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An Ode To NumPy!

On– the morning of April 10th 2019, Katie Bouman,  a 29 year old computer scientist and a professor at CalTech shared a picture of her from the previous night on social media. The picture showed her smiling and bracing herself looking at the camera. In the background was her laptop where a window was busy executing a piece of code. On top was a popup window covering fourth of the screen with an image truly from out of this world. The caption of her post read

Watching in disbelief as the first image I ever made of a black hole was in the process of being reconstructed.”

The image was of the halo made by the dust and gas situated 55 million light years away at the center of the galaxy named Messier 87. It was the first ever image and visual evidence of the existence of Black Holes.

Central to her challenge of constructing the image was to develop the primary algorithm and deploy a number of other algorithms developed by a team of more than 200+ scientist spread across the globe to process millions of terabytes of data collected by the Event Horizon Telescope (EHT) over a decade. Not surprisingly from data processing pipeline to image reconstruction were built using python libraries (SciPy, Matplotlib, eth-imaging, AstroPy, Pandas just to name a few) with NumPy at their core for array data processing.

Anyone in the scientific research community will agree that it is not any stretch of imagination to say the task Dr. Bauman and her team undertook was made a little if not a lot easier by the existence of Python’s NumPy library.

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