EuroPython are happy to announce the following posters which will be shown at EuroPython 2020.
The PDFs and previews will be uploaded closer to the event at https://ep2020.europython.eu/events/posters/
- Decision Science with Probabilistic Programming by Mattia Ferrini
- Design and Simulate your Control Systems with Python, Why Not? by Brilian Putra Amiruddin
- Developing a match-making algorithm between customers and Go-Jek products! by Gunjan Dewan
- Integration between ArcGIS Server (Rest API) and Django by Vinicius Cruvinel Rego
- Radio Astronomy with Python by Priscila Gutierres
- Reduce hardware costs in Internet of Things using Python by Vijay Sajjanar
- Resurrecting a django project in python 2.7 for 3.8 by Griffith Rees
- Simulation of logistic systems in Python with salabim by Ruud van der Ham
- Taking Part in the Greatest Experiment in History by Lil Anonymous
- The Phantom of Radon by Francesca Tedeschi
- Writing Extensions and Bindings for GPU made Easy by Krishna Kanta Singh
Generative Models are the Swiss Army Knife for the Decision Scientist. Generative models allow the simulation of scenarios based on different business hypotheses (Bayesian priors). With Probabilistic Programming, decision makers can simulate the impact of business drivers in times of great uncertainty.
Furthermore, Probabilistic Programming Languages provide all the inference tools necessary to identify the assumptions that have most likely generated an outcome. Inference is a statistical tool that enables optimal decision-making based on models that explicitly quantify uncertainty.
This talk addresses the use of Probabilistic Programming Languages in decision science. The talk will briefly introduce to Bayesian Machine Learning, Bayesian inference and inference algorithms through a number of use-cases developed in Pyro. The use cases will be simple yet will have practical relevance: the examples will illustrate scalability and verifiability challenges.
This talk is tailored to the hands-on practitioner and the sole prerequisite is an understanding of basic statistical concepts.
Many electrical engineering or control systems engineering students did not know that we can simulate our designed control algorithm with Python language, which is free and open source. In many colleges, to simulate the control algorithm it tends to use a paid programming language, but in many cases, the colleges did not provide the license for the students, so the students cannot afford it and ironically at the end, the students went to do an illegal act (software piracy).
Therefore, in this poster, I want to share about how can we design and simulate our control algorithm in Python using various methods, for instance:
- The conventional algorithm (PID Controller)
- Optimal control (LQR Controller)
- Learning control (Reinforcement Learning Control)
And all of the control algorithms are designed using scientific python libraries such as Numpy, Scipy, and Python Control as well after we finished design the algorithm then we will simulate and visualize it using Matplotlib or Open AI Gym to know how powerful and capable Python to use in control systems design.
The aim of this poster is to open a new perspective about Python to be used as an alternative language for control systems design in many colleges around the world. Don't worry if you have no background in control engineering cause I will introduce what control systems are at the beginning of the talks, so all of the audience would get a basic understanding of it.
by Gunjan Dewan
GoJek has millions of monthly active users in Indonesia across our 20+ products and services. A major problem we faced was targeting these customers with promos and vouchers that were relevant to them. We developed a generalized model that takes into account the transaction history of users and gives a ranked list of our services that they are most likely to use next. From here on, we are able to determine the vouchers that we can target these customers with.
In this talk, I will be talking about our process while developing the model, the challenges we faced during the time, how we used PySpark to tackle these challenges and the impact it had on our conversion rates.
The ESRI main software, ArcGIS, has a consolidated name in the market with many tools and also a complete web service platform called ArcGIS Server.
Considerer the dynamic evolution to integrate platforms it become more and more demanded and essential to get informations in real time.
This poster shows the workflow to integrate ArcGIS REST API with Django and also how it works and also some procedures to obtain data, populate the database and visualize them - in a very simple way.
- The ArcGIS REST API
- Django web framework.
- Creating models in, Populate database and display data from ArcGIS REST API.
- Updated in real time the information using jQuery.
Looking at higher redshifts is equivalent to looking back in time: they improve the studies of cosmology, expanding our knowledge of the universe. It allows us to study various physical phenomena like the power spectrum of galaxies which describes the distribution of galaxies on a range of scales, galaxy clustering, and large scales, the detection of the Baryon Acoustic Oscillation feature.
As a result, a significant amount of work has been done to increase the efficiency and accuracy of the process via new algorithms and optimization of existing ones.
Astronomical datasets are undergoing a rapid growth in size and complexity as past, ongoing and future surveys produce massive multi-temporal and multi-wavelength datasets, with huge information to be extracted and analyzed.
The alternative to a full spectroscopic survey is to obtain multi-color images of the sky and perform photometric redshift estimates for the galaxies we have available.
When dealing with this problem, there are two main approaches: model-driven data analysis (template fitting methods) and data-driven analysis, which can use machine learning methods.
To solve this problem, we use data-driven analysis, more specifically GPz (which uses Gaussian processes) and ANNz2 (which mainly uses neural networks), both python software.
Prerequisites: machine learning and basic math knowledge
I guide students in developing systems which are real world and need less hardware. This is possible in 2 steps. One,reduce collecting data which is already available online. Two, compute on cloud instead on the hardware.
Cloud computing and Web services have opened a plethora of ways which were science fiction for electronics academia. Python packages such as requests, beautifulsoup and pyserial automate electronics at the grassroot level pushing all complexities to the cloud.
This talk is about how I motivate students to use python for tasks which otherwise would require complex setup and costly hardware. I will present case studies of accessing websites (ethically per se!) of Indian Meteorological Department and Indian Railways for Internet of Things.
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Describe the process I went though to return to and finish the code I wrote for my PhD modelling the geographic growth and decline of a Bulletin Board Systems (BBS) community called FidoNet. I had to leave the project for 2 major surgeries and finally returned to finish, validate the results and present a paper (accepted for the Sunbelt conference in Paris this summer… we’ll see if that still happens).
- 5 min - Introduction and background on the research topic and my health situation
- 5 min - Examples of reading and documenting and adding unit-tests to the code to understand what I wrote. In some cases I had meticulously docstring-ed and while others were almost incomprehensible. Stress the value of PEP-8.
- 10 min - Look up the advantages and disadvantages of upgrading from python 2.7 to 3.8 and the excitement I needed to have the energy to keep going
- 5 min - upgrading from Django 1.11 to 3.0, including backwards incompatibility and changes to GeoDjango (some reference to upgrading from PostGIS 2 to 3)
- 5 min - Considering the options of Futurize and Modernize and why I went with Futurize
- 10 min - Pitfalls and examples and how I managed git forks throughout
- 5 min - Conclusion, thoughts and suggestions
If accepted for a 30 min talk I would skip the python 2.7-3.8 differences and documentation details and focus on the Django/PostGIS stuff
TBA; see poster link for updated info.
In the last few weeks, one of the largest and most exciting experiments in the history of mankind has taken place, with over 900 million children in over 190 countries being the recipients of online (virtual) education.
And in one corner of the globe in a high school in the UK, Python Programming lessons in an entirely online medium were delivered for the 1st time as opposed to the then normal face-to-face mode of delivery.
We will see in this poster how the teaching of Python Programming in this online fashion was both delivered and received, for a group of children aged 11-14.
We will discuss how great efforts were made to ensure that the online sessions were fit-for-purpose, educational and at the same time exciting and fun.
We will discuss how we discovered the pleasant surprises and advantages of teaching on an online forum.
We will look at some of the Python Programs, such as currency converters, modelling graphs using libraries, random password generaters, etc. produced by the children.
To conclude, it was a surprisingly pleasant experience for all concerned (i.e educators, students, parents, etc.) and we believe going forward the education system should also seriously incorporate this medium of online learning, not just for adults but also for children.
This project contains an open source Python library for image reconstruction in Axial Computed Tomography (TAC), based on the analytical Radon transforms of some classes of phantoms.
The package is available on GitHub at the following address: https://github.com/francescat93/Exact_sinogram.
The mathematical phantoms are fictitious images, composed of very simple geometric figures (ellipses, squares and rectangles) that, sampled with the Radon transform allows to build a fictitious signal, called (exact) sinogram. Using a phantom gives the advantage to test the reconstruction algorithm on a zero-noise data so the error we get is only due to numerical inaccuracies in the algorithm itself. We want to calculate two reconstructed images from the approximated and exact sinograms, obtained applying the iradon function of the Python library scikit-image on both of them. We expect a smaller error on the exact reconstructed image. This turns to be true on continuous regions, but near the discontinuities of the phantom the Gibbs phenomenon prevents us to obtain the same enhancement.
As Deep Learning Engineer and Researcher we are always trying to optimize some bottleneck computation in our programs. Sometimes we are faced with situations when scientific libraries like NumPy, SciPy aren't just cutting it or worse there are no libraries that implement the esoteric function on our expensive GPU hardware. *Writing Custom C and Cuda Extension becomes an important skill and necessity for applications that require really fast computation. *
In this talk, we go through a detailed example of image search on billions of items, we write custom C and Cuda kernel for distance computation and learn how to connect them seamlessly with our python codebase. We compare methods for writing these extensions and bindings for python in terms of both speed and ease of use. Finally, we make it all work together by hacking the setup.py file for easy deployment and sharing of the