SIPPY | Systems Identification Package for PYthon | Genomics library

by CPCLAB-UNIPI Python Version: Current License: LGPL-3.0

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

kandi X-RAY | SIPPY Summary

SIPPY is a Python library typically used in Artificial Intelligence, Genomics applications. SIPPY has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has low support. You can download it from GitHub.

Systems Identification Package for PYthon

Support

Quality

Security

License

Reuse

Support

SIPPY has a low active ecosystem.

It has 223 star(s) with 79 fork(s). There are 21 watchers for this library.

It had no major release in the last 6 months.

There are 20 open issues and 19 have been closed. On average issues are closed in 66 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SIPPY is current.

Quality

SIPPY has 0 bugs and 0 code smells.

Security

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

SIPPY code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SIPPY is licensed under the LGPL-3.0 License. This license is Weak Copyleft.

Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

Reuse

SIPPY 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 are available. Examples and code snippets are not available.

SIPPY saves you 1145 person hours of effort in developing the same functionality from scratch.

It has 3872 lines of code, 73 functions and 24 files.

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

Top functions reviewed by kandi - BETA

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

System identification
Performs OLSSID decomposition
SVD decomposition
Compute the dot product of a matrix
Dynamically approximation of a function

Get all kandi verified functions for this library.

SIPPY Key Features

No Key Features are available at this moment for SIPPY.

SIPPY Examples and Code Snippets

No Code Snippets are available at this moment for SIPPY.

Community Discussions

Trending Discussions on SIPPY

How to translate std::list from c++ to python with SIP

QUESTION

How to translate std::list from c++ to python with SIP

Asked 2021-Feb-24 at 12:56

I am using SIP version 6.0.1 with python 3.8 and have a given c++11 API. (on Ubuntu 18.04)

The goal is to store e.g. custom struct pointers (the structs I also translated with sip) in std::list using python.

I constructed a tiny example to make my case:

std_list.sip file

...

ANSWER

Answered 2021-Feb-24 at 12:56

The problem is not in the type_list.sip file, but in the test.py file.

The cmd of appending something to a translated python list with append like ...

temp_structContainerLvl2.listTestStruct.append(testStruct1)

can not be used for the sip translation of a python list.

As a workaround, one can use

temp_structContainerLvl2.listTestStruct = [testStruct1, testStruct2]

and

temp_structContainerLvl2.listTestStruct += [testStruct3]

I imagine it is not that efficient, but all I've got for now.

Source https://stackoverflow.com/questions/66282378

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

Vulnerabilities

No vulnerabilities reported

Install SIPPY

The code has been implemented in Python 2.7, compatible with Python 3.7, (download it here) and requires the following packages: NumPy, SciPy, control (version >= 0.8.2), math, Slycot, Future (See installation instruction here), CasADi (see here). The Slycot package is available here or alternatively the binaries can be found here. In order to make the installation easier, the user can simply use the quick command python setup.py install in order to gather all the required packages all together.
user_guide.pdf: documentation for Identification_code usage.
sippy/__init__.py: main file containing the function that has to be recalled to perform the identifications.
Examples/Ex_ARMAX_MIMO.py: example of usage of the Identification_code for ARMAX systems (multi input-multi output case).
Examples/Ex_ARX_MIMO.py: example of usage of the Identification_code for ARX systems (multi input-multi output case).
Examples/Ex_ARMAX.py: example of usage of the Identification_code for ARMAX systems (single input-single output case, using the information criteria).
Examples/SS.py: example of usage of the Identification_code for State-space systems.
Examples/Ex_OPT_GEN-INOUT.py: example of usage of the Identification_code for the input-output structures using optimization methods.
Examples/Ex_RECURSIVE.py: example of usage of the Identification_code for the input-output structures using recursive methods.
Examples/Ex_CST.py: example of usage of the Identification_code for a Continuous Stirred Tank system.
sippy/functionset.py: file containing most of the functions used by the identification functions and other useful functions (see the user_guide for the usage).
sippy/functionset_OPT.py: file containing the nonlinear optimization problem used by some of the identification methods.
sippy/functionsetSIM.py: additional functions used by the Subspace identification functions and other useful functions for state space models (see the user_guide for the usage).

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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