2. The DataServer module

Abstract

This chapter is dealing with the DataServer module which is the spine of the Uranie project. In this module crucial classes are defined to handle data and transmit them to other modules, along with basic statistics implementation.

The source files are in souRCE.git/dataSERVER/souRCE and the corresponding namespace is URANIE::DataServer.

• 2.1. Introduction
• 2.2. The TAttribute class
  • 2.2.1. Nature of the attribute
  • 2.2.2. List of variable information
  • 2.2.3. Examples of use of the class TAttribute
  • 2.2.4. Adding TAttribute when data are already available
  • 2.2.5. Introducing the TStochasticAttribute classes
    • 2.2.5.1. Uniform Law
    • 2.2.5.2. Log Uniform Law
    • 2.2.5.3. Triangular Law
    • 2.2.5.4. LogTriangular Law
    • 2.2.5.5. Normal law
    • 2.2.5.6. LogNormal law
    • 2.2.5.7. Trapezium law
    • 2.2.5.8. UniformByParts law
    • 2.2.5.9. Exponential law
    • 2.2.5.10. Cauchy law
    • 2.2.5.11. GumbelMax law
    • 2.2.5.12. Weibull law
    • 2.2.5.13. Beta law
    • 2.2.5.14. GenPareto law
    • 2.2.5.15. Gamma law
    • 2.2.5.16. InvGamma law
    • 2.2.5.17. Student Law
    • 2.2.5.18. Generalized normal law
    • 2.2.5.19. Composing law
• 2.3. Data handling
  • 2.3.1. Main format of input/output
    • 2.3.1.1. The Salome-table format
    • 2.3.1.2. The JSON format
  • 2.3.2. Import data from an ASCII file
  • 2.3.3. Import data from a TNtuple/TDSNtuple/TTree
  • 2.3.4. Adding attributes to a TDataServer
  • 2.3.5. Merging two DataServer
  • 2.3.6. Pattern selection
  • 2.3.7. Export to an ASCII file
• 2.4. Statistical treatments and operations
  • 2.4.1. Normalising the variable
  • 2.4.2. Computing the ranking
  • 2.4.3. Computing the elementary statistic
    • 2.4.3.1. Specific case of vectors
  • 2.4.4. The quantile computation
    • 2.4.4.1. computeQuantile
    • 2.4.4.2. \(\alpha\)-quantile
    • 2.4.4.3. Wilks-quantile computation
  • 2.4.5. Correlation matrix
    • 2.4.5.1. Special case of vector
• 2.5. Visualisation dedicated to uncertainties
  • 2.5.1. Histogram
  • 2.5.2. Box-and-whisker(“boxplot”)
  • 2.5.3. CDF, CCDF curves
  • 2.5.4. Graph 2D with contour levels
  • 2.5.5. Graph 2D “profile”
  • 2.5.6. Graph 2D “Tufte”
  • 2.5.7. Graph 2D “pairs”
  • 2.5.8. Graph “CobWeb”
  • 2.5.9. QQ plot
  • 2.5.10. PP plot
• 2.6. Combining these aspects: performing PCA
  • 2.6.1. PCA usage within Uranie
    • 2.6.1.1. Perform the PCA
    • 2.6.1.2. Interpretation of PCA results