semvec/pypi-packages huggingface dataset:
pyaerocom 0.36.0
pyaerocom model evaluation software
Requires Python: >=3.10

==========
pyaerocom 
==========

|logo|


|CI| |Docs| |Coverage| |DOI|

**Website and Documentation:** https://pyaerocom.readthedocs.io/en/latest/

**Installation:** https://pyaerocom.readthedocs.io/en/latest/install.html  

**Tutorials:** https://pyaerocom.readthedocs.io/en/latest/pyaerocom-tutorials/index.html

**AeroCom:** https://aerocom.met.no

**AeroVal** evaluation portal: https://aeroval.met.no/

.. |logo| image:: docs/pyaerocom_logo.png
  :width: 30%

.. |CI| image:: https://github.com/metno/pyaerocom/workflows/CI/badge.svg
	:target: https://github.com/metno/pyaerocom/actions

.. |Docs| image:: https://readthedocs.org/projects/pyaerocom/badge/?version=latest
 :target: https://pyaerocom.readthedocs.io/en/latest/?badge=latest
 :alt: Documentation Status

.. |Coverage| image:: https://codecov.io/gh/metno/pyaerocom/branch/main-dev/graph/badge.svg?token=A0AdX8YciZ
    :target: https://codecov.io/gh/metno/pyaerocom

.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.17492065.svg
  :target: https://doi.org/10.5281/zenodo.17492065
tracecov-community 0.15.2
Schema-level API coverage. Install 'tracecov' for the full package.
Requires Python: >=3.10
Keywords: api, testing, coverage, openapi, swagger, api-testing, schema, json-schema, schemathesis

# TraceCov Community

This package contains the native extension for TraceCov.

**For installation, use the main package:**

```bash
pip install tracecov
```

See the [TraceCov documentation](https://docs.tracecov.sh) for usage instructions.
tracecov 0.15.2
Schema-level API coverage down to individual keywords.
Requires Python: >=3.10
Keywords: api, api-testing, coverage, json-schema, openapi, schema, schemathesis, swagger, testing

# TraceCov

**Schema-level API coverage down to individual keywords.**

Your API tests pass. But did you actually test the validation rules?

Hitting `POST /users` with `{"name": "Alice", "age": 30}` gives you 100% endpoint coverage—but you haven't tested empty names, negative ages, or strings that are too long.

TraceCov measures coverage at the **schema keyword level**: `minLength`, `pattern`, `enum`, `minimum` - not just endpoints.

![API Coverage Report](https://docs.tracecov.sh/report-demo.png)

## Install

```bash
pip install tracecov
```

## Quick Start

```python
import tracecov
import requests

# Load your OpenAPI schema
coverage = tracecov.CoverageMap.from_path("openapi.json")

# Wrap your session to track requests
session = coverage.requests.track_session(requests.Session())

# Run your tests as usual
session.post("https://api.example.com/users", json={"name": "Alice"})
session.get("https://api.example.com/users/123")

# Generate the report
coverage.save_report()
```

## What It Tracks

| Dimension | What it measures |
|-----------|------------------|
| **Operations** | HTTP method + path combinations called |
| **Parameters** | Path, query, header, cookie, body coverage |
| **Keywords** | JSON Schema validation rules (`minLength`, `pattern`, etc.) |
| **Examples** | Schema examples and defaults used |
| **Responses** | HTTP status codes returned |

## Documentation

https://docs.tracecov.sh/

## Feedback

Help shape TraceCov: [2-minute survey](https://forms.gle/53Zcpt4NxbvmXbwFA)

## Professional Edition

For advanced features, contact info@tracecov.sh.
growthctl 0.1.3
Marketing as Code - Manage ad campaigns like infrastructure
Requires Python: >=3.10

![growthctl logo](docs/website/static/img/logo_readme.svg)


# growthctl

[![standard-readme compliant](https://img.shields.io/badge/readme%20style-standard-brightgreen.svg?style=flat-square)](https://github.com/RichardLitt/standard-readme)
[![PyPI version](https://badge.fury.io/py/growthctl.svg)](https://badge.fury.io/py/growthctl)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)

> Marketing as Code CLI for managing ad campaigns declaratively using YAML and Git.
>
> **Note**: This project is currently in active development. APIs and configuration schemas are subject to change.

Plan and apply campaign changes safely with Terraform-style dry runs. Version control your ad strategy and collaborate using standard Git workflows. Manage Meta (Facebook/Instagram) ads with built-in provider support.

![growthctl demo](docs/website/static/img/terminal-demo.svg)

## Table of Contents

- [Install](#install)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)

## Install

Install using uv (recommended):

```bash
uv tool install growthctl
```

Install using pipx:

```bash
pipx install growthctl
```

Or using pip:

```bash
pip install growthctl
```

## Usage

Define your campaign in `campaign.yaml`:

```yaml
version: "1.0"
campaigns:
  - id: summer-sale
    name: Summer Sale 2026
    objective: OUTCOME_SALES
    status: ACTIVE
    ad_sets:
      - id: us-audience
        name: US Audience
        status: ACTIVE
        budget_daily: 5000
        targeting:
          locations: ["US"]
          age_min: 25
          age_max: 54
```

Preview and apply changes:

```bash
# Preview changes
growthctl plan campaign.yaml

# Apply to live
growthctl apply campaign.yaml
```

## Contributing

See [the contributing file](CONTRIBUTING.md) for details.

## License

[MIT](LICENSE) © growthctl contributors

## Contact

For questions or feedback, please reach out to [growthctl@gmail.com](mailto:growthctl@gmail.com).
CTlessPET 0.2.4
CTlessPET for synthetic CT from NAC PET data
Requires Python: >=3.8

# CTlessPET 

## Install
`pip install CTlessPET`

## Use
CTlessPET only requires an NAC-PET dataset and a CT dataset. The CT is used as the container for the synthetic CT, and can be an empty CT acquired before the patient enters the scanner. The NAC-PET should be reconstructed using OSEM with Time-of-Flight enabled but no PSF modeling. Reconstruction at should be at 440x440 matrix size with a 4 mm Gaussian post filter. Only support for Siemens Bigraph Vision scanners (including the Quadra) has been tested.

### Dicom data
Using a folder containing both NAC and CT data:
```
CTlessPET -i <input_folder> --output <output_folder>
```
or in seperate folders:
```
CTlessPET -i <input_NAC_folder> --CT <input_CT_folder> --output <output_folder>
```

### Nifti data
```
CTlessPET -i <input_NAC_nii> --CT <input_CT_nii> --output <output_nii>
```

### Choice of model
The network has been trained for FDG-PET (adult and pediatric).

The type is automatically selected when running the model with dicom data. You can overwrite the choice of the model using the `--model` flag, e.g. `--model FDG_Pediatric`.

### Optional arguments
You can change the batch size using `--batch_size`.

Some models (e.g. the adult FDG) comes with multiple kfold submodels. Default is to use the median of the individual predictions. If you only wish to infer using one model to speed the inference up, use `--fast`.

The default is to insert the bed from the empty CT scan used as the container. However, if this is not wanted, you can turn it off with `--no_bed`.

## Citation

![image](https://github.com/DEPICT-RH/CTlessPET/assets/108402980/1de108d4-0d1b-40cb-b88c-ed5d18e5b0c9)

If you are using CTlessPET, please cite the following [manuscript for CTlessPET in adults](https://doi.org/10.3390/diagnostics13243661):

Montgomery ME, Andersen FL, d’Este SH, Overbeck N, Cramon PK, Law I, Fischer BM, Ladefoged CN. 
Attenuation Correction of Long Axial Field-of-View Positron Emission Tomography Using Synthetic
Computed Tomography Derived from the Emission Data: Application to Low-Count Studies and Multiple Tracers.
Diagnostics. 2023; 13(24):3661. https://doi.org/10.3390/diagnostics13243661

or the [pediatric version](https://doi.org/10.3390/diagnostics14242788):

Montgomery ME, Andersen FL, Mathiasen R, Borgwardt L, Andersen KF, Ladefoged CN. 
CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View 
Positron Emission Tomography Using Synthetic CT from Emission Data. 
Diagnostics. 2024; 14(24):2788. https://doi.org/10.3390/diagnostics14242788
tarxemo-django-graphene-utils 0.1.3
Shared Django Graphene utilities and DTOs for efficient API development
Requires Python: >=3.6

# TarXemo Django Graphene Utils

[![PyPI version](https://img.shields.io/pypi/v/tarxemo-django-graphene-utils.svg)](https://pypi.org/project/tarxemo-django-graphene-utils/)
[![Framework :: Django](https://img.shields.io/badge/framework-django-red.svg)](https://www.djangoproject.com/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Shared Django Graphene utilities and DTOs for efficient API development. This package provides standardized response builders, pagination helpers, and common GraphQL types.

## Installation

```bash
pip install tarxemo-django-graphene-utils
```

## Usage

### Standardized Responses

```python
from tarxemo_django_graphene_utils import build_success_response, build_error

def resolve_something(root, info):
    if success:
        return build_success_response("Operation successful")
    return build_error("Something went wrong")
```

### Pagination

```python
from tarxemo_django_graphene_utils import get_paginated_and_non_paginated_data

def resolve_items(root, info, **kwargs):
    return get_paginated_and_non_paginated_data(
        model=MyModel,
        filtering_object=kwargs,
        graphene_type=MyModelType
    )
```

## Building and Publishing

1. Build the package:
   ```bash
   python setup.py sdist bdist_wheel
   ```

2. Upload to PyPI (requires twine):
   ```bash
   twine upload dist/*
   ```
# tarxemo-django-graphene-utils
wristband-django-auth 1.0.1
SDK for integrating your Python Django application with Wristband. Handles user authentication, session management, and token management. Optional Django Rest Framework (DRF) support included.
Requires Python: >=3.10
Keywords: api, auth, authentication, authorization, django, jwt, multi-tenant, multi-tenancy, oauth, oidc, sdk, secure, security, sso, wristband

# Wristband Multi-Tenant Authentication SDK for Python Django

Wristband provides enterprise-ready auth that is secure by default, truly multi-tenant, and ungated for small businesses.

- Website: [Wristband Website](https://wristband.dev)
- Documentation: [Wristband Docs](https://docs.wristband.dev/)

For detailed setup instructions and usage guidelines, visit the project's GitHub repository.

- [Django Auth SDK - GitHub](https://github.com/wristband-dev/django-auth)


## Details

This SDK facilitates seamless interaction with Wristband for user authentication within multi-tenant Django applications. It follows OAuth 2.1 and OpenID standards and is supported for Python 3.10+. Key functionalities encompass the following:

- Initiating a login request by redirecting to Wristband.
- Receiving callback requests from Wristband to complete a login request.
- Retrieving all necessary JWT tokens and userinfo to start an application session.
- Logging out a user from the application by revoking refresh tokens and redirecting to Wristband.
- Checking for expired access tokens and refreshing them automatically, if necessary.
- Function-based view decorators and class-based view mixins for Django authentication.
- Multi-strategy authentication for API endpoints (sessions + JWTs).
- Django REST Framework authentication class support for session and JWT bearer token strategies.
- Authentication backend for syncing Wristband users to Django's User model.
- Encrypted cookie-based session engine for secure, stateless session management.

You can learn more about how authentication works in Wristband in our documentation:

- [Backend Server Auth Integration](https://docs.wristband.dev/docs/backend-server-integration)
- [Login Workflow In Depth](https://docs.wristband.dev/docs/login-workflow)

## Questions

Reach out to the Wristband team at <support@wristband.dev> for any questions regarding this SDK.
sysom-hang-analyzer 1.0.16
Distributed stack collection and analysis tool
Requires Python: >=3.6

# SYSOM Hang Analyzer

分布式堆栈收集和分析工具包，用于收集和分析多节点Python程序的堆栈信息。

## 功能特性

- 自动检测GPU进程并收集Python和原生堆栈
- 分布式收集支持（多节点协调）
- 堆栈数据分析和可视化
- 生成火焰图(flame graphs)


## 系统依赖

在安装Python包之前，请确保安装了必要的系统依赖：

<!-- ### Ubuntu/Debian:
```bash
sudo apt-get update
sudo apt-get install gdb

### CentOS/RHEL:
```bash
sudo yum install gdb

### Fedora:
```bash
sudo dnf install gdb -->

## 安装

```bash
pip install sysom-hang-analyzer
prompttube-sdk 0.1.1
Tiny Python wrapper for the PromptTube Public API v0.
Requires Python: >=3.9

# PromptTube Python SDK (v0.1.0)

Tiny wrapper around the PromptTube Public API v0.

## Install

Copy `prompttube.py` into your project, or publish this folder as a package.

## Usage

```python
from prompttube import PromptTube

client = PromptTube(
    api_key="ptu_...",
    base_url="https://prompttube.ai",
)

prompts = client.list_prompts(limit=10)
run = client.run(
    messages=[{"role": "user", "content": "Summarize this text."}],
    tier="basic",
)
summary = client.create_summary(
    text=run["text"],
    intent="Conversation recap",
    reusable_prompt="You are a helpful summarizer.",
)

print(len(prompts["items"]), run["text"], summary["summaryId"])
```

## Endpoints covered

- Read: `/api/v0/prompts`
- Run: `/api/v0/run`
- Summaries: `/api/v0/summaries`