The problem of extracting basic (e.g. bond lengths and angles) and advanced (e.g. polyhedron volumes and effective coordination numbers) crystal chemical parameters from large datasets of CIFs in a quick and flexible ...
详细信息
The problem of extracting basic (e.g. bond lengths and angles) and advanced (e.g. polyhedron volumes and effective coordination numbers) crystal chemical parameters from large datasets of CIFs in a quick and flexible way is addressed by a lightweight python library with a graphical user interface (GUI). A description of library functionality in the GUI and scripting modes followed by examples based on open-access data demonstrate its advantages for crystallographers working with pressure-, temperature- and chemistry-induced structural variations, as well as with analysis of structural databases.
The analysis of experimental results with python often requires writing many code scripts which all need access to the same set of functions. In a common field of research, this set will be nearly the same for many us...
详细信息
The analysis of experimental results with python often requires writing many code scripts which all need access to the same set of functions. In a common field of research, this set will be nearly the same for many users. The qspec python package was developed to provide functions for physical formulas, simulations and data analysis routines widely used in laser spectroscopy and related fields. Most functions are compatible with numpy arrays, enabling fast calculations with large samples of data. A multidimensional linear regression algorithm enables a King plot analyses over multiple atomic transitions. A modular framework for constructing lineshape models can be used to fit large sets of spectroscopy data. A simulation module within the package provides user-friendly methods to simulate the coherent time-evolution of atoms in electromagnetic fields without the need to explicitly derive a Hamiltonian.
python is a high-level programming language that is strongly, but dynamically typed. In this paper, we propose a type inference framework to compute specifications for python functions in isolation. To achieve this, w...
详细信息
python is a high-level programming language that is strongly, but dynamically typed. In this paper, we propose a type inference framework to compute specifications for python functions in isolation. To achieve this, we aim to use an abstract-interpretation-based data flow analysis to infer variable types on a subset of python programs that use built-in types, operators and functions. To evaluate the expressions found in every program point, specifications for the encountered operations functions are required. We propose a method for extracting these specifications from the Typeshed project, which contains a set of annotations for built-in and popular third-party libraries. These specifications will be used then to extend the proposed type inference to large python programs.
python has become the de facto language for scientific computing. Programming in python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the ...
详细信息
python has become the de facto language for scientific computing. Programming in python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for python support in High-Performance Computing (HPC) has skyrocketed. However, the python language itself does not necessarily offer high performance. This work presents a workflow that retains python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated python, and up to 93.16% scaling efficiency on 512 nodes. Our benchmarks were reproduced in the Student Cluster Competition (SCC) during the Supercomputing Conference (SC) 2022. We present and discuss the student teams' results.
Multi-omic integration involves the management of diverse omic datasets. Conducting an effective analysis of these datasets necessitates a data management system that meets a specific set of requirements, such as rapi...
详细信息
Multi-omic integration involves the management of diverse omic datasets. Conducting an effective analysis of these datasets necessitates a data management system that meets a specific set of requirements, such as rapid storage and retrieval of data with varying numbers of features and mixed data-types, ensurance of reliable and secure database transactions, extension of stored data row and column-wise and facilitation of data distribution. SQLite and DuckDB are embedded databases that fulfil these requirements. However, they utilize the structured query language (SQL) that hinders their implementation by the uninitiated user, and complicates their use in repetitive tasks due to the necessity of writing SQL queries. This study offers Omilayers, a python package that encapsulates these two databases and exposes a subset of their functionality that is geared towards frequent and repetitive analytical procedures. Synthetic data were used to demonstrate the use of Omilayers and compare the performance of SQLite and DuckDB.
Accurate landslide susceptibility assessments (LSA) are crucial for civil protection and land use planning. This study introduces PSLSA v2.0 as an open-source python package that can conduct LSA automatically. It inte...
详细信息
Accurate landslide susceptibility assessments (LSA) are crucial for civil protection and land use planning. This study introduces PSLSA v2.0 as an open-source python package that can conduct LSA automatically. It integrates six sophisticated machine learning algorithms (C5.0, SVM, LR, RF, MLP, XGBoost), and allows arbitrary combinations of influencing factors to generate landslide susceptibility index (LSI). We demonstrate how factor contribution and hyperparameter optimization as additional outputs can enhance the model interpretability. We apply PSLSA to a case study focused from Linzhi City in the Tibetan Plateau of China, that has undergone significant engineering modifications on its slopes. The results reveal that slope and aspect are the dominant factors in determining landslide susceptibility. All the six algorithms have an accuracy of over 80%. Although the distribution patterns of LSI vary, the C5.0 model is set apart with the best performance. PSLSA provides a powerful tool for stakeholders especially the non-geohazard professionals.
Agrivoltaic systems integrate photovoltaic (PV) energy production with agricultural activities, addressing the critical challenges of land use optimization and sustainable energy generation in the context of climate c...
详细信息
Agrivoltaic systems integrate photovoltaic (PV) energy production with agricultural activities, addressing the critical challenges of land use optimization and sustainable energy generation in the context of climate changes and food security. These systems are pivotal in offering a promising solution in mitigating the environmental and social impacts of utility-scale PV installations, such as habitat disruption and competition with agricultural land. This study evaluates a patented V-shaped bifacial photovoltaic system with a single-axis solar tracking, designed to optimize energy capture but also to minimize shading effects on crops like vineyards. A custom python-based algorithm using PVlib was developed to simulate the performance of the system, accounting for mutual shading, multiple solar radiation reflections, and dynamic tilt adjustments. Simulations conducted for Palermo, Italy, revealed that the system collects 5.2 % less solar irradiation than traditional side-by-side configurations but achieves an annual energy output of 2089.3 kWh per pair of panels, along with 24 % reduction in land use. These results highlight the system capability to optimize spatial efficiency while maintaining high energy production. The novelty of this work lies in its tailored simulation approach, addressing the unique geometry and operational dynamics of the V-shaped configuration, and its potential adaptability to diverse agrivoltaics scenarios. Unlike existing tools and methodologies in the literature, this work introduces a customized python-based model specifically designed to analyse the performance of this innovative structure, which is of recent conception and lacks precedent in both academic studies and commercial software solutions. By advancing the methodological framework for integrating renewable energy with agriculture, this study contribute to the broader goals of sustainable development and climate resilience.
Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally e...
详细信息
Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally expensive and the exact observation functions that can be applied in these systems are difficult to obtain. It prompts growing interest in integrating deep learning models within data assimilation workflows, but current software packages for data assimilation cannot handle deep learning models inside. This study presents a novel python package seamlessly combining data assimilation with deep neural networks to serve as models for state transition and observation functions. The package, named TorchDA, implements Kalman Filter, Ensemble Kalman Filter (EnKF), 3D Variational (3DVar), and 4D Variational (4DVar) algorithms, allowing flexible algorithm selection based on application requirements. Comprehensive experiments conducted on the Lorenz 63 and a two-dimensional shallow water system demonstrate significantly enhanced performance over standalone model predictions without assimilation. The shallow water analysis validates data assimilation capabilities mapping between different physical quantity spaces in either full space or reduced order space. Overall, this innovative software package enables flexible integration of deep learning representations within data assimilation, conferring a versatile tool to tackle complex high dimensional dynamical systems across scientific domains.
Building performance simulations (BPS) can be used to estimate the energy required to deliver indoor environmental conditions acceptable for the occupants. Although the adaptive approach has been historically addresse...
详细信息
Building performance simulations (BPS) can be used to estimate the energy required to deliver indoor environmental conditions acceptable for the occupants. Although the adaptive approach has been historically addressed only to naturally ventilated spaces, recent research has found that it could also be applied to air-conditioning spaces. Thus, it is possible to use setpoint temperatures based on adaptive comfort models as energy-saving measures. This study presents a seamless methodology based on the use of accim, an open-source software tool to automate the use of adaptive setpoint temperatures in building performance simulations. accim allows to use script-based workflows to perform all actions within the development of a simulation-based thermal comfort study. A case study is used to demonstrate the capabilities of accim. The results show that accim provides a wide range of new possibilities for developing studies related to the energy implications of adaptive thermal comfort.
The Protein Secondary Structure Visualizer (ProS2Vi) is a novel python-based visualization tool designed to enhance the analysis and information accessibility of protein secondary structures, calculated and identified...
详细信息
The Protein Secondary Structure Visualizer (ProS2Vi) is a novel python-based visualization tool designed to enhance the analysis and information accessibility of protein secondary structures, calculated and identified using the Dictionary of Secondary Structure of Proteins (DSSP) algorithm. Leveraging robust python libraries such as "Biopython" for data handling, "Flask" for Graphical User Interface (GUI), "Jinja2", and "wkhtmltopdf" for visualization, ProS2Vi offers a modern and intuitive representation for visualization of the DSSP assigned secondary structures to each residue of any proteins' amino acid sequence. Significant features of ProS2Vi include customizable icon colors, the number of residues per line, and the ability to export visualizations as scalable PDFs, enhancing both visual appeal and functional versatility through a user-friendly GUI. We have designed ProS2Vi specifically for secure and local operation, which significantly increases security when working with novel protein data.
暂无评论