semvec/pypi-packages huggingface dataset:
continual-flame 1.0
A continual learning PyTorch package
Requires Python: >=3.8

[![Build Status](https://travis-ci.com/andrew-r96/ContinualFlame.svg?branch=main)](https://travis-ci.com/andrew-r96/ContinualFlame)
# ContinualFlame
Small lightweight package for Continual Learning in PyTorch.
## Installation
For now the package is hosted on TestPyPi. To install it you just need to run:
```bash
pip install continual-flame
```
## Usage
To use the package you just need to import it inside your project.
```python
import contflame as cf
```
At the moment the package contains just the dataset module.
# Dataset
This module contains datasets normally used in the continual learning scenario. The main ones are:
- SplitMNIST - MNIST dataset split in classes. It allows to create different subtasks by including custom subsets of classes.
- PermutedMNIST - permuted MNIST dataset. It allows to choose the shape of the applied permutation.
- SplitCIFAR100
- PermutedCIFAR100
# Examples
**SplitMNIST**

In the following example the training tasks are five binary classification tasks on subsequent pairs of digit (i.e task 1 (0, 1), task 2 (2, 3), ...)
```python
from cont_flame.dataset import SplitMNIST

valid = []
for i in range(1, 10, 2)
  train_dataset = SplitMNIST(classes=[i, i+1], dset='train', valid=0.2)
  valid.append(SplitMNIST(classes=[i, i+1], dset='valid', valid=0.2))
  
  for e in epochs:
    # train the model on train_dataset
    # ...
    
  for v in valid:
    # test the model on the current and the previous tasks
    # ...
```
**PermutedMNIST**

To get a random permutation set tile to (1, 1). The same random permutation, selected by the task id, will be applied to all the data points.

```python
PermutedMNIST(tile=(1, 1), task=1)
```
<img style="float: right;" src="https://user-images.githubusercontent.com/47559809/96425928-da7d2f00-11fc-11eb-95d9-8035dde0e333.png" width="96">
You can also apply the permutation row (or column) wise by setting the corresponding dimension of the tile equal to the one of the image

```python
PermutedMNIST(tile=(1, 28), task=1)
```
<img style="float: right;" src="https://user-images.githubusercontent.com/47559809/96428444-0e0d8880-1200-11eb-814b-376496129f63.png" width="96">
Or try to maintain high level spatial feature by setting a bigger tile.

```python
PermutedMNIST(tile=(8, 8), task=1)
```
<img style="float: right;" src="https://user-images.githubusercontent.com/47559809/96429174-ef5bc180-1200-11eb-81bf-3bbb1dd6a515.png" width="96">

To get the images without any permutation set the tile to (28, 28) (default value).
mesconduit 1.1.2
Python & Conduit Integration Library
Requires Python: >=3.6

42Q- MES - Conduit Integration Setup!

Author  - Melvin Paul Miki
Version - 1.1.2

#Importing the Module:

    > import mesconduit

#Creating a session:

    > session = mesconduit.conduit(url,username,password,device_id,clinet_id)
    
        Attributes Descriptions

                #  url        => Enter the url to do the conduit
                #  Username   => Enter the username of the user
                #  password   => Enter the password of the user
                #  station_id => Enter the device id to be processed
                #  client_id  => Enter the device id to be processed
            


Login Valdation:
    > session.mes_login()
        
        ==> True  - Authorised User
        ==> False - Unathorised User    


Stage Pasing the unit

    > session.mes_PassUnit("123456")

        ==> STATUS :: Respective Message from the MES

        Attributes Descriptions

                #  Unit Serial Number   => "123456"
        
Mutli Units Pasing 

    > session.mes_MultiPass(["1234","5678",.,.,.,.,n])

        ==> [STATUS :: Respective Message from the MES,STATUS :: Respective Message from the MES,.,.,.,.,.,n]

        Attributes Descriptions

                #  Unit Serial Numbers   => ["123456","5678"]


Applying MEO the Serial Number

    > session.mes_ApplyMeo("123456","Screw 1 - Passed")

        ==> STATUS :: Respective Message from the MES

        Attributes Descriptions

                #  Unit Serial Number   => "123456"
                #  Added MEO Message    => "Screw = 1" 



Adding non tracked component 


    > session.mes_AddNonTrackedComponent("123456","Strip","345363"):

        ==> STATUS :: Respective Message from the MES

        Attributes Descriptions

                #  Unit Serial Number   => "123456"
                #  Component Key        => "Strip" 
                #  Component Value      => "345363"  

Data Collector
    Execute in Command Prompt
        >python -c "import  mesconduit; mesconduit.start_app()"
    
    Ex:
        $Enter the conduit URL
            >www.conduit.com
        $Enter the operator Username
            >1234567
        $Enter the operator Password
            >1234567
        $Enter the Station Id
            >123
        $Enter the Client Id
            >abcb123
        $Serial Number?
            >12334BC123
        $STATUS :: Respective Message from the MES
3DCORE 1.1.4
3D Coronal Rope Ejection Model
Keywords: astrophysics,solar physics,space weather

3DCORE
========

3D Coronal Rope Ejection modelling techniqe for coronal mass ejection (CME) flux ropes.

Installation
------------

Install the latest version manually using `git` and `pip`:

    git clone https://github.com/ajefweiss/py3DCORE
    cd 3DCORE
    pip install .
gluelib 1.2.1
A simple library for the pastebin API
Requires Python: >=3.6

# gluelib
This is a simple library for interacting with the pastebin API. The code is fairly simple and self-documenting, and all other information needed is contained within this file. Install with `pip3 install gluelib`.

(It's called gluelib because pastelib was taken)

## Terminology
### Session Keys
When using this library, the first thing to do is generate a session key. This is what you will use instead of a username and password. Call `getSessionKey` with your pastebin username, password, and developer key, and you will receive a session key. This is used in all future API calls. Keep it in memory, and generate a new one for every session. You do not need to use your username and password again.

### Paste Keys
These are the parts of a pastebin URL that identify a specific paste. (e.g. `zvwcdXyn` in `https://pastebin.com/zvwcdXyn`)

## Functions

### `getPaste(pasteKey, sessionKey, devKey)`
Returns the text of a paste given the above parameters.

### `makePaste(pasteName, pasteText, sessionKey, devKey, expiryDate = "N", privacy = "0")`
Uploads a paste to pastebin with the above parameters. See [the pastebin API page](https://pastebin.com/doc_api "the pastebin API page") for more detail.

### `deletePaste(pasteKey, sessionKey, devKey)`
Deletes a paste given the above parameters.

### `listUserPastes(sessionKey, devKey, resultsLimit=50)`
Returns information about your pastes in XML format. Will only display last `resultslimit` results.

### `getUserData(sessionKey, devKey)`
Returns information about your user profile in XML format.

### `getSessionKey(username, password, devKey)`
Returns a session key when given a pastebin username, password, and developer key. See the section on session keys for more.

### `outputBytesToString(outputBytes)`
This function formats a response from the pastebin API. It removes the prefix and suffix bytes, converts the response to a string, and returns it. All other functions in this library pass their output through it. You do not need to use this function in your code.