Snow Profile Alignment, Aggregation, and Clustering
Convert 'similarity' matrix to 'distance' matrix
Similarity measure between snow profile pairs
Average a group of snow profiles
Compute a seasonal timeseries of an average snowprofile
Backtrack layers from average or summary profile
Get index of appropriate initial condition average profile
Cluster snow profiles
Compute centroids/medoids for clustered snow profiles
Configure clusterSP computation
sarp.snowprofile.alignment: Snow Profile Alignment, Aggregation, and C...
K-dimensional barycentric average clustering for snow profiles
Concatenate time series of average profiles
Deposition Date Distance
Difference in layer density
Compute pairwise distances between snow profiles
Calculate a multidimensional distance matrix between two profiles
Calculate DTW alignment of two snow profiles
Extract from Scoring matrix
Flip snow profile layers top down
Grain Type similarity matrix for DTW alignments
Grain type similarity matrix for evaluation purposes
Difference in Hand Hardness
Run interactive alignment app
Weighting scheme for preferential layer matching
Match with numeric tolerance
Find the medoid snow profile among a group of profiles
Merge layers with identical properties
Difference in layer ogs
Plot clustered snow profiles
Plot alignment cost density and warping path
Align and plot two snow profiles using DTW
Difference in layer stability p_unstable
Scale total height of a snow profile
Resample snowprofile
Resample a pair of profiles
Rescale and resample a snow profile list
Return conceptually similar grain types
Remove layers with a thickness of 'zero cm'
Warp one snow profile onto another one
Restrict the DTW warping window for snow profiles alignment
Snow profiles describe the vertical (1D) stratigraphy of layered snow with different layer characteristics, such as grain type, hardness, deposition date, and many more. Hence, they represent a data format similar to multivariate time series containing categorical, ordinal, and numerical data types. Use this package to align snow profiles by matching their individual layers based on Dynamic Time Warping (DTW). The aligned profiles can then be assessed with an independent, global similarity measure that is geared towards avalanche hazard assessment. Finally, through exploiting data aggregation and clustering methods, the similarity measure provides the foundation for grouping and summarizing snow profiles according to similar hazard conditions. In particular, this package allows for averaging large numbers of snow profiles with DTW Barycenter Averaging and thereby facilitates the computation of individual layer distributions and summary statistics that are relevant for avalanche forecasting purposes. For more background information refer to Herla, Horton, Mair, and Haegeli (2021) <doi:10.5194/gmd-14-239-2021>, Herla, Mair, and Haegeli (2022) <doi:10.5194/tc-16-3149-2022>, and Horton, Herla, and Haegeli (2024) <doi:10.5194/egusphere-2024-1609>.