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AplPy is an astronomical plotting library in Python aimed at producing publication quality plots of astronomical imaging data in FITS format. It can be used to show ggrayscale, colorscale, and 3-color RGB images with contours, coordinate grids and more. 🌌💻 Install it from here: https://aplpy.github.io/
Anya is live and ready to show you everything. Watch her strip, dance, and perform exclusive shows just for you. Interact in real-time and make your fantasies come true.
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Installation view from recent exhibition Uncertain Ruins at Swiss Cottage Gallery (18 Oct – 10 Jan 2020). Through Machine & Darkness was displayed on a 48″ LCD screen and mounted on scaffolding structures that related to the diagrams of neural networks.
Further information: www.passen-gers.co.uk/uncertainruins
Example of RAW Hubble Image data, preprocessed for DCGAN.
The first development phase of the project was run on a training set of 45,000+ images from the Advanced Camera for Surveys (ACS WFC) on board the HST. The RAW image data was chosen as it represents, as closely as possible, what the telescope ‘sees’ and has not been treated with false colour or cleaned of artefacts such as cosmic dust and rays. The training set contains images that have been taken using filters for various wavelengths, some beyond what the human eye can see such as the infrared and ultraviolet.
The dataset of RAW image data pulled from Hubble Space Telescope Public Data which is freely available on AWS at the link below.
https://registry.opendata.aws/hst/
Through Machine & Darkness Video installation, 2018 –19 Projection 2.5m wide 35 minutes, looped
Programmer: Doug Neal Scientific Advice: STScI
First exhibited as part of the LCN Showcase | Edition 5 at SPACE, London, 13–15 Sep 2018. Of Stars & Chasms, ArthouSE1, London, 1–24 Feb 2019.
The video developed as part of Phase 1 consists of image outputs taken at increments from the DCGAN’s training, which have been edited together to form a moving image work. As the video transitions, the images become more resolved, giving an insight into how the machine learns: a computer generated parallel universe comes into view. The slow, almost meditative pace of the video slows down the speed of algorithmic operations which function in fractions of seconds. The work provides an experience that oscillates between human, celestial and technological scales.
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