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1. Introduction
2. Installing Karrigell 3. The Web server 4. Configuration options 5. Working with Apache, LightTPD or Xitami 6. Programming 7. Debugging 8. Python scripts 9. CGI scripts 10. Karrigell Services 11. Python Inside HTML 11.1 Python variables 11.2 Strings for translation 11.3 Indentation 11.4 PIH as a templating system 11.5 Debugging 12. HTML Inside Python 13. HTMLTags - generate HTML in Python 14. Including documents 15. Sessions 16. Authentication 17. Translation and Unicode 18. Summary of built-in names |
11. Python Inside HTMLPython Inside HTML behaves much like Microsoft's Active Server Pages, Sun's Java Server Pages and PHP : it's basically a HTML document, in which you insert portions of code written in a programming language - here Python In Python Inside HTML, these portions of code are separated from the HTML code inside special tags : <% and %> Suppose you want to display the current date, you'll mix html and Python code this way :
With a text editor, write the code above and save it under time.pih in your Root Directory. Enter http://localhost/time.pih and see what happens You'll notice that the code inside the 11.1 Python variablesWhen you only want to print a variable's value, instead of<% print var %> you can use the shortcut
<%= var %> :
os module : for convenience, it is already in the namespace
when you execute the script ; so are two other modules,
string and Cookie , because they will probably be
used in many scripts (but of course, if you explicitely write import
string your script will work as well)
11.2 Strings for translationSince internationalization is important in Karrigell, there is a shortcut for the strings or string variables you'll want to be translated according to user preferences : use<%_ string %>
If you have prepared a translation for the string See Karrigell support for internationalization 11.3 IndentationThe result of processing a PIH file is Python code ; this code must be indented. Since a PIH script is a mixture of HTML, in which indentation has no other meaning than readability, and of chunks of Python code, it may be difficult to produce a code that is easily readable and correctly indented 11.3.1 BasicsPythonInsideHTML follows simple rules :
A simple condition example :
and a
Without this A last one with two levels of indentation
Note that after the 1st line the tag must be closed by %>, if not, the indentation after the second line will be only 1 11.3.3 The
For longer or more complex code the repetitive use of |
# indentation |
|
<indent> <% if hour<12: %> Good morning <% elif hour<18: %> Good afternoon <% else: %> Good evening Ladies and Gentlemen </indent> |
# 0 # 0 # 1 # 0 # 1 # 0 # 1 # 0 |
Second one :
# indentation |
|

 Number	 Square
 <indent> <% for i in range(10): %>
 <% print i %>	 <% print i**2 %>
 </indent>
 |
# 0 # 0 # 0 # 0 # 0 # 0 # 0 (A) # 1 # 1 # 1 # 1 # next one : 0 # 0 |
On the line noted (A) above you see that the indentation of the line
is relative to the indentation of the <indent>
tag
Also note that after an indented part (after the
</indent>
tag) indentation returns to zero
An example with embedded loops :
<indent>
 <% for i in ['h']+range(10): %>
 <% for j in ['h']+range(10): %> <% if i!='h' and j!='h': %> <%= i*j %>	 <% elif i!='h': %> <%= i %>	 <% elif j!='h': %> <%= j %>	 <% else: %> *

 </indent>


[ARTICLE XXV.--1966]
Section 1. In case of the removal of the President from office or of his death or resignation, the Vice President shall become President.


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Software products Projects Partner Projects DIANA Topical Meetings Presentations by the DIANA team Papers / Documents
Software products
-
Carl: a toolbox for likelihood-free inference - docs, GitHub
-
Histogrammar: a multilingual specification for histogram aggregation with implementations in Python and Scala - docs, GitHub
-
Histbook: a versatile, high-performance histogram toolkit for Numpy - docs, GitHub
-
uproot: pure Python implementation of ROOT I/O - GitHub
-
OAMap: tools for handling hierarchically nested data as arrays with an Object Array Map - GitHub
-
yadage: a language and engine for flexible, distributed workflows - docs GitHub
-
TreeNiN: QCD-aware recursive neural networks - GitHub
-
pyhf: a pure-python implementation of the widely-used HistFactory p.d.f. template - GitHub
-
MadMiner: likelihood-free inference for particle physics using machine learning - GitHub
Projects
- PyHEP: creation of a "Python in HEP" community. Several activities / actions:
- PyHEP 2018 Workshop
- GitHub repository for "Python in HEP" resources
-
Gitter channel for informal discussions of Python in HEP
-
Scikit-HEP: a community-driven Python project for HEP core and common tools, containing at present about 12 packages (web page, GitHub)
-
Participation in the GSoC 2017 program:
-
Project Efficient Python routines for analysis on massively multi-threaded platforms - Python bindings for the Hydra C++ library - Eduardo Rodrigues, mentor for this CERN-HSF organisation project.
-
Participation in the GSoC 2018 program:
- Project ROOT Package Manager - Oksana Shadura, Brian Bockelman - mentor in CERN-HSF organisation project.
Partner Projects
-
Scikit-Optimize: a library for sequential model-based optimization (documentation, code)
-
REANA: a system that permits to instantiate research data analyses on the cloud. website docs code
-
RooFit / RooStats: workbook
DIANA Topical Meetings
The DIANA team organizes periodic meetings on various topics of interest, with both invited external speakers and DIANA team contributions. These and other DIANA meetings can be found in the DIANA-HEP Organization in the INDICO agenda system. Videos of these meetings are published in YouTube.
Papers / Documents
-
Inferring the quantum density matrix with machine learning - Kyle Cranmer, Siavash Golkar, Duccio Pappadopulo arXiv:1904.05903
-
Machine learning and the physical sciences - Guiseppe Carleo, Ignacio Cirac, Kyle Cranmer, et. al. submitted to Reviews of Modern Physics
-
The Machine Learning Landscape of Top Taggers - G. Kasieczka, T. Plehn, Kyle Cranmer, Sebastian Macaluso, et. al. submitted to SciPost
-
Optimizing Frameworks Performance Using C++ Modules Aware ROOT - Yuka Takahashi, Vassil Vassilev, Oksana Shadura, Raphael Isemann. Proceedings of the 23nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) (Sofia, Bulgaria) arXiv:1812.03992
-
Continuous Performance Benchmarking Framework for ROOT - Oksana Shadura, Vassil Vassilev, Brian Paul Bockelman. Proceedings of the 23nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) (Sofia, Bulgaria) arXiv:1812.03149
-
Extending ROOT through Modules - Oksana Shadura, Brian Paul Bockelman, Vassil Vassilev. Proceedings of the 23nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) (Sofia, Bulgaria) arXiv:1812.03145
-
HEP Community White Paper on Machine Learning - Kyle Cranmer, Jim Pivarski, Eduardo Rodrigues, Michael D Sokoloff in K. Albertsson et al. arXiv:1807.02876
-
Open is not enough Xiaoli Chen, Sünje Dallmeier-Tiessen, Kyle Cranmer, Lukas Heinrich, et. al. Nature Phys. 15 (2019) no.2, 113-119 (2018-11-15) DOI: 10.1038/s41567-018-0342-2
-
Likelihood-free inference with an improved cross-entropy estimator. -- Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer. arXiv:1808.00973
-
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. - Atilim Gunes Baydin et al. arXiv:1807.07706
-
Deep Learning and its Application to LHC Physics. - Dan Guest, Kyle Cranmer, Daniel Whiteson. 10.1146/annurev-nucl-101917-021019. Ann.Rev.Nucl.Part.Sci. 68 (2018) 161-181
-
Backdrop: Stochastic Backpropagation - Siavash Golkar, Kyle Cranmer. arXiv:1806.01337
-
Mining gold from implicit models to improve likelihood-free inference - Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer. arXiv:1805.12244 [stat.ML].
-
A Guide to Constraining Effective Field Theories with Machine Learning - Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez. arXiv:1805.00020 [hep-ph].
-
Constraining Effective Field Theories with Machine Learning - Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez. arXiv:1805.00013 [hep-ph].
-
HEP Community White Paper on Data analysis and interpretation - Brian Bockelman, Kyle Cranmer, Peter Elmer, David Lange, Jim Pivarski, Eduardo Rodrigues, Michael D Sokoloff in Lothar Bauerdick et al. arXiv:1804.03983
-
HEP Community White Paper on Software trigger and event reconstruction: Executive Summary - David Lange, Eduardo Rodrigues, Michael D Sokoloff in Johannes Albrecht et al. arXiv:1802.08640
-
HEP Community White Paper on Software trigger and event reconstruction - David Lange, Eduardo Rodrigues, Michael D Sokoloff in Johannes Albrecht et al. arXiv:1802.08638
-
A Roadmap for HEP Software and Computing R&D for the 2020s - Brian Bockelman, Kyle Cranmer, Peter Elmer, Lukas Heinrich, David Lange, Eduardo Rodrigues, Michael D Sokoloff in Antonio Augusto Alves Jr et al. arXiv:1712.06982
-
Increasing Parallelism in the ROOT I/O subsystem - Guilherme Amadio, Brian Bockelman, Philippe Canal, Danilo Piparo, Enric Tejedor, Zhe Zhang, submitted to Proceedings of 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle) arXiv:1804.03326
-
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators - By Mario Lezcano Casado et al. arXiv:1712.07901 [cs.AI].
-
Optimizing ROOT IO For Analysis - Brian Bockelman, Zhe Zhang, Jim Pivarski, submitted to Proceedings of 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle) arXiv:1711.02659
-
Toward real-time data query systems in HEP - Jim Pivarski, David Lange, Thanat Jatuphattharachat, submitted to Proceedings of 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle) arXiv:1711.01229
-
CMS Analysis and Data Reduction with Apache Spark - Oliver Gutsche, Luca Canali, Illia Cremer, Matteo Cremonesi, Peter Elmer, Ian Fisk, Maria Girone, Bo Jayatilaka, Jim Kowalkowski, Viktor Khristenko, Evangelos Motesnitsalis, Jim Pivarski, Saba Sehrish, Kacper Surdy, Alexey Svyatkovskiy, submitted to Proceedings of 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) (Seattle) arXiv:1708.08319
-
Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes -Meghan Frate, Kyle Cranmer, Saarik Kalia, Alexander Vandenberg-Rodes, Daniel Whiteson. arXiv:1709.05681 [physics.data-an].
-
Fast Access to Columnar, Hierarchical Data via Code Transformation - Jim Pivarski, Peter Elmer, Brian Bockelman, and Zhe Zhang. Submitted to IEEE Big Data 2017 (Boston) arXiv:1708.08319
-
Adversarial Variational Optimization of Non-Differentiable Simulators - Gilles Louppe and Kyle Cranmer arXiv:1703.07113 | source and code
-
Yadage and Packtivity - analysis preservation using parametrized workflows - Kyle Cranmer, Lukas Heinrich. Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco) arXiv:1706.01878 [physics.data-an]. DOI: 10.1088/1742-6596/898/10/102019. J.Phys.Conf.Ser. 898 (2017) no.10, 102019.
-
HEPData: a repository for high energy physics data - Eamonn Maguire, Lukas Heinrich, Graeme Watt. Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco) arXiv:1704.05473
-
QCD-Aware Recursive Neural Networks for Jet Physics - G.Louppe, K.Cho, C.Becot, K.Cranmer arXiv:1702.00748
-
Exploring compression techniques for ROOT IO - Z.Zhang, B.Bockelman; Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco) arXiv:1704.06976
-
Big Data in HEP: A comprehensive use case study - O.Gutsche, M.Cremonesi, P.Elmer, B.Jayatilaka, J.Kowalkowski, J.Pivarski, S.Sehrish, C.Mantilla Surez, A.Svyatkovskiy, N.Tran; Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco) arXiv:1703.04171
-
Analysis Preservation and Reinterpretation, Lukas Heinrich Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016) (San Francisco)
-
Better Higgs boson measurements through information geometry - Johann Brehmer, Kyle Cranmer, Felix Kling, Tilman Plehn. arXiv:1612.05261 [hep-ph]. DOI: 10.1103/PhysRevD.95.073002. Phys.Rev. D95 (2017) no.7, 073002.
-
Learning to Pivot with Adversarial Networks - G.Louppe, M.Kagan, K.Cranmer arXiv:1611.01046
-
Carl in the Journal of Open Source Software - G.Louppe, K.Cranmer, J.Pavez;
-
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers - K.Cranmer, J.Pavez, G.Louppe arXiv:1506.02169
-
Visualization of Publication Impact - E.Maguire, J.Montull, G.Louppe; Proceedings of Eurographics Conference on Visualization (EuroVis) 2016 (Groningen) arXiv:1605.06242
-
NSF Proposal - Data-Intensive Analysis for High Energy Physics (DIANA/HEP)
Presentations by the DIANA team
-
13 Mar 2019 - "Deep Learning in Science", Kyle Cranmer, National Academy of Science, Sackler Colloquia
-
18 Mar 2019 - "Overview of Likelihood-Free Inference for Physics", Kyle Cranmer, Likelihood-Free Inference Workshop
-
15 Apr 2019 - "Future areas of focus for ML in particle physics", Kyle Cranmer, 3rd IML Machine Learning Workshop
-
18 Mar 2019 - "'Mining gold' from simulators to improve likelihood-free inference", Johann Brehmer, Likelihood-free inference workshop
-
14 Mar 2019 - "Keynote: Constraining effective field theories with machine learning", Johann Brehmer, International Workshop on Advanced Computing and Analysis Techniques in Physics Research
-
13 Mar 2019 - Migrating large codebases to C++ Modules, Yuka Takahashi, Oksana Shadura, Vassil Vassilev, ACAT 2019
-
10 Mar 2019 - ROOT I/O compression algorithms and their performance impact within Run 3, Oksana Shadura, Brian Paul Bockelman, ACAT 2019
-
10 Mar 2019 - Speeding HEP Analysis with ROOT Bulk I/O, Brian Paul Bockelman, Zhe Zhang, Oksana Shadura, ACAT 2019
-
10 Mar 2019 - Evolution of ROOT package management, Oksana Shadura, Brian Paul Bockelman, Vassil Vassilev, ACAT 2019
-
7 Mar 2019 - "What does the Revolution in Artificial Intelligence Mean for Physics?", Kyle Cranmer, UC Riverside Physics Colloquium
-
28 Feb 2019 - "Bringing together simulations, physics insight, and machine learning to constrain new physics", Johann Brehmer, Dark universe seminar
-
12 Feb 2019 - "Panel Discussion on Machine Learning and Physics", Kyle Cranmer, At the Crossroad of Physics and Machine Learning
-
25 Jan 2019 - "Inverting simulation: experiences with inverse problems in particle physics", Kyle Cranmer, MATH + X Symposium on Inverse Problems and Deep Learning in Space Exploration
-
15 Jan 2019 - Computing & software work - recognition & citations in publications, Eduardo Rodrigues, LHCb Weekly Meeting, CERN
-
14 Jan 2018 - "Meticulous measurements with matrix elements and machine learning", Johann Brehmer, ITS/CHEP joint seminar
-
11 Jan 2019 - "Improving inference with matrix elements and machine learning", Johann Brehmer, HK IAS Program on High Energy Physics
-
19 Nov 2018 - Updates on ROOT C++ modules, Yuka Takahashi, DIANA meeting
-
19 Nov 2018 - Updates on ROOT Package Manager, Oksana Shadura, DIANA meeting
-
15 Nov 2018 - Python/Numpy for High-Performance Numerical Processing, Jim Pivarski, Princeton Research Computing Mini-Course, Princeton
-
12 Nov 2018 - ROOT package management: lazy install approach, Oksana Shadura, ROOT Team meeting
-
23 Oct 2018 - HEP Software Foundation (R&D) activities, Eduardo Rodrigues, CMS ECOM2X WG7 Meeting, CERN
-
17 Oct 2018 - Automatic Differentiation in C/C++ Using Clang Plugin Infrastructure, Vassil Vassilev, Aleksandr Efremov, LLVM developers' meeting 2018
-
15 Oct 2018 - I/O for uproot, Pratyush Das (DIANA Fellow), DIANA meeting
-
15 Oct 2018 - Updates about ROOT I/O compression algorithms, Oksana Shadura, DIANA meeting
-
15 Oct 2018 - Updates about ROOT I/O, Brian Bockelman, DIANA meeting
-
1 Oct 2018 - Histogram interoperability, Jim Pivarski, DIANA meeting
-
20 Sep 2018 - "Learning to constrain new physics", Johann Brehmer, IPPP seminar
-
18 Sep 2018 - Python in HEP - first workshop, community activities, Eduardo Rodrigues, LHCb Weekly Meeting, CERN
-
13 Sep 2018 - "Learning to constrain new physics", Johann Brehmer, Pheno & Vino Seminar
-
12 Sep 2018 - ROOT prompt usability features and the strict option, Yuka Takahashi, ROOT 2018 Users Workshop
-
12 Sep 2018 - Future of ROOT runtime C++ modules, Yuka Takahashi, ROOT 2018 Users Workshop
-
12 Sep 2018 - ROOT Package Manager, Oksana Shadura, Brian Bockleman, Vassil Vassilev, ROOT 2018 Users Workshop
-
11 Sep 2018 - Interactive documentation for advanced statistical modelling with RooFit and RooStats, Vince Croft, ROOT 2018 Users Workshop
-
10 Sep 2018 - Writing ROOT files with uproot, Pratyush Das, Jim Pivarski, ROOT 2018 Users Workshop
-
10 Sep 2018 - ROOT I/O compression algorithms, Oksana Shadura, Brian Bockelman, ROOT 2018 Users Workshop
-
10 Sep 2018 - Vectorized processing of nested data, Jim Pivarski, Jaydeep Nandi, David Lange, ROOT 2018 Users Workshop
-
7 Sep 2018 - "What does the Revolution in Artificial Intelligence Mean for Physics?", Kyle Cranmer, Harvard Physics Colloquium
-
19 Sep 2018 - "What does the Revolution in Artificial Intelligence Mean for Physics?", Kyle Cranmer, Jefferson Lab Colloquium
-
27 Aug 2018 - "Learning to constrain new physics", Johann Brehmer, Elementary particle seminar
-
23 Jul 2018 - "Machine Learning to Probe a BSM Higgs Sector", Johann Brehmer, Higgs Hunting
-
10 July 2018 - Continuous Performance Benchmarking Framework for ROOT, Oksana Shadura, Brian Paul Bockelman, Vassil Vassilev, CHEP 2018 Conference
-
9 July 2018 - Extending ROOT through Modules, Shadura Oksana, Brian Paul Bockelman, Vassil Vassilev CHEP 2018 Conference
-
9 July 2018 - The Scikit-HEP project, Eduardo Rodrigues, CHEP 2018 Conference
-
8 July 2018 - PyHEP Workshop Summary and Outcomes, Graeme Stewart, Eduardo Rodrigues, PyHEP 2018 Workshop
-
7 July 2018 - Welcome and workshop overview, Eduardo Rodrigues, PyHEP 2018 Workshop
-
27 Jun 2018 - "Constraining EEffective Theories with Machine Learning", Johann Brehmer, Theory seminar
-
4 June 2018 - RNNs and Beyond, Kyle Cranmer IML Working Group
-
29 Mar 2018 - Summary of Programming for Concurrency and Co-Processors, Jim Pivarski (with Vincenzo Innocente), Joint WLCG/HSF Workshop 2018
-
29 Mar 2018 - Summary of Analysis Facilities and Use Cases, Eduardo Rodrigues (with Oliver Gutsche), Joint WLCG/HSF Workshop 2018
-
28 Mar 2018 - Overview of Serialization Technologies, Jim Pivarski, Joint WLCG/HSF Workshop 2018
-
28 Mar 2018 - Becoming Mainstream, Eduardo Rodrigues, Joint WLCG/HSF Workshop 2018
-
27 Mar 2018 - Spark-like and query-like analysis systems and tools, Jim Pivarski, Joint WLCG/HSF Workshop 2018
-
26 Mar 2018 - CWP Roadmap, Eduardo Rodrigues, Joint WLCG/HSF Workshop 2018
-
28 Feb 2018 - Uproot: rapidly moving data from ROOT to Numpy and Pandas, Jim Pivarski, IML Meeting
-
26 Feb 2018 - APPAVO - Augmented tools for Particle Physics Analysis, Visualisation and Outreach, Eduardo Rodrigues, CERN EP Department Software R&D Second Lightning Talks Session
-
21 Feb 2018 - Enhance TTreeCache defaults, Brian Bockelman, ROOT I/O Workshop, Early Spring 2018
-
21 Feb 2018 - LZ4, ZStd updates, Oksana Shadura, ROOT I/O Workshop, Early Spring 2018
-
21 Feb 2018 - Parquet Data Format Performance, Jim Pivarski, ROOT I/O Workshop, Early Spring 2018
-
12 Feb 2018 - GooFit Python bindings, Himadri Pandey, DIANA Meeting
-
5 Feb 2018 - Activities in Data Analysis, Jim Pivarski, CMS Offline/Computing R&D
-
26 Jan 2018 - ZSTD and CloudFlare/ZLIB update, Oksana Shadura, ROOT I/O Meeting
-
11 Dec 2017 - Updates on ROOT Modularization Project, Oksana Shadura, DIANA Meeting
-
16 Nov 2017 - Data Management with Columnwise Stores, Jim Pivarski, Workshop on Data Organisation, Management and Access (DOMA) in Astronomy, Genomics and High Energy Physics
-
13 Nov 2017 - Unfolding in python, Vincent Croft, DIANA Meeting
-
25 Oct 2017 - Bridging the Particle Physics and Big Data Worlds [ talk abstract ], Jim Pivarski, APS Division of Nuclear Physics 2017
-
11 Oct 2017 - In-memory file merging, Vassil Vassilev, Oksana Shadura ROOT I/O Workshop
-
11 Oct 2017 - LZ4, BulkIO, and offset removal performance, Jim Pivarski, ROOT I/O Workshop
-
11 Oct 2017 - DIANA I/O Contributions Update, Brian Bockelman, ROOT I/O Workshop
-
11 Oct 2017 - How to make a petabyte ROOT file: proposal for managing data with columnar granularity, Jim Pivarski, ROOT I/O Workshop
-
30 Sep 2017 - Particle Physics, 10000 Times Faster, [ talk abstract, talk video ], Jim Pivarski, Strange Loop Conference
-
25 Sep 2017 - DIANA Big Data projects, Jim Pivarski, DIANA Meeting
-
11 Sep 2017 - Scikit-HEP Project Status, Eduardo Rodrigues, DIANA Meeting
-
24 Aug 2017 - Optimizing ROOT I/O for Analysis (poster), Brian Bockelman, Zhe Zhang, Jim Pivarski, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017), Seattle
-
22 Aug 2017 - Toward real-time data query systems in HEP, Jim Pivarski, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017), Seattle
-
4 Aug 2017 and 23 June 2017 - BulkIO to Numpy in ROOT (with sample notebooks), Jim Pivarski
-
28 Jul 2017 - RECAST and Data Reinterpretation, Lukas Heinrich at Open Session of US ATLAS Workshop, ANL
-
19-27 Jul 2017 - Broad HEP/ML Framing and Future Directions, Kyle Cranmer at Hammers and Nails Workshop, Weizmann Institute
-
20 Jul 2017 - Perspective of ML person working with physicists and Adversarial variational optimization of non-differentiable simulators, Gilles Louppe at Hammers and Nails Workshop, Weizmann Institute
-
13 Jul 2017 - Language Interoperability, Jim Pivarski, CoDaS-HEP school (CoDaS-HEP 2017 program), Princeton [Training]
-
10 Jul 2017 - Functional Programming for Data Analysis, Jim Pivarski, CoDaS-HEP school (CoDaS-HEP 2017 program), Princeton [Training]
-
17 Jul 2017 - IPCC ROOT Code Modernization Project, Vassil Vassilev, DIANA Meeting
-
19 Jun 2017 - Watch this space: emerging trends and techniques that could transform physics and astronomy, Kyle Cranmer at Big data tools for physics and astronomy at GRAPPA Institute, Amsterdam, Netherlands
-
12 Jun 2017 - DIANA/HEP: TTree Bulk I/O and More, Brian Bockelman, ROOT I/O Workshop, CERN
-
12 Jun 2017 - Compression using on-chip hardware, David Abdurachmanov, ROOT I/O Workshop, CERN
-
12 Jun 2017 - Parallel Unzipping, Zhe Zhang, ROOT I/O Workshop, CERN
-
24 May 2017 - QCD-Aware Neural Networks for Jet Physics, Kyle Cranmer, CERN Theory Colloquium
-
23 May 2017 - Femtocode, Jim Pivarski, HSF Analysis Ecosystem Workshop, Amsterdam
-
23 May 2017 - Survey of data formats and conversion tools, Jim Pivarski, HSF Analysis Ecosystem Workshop, Amsterdam
-
22 May 2017 - The Analysis Ecosystem - My Remarks, Eduardo Rodrigues, HSF Analysis Ecosystem Workshop, Amsterdam
-
22 May 2017 - Data Analysis in LHCb, Eduardo Rodrigues, HSF Analysis Ecosystem Workshop, Amsterdam
-
11 May 2017 - Data plumbing: moving data across frameworks, Jim Pivarski, DS@HEP 2017, FNAL
-
10 May 2017 - QCD-Aware Recursive Neural Networks, Kyle Cranmer, DS@HEP 2017, FNAL
-
10 May 2017 - Learning to Pivot with Adversarial Networks, Gilles Louppe, DS@HEP 2017, FNAL
-
9 May 2017 - Use of advanced ML techniques in operations and data analysis at LHCb, Mike Sokoloff, DS@HEP 2017, FNAL
-
3 May 2017 - Cyberinfrastructure for high-level science goals, Kyle Cranmer, Future Trends in Nuclear Physics Computing Workshop, Jefferson Lab
-
3 May 2017 - Lowering Boundaries between Data Analysis Ecosystems, Jim Pivarski, Future Trends in Nuclear Physics Computing Workshop, Jefferson Lab
-
2 May 2017 - Emerging trends in software for statistics and machine learning, Kyle Cranmer, 2nd S2I2 HEP/CS Workshop, Princeton University
-
2 May 2017 - Femtocode: query system for HEP (poster), Jim Pivarski, 2nd S2I2 HEP/CS Workshop, Princeton University
-
2 May 2017 - Data Intensive Analysis Tools & Visualization (Parallel Session Introduction), Jim Pivarski, 2nd S2I2 HEP/CS Workshop, Princeton University
-
17 Apr 2017 - Femtocode: querying HEP data - Status update and illustration of working examples, Jim Pivarski, DIANA Meeting, [ Video Recording ]
-
8 Apr 2017 - Bayesian Optimization with Scikit-Optimize, Gilles Louppe, PyData Amsterdam, The Netherlands
-
7 Apr 2017 - Learning to Pivot with Adversarial Networks, Gilles Louppe, Seminar at IIHE, VUB, Brussels
-
6 Apr 2017 - The Scikit-HEP Project, Eduardo Rodrigues, LHCb Analysis & Software Week, CERN
-
20-22 March 2017 - Introduction to Machine Learning, Gilles Louppe, IML Machine Learning workshop, CERN
-
27 Feb 2017 - The Scikit-HEP Project, Eduardo Rodrigues, DIANA Meeting, [ Video Recording ]
-
21-22 Feb 2017 - Data-Intensive Analysis for High Energy Physics (DIANA/HEP), poster at 2017 NSF SI2 PI Workshop
-
8 Feb 2017 - Survey of columnar file formats and the techniques they use, Jim Pivarski, ROOT I/O Workshop
-
8 Feb 2017 - Executing code on columnar data, Jim Pivarski, ROOT I/O Workshop
-
8 Feb 2017 - CMS/DIANA I/O Update, Brian Bockelman, ROOT I/O Workshop
-
26 Jan 2017 - DIANA Advisory Board Meeting: , Cincinnati, NYU, Princeton, Nebraska
-
16 Dec 2016 - Updates & planning: Nebraska, Cincinnati, NYU, Princeton
-
14 Dec 2016 - Data Plumbing (whiteboard talk), Jim Pivarski, LPC Coffee Hour
-
13 Dec 2016 - Machine Learning & Likelihood Free Inference in Particle Physics, Kyle Cranmer NIPS 2016 Keynote
-
12 Dec 2016 - Femtocode: development of a query language for HEP, Jim Pivarski, DIANA Meeting
-
5 Dec 2016 - Focus groups: physicist attitudes toward software, Jim Pivarski, DIANA Meeting
-
17 Nov 2016 - Learning to Pivot with Adversarial Networks, Gilles Louppe, ATLAS ML Forum
-
14 Nov 2016 - TTreeProcessor: A toy framework for parallel ntuple processing, Brian Bockelman, DIANA Meeting
-
7 Nov 2016 - Overview of file formats, Jim Pivarski, DIANA Meeting
-
7 Nov 2016 - Reading ROOT data in Java and Spark, Jim Pivarski, ROOT Team meeting; follow up with CERN IT
-
30 Oct 2016 - Plotting data on GPUs with Histogrammar, Jim Pivarski, Parallel Kalman Meeting
-
13 Oct 2016 - Analysis Preservation and Reinterpretation, Lukas Heinrich CHEP 2016
-
13 Oct 2016 - HEPData - a repository for high energy physics data exploration, Lukas Heinrich & Eamonn Maguire for HEPData team CHEP 2016
-
10 Oct 2016 - Exploring Compression Techniques for ROOT IO, Zhe Zhang, CHEP 2016
-
20 Oct 2016 - Histogrammar: Making Histograms Functional, Jim Pivarski, Chicago Hadoop User's Group (CHUG)
-
17-21 Oct 2016 - Lectures on Machine Learning, Gilles Louppe, ICCUB School Machine Learning and Data Science in Physics, Barcelona, Spain
-
21 Sep 2016 - Reconstructing Particle Trajectories in High Energy Physics with Xeon and Xeon Phi, David Abdurachmanov, Intel Xeon Phi User Group Meeting 2016
-
16 Sep 2016 - Expressing Complex Data Aggregations With Histogrammar, [ talk abstract, talk video ], Jim Pivarski, Strange Loop Conference
-
18 Aug 2016 - Computing for Data Analysis, Jim Pivarski, LPC Physics Forum
-
3 Aug 2016 - Learning to generate with adversarial networks, Gilles Louppe, US ATLAS Physics Support, Software and Computing meeting
-
21 Jul 2016 - Learning to generate with adversarial networks, Gilles Louppe, ATLAS ML forum
-
18 Jul 2016 - Potential Issues with Julia, Jim Pivarski, DIANA Meeting
-
7 Jul 2016 - Learning to generate with adversarial networks, Gilles Louppe, DS @ HEP, New York, USA
-
27 Jun 2016 - Learning to generate with adversarial networks, Gilles Louppe, Software Tech Forum
-
22 Jun 2016 - Approximating likelihood ratios with Calibrated Classifiers, Gilles Louppe, 2nd MLHEP summer school, Lund, Sweden
-
20 Jun 2016 - High-level analysis scripts with low-level performance, Jim Pivarski, DIANA Meeting
-
13 Jun 2016 - Python/ROOT Interoperability, David Lange, DIANA Meeting
-
25 May 2016 - DIANA IO Update, Brian Bockelman, ROOT I/O Workshop
-
23 May 2016 - Chains of functional primitives, Jim Pivarski, ROOT Team Meeting
-
19 May 2016 - The DIANA-HEP Project, Jim Pivarski, US-CMS Collaboration Meeting
-
2 May 2016 - Introduction to DIANA/HEP, Peter Elmer, HEP Software Foundation Workshop
-
25 Apr 2016 - Histogramming in map-reduce, Jim Pivarski, DIANA Meeting
-
18 Apr 2016 - Introduction to DIANA/HEP, Peter Elmer, ROOT Team Meeting
-
13 Apr 2016 - Robust and Calibrated Classifiers with Scikit-Learn, Gilles Louppe, Zurich ML meetup, Switzerland
-
13 Apr 2016 - Approximating likelihood ratios with Calibrated Classifiers, Gilles Louppe, ETH, Zurich, Switzerland
-
11 Apr 2016 - Bayesian Optimization, Gilles Louppe, DIANA Meeting
-
11 Apr 2016 - Introduction to DIANA/HEP, Peter Elmer, DIANA Meeting
-
30 Mar 2016 - Bayesian Optimization, Gilles Louppe, ATLAS ML workshop
-
29 Mar 2016 - Approximating likelihood ratios with Calibrated Classifiers, Gilles Louppe, ATLAS ML workshop
-
7 Mar 2016 - Computing practices observed in industry, Jim Pivarski, ROOT Team Meeting
-
7 Mar 2016 - ROOT-Spark integration, Jim Pivarski, ROOT Team Meeting
-
3 Mar 2016 - The DIANA/HEP Project, Eduardo Rodrigues, LHCb-wide Amplitude Analysis Meeting on an Ampl. Ana. software library
-
22 Feb 2016 - Accessing ROOT from the JVM (update), Jim Pivarski, DIANA Meeting
-
22 Feb 2016 - Notes from Heavy Flavour Data Mining Workshop, Eduardo Rodrigues, DIANA Meeting
-
22 Feb 2016 - Approximating density ratios with machine learning for likelihood-free inference, Gilles Louppe, DIANA Meeting
-
18 Feb 2016 - An introduction to machine learning with Scikit-Learn, Gilles Louppe, Heavy Flavour Data Mining workshop
-
16-17 Feb 2016 - Data-Intensive Analysis for High Energy Physics (DIANA/HEP), Peter Elmer, 2016 NSF SI2 PI Workshop
-
8 Feb 2016 - Accessing ROOT from the JVM (Java/Scala), Jim Pivarski, CMS Software and Computing R&D Working Meeting


Theoretical Physicist + Research Scientist + Quantum Software Developer @ IBM-Q
Developing techniques for characterization, verification, and validation of the errors that occur in current quantum hardware.
Methods to supress and mitigate errors in hardware to improve the performance of current quantum hardware for near term applications.
Developing theoretical models for noise processes in superconducting systems, and the simulations of noise processes in open quantum systems.
Qiskit Aer is a high-performance simulator framework for studying quantum computing algorithms and applications in the noisy intermediate scale quantum (NISQ) regime. It is written in C++ and Python and integrates with Qiskit-Terra. Some features of this library include:
Qiskit Terra is an open source Python software library for quantum computing developed at IBM-Q. My contributions to this project are in the development of the following modules:
QuantumUtils for Mathematica is a Mathematica software library for symbolic and numerical simulations of quantum systems including and numerous tools useful for quantum information science. It was developed by Christopher Granade, Ian Hincks and myself during our PhDs. Some features of this library include:
DC McKay, T Alexander, L Bello, MJ Biercuk, L Bishop, J Chen, JM Chow, AD Córcoles, D Egger, S Filipp, J Gomez, M Hush, A Javadi-Abhari, D Moreda, P Nation, B Paulovicks, E Winston, CJ Wood, J Wootton, JM Gambetta, arXiv preprint arXiv:1809.03452 (2018)
CJ Wood and JM Gambetta, Phys. Rev. A 97 032306 (2018)
DC McKay, CJ Wood, S Sheldon, JM Chow, JM Gambetta, Phys. Rev. A 96, 022330 (2017)
J Nsofini, D Sarenac, CJ Wood, DG Cory, M Arif, CW Clark, MG Huber, DA Pushin, Phys. Rev. A 94, 013605 (2016)
CJ Wood and DG Cory, Phys. Rev. A 93 023414 (2016)
M Ringbauer, CJ Wood, K Modi, A Gilchrist, AG White, A Fedrizzi, Phys. Rev. Lett. 114, 090402 (2015)
CJ Wood, JD Biamonte, DG Cory, Quant. Inf. Comp. 15, 0759-0811 (2015)
CJ Wood and RW Spekkens, New J. Phys. 17 033002 (2015)
CJ Wood, TW Borneman, DG Cory, Phys. Rev. Lett. 112, 050501 (2014)
CJ Wood, MO Abutaleb, MG Huber, M Arif, DG Cory, DA Pushin, Phys. Rev. A 90, 032315 (2014)
DA Pushin, MG Huber, M Arif, CB Shahi, J Nsofini, CJ Wood, D Sarenac, and DG Cory, Adv. H.E.P. 2014, 687480 (2014)
A Brodutch, A Gilchrist, D Terno, CJ Wood, J. Phys.: Conf. Ser. 306 012030 (2011)
A Gilchrist, D Terno, CJ Wood, arXiv:0911.2539 [quant-ph] (2009)
Quantum computing theory group at IBM-Q.
Quantum computing theory group at IBM-Q.
Thesis: Initialization and characterization of open quantum systems.
Thesis: Nonlocal correlations from the perspective of causal Bayesian networks.
Thesis: Non-completely positive maps: properties and applications.


Microscheme Literature
A functional programming language for the Arduino
Microscheme was originally a final-year undergraduate project, and had to be closed-source for examination purposes. The project has changed since then, and there have been rapid improvements since it was made open-source. However, much of the information in the original dissertation is still relevant.
More recently, a short paper was prepared for the Scheme and Functional Programming workshop, containing a concise exposition of the Microscheme runtime system, and some of the considerations necessary for very resource-constrained platforms such as the ATMega controllers. This paper was very kindly presented by Will Byrd.
Original Dissertation
An extensive account of the project motivations and development.
Download (PDF) From June 2014
SFP ‘14 Paper
A condensed technical exposition of the project.
SFP 2014
Download (PDF) From October 2014


In the first part I spoke about how to build our working environment to work with UI5 locally instead of using WebIDE. Now, in this second part of the post, we’ll see how to do it using docker to set up our environment.
I’ll use docker-compose to set up the project. Basically, as I explain in the first part, the project has two parts. One backend and one frontned. We’re going to use exactly the same code for the frontend and for the backend.
The frontend is build over a localneo. As it’s a node application we’ll use a node:alpine base host
FROM node:alpine
EXPOSE 8000
WORKDIR /code/src
COPY ./frontend .
RUN npm install
ENTRYPOINT ["npm", "run", "serve"]
In docker-compose we only need to map the port that we´ll expose in our host and since we want this project in our depelopemet process, we also will map the volume to avoid to re-generate our container each time we change the code.
...
ui5:
image: gonzalo123.ui5
ports:
- "8000:8000"
restart: always
build:
context: ./src
dockerfile: .docker/Dockerfile-ui5
volumes:
- ./src/frontend:/code/src
networks:
- api-network
The backend is a PHP application. We can set up a PHP application using different architectures. In this project we’ll use nginx and PHP-FPM.
for nginx we’ll use the following Dockerfile
FROM nginx:1.13-alpine
EXPOSE 80
COPY ./.docker/web/site.conf /etc/nginx/conf.d/default.conf
COPY ./backend /code/src
And for the PHP host the following one (with xdebug to enable debugging and breakpoints):
FROM php:7.1-fpm
ENV PHP_XDEBUG_REMOTE_ENABLE 1
RUN apt-get update && apt-get install -my \
git \
libghc-zlib-dev && \
apt-get clean
RUN apt-get install -y libpq-dev \
&& docker-php-ext-configure pgsql -with-pgsql=/usr/local/pgsql \
&& docker-php-ext-install pdo pdo_pgsql pgsql opcache zip
RUN curl -sS https://getcomposer.org/installer | php -- --install-dir=/usr/local/bin --filename=composer
RUN composer global require "laravel/lumen-installer"
ENV PATH ~/.composer/vendor/bin:$PATH
COPY ./backend /code/src
And basically that’s all. Here the full docker-compose file
version: '3.4'
services:
nginx:
image: gonzalo123.nginx
restart: always
ports:
- "8080:80"
build:
context: ./src
dockerfile: .docker/Dockerfile-nginx
volumes:
- ./src/backend:/code/src
- ./src/.docker/web/site.conf:/etc/nginx/conf.d/default.conf
networks:
- api-network
api:
image: gonzalo123.api
restart: always
build:
context: ./src
dockerfile: .docker/Dockerfile-lumen-dev
environment:
XDEBUG_CONFIG: remote_host=${MY_IP}
volumes:
- ./src/backend:/code/src
networks:
- api-network
ui5:
image: gonzalo123.ui5
ports:
- "8000:8000"
restart: always
build:
context: ./src
dockerfile: .docker/Dockerfile-ui5
networks:
- api-network
networks:
api-network:
driver: bridge
If we want to use this project you only need to:
- clone the repo fron github
- run ./ui5 up
With this configuration we’re exposing two ports 8080 for the frontend and 8000 for the backend. We also are mapping our local filesystem to containers to avoid to regenerate our containers each time we change the code.
We also can have a variation. A “production” version of our docker-compose file. I put production between quotation marks because normally we aren’t going to use localneo as a production server (please don’t do it). We’ll use SCP to host the frontend.
This configuration is just an example without filesystem mapping, without xdebug in the backend and without exposing the backend externally (Only the frontend can use it)
version: '3.4'
services:
nginx:
image: gonzalo123.nginx
restart: always
build:
context: ./src
dockerfile: .docker/Dockerfile-nginx
networks:
- api-network
api:
image: gonzalo123.api
restart: always
build:
context: ./src
dockerfile: .docker/Dockerfile-lumen
networks:
- api-network
ui5:
image: gonzalo123.ui5
ports:
- "8000:8000"
restart: always
build:
context: ./src
dockerfile: .docker/Dockerfile-ui5
networks:
- api-network
networks:
api-network:
driver: bridge
And that’s all. You can see the all the source code in my github account


This widget is configurable to hide some of the subviews. Use the attribute "excludeView" with flags corresponding to each subview to hide. Here are all the flags:
Note that multiple flags can be used simultaneously by logically OR'ing them. For example, if user wants to hide voltage subviews in the widget, it can be done by following two steps.
Then, add following attribute to the BatteryWidget:
custom:excludeView="singleVoltage|doubleVoltage"
Display:
Battery icon has two color states. White color indicates everything is ok. Icon will change into red color if aircraft needs to return home, land immediately, or battery connection is bad. Text shows percentage of charge remaining in battery. Text also has two color states. Green indicates the percentage is ok. Red indicates the percentage is either below or just enough for returning home or landing immediately.
Normal, means the battery energy is good. Low, means the battery energy is OK to go home. VeryLow, means the battery energy is only for landing immediately.


Select a .DST, .EXP, .EXY, .JEF, .PCS, .PEC, .PES, .SEW, .VP3, .XXX or .Z00 file or drag and drop one to the drop box below:
Drop files here.
Download


climate Science chemical Sciences {border-style monsoon px;} #ffffff;} China Fig Quaternary records weathering Earth Heinrich ICV Asian Xiaozhongdian Zhang climatic geochemical Basin events lacustrine section environmental CIA Plateau global study (CaOK O)/Al ONa Yunnan change elements lake samples –): Climate Geology Tibetan Wang changes deposits proxies sediment characteristics events. evolution indicated px; value {font-family .–. Geochemical Plant SETP average effect element font-size record remains values Chen Cheng Lake analysis cal content data double; index indicate minerals regional summer weak Acta CIA, Dynamics Key Laboratory Lacustrine Ming Province Reviews SiO Therefore, analyses approximately based border-color border-width clay drought good high intensity mainly major oxide recorded research signals solid; southwest used variability weathering, {font-style {font-weight ./science. Aquatic Chinese Edwards Environmental Holocene Hulu Kunming, Lee Liu Loess Marine Niu Paleobotany Paleontology: Provenance Research SWAM Shi Yang Zhu action age basin bold catchment conditions correlation correlations courier; degree em; environment high-resolution humid latitudes model past precipitation processes pt;} range rate reduced relatively section. sediments suction


Processing of uploaded funding data file {{filename}} was completed successfully, with {{row_count}} funding(s) written There were {{error_count}} error(s).
You can export a json of the results by clicking this link: {{export_url}}
If you would prefer the file in an alternative format, just visit the Hub and navigate to Affiliations>Uploaded Tasks and view this task. From this page, you can select an export from any of a wide range of data formats


NATS - Crystal Client
Simple NATS client for the Crystal programming language.
Installation
- Add the dependency to your
shard.yml
:
dependencies:
nats:
github: nats-io/nats.cr
- Run
shards install
Usage
require "nats"
nc = NATS::Connection.new("demo.nats.io")
nc.subscribe("foo") { |msg| puts "Received '#{msg}'"}
nc.publish("foo", "Hello!")
sub = nc.subscribe("req") do |msg|
msg.respond("ANSWER is 42")
end
answer = nc.request("req", "Help!")
puts "Received a response '#{answer}'!"
sub.close
nc.flush
nc.close
License
Unless otherwise noted, the NATS source files are distributed under the Apache Version 2.0 license found in the LICENSE file.


{% load staticfiles %}
Si elegans project will deliver a parallel computing framework, consisting of Field Programmable Gate Arrays (FPGAs) that accurately simulates the complexity of the C. elegans’ behaviour.
{% if not user.is_authenticated %} Register New Account {% endif %}Why Si elegans?
Learn more
Want to dive straight in? Try our guide:
Quick Start Guide


---
layout: default
---
{% comment %}
{% include landing.html %}
{% include banner.html %}
{% include features.html features=site.data.features %}
{% include tools.html tools=site.data.tools %}
{% include documentation.html documentation=site.data.documentation %}
{% include rules.html %}
{% include support.html %}
{% endcomment %}


|
Core Values
|
Strengths
(Cake) |
Personality
(Icing) |
Brand Image
(Cake & Icing) |
Brand Promise
(Cake & Icing) |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Hard skills
|
Personality Attributes
|
Brand Associations
|
Rational Value
|
||||||||
|
What I Love Doing
|
Soft skills
|
Type of leader, worker
|
External Image
|
Emotional Value
|
||||||||
|
My life/career dreams
|
Expertise
|
Brand Metaphor
|
Relationship Image
|
Brand Experience
|
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Key Brand Descriptors
|


--- layout: post title: About me date: 13-11-2015 excerpt: "About me" share: true ---
Hi! I'm Enol, a software engineer from Gijón, Asturias. In the north of Spain.
I love music, arcade video games, films and series. And I also enjoy reading books (specially Game of Thrones) and comics. In my free time I try to learn to play guitar. I'd like to write about Ruby, RoR, Elixir, Phoenix. And everything related to technology...
Be more than welcome!


so, I woke up to this:
Dear Customer,
Regarding the following subscriptions:
16384 MB Server - <IP_ADDRESS> (LABS_pool) in Paris
Our monitoring system indicated an issue with the hardware node hosting the instances listed in this email. Our engineering team has investigated the issue and initiated a restart of the host node in question.
Please note: While this event rebooted the instances listed in this email, we expect no impact on data and/or configurations.


{% extends 'serenity_tools_base.html' %} {% block app_title %} About {% endblock %} {% block app_content %}
This is a little holding pen for web tools I've developed - none are anything fancy, and they may well have bugs in!
Suggestions, bug reports, and pull requests welcome on GitHub.
The tools on this website are provided in the hope that they may be of use to you. However, they are all provided “as-is”. We make no warranties, express or implied, and hereby disclaim all implied warranties, including any warranty of merchantability and warranty of fitness for a particular purpose.
Richard Jenkins


None.
Quality:
Aortic arch:
Right brachiocepahlic artery:
Common carotid artery origin:
Common carotid artery cervical segment:
Carotid artery bifurcation:
External carotid artery origin:
Internal carotid artery origin:
Distal internal carotid artery:
Vertebral artery origin:
V1 segment:
V2 segment:
V3 segment:
V4 segment:


WITNESS LIST Natural Resources Committee August 27, 2002-9:00A Water Security On: Burrer, Todd A. (Self and ECO Resources and Assoc. of Water Board Directors) Campbell, Curtis W. (Red River Authority of Texas) Colley, Jack (Texas Department of Public Safety) Glenn, Ron (Red River Authority of Texas and Region B Water Planning Group) Henderson, Buck (TNRCC) Holaday, Robert (Self) Iltis, Mike (Brazos River Authority) Lehfeldt, David (City of Wichita Falls) Petersen, Ken (Texas Rural Water Association)


 The more data you give, the better we can assess the impact. Enter 0 where there's no data.
For trade associations, give a cumulative figure. .
{% endblock %}


Porter Remy:
Oh, I'm so sorry! There's something urgent I must attend to! Excuse me, I must go!
Quest


To fire your trebuchet, please supply your 2FA token; we've texted you a code
Thank you for securing your IoT Trebuchet™ - you're helping prevent war amongst our village!


Oklahoma City Community College September 2009 to October 2009
Development Resource
GoRails.com
Freecodecamp.org
KhanAcademy.com
Work independently for software web and mobile development shop. Focus on front-end development and back-end development to make website features work.
• Rails • NodeJS • Javascript • React Postgres Sql • Jest Testing • Express • Heroku • HTML • CSS • SCSS • Rest API Development • MVC Development • Programming
Taking calls, actively listening to understand customers’ needs, and answering questions. Investigate, evaluate, and adjust low to moderately complex auto or property claims presented by or against insured to confirm coverage, and determine legal liability in compliance with all state regulatory requirements. Contributed to business goals, performance metrics, and effectively used tools and technology.
• Oral and Written Communication Skills • Data Entry • Multi-tasking • Customer Service Skills • Microsoft Office • Outlook • Analyzing Bills • Sales Experience


---
layout: post
authors: ["Anelda van der Walt"]
title: "March 23-30, 2015: A Lab Meeting, a Dataset, NGS Course at MSU, and Postdoc Positions at BIDS"
date: 2015-03-30
time: "09:30:00"
tags: ["Community"]
---
Highlights
Resources
-
If you are looking for a teaching-ready dataset, take a look at Ethan Whites simplified version of the Portal Projects Database now availble in csv, json, and sqlite.
Events
Opportunities
Details
-
Ben Marwick wrote an inspirational post about teaching a very novice audience in an environment with no/limited bandwith.
-
Ever wondered what the most common R errors are as enquired about in Stack Overflow? Noam Ross used the Stack Overflow API and R package, stackr, to analyse the data. Seems like R is mostly looking for things that are not there.
-
What motivates researchers to participate in Software Carpentry workshops? Matthew Aiello-Lammens beliefs understanding participants' motivation may help instructors to support those struggling to keep up during the workshop.
-
A number of people completed the Software Carpentry instructor training. David Dotson, from the Center for Biological Physics at Arizona State University, wrote about how he will be putting this qualification to use in future.
-
Three workshop were run last week: Havard School of Public Health, University of Oklahoma Libraries, and Mahidol University (our first workshop in Thailand).
-
Several workshops have been confirmed for the next few months.
Upcoming workshops can be viewed on our website.
Newly added workshops are indicated with a "*".
-
April:
Weill Cornell Medical College*,
University of Miami*,
University of Melbourne*,
University of Washinghton - Seattle,
Imperial College London,
Northwestern University,
University of California - Santa Barbara,
Wrigley Building, Arizona State University*,
University of Texas at Arlington,
Washington State University,
Simon Fraser University
-
May:
Western University,
University of Melbourne
-
July:
The University of Queensland,
The University of Queensland - AMOS sciences
Repository Activity
| Project | Issues | Pull Requests |
| Opened | Closed | Comments | Opened | Closed | Comments |
| shell-novice | | | 5 | | 4 | |
| git-novice | 2 | 1 | 25 | 3 | 5 | 1 |
| hg-novice | | | | | 1 | |
| sql-novice-survey | | | 9 | 12 | 14 | |
| python-novice-inflammation | | | 12 | 1 | 2 | 3 |
| r-novice-inflammation | | 1 | 9 | | 4 | |
| matlab-novice-inflammation | | | | | 1 | |
| slideshows | | | | | | |
| instructor-training | | | | | | |
| amy | | | 1 | | | 1 |
| site | 2 | 12 | 31 | 8 | 9 | 7 |
| Total | 4 | 14 | 92 | 24 | 40 | 12 |


What is the relationship between mindfulness and self awareness?
The book says that it is the same thing and that they are both trainable.
Mindfulness means paying attention in a particular way - on purpose, in the present moment and non-judgmentally - Daniel Goleman
Self-awareness is a neutral mode that maintains self-reflectiveness even in the midst of turbulent emotions. - Jon Kabat-Zinn
With understanding of mindfulness, a person is able to look at their own strengths, weaknesses and emotions without judgement. After that, they can get a more accurate picture of themselve without being clouded by distractions or feelings.
Try at least one of the self awareness practices from the chapter (Body Scan, Journaling). What was your experience of it? How did you feel?
I did Body Scan twice this week. The first time was on Monday. I wasn’t sure what I was doing or what I was going to expect from it. I gave up after minute 5 and just went back to my normal meditation. I thought meditation was already enough for me and I couldn’t be bothered with this. Beside, I also had a lot of assignments that I need to finish.
The second time was on Wednesday. I told myself I should give another try so just that I could write something about it. It happened again… first 5 minutes nothing. Told myself not going to think about it and just feel… then all the sudden, I could feel slight throbbing near the temple and forehead - not in a negative way. I guess what I was feeling blood circulate through my head. Its like when you put your finger on your wrist you can feel the vein pumping… interesting but whatever.
The other sensation I felt was on my lower back. I felt slight discomfort. I think this happened because I haven’t gone to gym more than a week now.
I think I would like to incorporate Body Scan with the meditation so I can be aware what's going on with me physically. I also notice that since I tried meditation I'm more aware on how I sit infront of the computer. I keep fixing my sitting posture.
What did you take away from the "My Emotions are Not Me" section?
I think its accurate. We are not our emotion. Emotions can be different from time to time - it comes and goes. Its not permanent so using I am ___ is not right.


Fitness Function¶
Fitness class also redefines the relational operator in order to handle different types of optimization (eg. maximization, minimization) and to provide limited support to more complex scenarios (eg. multi-objective optimization).
How do we handle a different scenario¶
When subclassing, one’s fitness should only redefine ‘is_fitter’, and optionally ‘is_distinguishable’ and ‘is_dominant’; ‘is_dominant’ must be changed if ‘is_fitter’ is randomized (the result is uncertain).
The idea of several, different scenarios is the following:
`a == b`
In this case, fitness a
cannot be distinguished from fitness b
.
`a != b`
In this case, fitness a
is distinguishable from fitness b
.
`a > b`
In this case, fitness a
is fitter than fitness b
.
(may not always be the case, see lexicographic)
`a >= b`
In this case, fitness a
is fitter or not distinguishable from fitness b
.
`a < b`
In this case, fitness b
is fitter than fitness a
, respectively.
(may not always be the case, see lexicographic)
`a <= b`
In this case, fitness b
is fitter or not distinguishable from fitness a
, respectively.
`a >> b`
In this case, fitness a
dominates fitness b
which is a certain case.
`a << b`
In this case, fitness a
is dominated by fitness b
, accordingly.
Multi-Objective Evolutionary Algorithm¶
The problem becomes more interesting in case that there exist more than one characteristic that should be compared in order to decide which individual is “better”. It is very important to find rules that describe characteristics with respect to a property of interest. The MOEA approach, a method of combining the traditional genetic algorithm (TGA) with the multi-objective method, can consider the relation between the parameters and the objective spaces in the same time then explore the optimum solution. Our multi-objective evolutionary algorithm uses a ‘helper’ function which can decide the best individual when there are two comparable characteristics.
For this reason, SGX implements a specific function called Lexicase which can be found at
sgx.fitness.multi_objective
. Lexicase is a selection method that considers training cases individually, rather than in aggregate, when performing individual selection.
This special Multi-Objective scenario can be illustrated with an airplaine ticket purchase. Let us consider the example of buying a flight ticket where the price of ticket and travel time are the decision-making criteria.
The points A,B,C,D,E and F represent the options for flying between two cities. We assume that difference in travel time is due to the waiting time for connecting flight at transit. Option A is the most expensive with ticket price of $4000, but with least travel time of 16 hours. The cheapest ticket is of $2000 with travel time of 32 hours if one takes flying Option E. Here the decision-making process of flight booking is not a single objective of either price or travel time. The traveler has few options to choose from with some trade-off between travel time and price. If one selects Option B instead of Option A, he will be saving on his ticket price by spending more time on transit. Again, if the traveler selects Option D instead of Option E, he has to sell out more money for buying the ticket, but he can save a few hours. If someone chooses Option F, he is definitely losing. He can go for Option B at same price with less travel time, or Option D of same travel duration at lower price. The points A,B,C,D and E are called Pareto Optimal Points, named after the famous Italian economist Vilfredo Pareto. They are also called Non-Dominated solutions. The example of flight options is for a two-objective optimization. In multi-objective optimization there can be more than two objectives.


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Page 1 of 1
maybe you'll want this
Posted:
Wed Jan 16, 2008 11:26 pm
by Telex
it's just something i found saved that i had made a while ago, seems to fit the theme i guess. i'd convert to .ogg but i don't know how
http://www.sendspace.com/file/56pga9
Re: maybe you'll want this
Posted:
Thu Jan 17, 2008 12:03 am
by thadiusofx3
sounds really great!
made anything else?
Re: maybe you'll want this
Posted:
Thu Jan 17, 2008 11:09 am
by Karl G.
WOW!!!!
Can we please use this?
Can you make more?
Re: maybe you'll want this
Posted:
Thu Jan 17, 2008 12:46 pm
by Telex
haha, wow
i didn't expect this kind of response.
yeah, you're free to use it however you want, and i'd be glad to make more.
Re: maybe you'll want this
Posted:
Thu Jan 17, 2008 1:51 pm
by thadiusofx3
seriously, you're really talented
we could use your skills on the project
Re: maybe you'll want this
Posted:
Thu Jan 17, 2008 2:51 pm
by Telex
i'd be glad to help out with the project. i'd say my best area is in creating soundscapes. so if you need environmental background noises, eerie dungeon sounds, or something similar, i could get those made really quickly.
Re: maybe you'll want this
Posted:
Thu Jan 17, 2008 2:54 pm
by Karl G.
That's actually what we need the most. Long-loop background sounds...forest, town, city, ice-cave (I've got something planned
) and multiple dungeon themes are all necessary.


{% extends 'base.html' %}
{% block head %}Add a user to the IEEE Member Database. You are currently logged in as: {{ adminData.first_Name }} {{ adminData.last_Name }}
{% else %} {% include "app_Transaction/userHeader.html" %}You are currently logged in as: {{ adminData.first_Name }} {{ adminData.last_Name }}
{% endif %}


With development of such inventions like the internet, the popularity with educational technology is growing every day. Nowadays it is one of the most preferred methods of learning. Traditional lessons have been supplemented by virtual ones. Online classes include transferring files, chat rooms and even board of progress to follow the students’ success. Another benefit is that students can maintain a flexible schedule that is convenient for them (anytime, anywhere learning). It helps to combine distance education and work. There are various ways of improving teaching efficiency with technology in education. Thanks to the technology they have more than one way to keep an eye on the student’s progress. Moreover audio-visual presentation, wide-screen televisions, projectors can be used for improving the delivery of instruction to actually improve learning and increasing the comprehension level among the students.


PowerBall and PowerBall Plus results, Friday, 22 November, 2019
Here are the winning PowerBall lottery numbers from Friday night’s live draw.
by Citizen reporterPowerBall/Lotto Picture: iStock
The winning numbers from the live PowerBall draw on Friday, 22 November, can be found below. Draw 1044.
PowerBall winning numbers:
5, 17, 18, 20, 26. PowerBall: 7
PowerBall Plus winning numbers:
3, 14, 15, 27, 32. PowerBall: 12
The PowerBall jackpot was estimated at R107 million.
The PowerBall Plus jackpot was estimated at R4 million.
For more details, please visit the National Lottery’s website. While great care has been taken to ensure accuracy, The Citizen can take no responsibility for any error in the results.


SHAILESH
BHAT
FITNESS
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Username
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Welcome to Battle-Banana!
Ready, Aim...
Play!
Instructions
Game Variations (coming soon...)
Game by
Adam Wilson
2016. Theme music courtesy of Tyopos from Defense of Lanosia
-->


Appartamento Mirtillo
FAQ
Is it possible to receive bottles or cans without coming to the company?
Certainly, some customers come to the company because they live nearby or because it is still an interesting experience to visit our villa, the park and our oil cellar. But if you live far away, the best option is to receive the oil perfectly packaged at home via GLS courier. Shipping costs within the EU are not high and the package arrives very quickly. Since transport costs always refer to the weight of the shipment, please always write an email for an overall quote, both for EU countries, and for Great Britain or the United States, for example.
I am unsure between bottles or cans, what do you recommend?
If you use a lot of oil in the kitchen, it is cheaper to buy 5-liter cans. Obviously the bottles are more practical for daily use. The small bottles, 100 ml and 250 ml, extra virgin or flavored oil, are very suitable for giving gifts. There are also elegant packs of two or three 250ml bottles for this purpose.
How can I best preserve extra virgin olive oil?
An extra virgin olive oil must never be exposed to the sun or be near heat sources. The dark glass bottle and the can already offer a first protection. Ideal is to keep a larger quantity in a cool cellar and keep a bottle in the kitchen, if possible in a cabinet.
Why is extra virgin olive oil very healthy?
Recent medical studies have shown that the daily use of extra virgin oil significantly decreases the risk of breast cancer for women. In general, the so-called Mediterranean diet, based on the consumption of vegetables, legumes, fruit and olive oil is very healthy for the heart, where the most important factor against the risk of cardiovascular disease is undoubtedly the extra virgin oil for its precious content of unsaturated fats.
Is it true that cooking with extra virgin olive oil is not recommended?
It is absolutely false, contrary to popular opinion. Not only is extra virgin oil very stable even at high temperatures, but above all its antioxidant components (polyphenols and tocopherols) prevent the formation of carcinogenic substances, for example in cooking meat. As for taste, how can you resist roasted potatoes in extra virgin olive oil or Milanese cutlets fried in oil?
How to buy a quality extra virgin olive oil?
Faced with many offers of extra virgin olive oil with very different prices, you can be a little confused. Here are some recommendations: always check on the label behind the bottle or can that the name of the producer is present, which must be the same as the bottler. A label bearing only the company that bottled the oil reveals that it is in fact not a producer company, but a company that buys oil of different origins, probably transported from afar, not always adequately.
Why are organoleptic properties (aroma, flavor, color) very important?
A quality extra virgin olive oil always has a complex scent, of grass or other aromas and has an intense yellow color, which can still be greenish in the first two to three months after the November harvest. The flavor can be delicate or fruitier or spicier, even tingling in the throat, all signs of its authenticity. On the other hand, a transparent oil, or even dark green, which has no aroma, is always an unnatural product, but chemically refined.
How are your flavored oils produced?
Our flavored oils are generally produced by infusion. The chili oil remains soaked with Calabrian chili for about 40 days in a hermetically sealed container, the same as the rosemary oil collected in our garden. The same procedure would be more difficult for our latest product, coriander and pink pepper oil: in this case, drops of organic oil extracted from coriander and pink pepper are added to the extra virgin oil.
Why is a flavored oil important in the kitchen?
A flavored oil is a practical variant for those who are not very expert in the kitchen and at the same time a refined variant to create original dishes. Chili oil is, for example, perfect for a slightly spicy sauce for pasta, but also for enhancing the taste of sautéed or grilled peppers in the oven. Rosemary oil is perfect for cooking baked potatoes or adding raw to grilled meats and fish. Coriander and pink pepper oil perfectly season original salads (for example: tuna, olives, feta, salad and cucumber), but it also goes perfectly with vegetables cooked in a pan, such as zucchini and carrots, giving them a very refined taste. Try it with imagination!
Why are oil-based cosmetic products so good for the skin?
Our extra virgin olive oil is every year very rich in polyphenols, antioxidant substances. This is why it is not only very useful for health, but also nourishes and protects the skin and the hair. Try an anti-aging mask: leave a mask with extra virgin oil and a few drops of lemon on the face for about fifteen minutes, then dab the rest that has not been absorbed by the skin.


The cottage garden is a distinct style of garden that uses an informal design, dense plantings, and a mixture of ornamental and edible plants.
The Cottage Garden originated in England and its history can be traced back for centuries, although they were re-invented in 1870's England, when stylized versions were formed as a reaction to the more structured and rigorously maintained English estate gardens.
The earliest cottage gardens were more practical than their modern descendants, with an emphasis on vegetables and herbs, along with some fruit trees.


Twitter CEO Jack Dorsey visits CcHub, discloses return date
by Abiola OdutolaTwitter boss, Jack Dorsey, visited CcHub early on Friday, about 24 hours after he landed on Nigerian soil.
During his visit to one of Nigeria’s leading tech hub, which was covered by Nairametrics, Jack only took photographs with owners of the facility and other tech innovators at the venue before he left for another undisclosed venue.
According to Siera, head, Logistics, Twitter, the visit is a low key event and that he was not meant to make any official statement.
She said, “He only came to learn a few things and leave for his base. But he will be back next year.”
But the question is what he came to learn. Watch out for that details on Nairametrics shortly.
[Read Also: CcHUB unveils Nyota, showcases Africa’s top tech pioneers ]
Meanwhile, the Twitter boss has already learnt a Yoruba sentence, ‘Bawo ni’, which means, How are You? That was how he greeted some twitter influencers at the welcome dinner organized for him in Lagos on Thursday.
Back story: Jack had announced in October where he would be in Africa for the whole of November, visiting four African countries. Nigeria and Ghana in West Africa, Ethiopia in East Africa and South Africa.
He is expected to meet with leaders in the local technology and business world, in his words “entrepreneurs”- however, the reason for this meeting is not yet known.
[Read Also: United Kingdom to deepen fintech partnership in Nigeria ]
This will not be the first known developer to be in Nigeria, three years, there was a similar visit to Nigeria by Facebook CEO Mark Zuckerberg. After jogging through the streets of Lagos and meeting with developers, he traveled to Abuja where he met with President Buhari at the presidency.


|
mxGraph
|
Implements a graph model. The graph model acts as a wrapper around the cells which are in charge of storing the actual graph datastructure. The model acts as a transactional wrapper with event notification for all changes, whereas the cells contain the atomic operations for updating the actual datastructure. More...
Public Member Functions | |
| mxGraphModel () | |
| Constructs a new empty graph model. More... | |
| mxGraphModel (Object root) | |
| Constructs a new graph model. If no root is specified then a new root mxCell with a default layer is created. More... | |
| void | Clear () |
| Sets a new root using createRoot. More... | |
| Object | CreateRoot () |
| Creates a new root cell with a default layer (child 0). More... | |
| Object | GetCell (string id) |
| Object [] | CloneCells (Object[] cells, bool includeChildren) |
| Returns an array of clones for the given array of cells. Depending on the value of includeChildren, a deep clone is created for each cell. Connections are restored based if the corresponding cell is contained in the passed in array. More... | |
| bool | IsAncestor (Object parent, Object child) |
| Returns true if the given parent is an ancestor of child. More... | |
| bool | Contains (Object cell) |
| Returns true if the model contains the given cell. More... | |
| Object | GetParent (Object child) |
| Returns the parent of the given cell. More... | |
| Object | Add (Object parent, Object child, int index) |
| Adds the specified child to the parent at the given index. If no index is specified then the child is appended to the parent's array of children. More... | |
| string | CreateId (Object cell) |
| Creates a new Id for the given cell and increments the global counter for creating new Ids. More... | |
| Object | Remove (Object cell) |
| see com.mxgraph.mxIGraphModel.Remove(Object) More... | |
| int | GetChildCount (Object cell) |
| Returns the number of children in the given cell. More... | |
| Object | GetChildAt (Object parent, int index) |
| Returns the child of the given parent at the given index. More... | |
| Object | GetTerminal (Object edge, bool isSource) |
| Returns the source or target terminal of the given edge depending on the value of the boolean parameter. More... | |
| Object | SetTerminal (Object edge, Object terminal, bool isSource) |
| Sets the source or target terminal of the given edge using. More... | |
| void | UpdateEdgeParents (Object cell) |
| Updates the parents of the edges connected to the given cell and all its descendants so that each edge is contained in the nearest common ancestor. More... | |
| void | UpdateEdgeParents (Object cell, Object root) |
| Updates the parents of the edges connected to the given cell and all its descendants so that the edge is contained in the nearest-common-ancestor. More... | |
| void | UpdateEdgeParent (Object edge, Object root) |
| Inner helper method to update the parent of the specified edge to the nearest-common-ancestor of its two terminals. More... | |
| mxPoint | GetOrigin (Object cell) |
| Returns the absolute, cummulated origin for the children inside the given parent. More... | |
| Object | GetNearestCommonAncestor (Object cell1, Object cell2) |
| Returns the nearest common ancestor for the specified cells. More... | |
| int | GetEdgeCount (Object cell) |
| see com.mxgraph.mxIGraphModel.GetEdgeCount(Object) More... | |
| Object | GetEdgeAt (Object parent, int index) |
| see com.mxgraph.mxIGraphModel.GetEdgeAt(Object, int) More... | |
| bool | IsVertex (Object cell) |
| see com.mxgraph.mxIGraphModel.IsVertex(Object) More... | |
| bool | IsEdge (Object cell) |
| see com.mxgraph.mxIGraphModel.IsEdge(Object) More... | |
| bool | IsConnectable (Object cell) |
| see com.mxgraph.mxIGraphModel.IsConnectable(Object) More... | |
| Object | GetValue (Object cell) |
| see com.mxgraph.mxIGraphModel.GetValue(Object) More... | |
| Object | SetValue (Object cell, Object value) |
| see com.mxgraph.mxIGraphModel.SetValue(Object, Object) More... | |
| mxGeometry | GetGeometry (Object cell) |
| see com.mxgraph.mxIGraphModel.GetGeometry(Object) More... | |
| mxGeometry | SetGeometry (Object cell, mxGeometry geometry) |
| see com.mxgraph.mxIGraphModel.SetGeometry(Object, mxGeometry) More... | |
| string | GetStyle (Object cell) |
| see com.mxgraph.mxIGraphModel.GetStyle(Object) More... | |
| string | SetStyle (Object cell, string style) |
| see com.mxgraph.mxIGraphModel.SetStyle(Object, string) More... | |
| bool | IsVisible (Object cell) |
| see com.mxgraph.mxIGraphModel.IsVisible(Object) More... | |
| bool | SetVisible (Object cell, bool visible) |
| see com.mxgraph.mxIGraphModel.SetVisible(Object, bool) More... | |
| bool | IsCollapsed (Object cell) |
| see com.mxgraph.mxIGraphModel.IsCollapsed(Object) More... | |
| bool | SetCollapsed (Object cell, bool collapsed) |
| see com.mxgraph.mxIGraphModel.SetCollapsed(Object, bool) More... | |
| void | BeginUpdate () |
| see com.mxgraph.mxIGraphModel.BeginUpdate() More... | |
| void | EndUpdate () |
| see com.mxgraph.mxIGraphModel.EndUpdate() More... | |
| void | MergeChildren (mxICell from, mxICell to, bool cloneAllEdges) |
| Merges the children of the given cell into the given target cell inside this model. All cells are cloned unless there is a corresponding cell in the model with the same id, in which case the source cell is ignored and all edges are connected to the corresponding cell in this model. Edges are considered to have no identity and are always cloned unless the cloneAllEdges flag is set to false, in which case edges with the same id in the target model are reconnected to reflect the terminals of the source edges. More... | |
Static Public Member Functions | |
| static int | GetDirectedEdgeCount (mxIGraphModel model, Object cell, bool outgoing) |
| Returns the number of incoming or outgoing edges. More... | |
| static int | GetDirectedEdgeCount (mxIGraphModel model, Object cell, bool outgoing, Object ignoredEdge) |
| Returns the number of incoming or outgoing edges, ignoring the given edge. More... | |
| static Object [] | GetEdges (mxIGraphModel model, Object cell) |
| Returns all edges connected to this cell including loops. More... | |
| static Object [] | GetConnections (mxIGraphModel model, Object cell) |
| Returns all edges connected to this cell without loops. More... | |
| static Object [] | GetIncomingEdges (mxIGraphModel model, Object cell) |
| Returns the incoming edges of the given cell without loops. More... | |
| static Object [] | GetOutgoingEdges (mxIGraphModel model, Object cell) |
| Returns the outgoing edges of the given cell without loops. More... | |
| static Object [] | GetEdges (mxIGraphModel model, Object cell, bool incoming, bool outgoing, bool includeLoops) |
| Returns all distinct edges connected to this cell. If at least one of incoming or outgoing is true, then loops are ignored, otherwise if both are false, then all edges connected to the given cell are returned including loops. More... | |
| static Object [] | GetEdgesBetween (mxIGraphModel model, Object source, Object target) |
| Returns all edges between the given source and target mxCells. If the optional boolean directed argument is false, then a matching edge is returned regardless of its direction. More... | |
| static Object [] | GetEdgesBetween (mxIGraphModel model, Object source, Object target, bool directed) |
| Returns all edges between the given source and target mxCells. If the optional boolean directed argument is false, then a matching edge is returned regardless of its direction. More... | |
| static Object [] | GetOpposites (mxIGraphModel model, Object[] edges, Object terminal, bool sources, bool targets) |
| Returns all opposite vertices wrt terminal for the given edges, only$ returning sources and/or targets as specified. The result is returned as an array of mxCells. More... | |
| static void | SetTerminals (mxIGraphModel model, Object edge, Object source, Object target) |
| Sets the source and target of the given edge in a single atomic change. More... | |
| static Object [] | getChildVertices (mxIGraphModel model, Object parent) |
| Returns the child vertices of the given parent. More... | |
| static Object [] | getChildEdges (mxIGraphModel model, Object parent) |
| Returns the child edges of the given parent. More... | |
| static Object [] | getChildCells (mxIGraphModel model, Object parent, bool vertices, bool edges) |
| Returns the children of the given cell that are vertices and/or edges depending on the arguments. More... | |
Protected Member Functions | |
| Object | CloneCell (Object cell, Hashtable mapping, bool includeChildren) |
| Inner helper method for cloning cells recursively. More... | |
| void | RestoreClone (Object clone, Object cell, Hashtable mapping) |
| Inner helper method for restoring the connections in a network of cloned cells. More... | |
| void | CellAdded (Object cell) |
| Invoked after a cell has been added to a parent. This recursively creates an Id for the new cell and/or resolves Id collisions. More... | |
| void | CellRemoved (Object cell) |
| Invoked after a cell has been removed from the model. This recursively removes the cell from its terminals and removes the mapping from the Id to the cell. More... | |
| void | MergeChildrenImpl (mxICell from, mxICell to, bool cloneAllEdges, Dictionary< Object, Object > mapping) |
| Clones the children of the source cell into the given target cell in this model and adds an entry to the mapping that maps from the source cell to the target cell with the same id or the clone of the source cell that was inserted into this model. More... | |
Protected Attributes | |
| mxICell | root |
| Holds the root cell, which in turn contains the cells that represent the layers of the diagram as child cells. That is, the actual element of the diagram are supposed to live in the third generation of cells and below. More... | |
| Dictionary< Object, Object > | cells |
| Maps from Ids to cells. More... | |
| bool | createIds = true |
| Specifies if edges should automatically be moved into the nearest common ancestor of their terminals. Default is true. More... | |
| bool | maintainEdgeParent = true |
| Specifies if the parent of edges should be automatically change to point to the nearest common ancestor of its terminals. Default is true. More... | |
| int | nextId = 0 |
| Specifies the next Id to be created. Initial value is 0. More... | |
| int | updateLevel = 0 |
| Counter for the depth of nested transactions. Each call to beginUpdate increments this counter and each call to endUpdate decrements it. When the counter reaches 0, the transaction is closed and the respective events are fired. Initial value is 0. More... | |
Properties | |
| bool | IsMaintainEdgeParent [get, set] |
| Sets of returns if edges should automatically be moved into the nearest common ancestor of their terminals. More... | |
| bool | IsCreateIds [get, set] |
| Sets or returns if the model automatically creates Ids and resolves Id collisions. More... | |
| Object | Root [get, set] |
| Properties inherited from com.mxgraph.mxIGraphModel | |
| Object | Root [get, set] |
| Holds the root cell. More... | |
Events | |
| mxGraphModelChangeEventHandler | GraphModelChange |
| Fires when the graph model has changed. More... | |
| Events inherited from com.mxgraph.mxIGraphModel | |
| mxGraphModelChangeEventHandler | GraphModelChange |
| Called when the graph model has changed. More... | |
Implements a graph model. The graph model acts as a wrapper around the cells which are in charge of storing the actual graph datastructure. The model acts as a transactional wrapper with event notification for all changes, whereas the cells contain the atomic operations for updating the actual datastructure.
|
inline |
Constructs a new empty graph model.
|
inline |
Constructs a new graph model. If no root is specified then a new root mxCell with a default layer is created.
| root | Cell that represents the root cell. |
|
inline |
Adds the specified child to the parent at the given index. If no index is specified then the child is appended to the parent's array of children.
| parent | Cell that specifies the parent to contain the child. |
| child | Cell that specifies the child to be inserted. |
| index | Integer that specifies the index of the child. |
Implements com.mxgraph.mxIGraphModel.
|
inline |
see com.mxgraph.mxIGraphModel.BeginUpdate()
Implements com.mxgraph.mxIGraphModel.
|
inlineprotected |
Invoked after a cell has been added to a parent. This recursively creates an Id for the new cell and/or resolves Id collisions.
| cell | Cell that has been added. |
References com.mxgraph.mxICell.ChildCount(), com.mxgraph.mxICell.GetChildAt(), and com.mxgraph.mxICell.Id.
|
inlineprotected |
Invoked after a cell has been removed from the model. This recursively removes the cell from its terminals and removes the mapping from the Id to the cell.
| cell | Cell that has been removed. |
References com.mxgraph.mxICell.ChildCount(), com.mxgraph.mxICell.GetChildAt(), com.mxgraph.mxICell.Id, and com.mxgraph.mxICell.RemoveFromTerminal().
|
inline |
Sets a new root using createRoot.
|
inlineprotected |
Inner helper method for cloning cells recursively.
References com.mxgraph.mxICell.Insert().
|
inline |
Returns an array of clones for the given array of cells. Depending on the value of includeChildren, a deep clone is created for each cell. Connections are restored based if the corresponding cell is contained in the passed in array.
| cells | Array of cells to be cloned. |
| includeChildren | Boolean indicating if the cells should be cloned with all descendants. |
Implements com.mxgraph.mxIGraphModel.
|
inline |
Returns true if the model contains the given cell.
| cell | Cell to be checked. |
Implements com.mxgraph.mxIGraphModel.
|
inline |
Creates a new Id for the given cell and increments the global counter for creating new Ids.
| cell | Cell for which a new Id should be created. |
|
inline |
Creates a new root cell with a default layer (child 0).
References com.mxgraph.mxCell.Insert().
|
inline |
see com.mxgraph.mxIGraphModel.EndUpdate()
Implements com.mxgraph.mxIGraphModel.
|
inline |
| id |
|
inline |
Returns the child of the given parent at the given index.
| parent | Cell that represents the parent. |
| index | Integer that specifies the index of the child to be returned. |
Implements com.mxgraph.mxIGraphModel.
|
inlinestatic |
Returns the children of the given cell that are vertices and/or edges depending on the arguments.
| model | Model that contains the hierarchical information. |
| parent | Cell whose child vertices or edges should be returned. |
| vertices | Boolean indicating if child vertices should be returned. |
| edges | Boolean indicating if child edges should be returned. |
References com.mxgraph.mxIGraphModel.GetChildAt(), com.mxgraph.mxIGraphModel.GetChildCount(), com.mxgraph.mxIGraphModel.IsEdge(), and com.mxgraph.mxIGraphModel.IsVertex().
Referenced by com.mxgraph.mxGraph.GetChildCells().
|
inline |
Returns the number of children in the given cell.
| cell | Cell whose number of children should be returned. |
Implements com.mxgraph.mxIGraphModel.
|
inlinestatic |
Returns the child edges of the given parent.
| model | Model that contains the hierarchical information. |
| parent | Cell whose child edges should be returned. |
|
inlinestatic |
Returns the child vertices of the given parent.
| model | Model that contains the hierarchical information. |
| parent | Cell whose child vertices should be returned. |
|
inlinestatic |
Returns all edges connected to this cell without loops.
| model | Model that contains the connection information |
| cell | Cell whose connections should be returned |
|
inlinestatic |
Returns the number of incoming or outgoing edges.
| model | Graph model that contains the connection data. |
| cell | Cell whose edges should be counted. |
| outgoing | Boolean that specifies if the number of outgoing or incoming edges should be returned. |
|
inlinestatic |
Returns the number of incoming or outgoing edges, ignoring the given edge.
| model | Graph model that contains the connection data. |
| cell | Cell whose edges should be counted. |
| outgoing | Boolean that specifies if the number of outgoing or incoming edges should be returned. |
| ignoredEdge | Object that represents an edge to be ignored. |
References com.mxgraph.mxIGraphModel.GetEdgeAt(), com.mxgraph.mxIGraphModel.GetEdgeCount(), and com.mxgraph.mxIGraphModel.GetTerminal().
|
inline |
|
inline |
|
inlinestatic |
Returns all edges connected to this cell including loops.
| model | Model that contains the connection information |
| cell | Cell whose connections should be returned |
Referenced by com.mxgraph.mxFastOrganicLayout.execute(), and com.mxgraph.mxGraph.GetEdges().
|
inlinestatic |
Returns all distinct edges connected to this cell. If at least one of incoming or outgoing is true, then loops are ignored, otherwise if both are false, then all edges connected to the given cell are returned including loops.
| model | Model that contains the connection information |
| cell | Cell whose connections should be returned |
| incoming | Specifies if incoming edges should be returned |
| outgoing | Specifies if outgoing edges should be returned |
| includeLoops | Specifies if loops should be returned |
References com.mxgraph.mxIGraphModel.GetEdgeAt(), com.mxgraph.mxIGraphModel.GetEdgeCount(), and com.mxgraph.mxIGraphModel.GetTerminal().
|
inlinestatic |
Returns all edges between the given source and target mxCells. If the optional boolean directed argument is false, then a matching edge is returned regardless of its direction.
| model | |
| source | |
| target |
|
inlinestatic |
Returns all edges between the given source and target mxCells. If the optional boolean directed argument is false, then a matching edge is returned regardless of its direction.
| model | The graph model that contains the graph. |
| source | mxCell that defines the source terminal of the edge to be returned. |
| target | mxCell that defines the target terminal of the edge to be returned. |
| directed | Optional boolean that specifies if the direction of the edge should be taken into account. Default is true. |
References com.mxgraph.mxIGraphModel.GetEdgeAt(), com.mxgraph.mxIGraphModel.GetEdgeCount(), and com.mxgraph.mxIGraphModel.GetTerminal().
|
inline |
|
inlinestatic |
Returns the incoming edges of the given cell without loops.
| model | Graphmodel that contains the edges |
| cell | Cell whose incoming edges should be returned |
|
inline |
Returns the nearest common ancestor for the specified cells.
| cell1 | Cell that specifies the first cell in the tree. |
| cell2 | Cell that specifies the second cell in the tree. |
References com.mxgraph.mxCellPath.Create(), com.mxgraph.mxCellPath.GetParentPath(), and com.mxgraph.mxCellPath.PATH_SEPARATOR.
|
inlinestatic |
Returns all opposite vertices wrt terminal for the given edges, only$ returning sources and/or targets as specified. The result is returned as an array of mxCells.
| model | Model that contains the graph. |
| edges | Array of edges to be examined. |
| terminal | Cell that specifies the known end of the edges. |
| sources | Boolean that specifies if source terminals should be contained in the result. Default is true. |
| targets | Boolean that specifies if target terminals should be contained in the result. Default is true. |
References com.mxgraph.mxIGraphModel.GetTerminal().
Referenced by com.mxgraph.mxFastOrganicLayout.execute().
|
inline |
Returns the absolute, cummulated origin for the children inside the given parent.
References com.mxgraph.mxPoint.X, and com.mxgraph.mxPoint.Y.
|
inlinestatic |
Returns the outgoing edges of the given cell without loops.
| model | Graphmodel that contains the edges |
| cell | Cell whose outgoing edges should be returned |
|
inline |
Returns the parent of the given cell.
| child | Cell whose parent should be returned. |
Implements com.mxgraph.mxIGraphModel.
|
inline |
see com.mxgraph.mxIGraphModel.GetStyle(Object)
Implements com.mxgraph.mxIGraphModel.
|
inline |
Returns the source or target terminal of the given edge depending on the value of the boolean parameter.
| edge | Cell that specifies the edge. |
| source | Boolean indicating which end of the edge should be returned. |
Implements com.mxgraph.mxIGraphModel.
|
inline |
see com.mxgraph.mxIGraphModel.GetValue(Object)
Implements com.mxgraph.mxIGraphModel.
|
inline |
Returns true if the given parent is an ancestor of child.
| parent | Cell that specifies the parent. |
| child | Cell that specifies the child. |
Implements com.mxgraph.mxIGraphModel.
|
inline |
|
inline |
|
inline |
see com.mxgraph.mxIGraphModel.IsEdge(Object)
Implements com.mxgraph.mxIGraphModel.
|
inline |
see com.mxgraph.mxIGraphModel.IsVertex(Object)
Implements com.mxgraph.mxIGraphModel.
|
inline |
see com.mxgraph.mxIGraphModel.IsVisible(Object)
Implements com.mxgraph.mxIGraphModel.
Merges the children of the given cell into the given target cell inside this model. All cells are cloned unless there is a corresponding cell in the model with the same id, in which case the source cell is ignored and all edges are connected to the corresponding cell in this model. Edges are considered to have no identity and are always cloned unless the cloneAllEdges flag is set to false, in which case edges with the same id in the target model are reconnected to reflect the terminals of the source edges.
| from | |
| to | |
| cloneAllEdges |
|
inlineprotected |
Clones the children of the source cell into the given target cell in this model and adds an entry to the mapping that maps from the source cell to the target cell with the same id or the clone of the source cell that was inserted into this model.
| from | |
| to | |
| cloneAllEdges | |
| mapping |
References com.mxgraph.mxICell.ChildCount(), com.mxgraph.mxICell.Clone(), com.mxgraph.mxICell.GetChildAt(), com.mxgraph.mxICell.Id, and com.mxgraph.mxICell.Insert().
|
inline |
see com.mxgraph.mxIGraphModel.Remove(Object)
| cell |
Implements com.mxgraph.mxIGraphModel.
References com.mxgraph.mxICell.RemoveFromParent().
|
inlineprotected |
Inner helper method for restoring the connections in a network of cloned cells.
References com.mxgraph.mxICell.InsertEdge().
|
inline |
|
inline |
|
inline |
|
inline |
Sets the source or target terminal of the given edge using.
| edge | Cell that specifies the edge. |
| terminal | Cell that specifies the new terminal. |
| source | Boolean indicating if the terminal is the new source or target terminal of the edge. |
Implements com.mxgraph.mxIGraphModel.
References com.mxgraph.mxICell.GetTerminal(), and com.mxgraph.mxICell.RemoveEdge().
|
inlinestatic |
Sets the source and target of the given edge in a single atomic change.
| model | Model that contains the graph. |
| edge | Cell that specifies the edge. |
| source | Cell that specifies the new source terminal. |
| target | Cell that specifies the new target terminal. |
References com.mxgraph.mxIGraphModel.BeginUpdate(), com.mxgraph.mxIGraphModel.EndUpdate(), and com.mxgraph.mxIGraphModel.SetTerminal().
|
inline |
|
inline |
|
inline |
Inner helper method to update the parent of the specified edge to the nearest-common-ancestor of its two terminals.
| edge | Specifies the edge to be updated. |
| root | Current root of the model. |
References com.mxgraph.mxGeometry.Clone(), com.mxgraph.mxGeometry.Translate(), com.mxgraph.mxPoint.X, and com.mxgraph.mxPoint.Y.
|
inline |
Updates the parents of the edges connected to the given cell and all its descendants so that each edge is contained in the nearest common ancestor.
| cell | Cell whose edges should be checked and updated. |
|
inline |
Updates the parents of the edges connected to the given cell and all its descendants so that the edge is contained in the nearest-common-ancestor.
| cell | Cell whose edges should be checked and updated. |
| root | Root of the cell hierarchy that contains all cells. |
|
protected |
Maps from Ids to cells.
|
protected |
Specifies if edges should automatically be moved into the nearest common ancestor of their terminals. Default is true.
|
protected |
Specifies if the parent of edges should be automatically change to point to the nearest common ancestor of its terminals. Default is true.
|
protected |
Specifies the next Id to be created. Initial value is 0.
|
protected |
Holds the root cell, which in turn contains the cells that represent the layers of the diagram as child cells. That is, the actual element of the diagram are supposed to live in the third generation of cells and below.
|
protected |
Counter for the depth of nested transactions. Each call to beginUpdate increments this counter and each call to endUpdate decrements it. When the counter reaches 0, the transaction is closed and the respective events are fired. Initial value is 0.
|
getset |
Sets or returns if the model automatically creates Ids and resolves Id collisions.
|
getset |
Sets of returns if edges should automatically be moved into the nearest common ancestor of their terminals.
| mxGraphModelChangeEventHandler com.mxgraph.mxGraphModel.GraphModelChange |
Fires when the graph model has changed.


I made a OTRS 2.2.6 virtual machine for anyone who wants to test both OTRS and OTRS::ITSM. It was built on OpenSUSE 10.2, including OTRS 2.2.6 and OTRS::ITSM 1.0.94. OTRS::ITSM implements ITIL (R) focused IT service management. OTRS::ITSM 1.0.94 is still on Beta, you might have some bugs when using this virtual machine. Please download from my box.net, if you have an comment please just let me know. I’d like to invite you to improve this virtual machine if you have any good ideal.
Download it from here 点此下载。 This source is not available any longer. I will offer it by DVD. 这个地址已经无法下载，请关注我即将推出的DVD版虚机。
Thanks BigYue.com for donated upload space. 感谢BigYue.com的空间捐助。
Credentials 登录密码如下
-
OpenSuSE 10.2 : username / password is “root/ca1234”
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MySQL : username / password is “root/ “
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OTRS : admin username and password is root@localhost/root
Run 运行压缩文件中的虚拟机
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Download and install VM Player, the machine should have 1GB RAM at least.
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Un-zip OSS.rar in a partition, freespace must be 2.5+ GB.
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Double MartinLiu.cn.vmx and power on this machine.
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Click “keep” and OK button when you get a popup box。
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The network connection of virtual machine is ‘bridged’. You will see the ip address of this virtual machine at 5th line one the top of boot screen. NOTE: you should have DHCP server on your network.
Access 访问虚拟机
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SSH to it.
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Agent login page: http://ip_address/otrs/index.pl
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Customer login page: http://ip_address/otrs/customer.pl
Tips of OTRS installation 安装经验
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Apache should have perl mod, configure perl for apache before you install OTRS. 这是一个Perl写的程序，所以在安装之前最好吧apache上的perl配置好，确认apache能运行perl的东西
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Make sure your database accepts packages over 5 MB in size. Increase max_allowed_packet in my.cnf to 20MB. 修改my.cnf中的max_allowed_packet 参数到20MB，否则ITSM的包安装有问题。
-
OTRS需要一写附件的perl包的支持，手头最好有操作系统的安装dvd或者直接从网上安装。
Usage 用途
-
You can use it for any purpose, if you need any support you will contact OTRS。你可以用于任何用途，本博客不提供技术支持，请联系OTRS的邮件列表。
-
ITIL training, OTRS::ITSM implements ITIL (R) focused IT service management. 可以用于ITIL的培训，它能演示：incident management, problem management, configuration management 等。
-
This virtual machine comes with ABSOLUTELY NO WARRANTY. DO NOT USE it for production. 仅供测试使用，切勿用于生产环境。
If you like OTRS, you may try Request Tracer
今天一个网友告诉我另外一个开源的类似系统Request Tracer ，有空的话回头也试试。