semvec/pypi-packages huggingface dataset:
pyresid 0.6.0
Python tools for mining Protein Residuals from Fulltext articles using PMC number, ePMC and PDB

Python tools for mining Protein Residuals from Fulltext articles using PMC number, ePMC and PDB.

BSD 3-Clause License

Copyright (c) 2018, Robert Elliot Firth (Science and Techology Faclities Council)
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
noteutil 0.0.7.1
A package for handling specially written notes

# noteutil
Noteutil is a Python3 program to turn "notes" into usable data structures.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install noteutil.

```bash
python3 -m pip install noteutil
```

## Usage

```python
import noteutil as nu
noteutil = nu.NoteUtil("my_config.txt")
noteutil.get(content="Note's content")
```


## License
[GPL 3.0](https://choosealicense.com/licenses/gpl-3.0/)
gpt2 0.0.4
API client for GPT-2 text generator cloud hosted by Open Medical IO

# gpt2
API client for GPT-2 text generator hosted on the cloud by Open Medical IO

Generate synthetic text from your custom prompt with  the latest released 774M model of OpenAI's GPT-2.
We take care of the GPU backend.
See [openmedical.io/gpt2](https://openmedical.io/gpt2) for product demo and obtaining an API key!

## Installation 
pip install --upgrade gpt2

## Usage
```python
from gpt2 import Client
c = Client('API SECRET')
output_list = c.fast_generate(prompt='On a perfect summer day we went to')

"""
Generate text on the large 774M model using your own prompt and standard parameters.
This only takes a few seconds per query.
We're training additional models for your use in the near future, such as 774M finetuned on Chinese, Spanish, Wikipedia...
We're also releasing finetuning APIs in September 2019.
Stay tuned :)
"""
```
chaintrailapi 0.0.6
A small example package

# Chaintrail API Wrapper

Installation

```bash
pip3 install chaintrailapi
```

Import and use the package as demonstrated in `example.py` below:

```python
from chaintrailapi import ChainTrail, Accounts, Dataset
import matplotlib.pyplot as plt

api = ChainTrail("localhost:3000", Dataset.JAN_2019)

account_data = api.get_accounts(['acct_address'])
account_scaled = api.get_accounts(['tx_count','avg_tx_in','avg_tx_out','std_tx_in','std_tx_out']).get_scaled()

print(account_scaled.shape)

pcadata = ChainTrail.calculate_pca(account_scaled, components=2)
inliers, outliers, xx, yy, z_train = ChainTrail.predict_outliers(pcadata)

# plot the training points
plt.contour(xx,yy,z_train, levels=[0], linewidths=2, colors='darkred')
plt.scatter(pcadata[inliers,0], pcadata[inliers,1], color='blue', alpha=0.2)
plt.scatter(pcadata[outliers,0], pcadata[outliers,1], color='red', alpha=0.4)
plt.show()
```