ApproxMVBB - Fast algorithms to compute an approximation of the minimal volume oriented bounding box of a point cloud in 3D

•    C++

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Computing the minimal volume oriented bounding box for a given point cloud in 3D is a hard problem in computer science. Exact algorithms are known and of cubic order in the number of points in 3D. A faster exact algorithm is currently not know. However, for lots of applications an approximation of the minimum volume oriented bounding box is acceptable and already accurate enough. This project was developed for research in Granular Rigidbody Dynamics. This small standard compliant C++11 library can either be built into a shared object library or directly be included in an existing C++ project. Download these and install it on your system.

kd-tree point-cloud volume bounding-boxes outlier-removal oobb

visualqc - VisualQC : assistive tool to ease the quality control workflow of neuroimaging data.

•    Python

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Note: VisualQC employs release early and release often mechanism to seek user feedback and for thorough testing. Hence you might find some rough edges in docs or examples - please let us know if you do. Contributions are welcome.

neuroimaging anatomical-mri cortex neuroscience quality-control freesurfer mri-images medical-imaging outlier-detection fmri qc outlier-removal registration alignment

NYCBuildingEnergyUse - Creating Regression Models Of Building Emissions On Google Cloud

•    Jupyter

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In indentifying outliers I will cover both visual inspection as well a machine learning method called Isolation Forests. Since I will completing this project over multiple days and using Google Cloud, I will go over the basics of using BigQuery for storing the datasets so I won't have to start all over again each time I work on it. At the end of this blogpost I will summarize the findings, and give some specific recommendations to reduce mulitfamily and office building energy usage. In this second post I cover imputations techniques for missing data using Scikit-Learn's impute module using both point estimates (i.e. mean, median) using the SimpleImputer class as well as more complicated regression models (i.e. KNN) using the IterativeImputer class. The later requires that the features in the model are correlated. This is indeed the case for our dataset and in our particular case we also need to transform the feautres in order to discern a more meaningful and predictive relationship between them. As we will see, the transformation of the features also gives us much better results for imputing missing values.

data-science scikit-learn exploratory-data-analysis regression xgboost bokeh outlier-detection missing-data google-app-engine regression-models energy-efficiency outlier-removal missing-values

EddyCleanR - Fills gaps and removes outliers in eddy covariance data

•    R

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EddyCleanR is used to clean eddy covariance tower data from IRRI Ecological Intensification (EI) platform. The IRRI Ecological Intensification platform is a field laboratory where probable futuristic rice production systems are developed and researched. The EI platform makes use of mechanization in rice production in conjunction with efficient irrigation, which this script is used for, and for the study of intensification and diversification of cropping systems and scheduling. The EI aims to produce three crops per year, two rice crops in rotation with a third non-rice crop (maize or mungbean) while being environmentally and ecologically sustainable.

statistics evapotranspiration filter calculations irrigation filtering agronomy eddycovariance eddy-covariance-data rice-crops maize-crops corn-crops outlier-removal mungbean-crops rice-production maize-production tropical-agriculture