semvec/pypi-packages huggingface dataset:
pySATSI 0.2.3
Python package for focal mechanism stress inversions
Requires Python: >=3.8

# pySATSI

Python package for focal mechanism stress inversions.

Author: Robert Skoumal, U.S. Geological Survey | rskoumal@usgs.gov

This project contains Python code to compute damped stress inversions using earthquake focal mechanism solutions.

## How to Use
#### 1. Install the latest release in your virtual environment:
<pre>
# If you <i><b>don't</b></i> want to create any plots:
pip install -U pySATSI

# If you <i><b>do</b></i> want to create plots:
pip install -U 'pySATSI[plot]'
</pre>
Python 3.8+ versions are supported.

#### 2. Run pySATSI with your desired control file:
```
pySATSI path_to/control_file.txt
```

## More information
### Running examples
A variety of examples are provided in the repository. It's recommended you play around with these examples to learn about some of the features.
1. Download the examples folder available [HERE](https://code.usgs.gov/esc/pySATSI/-/archive/main/pySATSI-main.zip?path=examples).
2. Unzip the folder.
3. Navigate to the examples folder you just downloaded, e.g.:
`
cd examples
`
4. Run pySATSI with the example of your choice, e.g.:
`pySATSI 0D/control_file.txt`

### Manual
Refer to the [manual](https://code.usgs.gov/esc/pySATSI/-/blob/main/pySATSI_manual.pdf) or the [wiki](https://code.usgs.gov/esc/pySATSI/-/wikis/home) for additional information about running the code.

## Citation
Please cite our paper if you use anything in this project:

- Skoumal, R.J., Hardebeck, J.L., Michael, A.J. (2026). pySATSI: A Python package for computing focal mechanism stress inversions. _Seismological Research Letters_. https://doi.org/10.1785/0220250182

Significant portions of this algorithm are based on [SATSI](https://www.usgs.gov/node/279402):

- Hardebeck, J.L., & Michael, A.J. (2006). Damped regional‐scale stress inversions: Methodology and examples for southern California and the Coalinga aftershock sequence. Journal of Geophysical Research: Solid Earth, 111(B11). https://doi.org/10.1029/2005JB004144

## License and Disclaimer
[License](https://code.usgs.gov/esc/pySATSI/-/blob/main/LICENSE.md): This project is in the public domain.

[Disclaimer](https://code.usgs.gov/esc/pySATSI/-/blob/main/DISCLAIMER.md): This software is preliminary or provisional and is subject to revision.
craft-parts 2.29.0
Craft parts tooling
Requires Python: >=3.10

# Craft Parts

[![Documentation Status][rtd-badge]][rtd-latest]
[![Codecov Status][codecov-badge]][codecov-status]
[![Code Style][ruff-badge]][ruff-site]

Craft Parts supports the _parts_ mechanism common to all craft applications. It
provides a declarative way to obtain, process, and organize data from different sources
before it is packaged into the final artifact.

This library is most useful for app developers in the Starcraft family.

## Documentation

The [Craft Parts documentation][rtd-latest] describes the library's key components and
provides guidance on its usage.

## Community and support

You can report any issues or bugs on the project's [GitHub
repository](https://github.com/canonical/craft-parts/issues).

Craft Parts is covered by the [Ubuntu Code of
Conduct](https://ubuntu.com/community/ethos/code-of-conduct).

## Contribute to Craft Parts

Craft Parts is open source and part of the Canonical family. We would love
your help.

If you're interested, start with the [contribution guide](CONTRIBUTING.md).

We welcome any suggestions and help with the docs. The [Canonical Open
Documentation Academy](https://github.com/canonical/open-documentation-academy)
is the hub for doc development, including Craft Parts docs. No prior
coding experience is required.

## License and copyright

Craft Parts is released under the [LGPL-3.0 license](LICENSE).

© 2021-2025 Canonical Ltd.

[rtd-badge]: https://readthedocs.com/projects/canonical-craft-parts/badge/?version=latest
[rtd-latest]: https://canonical-craft-parts.readthedocs-hosted.com/en/latest/
[ruff-badge]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
[ruff-site]: https://github.com/astral-sh/ruff
[codecov-badge]: https://codecov.io/github/canonical/craft-parts/coverage.svg?branch=main
[codecov-status]: https://codecov.io/github/canonical/craft-parts?branch=main
scrapemyst 1.1.0
A lean wrapper for requests with stealth features.
Requires Python: >=3.6

# ScrapeMyst

a lean, human-like wrapper for Python `requests`. Handles user-agent rotation, proxy setup, and random delays so you don't have to.

## Installation

```bash
pip install scrapemyst
```

## Usage

### Setup

```python
from scrapemyst import ScrapeMyst
```

### Initialize with proxies

```python
proxies = ["127.0.0.1:8000", "example.com:8080"]
scrapemyst = ScrapeMyst(proxies=proxies)
```

### Or use the default instance for quick tasks

```python
from scrapemyst import scrapemyst
```

## GET request

Supports fixed sleep (`sleep=3`) or random ranges (`sleep=(1, 5)`).

```python
res = scrapemyst.send_get(
    "https://example.com",
    params={"id": "123"},
    sleep=(2, 4),
    referer="https://google.com"
)

if res["success"]:
    print(f"status: {res['status_code']}")
    # data is the raw requests response object
    print(res["data"].text)
```

## POST request

Works with standard form data or JSON.

### POST form data

```python
scrapemyst.send_post(
    "https://example.com/login",
    data={"user": "admin"}
)
```

### POST JSON

```python
res = scrapemyst.send_post(
    "https://api.com/v1",
    json={"key": "val"},
    sleep=2
)
```

## Response structure

Every method returns a simple dictionary:

* `success`: bool â€” `True` if the request worked
* `status_code`: int â€” HTTP status code (200, 404, etc.)
* `data`: the raw response object (if successful)
* `error_message`: string (if it failed)

## Customization

### Add new headers

```python
scrapemyst.update_headers({"X-Custom": "Value"})
```

### Overwrite all headers

```python
scrapemyst.update_headers({"User-Agent": "MyBot"}, replace=True)
```

## License

MIT
nmdb-py 0.1.1
Python client library for Neural Message Data Bus (NMDB)
Requires Python: >=3.8

# NMDB Python Client

Python client library for the Neural Message Data Bus (NMDB).

## Installation

```bash
pip install nmdb-py
```

Or install from source:

```bash
cd clients/python
pip install -e .
```

## Quick Start

```python
from nmdb import NMDBClient

# Connect to channel
client = NMDBClient("/tmp/nmdb/c1.sock")
client.connect()

# Send text message
client.send_text("Hello, NMDB!")

# Set message callback
def on_message(data: bytes):
    print(f"Received: {data.decode('utf-8')}")

client.set_message_callback(on_message)

# Keep running
import time
time.sleep(10)

# Disconnect
client.disconnect()
```

## Context Manager

```python
from nmdb import NMDBClient

with NMDBClient("/tmp/nmdb/c1.sock") as client:
    client.send_text("Hello, NMDB!")
    # Auto-disconnect on exit
```

## License

MIT License - see [LICENSE](../../LICENSE) for details.