PrecognitionÔ

(Release 4.2)

 

User Guide and Reference

 

John Zhong Ren

Renz Research, Inc.

 

 

 
Westmont, Illinois, U. S. A.

 

 

 

All rights reserved
12/10/2004 3:41 PM

 

 

Chapter 1 Overview
Chapter 2 Indexing of Laue Pattern
Chapter 3 Geometric Refinement
Chapter 4 Spot Integration
Chapter 5 Data Reduction

Chapter 11 Reference
Chapter 12 Tutorials

Appendix 1 Utilities

 

 


PrecognitionÔ is a trademark of Renz Research, Inc.

 

 

 

 

John Zhong Ren

 

Renz Research, Inc.

P. O. Box 605

Westmont, IL 60559

U.S.A.

 

E-mail  renz@renzresearch.com

WWW http://renzresearch.com

Tel.      630-230-0272

Fax      435-514-2645


What¡¯s New?

 

12/1/2004

Precognition 4.2.0

 

  • Fixing and freeing geometric parameters become more convenient.
  • Calibration of distance and cell constants during geometric refinement.
  • A new integration mode nonlinearAnalytical is available.
  • Analytical profile fitting works with a numerical compensation, which improves the accuracy of integration for strong spots.
  • Negative integrated intensity becomes possible.  Dealing with negative integrated intensities in Laue diffraction is complex.  It is not recommended for general users.
 

Epinorm 4.2.0

 

  • Negative integrated intensity may contribute to scaling and be included in final results.  Dealing with negative integrated intensities in Laue diffraction is complex.  It is not recommended for general users.
  • Frame-specific l-curves are possible.
 

7/12/2004

Precognition 4.1.0

 

  • Connection to the older program LaueView is no longer the default, but still possible by explicit command option, which signals the independence of Precognition system.
 

Epinorm 4.1.0

 

  • Extremely-close spatial overlaps can be deconvoluted by the same algorithm as harmonic deconvolution.
  • All effects of energy partial are completely modeled in deconvolution of extremely-close spatial overlaps.
  • Friedel pairs can be separated during data scaling and/or merging if anomalous scattering should be considered.
 

5/5/2004

Epinorm 4.0.0

 

  • Temperature factors can be fixed without refinement during scaling.
  • Harmonic deconvolution is available, which marks the completion of all major functionalities in this software system.
  • Crystal mosaicity, energy resolution and energy partial of harmonic reflection are completely modeled in harmonic deconvolution.
 

4/9/2004

Precognition 3.1.1

 

  • Bug fixing.
 

3/26/2004

Precognition 3.1.0

 

  • Soft limits and initial source spectrum can be recognized before and after indexing of a pattern.
  • A new refinement mode drunk is added for solution of frequent crystal jumping.
  • Random walk-around of crystal orientation is improved.  Number of steps and radius can be automatically adjusted.
  • Uncertainties of detector parameters can be input by user, so that freedom of these parameters are limited.
  • User may choose to fix some parameters during geometry refinement.
  • Manual geometry refinement is possible.

 

Epinorm 3.1.0

 
  • Crystal mosaicity is refined.
  • Partials in Laue diffraction due to a finite energy bandpass, called energy partial, are reduced.
  • l-curve becomes l-surface.  Wavelength normalization factor not only depends on wavelength, but also mosaicity and Bragg angle.
  • Spike removal in l-curve.
  • Data rejection in the last release was based absolute residual of fit which resulted in near constant final R-factors.  It is changed in this release so that rejection is based on root-mean-squares of fitting residuals.
  • Set initial values of parameters.
  • Save and restore intermediate results.

 

11/6/2003

Epinorm 3.0.0

 

  • The major addition to this release is a new program Epinorm (Energy Partial Improved NORMalization).  Epinorm reduces Laue integrated intensities to structure factor amplitudes.  Wavelength normalization, scaling, temperature factors, and beam polarization are determined.  Compared to LaueView, Epinorm has no multiple sessions, no interactive data rejection, no limitation to number of images, and runs faster.  Epinorm makes anisotropic scaling possible.

 

9/23/2003

Precognition 2.2.0

 

  • Indexing consistency check is available for re-indexing in middle of a set.
  • Sorting of filenames in .gon file is removed.  Frames are ordered as the sequence of loading.
  • A major addition is residual pattern recognition (RPR) in geometry refinement.  Residual pattern consists of all residual vectors from predicted spot locations to observed ones.  This pattern varies according to the remaining errors in the parameters, and shrinks to a small Gaussian distribution when refinement is done well.  Precognition automatically recognizes residual pattern using the newly developed RPR technique, makes geometry refinement more robust.

 

Precognition.py 2.2.0

 

  • This is the first released trial GUI, including image display, zooming, contrast, brightness, marking recognized spots, etc.


Contents

 

 

What¡¯s New

iii

 

Contents

vii

 

 

 

Chapter 1

Overview

1

 

1.1

Introduction

1

 

1.2

Edition, Version, Release, and Patch

2

 

1.3

Major Functionalities and Features

3

 

1.3.1

Major functionalities of Precognition, a pattern recognition software for Laue diffraction

3

 

1.3.2

Major functionalities of Epinorm, a wavelength normalization and data scaling software for Laue diffraction

3

 

1.3.3

Major features

3

 

1.4

A Tour of This Book

4

 

1.5

Conventions Used

6

 

1.5.1

Notations

6

 

1.6

Related Software

6

 

1.6.1

GCC

6

 

1.6.2

Python

7

 

1.6.3

Numeric/Numarray

7

 

1.6.4

PIL

8

 

1.6.5

Pmw

8

 

1.6.6

SWIG

9

 

1.6.7

FFTW

9

 

1.6.8

TNT

9

 

1.6.9

LAPACK and BLAS

10

 

1.6.10

GRACE

10

 

1.6.11

Gnuplot

11

 

1.7

Installation and Execution

11

 

1.7.1

Program Installation

11

 

1.7.2

License Installation

12

 

1.7.3

Execution

13

 

 

 

Chapter 2

Indexing of Laue Pattern

15

 

2.1

Required Input

15

 

2.1.1

Crystal information

15

 

2.1.2

Crystal-to-detector distance

16

 

2.1.3

Direct-beam center

16

 

2.1.4

Pixel size

17

 

2.1.5

Image format

18

 

2.1.6

Image file

18

 

2.1.7

Resolution and wavelength ranges

18

 

2.1.8

Exit from Input menu

18

 

2.2

First Indexing

19

 

2.2.1

Spot recognition

19

 

2.2.2

Auto-recognition of diffraction limit

24

 

2.2.3

Learning spot profile

25

 

2.2.4

Ellipse recognition

25

 

2.2.5

Nodal recognition

25

 

2.2.6

Indexing

26

 

2.3

Validation of Indexing

27

 

2.4

Routine Indexing

28

 

2.5

Mis-indexing

29

 

2.5.1

Check all possible matching

30

 

2.5.2

Use more nodal spots

31

 

2.5.3

Manual indexing

32

 

2.5.4

Other options

32

 

2.6

Quick Indexing

33

 

2.7

Goniometer Setting

33

 

 

 

 

Chapter 3

Geometric Refinement

35

 

3.1

Estimates of Soft Limits and Source Spectrum

35

 

3.1.1

Before indexing

35

 

3.1.2

After indexing

37

 

3.2

Goniometer Setting

39

 

3.3

Indexing and Refinement of a Set of Patterns

41

 

3.4

Crystal Slipping

44

 

3.5

Repairing an Individual Frame

45

 

3.6

Fixing Parameters and Limited Refinement

46

 

3.7

Calibration of Crystal-to-detector Distance and Cell Constants

47

 

3.8

Re-index

48

 

3.9

Manual Refinement

49

 

3.10

Final Refinement

56

 

 

 

 

Chapter 4

Spot Integration

59

 

4.1

General Issues for Integration

59

 

4.2

Integration On-the-fly, box Mode

61

 

4.3

Elliptical Peak Area, fixedElliptical Mode

62

 

4.4

Learned Elliptical Peak Area, variableElliptical Mode

62

 

4.5

Numerical Profile, numeric Mode

62

 

4.6

Analytical Profile, linearAnalytical Mode

62

 

4.7

Simultaneous Profile Fitting and Spatial-overlap Deconvolution, nonlinearAnalytical Mode

63

 

4.8

Numerical Compensation to Analytical Profiles

64

 

4.9

Hybrid Mode

65

 

 

 

 

Chapter 5

Data Reduction

67

 

5.1

Input Data and Control Parameters

67

 

5.2

Data Selection and Parameter Fitting

70

 

5.2.1

Data selection

70

 

5.2.2

Data isotropy

71

 

5.2.3

Crystal mosaicity

72

 

5.2.4

Absorption correction

72

 

5.2.5

Initial scaling

73

 

5.2.6

Minimization cycle and statistical report

74

 

5.2.7

Results

76

 

5.3

Saving and Restoring Intermediate Results

79

 

5.4

Applying scale Factors

82

 

5.5

Harmonic Deconvolution

84

 

5.6

Extremely Close Spatial Overlap

85

 

 

 

 

Chapter 11

Reference

87

 

11.1

Precognition_E¡Á.¡Á.¡Á_P¡Á.¡Á

precognition_E¡Á.¡Á.¡Á_P¡Á.¡Á

Precognition

precognition

87

 

11.1.1

Precognition:Input (I|i)

88

 

11.1.1.1

Precognition:Input:Format (F|f)

88

 

 

Precognition:Input:Format:Bas2000 (B|b)

88

 

 

Precognition:Input:Format:ESRF (E|e)

88

 

 

Precognition:Input:Format:Mar345 (M|m)

88

 

 

Precognition:Input:Format:MarCCD (C|c)

88

 

 

Precognition:Input:Format:Quantum4 (4)

88

 

11.1.1.2

Precognition:Input:Image (I|i)

88

 

11.1.1.3

Precognition:Input:Crystal (X|x)

89

 

11.1.1.4

Precognition:Input:Omega (O|o)

89

 

11.1.1.5

Precognition:Input:Goniometer (G|g)

89

 

11.1.1.6

Precognition:Input:Matrix (M|m)

90

 

11.1.1.7

Precognition:Input:Spot (L|l)

90

 

11.1.1.8

Precognition:Input:Distance (D|d)

91

 

11.1.1.9

Precognition:Input:Center (C|c)

91

 

11.1.1.10

Precognition:Input:Pixel (P|p)

91

 

11.1.1.11

Precognition:Input:Swing (S|s)

92

 

11.1.1.12

Precognition:Input:Tilt (T|t)

92

 

11.1.1.13

Precognition:Input:Bulge (B|b)

93

 

11.1.1.14

Precognition:Input:Resolution (R|r)

93

 

11.1.1.15

Precognition:Input:Wavelength (W|w)

93

 

11.1.1.16

Precognition:Input:Chebyshev (V|v)

93

 

11.1.1.17

Precognition:Input:Ellipse (E|e)

94

 

11.1.1.18

Precognition:Input:Nodal (N|n)

94

 

11.1.1.19

Precognition:Input:Anomalous (A|a)

94

 

11.1.1.20

Precognition:Input:Quit (Q|q)

95

 

11.1.2

Precognition:Spot (S|s)

95

 

11.1.3

Precognition:Profile (F|f)

95

 

11.1.4

Precognition:Ellipse (E|e)

95

 

11.1.5

Precognition:Nodal (N|n)

95

 

11.1.6

Precognition:Pattern (P|p)

96

 

11.1.7

Precognition:Limits (L|l)

96

 

11.1.8

Precognition:Dataset (D|d)

97

 

11.1.8.1

Mode of processing

97

 

 

normal, Normal, or NORMAL

97

 

 

progressive, Progressive, or PROGRESSIVE

97

 

 

drunk, Drunk, or DRUNK

98

 

 

calibration, Calibration, or CALIBRATION

98

 

 

final, Final, or FINAL

98

 

 

integration, Integration, or INTEGRATION

98

 

 

box, Box, or BOX

98

 

 

fixedElliptical, fixedelliptical, FixedElliptical, Fixedelliptical, or FIXEDELLIPTICAL

99

 

 

variableElliptical, variableelliptical, VariableElliptical, Variableelliptical, or VARIABLEELLIPTICAL

99

 

 

numeric, Numeric, or NUMERIC

99

 

 

linearAnalytical, linearanalytical, LinearAnalytical, Linearanalytical, or LINEARANALYTICAL

99

 

 

nonlinearAnalytical, nonlinearanalytical, NonlinearAnalytical, Nonlinearanalytical, or NONLINEARANALYTICAL

100

 

 

hybrid, Hybrid, or HYBRID

100

 

11.1.8.2

Precognition:Dataset:In (I|i)

100

 

11.1.8.3

Precognition:Dataset:Out (O|o)

100

 

11.1.8.4

Precognition:Dataset:Path (P|p)

100

 

11.1.8.5

Precognition:Dataset:OK (K|k)

100

 

11.1.8.6

Precognition:Dataset:Resolution (R|r)

100

 

11.1.8.7

Precognition:Dataset:Wavelength (W|w)

101

 

11.1.8.8

Precognition:Dataset:Quit (Q|q)

101

 

11.1.8.9

Connection to LaueView

101

 

11.1.9

Precognition:Quit (Q|q)

101

 

11.2

Epinorm_E´.´.´_P´.´

epinorm_E´.´.´_P´.´

Epinorm

epinorm

101

 

11.2.1

Epinorm:Input (I|i)

102

 

11.2.2

Epinorm:Restore (R|r)

102

 

11.2.2.1

Saving intermediate parameters

102

 

11.2.2.2

Epinorm:Restore:Polarization (P|p)

102

 

11.2.2.3

Epinorm:Restore:Mosaicity (M|m)

102

 

11.2.2.4

Epinorm:Restore:Scale (S|s)

102

 

11.2.2.5

Epinorm:Restore:Temperature (T|t)

102

 

11.2.2.6

Epinorm:Restore:AnisoScale (A|a)

102

 

11.2.2.7

Epinorm:Restore:AnisoTemperature (N|n)

103

 

11.2.2.8

Epinorm:Restore:Wavelength (W|w)

103

 

11.2.2.9

Epinorm:Restore:Coefficient (C|c)

103

 

11.2.2.10

Epinorm:Restore:Quit (Q|q)

103

 

11.2.3

Epinorm:Scale (S|s)

104

 

11.2.3.1

Data selection

104

 

11.2.3.2

Initial scaling

104

 

11.2.3.3

Reference frame

104

 

11.2.3.4

Data isotropy

104

 

11.2.3.5

Crystal mosaicity

105

 

11.2.4

Epinorm:Lambda (L|l)

105

 

11.2.5

Epinorm:Apply (A|a)

105

 

11.2.5.1

Data selection

105

 

11.2.5.2

Apply scaling parameters to single reflections

105

 

11.2.5.3

Merging single reflections

105

 

11.2.5.4

Harmonic deconvolution

106

 

11.2.5.5

Deconvolution of extremely-close spatial overlaps

106

 

11.2.6

Epinorm:Quit (Q|q)

106

 

11.3

TBA

 

 

11.4

Precognition.py

 

 

11.5

Utilities and Diagnostic Tools

 

 

11.6

cpl.Precognition Module

 

 

11.7

Coordinates Systems

 

 

11.8

Files

107

 

11.8.1

File selection

107

 

11.8.1.1

File selection:Filename (F|f)

107

 

11.8.1.2

File selection:Path (P|p)

107

 

11.8.1.3

File selection:Name (N|n)

107

 

11.8.1.4

File selection:Middle (M|m)

107

 

11.8.1.5

File selection:Extension (E|e)

107

 

11.8.1.6

File selection:Cancel (C|c)

107

 

11.8.1.7

File selection:OK (O|o)

108

 

11.8.1.8

Previously-selected filename

108

 

11.8.2

Crystal information file .xtl

108

 

11.8.3

Goniometer file .gon

108

 

11.8.4

l-curve .lam

109

 

11.8.5

Spot file .spt

109

 

11.8.6

Integrated intensity file .ii

109

 

11.8.7

Energy-dispersive intensity file .edi

109

 

11.8.8

Structure factor amplitude file .hkl

109

 

 

 

 

Chapter 12

Tutorials

111

 

12.1

An Undulator Laue Dataset from a Protein Crystal

111

 

12.1.1

Visual evaluation of images and estimates of soft limits

111

 

12.1.2

Indexing

115

 

12.1.2.1

Indexing

116

 

12.1.2.2

Estimation of more soft limits

119

 

12.1.2.3

Indexing problems

121

 

12.1.3

Geometric refinement

122

 

12.1.4

Integration

124

 

12.1.5

Wavelength normalization and scaling

125

 

12.1.6

Data merging, harmonic and spatial deconvolution

127

 

12.1.7

Structure refinement

128

 

 

 

 

Appendix 1

Utilities

131

 

A1.1

plot.py

131

 

A1.2

swap1.py

131