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# ai2es-sharpness
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This is a repository containing implementations and utility functions for a variety of metrics that can be used to analyze the sharpness of meteorological image, as well as transform functions and a selection of synthetic and real data for use in examples. This is work conducted under the umbrella of [The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)](https://www.ai2es.org/). To find out more about the metrics included and how they can be used, see the [accompanying preprint](https://www.ai2es.org/sharpness/), and if you make use of this repository, please cite that paper. A peer-reviewed version is expected soon.
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This is a repository containing implementations and utility functions for a variety of metrics that can be used to analyze the sharpness of meteorological image, as well as transform functions and a selection of synthetic and real data for use in examples. This is work conducted under the umbrella of [The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)](https://www.ai2es.org/). To find out more about the metrics included and how they can be used, see the [accompanying preprint](https://backup.ai2es.org/sharpness/), and if you make use of this repository, please cite that paper. A peer-reviewed version is expected soon.
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## Installation
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All functions take as their input a single `n x m` grayscale image. If using these methods on multi-channel imagery, either convert the image to grayscale (if it is an RGB image) or loop over the channels and compute sharpness statistics individually.
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Input data should be of float type; if the data are not already floats, certain metrics which require float type inputs will internally convert the data to floats.
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Input data should be of float type; if the data are not already floats, certain metrics which require float type inputs will internally convert the data to floats.

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