overholt | Example Flask application

by mattupstate Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | overholt Summary

overholt is a Python library typically used in Template Engine applications. overholt has build file available, it has a Permissive License and it has medium support. However overholt has 6 bugs and it has 1 vulnerabilities. You can download it from GitHub.

Overholt is an example Flask application illustrating some of my common practices.

Support

Quality

Security

License

Reuse

Support

overholt has a medium active ecosystem.

It has 1590 star(s) with 243 fork(s). There are 60 watchers for this library.

It had no major release in the last 6 months.

There are 18 open issues and 18 have been closed. On average issues are closed in 32 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of overholt is current.

Quality

overholt has 6 bugs (0 blocker, 0 critical, 6 major, 0 minor) and 32 code smells.

Security

overholt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

overholt code analysis shows 1 unresolved vulnerabilities (1 blocker, 0 critical, 0 major, 0 minor).

There are 7 security hotspots that need review.

License

overholt is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

overholt releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

overholt saves you 551 person hours of effort in developing the same functionality from scratch.

It has 1289 lines of code, 139 functions and 58 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed overholt and discovered the below as its top functions. This is intended to give you an instant insight into overholt implemented functionality, and help decide if they suit your requirements.

Create Celery app
Create Flask application
Register Blueprint modules
Calls the given request
Get the value from the querystring
Update a model
Save a model instance
Checks if the given model is of type
Preprocess params
Preprocess kwargs
Get all objects with given ids
Fetch all entities
Create a new model
Create a new model instance
Run migrations
Adds a product to store
Remove a manager from the store
Delete model instance
Remove a product from the store
Add a manager to store

Get all kandi verified functions for this library.

overholt Key Features

No Key Features are available at this moment for overholt.

overholt Examples and Code Snippets

No Code Snippets are available at this moment for overholt.

Community Discussions

Trending Discussions on overholt

How to find optimum combination for Cutting Stock Problem using Knapsack

QUESTION

How to find optimum combination for Cutting Stock Problem using Knapsack

Asked 2019-Dec-31 at 19:35

EDIT (31-12-2019) - https://jonathan.overholt.org/projects/cutlist

Above is the link of the free project which is what exactly I am looking for. I am just looking for proper guidance so that I can make it work.

I am working on minimizing the wastage of aluminium extrusion cutting for Aluminium Sliding Window Fabricators and I am not able to figure out which Algorithm/Data Structure I should go with for the problem.

I have done basic research and found that the problem falls in Cutting Stock Problem (Also called One dimensional cutting problem), Linear Programming Problem, Greedy Algorithm. BUT I am unable to decide which one I should go with and also how to start with.

Brief about the problem :

Basically the window fabricators have 3 sizes of material options to purchase.

...

ANSWER

Answered 2019-Jul-17 at 14:02

There is no algorithm that will guarantee you the optimal solution other than brute-force checking all possible combinations. That's obviously not a good algorithm, at least not if you have large data sets.

You should have a look at heuristic search algorithms like Simulated Annealing, MCTS or the like. It should be no problem finding existing implementations of heuristic search engines that allow you to provide them with

an input set (random distribution of pieces on material),
a transition function (move pieces between materials),
and an evaluation function (the amount of waste)

and compute a near-optimal solution for you.

Source https://stackoverflow.com/questions/57000755

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install overholt

The following assumes you have all of the recommended tools listed above installed.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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