The document discusses the new features of Python 3.8, which was recently released. Some key updates include positional-only arguments, the walrus operator for variable assignment, improved static typing support, and performance enhancements. The document also notes additional developer changes and provides resources for obtaining Python 3.8.
Datasets with millions of events in charm decays at LHCb have prompted the development of powerful fitting and analysis tools capable of handling unbinned datasets using GPUs and multithreaded architectures.
GooFit, the original GPU fitting program with a familiar syntax resembling classic RooFit, has undergone significant redesign and has expanded physics and computing capabilities. The performance has been improved and tested on a variety of systems. GooFit 2.0 is easier than ever to install, develop, and use on any system.
A new templated header-only library, Hydra, provides highly optimized general framework for fits, Monte Carlo generation, integration, and more. The design and benefits of this system along with initial tests will be shown.
Finally, a model-independent search for direct CP violation using an unbinned approach called an energy test was performed directly using the Thrust library (which both of the previous packages are based on). Public results from this analysis and performance comparisons will be presented.
Digital RSE: automated code quality checks - RSE group meetingHenry Schreiner
Given at a local RSE group meeting. Covers code quality practices, focusing on Python but over multiple languages, with useful tools highlighted throughout.
Datasets with millions of events in charm decays at LHCb have prompted the development of powerful fitting and analysis tools capable of handling unbinned datasets using GPUs and multithreaded architectures.
GooFit, the original GPU fitting program with a familiar syntax resembling classic RooFit, has undergone significant redesign and has expanded physics and computing capabilities. The performance has been improved and tested on a variety of systems. GooFit 2.0 is easier than ever to install, develop, and use on any system.
A new templated header-only library, Hydra, provides highly optimized general framework for fits, Monte Carlo generation, integration, and more. The design and benefits of this system along with initial tests will be shown.
Finally, a model-independent search for direct CP violation using an unbinned approach called an energy test was performed directly using the Thrust library (which both of the previous packages are based on). Public results from this analysis and performance comparisons will be presented.
Digital RSE: automated code quality checks - RSE group meetingHenry Schreiner
Given at a local RSE group meeting. Covers code quality practices, focusing on Python but over multiple languages, with useful tools highlighted throughout.
CHEP 2018: A Python upgrade to the GooFit package for parallel fittingHenry Schreiner
A Python upgrade to the GooFit package for parallel fitting
9 Jul 2018, 15:30
15m
Hall 3 (National Palace of Culture)
presentation Track 5 – Software development T5 - Software development
Speaker
Henry Fredrick Schreiner (University of Cincinnati (US))
Description
The GooFit highly parallel fitting package for GPUs and CPUs has been substantially upgraded in the past year. Python bindings have been added to allow simple access to the fitting configuration, setup, and execution. A Python tool to write custom GooFit code given a (compact and elegant) MINT3/AmpGen amplitude description allows the corresponding C++ code to be written quickly and correctly. New PDFs have been added. The most recent release was built on top of the December 2017 2.0 release that added easier builds, new platforms, and a more robust and efficient underlying function evaluation engine.
iminuit is an external Python interface to the Minuit2 C++ code,
which can be compiled standalone without the rest of ROOT. iminuit
has recently seen a boost of development which culminated in the
latest 1.3 release and will join the Scikit-HEP project in this year.
To simplify Minuit’s use as a standalone CMake package, for projects
like iminuit and GooFit, a new standalone build system
was implemented for Minuit2, and has been
included in the latest release of ROOT. This system uses modern CMake
patterns, and lives in a peaceful coexistence with the ROOT build
system. The production of source packages is handled without external
scripts, and the system even supports building from inside ROOT.
Integrating this into the ROOT source and build system provided several
challenges, with some interesting solutions that will be shown.
The GooFit package provides physicists a simple, familiar syntax for manipulating probability density functions and performing fits, but is highly optimized for data analysis on NVIDIA GPUs and multithreaded CPU backends. GooFit is being updated to version 2.0, bringing a host of new features. A completely revamped and redesigned build system makes GooFit easier to install, develop with, and run on virtually any system. Unit testing, continuous integration, and advanced logging options are improving the stability and reliability of the system. Developing new PDFs now uses standard CUDA terminology and provides a lower barrier for new users. The system now has built-in support for multiple graphics cards or nodes using MPI, and is being tested on a wide range of different systems.
GooFit also has significant improvements in performance on some GPU architectures due to optimized memory access. Support for time-dependent four body amplitude analyses has also been added.
Presentació a càrrec d'Adrián Macía, cap de Càlcul Científic del CSUC, duta a terme a la "4a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 17 de març de 2021 en format virtual.
CHEP 2018: A Python upgrade to the GooFit package for parallel fittingHenry Schreiner
A Python upgrade to the GooFit package for parallel fitting
9 Jul 2018, 15:30
15m
Hall 3 (National Palace of Culture)
presentation Track 5 – Software development T5 - Software development
Speaker
Henry Fredrick Schreiner (University of Cincinnati (US))
Description
The GooFit highly parallel fitting package for GPUs and CPUs has been substantially upgraded in the past year. Python bindings have been added to allow simple access to the fitting configuration, setup, and execution. A Python tool to write custom GooFit code given a (compact and elegant) MINT3/AmpGen amplitude description allows the corresponding C++ code to be written quickly and correctly. New PDFs have been added. The most recent release was built on top of the December 2017 2.0 release that added easier builds, new platforms, and a more robust and efficient underlying function evaluation engine.
iminuit is an external Python interface to the Minuit2 C++ code,
which can be compiled standalone without the rest of ROOT. iminuit
has recently seen a boost of development which culminated in the
latest 1.3 release and will join the Scikit-HEP project in this year.
To simplify Minuit’s use as a standalone CMake package, for projects
like iminuit and GooFit, a new standalone build system
was implemented for Minuit2, and has been
included in the latest release of ROOT. This system uses modern CMake
patterns, and lives in a peaceful coexistence with the ROOT build
system. The production of source packages is handled without external
scripts, and the system even supports building from inside ROOT.
Integrating this into the ROOT source and build system provided several
challenges, with some interesting solutions that will be shown.
The GooFit package provides physicists a simple, familiar syntax for manipulating probability density functions and performing fits, but is highly optimized for data analysis on NVIDIA GPUs and multithreaded CPU backends. GooFit is being updated to version 2.0, bringing a host of new features. A completely revamped and redesigned build system makes GooFit easier to install, develop with, and run on virtually any system. Unit testing, continuous integration, and advanced logging options are improving the stability and reliability of the system. Developing new PDFs now uses standard CUDA terminology and provides a lower barrier for new users. The system now has built-in support for multiple graphics cards or nodes using MPI, and is being tested on a wide range of different systems.
GooFit also has significant improvements in performance on some GPU architectures due to optimized memory access. Support for time-dependent four body amplitude analyses has also been added.
Presentació a càrrec d'Adrián Macía, cap de Càlcul Científic del CSUC, duta a terme a la "4a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 17 de març de 2021 en format virtual.
Python is an object-oriented programming language created by Guido Rossum in 1989. It is ideally designed for rapid prototyping of complex applications. It has interfaces to many OS system calls and libraries and is extensible to C or C++. Many large companies use the Python programming language, including NASA, Google, YouTube, BitTorrent, etc.
2018 cosup-delete unused python code safely - englishJen Yee Hong
The talk is about doing cleanup and refactor for legacy Python code base in a safer way. I introduced several existing tools for this task and demonstrated how (surprisingly) Python ast module can also help in this case.
中文摘要:
不管是 open source 專案還是工作上,經過長時間開發累積,source code 內可能會殘留許多不再需要的 code,造成維護以及 refactor 的困難,也造成新手 trace code 時的障礙。
對 C/C++ 這類編譯式語言來說,開啟編譯器最佳化能自動清除 dead code,但對於 Python 這類動態語言,則沒有公認完美的方法。
本議程分享一些相關經驗,佐以利用 Python AST 的簡易自製工具,討論如何從較複雜的 python source tree 中,安全的清除不再需要的 code。
Code: https://github.com/PCMan/python-find-unused-func
Everything You Always Wanted to Know About Memory in Python But Were Afraid t...Piotr Przymus
Have you ever wondered what happens to all the precious RAM after running your 'simple' CPython code? Prepare yourself for a short introduction to CPython memory management! This presentation will try to answer some memory related questions you always wondered about. It will also discuss basic memory profiling tools and techniques.
A Gentle Introduction to Coding ... with PythonTariq Rashid
A gentle introduction to coding (programming) for complete beginners. Starting from then basics - electrical wires - proceeding through variables, data structures, loops, functions, and exploring libraries for visualisation and specialist tools. Finally we use flask to make a very simple twitter clone web application.
Modern binary build systems have made shipping binary packages for Python much easier than ever before. This talk discusses three of the most popular build systems for Python packages using the new standards developed for packaging.
SciPy22 - Building binary extensions with pybind11, scikit build, and cibuild...Henry Schreiner
Building binary extensions is easier than ever thanks to several key libraries. Pybind11 provides a natural C++ language for extensions without requiring pre-processing or special dependencies. Scikit-build ties the premier C++ build system, CMake, into the Python extension build process. And cibuildwheel makes it easy to build highly compatible wheels for over 80 different platforms using CI or on your local machine. We will look at advancements to all three libraries over the last year, as well as future plans.
Scikit-HEP has grown rapidly over the last few years, not just to serve the needs of the High Energy Physics (HEP) community, but in many ways, the Python ecosystem at large. AwkwardArray, boost-histogram/hist, and iMinuit are examples of libraries that are used beyond the original HEP focus. In this talk, we will look key packages in the ecosystem, at how the collection of 30+ packages was developed and maintained, and the software ecosystem contributions made to packages like cibuildwheel, pybind11, nox, scikit-build, build, and pipx that support this effort, and the Scikit-HEP developer pages, and initial WebAssembly support.
PyCon 2022 -Scikit-HEP Developer Pages: Guidelines for modern packagingHenry Schreiner
This was a PyCon 2022 lightning talk over the Scikit-HEP developer pages. It highlights best practices and guides shown there, and the quick package creation cookiecutter. And finally it demos the Pyodide WebAssembly app embedded into the Scikit-HEP developer pages!
Talk at PyCon2022 over building binary packages for Python. Covers an overview and an in-depth look into pybind11 for binding, scikit-build for creating the build, and build & cibuildwheel for making the binaries that can be distributed on PyPI.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
2. New in Python
• Python 3.6
▶ Big update
▶ First version fast as 2.7
▶ New f-strings very popular
• Python 3.7
▶ First version faster than 2.7
▶ OpenSSL update has affected adoption
• Python 3.8
▶ Released just a few days ago!
▶ What’s new?
1/13Henry Schreiner Python 3.8 October 18, 2019
4. Python 3.8: Positional-only arguments
d = dict(one=1, two=2)
d = dict({'one':1, 'two':2}
Challenge: write dict yourself
3/13Henry Schreiner Python 3.8 October 18, 2019
5. Python 3.8: Positional-only arguments
d = dict(one=1, two=2)
d = dict({'one':1, 'two':2}
Challenge: write dict yourself
def dict(arg=None, **kargs):
...
dict(arg=3) # OOPS!
You have to use *args, and limit to 1 arg yourself. Or…
3/13Henry Schreiner Python 3.8 October 18, 2019
6. Python 3.8: Positional-only arguments (2)
If you look at the signatures of dict, pow, or some other builtins, you will see something kind of
like this:
def dict(arg=None, /, **kargs):
...
That is now valid Python in 3.8!
Why is it useful?
• Allow kwargs and positional arguments without overlap
• Force positional arguments without names
• You can change the internal name - not part of your API
def f(pos, /, pos_or_kw, *, kw_required, kw_optional=None):
...
4/13Henry Schreiner Python 3.8 October 18, 2019
7. Python 3.8: Walrus operator
Assignment in Python is a statement (limited):
item = ... # simple
item[...] = ... # item
item.attr = ... # attr
a, b, = ... # tuple
a = b = ... # chained (special case)
But what about assigning in other places, like in the C languages?
if x = True: # Will never be allowed, too easy to make mistake
f(x=True) # Keyword argument
5/13Henry Schreiner Python 3.8 October 18, 2019
8. Python 3.8: Walrus operator (2)
Solution: A new operator!
• Spelling: := (looks like a sideways walrus)
• Works almost anywhere normal = doesn’t (one way to do things)
• Often requires parenthesis for clarity
if res := check():
print(res)
a = [None, 0, 1, 2]
while a := b.pop():
print(a)
• Use carefully: could make code harder to read
• C++17/C++20 adding variable defines in loops for similar reasons
• Scope leaks, (un?)fortunately
6/13Henry Schreiner Python 3.8 October 18, 2019
9. Python 3.8: f-string debugging
In Python 3.6, f-strings make string interpolation easy:
>>> x = 3
>>> print(f"x = {x}")
x = 3
Debugging is now DRY with the = specifier:
>>> print(f"{x = }")
x = 3
• Spaces respected
• Mix with complex expressions or formatting
7/13Henry Schreiner Python 3.8 October 18, 2019
10. Python 3.8: Static typing
Static type hints are a big feature of Python 3, and now they are much more powerful:
Literals type (AKA make-shift enums)
def f(val : Literal['yes', 'no', 'auto']): ...
Final (AKA const)
x : Final[bool] = True
x = False # Invalid in type checker like mypy
Protocols (ACA C++20 Concepts / Java Interface)
class HasName(Protocol):
name: str
Now you can use HasName, will require a name attribute.
8/13Henry Schreiner Python 3.8 October 18, 2019
11. TypedASDT
• TypedAST merged into Python!
• feature_version selector (3.4+)
>>> import ast
>>> s = ast.parse('x = 2 # type: Int', type_comments=True)
>>> ast.dump(s)
"Module(body=[Assign(
targets=[Name(id='x', ctx=Store())],
value=Constant(value=2, kind=None),
type_comment='Int')],
type_ignores=[])"
• ast.get_source_segment gets the source for a bit of ast, if location information is
present.
9/13Henry Schreiner Python 3.8 October 18, 2019
12. Other features
• TypedDict gives types to dict parts
• importlib.metadata gives you info from installed packages (like importlib.resources)
• math and statistics have new functions
• namedtuple, pickle, and more are faster
• SyntaxError messages are more detailed in some common cases
• multiprocessing.shared_memory – can avoid pickle transfer of objects
• reversed works on dicts
• Unpacking in return/yield
10/13Henry Schreiner Python 3.8 October 18, 2019
13. Other developer changes
• --libs no longer include libpython
• Single ABI for debug/release
• Runtime audit hooks
• New C API for initialization
• Provisional vectorcall protocol – fast calling of C functions
• Pickle support out-of-band data (multiple streams) (Protocol 5)
• __code__ now has .replace, like __signature__
11/13Henry Schreiner Python 3.8 October 18, 2019
14. Final Words
Released
• Python.org released
• Docker: docker run --rm -it python:3.8
• Scikit-HEP GCC 9.2 ManyLinux1 containers
Not yet released
• Conda: Coming soon
• Numpy wheels
• Homebrew: In progress
• Still missing in Azure, etc.
• Scikit-HEP build tools (Windows, ManyLinux1 + GCC 9.2)
12/13Henry Schreiner Python 3.8 October 18, 2019
15. Sources
• Official docs
• RealPython article
• DeepSource
This presentation available at /henryiii/python38.
13/13Henry Schreiner Python 3.8 October 18, 2019