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Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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Biologia Computacional/métodos , Matemática , Linguagens de Programação , Design de SoftwareRESUMO
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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Algoritmos , Biologia Computacional/métodos , Linguagens de Programação , Software , Biologia Computacional/história , Simulação por Computador , História do Século XX , História do Século XXI , Modelos Lineares , Modelos Biológicos , Dinâmica não Linear , Processamento de Sinais Assistido por ComputadorRESUMO
Scanning (electrical) impedance imaging (SII) is a novel high-resolution imaging modality that has the potential of imaging the electrical properties of thin biological tissues. In this paper, we apply the reciprocity principle to the modeling of the SII system and develop a fast nonlinear inverse method for image reconstruction. The method is fast because it uses convolution to eliminate the requirement of a numerical solver for the 3-D electrostatic field in the SII system. Numerical results show that our approach can accurately reveal the exact conductivity distribution from the measured current map for different 2-D simulation phantoms. Experiments were also performed using our SII system for a piece of butterfly wing and breast cancer cells. Two-dimensional current images were measured and corresponding quantitative conductivity images were restored using our approach. The reconstructed images are quantitative and reveal details not present in the measured images.
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Algoritmos , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Microscopia de Varredura por Sonda/métodos , Pletismografia de Impedância/métodos , Sistemas Computacionais , Dinâmica não Linear , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Scanning impedance imaging (SH) uses a noncontacting electrical probe held at a known voltage and scanned over a thin sample on a ground plane in a conductive medium to obtain images of current. The current image is related in a nonlinear way to the conductivity of the sample. This paper develops the theory behind SII showing how the measured current relates to the desired conductivity. Also included is the development of a simplified, linear model that is effective in explaining many of the experimental results. Good agreement of the linear model with step-response data over an insulator is shown. The linear model shows that the current is a blurred version of the conductivity. Simple deblurring methods can, therefore, be applied to obtain relative conductivity images from the raw current data. Raw SII data from a flower-petal and a leaf sample are shown as well as relative conductivity images deblurred using the linear model.
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Algoritmos , Impedância Elétrica , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Modelos Biológicos , Tomografia/métodos , Simulação por Computador , Modelos Lineares , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Scanning electrical impedance imaging (SII) has been developed and implemented as a novel high resolution imaging modality with the potential of imaging the electrical properties of biological tissues. In this paper, a fast linear model is derived and applied to the impedance image reconstruction of scanning impedance imaging. With the help of both the deblurring concept and the reciprocity principle, this new approach leads to a calibrated approximation of the exact impedance distribution rather than a relative one from the original simplified linear method. Additionally, the method shows much less computational cost than the more straightforward nonlinear inverse method based on the forward model. The kernel function of this new approach is described and compared to the kernel of the simplified linear method. Two-dimensional impedance images of a flower petal and cancer cells are reconstructed using this method. The images reveal details not present in the measured images.
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Flores , Interpretação de Imagem Assistida por Computador , Pletismografia de Impedância , Algoritmos , Processamento de Imagem Assistida por Computador , Modelos Lineares , Modelos Biológicos , Pletismografia de Impedância/métodosRESUMO
We are interested in applying electrical impedance imaging to a single cell because it has potential to reveal both cell anatomy and cell function. Unfortunately, classic impedance imaging techniques are not applicable to this small scale measurement due to their low resolution. In this paper, a different method of impedance imaging is developed based on a noncontact scanning system. In this system, the imaging sample is immersed in an aqueous solution allowing for the use of various probe designs. Among those designs, we discuss a novel shield-probe design that has the advantage of better signal-to-noise ratio with higher resolution compared to other probes. Images showing the magnitude of current for each scanned point were obtained using this configuration. A low-frequency linear physical model helps to relate the current to the conductivity at each point. Line-scan data of high impedance contrast structures can be shown to be a good fit to this model. The first two-dimensional impedance image of biological tissues generated by this technique is shown with resolution on the order of 100 mum. The image reveals details not present in the optical image.
