LEADER 07291nam 2200493 450 001 9910554854903321 005 20220423083437.0 010 $a1-119-60691-8 010 $a1-119-60689-6 010 $a1-119-60692-6 035 $a(CKB)4100000011993129 035 $a(MiAaPQ)EBC6692400 035 $a(Au-PeEL)EBL6692400 035 $a(OCoLC)1263872085 035 $a(EXLCZ)994100000011993129 100 $a20220423d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEarth observation using Python $ea practical programming guide /$fRebekah Bradley Esmaili 210 1$aHoboken, New Jersey :$cAGU :$cWiley,$d[2021] 210 4$dİ2021 215 $a1 online resource (300 pages) 225 1 $aSpecial publications series ;$v75 311 $a1-119-60688-8 327 $aCover -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- Introduction -- Part I Overview of Satellite Datasets -- Chapter 1 A Tour of Current Satellite Missions and Products -- 1.1 History of Computational Scientific Visualization -- 1.2 Brief Catalog of Current Satellite Products -- 1.2.1 Meteorological and Atmospheric Science -- 1.2.2 Hydrology -- 1.2.3 Oceanography and Biogeosciences -- 1.2.4 Cryosphere -- 1.3 The Flow of Data from Satellites to Computer -- 1.4 Learning Using Real Data and Case Studies -- 1.5 Summary -- References -- Chapter 2 Overview of Python -- 2.1 Why Python? -- 2.2 Useful Packages for Remote Sensing Visualization -- 2.2.1 NumPy -- 2.2.2 Pandas -- 2.2.3 Matplotlib -- 2.2.4 netCDF4 and h5py -- 2.2.5 Cartopy -- 2.3 Maturing Packages -- 2.3.1 xarray -- 2.3.2 Dask -- 2.3.3 Iris -- 2.3.4 MetPy -- 2.3.5 cfgrib and eccodes -- 2.4 Summary -- References -- Chapter 3 A Deep Dive into Scientific Data Sets -- 3.1 Storage -- 3.1.1 Single Values -- 3.1.2 Arrays -- 3.2 Data Formats -- 3.2.1 Binary -- 3.2.2 Text -- 3.2.3 Self-Describing Data Formats -- 3.2.4 Table-Driven Formats -- 3.2.5 geoTIFF -- 3.3 Data Usage -- 3.3.1 Processing Levels -- 3.3.2 Product Maturity -- 3.3.3 Quality Control -- 3.3.4 Data Latency -- 3.3.5 Reprocessing -- 3.4 Summary -- References -- Part II Practical Python Tutorials for Remote Sensing -- Chapter 4 Practical Python Syntax -- 4.1 "Hello Earth" in Python -- 4.2 Variable Assignment and Arithmetic -- 4.3 Lists -- 4.4 Importing Packages -- 4.5 Array and Matrix Operations -- 4.6 Time Series Data -- 4.7 Loops -- 4.8 List Comprehensions -- 4.9 Functions -- 4.10 Dictionaries -- 4.11 Summary -- References -- Chapter 5 Importing Standard Earth Science Datasets -- 5.1 Text -- 5.2 NetCDF -- 5.2.1 Manually Creating a Mask Variable Using True and False Values. 327 $a5.2.2 Using NumPy Masked Arrays to Filter Automatically -- 5.3 HDF -- 5.4 GRIB2 -- 5.5 Importing Data Using Xarray -- 5.5.1 netCDF -- 5.5.2 Examining Vertical Cross Sections -- 5.5.3 Examining Horizontal Cross Sections -- 5.5.4 GRIB2 using Cfgrib -- 5.5.5 Accessing Datasets Using OpenDAP -- 5.6 Summary -- References -- Chapter 6 Plotting and Graphs for All -- 6.1 Univariate Plots -- 6.1.1 Histograms -- 6.1.2 Barplots -- 6.2 Two Variable Plots -- 6.2.1 Converting Data to a Time Series -- 6.2.2 Useful Plot Customizations -- 6.2.3 Scatter Plots -- 6.2.4 Line Plots -- 6.2.5 Adding Data to an Existing Plot -- 6.2.6 Plotting Two Side-by-Side Plots -- 6.2.7 Skew-T Log-P -- 6.3 Three Variable Plots -- 6.3.1 Filled Contour Plots -- 6.3.2 Mesh Plots -- 6.4 Summary -- References -- Chapter 7 Creating Effective and Functional Maps -- 7.1 Cartographic Projections -- 7.1.1 Geographic Coordinate Systems -- 7.1.2 Choosing a Projection -- 7.1.3 Some Common Projections -- 7.2 Cylindrical Maps -- 7.2.1 Global Plots -- 7.2.2 Changing Projections -- 7.2.3 Regional Plots -- 7.2.4 Swath Data -- 7.2.5 Quality Flag Filtering -- 7.3 Polar Stereographic Maps -- 7.4 Geostationary Maps -- 7.5 Creating Maps from Datasets Using OpenDAP -- 7.6 Summary -- References -- Chapter 8 Gridding Operations -- 8.1 Regular One-Dimensional Grids -- 8.2 Regular Two-Dimensional Grids -- 8.3 Irregular Two-Dimensional Grids -- 8.3.1 Resizing -- 8.3.2 Regridding -- 8.3.3 Resampling -- 8.4 Summary -- References -- Chapter 9 Meaningful Visuals through Data Combination -- 9.1 Spectral and Spatial Characteristics of Different Sensors -- 9.2 Normalized Difference Vegetation Index (NDVI) -- 9.3 Window Channels -- 9.4 RGB -- 9.4.1 True Color -- 9.4.2 Dust RGB -- 9.4.3. Fire/Natural RGB -- 9.5 Matching with Surface Observations -- 9.5.1 With User-Defined Functions -- 9.5.2 With Machine Learning. 327 $a9.6 Summary -- References -- Chapter 10 Exporting with Ease -- 10.1 Figures -- 10.2 Text Files -- 10.3 Pickling -- 10.4 NumPy Binary Files -- 10.5 NetCDF -- 10.5.1 Using netCDF4 to Create netCDF Files -- 10.5.2 Using Xarray to Create netCDF Files -- 10.5.3 Following Climate and Forecast (CF) Metadata Conventions -- 10.6 Summary -- Part III Effective Coding Practices -- Chapter 11 Developing a Workflow -- 11.1 Scripting with Python -- 11.1.1 Creating Scripts Using Text Editors -- 11.1.2 Creating Scripts from Jupyter Notebook -- 11.1.3 Running Python Scripts from the Command Line -- 11.1.4 Handling Output When Scripting -- 11.2 Version Control -- 11.2.1 Code Sharing though Online Repositories -- 11.2.2 Setting up on GitHub -- 11.3 Virtual Environments -- 11.3.1 Creating an Environment -- 11.3.2 Changing Environments from the Command Line -- 11.3.3 Changing Environments in Jupyter Notebook -- 11.4 Methods for Code Development -- 11.5 Summary -- References -- Chapter 12 Reproducible and Shareable Science -- 12.1 Clean Coding Techniques -- 12.1.1 Stylistic Conventions -- 12.1.2 Tools for Clean Code -- 12.2 Documentation -- 12.2.1 Comments and Docstrings -- 12.2.2 README File -- 12.2.3 Creating Useful Commit Messages -- 12.3 Licensing -- 12.4 Effective Visuals -- 12.4.1 Make a Statement -- 12.4.2 Undergo Revision -- 12.4.3 Are Accessible and Ethical -- 12.5 Summary -- References -- Conclusion -- Appendix A Installing Python -- A.1. Download Tutorials for This Book -- A.2. Download and Install Anaconda -- A.3. Package Management in Anaconda -- Appendix B Jupyter Notebook -- B.1. Running on a Local Machine (New Coders) -- B.2. Running on a Remote Server (Advanced) -- B.3. Tips for Advanced Users -- B.3.1. Customizing Notebooks with Configuration Files -- B.3.2. Starting and Ending Python Scripts -- B.3.3. Creating Git Commit Templates. 327 $aAppendix C Additional Learning Resources -- Appendix D Tools -- D.1. Text Editors and IDEs -- D.2. Terminals -- Appendix E Finding, Accessing, and Downloading Satellite Datasets -- E.1. Ordering Data from NASA EarthData -- E.2. Ordering Data from NOAA/CLASS -- Appendix F Acronyms -- Index -- EULA. 410 0$aSpecial publication (American Geophysical Union) ;$v75. 606 $aEarth sciences$xData processing 608 $aElectronic books. 615 0$aEarth sciences$xData processing. 676 $a550.2855133 700 $aEsmaili$b Rebekah Bradley$01222024 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910554854903321 996 $aEarth observation using Python$92834034 997 $aUNINA LEADER 00981cam0 2200289 450 001 E600200019670 005 20240502085534.0 100 $a20060630d2006 |||||ita|0103 ba 101 $aita 102 $aIT 200 1 $aICI 2006$eimposta comunale sugli immobili$fElisabetta Casari$gJgor Merighi$gArianna Santoni 210 $aTrento$cSEAC$d2006 215 $a450 p.$cill.$d30 cm 225 2 $aLinea tributi 410 1$1001LAEC00022486$12001 $a*Linea tributi 700 1$aCasari$b, Elisabetta$3A600200037256$4070$0612743 702 1$aMerighi, Jgor$3A600200037257$4070 702 1$aSantoni, Arianna$3A600200037258$4070 801 0$aIT$bUNISOB$c20240502$gRICA 850 $aUNISOB 852 $aUNISOB$j330$m130432 912 $aE600200019670 940 $aM 102 Monografia moderna SBN 941 $aM 957 $a330$bVIII$i- (8)$gSi$d130432$racquisto$1pomicino$2UNISOB$3UNISOB$420060630094607.0$520151124130643.0$6bethb 996 $aICI 2006$91692285 997 $aUNISOB LEADER 03727nam 22006855 450 001 9910506392003321 005 20251113210121.0 010 $a9783030824082 010 $a303082408X 024 7 $a10.1007/978-3-030-82408-2 035 $a(CKB)5340000000068499 035 $a(MiAaPQ)EBC6789937 035 $a(Au-PeEL)EBL6789937 035 $a(OCoLC)1280459741 035 $a(MiFhGG)9783030824082 035 $a(DE-He213)978-3-030-82408-2 035 $a(EXLCZ)995340000000068499 100 $a20211022d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Substance and Behavioral Addiction $eThe Role of Executive Functions /$fedited by Michela Balconi, Salvatore Campanella 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (259 pages) 225 1 $aAdvances in Mental Health and Addiction,$x2570-3404 300 $aIncludes index. 311 08$a9783030824075 311 08$a3030824071 327 $aChapter 1. Similarities and differences between old and behavioral addictions: Focus on executive functions -- Chapter 2. The assessment of executive functions: A new neuropsychological tool for the assessment of executive functions -- Chapter 3. Analysis of executive functions in pathological gambling disorder -- Chapter 4. Neuroenhancement of executive functions in addictions -- Chapter 5. Mindfulness as a way for treating addiction-related disorders -- Chapter 6. Neuromodulation techniques in the treatment of addictions -- Chapter 7. Comorbidities between mental disorders and addictions -- Chapter 8. Impulse control and reward systems in old and new addictions -- Chapter 9. Interoception: an etiological mechanism or a tool for rehabilitation? -- Chapter 10. The theme of new addiction in the developmental age. 330 $aThis book deals with recent perspectives on the panel of addiction behavior in a vast amount of population (young and adult). Thanks to the contribution of experts of the topic of addiction the volume will furnish new perspectives to formulate assessment, diagnosis and intervention in response to the increasing variety of addictions. It focuses the assessment of executive functions in substance and behavioral addictions. More specifically, this assessment consists of a new approach not only inherent to the diagnosis, but also to the treatment and prevention of addictions. 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