China
Africa
Explore chapters and articles related to this topic
Introduction
Published in S. Poonkuntran, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Object Detection with Deep Learning Models, 2023
Some of the image repositories are:Scikit-ImageOpenCVPython Image Library (Pillow/PIL)ScipySimpleITKMatplotlibNumpyMahotas
Python API Modules for Machine Learning and Arduino
Published in Amartya Mukherjee, Nilanjan Dey, Smart Computing with Open Source Platforms, 2019
Amartya Mukherjee, Nilanjan Dey
Matplotlib is the most powerful plotting library component in Python. Any type of data visualization, graph, or chart generation can be done using Matplotlib package. Designing of two- and three-dimensional graphs, bar charts, and pie charts is a very popular component of Matplotlib package. One of the advantages of Matplotlib is that it is a continuously evolving software, that is, new features and functionalities are ready to incorporate each time as it adds some new components. Some of the core features are as follows: It comprises 70,000 lines of code.It is a home to various interfaces and is able to interact with different back-end services.
Methodological framework
Published in Isnaeni Murdi Hartanto, Integrating Multiple Sources of Information for Improving Hydrological Modelling: An Ensemble Approach, 2019
The flowchart of the calculation using multiprocessing Python module is presented in Figure 3-4. In this research the above method is utilised when computing several independent model runs. A run directory is created for each CPU core. The model input is prepared and copied to each directory based on the sampled parameters. Each of the CPU cores then computes a model in the run directory, when the model run is finished, the output files are compressed and saved in the output directory. Python modules such as Pandas, Matplotlib and Numpy are used in the process.
Inundation over land with increasing water depth using self-similarity analysis
Published in Journal of Hydraulic Research, 2022
Hidekazu Shirai, How Tion Puay, Takashi Hosoda
The numerical model was coded in FORTRAN. The code was executed by using the Intel compiler on a personal computer with Intel R Core i7-4650U processor, 2.29 GHz base frequency (CPU), and 16 GB shared memory (RAM). The results were visualized by Matplotlib (version 3.3.2), which is a plotting library for the Python programming language.