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Peaks map - style exploration #15
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One thing to save a ton of size/space on this is to use discrete categories for the GW heights (the different shapes) and use those as animated masks over a simple shape which also would transition in color. You could define those masks as say 5 shapes and animate between them. That way you only define 5 fancy paths instead of site * 5 paths. |
☝️ Yes, that approach would save lots of space (so referencing #18, too). Putting this here to remember: currently, the number of unique peaks paths generated (bc we use the # Just using values straight up
read_csv("2_process/out/gw_data_anomalies.csv") %>%
pull(daily_quant) %>%
unique() %>%
length()
[1] 215942
# At a minimum, we should round:
read_csv("2_process/out/gw_data_anomalies.csv") %>%
mutate(daily_quant = round(daily_quant)) %>%
pull(daily_quant) %>%
unique() %>%
length()
[1] 102 Turns out I am already rounding here, so we are already benefitting from this basic rounding technique. |
Per #26 style and implementation of the peaks can now be driven via css and js, although are currently not animated and still derived from the R processing. This has implications for how the animation is structured. In initial explorations, I looked into tweening among set path shapes for each For now, I am trying to implement a similar process that LP used in R to define the paths - by taking a data sheet with the svg-converted percentile for each site as input and running a d3 transition on the midpoint of the path 'd' . As illustrated here: https://github.com/pbeshai/d3-interpolate-path. This does seem like it could be a lot to process in browser, TBD. |
This is complete, especially since we are near release. Styles successfully explored. Optimization issues were further explored in #18 |
Mimic river conditions with negative space for low values?
Have totally diff colors? Have background color?
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