Python Programming Environment Research Articles

Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

Abstract Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

Сравнение статистических методов и применения критерия Граббса для определения и устранения противоречия

The use of conflict detection methods is not limited to the search and correction of errors in the data, they can also be used to find distorted data. Text analysis algorithms are used to detect inconsistencies and identify artificially created copies of original texts. This article discusses the effectiveness of statistical methods used to identify and eliminate inconsistencies and anomalies in large data. Compliance with the law of normal distribution of data was determined. The study is based on the comparison of statistical criteria with the Grubbs criterion, which gave high results in identifying conflicting data. A block diagram of the general algorithm of the Grubbs criterion was created and the results of iterations obtained in the Python programming environment were presented.

Logomaker: beautiful sequence logos in Python.

SummarySequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as native matplotlib objects that are easy to stylize and incorporate into multi-panel figures.Availability and implementationLogomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.

Data Analysis for the Preventive Maintenance of Machinery

This article shows the importance of data for the resolution of current problems, through a case study of preventive maintenance of a company’s machines. From a given inspection of the condition of the machines, enough data is obtained to simulate the operation of the machines under specific conditions using Python programming environment, for a long enough period of time to obtain reliable information about the machines’ lifetime. By using “R”, the statistical analysis of the data is performed to determine the optimal period between repairs, schedule the company’s preventive maintenance and show the possibility of solving complex problems from a simple data set.

Development and implementation of mathematical models of working mediums for gas part of combined cycle gas turbine system in Python programming environment

The working mediums in the gas turbine systems are: atmospheric air, natural gas and exhaust gases. For the detailed analysis of thermodynamic performance and values at characteristic points of the cycle it is necessary to know the relations defining: specific volume, specific isobaric and isochoric heat capacity, isentropic exponent, specific enthalpy and specific entropy. Mathematical models of thermodynamic parameters for the mentioned mediums were developed based on dependencies for mixtures of ideal and semi-ideal gases. The functions obtained for semi-ideal gas mixtures were extended by pressure correction factors derived from the Redlich-Kwong and Peng-Robinson equations of state. The thermodynamic parameters of the working mediums were dependent on the mass fractions of the components, temperature and pressure. Developed models approximated the behaviour and parameters of real gas mixtures. All calculation algorithms were implemented and optimized using appropriate numerical methods in the Python programming environment. As a result, mathematical models of working mediums for the gas part of the combined cycle gas turbine system were obtained.

Control strategy for battery-supported photovoltaic systems aimed at peak load reduction

The use of photovoltaic (PV) technologies is one of the key means for achieving the balance between operational power demand and generation in net Zero Energy Buildings (nZEBs). However, direct use of PV power on-site is limited due to wide variability and uncertainty of PV output, the temporal mismatch between PV generation and load and other factors. Consequently, in addition to low self-consumption rates, the problem of peak grid load and peak PV feed into the grid persists. Batteries that are coupled to PV units may partially offer the solution to these problems, if operated under an intelligent control strategy. In this paper we proposed a forecast-based control strategy for battery-to-grid interaction aimed at enhancing selfconsumption and at reducing peak load. Python programming environment was used for data processing and algorithm development. Exemplification was made based on the reported hourly energy demand in one office building of 3000 m2 heated floor area located in Trondheim, Norway. Forecasting of electricity load profiles was based on the seasonal autoregressive integral moving average (SARIMA) model. For PV power forecasting, the algorithm communicated with external service – Solcast API. The search method for optimal scheduling of operational time and the extent of charging/discharging was proposed. The results showed that as opposed to conventional battery use, this control strategy allowed to achieve significantly more consistent grid interaction. It offered highly accurate battery scheduling on a day-ahead basis while utilising minimum historical data and computational resources. The algorithm may be beneficial for end-users and grid operators, and thus, it has a high potential for future integration into building energy supply systems.

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran).

BackgroundThe level of groundwater resources is changing rapidly and this requires the discovery of newer groundwater resources. Drought is one of the most significant natural phenomena affecting different aspects of human life and environment. During the last decades, the application of artificial intelligent techniques has been recognized as effective approaches to forecast an annual precipitation rate.MethodIn this study, the association analysis of climate drought and a decline in groundwater level is addressed using Gaussian process classification (GPC) and backpropagation (BP) artificial neural network (ANN). This methodology is proposed to create a framework for decision making and reduce uncertainty in water resource management calculations, and in particular to optimize the management of groundwater drinking water sources.ResultsUnderground water levels in 609 study plains in Iran were used to predict drought over the test period, extending from 2017 to 2021. The artificial intelligence methods were implemented in the Python programming environment to achieve an annual precipitation rate. A statistical summary of the Rasterized Cells of the zoning maps was used to validate the prediction results. Considering the relationship between water quality reductions and drought in Iranian aquifers due to the occurrence of groundwater drought periods, the results were validated by analysis of the effect of climate drought using the Standardized Precipitation Index (SPI) on the occurrence of observed droughts with the Groundwater Resources Index (GRI). The results are well-illustrated by the observation of the predicted digits in the third dimension of the Gaussian distribution.ConclusionAccording to the SPI indicator, the southern regions of the country, and especially the central parts of the plain, can be considered the most affected areas by the most severe future droughts. The prediction results indicate a decrease in drought severity as part of a two-year sequence involving a recurrence of drought exacerbation and relative decline, as well as a failed state after the critical condition of aquifers.

A software platform for the analysis of dermatology images

The purpose of this paper is to present a software platform developed in Python programming environment that can be used for the processing and analysis of dermatology images. The platform provides the capability for reading a file that contains a dermatology image. The platform supports image formats such as Windows bitmaps, JPEG, JPEG2000, portable network graphics, TIFF. Furthermore, it provides suitable tools for selecting, either manually or automatically, a region of interest (ROI) on the image. The automated selection of a ROI includes filtering for smoothing the image and thresholding. The proposed software platform has a friendly and clear graphical user interface and could be a useful second-opinion tool to a dermatologist. Furthermore, it could be used to classify images including from other anatomical parts such as breast or lung, after proper re-training of the classification algorithms.

ATK-ForceField: a new generation molecular dynamics software package

ATK-ForceField is a software package for atomistic simulations using classical interatomic potentials. It is implemented as a part of the Atomistix ToolKit (ATK), which is a Python programming environment that makes it easy to create and analyze both standard and highly customized simulations. This paper will focus on the atomic interaction potentials, molecular dynamics, and geometry optimization features of the software, however, many more advanced modeling features are available. The implementation details of these algorithms and their computational performance will be shown. We present three illustrative examples of the types of calculations that are possible with ATK-ForceField: modeling thermal transport properties in a silicon germanium crystal, vapor deposition of selenium molecules on a selenium surface, and a simulation of creep in a copper polycrystal.

BlockPy: An Open Access Data-Science Environment for Introductory Programmers

Non-computer science majors often struggle to find relevance in traditional computing curricula that tend to emphasize abstract concepts, focus on nonpractical entertainment, or rely on decontextualized settings. BlockPy, a web-based, open access Python programming environment, supports introductory programmers in a data-science context through a dual block/text programming view. The web extra at https://youtu.be/RzaOPqOpMoM illustrates BlockPy features discussed in the article.

A Python Program for Solving Schrödinger’s Equation in Undergraduate Physical Chemistry

In undergraduate physical chemistry, Schrodinger’s equation is solved for a variety of cases. In doing so, the energies and wave functions of the system can be interpreted to provide connections with the physical system being studied. Solving this equation by hand for a one-dimensional system is a manageable task, but it becomes time-consuming once students aim to make various changes and investigate the impact of those changes on the results. To address this challenge, numerical methods, such as the shooting and linear finite-difference methods, have been utilized to quickly solve Schrodinger’s equation. In this technology report, we use the Python programming environment and the three-point finite-difference numerical method to find the solutions and plot the results (wave functions or probability densities) for a particle in an infinite, finite, double finite, harmonic, Morse, or Kronig–Penney finite potential energy well. We believe that this technology report will educate undergraduates on the basic ...

Curvilinearly T-Stiffened Panel-Optimization Framework Under Multiple Load Cases Using Parallel Processing

Future aerospace vehicles like hybrid wing/body, truss-braced wing, and “double bubble” would have pressurized noncircular fuselage structures and complex wing geometry. Traditional aircraft designs have led to the confidence and experience of designing such structures using the knowledge base built over the years and the resulting rules of thumb. However, there is a lack of experience of load calculations and design of complex, multifunctional, aircraft structural concepts for future aerospace vehicles. Designing such structures will require a physics-based optimization framework. Therefore, a new optimization framework, EBF3PanelOpt, is being developed. Commercial software MD-PATRAN (geometry modeling and mesh generation) and MD-NASTRAN (finite-element analysis) are integrated in EBF3PanelOpt framework using the Python programming environment to design stiffened panels with curvilinear stiffeners. Currently, EBF3PanelOpt optimizes the stiffened panel with curvilinear blade stiffeners, where the loads are applied only through the plate. During the optimization, the mass is minimized with the constraints on buckling, von Mises stress, and crippling or local failure of the stiffeners. EBF3PanelOpt is enhanced to have curvilinear T stiffeners with or without axial loads in addition to loads through plate. The panel/stiffener geometry is defined in a parametric fashion based on design variables that include variables for orientation and shape of the stiffeners, the thicknesses and heights of the webs and flanges of the stiffeners, and the plate-pocket thicknesses. This framework is supported with coarse-grained parallelism using Python to analyze multiple designs on the cluster. Using this framework, a vertical stabilizer skin panel of transport aircraft panel having two extreme load cases is optimized using with or without stiffener loads. When the equivalent uniform loads are applied only through the plate, the plate buckling becomes critical, but combined buckling of plate and stiffeners becomes critical when the loads are applied through both the plate and the stiffeners. When the uniform in-plane compressive loads are applied through both the plate and the stiffeners, the panel with straight T stiffeners is more optimal than the panel with curvilinear T stiffeners.

EarSketch: An integrated approach to teaching introductory computer music

EarSketch is an all-in-one approach to supporting a holistic introductory course to computer music as an artistic pursuit and a research practice. Targeted to the high school and undergraduate levels, EarSketch enables students to acquire a strong foundation in electroacoustic composition, computer music research and computer science. It integrates a Python programming environment with a commercial digital audio workstation program (Cockos’ Reaper) to provide a unified environment within which students can use programmatic techniques in tandem with more traditional music production strategies to compose music. In this paper we discuss the context and goals of EarSketch, its design and implementation, and its use in a pilot summer camp for high school students.

EBF3PanelOpt: An optimization framework for curvilinear blade-stiffened panels

A new framework, EBF3PanelOpt, is being developed for design and optimization of complex, multifunctional, aircraft structural concepts like pressurized non-circular fuselage structures to be used in hybrid wing/body vehicles that are subjected to complex structural loading cases. This tool can be used to integrate materials and structural concepts to exploit emerging additive manufacturing processes that offer the ability to efficiently fabricate complex structural configurations. Commercial software packages, MD-Patran (geometry modeling and mesh generation), MD-Nastran (Finite Element Analysis), are integrated in the EBF3PanelOpt framework using Python programming environment to design stiffened panels with curvilinear stiffeners. Typically, these panels experience multiple loading conditions during the operations of these vehicles. EBF3PanelOpt has the capability to optimize flat/curved multi-sided panels with straight/curved edges having curvilinear, blade-type stiffeners under multiple loading conditions. The mass of the panel is minimized subjected to constraints on buckling, von Mises stress, and crippling or local failure of the stiffener using global optimization techniques or gradient based optimization techniques. The panel/stiffener geometry is parametrized using design variables that include variables for orientation and shape of the stiffeners, the thicknesses and height of the stiffeners, and the plate thickness. The plate can have uniform thickness or non-uniform thicknesses for the pockets created by the stiffeners. During optimization, constraints can be applied for each of the loading conditions by aggregating all the responses using Kreisselmeir–Steinhauser criteria or using worst response amongst all the responses or applying all the constraints. Initially, the flat rectangular panel is optimized for the single load-case to study the effectiveness of the panel thickness option. Then, the optimization of flat rectangular and cylindrical panels is carried out for three sample load cases of practical interest. This paper discusses the advantages and disadvantages of the proposed constraints' application.

Parallel distributed computing using Python

This work presents two software components aimed to relieve the costs of accessing high-performance parallel computing resources within a Python programming environment: MPI for Python and PETSc for Python. MPI for Python is a general-purpose Python package that provides bindings for the Message Passing Interface (MPI) standard using any back-end MPI implementation. Its facilities allow parallel Python programs to easily exploit multiple processors using the message passing paradigm. PETSc for Python provides access to the Portable, Extensible Toolkit for Scientific Computation (PETSc) libraries. Its facilities allow sequential and parallel Python applications to exploit state of the art algorithms and data structures readily available in PETSc for the solution of large-scale problems in science and engineering. MPI for Python and PETSc for Python are fully integrated to PETSc-FEM, an MPI and PETSc based parallel, multiphysics, finite elements code developed at CIMEC laboratory. This software infrastructure supports research activities related to simulation of fluid flows with applications ranging from the design of microfluidic devices for biochemical analysis to modeling of large-scale stream/aquifer interactions.

High performance simulations of electrokinetic flow and transport in microfluidic chips

This article discusses high performance numerical simulations of electrokinetic flow and transport phenomena in microfluidic chips. Modeling grounds on conservation equations of mass, momentum and electric charge in the framework of continuum mechanics. Two examples of interest in microfluidics are considered as study cases. Three dimension effects and whole chip geometries are taking into account. All numerical simulations presented are performed with PETSc-FEM within a Python programming environment employing parallel computing. Computation time and parallel efficiency are measured in order to study additive Schwarz method performance as domain decomposition technique in solving common ill-conditioned microfluidics problems.