Tensorflow - Computer Vision

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Django middleware - Crawler detection

Sometimes you have to detect search engine crawling activity on your system, to handle the workflow for this type of request. On our website, there is a high crawling activity, because BV FAPESP (https://bv.fapesp.br) provides useful information abour reaserch for science, technology and academy in Sao Paulo state of Brazil. Recently, I deployed a feature on our system to allow people to store their queries and filters they do on our website. This is an open feature for everybody who navigates on our system. It is based on the HTTP session of the brower, which is stored on Python/Django server-side. When we were tunning Django session table in the database, we found out that there were a lot of sessions created by crawling activities. To take control of the session creation, I wrote the middleware that follows.  The list of crawlers, I get from ngix logs, on a very small timeframe, so maybe there are some more search engines that are not listed. The use-case presented, ...
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Atom - Jupyter / Hydrogen

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I will show you how to use the Atom editor with Jupyter Notebook to debug Python/Django views using Hydrogen plugin. On the Internet you can find some examples using this setup to debug Python but I found nothing related to Django, so I decided to publish this notes. This is not a high performance setup, because it involves many technologies at several layers. This is more a proof of concept of nowadays technologies which are available to the programmer's toolbox. This notes has been written during the development and improvement of the automated tests of the Virtual Library of FAPESP ( http://www.bv.fapesp.br ). The environment The diagram below shows an actual infrastructure to use this setup in a development environment. Technologies It is not expected that you know all of the technologies in detail, but there are some trick points. This paper is oriented for Python programmer who already use Atom and Jupyter and enjoy explore the edges of these tools. ...
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Machine Learning - Moving Linear Regression

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About the Moving Linear Regression This type of regression to be used when data of sub periods are different inside periods. For example, hours of day, days of week, months of year. For example, the variance of  the frequency of people in a commercial mall varies when comparing the days of the week. The same applies to an amount of cars on downtown or roads when comparing the days in a week, or even when comparing months, due to holidays and vacancies. The chart bellow displays a seven year Moving Linear Regression applied to each month, for the number of ongoing research projects.  About the data The data is public available on the open government initiative of the State of São Paulo - Brazil. State of São Paulo / Brazil - Transparency Portal Disclaimer - The sole purpose of this presentation is to carry out tests with machine learning, using public and open data to improve the use of technology for future application in the FAPESP Virtual Library. ...
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