semvec/pypi-packages huggingface dataset:
idea-atlas 0.2.3
Atlas is a suite of standalone AI agents powered by crewai you can plug into your workflows.
Requires Python: >=3.11

# gds-idea-atlas
✨ Let Atlas carry your AI workflows ✨

Atlas is a suite of standalone AI agents powered by crewai you can plug into your workflows.
Each agent is designed to perform a focused task well, and can be used either on its own or as part of a larger crew or automation pipeline.

The first available agent is the Researcher.

💡 Use Cases

* Information Gathering → Expand a query into reliable sources and discover material you might have missed.

* Summarization & Synthesis → Turn multiple sources into a concise Markdown or JSON summary.

* Academic & Technical Research → Pull structured insights from research papers and technical documents.

* Workflow Automation → Feed structured results into pipelines, dashboards, or agent frameworks.

* Business & Product Use → Scan markets, competitors, or reviews and generate quick decision-support reports.

📦 Outputs

* Structured JSON validated with Pydantic

* Markdown Summaries for human-readable reports

🔧 Installation

Clone this repository and install dependencies in your preferred environment (e.g. uv, pip, or poetry):

``` 
pip install idea-atlas

or 

uv add idea-atlas
```


You’ll need Python 3.11+.

⚙️ Configuration

Create a .env file at the project root. Add the model and API keys you want to use:

``` MODEL=bedrock/anthropic.claude-3-7-sonnet-20250219-v1:0
AWS_ACCESS_KEY_ID=your-aws-access-key-id
AWS_SECRET_ACCESS_KEY=your-aws-secret-access-key
AWS_REGION_NAME=your-aws-region-name
SERPER_AI_KEY=your-serper-api-key 
```
plantuml-sequence 0.5.1
Create PlantUML sequence diagrams programmatically from Python
Requires Python: >=3.10
Keywords: diagram, plantuml

# plantuml-sequence

Create PlantUML sequence diagrams programmatically from Python.

The basic example of the [PlantUML Documentation](https://plantuml.com/sequence-diagram) can be implemented with the
following Python script:

``` python
from plantuml_sequence import Diagram

with open("my-diagram.puml", "w") as file, Diagram(file) as sequence:
    (
        sequence.message("Alice", "Bob", "Authentication Request")
        .message("Bob", "Alice", "Authentication Response", arrow_style="-->")
        .blank_line()
        .message("Alice", "Bob", "Another authentication Request")
        .message("Alice", "Bob", "Another authentication Response", arrow_style="<--")
    )

```

Its output inside *my-diagram.puml* is:

``` puml
@startuml
Alice -> Bob: Authentication Request
Bob --> Alice: Authentication Response

Alice -> Bob: Another authentication Request
Alice <-- Bob: Another authentication Response
@enduml
```

This file be compiled to an image using the `plantuml` command-line or a online server. See the
[PlantUML documentation](https://plantuml.com/starting) for more details.

---

**[Read the documentation on ReadTheDocs!](https://plantuml-sequence.readthedocs.io/)**

---

## Installation and supported Python versions

```sh
pip install plantuml-sequence
```

Python 3.10+ is supported

## Supported PlantUML features

Not all of the features of message sequence charts are supported yet. See the list of implemented features below.

### General

* [x] Use *teoz* renderer

### Participants

* [x] Declaring participants
* [x] Multiline participants
* [ ] Create participant with message
* [x] Lifeline activation / deactivation
* [ ] Lifeline auto-activate
* [x] Participants encompass (Box around participants)
* [x] Remove foot boxes

### Messages

* [x] Messages
* [x] Basic `autonumber` message numbering
* [ ] Advanced auto numbering
  * [ ] `autonumber` formats
  * [ ] `autonumber` sequence increments
* [x] Hide unlinked participants
* [ ] Mainframe

### Notes

* [x] Different note shapes (`note`, `hnote`, `rnote`)
* [x] Notes on messages
* [x] Notes over / left / right of lifelines
* [x] Notes across all participants
* [ ] Multiple notes at the same level

### Flow

* [ ] Basic message grouping
* [ ] `alt` / `else` groups
* [x] Divider
* [ ] Reference
* [x] Delay
* [x] Space

### Styling

* [ ] Colored groups
* [x] Arrow style change
  * [x] Standard arrow styles
  * [x] Short arrows
* [ ] Slanted arrows
* [ ] Styling changes using the [`skinparam`](https://plantuml.com/skinparam) command

### Preprocessing

> Currently no preprocessing features are planned to be supported

* [ ] Variable definition
* [ ] Boolean expression
* [ ] Conditions
* [ ] While loops
* [ ] Procedures & functions blocks
* [ ] Include directive
* [ ] Memory dump
* [ ] Assertions
gemseo-mlearning 3.1.0
A GEMSEO extension for machine learning.
Requires Python: <3.14,>=3.10

<!--
Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0
International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative
Commons, PO Box 1866, Mountain View, CA 94042, USA.
-->
# gemseo-mlearning

[![PyPI - License](https://img.shields.io/pypi/l/gemseo-mlearning)](https://www.gnu.org/licenses/lgpl-3.0.en.html)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/gemseo-mlearning)](https://pypi.org/project/gemseo-mlearning/)
[![PyPI](https://img.shields.io/pypi/v/gemseo-mlearning)](https://pypi.org/project/gemseo-mlearning/)
[![Codecov branch](https://img.shields.io/codecov/c/gitlab/gemseo:dev/gemseo-mlearning/develop)](https://app.codecov.io/gl/gemseo:dev/gemseo-mlearning)

## Overview

`gemseo-mlearning` is a plugin of the library [GEMSEO](https://www.gemseo.org), dedicated to machine learning.

This package adds new [regression models](https://gemseo.gitlab.io/dev/gemseo-mlearning/latest/user_guide/optimization/smt/)
and [optimization algorithms](https://gemseo.gitlab.io/dev/gemseo-mlearning/latest/user_guide/regression/smt/)
based on [SMT](https://smt.readthedocs.io/).

A [package for active learning](https://gemseo.gitlab.io/dev/gemseo-mlearning/latest/user_guide/active_learning/what_active_learning_is/) is also available,
deeply based on the core GEMSEO objects for optimization,
as well as a
[SurrogateBasedOptimization](https://gemseo.gitlab.io/dev/gemseo-mlearning/latest/user_guide/optimization/al/)
library built on its top.
An effort is being made to improve both content and performance in future versions.

## Installation

Install the latest version with `pip install gemseo-mlearning`.

See [pip](https://pip.pypa.io/en/stable/getting-started/) for more information.

## Bugs and questions

Please use the [gitlab issue tracker](https://gitlab.com/gemseo/dev/gemseo-mlearning/-/issues)
to submit bugs or questions.

## Contributing

See the [contributing section of GEMSEO](https://gemseo.readthedocs.io/en/stable/software/developing.html#dev).

## Contributors

- Antoine Dechaume
- Benoît Pauwels
- Clément Laboulfie
- Matthias De Lozzo
chrometrace 0.2.1
A Python library for Chrome Trace Viewer files
Requires Python: >=3.11
Keywords: chromium, trace

# chrometrace

A Python library for creating Chrome Trace Viewer files. The Chrome Trace Viewer can be opened in
Chromium-based browsers (e.g. Google Chrome, Microsoft Edge, Chromium, ...) by entering _chrome://tracing_ into the
address bar.

The trace event format and types are defined in
[this document](https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU).

## Installation

Install the package from PyPi using the following command.

```sh
pip install chrometrace
```

## Usage

```python
import chrometrace

# Create the trace sink as a context manager
with chrometrace.TraceSink("trace.json") as trace_sink:
    # Create a process tracer called myapp from the trace sink
    myapp_tracer = trace_sink.process_tracer("myapp", process_id=1337)
    # Create a thread tracer for the renderer thread from the process tracer
    renderer_thread_tracer = myapp_tracer.thread_tracer("RendererThread", 1)

    # Write a complete event at 10 us taking 1000 us with the name my_function
    renderer_thread_tracer.complete("my_function", timestamp_us=10, duration_us=1000)
```

## Supported trace formats

Currently only the _JSON Array Format_ is supported due to its simplicity. Support for the _JSON Object Format_ might be
added in the future.

## Supported trace events

* [x] Duration Events
  * Begin
  * End
* [x] Complete Events
* [x] Instant Events
  * With different scopes
* [x] Counter Events
* [ ] Async Events
* [ ] Flow Events
* [x] Metadata Events
  * Name process / thread
  * Define process / thread sort index
  * Process labels
* [ ] Sample Events
* [ ] Object Events
* [ ] Mark Events
* [ ] Clock Sync Events
* [ ] Context Events
dism-core 1.5.1
Validation tools for the yaml template used by DIALS Inference Service Manager.
Requires Python: >=3.9

![Build Status](https://gitlab.cern.ch/cms-dqmdc/libraries/dism-core/badges/develop/pipeline.svg)
![Coverage](https://gitlab.cern.ch/cms-dqmdc/libraries/dism-core/badges/develop/coverage.svg)
![Latest Release](https://gitlab.cern.ch/cms-dqmdc/libraries/dism-core/-/badges/release.svg)

# dism-core

DIALS Inference Service Manager Core or simply `dism-core` is a python package that provides core functionality for validating custom YAML templates used to manage custom `InferenceService` resources in KServe managed by DIALS. It leverages the `pydantic` library to ensure that the YAML templates conform to the required schema.

> [!WARNING]
> This package is not intended to be installed or used directly. Instead, it serves as a foundational library for other packages that require YAML validation for KServe custom `InferenceService` deployments.

## Development

Install the dependencies and the package using `uv`:

```shell
uv sync --all-groups
uv run pre-commit install
uv pip install -e .
```

### Running tests

Run tests with `pytest`:

```shell
uv run pytest tests
```

#### Tox

You may also want to run the tests with `tox` to test against multiple python versions:

```shell
uv run tox
```

**[asdf](https://asdf-vm.com/) users**

tox requires multiple versions of Python to be installed. Using `asdf`, you have multiple versions installed, but they aren’t normally exposed to the current shell. You can use the following command to expose multiple versions of Python in the current directory:

```bash
asdf set python 3.12.9 3.11.10 3.10.13
```

This will use `3.12.9` by default (if you just run `python`), but it will also put `python3.11` and `python3.10` symlinks in your PATH so you can run those too (which is exactly what tox is looking for).

### Releasing the package on PyPI

The package is available in PyPI at [cmsdials](https://pypi.org/project/dism-core/), under the [cmsdqm](https://pypi.org/org/cms-dqm/) organization. You'll need at leat Mantainer rights to be able to push new versions. This package is not meant to be deployed in PyPI, since it is simply a foundational library for `dials-service` and `dism-cli`.

### CI

The GitLab CI is configured to automatically publish the release notes in GitLab whever a tag is pushed to the repo.

> [!NOTE]
> For this to work the CI/CD variables named `UV_PUBLISH_TOKEN` and `GITLAB_TOKEN` should be registered in gitlab. The `GITLAB_TOKEN` is a Project Access Token with api read/write rights, which is needed to read merge requests using the `glab-cli`.
> https://gitlab.cern.ch/cms-dqmdc/libraries/dism-core/-/settings/access_tokens
gemseo-mma 4.0.0
A gemseo wrapper of Python version of Method of Moving Asymptothes in the implementation of Arjen Deetman.
Requires Python: <3.14,>=3.10

<!--
Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0
International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative
Commons, PO Box 1866, Mountain View, CA 94042, USA.
-->

# gemseo-mma

[![PyPI - License](https://img.shields.io/pypi/l/gemseo-mma)](https://www.gnu.org/licenses/gpl-3.0.en.html)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/gemseo-mma)](https://pypi.org/project/gemseo-mma/)
[![PyPI](https://img.shields.io/pypi/v/gemseo-mma)](https://pypi.org/project/gemseo-mma/)
[![Codecov branch](https://img.shields.io/codecov/c/gitlab/gemseo:dev/gemseo-mma/develop)](https://app.codecov.io/gl/gemseo:dev/gemseo-mma)

## Overview

A gemseo wrapper of Python version of Method of Moving Asymptothes in the implementation of Arjen Deetman.

## Installation

Install the latest version with `pip install gemseo-mma`.

See [pip](https://pip.pypa.io/en/stable/getting-started/) for more information.

## Bugs and questions

Please use the [gitlab issue tracker](https://gitlab.com/gemseo/dev/gemseo-mma/-/issues)
to submit bugs or questions.

## Contributing

See the [contributing section of GEMSEO](https://gemseo.readthedocs.io/en/stable/software/developing.html#dev).

## Contributors

- Simone Coniglio
- Antoine Dechaume
- Original implementation of Arjen Deetman, see [here](https://github.com/arjendeetman/GCMMA-MMA-Python).

## References

Svanberg, K. (1987). The Method of Moving Asymptotes -- A new method for
structural optimization. International Journal for Numerical Methods in
Engineering 24, 359-373. `doi:10.1002/nme.1620240207`,
see [here](https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.1620240207).

Svanberg, K. (n.d.). MMA and GCMMA -- two methods for nonlinear
optimization.
Retrieved August 3, 2017 from [this](https://people.kth.se/~krille/mmagcmma.pdf).