semvec/pypi-packages huggingface dataset:
django-authenticator-gdaley 0.1.5a0
Authenticate to JupyterHub using an active Django login session
Requires Python: >=3.6
Keywords: jupyterhub,django

# JupyterHub django-session-authenticator - A Django Session Authenticator for JupyterHub

Authenticate to JupyterHub using an active Django login session, which was created by a Django-based service running on a server in the same domain as the JupyterHub service.

## Prerequisites

The main purpose of this authenticator is to provide single sign-on (SSO) capability into JupyterHub, in cases where users are already authenticating with a web application based on the Django framework. This is a simpler alternative to a more centralized authentication solution, such as Cognito.

To allow this JupyterHub (and this authenticator) to "see" the session cookie generated by Django:

* Django and JupyterHub must be hosted under the same domain. For example, django.hostname.com and jupyterhub.hostname.com
* Django's SESSION_COOKIE_DOMAIN must be set to allow cross-domain cookies. For example, hostname.com

*Note that only MySQL database-backed sessions are supported at present.*

## Installation

This package can be installed with pip:

```
pip install django-authenticator-gdaley
```

Alternately, you can clone the source repository and run:

```
cd django-authenticator
pip install -e .
```

## Configuration

You should edit your file `jupyterhub_config.py` to set the authenticator class. The DjangoSessionLocalAuthenticator provides features such as local user creation. If you already have local users then you may use the DjangoSessionAuthenticator authenticator class, as follows:

### For authentication and local user creation
```
c.JupyterHub.authenticator_class = 'djangoauthenticator.djangoauthenticator.DjangoSessionLocalAuthenticator'
```

This class is derived from LocalAuthenticator and therefore provides features such as the ability to add local accounts through the admin interface if configured to do so.

### For authentication of the Django session only

```
c.JupyterHub.authenticator_class = 'djangoauthenticator.djangoauthenticator.DjangoSessionAuthenticator'
```

### Required configuration

There are a number of configuration settings, which need to be made. The name of the session cookie used by Django is optional. If you are using the default session cookie name ("sessionid"), there is no need to set it here.
```
# Django session cookie name
c.DjangoSessionAuthenticator.django_session_cookie_name = '<insert-session-cookie-name-here>'
# Django MySQL database hostname
c.DjangoSessionAuthenticator.mysql_hostname = '<insert-db-hostname-here>'
# Django MySQL username
c.DjangoSessionAuthenticator.mysql_username = '<insert-db-username-here>'
# Django MySQL password
c.DjangoSessionAuthenticator.mysql_password = '<insert-db-password-here>'
```

## Issues

If you have any issues or bug reports, all are welcome in the issues section. We'll do our best to respond quickly.

## Contribution

If you want to fix the bugs yourself then raise a PR and we'll take a look :)
Explain-LISA-CNN-Research 0.0.5
Unified Explanation Provider For CNNs
Keywords: LIME,Integrated gradients,SHAP,Anchors,Explainable AI,XAI,CNN Explainer


# Explain LISA
It takes the following.
img: local path of img to be explained
class_names: the classes available as predictions for the given model
img_shape: shape of the image accepts by the neural network
model: the model to be explained get from tf.keras.models.load_model("your model path")
img1: local path background data point for produce explanations with SHAP
img2: local path background data point for produce explanations with SHAP
scale: for manual image scaling if scaling layer absent in the model to be explained 
filter_radius: the pixel value of the radius of the High pass filter

## Installation
```pip install Explain_LISA_CNN_Research```

## How to use it?
Open terminal and type python/python3 according to your OS.


``` import Explain_LISA_CNN_Research as e```
``` e.ExplainLISA(img,class_names,img_shape,model,img1,img2,scale,filter_radius)```

## License

© 2021 Sudil H.P Abeyagunasekera

This repository is licensed under the MIT license. See LICENSE for details.
aiot 0.0.6
aiot sdk in python

## sdk python for mobifone aiot platform

this is an example project demonstrating how to publish a python module to PyPI.

## Installation

Run the followin gto install

```python
pip install aiot
```

## Developing aiot

To install Helloworld, along with the tools you need to develop and run tests, run the following in yout virtualenv:

```bash
$ pip install -e .[dev]
```
DoraPy 1.1.0
Dorapy is a deep learning framework that focuses on data preprocessing.🛸
Requires Python: >=3.6

```
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣤⣴⣶⣶⣶⣶⣶⠶⣶⣤⣤⣀⠀⠀⠀⠀   
⠀⠀⠀⠀⠀⠀⠀⢀⣤⣾⣿⣿⣿⠁⠀⢀⠈⢿⢀⣀⠀⠹⣿⣿⣿⣦⣄⠀⠀  
⠀⠀⠀⠀⠀⠀⣴⣿⣿⣿⣿⣿⠿⠀⠀⣟⡇⢘⣾⣽⠀⠀⡏⠉⠙⢛⣿⣷⡖⠀ 
⠀⠀⠀⠀⠀⣾⣿⣿⡿⠿⠷⠶⠤⠙⠒⠀⠒⢻⣿⣿⡷⠋⠀⠴⠞⠋⠁⢙⣿⣄ 
⠀⠀⠀⠀⢸⣿⣿⣯⣤⣤⣤⣤⣤⡄⠀⠀⠀⠀⠉⢹⡄⠀⠀⠀⠛⠛⠋⠉⠹⡇ 
⠀⠀⠀⠀⢸⣿⣿⠀⠀⠀⣀⣠⣤⣤⣤⣤⣤⣤⣤⣼⣇⣀⣀⣀⣛⣛⣒⣲⢾⡷ 
⢀⠤⠒⠒⢼⣿⣿⠶⠞⢻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⠁⠀⣼⠃ 
⢮⠀⠀⠀⠀⣿⣿⣆⠀⠀⠻⣿⡿⠛⠉⠉⠁⠀⠉⠉⠛⠿⣿⣿⠟⠁⠀⣼⠃⠀ 
⠈⠓⠶⣶⣾⣿⣿⣿⣧⡀⠀⠈⠒⢤⣀⣀⡀⠀⠀⣀⣀⡠⠚⠁⠀⢀⡼⠃⠀  
⠀⠀⠀⠈⢿⣿⣿⣿⣿⣿⣷⣤⣤⣤⣤⣭⣭⣭⣭⣭⣥⣤⣤⣤⣴⣟⠁    
███████                             ███████          
░██░░░░██                           ░██░░░░██  ██   ██
░██    ░██  ██████  ██████  ██████  ░██   ░██ ░░██ ██ 
░██    ░██ ██░░░░██░░██░░█ ░░░░░░██ ░███████   ░░███  
░██    ░██░██   ░██ ░██ ░   ███████ ░██░░░░     ░██   
░██    ██ ░██   ░██ ░██    ██░░░░██ ░██         ██    
░███████  ░░██████ ░███   ░░████████░██        ██     
░░░░░░░    ░░░░░░  ░░░     ░░░░░░░░ ░░        ░░      
```

## **Dorapy** 

#### is a deep learning framework that focuses on data preprocessing.🛸



### INSTALLING DORAPY

If you use `pip`, you can install DoraPy with:

```bash
pip install dorapy
```



### DoraPy quickstart
Explain-LISA-CNN 0.0.4
Unified Explanation Provider For CNNs
Keywords: LIME,Integrated gradients,SHAP,Anchors,Explainable AI,XAI,CNN Explainer


# Explain LISA
It takes the following.
img: local path of img to be explained
class_names: the classes available as predictions for the given model
img_shape: shape of the image accepts by the neural network
model: the model to be explained get from tf.keras.models.load_model("your model path")
img1: local path background data point for produce explanations with SHAP
img2: local path background data point for produce explanations with SHAP
scale: for manual image scaling if scaling layer absent in the model to be explained 
filter_radius: the pixel value of the radius of the High pass filter

## Installation
```pip install ExplainLISA```

## How to use it?
Open terminal and type python/python3 according to your OS.


``` import ExplainLISA as e```
``` e.ExplainLISA(img,class_names,img_shape,model,img1,img2,scale,filter_radius)```
``` e.getExplanations()```

## License

© 2021 Sudil H.P Abeyagunasekera

This repository is licensed under the MIT license. See LICENSE for details.
Explain-LISA 0.0.3
Unified Explanation Provider For CNNs
Keywords: LIME,Integrated gradients,SHAP,Anchors,Explainable AI,XAI,CNN Explainer


# Explain LISA
It takes the following.
img: local path of img to be explained
class_names: the classes available as predictions for the given model
img_shape: shape of the image accepts by the neural network
model: the model to be explained get from tf.keras.models.load_model("your model path")
img1: local path background data point for produce explanations with SHAP
img2: local path background data point for produce explanations with SHAP
scale: for manual image scaling if scaling layer absent in the model to be explained 
filter_radius: the pixel value of the radius of the High pass filter

## Installation
```pip install ExplainLISA```

## How to use it?
Open terminal and type python/python3 according to your OS.


``` import ExplainLISA as e```
``` e.ExplainLISA(img,class_names,img_shape,model,img1,img2,scale,filter_radius)```
``` e.getExplanations()```

## License

© 2021 Sudil H.P Abeyagunasekera

This repository is licensed under the MIT license. See LICENSE for details.
geo-dome 1.0.0
A performant python Geodesic Dome library developed for the University of Sydney capston unit
Requires Python: >=3.6

## CP38 - Development of Python Library for the Geodesic Dome
This project aims to complete a python library implementing 3 key features
related to the Geodesic Dome:

- Create a Geodesic Dome with the ability to continuously tessellate each face
  according to a specified frequency
- Tessellate a subset of faces
- Implement a neighbour search algorithm to find all points within a specified
  distance, where each neighbour can be found in O(1) time

We aim to implement these three features correctly while also trying to make the
code performant both in terms of time and memory.
jjckdockertool 2
自动部署容器环境

Copyright (c) 2005-2020, NumPy Developers.
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 NumPy Developers nor the names of any
       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
OWNER 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.
tensordiffeq 0.2.0
Distributed PDE Solver in Tensorflow
Requires Python: >=3.6


![TensorDiffEq logo](tdq-banner.png)


![Package Build](https://github.com/tensordiffeq/TensorDiffEq/workflows/Package%20Build/badge.svg)
![Package Release](https://github.com/tensordiffeq/TensorDiffEq/workflows/Package%20Release/badge.svg)
![pypi](https://img.shields.io/pypi/v/tensordiffeq)
![downloads](https://img.shields.io/pypi/dm/tensordiffeq)
![python versions](https://img.shields.io/pypi/pyversions/tensordiffeq)

## Efficient and Scalable Physics-Informed Deep Learning

#### Collocation-based PINN PDE solvers for prediction and discovery methods on top of [Tensorflow](https://github.com/tensorflow/tensorflow) 2.X for multi-worker distributed computing. 

Use TensorDiffEq if you require:
- A meshless PINN solver that can distribute over multiple workers (GPUs) for
  forward problems (inference) and inverse problems (discovery)
- Scalable domains - Iterated solver construction allows for N-D spatio-temporal support
  - support for N-D spatial domains with no time element is included
- Self-Adaptive Collocation methods for forward and inverse PINNs
- Intuitive user interface allowing for explicit definitions of variable domains, 
  boundary conditions, initial conditions, and strong-form PDEs 


What makes TensorDiffEq different?
- Completely open-source
- [Self-Adaptive Solvers](https://arxiv.org/abs/2009.04544) for forward and inverse problems, leading to increased accuracy of the solution and stability in training, resulting in 
  less overall training time 
- Multi-GPU distributed training for large or fine-grain spatio-temporal domains
- Built on top of Tensorflow 2.0 for increased support in new functionality exclusive to recent TF releases, such as [XLA support](https://www.tensorflow.org/xla), 
[autograph](https://blog.tensorflow.org/2018/07/autograph-converts-python-into-tensorflow-graphs.html) for efficent graph-building, and [grappler support](https://www.tensorflow.org/guide/graph_optimization)
  for graph optimization* - with no chance of the source code being sunset in a further Tensorflow version release
  
- Intuitive interface - defining domains, BCs, ICs, and strong-form PDEs in "plain english"
  

*In development

If you use TensorDiffEq in your work, please cite it via:

```code
@article{mcclenny2021tensordiffeq,
  title={TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks},
  author={McClenny, Levi D and Haile, Mulugeta A and Braga-Neto, Ulisses M},
  journal={arXiv preprint arXiv:2103.16034},
  year={2021}
}
```

### Thanks to our additional contributors: 
@marcelodallaqua, @ragusa, @emiliocoutinho