Python recipes for earth sciences / Martin H. Trauth.
Material type:
TextSeries: Springer textbooks in earth sciences, geography and environmentPublication details: Cham, Switzerland : Springer, 2022.Edition: First editionDescription: xii, 453 pages : illustrations (chiefly color) ; 24 cmISBN: - 9783031077180
- 23 005.133 T777
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 005.133 T777 (Browse shelf(Opens below)) | Available | 138831 |
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| 005.133 T468 Basic: a modular approach/ | 005.133 T475 Haskell the craft of functional programming | 005.133 T625 C for professional programmers | 005.133 T777 Python recipes for earth sciences / | 005.133 T821 COBOL programming | 005.133 Uc17 Problem solving using C ++ : structured and object oriented programming techniques | 005.133 Uc17 Problem solving using C ++ : structured and object oriented programming techniques |
Includes bibliographical references and index.
Introduction to Python -- Scientific computing and visualization -- Univariate and multivariate statistics -- Time series analysis -- Signal processing -- Spatial and directional data analysis -- Image processing and remote sensing -- Geoscientific applications and modeling.
This textbook introduces the use of Python programming for data analysis and computational applications in the earth sciences. Designed for students, researchers, and professionals in geosciences, the book explains how Python can be employed for statistical analysis, signal processing, image analysis, spatial data interpretation, and scientific visualization. Through practical examples and geoscientific datasets, the author demonstrates methods for handling univariate, bivariate, and multivariate data, analyzing time series, processing remote sensing imagery, and working with geographic and environmental information. The work combines theoretical explanations with executable Python recipes and computational workflows, enabling readers to develop practical programming skills for geoscientific research and modeling. Supplementary materials and sample datasets further support independent learning and advanced analytical applications in earth science investigations.
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