scholarly journals Python-Microscope – a new open-source Python library for the control of microscopes

2021 ◽  
Vol 134 (19) ◽  
Author(s):  
David Miguel Susano Pinto ◽  
Mick A. Phillips ◽  
Nicholas Hall ◽  
Julio Mateos-Langerak ◽  
Danail Stoychev ◽  
...  
Keyword(s):  
Open Source ◽  
High Speed ◽  
High Performance ◽  
Simulation Tool ◽  
Project Control ◽  
Performance Control ◽  
Python Programming Language ◽  
Architectural Features ◽  
Design Integration ◽  
Python Programming

ABSTRACT Custom-built microscopes often require control of multiple hardware devices and precise hardware coordination. It is also desirable to have a solution that is scalable to complex systems and that is translatable between components from different manufacturers. Here we report Python-Microscope, a free and open-source Python library for high-performance control of arbitrarily complex and scalable custom microscope systems. Python-Microscope offers simple to use Python-based tools, abstracting differences between physical devices by providing a defined interface for different device types. Concrete implementations are provided for a range of specific hardware, and a framework exists for further expansion. Python-Microscope supports the distribution of devices over multiple computers while maintaining synchronisation via highly precise hardware triggers. We discuss the architectural features of Python-Microscope that overcome the performance problems often raised against Python and demonstrate the different use cases that drove its design: integration with user-facing projects, namely the Microscope-Cockpit project; control of complex microscopes at high speed while using the Python programming language; and use as a microscope simulation tool for software development.

scholarly journals Python-Microscope: High performance control of arbitrarily complex and scalable bespoke microscopes

2021 ◽  
Author(s):  
David Miguel Susano Pinto ◽  
Mick A Phillips ◽  
Nicholas Hall ◽  
Julio Mateos–Langerak ◽  
Danail Stoychev ◽  
...  
Keyword(s):  
Software Development ◽  
Programming Language ◽  
High Speed ◽  
High Performance ◽  
Light Sources ◽  
Simulation Tool ◽  
Control Solution ◽  
Performance Control ◽  
Python Programming Language ◽  
Python Programming

AbstractBespoke microscopes often require control of multiple hardware devices and precise hardware coordination. It is also desirable to have a control solution that is scalable to more complex systems and translatable between components from different manufacturers. Here we report Python-Microscope, a free and open source Python library for high performance control of arbitrarily complex and scalable bespoke microscopes. Python-Microscope offers an elegant pythonic software platform to control microscopes, abstracting differences between physical devices by providing a defined interface for different device types. These include cameras, filter wheels, light sources, deformable mirrors, and stages. Concrete implementations are provided for a range of specific hardware and a framework is in place for further expansion. Python-Microscope supports the distribution of devices over multiple computers while maintaining synchronisation via highly precise hardware triggers. We discuss the architecture choices of Python-Microscope that overcome the performance problems often raised against Python and demonstrate the different use cases that drove its design: its integration in user facing projects, namely in the Microscope-Cockpit project; in controlling complex microscopes at high speed while using the Python programming language; and as a microscope simulation tool for software development.

An Open Source Finance System for Stocks Backtesting Trade Strategies

2021 ◽  
Vol 12 (2) ◽  
pp. 52-65
Author(s):  
Eviatar Rosenberg ◽  
Dima Alberg
Keyword(s):  
User Interface ◽  
Open Source ◽  
Programming Language ◽  
Capital Market ◽  
Historical Data ◽  
Trading Strategies ◽  
Market Volatility ◽  
Finance System ◽  
Python Programming Language ◽  
Python Programming

A significant part of pension savings is in the capital market and exposed to market volatility. The COVID-19 pandemic crisis, like the previous crises, damaged the gains achieved in those funds. This paper presents a development of open-source finance system for stocks backtesting trade strategies. The development will be operated by the Python programming language and will implement application user interface. The system will import historical data of stocks from financial web and will produce charts for analysis of the trends in stocks price. Based on technical analysis, it will run trading strategies which will be defined by the user. The system will output the trade orders that should have been executed in retrospect and concluding charts to present the profit and loss that would occur to evaluate the performance of the strategy.

scholarly journals scikit-image: Image processing in Python

2014 ◽  
Author(s):  
Stéfan van der Walt ◽  
Johannes L Schönberger ◽  
Juan Nunez-Iglesias ◽  
François Boulogne ◽  
Joshua D Warner ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Programming Language ◽  
Real World ◽  
Research Education ◽  
Python Programming Language ◽  
Image Library ◽  
Industry Applications ◽  
Python Programming ◽  
World Image

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image.

scholarly journals High-performance Python for crystallographic computing

2019 ◽  
Vol 52 (4) ◽  
pp. 882-897 ◽  
Author(s):  
A. Boulle ◽  
J. Kieffer
Keyword(s):  
Programming Language ◽  
High Performance ◽  
Scientific Computing ◽  
State Of The Art ◽  
Third Party ◽  
Improve Performance ◽  
Python Language ◽  
Python Programming Language ◽  
Numerical Computing ◽  
Python Programming

The Python programming language, combined with the numerical computing library NumPy and the scientific computing library SciPy, has become the de facto standard for scientific computing in a variety of fields. This popularity is mainly due to the ease with which a Python program can be written and executed (easy syntax, dynamical typing, no compilation etc.), coupled with the existence of a large number of specialized third-party libraries that aim to lift all the limitations of the raw Python language. NumPy introduces vector programming, improving execution speeds, whereas SciPy brings a wealth of highly optimized and reliable scientific functions. There are cases, however, where vector programming alone is not sufficient to reach optimal performance. This issue is addressed with dedicated compilers that aim to translate Python code into native and statically typed code with support for the multi-core architectures of modern processors. In the present article it is shown how these approaches can be efficiently used to tackle different problems, with increasing complexity, that are relevant to crystallography: the 2D Laue function, scattering from a strained 2D crystal, scattering from 3D nanocrystals and, finally, diffraction from films and multilayers. For each case, detailed implementations and explanations of the functioning of the algorithms are provided. Different Python compilers (namely NumExpr, Numba, Pythran and Cython) are used to improve performance and are benchmarked against state-of-the-art NumPy implementations. All examples are also provided as commented and didactic Python (Jupyter) notebooks that can be used as starting points for crystallographers curious to enter the Python ecosystem or wishing to accelerate their existing codes.

scholarly journals APPLICATION OF NEURAL NETWORKS TO DETERMINE THE COORDINATES OF THE SEAT OF FIRE BY MULTIPOINT ELECTRO-OPTICAL SYSTEM

2021 ◽  
pp. 248-251
Author(s):  
Д.Ф. Пирова ◽  
Б.Э. Забержинский ◽  
А.Г. Золин
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
Network Models ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Python Programming Language ◽  
Python Programming

Статья посвящена исследованию методов проектирования интеллектуальных информационных систем и применение моделей искусственных нейронных сетей для диагностического прогнозирования развития пневмонии посредством анализа рентгеновских снимков. В этой работе основное внимание уделяется классификации пневмонии и туберкулеза - двух основных заболеваний грудной клетки - на основе рентгеновских снимков грудной клетки. Данное исследование проводилось при помощи открытой нейросетевой библиотеки Keras и языка программирования Python. Система дает пользователю заключение о том, болен он или нет, тем самым помогая врачам и медицинскому персоналу принять быстрое и информированное решение о наличии заболевания. Разработанная модель, может определить, является ли рентгеновский снимок нормальным или имеет отклонения, которые могут быть пневмонией с точностью 94,87%. Полученные результаты указывают на высокую эффективность применения нейронных сетей при диагностировании пневмонии по рентгеновским снимкам. This paper is devoted to the study of methods of designing intellectual information systems and neural network models application on diagnostic prediction of pneumonia development by X-ray images analysis. This article focuses on the classification of pneumonia and tuberculosis - the two main chest diseases - based on chest x-rays. This study was carried out using the Keras open neural network library and the Python programming language. System returns user a conclusion whether the patient is ill or not helping medical staff to make a quick and informed decision about the presence of the disease. The developed model can determine is the X-ray image normal or has anomalies that can be pneumonia with accuracy up to 94.87%. The results obtained indicate the high performance of the applying neural networks in the diagnosis of pneumonia by X-ray images.

scholarly journals PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Forrest Sheng Bao ◽  
Xin Liu ◽  
Christina Zhang
Keyword(s):  
Feature Extraction ◽  
Open Source ◽  
Physiological Signals ◽  
Eeg Signals ◽  
Python Programming Language ◽  
Computer Aided ◽  
Python Programming ◽  
Neural Diseases ◽  
Aided Diagnosis ◽  
Eeg Features

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

scholarly journals scikit-image: Image processing in Python

2014 ◽  
Author(s):  
Stéfan van der Walt ◽  
Johannes L Schönberger ◽  
Juan Nunez-Iglesias ◽  
François Boulogne ◽  
Joshua D Warner ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Programming Language ◽  
Real World ◽  
Research Education ◽  
Python Programming Language ◽  
Image Library ◽  
Industry Applications ◽  
Python Programming ◽  
World Image

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image.

scholarly journals DNA Features Viewer: a sequence annotation formatting and plotting library for Python

2020 ◽  
Vol 36 (15) ◽  
pp. 4350-4352 ◽  
Author(s):  
Valentin Zulkower ◽  
Susan Rosser
Keyword(s):  
Computational Biology ◽  
Open Source ◽  
Programming Language ◽  
Source Code ◽  
Supplementary Information ◽  
Use Case ◽  
Sequence Annotation ◽  
Supplementary Data ◽  
Python Programming Language ◽  
Python Programming

Abstract Motivation Although the Python programming language counts many Bioinformatics and Computational Biology libraries; none offers customizable sequence annotation visualizations with layout optimization. Results DNA Features Viewer is a sequence annotation plotting library which optimizes plot readability while letting users tailor other visual aspects (colors, labels, highlights etc.) to their particular use case. Availability and implementation Open-source code and documentation are available on Github under the MIT license (https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer). Supplementary information Supplementary data are available at Bioinformatics online.

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