scholarly journals ordpy: A Python package for data analysis with permutation entropy and ordinal network methods

2021 ◽  
Vol 31 (6) ◽  
pp. 063110
Author(s):  
Arthur A. B. Pessa ◽  
Haroldo V. Ribeiro
Keyword(s):  
Data Analysis ◽  
Permutation Entropy ◽  
Network Methods ◽  
Python Package

scholarly journals OceanSpy: A Python package to facilitate ocean model data analysis and visualization

2019 ◽  
Vol 4 (39) ◽  
pp. 1506 ◽  
Author(s):  
Mattia Almansi ◽  
Renske Gelderloos ◽  
Thomas Haine ◽  
Atousa Saberi ◽  
Ali Siddiqui
Keyword(s):  
Data Analysis ◽  
Ocean Model ◽  
Model Data ◽  
Python Package

scholarly journals An Approach for Reconstruction of Realistic Economic Data Based on Frequency Characteristics between IMFs

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Du ◽  
Hao Gong ◽  
Suyue Han ◽  
Peng Zheng ◽  
Bin Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Low Frequency ◽  
Test Method ◽  
Series Data ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Economic Data ◽  
Index Method

Reconstruction of realistic economic data often causes social economists to analyze the underlying driving factors in time-series data or to study volatility. The intrinsic complexity of time-series data interests and attracts social economists. This paper proposes the bilateral permutation entropy (BPE) index method to solve the problem based on partly ensemble empirical mode decomposition (PEEMD), which was proposed as a novel data analysis method for nonlinear and nonstationary time series compared with the T-test method. First, PEEMD is extended to the case of gold price analysis in this paper for decomposition into several independent intrinsic mode functions (IMFs), from high to low frequency. Second, IMFs comprise three parts, including a high-frequency part, low-frequency part, and the whole trend based on a fine-to-coarse reconstruction by the BPE index method and the T-test method. Then, this paper conducts a correlation analysis on the basis of the reconstructed data and the related affected macroeconomic factors, including global gold production, world crude oil prices, and world inflation. Finally, the BPE index method is evidently a vitally significant technique for time-series data analysis in terms of reconstructed IMFs to obtain realistic data.

scholarly journals Two useful Python tools - dimpy and tablefile for data analysis applications

2021 ◽  
Author(s):  
Dwaipayan Deb
Keyword(s):  
Data Analysis ◽  
Data File ◽  
List Type ◽  
Python Package ◽  
Mathematics And Physics

Abstract Python’s list array is more powerful than arrays in other languages like C, C++, Fortran, or Java. However, in some cases it becomes tedious and complicated to construct a multidimensional ‘list’ type array in Python. A Python tool namely ‘dimpy’ is discussed in this paper which can easily generate any multidimensional ‘list’ type array in python. Another Python package called tablefile for reading and analysing column-wise data from a data-file is also discussed. How these two tools may be useful and reduce steps of programming is shown by using some mathematics and physics related problems.

scholarly journals JMASM 53: MicceriRD

2020 ◽  
Vol 18 (2) ◽  
pp. 2-4
Author(s):  
Michael Lance
Keyword(s):  
Monte Carlo ◽  
Data Analysis ◽  
Monte Carlo Simulations ◽  
Python Package ◽  
Fortran 77

Fortran 77 and 90 modules (REALPOPS.lib) exist for invoking the 8 distributions estimated by Micceri (1989). These respective modules were created by Sawilowsky et al. (1990) and Sawilowsky and Fahoome (2003). The MicceriRD (Micceri’s Real Distributions) Python package was created because Python is increasingly used for data analysis and, in some cases, Monte Carlo simulations.

scholarly journals Suistainable Tourism Deswita Berkembang menjadi Deswita Maju di Desa Wisata Sumberbulu

Media Wisata ◽  
2021 ◽  
Vol 19 (2) ◽  
pp. 255-258
Author(s):  
Erna Wigati ◽  
Wahyu Ari Indriastuti
Keyword(s):  
Data Analysis ◽  
Social Welfare ◽  
Qualitative Method ◽  
Ornamental Plants ◽  
Management Team ◽  
Community Members ◽  
Tour Guides ◽  
Network Methods ◽  
Tourist Village

This study aims to determine the quality of Sumberbulu tourism village in contributing to social welfare by having a local and regional economic network. Methods used in this research is to use descriptive qualitative method, with data analysis done inductively. The aim is to find out sustainable tourism village to develop into the tourism village of Sumberbulu Karanganyar. The result this study, Sumberbulu tourism village, in contributing to economic and social welfare has inderectly been seen by the presence of many buyers who buy ornamental plants when visiting Sumberbulu Village and orrdering snacks that have been produced by community members from the formation of small culinary teams from several residens houses. The quality of the tour guides in the Sumberbulu tourist village is not yet visible because everything is still one with Pokdarwis. The quality of accommodation (homestay) in the tourist village of Sumberbulu is getting better and better because the homestay management team continuously checks it from the homestay coordinator who has been formed from Pokdarwis

scholarly journals EMDA: A Python package for Electron Microscopy Data Analysis

2021 ◽  
pp. 107826
Author(s):  
Rangana Warshamanage ◽  
Keitaro Yamashita ◽  
Garib N. Murshudov
Keyword(s):  
Electron Microscopy ◽  
Data Analysis ◽  
Electron Microscopy Data ◽  
Microscopy Data ◽  
Python Package

scholarly journals Scedar: a scalable Python package for single-cell RNA-seq exploratory data analysis

2018 ◽  
Author(s):  
Yuanchao Zhang ◽  
Man S. Kim ◽  
Erin R. Reichenberger ◽  
Ben Stear ◽  
Deanne M. Taylor
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Large Scale ◽  
Exploratory Data Analysis ◽  
Statistical Distributions ◽  
Rna Seq ◽  
Exploratory Data ◽  
Further Development ◽  
Python Package ◽  
Open Source Development

AbstractIn single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar.

Detecting and Predicting Tipping Points

2019 ◽  
Vol 29 (08) ◽  
pp. 1930022 ◽  
Author(s):  
Xiaoyi Peng ◽  
Michael Small ◽  
Yi Zhao ◽  
Jack Murdoch Moore
Keyword(s):  
Time Series ◽  
Logistic Map ◽  
Pitchfork Bifurcation ◽  
Permutation Entropy ◽  
Dynamical Regime ◽  
Tipping Points ◽  
Edge Betweenness ◽  
Network Methods ◽  
Entropy Of Transition ◽  
Transition Networks

Tipping points are sudden, and sometimes irreversible and catastrophic, changes in a system’s dynamical regime. Complex networks are now widely used in the analysis of time series from a complex system. In this paper, we investigate the scope of network methods to indicate tipping points. In particular, we verify that the permutation entropy of transition networks constructed from time series observations of the logistic map can distinguish periodic and chaotic regimes and indicate bifurcations. The permutation entropy of transition networks, the mean edge betweenness of visibility graphs and the number of code words in compression networks, are each shown to indicate the onset of transition of a pitchfork bifurcation system. Our study shows that network methods are effective in detecting transitions. Network-based forecasts can be applied to models of real systems, as we illustrate by considering a lake eutrophication model.

scholarly journals USING PERMUTATION ENTROPY FOR AFM DATA ANALYSIS

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Dragana Malivuk ◽  
Saša Nježić ◽  
Sreten Lekić ◽  
Enes Škrgić ◽  
Zoran Rajilić
Keyword(s):  
Data Analysis ◽  
Driving Force ◽  
Initial Position ◽  
Computational Experiments ◽  
Permutation Entropy ◽  
Dynamic Mode ◽  
Force Amplitude ◽  
Scanning Velocity ◽  
Optimal Values ◽  
Afm Cantilever

We consider driven oscillations of the AFM cantilever in computational experiments. Complexity of motion is described by permutation entropy H(3), which we compute for data obtained during scanning. Our aim is to find the optimal values of the scanning velocity, initial position of the tip, and the driving force amplitude and frequency for imaging the nanostructure, in a specific dynamic mode of the AFM operation.

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