visualize.R

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# SPDX-License-Identifier: AGPL-3.0-or-later
# Copyright (C) 2025 SWGY, Inc
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.
#install.packages("ggplot2")
#install.packages("rgl")
library(ggplot2)
library(rgl)

ggplot(long_df, aes(x = ArmorCondition, y = Impulse)) +
  geom_boxplot() +
  theme_minimal()

ggplot(long_df, aes(x = ArmorCondition, y = Impulse)) +
  geom_violin(trim=FALSE) +
  geom_boxplot(width=0.1) +
  theme_minimal()

# compute 95th percentile
# This version "zooms in" to the interesting part
q95 <- quantile(long_df$Impulse, .95)
ggplot(long_df, aes(ArmorCondition, Impulse)) +
  geom_boxplot() +
  coord_cartesian(ylim = c(0, q95)) +
  theme_minimal()

# The log scale can help with seeing detail. It offers perhaps the
# best view, and makes sense given the spherical drop-off of the pressure wave
ggplot(long_df, aes(ArmorCondition, Impulse)) +
  geom_boxplot() +
  scale_y_log10() +
  theme_minimal()

ggplot(long_df, aes(x = ArmorCondition, y = Impulse)) +
  geom_violin(trim=FALSE) +
  geom_boxplot(width=0.1) +
  scale_y_log10() +
  theme_minimal()

df$diff_full_no <- df$full_armor - df$no_armor
df$diff_full_helmet <- df$full_armor - df$helmet_only
df$diff_vest_no <- df$vest_only - df$no_armor

with(df, {
       plot3d(
              x = bp_x,
              y = bp_y,
              z = bp_z,
              col = heat.colors(100)[as.numeric(cut(diff_full_no, 100))],
              size = 5,
              type = "s"
       )
})

intermediate_df$diff_full_no <- intermediate_df$full_armor - intermediate_df$no_armor
intermediate_df$diff_full_helmet <- intermediate_df$full_armor - intermediate_df$helmet_only
intermediate_df$diff_vest_no <- intermediate_df$vest_only - intermediate_df$no_armor

# signed log: compress both positive & negative extremes
intermediate_df$diff_log <- sign(intermediate_df$diff_full_no) *
  log1p(abs(intermediate_df$diff_full_no))

# then bin & color
col_idx <- as.numeric(cut(intermediat_df$diff_log, breaks = 100))
cols    <- heat.colors(100)[col_idx]

with(intermediate_df, {
       plot3d(
              bp_x, bp_y, bp_z,
              col  = cols,
              size = 1.7,
              type = "s"
       )
})


# 1) Filter to just the two conditions and set factor order
df2 <- intermediate_long %>%
  filter(ArmorCondition %in% c("helmet_only", "vest_only")) %>%
  mutate(ArmorCondition = factor(ArmorCondition, levels = c("helmet_only", "vest_only")))

# 2) Compute condition means
means <- df2 %>%
  group_by(ArmorCondition) %>%
  summarize(mean_impulse = mean(Impulse, na.rm = TRUE))

# 3) Plot
ggplot(df2, aes(x = ArmorCondition, y = Impulse)) +
  # subject‐level trajectories
  geom_line(aes(group = subject_id), color = "grey70", alpha = 0.6) +
  geom_point(aes(group = subject_id), color = "grey50", alpha = 0.6) +
  # overlay the means in red
  geom_point(data = means,
             aes(x = ArmorCondition, y = mean_impulse),
             color = "red", size = 3) +
  geom_line(data = means,
            aes(x = ArmorCondition, y = mean_impulse, group = 1),
            color = "red", size = 1.2) +
  theme_minimal() +
  labs(
    title    = "Intermediate Blasts: Helmet vs Vest",
    subtitle = "Grey lines = each blast point; red = group means",
    x        = "Armor Condition",
    y        = "Impulse"
  )

# 1) Filter to just the two conditions and set factor order
df2 <- intermediate_long %>%
  filter(ArmorCondition %in% c("full_armor", "helmet_only")) %>%
  mutate(ArmorCondition = factor(ArmorCondition, levels = c("full_armor", "helmet_only")))

# 2) Compute condition means
means <- df2 %>%
  group_by(ArmorCondition) %>%
  summarize(mean_impulse = mean(Impulse, na.rm = TRUE))

# 3) Plot
ggplot(df2, aes(x = ArmorCondition, y = Impulse)) +
  # subject‐level trajectories
  geom_line(aes(group = subject_id), color = "grey70", alpha = 0.6) +
  geom_point(aes(group = subject_id), color = "grey50", alpha = 0.6) +
  # overlay the means in red
  geom_point(data = means,
             aes(x = ArmorCondition, y = mean_impulse),
             color = "red", size = 3) +
  geom_line(data = means,
            aes(x = ArmorCondition, y = mean_impulse, group = 1),
            color = "red", size = 1.2) +
  theme_minimal() +
  labs(
    title    = "Intermediate Blasts: Full Armor vs Vest Only",
    subtitle = "Grey lines = each blast point; red = group means",
    x        = "Armor Condition",
    y        = "Impulse"
  )