semvec/pypi-packages huggingface dataset:
ubcpdk 3.3.3
ubcpdk pdk
Requires Python: <3.13,>=3.12
Keywords: python

# ubcpdk (SiEPIC Ebeam PDK) 3.3.3

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/gdsfactory/binder-sandbox/HEAD)
[![pypi](https://img.shields.io/pypi/v/ubcpdk)](https://pypi.org/project/ubcpdk/)
[![mit](https://img.shields.io/github/license/gdsfactory/ubc)](https://choosealicense.com/licenses/mit/)
[![codecov](https://codecov.io/gh/gdsfactory/ubc/branch/main/graph/badge.svg?token=T3kCV2gYE9)](https://codecov.io/gh/gdsfactory/ubc)
[![black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

SiEPIC Ebeam PDK adapted from [siepic Ebeam PDK](https://github.com/lukasc-ubc/SiEPIC_EBeam_PDK) for gdsfactory.
It provides a fully python driven flow alternative for the most advanced users taking the [edx course](https://www.edx.org/course/silicon-photonics-design-fabrication-and-data-ana)

## Installation

We recommend `uv`

```bash
# On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh
```

```bash
# On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```

### Installation for users

Use python 3.11, 3.12 or 3.13. We recommend [VSCode](https://code.visualstudio.com/) as an IDE.

```
uv pip install ubcpdk --upgrade
```

Then you need to restart Klayout to make sure the new technology installed appears.

### Installation for contributors


Then you can install with:

```bash
git clone https://github.com/gdsfactory/ubc.git
cd ubc
uv venv --python 3.12
uv sync --extra docs --extra dev
```

## Documentation

- [gdsfactory docs](https://gdsfactory.github.io/gdsfactory/)
- [UBCpdk docs](https://gdsfactory.github.io/ubc/) and [code](https://github.com/gdsfactory/ubc)
- [ubc1](https://github.com/gdsfactory/ubc1)
pickerxl 0.2.4
PickerXL is a large deep learning model to measure arrival times from noisy seismic signals.
Requires Python: >=3.13

# PickerXL

A large deep learning model to measure arrival times from noisy seismic signals. The model was developed by Chengping Chai, Derek Rose, Scott Stewart, Nathan Martindale, Mark Adams, Lisa Linville, Christopher Stanley, Anibely Torres Polanco and Philip Bingham.


## Introduction

This model is trained on STEAD (Mousavi et al., 2019) for Primary (P) and Secondary (S) wave arrival picking. The model was trained using earthquake data at local distances (0-350 km). The model uses 57s-long three-component seismograms sampled at 100 Hz as input. The output of the model are probability channels corresponding to P wave, S wave, and noise. These probabilities can be used to compute P- and S-wave arrival times.

## Installation

Creating a virtual environment is highly encouraged. 

```
pip install pickerxl
```

To install only for CPU (e.g., macOS), you may use the following commands.

```
pip3 install torch torchvision torchaudio
pip3 install pickerxl
```


## Example Usage

```
from pickerxl.pickerxl import Picker
import numpy as np
import h5py
model = Picker()
fid = h5py.File("example_waveforms.h5", "r")
data_group = fid["data"]
example_data = []
true_p_index = []
true_s_index = []
for akey in data_group.keys():
    dataset = data_group[akey]
    example_data.append(dataset[...])
    true_p_index.append(float(dataset.attrs["p_arrival_sample"]))
    true_s_index.append(float(dataset.attrs["s_arrival_sample"]))
fid.close()
preds = model.predict_probability(example_data)
p_index, s_index = model.predict_arrivals(example_data)
print("True P-wave arrival index:", true_p_index)
print("Predicted P-wave arrival index:", p_index)
print("True S-wave arrival index:", true_s_index)
print("Predicted S-wave arrival index:", s_index)
```

## Test the Package

First, go to the top directory of the package. Then, using the following commands.

```
cd tests
python run_tests.py
```

![example image](images/example_waveform_2.png)


## Known Limitations

* The model may have a less-than-optimal performance for earthquake data outside of a source-receiver distance range of 10–110 km and a magnitude range of 0–4.5 because of biases in the training data.
* The model may produce false detections when applied to continuous seismic data.
* The model may not perform well for earthquake data at larger distances or for non-earthquake sources.

## Reference

Chengping Chai, Derek Rose, Scott Stewart, Nathan Martindale, Mark Adams, Lisa Linville, Christopher Stanley, Anibely Torres Polanco, Philip Bingham; PickerXL, A Large Deep Learning Model to Measure Arrival Times from Noisy Seismic Signals. Seismological Research Letters 2025; 96 (4):2394-2404. doi: https://doi.org/10.1785/0220240353

## License

GNU GENERAL PUBLIC LICENSE version 3

## Credit

The architecture of the deep learning model was adapted from SeisBench (Woollam et al., 2022).
openlineage-dbt 1.44.1
OpenLineage integration with dbt
Requires Python: >=3.9
Keywords: openlineage

# OpenLineage dbt integration

Wrapper script for automatic metadata collection from dbt

## Features

**Metadata**

* Model run lifecycle
* Model inputs / outputs

## Requirements

- [Python >= 3.8](https://www.python.org/downloads)
- [dbt >= 0.20](https://www.getdbt.com/)

Right now, `openlineage-dbt` supports only these dbt adapters:

* `bigquery`
* `snowflake`
* `spark` (`thrift` and `odbc`, but not `local`)
* `redshift`
* `athena`
* `glue`
* `postgres`
* `clickhouse`
* `trino`
* `databricks`
* `sqlserver`
* `dremio`
* `duckdb`

## Installation

```bash
$ pip3 install openlineage-dbt
```

To install from source, run:

```bash
$ pip install .
```

## Configuration

### Transport

`openlineage-dbt` uses the OpenLineage Python client to push data to the OpenLineage backend, so any way of configuring Python client will work here as well:

```ini
OPENLINEAGE_URL=http://localhost:5000
OPENLINEAGE_API_KEY=abc
```

dbt integration-specific environment variables:

* `OPENLINEAGE_NAMESPACE` - set if you are using something other than the `default` namespace for job namespace.

### Logging

In addition to conventional logging approaches, the OpenLineage dbt wrapper script provides an alternative way of configuring its logging behavior. By setting the `OPENLINEAGE_DBT_LOGGING` environment variable, you can establish the logging level for the `openlineage.dbt` and its child modules.

You can also set log level of `dbtol` Python module but this is deprecated.

## Usage

To begin collecting dbt metadata with OpenLineage, replace `dbt run` with `dbt-ol run`.

Additional table and column level metadata will be available if `catalog.json`, a result of running `dbt docs generate`, will be found in the target directory.

If you need to send events without running the job you can use the command `dbt-ol send-events`, it will send the metadata of your last run without running the job.

## Development

For development setup instructions, see the [Python development setup guide](https://openlineage.io/docs/client/python/development/setup).

Quick start:
```bash
# From the repository root
$ make setup-dbt

# Or manually
$ cd integration/dbt
$ uv sync --extra dev
```

----
SPDX-License-Identifier: Apache-2.0\
Copyright 2018-2026 contributors to the OpenLineage project
brainsig 0.2.1
A package for computing 'neural signatures' from fMRI data.
Requires Python: >=3.10

# Welcome to brainsig

|        |        |
|--------|--------|
| Package | [![Latest PyPI Version](https://img.shields.io/pypi/v/brainsig.svg)](https://pypi.org/project/brainsig/) [![Supported Python Versions](https://img.shields.io/pypi/pyversions/brainsig.svg)](https://pypi.org/project/brainsig/) [![Documentation](https://readthedocs.org/projects/brainsig/badge/?version=latest)](https://brainsig.readthedocs.io/en/latest/?badge=latest) |
| Meta   | [![Code of Conduct](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](CODE_OF_CONDUCT.md) |

*TODO: the above badges that indicate python version and package version will only work if your package is on PyPI.
If you don't plan to publish to PyPI, you can remove them.*

brainsig is a project that (describe what it does here).

## Get started

You can install this package into your preferred Python environment using pip:

```bash
$ pip install brainsig
```

TODO: Add a brief example of how to use the package to this section

To use brainsig in your code:

```python
>>> from brainsig.dataset import Dataset
>>> from brainsig.model import NeuralSignature
```

Full documentation: brainsig.readthedocs.io/en/latest/


## Copyright

- Copyright © 2025 Tony Barrows.
- Free software distributed under the [MIT License](./LICENSE).
pbest 0.2.9
BioSim Registry is a project focused on delivering high quality processes and tooling in relation to process bi-graph.
Requires Python: <3.13,>=3.11
Keywords: python

[![Release](https://img.shields.io/github/v/release/biosimulators/biosim-registry)](https://img.shields.io/github/v/release/biosimulators/bsedic)
[![Build status](https://img.shields.io/github/actions/workflow/status/biosimulators/biosim-registry/main.yml?branch=main)](https://github.com/biosimulators/biosim-registry/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/biosimulators/biosim-registry/branch/main/graph/badge.svg)](https://codecov.io/gh/biosimulators/biosim-registry)
[![License](https://img.shields.io/github/license/biosimulators/biosim-registry)](https://img.shields.io/github/license/biosimulators/biosim-registry)

# BioSimulations Registry
A repository filled with high quality processes, steps, and composites for process bi-graph.
All used as a standard library for process bi-graph.


- **Github repository**: <https://github.com/biosimulators/biosim-registry/>
- **Documentation** <https://biosimulators.github.io/biosim-registry/>

---

Repository initiated with [fpgmaas/cookiecutter-uv](https://github.com/fpgmaas/cookiecutter-uv).