divebomb dive classification algorithm
Project description
Divebomb is a python package that uses pandas to divide a timeseries of depths into individual dives.
Each dive is then profiled with the following attributes:
max_depth - the max depth in the dive
dive_start - the timestamp of the first point in the dive
dive_end - the timestamp of the last point in the dive
bottom_start - the timestamp of the first point in the dive when the animal is at depth
td_bottom_duration - a timedelta object containing the duration of the time the animal is at depth in seconds
td_descent_duration - a timedelta object containing the duration of the time the animal is descending in seconds
td_ascent_duration - a timedelta object containing the duration of the time the animal is ascending in seconds
td_surface_duration - a timedelta object containing the duration of the time the animal is at the surface in seconds
bottom_variance - the variance of the depth while the animal is at the bottom of the dive
dive_variance - the variance of the depth for the entire dive.
descent_velocity - the average velocity of the descent
ascent_velocity - the average velocity of the descent
peaks - the number of peaks found in the dive profile
left_skew - a boolean of 1 or 0 indicating if the dive is left skewed
right_skew - a boolean of 1 or 0 indicating if the dive is right skewed
no_skew - a boolean of 1 or 0 indicating if the dive is not skewed
The dive profiles are reduced to 8 dimensions using Principal Component Analsysis. Guassian Mixed Models are generated using theses variables and the minimal Bayesian Information Criterion is used to determine the optimal number of clusters. The dives are split into the clutsers using Agglomerative Hierarchical Clustering (from sklearn). The dives are then display through iPython notebooks or saved to netCDF files organized by cluster.
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