China
Africa
Explore chapters and articles related to this topic
Image Processing Techniques in Remote Sensing
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
IDL, short for Interactive Data Language, is a scientific program with similar capabilities to MATLAB, also developed by Exelis VIS. It has been commonly used along with ENVI, an image processing software package built in IDL, for data analysis and image processing, particularly in remote sensing and medical imaging. Similar to other programming languages, IDL incorporates three essential capabilities including interactivity, graphics display, and array-oriented operation for data analysis. Its vectorized nature makes IDL capable of performing fast array computations, especially for numerically heavy computations, by taking advantage of the built-in vector operations.
Driving solar coronal MHD simulations on high-performance computers
Published in Geophysical & Astrophysical Fluid Dynamics, 2020
A typical example of the content of a var.h5 snapshot can be visualised with the tool ‘‘hdfview ’’; see figure 10. The datasets (like ux) are listed in groups (like data or settings) that can be opened and closed by a double mouseclick. Some fundamental parameters of the simulation can be contained in snapshot files, e.g. settings/precison holds either an ‘‘S ’’ for single or a ‘‘D ’’ for double precision. The time of the snapshot is stored as a separate scalar double-precision dataset named time. Datasets can also be multi-dimensional arrays and the order of these arrays is in the canonical Fortran way, which means the first dimension of the array is along the z-direction. This implies that one has to transpose multi-dimensional data arrays for post-processing with languages like C or Julia/Python, while the datasets are naturally aligned for languages like Fortran or IDL.
Improvement of Automatic Physics Data Analysis Environment for the LHD Experiment
Published in Fusion Science and Technology, 2018
M. Emoto, C. Suzuki, M. Yokoyama, M. Yoshinuma, R. Seki, K. Ida
The analysis program receives a shot number as an argument, calculates the target physical data, and registers it to the analyzed data server. Generally, analysis programs are offered by scientists who are specialists in their fields. Therefore, the programs are written in various languages, for example, Python, FORTRAN, PV-Wave, Shell script, and others. There is also a potential requirement to use other proprietary tools such as IDL and MATLAB because both tools are widely used as data analysis tools in the nuclear fusion community. However, both tools were not supported because the number of run-time licenses is limited. As previously mentioned, execution programs run simultaneously. The number of concurrent processes is 116 at most, but the number is easy to increase by adding Executer PC to the AutoAna, which consumes limited licenses. In order to make use of the limited license, a resource management scheme has been introduced. Figure 5 shows the procedure to manage computer resources. All the available resources and the resources that the module uses are written in a new JSON file, “resource.json.” In this example, there are ten licenses for IDL and five licenses for MATLAB. Then, a new tag “USE” is added in the module definition file. In the example, “module 1” uses one IDL license. When Executer runs module 1, it asks ResourceManager whether an IDL license is available. If the license is available, it rents one license from ResourceManager. When the execution is terminated, it returns the license to ResourceManager.
Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data
Published in International Journal of Digital Earth, 2018
Julia Wagemann, Oliver Clements, Ramiro Marco Figuera, Angelo Pio Rossi, Simone Mantovani
Alternatives to the Python API presented include other open-source solutions developed in Python, IDL or C or proprietary solutions using ENVI. The RGB composition tool is open-source and available on GitHub (Halder and Marco Figuera 2016). The Jupyter Notebooks developed by Wagemann et al. (2016) contain as well an example workflow taking advantage of the RGB composition tool.