library(Athlytics)
library(dplyr)

# Parse stream data from a FIT/TCX/GPX file
stream_data <- parse_activity_file("path/to/activity.fit")

# Flag quality issues
flagged_data <- flag_quality(
  stream_data,
  sport = "Run",
  hr_range = c(30, 220), # Valid HR range
  pw_range = c(0, 1500), # Valid power range
  max_run_speed = 7.0, # Max speed (m/s) ≈ 2:23/km pace
  max_hr_jump = 10, # Max HR change per second
  max_pw_jump = 300, # Max power change per second
  min_steady_minutes = 20, # Min duration for steady-state
  steady_cv_threshold = 8 # CV% threshold for steady-state
)

# View quality summary
summary_stats <- summarize_quality(flagged_data)
print(summary_stats)

# Load activities
activities <- load_local_activities("export_12345678.zip")

# Calculate ACWR using Rolling Average (traditional)
acwr_ra <- calculate_acwr_ewma(
  activities,
  activity_type = "Run",
  method = "ra",
  acute_period = 7,
  chronic_period = 28,
  load_metric = "duration_mins"
)

# Calculate ACWR using EWMA
acwr_ewma <- calculate_acwr_ewma(
  activities,
  activity_type = "Run",
  method = "ewma",
  half_life_acute = 3.5, # Acute load half-life (days)
  half_life_chronic = 14, # Chronic load half-life (days)
  load_metric = "duration_mins"
)

# Compare the two methods
library(ggplot2)
ggplot() +
  geom_line(
    data = acwr_ra, aes(x = date, y = acwr_smooth),
    color = "blue", linewidth = 1, linetype = "solid"
  ) +
  geom_line(
    data = acwr_ewma, aes(x = date, y = acwr_smooth),
    color = "red", linewidth = 1, linetype = "dashed"
  ) +
  labs(
    title = "ACWR: Rolling Average vs EWMA",
    subtitle = "Blue = RA (7:28d) | Red = EWMA (3.5:14d half-life)",
    x = "Date", y = "ACWR"
  ) +
  theme_minimal()

# Calculate EWMA with 95% confidence bands
acwr_ewma_ci <- calculate_acwr_ewma(
  activities,
  activity_type = "Run",
  method = "ewma",
  half_life_acute = 3.5,
  half_life_chronic = 14,
  load_metric = "duration_mins",
  ci = TRUE, # Enable confidence intervals
  B = 200, # Bootstrap iterations
  block_len = 7, # Block length (days) for bootstrap
  conf_level = 0.95 # 95% CI
)

# Plot with confidence bands
ggplot(acwr_ewma_ci, aes(x = date)) +
  geom_ribbon(aes(ymin = acwr_lower, ymax = acwr_upper),
    fill = "gray70", alpha = 0.5
  ) +
  geom_line(aes(y = acwr_smooth), color = "black", linewidth = 1) +
  geom_hline(yintercept = c(0.8, 1.3), linetype = "dotted", color = "green") +
  geom_hline(yintercept = 1.5, linetype = "dotted", color = "red") +
  labs(
    title = "ACWR with 95% Confidence Bands",
    subtitle = "Gray band = bootstrap confidence interval",
    x = "Date", y = "ACWR"
  ) +
  theme_minimal()

# Load data for multiple athletes
athlete1 <- load_local_activities("athlete1_export.zip") |>
  mutate(athlete_id = "athlete1", sex = "M", age_band = "25-35")

athlete2 <- load_local_activities("athlete2_export.zip") |>
  mutate(athlete_id = "athlete2", sex = "F", age_band = "25-35")

athlete3 <- load_local_activities("athlete3_export.zip") |>
  mutate(athlete_id = "athlete3", sex = "M", age_band = "35-45")

# Combine
cohort_data <- bind_rows(athlete1, athlete2, athlete3)

# Calculate ACWR for each athlete
cohort_acwr <- cohort_data |>
  group_by(athlete_id) |>
  group_modify(~ calculate_acwr_ewma(.x, method = "ewma")) |>
  ungroup() |>
  left_join(
    cohort_data |> select(athlete_id, sex, age_band) |> distinct(),
    by = "athlete_id"
  )

# Calculate cohort reference by activity type and sex
reference <- calculate_cohort_reference(
  cohort_acwr,
  metric = "acwr_smooth",
  by = c("type", "sex"), # Group by activity type and sex
  probs = c(0.05, 0.25, 0.5, 0.75, 0.95), # Percentiles
  min_athletes = 3 # Minimum athletes per group
)

# View reference structure
head(reference)

# Extract one athlete's data
individual <- cohort_acwr |>
  filter(athlete_id == "athlete1")

# Plot with cohort reference
p <- plot_with_reference(
  individual = individual,
  reference = reference |> filter(sex == "M"), # Match athlete's group
  metric = "acwr_smooth",
  bands = c("p25_p75", "p05_p95", "p50")
)

print(p)