semvec/pypi-packages huggingface dataset:
seestarpy 0.1.3
Python interface for the SeeStar telescope control system
Requires Python: >=3.11

![SeeStar-Py](docs/_static/seestar_py_logo_banner.png)
A light-weight python module to drive the Seestar smart telescopes

This package is on ReadTheDocs. See [seestarpy.readthedocs.io](https://seestarpy.readthedocs.io/en/latest/)

If you've found the package useful - how about [buying me a coffee](https://buymeacoffee.com/kdleschinsf), to keep the creative juices flowing ;)


Quickstart
----------
Install `seestarpy` using pip:

    $ pip install seestarpy

Usage example:

    from seestarpy import connection as conn
    from seestarpy import raw
        
    conn.DEFAULT_IP = "192.168.1.243"                # NOTE - set your own IP address! This is mine.
    raw.test_connection()                            # Test if the seestar is connected to the wifi in station mode

    raw.scope_move_to_horizon()                      # Turn on telescope
    raw.iscope_start_view(ra=13.4, dec=54.9, target_name="Mizar")  # Move to target and turn on camera
    raw.iscope_start_stack()                         # Start stacking sub-frames   

    raw.iscope_stop_view("Stack")                    # Turn off frame stacking
    raw.iscope_stop_view("ContinuousExposure")       # Turn off camera
    raw.pi_shutdown(True)                            # force=True to avoid mistakenly shutting down
onnx-ir 0.1.16
Efficient in-memory representation for ONNX
Requires Python: >=3.9

# <img src="docs/_static/logo-light.png" alt="ONNX IR" width="250"/>

[![PyPI - Version](https://img.shields.io/pypi/v/onnx-ir.svg)](https://pypi.org/project/onnx-ir)
[![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)
[![codecov](https://codecov.io/gh/onnx/ir-py/graph/badge.svg?token=SPQ3G9T78Z)](https://codecov.io/gh/onnx/ir-py)
[![PyPI Downloads](https://static.pepy.tech/badge/onnx-ir/month)](https://pepy.tech/projects/onnx-ir)

An in-memory IR that supports the full ONNX spec, designed for graph construction, analysis and transformation.

## Getting Started

[onnx-ir documentation](https://onnx.ai/ir-py/)

### Installation

Via pip:

```
pip install onnx-ir
```

Or from source:

```
pip install git+https://github.com/onnx/ir-py.git
```

## Features ✨

- Full ONNX spec support: all valid models representable by ONNX protobuf, and a subset of invalid models (so you can load and fix them).
- Low memory footprint: mmap'ed external tensors; unified interface for ONNX TensorProto, Numpy arrays and PyTorch Tensors etc. No tensor size limitation. Zero copies.
- Straightforward access patterns: Access value information and traverse the graph topology at ease.
- Robust mutation: Create as many iterators as you like on the graph while mutating it.
- Speed: Performant graph manipulation, serialization/deserialization to Protobuf.
- Pythonic and familiar APIs: Classes define Pythonic apis and still map to ONNX protobuf concepts in an intuitive way.
- No protobuf dependency: The IR does not require protobuf once the model is converted to the IR representation, decoupling from the serialization format.

## Concept Diagram

![Concept Diagram](docs/resource/onnx-ir-entities.svg)

## Code Organization 🗺️

- [`_protocols.py`](src/onnx_ir/_protocols.py): Interfaces defined for all entities in the IR.
- [`_core.py`](src/onnx_ir/_core.py): Implementation of the core entities in the IR, including `Model`, `Graph`, `Node`, `Value`, and others.
- [`_enums.py`](src/onnx_ir/_enums.py): Definition of the type enums that correspond to the `DataType` and `AttributeType` in `onnx.proto`.
- [`_name_authority.py`](src/onnx_ir/_name_authority.py): The authority for giving names to entities in the graph, used internally.
- [`_linked_list.py`](src/onnx_ir/_linked_list.py): The data structure as the node container in the graph that supports robust iteration and mutation. Internal.
- [`_metadata.py`](src/onnx_ir/_metadata.py): Metadata store for all entities in the IR.
pytractoviz 0.5.2
Python tools for diffusion tractography visualization
Requires Python: >=3.10

# pyTractoViz

[![ci](https://github.com/mcw-meier-lab/pytractoviz/workflows/ci/badge.svg)](https://github.com/mcw-meier-lab/pytractoviz/actions?query=workflow%3Aci)
[![documentation](https://img.shields.io/badge/docs-mkdocs-708FCC.svg?style=flat)](https://mcw-meier-lab.github.io/pytractoviz/)
[![pypi version](https://img.shields.io/pypi/v/pytractoviz.svg)](https://pypi.org/project/pytractoviz/)

Python tools for diffusion tractography visualization

## Installation

```bash
pip install pytractoviz
```

With [`uv`](https://docs.astral.sh/uv/):

```bash
uv tool install pytractoviz
```
nova-epics 0.2.1
A Python Package for retrieving unprivileged instrument status information from EPICS for use in NOVA applications.
Requires Python: <3.14,>=3.10
Keywords: NDIP, Python, Trame

nova-epics
==========

`nova-epics` is a Python package for retrieving unprivileged instrument status information from EPICS for use in NOVA applications.

You can install this package directly with

```commandline
pip install "nova-epics[trame]"
```

Developers: please read [this document](DEVELOPMENT.md)