semvec/pypi-packages huggingface dataset:
isddc-aliyun 1.1.3
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Home-page: http://www.gqylpy.com
Author: 竹永康
Author-email: zhuyk4@lenovo.com
License: MIT
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Utilities
Classifier: Topic :: Internet
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
drever 0.0.9
A framework for handling testvectors within an HDL testbench.

# Drever Framework

Drever is a framework for supporting the development of FPGA based IPs using Python as specification entry.

## Documentation (under development)

Auto-generated documentation for the latest version of the master branch can be found at [https://elpra.gitlab.io/drever-framework/drever](https://elpra.gitlab.io/drever-framework/drever).

## Requirements

- [Python](https://www.python.org/) 3.5 or above (recommended 3.7)
- [Numpy](https://www.numpy.org/)
- [VUnit](https://vunit.github.io/)

## Development Status

Drever is currently in `alpha` status, which means that newer Drever versions can break compability. It is recommended to tag freeze the Drever version within your development cycle until you have ensured that Drever updates do not have an unwanted impact on your existing code.

All alpha versions are tagged as 0.0.x, while x is counted up straight forward. When entering beta phase, version numbers will be 0.x.0. With the first stable release, the version number becames 1.0.0 and then follows semantic versioning.

## Test Reports

Some test reports are generated for quality ensurance. These can be found at:

- [Functional Verification / Unit Tests](https://elpra.gitlab.io/drever-framework/drever/reports/tests)
- [Code Coverage](https://elpra.gitlab.io/drever-framework/drever/reports/coverage)
- [Code Linting](https://elpra.gitlab.io/drever-framework/drever/reports/lint)

## Licensing

Drever is released under the terms of [GNU General Public License, Version 3](LICENSE).

Copyright (c) 2019, Tobias Baumann tobias.baumann@elpra.de

If you need support for the Drever framework under other license conditions, please get in contact by using the email mentioned above.
ChanChanAuth 0.0.4
A simple Python wrapper for https://api.ccauth.app/
Requires Python: >=3.6

# ChanChanAuth
Python wrapper for https://api.ccauth.app/
## Examples of Usage

### Login
```py
from chanchanauth import Client


client = Client(AID, APIKEY, SECRET)
response = client.login(USERNAME, PASSWORD, HWID)

if response.is_authenticated:
    print("Logged in")
```
### Register
```py
from chanchanauth import Client

client = Client(AID, APIKEY, SECRET)
response = client.register(USERNAME, PASSWORD, HWID, DISCORD, LICENSE)

if response.success:
    print("Success")
```
### Reset HWID
```py
from chanchanauth import Client

client = Client(AID, APIKEY, SECRET)
response = client.hwid_reset(USERNAME, PASSWORD, HWID, HWID_KEY)

if response.success:
    print("success")
 ```
 ### Generate License
 ```py
 from chanchanauth import Admin
 
 admin = Admin(AID, APIKEY, USERNAME, PASSWORD)
 response = admin.generate_license()
 
 if response.success:
    print(response.license)
```
 ### Generate HWID Key
 ```py
 from chanchanauth import Admin
 
 admin = Admin(AID, APIKEY, USERNAME, PASSWORD)
 response = admin.generate_hwidkey()
 
 if response.success:
    print(response.key)
```
covira 1.1.12
CoVIRA (Consensus by Voting with Iterative Re-weighting based on Agreement)
Keywords: bioinformatics machine-learning data science

# CoVIRA
![Travis CI status](https://travis-ci.org/fredericokremer/covira.svg?branch=master&status=unknown)

CoVIRA (*Consensus by Voting with Iterative Re-weighting based on Agreement*) is a method
to identify weights and produce consensus predictions based on a collection of results from
predictors for multiple samples. It employs a iterative recalculations of weights based on the
weighted "agreement" between the predictors, and allows the calculation of a final prediction as well.

This algorithm was created to help on the integration of results from multiple prediction tools in a
reverse vaccinology study where no validation dataset was available for all features been inferred. Therefore,
we created a unsupervised way to estimate how accurate each predictor was for that particular case considering
that the more the results of a predictor is "confirmed" by the others, the higher it's accuracy.

## Installing 

### From PyPI

```bash
$ pip install covira
```

### From source code

```bash
$ git clone https://github.com/fredericokremer/covira
$ cd covira
$ python setup.py build
$ python setup.py install
```

## Using

```python
>>> import pandas as pd
>>> from covira import Covira
>>> df = pd.read_csv('test_data/dataset_1.csv')
>>> predictions = df.drop(['gene'], axis=1).values
>>> predictions                          # each column represents the prediction from a different tool, while each row is a different sample
array([[1, 0, 0], 
       [0, 1, 0], 
       [1, 1, 0], 
       [1, 0, 0], 
       [1, 0, 0], 
       [1, 0, 0], 
       [1, 0, 0], 
       [1, 0, 1]])
>>> covira = Covira(max_iterations=1000) # max number of iterations in the weight calculation
>>> covira.fit(predictions)              # "fit" = calculates the weights
>>> covira.predict(predictions)          # calculates the consensus prediction
array([0.1875, 0.375 , 0.5625, 0.1875, 0.1875, 0.1875, 0.1875, 0.625 ])
>>> covira.weights                       # weights calculated for each predictor
array([0.1875, 0.375 , 0.4375])
```

## Reference

Grassmann AA, Kremer FS, Dos Santos JC, Souza JD, Pinto LDS, McBride AJA. *Discovery of Novel Leptospirosis Vaccine 
Candidates Using Reverse and Structural Vaccinology*. **Front Immunol**. 2017;8:463. Published 2017 Apr 27. 
[doi:10.3389/fimmu.2017.00463](doi:10.3389/fimmu.2017.00463)