tsam | based time series aggregation module which can be | Time Series Database library

by FZJ-IEK3-VSA Python Version: 2.3.1 License: Non-SPDX

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

kandi X-RAY | tsam Summary

tsam is a Python library typically used in Database, Time Series Database applications. tsam has no bugs, it has no vulnerabilities, it has build file available and it has low support. However tsam has a Non-SPDX License. You can install using 'pip install tsam' or download it from GitHub, PyPI.

We are the Techno-Economic Energy Systems Analysis department at the Institute of Energy and Climate Research: Electrochemical Process Engineering (IEK-3) belonging to the Forschungszentrum Jülich. Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and costs of energy systems. The results are used for performing comparative assessment studies between the various systems. Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and by conducting cost analysis studies for integrating new technologies into future energy market frameworks.

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Support

tsam has a low active ecosystem.

It has 91 star(s) with 25 fork(s). There are 11 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 23 have been closed. On average issues are closed in 214 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tsam is 2.3.1

Quality

tsam has 0 bugs and 0 code smells.

Security

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

tsam code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tsam 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

tsam releases are available to install and integrate.

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

It has 781 lines of code, 35 functions and 8 files.

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

Top functions reviewed by kandi - BETA

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

Creates the centroids for the time series
Calculate the representative centers for the given candidates
Return the duration representation of the candidates
Performs clustering
Fit the clustering algorithm
Checks if the array is valid
Calculate k - means k - medoids
Setup k - means model
Calculate k - maxoids clustering
Check that the data is valid
Calculate the k maxima for a given matrix X
Return the segment duration as a dictionary
Creates a new stochasticPeriods
Unnormalize the time series
Returns a dictionary of clusteringPeriods
Perform clustering
Return a list of the indices of the cluster
Returns the clustering order of the grid
ClusterPeriods

Get all kandi verified functions for this library.

tsam Key Features

No Key Features are available at this moment for tsam.

tsam Examples and Code Snippets

No Code Snippets are available at this moment for tsam.

Community Discussions

Trending Discussions on tsam

Is it possible to derive a classdef class from an old-style class?

QUESTION

Is it possible to derive a classdef class from an old-style class?

Asked 2021-Jan-16 at 21:43

Is it possible to derive a classdef class from an old-style class?

In Octave 4.0 the ss class is defined in the old style with @ss directory. I want to derive a class with a customized __freqresp__ method in a file myss.m located in path and with the following content:

...

ANSWER

Answered 2021-Jan-12 at 17:42

Extending my answer in the comments to an example.

Like I said in the comments, I don't think mixing classdef and classic object-oriented styles is possible. Therefore I would use the 'classic' OO-style to perform the 'overload' instead, which is fairly straightforward anyway.

However, like lots of other syntax in matlab/octave, the classic style uses filesystem semantics to define the respective namespaces, and you have the added requirement that you'd like to code all of this functionality in a single file.

The obvious workaround that comes to mind then, is to create the necessary files/namespaces on the filesystem 'on the fly', and then load that class definition as appropriate in your main code later. Obviously, this isn't really the recommended way to work with classes (new or old), however, if you really must work from a single file, then it is not that difficult to accomplish either.

Here's a (standalone) example demonstrating this (i.e. copy code into a script and just run the script in octave):

Source https://stackoverflow.com/questions/65680464

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

Vulnerabilities

No vulnerabilities reported

Install tsam

Directly install via pip as follows:. Alternatively, clone a local copy of the repository to your computer. Then install tsam via pip as follow. Or install directly via python as. In order to use the k-medoids clustering, make sure that you have installed a MILP solver. As default solver GLPK is used. Nevertheless, in case you have access to a license we recommend commercial solvers (e.g. Gurobi or CPLEX) since they have a better performance.

Support

Within the BMWi funded project METIS we extend the methodology together with the RWTH-Aachen (Prof. Aaron Praktiknjo), the EDOM Team at FAU (PD Lars Schewe) and the Jülich Supercomputing Centre.

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