scipy-lecture-notes | Tutorial material on the scientific Python ecosystem | Learning library

by scipy-lectures Python Version: 2020.1-beta License: Non-SPDX

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

kandi X-RAY | scipy-lecture-notes Summary

scipy-lecture-notes is a Python library typically used in Tutorial, Learning applications. scipy-lecture-notes has no vulnerabilities, it has build file available and it has medium support. However scipy-lecture-notes has 5 bugs and it has a Non-SPDX License. You can download it from GitHub.

Tutorial material on the scientific Python ecosystem

Support

Quality

Security

License

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Support

scipy-lecture-notes has a medium active ecosystem.

It has 2950 star(s) with 1171 fork(s). There are 177 watchers for this library.

It had no major release in the last 12 months.

There are 23 open issues and 141 have been closed. On average issues are closed in 692 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of scipy-lecture-notes is 2020.1-beta

Quality

scipy-lecture-notes has 5 bugs (0 blocker, 0 critical, 2 major, 3 minor) and 91 code smells.

Security

scipy-lecture-notes has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

scipy-lecture-notes code analysis shows 0 unresolved vulnerabilities.

There are 20 security hotspots that need review.

License

scipy-lecture-notes has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

scipy-lecture-notes releases are available to install and integrate.

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

scipy-lecture-notes saves you 3790 person hours of effort in developing the same functionality from scratch.

It has 8083 lines of code, 250 functions and 267 files.

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

Top functions reviewed by kandi - BETA

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

Implementation of IECA
Calculate the derivative of the gaussian correlation
Compute the function f
Compare two optimizers
Benchmark an optimizer
Apply a function to func
Bencher - Hessian Hessian
Generate the cost function for a given distribution
Compute the covariance matrix
Compute the symmetric symmetry operator
Plot a supervised plot
Creates the base plot
Load data from file
Compute the log - likelihood function
Fortran_module
Calculate the cost function for a given method
Return a configuration object
Random model
Find a module in sys path
Plot tick line
Returns a list of filenames sorted by their length
Create a dictionary of the cost function
Performs an iteration on an iterated image
Generate a nonlinear model
Generate the sakara matrix
Compute mandelbrot
Sort a list
Draw a box

Get all kandi verified functions for this library.

scipy-lecture-notes Key Features

No Key Features are available at this moment for scipy-lecture-notes.

scipy-lecture-notes Examples and Code Snippets

Jupyter Notebook

Lines of Code : 3

License : No License

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Required Reading:

Suggested Reading:

Additional Resources:

Community Discussions

Trending Discussions on scipy-lecture-notes

How does one acces numpy multidimensionnal array in c extensions?

QUESTION

How does one acces numpy multidimensionnal array in c extensions?

Asked 2019-May-22 at 03:06

I have been struggling for a few day to understand the access to numpy arrays in C extension, but I've trouble understanding the documentation.

Edit: Here is the code I would like to port to c (the grav function)

...

ANSWER

Answered 2019-May-18 at 10:58

Intro

Probably the best way to figure it out is to create the iterator in Python and experiment with it there. This will be slow, but it'll confirm what you're doing is right. You then use NpyIter_AdvancedNew, using the default parameters wherever possible.

I'm afraid I haven't actually translated this into C code myself - it was taking too long for me. I therefore suggest you don't accept this answer since it only gives a starting point really.

My guess would be that any performance improvements will be disappointing given the amount of effort that writing C code is (especially since I'd guess that writing fast code needs a deeper level of understanding). At the end of the answer I suggest a few of simpler alternatives that I'd recommend instead of using the C API.

Examples

I've translated a couple of lines from your code as examples:

Source https://stackoverflow.com/questions/56182259

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

Vulnerabilities

No vulnerabilities reported

Install scipy-lecture-notes

You can download it from GitHub.
You can use scipy-lecture-notes like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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