semvec/pypi-packages huggingface dataset:
penn-chime 0.0.3
Covid-19 Hospital Impact Model for Epidemics
Requires Python: >=3.7

chime
=====

PyPI hosted Python module for CHIME

Documentation
-------------

* `User Documentation <https://code-for-philly.gitbook.io/chime/>`_
* `Developer Documentation <https://codeforphilly.github.io/chime/>`_

Contributing
---------------

* `Contribution Overview <https://codeforphilly.github.io/chime/CONTRIBUTING.html>`_

Join our `Code For Philly <https://codeforphilly.org/>`_ project or our `Slack workspace <https://codeforphilly.org/chat>`_ in the `#covid19-chime-penn <https://codeforphilly.org/chat/covid19-chime-penn>`_ channel if you'd like to chat with us. We'd appreciate your `feedback <http://predictivehealthcare.pennmedicine.org/contact/>`_.

Thanks
------

* Created by `Predictive Healthcare @ Penn Medicine <http://predictivehealthcare.pennmedicine.org/>`_
* Project management provided by `Code for Philly <https://codeforphilly.org/>`_
* Production hosting provided by `Linode Kubernetes Engine <https://www.linode.com/products/kubernetes/>`_
blackfortpay 0.0.1
BlackFort Payment API client
Requires Python: >=3.6

# BlackFortPay

BlackFort Payment API client for Python 3.6+

## Installation

`pip install blackfortpay`

## Usage

```python
from blackfortpay import BlackFortPaymentAPI

# pass API User key and API Secret key or store them as environment variables BLACKFORT_API_USER_KEY and BLACKFORT_API_SECRET_KEY
>>> client = BlackFortPaymentAPI('api-user', 'api-secret')  # also you can pass pos_id and locale
>>> rate = client.start_payment(invoice_amount=100, invoice_currency='EUR', currency='BTC')
GetRateResponse(invoice_currency='EUR', invoice_amount='100.00', currency='BTC', rate='7014.0000', amount_exchange='0.01425719', network_processing_fee='0.00000452', amount='0.01425945', wait_time='7 minutes', fast_transaction_fee='0.00033656', fast_transaction_fee_currency='BTC/kB', payment_id='2a1eb341-5e87-4ad8-9f0b-089f5ff027f6')
>>> rate.rate
'7014.0000'
# Change pos_id and locale if needed by set_pos_id() and set_locale() methods
```

API reference available [here](https://pay.blackfort.exchange/payInfo.api)
subjective-test 0.0.2
Transforming the Mean Opinion Scores (MOS) values given their 95% Confidence Intervals to safely use rank based statictial techniques.
Requires Python: >=3.6

# MOS-transformation
Implementation of MOS-transformation to be used with Rank based statistical techniques.

The rank correlation coefficients and the ranked-based statistical tests (as a subset of non-parametric techniques) might 
be misleading when they are applied to subjectively collected opinion scores. Those techniques assume that the data is 
measured at least at an ordinal level and define a sequence of scores to represent a tied rank when they have precisely 
an equal numeric value.

In this paper, we show that the definition of tied rank, as mentioned above, is not suitable for Mean Opinion Scores 
(MOS) and might be misleading conclusions of rank-based statistical techniques. Furthermore, we introduce a method to 
overcome this issue by transforming the MOS values considering their 95% Confidence Intervals. The rank correlation 
coefficients and ranked-based statistical tests can then be safely applied to the transformed values. We also provide 
open-source software packages in different programming languages to utilize the application of our transformation method 
in the quality of experience domain.

## Code
How to use?

``` python
    mos = [10, 6, 5.5]
    ci = [4, 2, 0.2]
    t = transform_mos(mos, ci)
    #  expected_rank = [3, 1.5, 1.5]
```




## Contact

Babak Naderi, babak.naderi[at]tu-berlin.de

## Citation
Naderi B, Moeller S. [Transformation of Mean Opinion Scores to AvoidMisleading of Ranked based Statistical Techniques](https://arxiv) 
2020 Twelfth International Workshop on Quality of Multimedia Experience (QoMEX). IEEE, 2020.


## License
MIT License

Copyright 2020 (c) Dr. Babak Naderi.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
altair-images 0.1.4
Serving interactive charts with images
Keywords: altair flask colab ngrok demo

# Serving interactive charts with images - based on Altair
[![PyPI version](https://badge.fury.io/py/altair-images.svg)](https://badge.fury.io/py/altair-images)

Project fully supports Google Colab. For more details see [examples](examples/examples.ipynb)

## Installation
```sh
pip install altair-images
```

## How to use
```python
import numpy as np

from altair_images import plot_with_image

embedding_data = np.load('../tests/pca_data_100.npy')
sample_images = np.load('../tests/sample_images_100.npy')
labels = np.load('../tests/sample_labels_100.npy')

plot_with_image(embedding_data, labels, sample_images)
```

![Fashion MNIST embeddings](https://raw.githubusercontent.com/lc0/altair-images/master/altair-images.gif)

## TODO
- [x] Add examples in readme
- [ ] Add CI for tagging and publishing new version from master
- [ ] Add tests
- [ ] Example of line_plot and pictures(speed and frame, weight and body)