Transformation
You know it. We know it. NumPy is cool. Pandas is cool. We can bend them to our will, but sometimes they’re not the right tools for the job. Enter Xarray and Awkward Array. Read on for the four reasons why you need to learn these Python packages. Reason 1: You need labeled arrays of…
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March 9, 2023|Life Sciences, Materials Science, Transformation Making the Most of Small Data in Scientific R&D For many traditional innovation-driven organizations, scientific data is generated to answer specific immediate research questions and then archived to protect IP, with little attention paid to the future value of reusing the data to answer other similar or tangential…
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Like most people, I mostly interact with Python using the default REPL or with IPython. Yet, I often reach for one of the Python tools that come with the standard library. All these tools are implemented as “mains” in the various scripts and modules. Here are 7 I use on a semi-regular basis. 1. &…
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Recently, I’ve been working on a new course offering in Enthought Academy titled Software Engineering for Scientists and Engineers course. I’ve focused on distilling the software engineering best practices that we use at Enthought with our clients, with the twist of “what parts are most useful for a scientist who writes software for R&D?” After…
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Jun 8, 2023|Energy, Life Sciences, Materials Science, Technology, Transformation In the digital era, robust data tools are crucial for all companies and the science-driven industries like the life sciences, materials science, and chemistry are no exception. While artificial intelligence (AI) and machine learning (ML) are now essential parts of the modern research lab, many…
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if __name__ == “__main__”: When I was new to Python, I ran into a mysterious block of code that looked something like: def main(): # do some stuff if __name__ == “__main__”: main() Looking at the code, I could see that it ran the main() function after checking the status of…
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As a company that delivers Digital Transformation for Science, part of our job at Enthought is to understand the trends that will affect how our clients do their science. Below are three trends that caught our attention in 2022 that we predict will take center stage in 2023. ChatGPT This one just showed up on…
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When thinking about enhancing R&D processes, Newton’s second law of motion provides the perfect framework. Classical mechanics teaches us that putting a body into motion requires applying force. The resulting acceleration will be the sum of the forces applied to the body, divided by the body’s mass: a = F/m. So, if we want to…
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Dear Students and Friends of Enthought, I am pleased to announce Enthought Academy—the culmination of over twenty years of teaching Scientific Python. Since our founding in 2001, Enthought has worked in the Scientific Python ecosystem, consulting in both the public and private sectors, solving hard science problems. As the creators of the SciPy package, cofounders…
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In the blog post Configuring a Neural Network Output Layer we highlighted how to correctly set up an output layer for deep learning models. Here, we discuss how to make sense of what a neural network actually returns from the output layers. If you are like me, you may have been surprised when you first…
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