semvec/pypi-packages huggingface dataset:
epiphani-ecube 1.1.0
epiphani CLI for registering connectors & executing scripts in a sandbox
License: MIT
Requires Python: >=2.7

# ecube
epiphani execution engine (e3) allows running local scripts and commands in conjunction with other cloud based connectors to create low-code playbooks. These can be shared with other team members using collaboration tools such as Slack. For more information visit: https://www.epiphani.ai

## Installation
```
$ pip install epiphani-ecube
```

## ecube config & arguments

In order to use ecube, you need to first create an epiphani account. Once you have the account, you need to setup ecube by adding your credentials in **~/.e3.ini**
```
(ecube) pmadhav@ip-172-31-46-212:~$ e3 --help
usage: e3 <command> [<args>]
The following commands are supported:
    run      Run a local connector
    runcli   Run a cli as a connector 
    playbook Interact with the playbook engine 
    
Please create a config file ~/.e3.ini with following content:
[DEFAULT]
username = <epiphani username> <REQUIRED>
password = <epiphani password> <REQUIRED>
login = URL to epiphani <OPTIONAL>
log_file = foo.log <OPTIONAL>
local_install = true <OPTIONAL | Use local installation | Default: false>
local_install_host = localhost <OPTIONAL | IP/FQDN of local installation | Default: localhost>
local_install_port = 31050 <OPTIONAL | GraphQL server port number | Default: 31050>

If you set local_install_host or local_install_port, local_install is automatically set to true

e3 (ecube) -> epiphani execution engine

positional arguments:
  command     Subcommand to run

optional arguments:
  -h, --help  show this help message and exit
(ecube) pmadhav@ip-172-31-46-212:~$ 
```

## Using ecube examples

### runcli

```
e3 runcli --name pmCli
```
This will create a connector that will be executed on the machine that is running e3. It can be made a part of any playbooks on the playbook engine. Please see this: https://github.com/epiphani-inc/ecube/wiki/Install

## Use ecube to create & run playbooks

### Create a playbook

There are some simple sample playbooks in the gitrepo @ ./samples/playbooks
You will have to edit them to add you AWS secret and key information where it says <>.

Once you have the samples edited and e3 installed, you can just create the playbook like this:
```
e3 playbook create --directory ./samples/playbooks/threeNode --name EC2CL109
```

### Execute a playbook

To execute a playbook you just need to give the name of the playbook to run

```
e3 playbook run --vars '{"region": "us-west-2"}' --name EC2CL108
```

In this case the playbook needs variables - region where it is going to fetch the state from.
syntrans 0.5.0
This is a simple engine for language-indepdent document (LID, based on ideas on definitionary.com).
License: MIT

Syntrans
========

This toolkit is based on the idea of `Default Interlingual Synsets <https://0oo.li/method/895/default-interlingual-synsets>`_

You can write a hierarchy or graph in a YAML file, using precise concept meaning references from various namespaces of semantic web, notably, `WikiData <https://www.wikidata.org/>`_, automatically them converted into their representations in a language of
choice.

The aim is to continue with introducing default meanings for tokens in languages, so as to convert not to arbitrary first expressions, but to the default expressions for concepts in each language. Since, those default expressions are likely to coincide with the multi-meaning traditional expressions, we want to come up with a way to emphasize default meanings in such texts, perhaps prepending words with a symbol like in the ``examples/test.yml``, though, language references would also be required.


Usage
=====
Install it with ``pip install synsets``, then you can translate YAML documents with keys like in the ``examples`` directory, for example, using WikiData concept IDs with namespace ``WD:`` or other namespaces supported by `Metawiki
<https://github.com/wefindx/metawiki/>`_ package.

::

    syntrans -h
    usage: syntrans [-h] [-c CONCEPT] [-s SOURCE] [-l LANGUAGE] [-r REFRESH]

    optional arguments:
      -h, --help            show this help message and exit
      -s SOURCE, --source SOURCE
                            Source text file to parse.
      -l LANGUAGE, --language LANGUAGE
                            Language reference, e.g., en, lt, ja, ru, cn.
      -r CONCEPT, --concept CONCEPT
                            Single concept to refresh cache from source.

For example, let's translate the sample text.

Example source:

::

    $ cat examples/sample.yml
    WD:Q4503831:
      - WD:Q1156970 WD:Q7949 WD:Q8 WD:Q245962
      - WD:Q1378301 WD:Q3 WD:Q15290 WD:Q185957 WD:Q468777 WD:Q245962

Example to English:

::

    $ syntrans -s examples/sample.yml -l en

    goal:
    - humanity • truth • happiness • implementation
      - universal set • life • good • quality • existence • implementation

Example to Japanese:

::

    syntrans -s examples/sample.yml -l ja

    目標:
    - 人類 • 真理 • 幸福 • 実装
      - 普遍集合 • 生命 • 善 • 品質 • 存在 • 実装

Refresh concept from web:

``syntrans -r WD:Q1156970``

Delete ``.concept`` folder to refresh all cached concepts.
scArchest 0.0.1
Transfer learning with Architecture Surgery on Single-cell data
License: MIT

|PyPI| |PyPIDownloads| |Docs| |travis|

scArches (PyTorch) - single-cell architecture surgery
=========================================================================
.. raw:: html

 <img src="https://user-images.githubusercontent.com/33202701/89729020-15f7c200-da32-11ea-989b-1b9a3283f642.png" width="900px" align="center">

This is a Pytorch version of scArches which can be found `here <https://github.com/theislab/scArches/>`_. scArches is a package to integrate newly produced single-cell datasets into integrated reference atlases. Our method can facilitate large collaborative projects with decentralise training and integration of multiple datasets by different groups. scArches is compatible with `scanpy <https://scanpy.readthedocs.io/en/stable/>`_, and hosts efficient implementations of all conditional generative models for single-cell data.

What can you do with scArches?
-------------------------------
- Integrate many single-cell datasets and share the trained model and the data (if possible).
- Download a pre-trained model for your atlas of interest, update it with new datasets and share with your collaborators.
- Construct a customized reference by downloading a reference atlas, add a few  pre-trained adaptors (datasets) and project your own data in to this customized reference atlas.
- Project and integrate query datasets on the top of a reference and use latent representation for downstream tasks, e.g.: diff testing, clustering.

Usage and installation
-------------------------------
See `here <https://scarchest.readthedocs.io/>`_ for documentation and tutorials.

Support and contribute
-------------------------------
If you have a question or new architecture or a model that could be integrated into our pipeline, you can
post an `issue <https://github.com/theislab/scarchesp/issues/new>`__ or reach us by `email <mailto:cottoneyejoe.server@gmail.com,mo.lotfollahi@gmail.com,mohsen.naghipourfar@gmail.com>`_. Our package supports tf/keras now but pytorch version will be added very soon.


Reference
-------------------------------
If scArches is useful in your research, please consider citing this `preprint <https://www.biorxiv.org/content/10.1101/2020.07.16.205997v1/>`_.


.. |PyPI| image:: https://img.shields.io/pypi/v/scarchest.svg
   :target: https://pypi.org/project/scarchesp

.. |PyPIDownloads| image:: https://pepy.tech/badge/scarchest
   :target: https://pepy.tech/project/scarchesp

.. |Docs| image:: https://readthedocs.org/projects/scarchest/badge/?version=latest
   :target: https://scarchesp.readthedocs.io

.. |travis| image:: https://travis-ci.com/theislab/scarchest.svg?branch=master
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