semvec/pypi-packages huggingface dataset:
GISEA 0.1.1
Genetic Interaction Networks and Pathway Modules
Requires Python: >=3.8

GISEA
=======================

Genetic Interaction Networks and Pathway Modules

|image| |Downloads|

.. |image| image:: https://img.shields.io/pypi/v/GISEA.svg
   :target: https://pypi.python.org/pypi/GISEA
.. |Downloads| image:: https://static.pepy.tech/badge/GISEA
   :target: https://pepy.tech/project/GISEA
omop2fhir-vocab 1.1.6
Convert OMOP vocab to FHIR.
Requires Python: >=3.9.0


# OMOP2FHIR-vocab
Convert OMOP vocab to FHIR.

## Prerequisites
* [Python 3.9+](https://www.python.org/downloads/)
* [Java 11+](https://www.oracle.com/java/technologies/javase/jdk11-archive-downloads.html)

## Installation
`pip install omop2fhir-vocab`

## Running
Run: `omop2fhir-vocab OPTIONS`

### CLI
```
omop2fhir-vocab --help
usage: omop2fhir-vocab [-h] -c CONCEPT_CSV_PATH -r CONCEPT_RELATIONSHIP_CSV_PATH [-v VOCABS [VOCABS ...]] [-R RELATIONSHIPS [RELATIONSHIPS ...]] [-o OUT_DIR]
                       [-I]

Convert OMOP vocab to FHIR.

options:
  -h, --help            show this help message and exit
  -c CONCEPT_CSV_PATH, --concept-csv-path CONCEPT_CSV_PATH
                        Path to CSV of OMOP concept table.
  -r CONCEPT_RELATIONSHIP_CSV_PATH, --concept-relationship-csv-path CONCEPT_RELATIONSHIP_CSV_PATH
                        Path to CSV of OMOP concept_relationship table.
  -v VOCABS [VOCABS ...], --vocabs VOCABS [VOCABS ...]
                        Which vocabularies to include in the output? Usage: --vocabs "Procedure Type" "Device Type"
  -R RELATIONSHIPS [RELATIONSHIPS ...], --relationships RELATIONSHIPS [RELATIONSHIPS ...]
                        Which relationship types from the concept_relationship table's relationship_id field to include? Default is "Is a"
                        only. Passing "ALL" includes everything. Usage: --relationships "Is a" "Maps to"
  -o OUT_DIR, --out-dir OUT_DIR
                        Output directory. Defaults to current working directory.
  -V, --vocab-outputs   Create set of artefacts (CodeSystem, ConceptMap) for each vocabulary?
  -C, --combined-outputs
                        Create set of artefacts (CodeSystem, ConceptMap) for all of OMOP vocabulary combined?
  -e, --exclude-vocab-prefix
                        With this flag absent, the identifiers and file names for each vocabulary will be prefixed "OMOP".
  -S, --exclude-code-system
                        Exclude CodeSystem outputs.
  -M, --exclude-concept-map
                        Exclude ConceptMap outputs.
  -H {all,omop2fhir,omop2owl,owl2fhir} [{all,omop2fhir,omop2owl,owl2fhir} ...], --caching {all,omop2fhir,omop2owl,owl2fhir} [{all,omop2fhir,omop2owl,owl2fhir} ...]
                        Warning: Option currently in development. Does not differentiate between different settings (particularly --vocabs
                        and --relationships). It considers all cached files relevant each time you run it using a given --out-dir. So if
                        you want to use caching and also want to change your settings, either (a) clear your --out-dir or (b) use a
                        different --out-dir. Caching options: `all`: Turns on all cache options. `omop2fhir`: Saves any intermediate files
                        at the top level of this pipeline, which basically entails caching of output from omop2owl which is used by
                        owl2fhir. `omop2owl`: Intermediates used by that package, such as robot templates. `owl2fhir`: Intermediates used
                        by that package, such as Obographs JSON.
```
asfpy-phd 0.6.1
A collection of Python scripts for the Application Statement Feedback Program's logistics needs
Requires Python: >=3.6

# ASFPy

A collection of Python scripts for the Application Statement Feedback Program's
logistic needs.

<!-- toc -->

- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
  * [Packaging](#packaging)
  * [Authors](#authors)

<!-- tocstop -->

## Installation

This package requires Python 3.6+ and pip 20+, and recommends using the Python
3.8 runtime. To install, run 

```
pip install asfpy-phd
```

You can find further installation instructions and versioned distributions at https://pypi.org/project/asfpy-phd/.

## Usage 

Current usage requires importing the namespace directly:

```
from asfpy import asfpy
```

This can then be used to work with internal methods. For instance:

```
asfpy.allocate(applicants, editors)
```

will run the allocation algorithm, which returns a list of matchings between
applicants and two editors each. If two editors cannot be matched, the
applicant remains in an unmatched list.

## Contributing

### Packaging

Install via pip: `setuptools`, `wheel` and `twine`. Update version number in
`setup.py` before creating a new distribution.

From root of project, run `python setup.py sdist bdist_wheel` to create
distribution in `dist/*`. Then run `twine upload dist/*`.

### Authors

Ari Dyckovsky
keaml 0.1.3
KeaML Python SDK

# KeaML Deployments

This is a Python library that serves as an SDK for deploying models in KeaML. It provides a simple way to initialize a client and deploy machine learning models.

## Table of Contents

- [Installation](#installation)
- [Getting Started](#getting-started)
- [Usage](#usage)
- [API Key](#api-key)

## Installation

You can install the latest version of KeaML SDK from PyPI:

```bash
pip install keaml
```

## Getting Started

Before you can use KeaML SDK, you'll need to initialize the client with an API key.

### API Key

To get your API key, visit [KEAML Settings](https://app.keaml.com/settings) and generate a new API key.

## Usage

Here's a simple example to get you started:

```python
import keaml

# Initialize the client with your API key
kea_client = keaml.init(api_key="your_api_key_here")

# Deploy a machine learning model
kea_client.deploy(model_object, model_name, framework_name)
```

### Methods

#### `init(api_key: str) -> KeaMLSDKClient`

Initializes the KeaML SDK client.

- **Parameters:**
  - `api_key` (str): Your KEAML API key.

- **Returns:**
  - An instance of `KeaMLSDKClient`.

#### `KeaMLSDKClient.deploy(model: Any, name: str, framework: str) -> None`

Deploys a machine learning model.

- **Parameters:**
  - `model` (Any): The machine learning model object.
  - `name` (str): The name of the model.
  - `framework` (str): The framework used for the model (e.g., "tensorflow", "pytorch").

- **Returns:**
  - None
tflocalpattern 0.0.1
A Local Derivative Pattern layer for deep dearning models
Requires Python: >=3.7

# Local Derivative Pattern transform as a layer in Deep Learning models

This repository contains the Local Derivative Patterns as a Deep Learning layer implemented in the paper. The example on LFW - Labeled Faces in the Wild dataset compares a model with LDP layers and a baseline CNN model.

[Examples](https://github.com/algocodes/ldp_layer)