cyclotron | simple jQuery plugin for dragging 360 panoramas | Animation library

by mahonnaise JavaScript Version: Current License: Unlicense

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

kandi X-RAY | cyclotron Summary

cyclotron is a JavaScript library typically used in User Interface, Animation, jQuery applications. cyclotron has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

simple jQuery plugin for dragging 360 panoramas (cylindrical projection) and other seamlessly looping images around

Support

Quality

Security

License

Reuse

Support

cyclotron has a low active ecosystem.

It has 96 star(s) with 51 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 696 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cyclotron is current.

Quality

cyclotron has 0 bugs and 0 code smells.

Security

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

cyclotron code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cyclotron is licensed under the Unlicense License. This license is Permissive.

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

Reuse

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

Installation instructions are not available. Examples and code snippets are available.

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cyclotron Key Features

No Key Features are available at this moment for cyclotron.

cyclotron Examples and Code Snippets

No Code Snippets are available at this moment for cyclotron.

Community Discussions

Trending Discussions on cyclotron

Python Fit Arbitrary Gaussian Functions - Bad Fits?

QUESTION

Python Fit Arbitrary Gaussian Functions - Bad Fits?

Asked 2019-Nov-19 at 02:44

I'm trying to generalise some code to be able to fit multiple (n from 1 to >10) gaussian curves/peaks within a single dataset.

Using Scipy Optimise Curve_fit I can get pretty good fits when I hard code functions for 1-3 gaussians, and I've managed to produce functions which run without error for a generalise, arbitrary number of gaussians. However, the output fit is very poor. This is despite giving the input parameters which are identical to those used to generate the 'raw' data - i.e. a best case scenario.

Also, there is a non-zero chance the specific function may need to be modified from a simple gaussian at some point, but for now it should be OK.

Below is my code example, and the output figure is shown below.

...

ANSWER

Answered 2019-Nov-19 at 02:44

You might find lmfit (https://lmfit.github.io/lmfit-py/, disclosure: I am a lead author) useful for this. It provides an easy-to-use Model class for modeling data, including builtin Models for Gaussian, Voigt, and similar lineshapes making it easy to compare model functions.

Lmfit models can be added (or mulitplied) to make a Composite Model, making it easy to support 1, 2, 3, etc Gaussians and include different baseline functions as well. There are docs and several examples at the link above. A small rewrite of your example (including adding a bit of noise) might look like this:

Source https://stackoverflow.com/questions/58874920

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

Vulnerabilities

No vulnerabilities reported

Install cyclotron

You can download it from GitHub.

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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