semvec/pypi-packages huggingface dataset:
pytask-parallel 0.5.2
Parallelize the execution of tasks with pytask.
License: MIT
Requires Python: >=3.10

# pytask-parallel

[![PyPI](https://img.shields.io/pypi/v/pytask-parallel?color=blue)](https://pypi.org/project/pytask-parallel)
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[![image](https://img.shields.io/conda/vn/conda-forge/pytask-parallel.svg)](https://anaconda.org/conda-forge/pytask-parallel)
[![image](https://img.shields.io/conda/pn/conda-forge/pytask-parallel.svg)](https://anaconda.org/conda-forge/pytask-parallel)
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[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

______________________________________________________________________

Parallelize the execution of tasks with `pytask-parallel`, a plugin for
[pytask](https://github.com/pytask-dev/pytask).

## Documentation

You find the documentation at <https://pytask-parallel.readthedocs.io> with a
[quickstart guide](https://pytask-parallel.readthedocs.io/en/latest/quickstart.html).

## Changes

Consult the
[release notes](https://pytask-parallel.readthedocs.io/en/latest/changes.html) to find
out about what is new.
latent-spaces-by-example 0.1.1
Latent spaces by example.
License: MIT
Requires Python: >=3.12

## latent-spaces-by-example

Python package: `latent-spaces-by-example` (import as `latent_spaces_by_example`).

### What this repo is

This repo provides utilities for performing black-box search/optimisation over the outputs of generative (diffusion/flow matching) models.

This works by defining **surrogate latent spaces** that map a model’s high-dimensional latent representation into a low-dimensional space where standard black-box optimisation algorithms can be applied.

You form these spaces by providing a small set of latents corresponding to specific outputs of the generative model (called “seeds”). A surrogate latent space then defines a map:

- `u`: hypercube coordinates, shape \((N, n_seeds-1)\)
- `w`: nonnegative weights on the unit sphere, shape \((N, n_seeds)\)
- `z`: latent vectors, shape \((N, n_latent_dims)\)

The main API is `latent_spaces_by_example.SurrogateChart`.

### Paper

[Defining latent spaces by example: optimisation over the outputs of generative models](https://arxiv.org/abs/2509.23800)

### Install

Create / sync the environment (uses `uv`):

```bash
uv sync
```

### Quick usage

The minimal workflow is:

- choose `seed_latents` (shape `(num_seeds, latent_dim)`)
- build a chart (e.g. `knothe_rosenblatt_surrogate_chart(seed_latents)`)
- sample `u ~ Uniform([0,1]^(num_seeds-1))`
- map `u -> z` and optionally round-trip `z -> u`

See [`examples/basic_surrogate_chart.py`](examples/basic_surrogate_chart.py).

## Examples

### Flux + CLIP optimisation (3 seeds → 2D u-space)

This example optimises a 2D coordinate \(u \in [0,1]^2\) (formed from **three** basis latents) to maximise a **CLIP** similarity score of images generated by **Flux**. It also evaluates a 2D grid and saves a contour plot. In this example the basis latents are sampled randomly (i.e. they correspond to random images).

Install optional dependencies:

```bash
uv sync --group examples
```

Dry run (no model download; checks shape plumbing only):

```bash
uv run python examples/image_optimisation.py --dry-run
```

Small real run (downloads models; will be slow and may require GPU + `huggingface-cli login`):

```bash
uv run python examples/image_optimisation.py
```

## Development

### Run tests

```bash
uv run pytest
```

### Lint

```bash
uv run ruff check .
```

### Format

```bash
uv run isort --profile black . && black .
```
emc2 1.3.6
An open source framework for atmospheric model evaluation using observational data
License: MIT

EMC²: the Earth Model Column Collaboratory
==========================================

.. image:: https://img.shields.io/pypi/v/emc2.svg
    :target: https://pypi.python.org/pypi/emc2
    :alt: Latest PyPI version

.. image:: https://travis-ci.org/ARM-DOE/EMC2.png
   :target: https://travis-ci.org/ARM-DOE/EMC2
   :alt: Latest Travis CI build status

The Earth Model Column Collaboratory (EMC²) is an open-source framework for
atmospheric model evaluation against observational data and model
intercomparisons. It consisting of an instrument simulator and a sub-column
generator, which enables statistically emulating a higher spatial resolution.
This framework is specifically designed to simulate the `Atmospheric
Radiation Measurement (ARM) User Facility <http://www.arm.gov>`_ remote-
sensing measurements while being faithful to the representation of physical
processes and sub-grid scale assumptions in various state-of-the-art models,
thereby serving as a practical bridge between observations and models.


Detailed description of EMC² is provided in Silber et al. (GMD, 2022;
https://doi.org/10.5194/gmd-15-901-2022).


Useful links
============

- source code repository: https://github.com/ARM-DOE/EMC2
- EMC² Documentation: https://arm-doe.github.io/EMC2
- EMC² tutorial (from the 2022 ARM Open-Science Workshop): https://github.com/ARM-Development/ARM-Notebooks/blob/main/Tutorials/Open-Science-Workshop-2022/tutorials/EMC2_demo_w_E3SM.ipynb


Citing
======

If the Earth Model Column Collaboratory (EMC²) is used in your manuscript,
please cite:

    Silber, I., Jackson, R. C., Fridlind, A. M., Ackerman, A. S., Collis, S.,
    Verlinde, J., and Ding, J.: The Earth Model Column Collaboratory (EMC2)
    v1.1: an open-source ground-based lidar and radar instrument simulator and
    subcolumn generator for large-scale models, Geosci. Model Dev., 15,
    901–927, https://doi.org/10.5194/gmd-15-901-2022, 2022.

and references therein.


Installation
============

In order to install EMC², you can use either pip or anaconda. In a terminal, simply type either of::

$ pip install emc2
$ conda install -c conda-forge emc2

In addition, if you want to build EMC² from source and install, type in the following commands::

$ git clone https://github.com/ARM-DOE/EMC2
$ cd EMC2
$ pip install .


Dependencies
============

EMC² requires Python 3.10+ as well as: 
   * Atmoshperic Community Toolkit (https://arm-doe.github.io/ACT) 
   * Numpy (https://numpy.org)
   * Scipy (https://scipy.org)
   * Matplotlib (https://matplotlib.org)
   * Xarray (http://xarray.pydata.org)
   * Pandas (https://pandas.pydata.org/)
   * matplotlib (https://matplotlib.org/)
   * netCDF4 (https://github.com/Unidata/netcdf4-python)


Contributions
=============

As its acronym suggests, EMC² is a collaboratory...
Contributions are welcome and encouraged, provided that the code can be
distributed under the BSD 3-clause license (see the LICENSE.txt file).
See the  `contributor's guide. <https://github.com/ARM-DOE/EMC2/blob/master/CONTRIBUTING.rst>`_ for more information.
iflow-mcp_caretdev-mcp-server-iris 0.3.1
A Model Context Protocol server for InterSystems IRIS.
License: MIT
Requires Python: >=3.10
Keywords: automation, iris, llm, mcp

[![MseeP.ai Security Assessment Badge](https://mseep.net/pr/caretdev-mcp-server-iris-badge.png)](https://mseep.ai/app/caretdev-mcp-server-iris)

# mcp-server-iris: An InterSystems IRIS MCP server

<a href="https://glama.ai/mcp/servers/@caretdev/mcp-server-iris">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/@caretdev/mcp-server-iris/badge" />
</a>

# mcp-server-iris: An InterSystems IRIS MCP server

## Overview

A [Model Context Protocol](https://modelcontextprotocol.io/introduction) server for InterSystems IRIS database interaction and automation.

## Configure Claude

- [Claude Desktop](https://claude.ai/download)
- [uv](https://docs.astral.sh/uv/getting-started/installation/)

```json
{
  "mcpServers": {
    "iris": {
      "command": "uvx",
      "args": [
        "mcp-server-iris"
      ],
      "env": {
        "IRIS_HOSTNAME": "localhost",
        "IRIS_PORT": "1972",
        "IRIS_NAMESPACE": "USER",
        "IRIS_USERNAME": "_SYSTEM",
        "IRIS_PASSWORD": "SYS"
      }
    }
  }
}
```

![ClaudeIRISInteroperability](https://github.com/user-attachments/assets/ec5b90e6-1cd3-467a-8875-72a13606a747)