Exploratory orientation data analysis with ω sections

被引:51
|
作者
van den Boogaart, KG [1 ]
Schaeben, H
机构
[1] Univ Greifswald, Dept Math & Comp Sci, Inst F48, D-17489 Greifswald, Germany
[2] Freiberg Univ Min & Technol, Freiburg, Germany
来源
关键词
D O I
10.1107/S0021889804011446
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Since the domain of crystallographic orientations is three-dimensional and spherical, insightful visualization of them or visualization of related probability density functions requires (i) exploitation of the effect of a given orientation on the crystallographic axes, (ii) consideration of spherical means of the orientation probability density function, in particular with respect to one-dimensional totally geodesic submanifolds, and (iii) application of projections from the two-dimensional unit sphere S-2 subset of IR3 onto the unit disk D subset of IR2. The familiar crystallographic 'pole figures' are actually mean values of the spherical Radon R-1 transform. The mathematical Radon R-1 transform associates a real-valued function f defined on a sphere with its mean values R(1)f along one-dimensional circles with centre O, the origin of the coordinate system, and spanned by two unit vectors. The family of views suggested here defines omega sections in terms of simultaneous orientational relationships of two different crystal axes with two different specimen directions, such that their superposition yields a user-specified pole probability density function. Thus, the spherical averaging and the spherical projection onto the unit disk determine the distortion of the display. Commonly, spherical projections preserving either volume or angle are favoured. This rich family displays f completely, i.e. if f is given or can be determined unambiguously, then it is uniquely represented by several subsets of these views. A computer code enables the user to specify and control interactively the display of linked views, which is comprehensible as the user is in control of the display.
引用
收藏
页码:683 / 697
页数:15
相关论文
共 50 条
  • [41] EXPLORATORY ANALYSIS OF MARKETING DATA - REPLY
    ARMSTRON.JS
    JOURNAL OF MARKETING RESEARCH, 1971, 8 (04) : 511 - 513
  • [42] Learning metrics for exploratory data analysis
    Kaski, S
    NEURAL NETWORKS FOR SIGNAL PROCESSING XI, 2001, : 53 - 62
  • [43] THE PHILOSOPHY OF EXPLORATORY DATA-ANALYSIS
    GOOD, IJ
    PHILOSOPHY OF SCIENCE, 1983, 50 (02) : 283 - 295
  • [44] Reinforced Approximate Exploratory Data Analysis
    Garg, Shaddy
    Mitra, Subrata
    Yu, Tong
    Gadhia, Yash
    Kashettiwar, Arjun
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 6, 2023, : 7660 - 7669
  • [45] Scolopax: Exploratory Analysis of Scientific Data
    Okcan, Alper
    Riedewald, Mirek
    Panda, Biswanath
    Fink, Daniel
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2013, 6 (12): : 1298 - 1301
  • [46] Exploratory data analysis for complex models
    Gelman, A
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2004, 13 (04) : 755 - 779
  • [47] Exploratory data analysis with noisy measurements
    Wentzell, P. D.
    Hou, S.
    JOURNAL OF CHEMOMETRICS, 2012, 26 (06) : 264 - 281
  • [48] Graphical exploratory analysis of survival data
    Lumley, T
    Heagerty, P
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2000, 9 (04) : 738 - 749
  • [49] Comments on: Exploratory functional data analysis
    Lopez-Pintado, Sara
    TEST, 2025,
  • [50] Intelligent support for exploratory data analysis
    St Amant, R
    Cohen, PR
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 1998, 7 (04) : 545 - 558