plotAnimatedFlowmap: Generating flow maps and animations based on transition...

In HVT: Constructing Hierarchical Voronoi Tessellations and Overlay Heatmaps for Data Analysis

Generating flow maps and animations based on transition probabilities

Description

This is the main function for generating flow maps and animations based on transition probabilities including self states and excluding self states. Flow maps are a type of data visualization used to represent the transition probability of different states. Animations are the gifs used to represent the movement of data through the cells.

Usage

plotAnimatedFlowmap(
  hvt_model_output,
  transition_probability_df,
  df,
  animation = "All",
  flow_map = "All",
  fps_time = 1,
  fps_state = 1,
  time_duration = 2,
  state_duration = 2,
  cellid_column,
  time_column
)

Arguments

hvt_model_output	List. Output from a trainHVT function.
transition_probability_df	List. Output from getTransitionProbability function
df	Data frame. The input dataframe should contain two columns, cell ID from scoreHVT function and time stamp of that dataset.
animation	Character. Type of animation ('state_based', 'time_based', 'All' or NULL)
flow_map	Character. Type of flow map ('self_state', 'without_self_state', 'All' or NULL)
fps_time	Numeric. A numeric value for the frames per second of the time transition gif. (Must be a numeric value and a factor of 100). Default value is 1.
fps_state	Numeric. A numeric value for the frames per second of the state transition gif. (Must be a numeric value and a factor of 100). Default value is 1.
time_duration	Numeric. A numeric value for the duration of the time transition gif. Default value is 2.
state_duration	Numeric. A numeric value for the duration of the state transition gif. Default value is 2.
cellid_column	Character. Name of the column containing cell IDs.
time_column	Character. Name of the column containing time stamps

Value

A list of flow maps and animation gifs.

Author(s)

PonAnuReka Seenivasan <[e-mail]>, Vishwavani <[e-mail]>

See Also

trainHVT
scoreHVT
getTransitionProbability

Examples

dataset <- data.frame(date = as.numeric(time(EuStockMarkets)),
                      DAX = EuStockMarkets[, "DAX"],
                      SMI = EuStockMarkets[, "SMI"],
                      CAC = EuStockMarkets[, "CAC"],
                      FTSE = EuStockMarkets[, "FTSE"])
hvt.results<- trainHVT(dataset,n_cells = 60, depth = 1, quant.err = 0.1,
                       distance_metric = "L1_Norm", error_metric = "max",
                       normalize = TRUE,quant_method = "kmeans")
scoring <- scoreHVT(dataset, hvt.results)
cell_id <- scoring$scoredPredictedData$Cell.ID
time_stamp <- dataset$date
dataset <- data.frame(cell_id, time_stamp)
table <- getTransitionProbability(dataset, cellid_column = "cell_id",time_column = "time_stamp")
plots <- plotAnimatedFlowmap(hvt_model_output = hvt.results, transition_probability_df = table,
df = dataset, animation = 'All', flow_map = 'All',fps_time = 1,fps_state =  1,time_duration = 3,
state_duration = 3,cellid_column = "cell_id", time_column = "time_stamp")

HVT documentation built on April 3, 2025, 8:45 p.m.