Untitled

Removing excess warnings from plots

options(warn = -1)

Loading Libraries

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(lmerTest)

Loading required package: lme4

Loading required package: Matrix

Attaching package: 'lmerTest'

The following object is masked from 'package:lme4':

    lmer

The following object is masked from 'package:stats':

    step

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ readr     2.1.5
✔ ggplot2   3.5.1     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::expand() masks Matrix::expand()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
✖ tidyr::pack()   masks Matrix::pack()
✖ tidyr::unpack() masks Matrix::unpack()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(modelr)
library(purrr)
library(emmeans)

Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'

library(gridExtra)

Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine

library(writexl)
library(gt)
library(webshot2)
library(broom.mixed)
library(ggplot2)
library(isotree)
library(tidyr)
library(ggforce)
library(plotrix)

Loading in Data

load("Z:/Isaac/Visual Features/1-5/step1.RData")

creating a wide df for all sows for selected features

# Need to create a new wide df


features_to_use_mean <- c("Rightmost.X", "Width")

df_subset_mean <- df_10min_raw %>%
  select(sow, ttf, feature, mean_value)

df_wide_mean <- df_subset_mean %>%
  filter(feature %in% features_to_use_mean) %>%
  pivot_wider(
    names_from  = feature,
    values_from = mean_value,
    names_glue  = "{feature}_mean"
  ) %>% 
  select(sow,ttf,Width_mean,Rightmost.X_mean)

features_to_use_var <- c("Centroid.X", "Height","Major.Axis.Length","Rightmost.X","Width")

df_subset_var <- df_10min_raw %>%
  select(sow, ttf, feature, var_value)

df_wide_var <- df_subset_var %>%
  filter(feature %in% features_to_use_var) %>%
  pivot_wider(
    names_from  = feature,
    values_from = var_value,
    names_glue  = "{feature}_var"
  ) %>% 
  select(sow,ttf,Centroid.X_var, Height_var,Major.Axis.Length_var,Rightmost.X_var,Width_var)

df_wide_10 <- df_wide_mean %>% 
  left_join(df_wide_var,by=c("sow","ttf"))

colnames((df_wide_10))

[1] "sow"                   "ttf"                   "Width_mean"           
[4] "Rightmost.X_mean"      "Centroid.X_var"        "Height_var"           
[7] "Major.Axis.Length_var" "Rightmost.X_var"       "Width_var"            

defining function to get ttf value at peak

find_max<-function(x,y){
  min_pos<-x[which.max(y)]
  return(min_pos)
}

Nesting df per sow

keeps sows with greater than 50 hours of recording before farrowing

nested_df_of_selected <- df_wide_10 %>% 
  group_by(sow) %>% 
  filter(any(ttf < -50)) %>% 
  ungroup()

Nests by sow and has a full tibble (for testing) and a baseline tibble (for training)

nested_df_of_selected <- nested_df_of_selected %>% 
  nest(full = -sow) %>% 
  mutate(
    baseline=map(full,~.x %>% filter(between(ttf, -120,-50)))
  )

creates a new column for a model for each sow

nested_df_of_selected <- nested_df_of_selected %>% 
  mutate(
    iso_model = map(baseline, ~isolation.forest(data=.x %>% select(-ttf),ntrees = 500))
  )

Creating a nested df tibble for each sow for -50 to 0 ttf one hour at a time

cutoffs <- -49:120

nested_df_of_selected <- nested_df_of_selected %>% 
  mutate(
    rolling_windows = map(
      full,
      ~ map(
        cutoffs,
        function(cut){
          .x %>% 
            filter(ttf >= -120, ttf <= cut)
        }
      )
    )
  )

Scoring the full data set

nested_df_of_selected <- nested_df_of_selected %>% 
  mutate(
    full_scored = map2(
      iso_model,
      full,
      ~ .y %>%
          mutate(
            anomaly_score = predict(
              .x,
              newdata = .y %>% select(-ttf),
              type = "score"
            )
          )
    )
  )

Scoring each window

# Score anomalies across all rolling windows per sow
nested_df_of_selected <- nested_df_of_selected %>%
  mutate(
    rolling_scored = map2(
      iso_model,
      rolling_windows,
      ~ map(
          .y,
          function(win) {
            win %>%
              mutate(
                anomaly_score = predict(
                  .x,
                  newdata = win %>% select(-ttf),
                  type    = "score"
                )
              )
          }
        )
    )
  )

fitting the loess function to get fitted values

# Fit a loess smoother to anomaly scores within each rolling window per sow
nested_df_of_selected <- nested_df_of_selected %>%
  mutate(
    rolling_fitted = map(
      rolling_scored,
      ~ map(
          .x,
          function(win) {
            if (nrow(win) < 10) return(NULL)   # need enough points to fit
            fit <- loess(anomaly_score ~ ttf, data = win, span = 0.75)
            win %>%
              mutate(fitted_score = predict(fit, newdata = win))
          }
        )
    )
  )

Getting the peaks ttf and fitted value per window

# Extract ttf and fitted value at the peak (max fitted score) per window per sow
nested_df_of_selected <- nested_df_of_selected %>%
  mutate(
    peak_summary = map2(
      rolling_fitted,
      # carry the cutoff values alongside so we know which window each row came from
      list(cutoffs),
      ~ {
          map2_dfr(
            .x,
            .y,
            function(win, cut) {
              if (is.null(win)) return(tibble(
                window_cutoff    = cut,
                peak_ttf         = NA_real_,
                peak_fitted_score = NA_real_
              ))
              best_row <- win %>%
                filter(!is.na(fitted_score)) %>%
                slice_max(fitted_score, n = 1, with_ties = FALSE)
              tibble(
                window_cutoff     = cut,
                peak_ttf          = best_row$ttf,
                peak_fitted_score = best_row$fitted_score
              )
            }
          )
        }
    )
  )

Unnesting

# Unnest the peak summaries and build a per-sow table
peak_table <- nested_df_of_selected %>%
  select(sow, peak_summary) %>%
  unnest(peak_summary)

# Quick look at the table
peak_table %>%
  gt(groupname_col = "sow") %>%
  tab_header(
    title    = "Rolling Window Peak Anomaly Score by Sow",
    subtitle = "window_cutoff = upper TTF bound of the window; peak_ttf = TTF at highest fitted score"
  ) %>%
  fmt_number(
    columns  = c(peak_ttf, peak_fitted_score),
    decimals = 3
  ) %>%
  cols_label(
    window_cutoff     = "Window Cutoff (TTF)",
    peak_ttf          = "Peak TTF",
    peak_fitted_score = "Peak Fitted Score"
  )

Rolling Window Peak Anomaly Score by Sow
window_cutoff = upper TTF bound of the window; peak_ttf = TTF at highest fitted score
Window Cutoff (TTF)	Peak TTF	Peak Fitted Score
2
-49	−86.382	0.518
-48	−86.382	0.514
-47	−86.382	0.511
-46	−86.382	0.507
-45	−86.382	0.504
-44	−86.382	0.500
-43	−86.382	0.497
-42	−86.382	0.495
-41	−86.382	0.493
-40	−86.382	0.490
-39	−86.382	0.489
-38	−86.382	0.487
-37	−86.382	0.485
-36	−86.382	0.483
-35	−86.382	0.482
-34	−86.382	0.481
-33	−86.382	0.480
-32	−86.382	0.479
-31	−86.382	0.478
-30	−86.382	0.477
-29	−86.382	0.476
-28	−86.382	0.476
-27	−86.382	0.475
-26	−26.086	0.476
-25	−25.085	0.487
-24	−86.382	0.474
-23	−86.382	0.474
-22	−86.382	0.474
-21	−86.382	0.474
-20	−86.382	0.474
-19	−86.382	0.474
-18	−86.382	0.474
-17	−86.382	0.475
-16	−86.382	0.475
-15	−86.382	0.475
-14	−14.085	0.495
-13	−13.086	0.519
-12	−12.085	0.543
-11	−11.085	0.570
-10	−10.085	0.581
-9	−9.085	0.596
-8	−8.085	0.617
-7	−7.085	0.649
-6	−6.085	0.661
-5	−5.085	0.673
-4	−4.085	0.695
-3	−3.085	0.685
-2	−2.085	0.660
-1	−1.085	0.646
0	−0.085	0.623
1	0.915	0.600
2	1.915	0.574
3	2.915	0.541
4	−4.419	0.522
5	−7.085	0.515
6	−8.418	0.512
7	−9.252	0.511
8	−10.085	0.510
9	−11.252	0.510
10	−12.252	0.510
11	−13.252	0.512
12	−12.919	0.513
13	−12.252	0.513
14	−11.752	0.512
15	−10.919	0.511
16	−10.252	0.510
17	−9.919	0.509
18	−9.919	0.509
19	−9.919	0.509
20	−9.919	0.509
21	−9.919	0.509
22	−9.919	0.509
23	−9.919	0.509
24	−9.919	0.509
25	−9.919	0.509
26	−9.919	0.509
27	−9.919	0.509
28	−9.919	0.509
29	−9.919	0.509
30	−9.919	0.509
31	−9.919	0.509
32	−9.919	0.509
33	−9.919	0.509
34	−9.919	0.509
35	−9.919	0.509
36	−9.919	0.509
37	−9.919	0.509
38	−9.919	0.509
39	−9.919	0.509
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41	−9.919	0.509
42	−9.919	0.509
43	−9.919	0.509
44	−9.919	0.509
45	−9.919	0.509
46	−9.919	0.509
47	−9.919	0.509
48	−9.919	0.509
49	−9.919	0.509
50	−9.919	0.509
51	−9.919	0.509
52	−9.919	0.509
53	−9.919	0.509
54	−9.919	0.509
55	−9.919	0.509
56	−9.919	0.509
57	−9.919	0.509
58	−9.919	0.509
59	−9.919	0.509
60	−9.919	0.509
61	−9.919	0.509
62	−9.919	0.509
63	−9.919	0.509
64	−9.919	0.509
65	−9.919	0.509
66	−9.919	0.509
67	−9.919	0.509
68	−9.919	0.509
69	−9.919	0.509
70	−9.919	0.509
71	−9.919	0.509
72	−9.919	0.509
73	−9.919	0.509
74	−9.919	0.509
75	−9.919	0.509
76	−9.919	0.509
77	−9.919	0.509
78	−9.919	0.509
79	−9.919	0.509
80	−9.919	0.509
81	−9.919	0.509
82	−9.919	0.509
83	−9.919	0.509
84	−9.919	0.509
85	−9.919	0.509
86	−9.919	0.509
87	−9.919	0.509
88	−9.919	0.509
89	−9.919	0.509
90	−9.919	0.509
91	−9.919	0.509
92	−9.919	0.509
93	−9.919	0.509
94	−9.919	0.509
95	−9.919	0.509
96	−9.919	0.509
97	−9.919	0.509
98	−9.919	0.509
99	−9.919	0.509
100	−9.919	0.509
101	−9.919	0.509
102	−9.919	0.509
103	−9.919	0.509
104	−9.919	0.509
105	−9.919	0.509
106	−9.919	0.509
107	−9.919	0.509
108	−9.919	0.509
109	−9.919	0.509
110	−9.919	0.509
111	−9.919	0.509
112	−9.919	0.509
113	−9.919	0.509
114	−9.919	0.509
115	−9.919	0.509
116	−9.919	0.509
117	−9.919	0.509
118	−9.919	0.509
119	−9.919	0.509
120	−9.919	0.509
4
-49	−58.413	0.435
-48	−59.413	0.434
-47	−62.579	0.434
-46	−62.746	0.435
-45	−62.079	0.435
-44	−62.079	0.436
-43	−61.746	0.436
-42	−61.746	0.435
-41	−61.913	0.435
-40	−64.246	0.434
-39	−39.079	0.437
-38	−38.080	0.450
-37	−37.079	0.450
-36	−36.079	0.467
-35	−35.080	0.474
-34	−34.080	0.478
-33	−33.079	0.484
-32	−32.079	0.494
-31	−31.079	0.480
-30	−30.079	0.502
-29	−29.079	0.542
-28	−28.080	0.565
-27	−27.080	0.580
-26	−26.079	0.583
-25	−25.080	0.572
-24	−24.079	0.559
-23	−23.079	0.545
-22	−22.080	0.519
-21	−21.080	0.492
-20	−24.913	0.472
-19	−30.079	0.467
-18	−31.913	0.465
-17	−31.246	0.466
-16	−33.746	0.464
-15	−36.079	0.464
-14	−36.413	0.464
-13	−35.080	0.464
-12	−32.913	0.464
-11	−33.413	0.464
-10	−30.413	0.464
-9	−23.746	0.467
-8	−20.913	0.468
-7	−7.079	0.481
-6	−6.079	0.511
-5	−5.079	0.526
-4	−4.079	0.543
-3	−3.079	0.549
-2	−2.080	0.539
-1	−1.080	0.523
0	−0.079	0.519
1	0.920	0.509
2	1.921	0.491
3	−11.746	0.482
4	−16.246	0.479
5	−19.579	0.479
6	−21.913	0.479
7	−22.913	0.480
8	−22.413	0.480
9	−22.080	0.481
10	−21.746	0.481
11	−21.246	0.481
12	−20.746	0.481
13	−20.079	0.480
14	−19.746	0.480
15	−19.413	0.479
16	−19.246	0.480
17	−19.079	0.478
18	−19.579	0.477
19	−19.913	0.478
20	−19.913	0.478
21	−20.913	0.478
22	−22.246	0.478
23	−22.579	0.479
24	−23.246	0.479
25	−23.579	0.480
26	−23.246	0.480
27	−23.079	0.480
28	−22.913	0.480
29	−22.579	0.481
30	−22.413	0.481
31	−22.080	0.481
32	−21.746	0.481
33	−21.413	0.481
34	−20.913	0.481
35	−20.580	0.480
36	−20.246	0.480
37	−19.746	0.480
38	−19.413	0.480
39	−19.079	0.479
40	−18.746	0.479
41	−18.580	0.479
42	−18.246	0.478
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44	−18.079	0.477
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46	−18.079	0.476
47	−18.413	0.476
48	−18.580	0.476
49	−19.079	0.475
50	−19.746	0.475
51	−20.580	0.475
52	−21.246	0.474
53	−22.246	0.474
54	−22.913	0.474
55	−23.579	0.474
56	−24.079	0.474
57	−24.246	0.474
58	−24.413	0.474
59	−24.579	0.474
60	−24.413	0.474
61	−24.246	0.474
62	−24.079	0.474
63	−23.913	0.474
64	−23.579	0.475
65	−23.413	0.475
66	−23.079	0.475
67	−22.746	0.474
68	−22.746	0.474
69	−22.746	0.474
70	−22.746	0.474
71	−22.746	0.474
72	−22.746	0.474
73	−22.746	0.474
74	−22.746	0.474
75	−22.746	0.474
76	−22.746	0.474
77	−22.746	0.474
78	−22.746	0.474
79	−22.746	0.474
80	−22.746	0.474
81	−22.746	0.474
82	−22.746	0.474
83	−22.746	0.474
84	−22.746	0.474
85	−22.746	0.474
86	−22.746	0.474
87	−22.746	0.474
88	−22.746	0.474
89	−22.746	0.474
90	−22.746	0.474
91	−22.746	0.474
92	−22.746	0.474
93	−22.746	0.474
94	−22.746	0.474
95	−22.746	0.474
96	−22.746	0.474
97	−22.746	0.474
98	−22.746	0.474
99	−22.746	0.474
100	−22.746	0.474
101	−22.746	0.474
102	−22.746	0.474
103	−22.746	0.474
104	−22.746	0.474
105	−22.746	0.474
106	−22.746	0.474
107	−22.746	0.474
108	−22.746	0.474
109	−22.746	0.474
110	−22.746	0.474
111	−22.746	0.474
112	−22.746	0.474
113	−22.746	0.474
114	−22.746	0.474
115	−22.746	0.474
116	−22.746	0.474
117	−22.746	0.474
118	−22.746	0.474
119	−22.746	0.474
120	−22.746	0.474
6
-49	−49.165	0.458
-48	−48.165	0.452
-47	−47.165	0.429
-46	−49.665	0.427
-45	−86.498	0.424
-44	−86.998	0.423
-43	−87.665	0.423
-42	−87.998	0.422
-41	−57.165	0.422
-40	−56.665	0.422
-39	−55.998	0.422
-38	−55.665	0.423
-37	−55.498	0.423
-36	−55.332	0.423
-35	−55.332	0.423
-34	−55.498	0.423
-33	−57.332	0.423
-32	−57.999	0.424
-31	−57.832	0.424
-30	−57.665	0.424
-29	−29.165	0.449
-28	−28.165	0.474
-27	−27.165	0.485
-26	−26.165	0.504
-25	−25.165	0.530
-24	−24.165	0.556
-23	−23.165	0.580
-22	−22.165	0.605
-21	−21.165	0.614
-20	−20.165	0.606
-19	−19.165	0.609
-18	−18.165	0.594
-17	−17.165	0.565
-16	−16.165	0.557
-15	−15.165	0.547
-14	−14.165	0.549
-13	−13.165	0.525
-12	−12.165	0.517
-11	−11.165	0.509
-10	−10.165	0.491
-9	−14.832	0.482
-8	−15.165	0.481
-7	−13.165	0.484
-6	−12.165	0.486
-5	−5.332	0.493
-4	−4.165	0.497
-3	−3.165	0.512
-2	−2.165	0.525
-1	−1.165	0.522
0	−0.165	0.515
1	0.835	0.511
2	−1.665	0.503
3	−6.665	0.498
4	−9.332	0.494
5	−10.998	0.492
6	−12.998	0.490
7	−12.831	0.490
8	−13.498	0.490
9	−14.998	0.490
10	−14.498	0.490
11	−15.165	0.490
12	−16.832	0.491
13	−16.498	0.491
14	−15.998	0.491
15	−15.498	0.492
16	−14.998	0.492
17	−14.165	0.491
18	−13.998	0.492
19	−13.331	0.491
20	−13.165	0.491
21	−13.165	0.492
22	−12.831	0.491
23	−12.998	0.492
24	−12.998	0.492
25	−13.832	0.491
26	−13.832	0.491
27	−14.832	0.491
28	−15.332	0.491
29	−15.665	0.491
30	−15.498	0.492
31	−15.498	0.492
32	−15.165	0.492
33	−14.832	0.492
34	−14.498	0.493
35	−14.165	0.493
36	−13.665	0.493
37	−13.331	0.493
38	−12.998	0.493
39	−12.831	0.493
40	−12.498	0.493
41	−12.498	0.492
42	−12.332	0.492
43	−11.999	0.492
44	−11.665	0.492
45	−11.332	0.492
46	−10.998	0.492
47	−10.665	0.491
48	−10.332	0.491
49	−9.998	0.491
50	−9.665	0.490
51	−9.498	0.490
52	−9.165	0.490
53	−8.998	0.489
54	−8.998	0.489
55	−8.832	0.488
56	−8.832	0.488
57	−8.998	0.487
58	−9.165	0.486
59	−9.498	0.486
60	−9.998	0.485
61	−10.665	0.485
62	−11.332	0.484
63	−12.165	0.484
64	−12.998	0.484
65	−13.665	0.484
66	−14.165	0.484
67	−14.498	0.484
68	−14.665	0.484
69	−14.832	0.484
70	−14.832	0.484
71	−14.665	0.484
72	−14.498	0.484
73	−14.332	0.484
74	−14.165	0.484
75	−14.165	0.484
76	−14.165	0.484
77	−14.165	0.484
78	−14.165	0.484
79	−14.165	0.484
80	−14.165	0.484
81	−14.165	0.484
82	−14.165	0.484
83	−14.165	0.484
84	−14.165	0.484
85	−14.165	0.484
86	−14.165	0.484
87	−14.165	0.484
88	−14.165	0.484
89	−14.165	0.484
90	−14.165	0.484
91	−14.165	0.484
92	−14.165	0.484
93	−14.165	0.484
94	−14.165	0.484
95	−14.165	0.484
96	−14.165	0.484
97	−14.165	0.484
98	−14.165	0.484
99	−14.165	0.484
100	−14.165	0.484
101	−14.165	0.484
102	−14.165	0.484
103	−14.165	0.484
104	−14.165	0.484
105	−14.165	0.484
106	−14.165	0.484
107	−14.165	0.484
108	−14.165	0.484
109	−14.165	0.484
110	−14.165	0.484
111	−14.165	0.484
112	−14.165	0.484
113	−14.165	0.484
114	−14.165	0.484
115	−14.165	0.484
116	−14.165	0.484
117	−14.165	0.484
118	−14.165	0.484
119	−14.165	0.484
120	−14.165	0.484
8
-49	−90.367	0.429
-48	−90.367	0.427
-47	−90.534	0.426
-46	−90.700	0.424
-45	−91.367	0.422
-44	−64.700	0.422
-43	−66.033	0.422
-42	−66.867	0.423
-41	−67.034	0.424
-40	−66.700	0.424
-39	−66.367	0.424
-38	−65.867	0.424
-37	−37.034	0.444
-36	−36.034	0.462
-35	−35.034	0.464
-34	−34.034	0.461
-33	−33.033	0.484
-32	−32.033	0.491
-31	−31.034	0.525
-30	−30.034	0.580
-29	−29.034	0.629
-28	−28.034	0.654
-27	−27.034	0.664
-26	−26.033	0.643
-25	−25.034	0.612
-24	−24.034	0.607
-23	−23.034	0.581
-22	−22.034	0.574
-21	−21.034	0.554
-20	−20.033	0.543
-19	−19.033	0.515
-18	−18.034	0.498
-17	−21.867	0.482
-16	−24.534	0.478
-15	−24.034	0.479
-14	−21.034	0.484
-13	−13.033	0.495
-12	−15.034	0.493
-11	−11.034	0.504
-10	−10.034	0.514
-9	−9.033	0.528
-8	−8.034	0.558
-7	−7.033	0.586
-6	−6.033	0.600
-5	−5.034	0.587
-4	−4.034	0.583
-3	−3.034	0.584
-2	−2.034	0.580
-1	−1.034	0.574
0	−0.033	0.568
1	0.966	0.559
2	1.966	0.556
3	2.966	0.562
4	3.966	0.571
5	4.966	0.567
6	5.966	0.566
7	6.967	0.569
8	7.966	0.564
9	8.299	0.548
10	−1.867	0.537
11	−4.867	0.533
12	−4.867	0.533
13	−5.534	0.532
14	−6.367	0.531
15	−7.367	0.531
16	−8.200	0.531
17	−9.534	0.531
18	−10.700	0.531
19	−11.701	0.531
20	−12.533	0.532
21	−12.533	0.532
22	−12.034	0.533
23	−11.534	0.533
24	−11.200	0.533
25	−10.534	0.533
26	−10.200	0.533
27	−10.034	0.533
28	−9.867	0.533
29	−9.867	0.533
30	−9.867	0.533
31	−10.034	0.533
32	−10.200	0.533
33	−10.534	0.533
34	−11.200	0.533
35	−11.867	0.533
36	−12.367	0.533
37	−12.867	0.533
38	−13.033	0.534
39	−13.367	0.534
40	−13.367	0.534
41	−13.533	0.534
42	−13.367	0.535
43	−13.033	0.535
44	−12.700	0.535
45	−12.533	0.535
46	−12.200	0.535
47	−11.701	0.535
48	−11.367	0.534
49	−11.034	0.534
50	−10.534	0.533
51	−10.034	0.532
52	−9.700	0.532
53	−9.200	0.531
54	−8.867	0.530
55	−8.367	0.529
56	−8.034	0.528
57	−7.700	0.527
58	−7.534	0.526
59	−7.367	0.525
60	−7.200	0.524
61	−7.367	0.523
62	−7.534	0.522
63	−8.034	0.521
64	−8.867	0.520
65	−9.700	0.520
66	−10.867	0.519
67	−11.367	0.519
68	−11.367	0.519
69	−11.367	0.519
70	−11.367	0.519
71	−11.367	0.519
72	−11.367	0.519
73	−11.367	0.519
74	−11.367	0.519
75	−11.367	0.519
76	−11.367	0.519
77	−11.367	0.519
78	−11.367	0.519
79	−11.367	0.519
80	−11.367	0.519
81	−11.367	0.519
82	−11.367	0.519
83	−11.367	0.519
84	−11.367	0.519
85	−11.367	0.519
86	−11.367	0.519
87	−11.367	0.519
88	−11.367	0.519
89	−11.367	0.519
90	−11.367	0.519
91	−11.367	0.519
92	−11.367	0.519
93	−11.367	0.519
94	−11.367	0.519
95	−11.367	0.519
96	−11.367	0.519
97	−11.367	0.519
98	−11.367	0.519
99	−11.367	0.519
100	−11.367	0.519
101	−11.367	0.519
102	−11.367	0.519
103	−11.367	0.519
104	−11.367	0.519
105	−11.367	0.519
106	−11.367	0.519
107	−11.367	0.519
108	−11.367	0.519
109	−11.367	0.519
110	−11.367	0.519
111	−11.367	0.519
112	−11.367	0.519
113	−11.367	0.519
114	−11.367	0.519
115	−11.367	0.519
116	−11.367	0.519
117	−11.367	0.519
118	−11.367	0.519
119	−11.367	0.519
120	−11.367	0.519
10
-49	−49.117	0.467
-48	−48.117	0.448
-47	−47.117	0.446
-46	−46.117	0.451
-45	−45.117	0.456
-44	−44.118	0.443
-43	−43.117	0.444
-42	−42.117	0.453
-41	−41.951	0.434
-40	−47.784	0.429
-39	−49.117	0.427
-38	−50.284	0.426
-37	−50.784	0.427
-36	−53.284	0.427
-35	−52.617	0.428
-34	−51.950	0.428
-33	−51.450	0.428
-32	−48.117	0.427
-31	−44.617	0.427
-30	−38.950	0.429
-29	−29.117	0.433
-28	−28.117	0.460
-27	−27.117	0.459
-26	−26.117	0.474
-25	−25.117	0.490
-24	−24.117	0.500
-23	−23.117	0.495
-22	−22.117	0.511
-21	−21.117	0.558
-20	−20.117	0.573
-19	−19.117	0.595
-18	−18.117	0.606
-17	−17.117	0.607
-16	−16.117	0.598
-15	−15.117	0.598
-14	−14.117	0.602
-13	−13.117	0.587
-12	−12.117	0.570
-11	−11.117	0.555
-10	−10.117	0.546
-9	−9.117	0.532
-8	−8.117	0.537
-7	−7.117	0.533
-6	−6.117	0.535
-5	−5.117	0.522
-4	−4.117	0.543
-3	−3.117	0.539
-2	−2.117	0.548
-1	−1.117	0.559
0	−0.117	0.551
1	0.883	0.540
2	0.050	0.524
3	−8.950	0.513
4	−11.950	0.508
5	−13.617	0.506
6	−14.451	0.506
7	−14.784	0.506
8	−16.284	0.506
9	−17.617	0.506
10	−16.951	0.506
11	−16.951	0.507
12	−17.284	0.507
13	−16.451	0.507
14	−15.784	0.507
15	−15.617	0.507
16	−14.951	0.507
17	−14.617	0.507
18	−14.284	0.507
19	−14.284	0.508
20	−13.951	0.507
21	−13.951	0.507
22	−14.284	0.507
23	−14.451	0.507
24	−15.117	0.507
25	−15.784	0.506
26	−16.617	0.507
27	−17.117	0.507
28	−17.451	0.507
29	−16.951	0.507
30	−16.617	0.507
31	−16.617	0.507
32	−16.117	0.507
33	−15.784	0.507
34	−15.284	0.507
35	−14.784	0.507
36	−14.117	0.507
37	−13.617	0.507
38	−13.117	0.506
39	−12.784	0.506
40	−12.284	0.506
41	−12.117	0.505
42	−11.617	0.505
43	−11.284	0.505
44	−10.951	0.504
45	−10.617	0.504
46	−10.117	0.503
47	−9.784	0.503
48	−9.451	0.503
49	−9.284	0.502
50	−9.117	0.502
51	−9.117	0.501
52	−9.284	0.501
53	−9.451	0.500
54	−9.951	0.500
55	−10.451	0.499
56	−10.951	0.499
57	−11.617	0.499
58	−12.284	0.498
59	−12.950	0.498
60	−13.617	0.498
61	−13.951	0.497
62	−14.284	0.497
63	−14.617	0.497
64	−14.784	0.497
65	−14.784	0.497
66	−14.784	0.497
67	−14.617	0.497
68	−14.451	0.497
69	−14.284	0.497
70	−14.117	0.497
71	−13.784	0.497
72	−13.617	0.497
73	−13.284	0.497
74	−12.950	0.497
75	−12.617	0.497
76	−12.284	0.496
77	−12.117	0.497
78	−12.117	0.497
79	−12.117	0.497
80	−12.117	0.497
81	−12.117	0.497
82	−12.117	0.497
83	−12.117	0.497
84	−12.117	0.497
85	−12.117	0.497
86	−12.117	0.497
87	−12.117	0.497
88	−12.117	0.497
89	−12.117	0.497
90	−12.117	0.497
91	−12.117	0.497
92	−12.117	0.497
93	−12.117	0.497
94	−12.117	0.497
95	−12.117	0.497
96	−12.117	0.497
97	−12.117	0.497
98	−12.117	0.497
99	−12.117	0.497
100	−12.117	0.497
101	−12.117	0.497
102	−12.117	0.497
103	−12.117	0.497
104	−12.117	0.497
105	−12.117	0.497
106	−12.117	0.497
107	−12.117	0.497
108	−12.117	0.497
109	−12.117	0.497
110	−12.117	0.497
111	−12.117	0.497
112	−12.117	0.497
113	−12.117	0.497
114	−12.117	0.497
115	−12.117	0.497
116	−12.117	0.497
117	−12.117	0.497
118	−12.117	0.497
119	−12.117	0.497
120	−12.117	0.497
12
-49	−93.333	0.420
-48	−93.333	0.419
-47	−64.499	0.418
-46	−63.666	0.418
-45	−63.333	0.418
-44	−58.999	0.418
-43	−43.166	0.430
-42	−42.166	0.434
-41	−41.166	0.451
-40	−40.166	0.454
-39	−39.166	0.460
-38	−38.166	0.461
-37	−37.166	0.468
-36	−36.166	0.472
-35	−35.166	0.475
-34	−34.166	0.523
-33	−33.166	0.557
-32	−32.166	0.584
-31	−31.166	0.598
-30	−30.166	0.607
-29	−29.166	0.597
-28	−28.166	0.569
-27	−27.166	0.543
-26	−26.166	0.515
-25	−25.166	0.490
-24	−27.833	0.470
-23	−34.666	0.462
-22	−36.666	0.460
-21	−38.499	0.459
-20	−39.333	0.458
-19	−40.000	0.459
-18	−42.166	0.459
-17	−37.999	0.459
-16	−36.166	0.459
-15	−29.833	0.462
-14	−22.000	0.468
-13	−13.166	0.480
-12	−12.166	0.506
-11	−11.166	0.529
-10	−10.166	0.543
-9	−9.166	0.544
-8	−8.166	0.557
-7	−7.166	0.557
-6	−6.166	0.545
-5	−5.166	0.526
-4	−4.166	0.521
-3	−3.166	0.513
-2	−2.166	0.510
-1	−1.166	0.500
0	−5.499	0.493
1	−13.333	0.488
2	−17.000	0.485
3	−17.666	0.485
4	−19.333	0.484
5	−18.499	0.485
6	−19.666	0.485
7	−21.666	0.485
8	−21.833	0.485
9	−22.500	0.486
10	−22.000	0.486
11	−21.499	0.486
12	−20.833	0.486
13	−20.499	0.486
14	−20.166	0.486
15	−19.833	0.487
16	−19.666	0.487
17	−19.499	0.487
18	−19.333	0.487
19	−19.333	0.487
20	−19.333	0.487
21	−19.333	0.487
22	−19.666	0.487
23	−20.000	0.487
24	−20.833	0.487
25	−21.333	0.487
26	−22.000	0.487
27	−22.333	0.488
28	−22.833	0.488
29	−23.000	0.488
30	−23.000	0.489
31	−23.000	0.489
32	−22.833	0.489
33	−22.666	0.489
34	−22.500	0.490
35	−22.000	0.490
36	−21.499	0.490
37	−21.166	0.489
38	−20.833	0.489
39	−20.333	0.489
40	−20.000	0.488
41	−19.666	0.488
42	−19.333	0.487
43	−18.999	0.487
44	−18.833	0.486
45	−18.666	0.485
46	−18.666	0.484
47	−18.666	0.484
48	−18.833	0.483
49	−19.166	0.482
50	−19.833	0.481
51	−20.833	0.480
52	−22.166	0.480
53	−23.333	0.480
54	−24.666	0.480
55	−25.499	0.480
56	−25.999	0.480
57	−26.333	0.480
58	−26.500	0.480
59	−26.666	0.481
60	−26.500	0.481
61	−26.333	0.481
62	−26.166	0.481
63	−25.999	0.482
64	−25.999	0.482
65	−25.999	0.482
66	−25.999	0.482
67	−25.999	0.482
68	−25.999	0.482
69	−25.999	0.482
70	−25.999	0.482
71	−25.999	0.482
72	−25.999	0.482
73	−25.999	0.482
74	−25.999	0.482
75	−25.999	0.482
76	−25.999	0.482
77	−25.999	0.482
78	−25.999	0.482
79	−25.999	0.482
80	−25.999	0.482
81	−25.999	0.482
82	−25.999	0.482
83	−25.999	0.482
84	−25.999	0.482
85	−25.999	0.482
86	−25.999	0.482
87	−25.999	0.482
88	−25.999	0.482
89	−25.999	0.482
90	−25.999	0.482
91	−25.999	0.482
92	−25.999	0.482
93	−25.999	0.482
94	−25.999	0.482
95	−25.999	0.482
96	−25.999	0.482
97	−25.999	0.482
98	−25.999	0.482
99	−25.999	0.482
100	−25.999	0.482
101	−25.999	0.482
102	−25.999	0.482
103	−25.999	0.482
104	−25.999	0.482
105	−25.999	0.482
106	−25.999	0.482
107	−25.999	0.482
108	−25.999	0.482
109	−25.999	0.482
110	−25.999	0.482
111	−25.999	0.482
112	−25.999	0.482
113	−25.999	0.482
114	−25.999	0.482
115	−25.999	0.482
116	−25.999	0.482
117	−25.999	0.482
118	−25.999	0.482
119	−25.999	0.482
120	−25.999	0.482
14
-49	−74.440	0.487
-48	−74.114	0.486
-47	−73.783	0.486
-46	−46.107	0.489
-45	−45.116	0.503
-44	−44.107	0.510
-43	−43.117	0.510
-42	−42.107	0.505
-41	−41.101	0.508
-40	−40.119	0.529
-39	−39.109	0.534
-38	−38.106	0.535
-37	−37.099	0.544
-36	−36.107	0.576
-35	−35.106	0.587
-34	−34.107	0.595
-33	−33.107	0.600
-32	−32.106	0.604
-31	−31.102	0.618
-30	−30.117	0.614
-29	−29.096	0.611
-28	−28.103	0.604
-27	−27.104	0.587
-26	−26.103	0.564
-25	−28.273	0.547
-24	−32.106	0.539
-23	−33.273	0.536
-22	−33.440	0.536
-21	−33.935	0.535
-20	−34.440	0.536
-19	−35.106	0.536
-18	−34.107	0.536
-17	−33.107	0.537
-16	−33.440	0.537
-15	−33.107	0.537
-14	−33.273	0.538
-13	−32.940	0.538
-12	−32.773	0.537
-11	−32.940	0.538
-10	−33.107	0.538
-9	−33.107	0.539
-8	−33.107	0.539
-7	−33.107	0.539
-6	−33.440	0.539
-5	−33.607	0.539
-4	−33.607	0.539
-3	−33.440	0.539
-2	−33.607	0.539
-1	−33.273	0.539
0	−33.107	0.539
1	−32.606	0.538
2	−32.273	0.538
3	−31.766	0.537
4	−31.270	0.536
5	−30.771	0.535
6	−30.439	0.534
7	−30.117	0.533
8	−29.940	0.532
9	8.902	0.531
10	9.895	0.539
11	10.894	0.541
12	11.898	0.544
13	12.890	0.549
14	13.896	0.553
15	14.894	0.554
16	15.885	0.552
17	16.892	0.550
18	17.889	0.547
19	18.892	0.544
20	19.884	0.539
21	20.892	0.533
22	21.894	0.530
23	−32.773	0.527
24	23.879	0.535
25	24.894	0.531
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16
-49	−73.706	0.497
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18
-49	−89.985	0.408
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22
-49	−113.683	0.447
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24
-49	−49.094	0.428
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8	−6.761	0.496
9	−8.761	0.493
10	−8.261	0.493
11	−8.928	0.493
12	−9.594	0.492
13	−11.428	0.492
14	−13.094	0.492
15	−13.594	0.493
16	−14.761	0.494
17	−14.261	0.495
18	−13.761	0.495
19	−13.428	0.496
20	−12.928	0.496
21	−12.428	0.495
22	−12.095	0.496
23	−11.928	0.496
24	−11.428	0.494
25	−11.428	0.494
26	−11.595	0.494
27	−11.595	0.494
28	−11.928	0.494
29	−12.595	0.494
30	−13.428	0.494
31	−13.594	0.495
32	−14.094	0.495
33	−14.428	0.496
34	−14.594	0.496
35	−14.594	0.497
36	−14.594	0.497
37	−14.594	0.498
38	−14.428	0.498
39	−14.261	0.498
40	−14.094	0.499
41	−13.761	0.499
42	−13.428	0.498
43	−13.094	0.498
44	−12.595	0.498
45	−12.261	0.498
46	−11.761	0.497
47	−11.428	0.496
48	−10.928	0.496
49	−10.594	0.495
50	−10.261	0.494
51	−10.094	0.493
52	−9.761	0.492
53	−9.594	0.491
54	−9.428	0.490
55	−9.261	0.489
56	−9.261	0.488
57	−9.428	0.487
58	−9.761	0.486
59	−10.094	0.485
60	−10.761	0.484
61	−11.595	0.483
62	−12.761	0.483
63	−13.761	0.482
64	−14.761	0.482
65	−15.595	0.482
66	−16.261	0.482
67	−16.594	0.482
68	−16.761	0.482
69	−16.761	0.483
70	−16.761	0.483
71	−16.594	0.483
72	−16.428	0.483
73	−16.261	0.483
74	−15.928	0.483
75	−15.928	0.483
76	−15.928	0.483
77	−15.928	0.483
78	−15.928	0.483
79	−15.928	0.483
80	−15.928	0.483
81	−15.928	0.483
82	−15.928	0.483
83	−15.928	0.483
84	−15.928	0.483
85	−15.928	0.483
86	−15.928	0.483
87	−15.928	0.483
88	−15.928	0.483
89	−15.928	0.483
90	−15.928	0.483
91	−15.928	0.483
92	−15.928	0.483
93	−15.928	0.483
94	−15.928	0.483
95	−15.928	0.483
96	−15.928	0.483
97	−15.928	0.483
98	−15.928	0.483
99	−15.928	0.483
100	−15.928	0.483
101	−15.928	0.483
102	−15.928	0.483
103	−15.928	0.483
104	−15.928	0.483
105	−15.928	0.483
106	−15.928	0.483
107	−15.928	0.483
108	−15.928	0.483
109	−15.928	0.483
110	−15.928	0.483
111	−15.928	0.483
112	−15.928	0.483
113	−15.928	0.483
114	−15.928	0.483
115	−15.928	0.483
116	−15.928	0.483
117	−15.928	0.483
118	−15.928	0.483
119	−15.928	0.483
120	−15.928	0.483
26
-49	−93.507	0.449
-48	−93.507	0.447
-47	−93.507	0.443
-46	−46.060	0.441
-45	−45.060	0.473
-44	−44.060	0.460
-43	−43.060	0.508
-42	−42.060	0.514
-41	−41.060	0.484
-40	−40.060	0.478
-39	−39.060	0.477
-38	−38.060	0.453
-37	−37.060	0.466
-36	−36.060	0.450
-35	−35.060	0.449
-34	−34.060	0.422
-33	−70.894	0.410
-32	−70.560	0.409
-31	−70.394	0.408
-30	−69.893	0.407
-29	−69.727	0.405
-28	−44.560	0.406
-27	−40.393	0.406
-26	−42.893	0.407
-25	−42.560	0.407
-24	−41.393	0.407
-23	−40.727	0.407
-22	−36.393	0.407
-21	−21.060	0.421
-20	−20.060	0.423
-19	−19.060	0.431
-18	−18.060	0.452
-17	−17.060	0.471
-16	−16.060	0.481
-15	−15.060	0.544
-14	−14.060	0.600
-13	−13.060	0.649
-12	−12.060	0.679
-11	−11.060	0.717
-10	−10.060	0.714
-9	−9.060	0.702
-8	−8.060	0.687
-7	−7.060	0.669
-6	−6.060	0.648
-5	−5.060	0.632
-4	−4.060	0.649
-3	−3.060	0.622
-2	−2.060	0.619
-1	−1.060	0.606
0	−0.060	0.589
1	0.940	0.580
2	1.940	0.554
3	−1.393	0.535
4	−6.393	0.524
5	−8.393	0.518
6	−9.727	0.516
7	−9.727	0.515
8	−10.227	0.515
9	−10.560	0.515
10	−11.394	0.515
11	−12.393	0.515
12	−12.560	0.515
13	−12.726	0.515
14	−12.893	0.516
15	−12.393	0.516
16	−12.060	0.517
17	−11.727	0.517
18	−11.060	0.517
19	−10.894	0.517
20	−10.560	0.517
21	−10.560	0.517
22	−10.560	0.517
23	−10.560	0.517
24	−10.727	0.518
25	−10.727	0.518
26	−10.727	0.518
27	−10.894	0.518
28	−10.894	0.519
29	−10.894	0.519
30	−11.060	0.519
31	−11.727	0.519
32	−11.893	0.519
33	−11.893	0.519
34	−11.893	0.519
35	−11.727	0.519
36	−11.560	0.519
37	−11.227	0.519
38	−10.894	0.519
39	−10.560	0.519
40	−10.227	0.518
41	−9.893	0.518
42	−9.393	0.517
43	−9.060	0.516
44	−8.560	0.516
45	−8.393	0.514
46	−8.060	0.514
47	−7.893	0.512
48	−7.727	0.511
49	−7.727	0.510
50	−7.893	0.509
51	−8.227	0.508
52	−8.560	0.507
53	−9.227	0.506
54	−9.893	0.505
55	−10.727	0.505
56	−11.394	0.505
57	−11.893	0.504
58	−12.393	0.504
59	−12.726	0.504
60	−12.893	0.504
61	−13.060	0.504
62	−13.060	0.504
63	−13.060	0.504
64	−12.893	0.504
65	−12.726	0.504
66	−12.560	0.504
67	−12.226	0.503
68	−12.060	0.503
69	−11.727	0.503
70	−11.394	0.502
71	−11.060	0.502
72	−10.727	0.502
73	−10.393	0.501
74	−10.060	0.501
75	−9.560	0.500
76	−9.227	0.500
77	−8.893	0.499
78	−8.727	0.498
79	−8.393	0.498
80	−8.060	0.497
81	−7.727	0.496
82	−7.560	0.496
83	−7.393	0.495
84	−7.060	0.494
85	−6.893	0.493
86	−6.893	0.493
87	−6.727	0.492
88	−6.727	0.492
89	−6.727	0.492
90	−6.727	0.492
91	−6.727	0.492
92	−6.727	0.492
93	−6.727	0.492
94	−6.727	0.492
95	−6.727	0.492
96	−6.727	0.492
97	−6.727	0.492
98	−6.727	0.492
99	−6.727	0.492
100	−6.727	0.492
101	−6.727	0.492
102	−6.727	0.492
103	−6.727	0.492
104	−6.727	0.492
105	−6.727	0.492
106	−6.727	0.492
107	−6.727	0.492
108	−6.727	0.492
109	−6.727	0.492
110	−6.727	0.492
111	−6.727	0.492
112	−6.727	0.492
113	−6.727	0.492
114	−6.727	0.492
115	−6.727	0.492
116	−6.727	0.492
117	−6.727	0.492
118	−6.727	0.492
119	−6.727	0.492
120	−6.727	0.492
30
-49	−65.541	0.412
-48	−65.541	0.412
-47	−65.707	0.411
-46	−65.707	0.411
-45	−66.541	0.411
-44	−66.707	0.411
-43	−66.374	0.412
-42	−66.374	0.411
-41	−66.541	0.411
-40	−40.041	0.435
-39	−39.041	0.458
-38	−38.040	0.477
-37	−37.041	0.471
-36	−36.040	0.457
-35	−35.040	0.484
-34	−34.041	0.483
-33	−33.041	0.461
-32	−32.041	0.464
-31	−31.041	0.457
-30	−30.041	0.447
-29	−29.040	0.473
-28	−28.041	0.516
-27	−27.041	0.561
-26	−26.041	0.595
-25	−25.040	0.606
-24	−24.041	0.627
-23	−23.040	0.637
-22	−22.040	0.644
-21	−21.041	0.643
-20	−20.041	0.650
-19	−19.041	0.670
-18	−18.041	0.668
-17	−17.041	0.689
-16	−16.040	0.707
-15	−15.040	0.712
-14	−14.041	0.724
-13	−13.041	0.727
-12	−12.040	0.738
-11	−11.041	0.744
-10	−10.040	0.751
-9	−9.040	0.753
-8	−8.041	0.763
-7	−7.041	0.739
-6	−6.041	0.711
-5	−5.040	0.693
-4	−4.041	0.669
-3	−3.040	0.668
-2	−2.040	0.664
-1	−1.041	0.659
0	−0.041	0.652
1	0.959	0.648
2	1.959	0.645
3	2.959	0.645
4	3.960	0.646
5	2.793	0.632
6	−6.374	0.610
7	−8.374	0.604
8	−10.374	0.598
9	−12.040	0.595
10	−11.041	0.597
11	−10.207	0.599
12	−9.207	0.600
13	−8.540	0.602
14	−7.541	0.603
15	−6.707	0.605
16	−5.707	0.606
17	−3.874	0.609
18	−2.707	0.610
19	−1.041	0.613
20	0.293	0.614
21	−1.541	0.612
22	−2.874	0.611
23	−4.541	0.611
24	−5.374	0.611
25	−6.707	0.612
26	−7.374	0.612
27	−7.874	0.613
28	−7.707	0.614
29	−7.374	0.615
30	−7.041	0.615
31	−6.874	0.615
32	−6.707	0.616
33	−6.541	0.616
34	−6.374	0.616
35	−6.374	0.616
36	−6.374	0.616
37	−6.374	0.616
38	−6.374	0.616
39	−6.541	0.616
40	−6.874	0.615
41	−7.207	0.615
42	−7.541	0.615
43	−8.374	0.615
44	−8.874	0.615
45	−9.540	0.616
46	−9.874	0.615
47	−9.707	0.616
48	−9.707	0.617
49	−9.374	0.617
50	−9.207	0.617
51	−8.874	0.617
52	−8.374	0.617
53	−7.874	0.616
54	−7.541	0.615
55	−7.041	0.614
56	−6.707	0.613
57	−6.207	0.612
58	−5.874	0.611
59	−5.374	0.609
60	−4.874	0.607
61	−4.541	0.606
62	−4.374	0.604
63	−4.041	0.602
64	−3.874	0.600
65	−3.874	0.598
66	−4.041	0.596
67	−4.541	0.594
68	67.959	0.601
69	68.458	0.606
70	68.458	0.606
71	68.458	0.606
72	68.458	0.606
73	68.458	0.606
74	68.458	0.606
75	68.458	0.606
76	68.458	0.606
77	68.458	0.606
78	68.458	0.606
79	68.458	0.606
80	68.458	0.606
81	68.458	0.606
82	68.458	0.606
83	68.458	0.606
84	68.458	0.606
85	68.458	0.606
86	68.458	0.606
87	68.458	0.606
88	68.458	0.606
89	68.458	0.606
90	68.458	0.606
91	68.458	0.606
92	68.458	0.606
93	68.458	0.606
94	68.458	0.606
95	68.458	0.606
96	68.458	0.606
97	68.458	0.606
98	68.458	0.606
99	68.458	0.606
100	68.458	0.606
101	68.458	0.606
102	68.458	0.606
103	68.458	0.606
104	68.458	0.606
105	68.458	0.606
106	68.458	0.606
107	68.458	0.606
108	68.458	0.606
109	68.458	0.606
110	68.458	0.606
111	68.458	0.606
112	68.458	0.606
113	68.458	0.606
114	68.458	0.606
115	68.458	0.606
116	68.458	0.606
117	68.458	0.606
118	68.458	0.606
119	68.458	0.606
120	68.458	0.606
32
-49	−92.788	0.479
-48	−92.458	0.478
-47	−92.131	0.478
-46	−46.124	0.479
-45	−91.626	0.478
-44	−91.291	0.477
-43	−43.126	0.485
-42	−42.124	0.499
-41	−41.153	0.504
-40	−40.124	0.509
-39	−39.126	0.519
-38	−38.123	0.526
-37	−37.124	0.544
-36	−36.124	0.567
-35	−35.127	0.569
-34	−34.125	0.580
-33	−33.124	0.586
-32	−32.124	0.590
-31	−31.124	0.588
-30	−30.118	0.579
-29	−29.124	0.570
-28	−28.126	0.558
-27	−27.110	0.546
-26	−26.117	0.539
-25	−25.144	0.536
-24	−24.104	0.532
-23	−23.134	0.526
-22	−22.132	0.517
-21	−21.121	0.526
-20	−20.100	0.520
-19	−24.787	0.512
-18	−18.130	0.517
-17	−20.782	0.516
-16	−22.615	0.515
-15	−24.960	0.513
-14	−24.787	0.513
-13	−25.961	0.512
-12	−27.803	0.510
-11	−26.989	0.511
-10	−27.303	0.510
-9	−26.786	0.511
-8	−26.989	0.511
-7	−27.303	0.511
-6	−27.957	0.511
-5	−28.945	0.511
-4	−29.291	0.511
-3	−30.457	0.511
-2	−29.791	0.511
-1	−30.457	0.511
0	−29.124	0.511
1	−28.790	0.511
2	−26.786	0.511
3	−25.961	0.511
4	−26.989	0.512
5	−27.110	0.512
6	−27.303	0.512
7	−26.786	0.512
8	−26.117	0.511
9	−27.110	0.511
10	−28.945	0.511
11	−31.124	0.510
12	11.876	0.514
13	12.876	0.528
14	13.872	0.536
15	14.870	0.546
16	15.873	0.557
17	16.880	0.567
18	17.874	0.576
19	18.865	0.575
20	19.876	0.579
21	20.876	0.589
22	21.876	0.598
23	22.873	0.596
24	23.870	0.595
25	24.880	0.596
26	25.857	0.597
27	25.857	0.597
28	25.857	0.597
29	25.857	0.597
30	25.857	0.597
31	25.857	0.597
32	25.857	0.597
33	25.857	0.597
34	25.857	0.597
35	25.857	0.597
36	25.857	0.597
37	25.857	0.597
38	25.857	0.597
39	25.857	0.597
40	25.857	0.597
41	25.857	0.597
42	25.857	0.597
43	25.857	0.597
44	25.857	0.597
45	25.857	0.597
46	25.857	0.597
47	25.857	0.597
48	25.857	0.597
49	25.857	0.597
50	25.857	0.597
51	25.857	0.597
52	25.857	0.597
53	25.857	0.597
54	25.857	0.597
55	25.857	0.597
56	25.857	0.597
57	25.857	0.597
58	25.857	0.597
59	25.857	0.597
60	25.857	0.597
61	25.857	0.597
62	25.857	0.597
63	25.857	0.597
64	25.857	0.597
65	25.857	0.597
66	25.857	0.597
67	25.857	0.597
68	25.857	0.597
69	25.857	0.597
70	25.857	0.597
71	25.857	0.597
72	25.857	0.597
73	25.857	0.597
74	25.857	0.597
75	25.857	0.597
76	25.857	0.597
77	25.857	0.597
78	25.857	0.597
79	25.857	0.597
80	25.857	0.597
81	25.857	0.597
82	25.857	0.597
83	25.857	0.597
84	25.857	0.597
85	25.857	0.597
86	25.857	0.597
87	25.857	0.597
88	25.857	0.597
89	25.857	0.597
90	25.857	0.597
91	25.857	0.597
92	25.857	0.597
93	25.857	0.597
94	25.857	0.597
95	25.857	0.597
96	25.857	0.597
97	25.857	0.597
98	25.857	0.597
99	25.857	0.597
100	25.857	0.597
101	25.857	0.597
102	25.857	0.597
103	25.857	0.597
104	25.857	0.597
105	25.857	0.597
106	25.857	0.597
107	25.857	0.597
108	25.857	0.597
109	25.857	0.597
110	25.857	0.597
111	25.857	0.597
112	25.857	0.597
113	25.857	0.597
114	25.857	0.597
115	25.857	0.597
116	25.857	0.597
117	25.857	0.597
118	25.857	0.597
119	25.857	0.597
120	25.857	0.597

# Constrain peak search to pre-farrowing only (ttf <= 0)
# This is the biologically meaningful zone — we only care about
# whether the model identified the right pre-farrowing window
nested_df_of_selected <- nested_df_of_selected %>%
  mutate(
    peak_summary = map2(
      rolling_fitted,
      list(cutoffs),
      ~ {
          map2_dfr(
            .x,
            .y,
            function(win, cut) {
              if (is.null(win)) return(tibble(
                window_cutoff     = cut,
                peak_ttf          = NA_real_,
                peak_fitted_score = NA_real_
              ))
              best_row <- win %>%
                filter(!is.na(fitted_score), ttf <= 0) %>%  # pre-farrowing only
                slice_max(fitted_score, n = 1, with_ties = FALSE)
              
              if (nrow(best_row) == 0) return(tibble(
                window_cutoff     = cut,
                peak_ttf          = NA_real_,
                peak_fitted_score = NA_real_
              ))
              
              tibble(
                window_cutoff     = cut,
                peak_ttf          = best_row$ttf,
                peak_fitted_score = best_row$fitted_score
              )
            }
          )
        }
    )
  )

peak_table <- nested_df_of_selected %>%
  select(sow, peak_summary) %>%
  unnest(peak_summary)