semvec/pypi-packages huggingface dataset:
anna-client 1.1.9
anna API client

## anna client

### setup
```$ pip install anna-client```

### usage
#### initialization
```python
from anna_client.client import Client

client = Client(endpoint='http://localhost:5000/graphql')
```
#### get jobs
```python
# get all job ids
jobs = client.get_jobs()
# you can specify a where clause & the fields you wish to receive
jobs = client.get_jobs(where={'id_in', [...]}, fields=('driver','site','status'))
```
#### create jobs
```python
# create_jobs takes a list of dicts describing your jobs
jobs = client.create_jobs(data=[{'driver': 'firefox', 'site': 'test'}])
```
#### delete jobs
```python
# provide no parameters in order to delete all jobs
client.delete_jobs(where={})
# or delete specific jobs
client.delete_jobs(where={'id_in': my_jobs})
```
#### update jobs
```python
# provide no where parameter in order to update all jobs
client.update_jobs(data={'status': 'STOPPED'})
# or update specific jobs
client.delete_jobs(where={'id_in': my_jobs}, data={'status': 'STOPPED'})
```
#### reserve jobs
```python
# reserve_jobs takes a worker and a tuple of job ids
client.reserve_jobs(worker='worker', job_ids=my_jobs)
```
#### get tasks
```python
# get_tasks takes a namespace & returns a url and a list of tuples containing the task names & definitions
url, tasks = client.get_tasks(namespace='test')
```
neurocombat-sklearn 0.1.3
Harmonizing neuroimaging data across sites. Implementation of neurocombat using sklearn format

# NeuroCombat-sklearn
[![License: MIT](https://img.shields.io/github/license/Warvito/neurocombat_sklearn)](https://opensource.org/licenses/MIT) 
[![Version](https://img.shields.io/pypi/v/neurocombat-sklearn)](https://pypi.org/project/neurocombat-sklearn/)
[![PythonVersion](https://img.shields.io/pypi/pyversions/neurocombat-sklearn)]()

Implementation of Combat harmonization method in scikit-learn compatible format.


The Combat harmonization/normalization method uses an parametric empirical Bayes framework to robustly adjust data for site/batch effects. 
The scikit-learn compatible format was used to facilitates the use of this harmonization method in machine learning projects. 


This repository is developed by [Walter Hugo Lopez Pinaya](https://scholar.google.com/citations?user=jjT5-HUAAAAJ) at King's College London and community contributors.

## Installation

### Requirements
- Python (>= 3.5)
- [Scikit-Learn](https://scikit-learn.org/) (>= 0.21.0)


### User installation

If you already have a working installation of numpy and scipy,
the easiest way to install neurocombat-sklearn is using ``pip``   :

    pip install neurocombat-sklearn


## Citation
If you find this code useful for your research, please cite:

    @article{fortin2018harmonization,
      title={Harmonization of cortical thickness measurements across scanners and sites},
      author={Fortin, Jean-Philippe and Cullen, Nicholas and Sheline, Yvette I and Taylor, Warren D and Aselcioglu, Irem and Cook, Philip A and Adams, Phil and Cooper, Crystal and Fava, Maurizio and McGrath, Patrick J and others},
      journal={Neuroimage},
      volume={167},
      pages={104--120},
      year={2018},
      publisher={Elsevier}
    }

    @article{johnson2007adjusting,
      title={Adjusting batch effects in microarray expression data using empirical Bayes methods},
      author={Johnson, W Evan and Li, Cheng and Rabinovic, Ariel},
      journal={Biostatistics},
      volume={8},
      number={1},
      pages={118--127},
      year={2007},
      publisher={Oxford University Press}
    }

### Disclaimer

Based on:
 - https://github.com/ncullen93/neuroCombat
 - https://github.com/nih-fmrif/nielson_abcd_2018
 - https://github.com/Jfortin1/ComBatHarmonization