OISST Preparationvignette to match the improvements to the
category(), which allows the user to specify which season of the MHWs they are interested in:
Algierstime series for examples on using multiple thresholds for atmospheric data
ggplot2dependencies to imports
heatwaveRis now dependency free
geom_flame()to allow proper screening of heatspikes
event_line()to allow users to manipulate the output more without having to get into the source code.
exceedance()will no longer produce a
var = TRUEmay be given to produce this column
detect_events.R()for returning the proto events rather than a table for the event metrics
event_line()that caused it to graph events outside of the
ts2clm()to match the Python default settings
clim_calc()reinstated to allow for calculation of clims with missing data
varcalculations reinstated for documentation issues
make_whole_fast()to provide a cleaner internal output
ts2clm()that prevented calculation of clims with large contiguous missing periods of data (e.g. ice coverage).
category()that allows one to have the function also output the day-to-day (long) category values, rather than just the summary (wide) output.
clim_calc_cppnot being able to calculate clims from baselines not beginning and ending on the Julian year by making
clim_spreadplug the gaps beforehand with row-wise means.
make_whole_fastwhich did not create a whole, complete time series (i.e. missing dates were still present); the missing dates caused
proto_eventnow handles all event calculations ‘in house’
detect_eventto now be given a theoretically limitless number of thresholds
lolli_plotbeing asked to highlight more events than are present
detect_eventto better match Python version
clim_calc_ccp()for faster climatology calculations; speed of climatology calculation comes down from 50.6 ms in R to 3.4 ms in C++ on my MacBook Pro (15-inch, 2017) 2.9 GHz Intel Core i7 16 GB RAM computer
smooth_percentile()by using RcppRoll
clim_spread()now returns a matrix, not a data frame. This makes the loop in
clim_calc()much faster. In testing with the sst_WA data, it leads to a 3.7 fold speed improvement (520 ms down to 140 ms).
make_whole()(60 ms down to 40 ms)
detect_event()now passing checks
detect()function was unpacked and simplified. Internal code is now in new functions, most of which will not be seen by the user. They are
ts2clm()used instead of
exceedance()function testthat checks updated to account for change in variable naming
detect()has now been broken into
category()function returns the category results for events
heatwaveRhex logo added to site
detect()as requested by Maxime Marin (), The University of Tasmania (IMAS) – CSIRO (O&A), and which is present in the python version of the package