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Data and Process Visualisation for Graphic Communication : A Hands-On Approach with Python
| Data and Process Visualisation for Graphic Communication : A Hands-On Approach with Python |
| Autore | Bianconi Francesco |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (242 pages) |
| ISBN | 3-031-57051-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- How the Book Is Organized -- Figures, Data Listings, Scripts, Code Fragments, and Comment Boxes -- Companion Website -- Prerequisites -- Why Python? -- Alternative Tools -- References -- Acknowledgments -- Disclaimer -- Contents -- Part I Data -- 1 Introducing Data -- 1.1 Types of Variables -- 1.2 Measures and Dimensions -- References -- 2 Magnitudes -- 2.1 Bar Charts -- 2.1.1 Basic Bar Chart -- 2.1.2 Basic Bar Chart with Style Variations -- 2.1.3 Paired Bar Charts -- 2.1.4 Stacked Bar Charts -- 2.1.5 Multiple Bar Chart -- 2.1.6 Horizontal Bar Chart -- 2.2 Packed Bubble Chart -- References -- 3 Proportions -- 3.1 Pie Charts -- 3.1.1 Basic Pie Chart -- 3.1.2 Pie Chart With Side Legend -- 3.1.3 Pulling Out the Wedges -- 3.2 Doughnut Charts -- 3.3 Semi-Doughnut Charts -- 3.4 Waffle Charts -- 3.4.1 Multiple Waffle Charts -- 3.5 Hundred Percent Stacked Bar Charts -- 3.6 Divergent Hundred Percent Stacked Bar Charts -- 3.7 Tree Maps -- 3.7.1 One-Level Tree Map -- 3.7.2 Two-Level Tree Map -- References -- 4 One Variable as a Function of the Other -- 4.1 Line Charts -- 4.1.1 Single-Line Chart -- 4.1.2 Multi-Line Chart -- 4.1.3 Split Line Charts -- 4.2 Slope Charts -- 4.2.1 Basic Slope Chart -- 4.2.2 Slope Chart with a Legend -- References -- 5 Frequency Distributions -- 5.1 Histogram Plots -- 5.2 Dot Diagrams -- 5.3 Pyramid Plots -- 5.4 Area Charts -- 5.4.1 Single Area Chart -- 5.4.2 Multiple Area Charts -- References -- 6 Groups -- 6.1 Strip Plots -- 6.2 Swarm Plots -- 6.3 Box Plots -- 6.4 Combined Box and Strip Plots -- 6.5 Violin Plots -- 6.6 Combined Violin and Box Plots -- References -- 7 Relations -- 7.1 Chord Diagrams -- 7.1.1 Directed Chord Diagram -- 7.1.2 Undirected Chord Diagram -- 7.2 Sankey Diagrams -- 7.2.1 One-to-Many Sankey Diagram -- 7.2.2 Many-to-Many Sankey Diagram -- References -- 8 Bivariate Data.
8.1 Scatter Plots -- 8.1.1 Basic Scatter Plot for Correlation Analysis -- 8.1.2 Scatter Plot with Regression Lineand Confidence Interval -- 8.1.3 Scatter Plot Matrix for Pairwise Correlation Analysis -- 8.1.4 Scatter Plot for Cluster Visualization -- 8.1.5 Scatter Plot for Cluster Visualization (Fancy Version) -- 8.1.6 The Datasaurus Dozen -- References -- 9 Trivariate Data -- 9.1 Scatter Bubble Plots -- 9.1.1 Simple Scatter Bubble Plot -- 9.1.2 Scatter Bubble Plot with Annotations -- 9.2 Lattice Bubble Plots -- 9.3 Heat Maps -- 9.3.1 Heat Map with a Color Bar -- 9.3.2 Heat Map with Color Bar and Annotations -- References -- 10 Geospatial Data -- 10.1 Choroplet Maps -- 10.2 Hexgrid Maps -- 10.3 Proportional Symbol Maps -- 10.4 Cartograms -- References -- Part II Representing Processes -- 11 Timelines -- 11.1 Horizontal timeline -- 11.2 Vertical Timeline -- Reference -- 12 Flowcharts -- 12.1 A Simple Flowchart -- 12.1.1 Flowchart for Computing the Factorial of a Number -- References -- 13 Gantt Charts -- 13.1 A Simple Gantt Chart -- 13.2 Gantt Chart with Activities and Phases -- Reference -- 14 PERT Diagrams -- 14.1 AoN PERT Diagrams -- 14.2 AoA PERT Diagrams -- References -- Appendices -- A Mathematics and Statistics Review -- A.1 Set Theory -- A.1.1 Partial and Total Orders -- A.2 Correlation -- A.2.1 Pearson's Linear Correlation Coefficient -- A.2.2 Spearmans's Rank Correlation Coefficient -- A.2.3 Qualitative Interpretation of Correlation Coefficients -- B Matplotlib: A Primer -- B.1 Functional vs. Object-Oriented Interface -- B.2 Understanding Figure and Axes -- B.2.1 Adding Axes to a Figure -- B.2.2 Generating Insets -- B.2.3 Customizing Axes -- B.2.4 Managing Titles and Subtitles Through mpl-ornaments -- B.2.5 Changing the Background Color of Figure and Axes -- B.3 Depth Sorting -- C Color -- C.1 Background -- C.1.1 Color Spaces. C.2 Guidelines for Using Colors in Charts -- C.2.1 When to Use Color -- C.2.2 When Not to Use Color -- C.3 Color Palettes -- C.3.1 Sequential Color Palettes -- C.3.2 Diverging Color Palettes -- C.3.3 Qualitative Color Palettes -- C.4 Specifying Colors in Matplotlib -- C.4.1 Transparency -- D Geodesy and Cartography Notes -- D.1 The World Geodetic System 1984 (WGS 84) -- D.2 Map Projections -- D.2.1 Types of Projections -- D.2.2 Properties of Projections -- D.3 Data Models for GIS -- D.3.1 Storing Geospatial Data -- D.4 Generating Maps With Python and GeoPandas -- References -- Index. |
| Record Nr. | UNINA-9910861098903321 |
Bianconi Francesco
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| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Texture and Colour in Image Analysis
| Texture and Colour in Image Analysis |
| Autore | Bianconi Francesco |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (278 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
Machine vision
image analysis item counting device electro-deposition industry digital intraoral radiography image preprocessing periapical lesions texture analysis prostate cancer histopathology microscopic tissue image segmentation morphological quantitative classification SVM image resizing local Tchebichef moments (LTM) scaling scale-and-stretch seam carving faster R-CNN cutting pieces multi-period pattern skew angle period length colored texture pattern classification global-local texture classification color-texture features color-texture feature extraction bagging post-processing BQMP and Haralick global-local feature integration maceral components image segmentation coal petrography random forest two-level clustering deep neural networks adaptive gradient methods stochastic gradient descent bounded scheduling method image classification language modeling texture deep learning MB-LBP surface defect detection feature extraction defect recognition mammogram meta-heuristics optimization breast cancer detection skin microrelief water sorption aging hair mathematics of colour and texture hand-designed image descriptors rank features partial orders river scene segmentation local binary pattern hue variance surface reflection audio classification dissimilarity space siamese network ensemble of classifiers pattern recognition animal audio co-saliency omnidirectional images video saliency visual saliency estimation |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557518003321 |
|
Bianconi Francesco
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||