Python recipes for earth sciences / / Martin H. Trauth

• Autore: Trauth Martin H.
• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (463 pages)
• Disciplina: 550.028557
• Soggetto topico: Earth sciences - Data processing; Geophysics - Data processing; Python (Computer program language)
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- 1 Data Analysis in the Earth Sciences -- 1.1 Introduction -- 1.2 Data Collection -- 1.3 Types of Data -- 1.4 Methods of Data Analysis -- Recommended Reading -- 2 Introduction to Python -- 2.1 Introduction -- 2.2 Getting Started -- 2.3 Python Syntax -- 2.4 Array Manipulation -- 2.5 Data Types in Python -- 2.6 Data Storage and Handling -- 2.7 Control Flow -- 2.8 Scripts and Functions -- 2.9 Basic Visualization Tools -- 2.10 Generating Code to Recreate Graphics -- 2.11 Publishing and Sharing MATLAB Code -- 2.12 Creating Graphical User Interfaces -- Recommended Reading -- 3 Univariate Statistics -- 3.1 Introduction -- 3.2 Empirical Distributions -- 3.3 Examples of Empirical Distributions -- 3.4 Theoretical Distributions -- 3.5 Examples of Theoretical Distributions -- 3.6 Hypothesis Testing -- 3.7 The t-Test -- 3.8 The F-Test -- 3.9 The χ2-Test -- 3.10 The Kolmogorov-Smirnov Test -- 3.11 Mann-Whitney Test -- 3.12 The Ansari-Bradley Test -- 3.13 Distribution Fitting -- 3.14 Error Analysis -- Anchor 16 -- Recommended Reading -- 4 Bivariate Statistics -- 4.1 Introduction -- 4.2 Correlation Coefficients -- 4.3 Classical Linear Regression Analysis -- 4.4 Analyzing the Residuals -- 4.5 Bootstrap Estimates of the Regression Coefficients -- 4.6 Jackknife Estimates of the Regression Coefficients -- 4.7 Cross-Validation -- 4.8 Reduced Major Axis Regression -- 4.9 Curvilinear Regression -- 4.10 Nonlinear and Weighted Regression -- 4.11 Classical Linear Regression of Log-Transformed Data -- Recommended Reading -- 5 Time Series Analysis -- 5.1 Introduction -- 5.2 Generating Signals -- 5.3 Auto-Spectral and Cross-Spectral Analysis -- 5.4 Examples of Auto-Spectral and Cross-Spectral Analysis -- 5.5 Interpolating and Analyzing Unevenly Spaced Data -- 5.6 Evolutionary Power Spectrum -- 5.7 Lomb-Scargle Power Spectrum. ; 5.8 Wavelet Power Spectrum -- 5.9 Detecting Abrupt Transitions in Time Series -- 5.10 Aligning Stratigraphic Sequences -- 5.11 Nonlinear Time Series Analysis (by N. Marwan) -- Phase Space Portrait -- Recurrence Plots -- Recurrence Quantification -- Anchor 16 -- Recommended Reading -- 6 Signal Processing -- 6.1 Introduction -- 6.2 Generating Signals -- 6.3 Linear Time-Invariant Systems -- 6.4 Convolution, Deconvolution, and Filtering -- 6.5 Comparing Functions for Filtering Data Series -- 6.6 Recursive and Nonrecursive Filters -- 6.7 Impulse Response -- 6.8 Frequency Response -- 6.9 Filter Design -- 6.10 Adaptive Filtering -- Recommended Reading -- 7 Spatial Data -- 7.1 Introduction -- 7.2 The Global Geography Database GSHHG -- 7.3 The 1 Arc-Minute Gridded Global Relief Data ETOPO1 -- 7.4 The 30 Arc-Second Elevation Model GTOPO30 -- 7.5 The Shuttle Radar Topography Mission SRTM -- 7.6 Exporting 3D Graphics to Create Interactive Documents -- 7.7 Gridding and Contouring -- 7.8 Comparison of Methods and Potential Artifacts -- 7.9 Statistics of Point Distributions -- 7.10 Analysis of Digital Elevation Models (by R. Gebbers) -- 7.11 Geostatistics and Kriging (by R. Gebbers) -- Anchor 13 -- Recommended Reading -- 8 Image Processing -- 8.1 Introduction -- 8.2 Data Storage -- 8.3 Importing, Processing, and Exporting Images -- 8.4 Importing, Processing, and Exporting Landsat Images -- 8.5 Importing and Georeferencing Terra ASTER Images -- 8.6 Processing and Exporting EO-1 Hyperion Images -- 8.7 Digitizing from the Screen -- 8.8 Image Enhancement, Correction, and Rectification -- 8.9 Color Intensity Transects Across Varved Sediments -- 8.10 Grain Size Analysis from Microscopic images -- 8.11 Quantifying Charcoal in Microscopic images -- 8.12 Shape-Based Object Detection in Images -- 8.13 The Normalized Difference Vegetation Index NDVI -- Anchor 15. ; Recommended Reading -- 9 Multivariate Statistics -- 9.1 Introduction -- 9.2 Principal Component Analysis -- 9.3 Independent Component Analysis (by N. Marwan) -- 9.4 Discriminant Analysis -- 9.5 Cluster Analysis -- 9.6 Multiple Linear Regression -- 9.7 Aitchison's Log-Ratio Transformation -- Recommended Reading -- 10 Directional Data -- 10.1 Introduction -- 10.2 Graphical Representation of Circular Data -- 10.3 Empirical Distributions of Circular Data -- 10.4 Theoretical Distributions of Circular Data -- 10.5 Test for Randomness of Circular Data -- 10.6 Test for the Significance of a Mean Direction -- 10.7 Test for the Difference Between Two Sets of Directions -- 10.8 Graphical Representation of Spherical Data -- 10.9 Statistics of Spherical Data -- Recommended Reading.
• Titolo autorizzato: Python Recipes for Earth Sciences
• ISBN: 9783031077197; 9783031077180
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910616212103321
• Lo trovi qui: Univ. Federico II
• Serie: Springer Textbooks in Earth Sciences, Geography and Environment