semvec/pypi-packages huggingface dataset:
ali-amap-hmac-generate 0.1.1
requests middleware for generate signature amap apistore
License: MIT
Requires Python: >=2.7.0

# requests_middleware_ali_hmac 
### requests_middleware_ali_hmac demo 
```python
import requests
from requests_middleware import MiddlewareHTTPAdapter
from requests_middleware_ali_hmac import AliHmacGenerate
session = requests.Session()
middlewares = [
    AliHmacGenerate("testid", "testsecret"),
]
adapter = MiddlewareHTTPAdapter(middlewares)
session.mount('http://', adapter)
session.mount('https://', adapter)
```
allow requests methods "GET", "POST", "PUT", "DELETE", "PATCH"
volga 0.2.0
A python framework for deserialization
License: MIT
Requires Python: >=3.9,<4.0
Keywords: python,serialize,deserialize

# volga: flexible object deserialization

[![Build Status]][build] [![Azure DevOps coverage]][Azure coverage url] [![license]][license-file] [![release]][releases] [![python-version]][pypi]
[![Docs](https://img.shields.io/readthedocs/volga.svg)](https://volga.readthedocs.io)

[Build Status]: https://dev.azure.com/yefrigaitan/volga/_apis/build/status/yefrig.volga?branchName=main
[build]: https://dev.azure.com/yefrigaitan/volga/_build/latest?definitionId=9&branchName=main

[Azure DevOps coverage]: https://img.shields.io/azure-devops/coverage/yefrigaitan/volga/9
[Azure coverage url]: https://dev.azure.com/yefrigaitan/volga/_build/latest?definitionId=9&branchName=main

[license]: https://img.shields.io/github/license/yefrig/volga
[license-file]: https://github.com/yefrig/volga/blob/main/LICENSE

[release]: https://img.shields.io/github/v/release/yefrig/volga?include_prereleases&sort=semver
[releases]: https://github.com/yefrig/volga/releases

[python-version]: https://img.shields.io/pypi/pyversions/volga
[pypi]: https://pypi.org/project/volga/

## What is it?
**volga** provides fast, extensible, and expressive APIs
to deserialize any python data structure from any supported data format
(such as JSON and *eventually* YAML and more). Volga allows full customization of the deserialization 
behavior of your data structures resulting in schema-tized, validated, type-checked 
objects.

```python3
import volga

# Define your model
class User(volga.Schema):
    name: volga.fields.Str
    age: volga.fields.Int
    verified: volga.fields.Bool
  
json_data = '{"name":"bob","age":20,"verified":true}'

bob = volga.json.deserialize(json_data, User)

assert isinstance(bob, User)

print(bob) # prints object User(name='bob', age=20, verified=True)
```

## Main Features


## Documentation

Full documentation will soon be available on https://volga.readthedocs.io/en/latest/


## Where to get it
The source code is currently hosted on GitHub at:
https://github.com/yefrig/volga

Binary installers for the latest released version are available at the [Python
package index](https://pypi.org/project/volga).

```sh
pip install volga
```

## Main Contributors

- Yefri Gaitan [@yefrig](https://github.com/yefrig)

- Ecenaz (Jen) Ozmen [@eozmen410](https://github.com/eozmen410)
drew 0.0.11
drew
License: MIT

You can tell by the way I walk...

Change Log
======================

0.0.1 (11/14/20)
----------------------
- First Release

0.0.2 (11/14/20)
----------------------
- Added animation

0.0.3 (11/14/20)
----------------------
- Fix import bug

0.0.4 (11/14/20)
----------------------
- Fix import bug

0.0.11 (11/147/20)
----------------------
- Improve animation
ali-hmac-generate 0.1.0
requests middleware for generate signature amap apistore
License: MIT
Requires Python: >=2.7.0

#requests-middleware-add-hmac
###requests-middleware-add-hmac demo
```python
import requests
from requests_middleware import MiddlewareHTTPAdapter
from ali_hmac_generate import AliHmacGenerate
session = requests.Session()
middlewares = [
    AliHmacGenerate("testid", "testsecret"),
]
adapter = MiddlewareHTTPAdapter(middlewares)
session.mount('http://', adapter)
session.mount('https://', adapter)
```
allow requests methods "GET", "POST", "PUT", "DELETE", "PATCH"
topsis-101803128 1.4
A Python package implementing TOPSIS technique.
License: MIT
Keywords: Topsis,Topsis Ranking

# TOPSIS-Python

**Assignment 6: UCS538**


Submitted By: **DIVYAM JAIN-101803128**

***

## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
making method. TOPSIS chooses the alternative of shortest Euclidean distance
from the ideal solution, and greatest distance from the negative-ideal
solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-Divyam-101803128 can be run as in the following example:



### Python Script
```
from topsis_101803128.topsis import CalcTopsisScore
filename = "input.csv"
weight = "1,1,1,2"
impact = "+,+,-,+"
CalcTopsisScore(filename, weight, impact )
```
<br>


## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

Weights (`w`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.

<br>

## Output

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.77221 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.225599 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.438897 | 4
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.523878 | 3
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.811389 | 1


<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.

## License

Â© 2020 Divyam Jain

This repository is licensed under the MIT license. See LICENSE for details.
misso 0.1
Python package to compute mutual information matrix
License: MIT
Requires Python: >=3.6

MISSO


### Installation
 - Using `pip`
```
pip install misso
```
**Note:** In our benchmarks, multi-core version was always faster than the GPU accelerated version. So, we
highly recommend installing just the CPU version and using multi-core computation.

- Installing from source
```

```

### Usage
```
from misso import MISSO


```
For a more detailed usage, check out the Tutorials folder.

### Benchmarks

** Benchmarks were run on a machine with the following configuration
```
CPU:       6 core Intel Core i7-8750H (-MT-MCP-) [12 core with Hyperthreading]
           arch: Skylake rev.10 cache: 9216 KB
           flags: (lm nx sse sse2 sse3 sse4_1 sse4_2 ssse3 vmx) bmips: 26399
           clock speeds: max: 4100 MHz 1: 2479 MHz 2: 3013 MHz 3: 3211 MHz
           4: 3098 MHz 5: 3362 MHz 6: 3769 MHz 7: 3082 MHz 8: 3290 MHz
           9: 3090 MHz 10: 3141 MHz 11: 3055 MHz 12: 3650 MHz
Graphics:  Card-1: Intel Device 3e9b bus-ID: 00:02.0
           Card-2: NVIDIA Device 1f10 bus-ID: 01:00.0
           Display Server: x11 (X.Org 1.19.6 )
           drivers: modesetting,nvidia (unloaded: fbdev,vesa,nouveau)
           Resolution: 3840x1600@59.99hz
           OpenGL: renderer: GeForce RTX 2070 with Max-Q Design/PCIe/SSE2
           version: 4.6.0 NVIDIA 440.100 Direct Render: Yes
```

### License

### TODO
- [ ] Try gradient-based solvers 
    - [ ] Conjugate-gradient descent
- [x] Multi-processing for `lsmi` computation
    - [x] Reduce interprocess overhead
    - [x] Try other methods to parallelize the code
- [ ] Benchmarks
    - [x] Multiprocessing
    - [x] GPU benchmarks
    - [ ] Solver Benchmarks
    - [ ] Run benchmarks on multiple machines and put in benchmark reports
- [ ] Detailed comparison with graphical Lasso (Tutorials)
    - [ ] Toy Example
    - [ ] Time Series: Stationary & Dynamic [link](https://academic.oup.com/cercor/article-pdf/24/3/663/14099596/bhs352.pdf)
    - [ ] Comparison of MISSO and GLASSO on indirect coupling [link](https://academic.oup.com/bioinformatics/article-pdf/28/2/184/16908913/btr638.pdf)
- [ ] GPU Acceleration    
    - [x] Use Cupy for solving
    - [x] Reduce GPU overhead 
    - [ ] Verify correctness (Still an issue)
    - [x] Try torch for GPU acceleration
- [x] tqdm for Notebook and Script
- [ ] Pandas DataFrame support
- [ ] Packaging
    - [ ] pip package
    - [ ] Travis CI
- [ ] Readme