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Miscellaneous Aspects
Published in B.K. Raghu Prasad, Structural Dynamics in Earthquake and Blast Resistant Design, 2020
Python’s massive customary library, commonly cited as one of its greatest strengths, provides tools suited to several tasks. Some of the libraries are listed below:NumPy- It supports some advance math functionalities to Python.SciPy- It is a library consisting of algorithms and mathematical tools for Python.matplotlib- It is a numerical plotting library. It is very useful for any data analyzer or any data scientist.Pygame- This library helps to achieve your goal of 2D game development.pywin32- A Python library providing some useful methods and classes for interacting with windows.SymPy- A library that can do algebraic evaluation, differentiation, expansion, complex numbers, etc.IPython- It provides completion, history, shell capabilities, and a lot more.Pandas- It offers data structures and operations for manipulating numerical tables and time series.
Semantic and syntactic interoperability in online processing of big Earth observation data
Published in International Journal of Digital Earth, 2018
Martin Sudmanns, Dirk Tiede, Stefan Lang, Andrea Baraldi
The Jupyter Notebook is an open source and web-based computing environment for data exploration and scientific computing based on narratives (Kluyver et al. 2016). It creates documents, called notebooks, containing code, including results, documentations or comments in Markdown syntax, and visualisations. While it has been launched in the Python environment IPython (Pérez and Granger 2007), the Jupyter project is meanwhile agnostic to the programming language and connects to different interpreters, called kernels. Although Jupyter notebooks have not been designed explicitly for processing geospatial data, it gains popularity in data science and related disciplines. It supports collaborative work as well as access to parallel computing facilitating large-scale processing and complex tasks.
Neural network protocol to predict interfacial tension for CO2/CH4/Water-Brine ternary systems under reservoir temperature and pressure ranges
Published in Petroleum Science and Technology, 2022
Andréa da Silva Pereira, Arthur Reys Carvalho de Oliveira, Pedro F. G. Silvino, Moises Bastos-Neto, Sebastião M. P. Lucena
The ANNs herein described were developed through an interactive process implemented in Python/IPython Notebook version 2.7.8, the PyBrain associated with the PyBrain (Schaul and Felder 2010)and PySwarm packages for machine learning and evolutionary optimization, respectively. In this algorithm, the neural network model is obtained after a linear combination of the internal activation functions applied to neurons in the hidden layers (hyperbolic) and the output layer (linear).
Tutorial: The LuxPy Python Toolbox for Lighting and Color Science
Published in LEUKOS, 2020
Spyder is an Interactive Development Environment (IDE) for scientific programming in the Python language. It supports IPython and popular Python libraries such as NumPy, SciPy, or matplotlib. It should come installed with the Anaconda distribution in the conda virtual environment. If not, it can be installed by typing: