No weights generated after training the model and also it got executed in only few minutes - yolov4

Hello i was training a yolo model for performing ocr detection i created the dataset and also started training my model but it executed for only few minutes and got completed and no weights were generated and i also all of the steps carefully i don't know how to resolve it could anyone help
Here is the youtube link whose code i referred: https://www.youtube.com/watch?v=hTCmL3S4Obw&t=1905s
Here is the code
!darknet/darknet detector train custom_data/labelled_data.data darknet/cfg/yolov2_custom.cfg custom_weight/darknet53.conv.74 -dont_show
yolov2_custom
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BFLOPs
1 conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BFLOPs
2 conv 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BFLOPs
3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BFLOPs
4 res 1 208 x 208 x 64 -> 208 x 208 x 64
5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BFLOPs
6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BFLOPs
7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BFLOPs
8 res 5 104 x 104 x 128 -> 104 x 104 x 128`enter code here`
9 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BFLOPs
10 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BFLOPs
11 res 8 104 x 104 x 128 -> 104 x 104 x 128
12 conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BFLOPs
13 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
14 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
15 res 12 52 x 52 x 256 -> 52 x 52 x 256
16 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
17 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
18 res 15 52 x 52 x 256 -> 52 x 52 x 256
19 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
20 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
21 res 18 52 x 52 x 256 -> 52 x 52 x 256
22 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
23 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
24 res 21 52 x 52 x 256 -> 52 x 52 x 256
25 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
26 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
27 res 24 52 x 52 x 256 -> 52 x 52 x 256
28 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
29 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
30 res 27 52 x 52 x 256 -> 52 x 52 x 256
31 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
32 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
33 res 30 52 x 52 x 256 -> 52 x 52 x 256
34 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
35 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
36 res 33 52 x 52 x 256 -> 52 x 52 x 256
37 conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BFLOPs
38 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
39 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
40 res 37 26 x 26 x 512 -> 26 x 26 x 512
41 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
42 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
43 res 40 26 x 26 x 512 -> 26 x 26 x 512
44 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
45 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
46 res 43 26 x 26 x 512 -> 26 x 26 x 512
47 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
48 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
49 res 46 26 x 26 x 512 -> 26 x 26 x 512
50 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
51 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
52 res 49 26 x 26 x 512 -> 26 x 26 x 512
53 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
54 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
55 res 52 26 x 26 x 512 -> 26 x 26 x 512
56 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
57 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
58 res 55 26 x 26 x 512 -> 26 x 26 x 512
59 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
60 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
61 res 58 26 x 26 x 512 -> 26 x 26 x 512
62 conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BFLOPs
63 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs
64 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
65 res 62 13 x 13 x1024 -> 13 x 13 x1024
66 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs
67 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
68 res 65 13 x 13 x1024 -> 13 x 13 x1024
69 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs
70 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
71 res 68 13 x 13 x1024 -> 13 x 13 x1024
72 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs
73 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
74 res 71 13 x 13 x1024 -> 13 x 13 x1024
75 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs
76 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
77 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs
78 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
79 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs
80 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
81 conv 45 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 45 0.016 BFLOPs
82 yolo
83 route 79
84 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BFLOPs
85 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256
86 route 85 61
87 conv 256 1 x 1 / 1 26 x 26 x 768 -> 26 x 26 x 256 0.266 BFLOPs
88 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
89 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
93 conv 45 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 45 0.031 BFLOPs
94 yolo
95 route 91
96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BFLOPs
97 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
98 route 97 36
99 conv 128 1 x 1 / 1 52 x 52 x 384 -> 52 x 52 x 128 0.266 BFLOPs
100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
105 conv 45 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 45 0.062 BFLOPs
106 yolo
Loading weights from custom_weight/darknet53.conv.74...Done!
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005

Related

Group by sum specific column in R

df <- data.frame(items=sample(LETTERS,replace= T),quantity=sample(1:100,26,replace=FALSE),price=sample(100:1000,26,replace=FALSE))
I want to group_by sum quantity is about 500(ballpark) ,
When count close about 500 put the same group,like below
Any help would be appreciated.
Updated
Because the condition need to change, I reset the threshold to 250,
I summarize to find the max total value for each group, and then,
How could I change the the total of group6 < 200 , into group5.
I think about using ifelse but can't work successfully.
set.seed(123)
df <- data.frame(items=sample(LETTERS,replace= T),quantity=sample(1:100,26,replace=FALSE),price=sample(100:1000,26,replace=FALSE))
df$group=cumsum(c(1,ifelse(diff(cumsum(df$quantity)%% 250) < 0,1,0)))
df$total=ave(df$quantity,df$group,FUN=cumsum)
df %>% group_by(group) %>% summarise(max = max(total, na.rm=TRUE))
# A tibble: 6 × 2
group max
<dbl> <int>
1 1 238
2 2 254
3 3 256
4 4 246
5 5 237
6 6 101
I want get like
> df
items quantity price group total
1 O 36 393 1 36
2 S 78 376 1 114
3 N 81 562 1 195
4 C 43 140 1 238
5 J 76 530 2 76
6 R 15 189 2 91
7 V 32 415 2 123
8 K 7 322 2 130
9 E 9 627 2 139
10 T 41 215 2 180
11 N 74 705 2 254
12 V 23 873 3 23
13 Y 27 846 3 50
14 Z 60 555 3 110
15 E 53 697 3 163
16 S 93 953 3 256
17 Y 86 138 4 86
18 Y 88 258 4 174
19 I 38 851 4 212
20 C 34 308 4 246
21 H 69 473 5 69
22 Z 72 917 5 141
23 G 96 133 5 237
24 J 63 615 5 300
25 I 13 112 5 376
26 S 25 168 5 477
Thank you for any helping all the time.

Base R
set.seed(123)
df <- data.frame(items=sample(LETTERS,replace= T),quantity=sample(1:100,26,replace=FALSE),price=sample(100:1000,26,replace=FALSE))
df$group=cumsum(c(1,ifelse(diff(cumsum(df$quantity)%%500)<0,1,0)))
df$total=ave(df$quantity,df$group,FUN=cumsum)
items quantity price group total
1 O 36 393 1 36
2 S 78 376 1 114
3 N 81 562 1 195
4 C 43 140 1 238
5 J 76 530 1 314
6 R 15 189 1 329
7 V 32 415 1 361
8 K 7 322 1 368
9 E 9 627 1 377
10 T 41 215 1 418
11 N 74 705 1 492
12 V 23 873 2 23
13 Y 27 846 2 50
14 Z 60 555 2 110
15 E 53 697 2 163
16 S 93 953 2 256
17 Y 86 138 2 342
18 Y 88 258 2 430
19 I 38 851 2 468
20 C 34 308 2 502
21 H 69 473 3 69
22 Z 72 917 3 141
23 G 96 133 3 237
24 J 63 615 3 300
25 I 13 112 3 313
26 S 25 168 3 338

You could use Reduce(..., accumulate = TRUE) to find where the first cumulative quantity >= 500.
set.seed(123)
df <- data.frame(items=sample(LETTERS,replace= T),quantity=sample(1:100,26,replace=FALSE),price=sample(100:1000,26,replace=FALSE))
library(dplyr)
df %>%
group_by(group = lag(cumsum(Reduce(\(x, y) {
z <- x + y
if(z < 500) z else 0
}, quantity, accumulate = TRUE) == 0) + 1, default = 1)) %>%
mutate(total = sum(quantity)) %>%
ungroup()
# A tibble: 26 × 5
items quantity price group total
<chr> <int> <int> <dbl> <int>
1 O 36 393 1 515
2 S 78 376 1 515
3 N 81 562 1 515
4 C 43 140 1 515
5 J 76 530 1 515
6 R 15 189 1 515
7 V 32 415 1 515
8 K 7 322 1 515
9 E 9 627 1 515
10 T 41 215 1 515
11 N 74 705 1 515
12 V 23 873 1 515
13 Y 27 846 2 548
14 Z 60 555 2 548
15 E 53 697 2 548
16 S 93 953 2 548
17 Y 86 138 2 548
18 Y 88 258 2 548
19 I 38 851 2 548
20 C 34 308 2 548
21 H 69 473 2 548
22 Z 72 917 3 269
23 G 96 133 3 269
24 J 63 615 3 269
25 I 13 112 3 269
26 S 25 168 3 269

Here is a base R solution. The groups break after the cumulative sum passes a threshold. The output of aggregate shows that all cumulative sums are above thres except for the last one.
set.seed(2022)
df <- data.frame(items=sample(LETTERS,replace= T),
quantity=sample(1:100,26,replace=FALSE),
price=sample(100:1000,26,replace=FALSE))
f <- function(x, thres) {
grp <- integer(length(x))
run <- 0
current_grp <- 0L
for(i in seq_along(x)) {
run <- run + x[i]
grp[i] <- current_grp
if(run > thres) {
current_grp <- current_grp + 1L
run <- 0
}
}
grp
}
thres <- 500
group <- f(df$quantity, thres)
aggregate(quantity ~ group, df, sum)
#> group quantity
#> 1 0 552
#> 2 1 513
#> 3 2 214
ave(df$quantity, group, FUN = cumsum)
#> [1] 70 133 155 224 235 327 347 409 481 484 552 29 95 129 224 263 294 377 433
#> [20] 434 453 513 50 91 182 214
Created on 2022-09-06 by the reprex package (v2.0.1)
Edit
To assign groups and total quantities to the data can be done as follows.
df$group <- f(df$quantity, thres)
df$total_quantity <- ave(df$quantity, df$group, FUN = cumsum)
head(df)
#> items quantity price group total_quantity
#> 1 D 70 731 0 70
#> 2 S 63 516 0 133
#> 3 N 22 710 0 155
#> 4 W 69 829 0 224
#> 5 K 11 887 0 235
#> 6 D 92 317 0 327
Created on 2022-09-06 by the reprex package (v2.0.1)
Edit 2
To assign only the total quantity per group use sum instead of cumsum.
df$total_quantity <- ave(df$quantity, df$group, FUN = sum)

Data frame from wide to long with multiple variables and ids R [duplicate]

This question already has answers here:
Reshaping multiple sets of measurement columns (wide format) into single columns (long format)
(8 answers)
Closed 2 years ago.
I have a dataframe with participants' judgments for two texts. Suppose each text has a correct answer and an identifier, and each text is judged multple times.
set.seed(123)
wide_df = data.frame('participant_id' = LETTERS[1:12]
, 'judgment_1' = round(rnorm(12)*100)
, 'correct_1' = round(rnorm(12)*100)
, 'text_id_1' = sample(1:12, 12, replace = F)
, 'judgment_2' = round(rnorm(12)*100)
, 'correct_2' = round(rnorm(12)*100)
, 'text_id_2' = sample(13:24, 12, replace = F)
)
So that:
participant_id judgment_1 correct_1 text_id_1 judgment_2 correct_2 text_id_2
1 A -56 40 4 43 -127 17
2 B -23 11 10 -30 217 14
3 C 156 -56 1 90 121 22
4 D 7 179 12 88 -112 15
5 E 13 50 7 82 -40 13
...
I would want to convert this to the long format with the columns:
participant_id text_id judgment correct
A 4 -56 40
A 17 43 127
...
I found and followed the SO advice here:
wide_df %>%
gather(v, value, judgment_1:text_id_2) %>%
separate(v, c("var", "col")) %>%
arrange(participant_id) %>%
spread(col, value)
But that way of reshaping returns the error Error: Duplicate identifiers for rows (3, 6), (9, 12)
I think I do something conceptually wrong but can't quite find it. Where is my mistake? Thanks!

Here's a more dynamic way compared to my other answer. This does not require manually uniting the required columns but does rely on column name pattern.
wide_df %>%
gather(variable, value, -participant_id) %>%
mutate(
variable = substr(variable, 1, nchar(variable)-2),
rn = ave(1:length(participant_id), participant_id, variable, FUN = seq_along)
) %>%
spread(variable, value) %>%
select(-rn)
participant_id correct judgment text_id
1 A 40 -56 4
2 A -127 43 17
3 B 11 -23 10
4 B 217 -30 14
5 C -56 156 1
6 C 121 90 22
7 D 179 7 12
8 D -112 88 15
9 E 50 13 7
10 E -40 82 13
11 F -197 172 11
12 F -47 69 19
13 G 70 46 9
14 G 78 55 24
15 H -47 -127 2
16 H -8 -6 20
17 I -107 -69 8
18 I 25 -31 21
19 J -22 -45 3
20 J -3 -38 16
21 K -103 122 5
22 K -4 -69 23
23 L -73 36 6
24 L 137 -21 18

Answer already exists here: https://stackoverflow.com/a/12466668/2371031
e.g.,
set.seed(123)
wide_df = data.frame('participant_id' = LETTERS[1:12]
, 'judgment_1' = round(rnorm(12)*100)
, 'correct_1' = round(rnorm(12)*100)
, 'text_id_1' = sample(1:12, 12, replace = F)
, 'judgment_2' = round(rnorm(12)*100)
, 'correct_2' = round(rnorm(12)*100)
, 'text_id_2' = sample(13:24, 12, replace = F)
)
dl <- reshape(data = wide_df,
idvar = "participant_id",
varying = list(judgment=c(2,5),correct=c(3,6),text_id=c(4,7)),
direction="long",
v.names = c("judgment","correct","text_id"),
sep="_")
Result:
participant_id time judgment correct text_id
A.1 A 1 -56 40 4
B.1 B 1 -23 11 10
C.1 C 1 156 -56 1
D.1 D 1 7 179 12
E.1 E 1 13 50 7
F.1 F 1 172 -197 11
G.1 G 1 46 70 9
H.1 H 1 -127 -47 2
I.1 I 1 -69 -107 8
J.1 J 1 -45 -22 3
K.1 K 1 122 -103 5
L.1 L 1 36 -73 6
A.2 A 2 43 -127 17
B.2 B 2 -30 217 14
C.2 C 2 90 121 22
D.2 D 2 88 -112 15
E.2 E 2 82 -40 13
F.2 F 2 69 -47 19
G.2 G 2 55 78 24
H.2 H 2 -6 -8 20
I.2 I 2 -31 25 21
J.2 J 2 -38 -3 16
K.2 K 2 -69 -4 23
L.2 L 2 -21 137 18

Some playing around with data.table:
library(data.table)
superlong_df <- melt(wide_df, id.vars = "participant_id")
setDT(superlong_df)[, `:=`(varn = gsub(".*_(\\d)", "\\1", variable),
variable = gsub("_\\d$", "", variable))]
dcast(superlong_df, participant_id + varn ~ variable)[, !"varn"]
participant_id correct judgment text_id
1: A 40 -56 4
2: A -127 43 17
3: B 11 -23 10
4: B 217 -30 14
5: C -56 156 1
6: C 121 90 22
7: D 179 7 12
8: D -112 88 15
9: E 50 13 7
10: E -40 82 13
11: F -197 172 11
12: F -47 69 19
13: G 70 46 9
14: G 78 55 24
15: H -47 -127 2
16: H -8 -6 20
17: I -107 -69 8
18: I 25 -31 21
19: J -22 -45 3
20: J -3 -38 16
21: K -103 122 5
22: K -4 -69 23
23: L -73 36 6
24: L 137 -21 18
participant_id correct judgment text_id

Here's way with tidyr. Basically you'll do unite() + separate_rows(). unite to combine columns and separate_rows to get them in rows -
wide_df %>%
unite(text_id, text_id_1, text_id_2) %>%
unite(judgment, judgment_1, judgment_2) %>%
unite(correct, correct_1, correct_2) %>%
separate_rows(2:4, sep = "_")
participant_id judgment correct text_id
1 A -56 40 4
2 A 43 -127 17
3 B -23 11 10
4 B -30 217 14
5 C 156 -56 1
6 C 90 121 22
7 D 7 179 12
8 D 88 -112 15
9 E 13 50 7
10 E 82 -40 13
11 F 172 -197 11
12 F 69 -47 19
13 G 46 70 9
14 G 55 78 24
15 H -127 -47 2
16 H -6 -8 20
17 I -69 -107 8
18 I -31 25 21
19 J -45 -22 3
20 J -38 -3 16
21 K 122 -103 5
22 K -69 -4 23
23 L 36 -73 6
24 L -21 137 18

Filter using paste and name in dplyr

Sample data
df <- data.frame(loc.id = rep(1:5, each = 6), day = sample(1:365,30),
ref.day1 = rep(c(20,30,50,80,90), each = 6),
ref.day2 = rep(c(10,28,33,49,67), each = 6),
ref.day3 = rep(c(31,49,65,55,42), each = 6))
For each loc.id, if I want to keep days that are >= then ref.day1, I do this:
df %>% group_by(loc.id) %>% dplyr::filter(day >= ref.day1)
I want to make 3 data frames, each whose rows are filtered by ref.day1, ref.day2,ref.day3 respectively
I tried this:
col.names <- c("ref.day1","ref.day2","ref.day3")
temp.list <- list()
for(cl in seq_along(col.names)){
col.sub <- col.names[cl]
columns <- c("loc.id","day",col.sub)
df.sub <- df[,columns]
temp.dat <- df.sub %>% group_by(loc.id) %>% dplyr::filter(day >= paste0(col.sub)) # this line does not work
temp.list[[cl]] <- temp.dat
}
final.dat <- rbindlist(temp.list)
I was wondering how to refer to columns by names and paste function in dplyr in order to filter it out.

The reason why your original code doesn't work is that your col.names are strings, but dplyr function uses non-standard evaluation which doesn't accept strings. So you need to convert the string into variables.rlang::sym() can do that.
Also, you can use map function in purrr package, which is much more compact:
library(dplyr)
library(purrr)
col_names <- c("ref.day1","ref.day2","ref.day3")
map(col_names,~ df %>% dplyr::filter(day >= UQ(rlang::sym(.x))))
#it will return you a list of dataframes
By the way I removed group_by() because they don't seem to be useful.
Returned result:
[[1]]
loc.id day ref.day1 ref.day2 ref.day3
1 1 362 20 10 31
2 1 69 20 10 31
3 1 65 20 10 31
4 1 88 20 10 31
5 1 142 20 10 31
6 2 355 30 28 49
7 2 255 30 28 49
8 2 136 30 28 49
9 2 156 30 28 49
10 2 194 30 28 49
11 2 204 30 28 49
12 3 129 50 33 65
13 3 254 50 33 65
14 3 279 50 33 65
15 3 201 50 33 65
16 3 282 50 33 65
17 4 351 80 49 55
18 4 114 80 49 55
19 4 338 80 49 55
20 4 283 80 49 55
21 5 199 90 67 42
22 5 141 90 67 42
23 5 241 90 67 42
24 5 187 90 67 42
[[2]]
loc.id day ref.day1 ref.day2 ref.day3
1 1 16 20 10 31
2 1 362 20 10 31
3 1 69 20 10 31
4 1 65 20 10 31
5 1 88 20 10 31
6 1 142 20 10 31
7 2 355 30 28 49
8 2 255 30 28 49
9 2 136 30 28 49
10 2 156 30 28 49
11 2 194 30 28 49
12 2 204 30 28 49
13 3 129 50 33 65
14 3 254 50 33 65
15 3 279 50 33 65
16 3 201 50 33 65
17 3 282 50 33 65
18 4 351 80 49 55
19 4 114 80 49 55
20 4 338 80 49 55
21 4 283 80 49 55
22 4 79 80 49 55
23 5 199 90 67 42
24 5 67 90 67 42
25 5 141 90 67 42
26 5 241 90 67 42
27 5 187 90 67 42
[[3]]
loc.id day ref.day1 ref.day2 ref.day3
1 1 362 20 10 31
2 1 69 20 10 31
3 1 65 20 10 31
4 1 88 20 10 31
5 1 142 20 10 31
6 2 355 30 28 49
7 2 255 30 28 49
8 2 136 30 28 49
9 2 156 30 28 49
10 2 194 30 28 49
11 2 204 30 28 49
12 3 129 50 33 65
13 3 254 50 33 65
14 3 279 50 33 65
15 3 201 50 33 65
16 3 282 50 33 65
17 4 351 80 49 55
18 4 114 80 49 55
19 4 338 80 49 55
20 4 283 80 49 55
21 4 79 80 49 55
22 5 199 90 67 42
23 5 67 90 67 42
24 5 141 90 67 42
25 5 241 90 67 42
26 5 187 90 67 42
You may also want to check these:
https://dplyr.tidyverse.org/articles/programming.html
Use variable names in functions of dplyr

Discrete probability distribution plot with given values

First of all, I could not draw the graphics myself. Because, unfortunately I have no computer and mathematical software.. I do not know how to use these softwares.
I need to create a discrete probabilistic distribution plot of the following values.
I tried Wolfram Alpha (as an online software), but it is impossible to do so because the number of values ​​is so long.
EXAMPLE:
I would like to set up a graph similar to the graphs of the following distribution functions:
P_1(x)={ 3/42,3/42,4/42,3/42,4/42,5/42,3/42,3/42,1/42,2/42,2/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,0/42,1/42,1/42,2/42,2/42,2/42,1/42}
P_2(x)={ 4/170,6/170,8/170,6/170,8/170,11/170,8/170,9/170,6/170,6/170,8/170,4/170,7/170,3/170,5/170,3/170,3/170,3/170,0/170,1/170,0/170,1/170,2/170,2/170,1/170,0/170,1/170,1/170,1/170,2/170,2/170,2/170,2/170,2/170,2/170,2/170,3/170,3/170,4/170,5/170,4/170,6/170,4/170,4/170,1/170,2/170,1/170,0/170,0/170,0/170,0/170,1/170}
P_3(x)={ 5/682,9/682,13/682,15/682,20/682,25/682,25/682,34/682,21/682,26/682,24/682,21/682,26/682,19/682,21/682,22/682,14/682,20/682,10/682,13/682,10/682,6/682,11/682,6/682,8/682,3/682,4/682,5/682,5/682,8/682,5/682,9/682,7/682,9/682,10/682,5/682,5/682,5/682,7/682,11/682,11/682,15/682,13/682,16/682,11/682,15/682,18/682,9/682,9/682,3/682,6/682,10/682,5/682,4/682,2/682,2/682,1/682,0/682,1/682,0/682,2/682,1/682,1/682,3/682,1/682,1/682}
P_4(x)={ 6/2730,12/2730,22/2730,28/2730,41/2730,55/2730,58/2730,80/2730,68/2730,91/2730,84/2730,72/2730,87/2730,65/2730,82/2730,89/2730,61/2730,80/2730,51/2730,66/2730,49/2730,47/2730,61/2730,39/2730,50/2730,21/2730,31/2730,34/2730,24/2730,40/2730,23/2730,36/2730,34/2730,31/2730,44/2730,27/2730,33/2730,18/2730,25/2730,37/2730,33/2730,50/2730,35/2730,52/2730,40/2730,52/2730,66/2730,45/2730,48/2730,32/2730,34/2730,43/2730,32/2730,37/2730,22/2730,29/2730,21/2730,15/2730,22/2730,10/2730,18/2730,11/2730,15/2730,22/2730,10/2730,13/2730,1/2730,4/2730,4/2730,1/2730,2/2730,1/2730,2/2730,0/2730,1/2730,2/2730,0/2730,1/2730,1/2730,0/2730,0/2730,0/2730,0/2730,0/2730,0/2730,0/2730,1/2730}
P_5(x)={ 7/10922,17/10922,33/10922,47/10922,77/10922,108/10922,126/10922,179/10922,167/10922,226/10922,245/10922,235/10922,308/10922,224/10922,303/10922,300/10922,263/10922,318/10922,216/10922,278/10922,204/10922,238/10922,282/10922,180/10922,229/10922,138/10922,185/10922,211/10922,145/10922,194/10922,122/10922,168/10922,137/10922,136/10922,168/10922,113/10922,150/10922,93/10922,123/10922,132/10922,118/10922,165/10922,124/10922,174/10922,156/10922,167/10922,215/10922,150/10922,204/10922,134/10922,167/10922,208/10922,127/10922,180/10922,107/10922,145/10922,119/10922,97/10922,131/10922,81/10922,100/10922,63/10922,78/10922,99/10922,61/10922,83/10922,43/10922,56/10922,58/10922,35/10922,46/10922,19/10922,26/10922,13/10922,14/10922,18/10922,7/10922,8/10922,4/10922,8/10922,9/10922,3/10922,6/10922,2/10922,6/10922,4/10922,6/10922,11/10922,5/10922,6/10922,2/10922,3/10922,6/10922,2/10922,4/10922,4/10922,2/10922,1/10922,2/10922,2/10922,1/10922,2/10922}
P_6(x)={ 8/43690,22/43690,48/43690,77/43690,133/43690,196/43690,262/43690,391/43690,396/43690,550/43690,607/43690,656/43690,877/43690,689/43690,919/43690,922/43690,904/43690,1104/43690,823/43690,1058/43690,806/43690,919/43690,1120/43690,792/43690,1028/43690,647/43690,876/43690,930/43690,721/43690,876/43690,585/43690,741/43690,621/43690,615/43690,777/43690,528/43690,695/43690,420/43690,544/43690,608/43690,488/43690,656/43690,458/43690,620/43690,588/43690,591/43690,775/43690,545/43690,761/43690,507/43690,666/43690,818/43690,551/43690,735/43690,466/43690,626/43690,560/43690,505/43690,659/43690,387/43690,509/43690,307/43690,401/43690,541/43690,314/43690,438/43690,259/43690,339/43690,361/43690,234/43690,318/43690,154/43690,214/43690,125/43690,161/43690,196/43690,90/43690,125/43690,61/43690,95/43690,114/43690,58/43690,88/43690,39/43690,65/43690,36/43690,43/43690,72/43690,41/43690,49/43690,28/43690,41/43690,50/43690,27/43690,32/43690,16/43690,25/43690,12/43690,20/43690,28/43690,11/43690,13/43690,6/43690,9/43690,13/43690,2/43690,6/43690,2/43690,2/43690,3/43690,3/43690,10/43690,3/43690,7/43690,2/43690,3/43690,4/43690,2/43690,4/43690,0/43690,1/43690,1/43690,1/43690,1/43690,1/43690,0/43690,0/43690,0/43690,1/43690}
P_7(x)={ 9/174762,27/174762,67/174762,118/174762,220/174762,348/174762,486/174762,771/174762,825/174762,1214/174762,1373/174762,1633/174762,2257/174762,1906/174762,2554/174762,2557/174762,2737/174762,3353/174762,2728/174762,3572/174762,2798/174762,3278/174762,3935/174762,3040/174762,3969/174762,2675/174762,3469/174762,3713/174762,2907/174762,3630/174762,2491/174762,3269/174762,2647/174762,2733/174762,3408/174762,2315/174762,3043/174762,1931/174762,2584/174762,2710/174762,2155/174762,2789/174762,1904/174762,2566/174762,2473/174762,2331/174762,3061/174762,2134/174762,2871/174762,1939/174762,2595/174762,3186/174762,2249/174762,3031/174762,1967/174762,2575/174762,2440/174762,2253/174762,2909/174762,1827/174762,2398/174762,1549/174762,1992/174762,2595/174762,1593/174762,2098/174762,1248/174762,1661/174762,1819/174762,1347/174762,1741/174762,991/174762,1320/174762,792/174762,964/174762,1310/174762,710/174762,939/174762,507/174762,694/174762,826/174762,473/174762,651/174762,341/174762,468/174762,264/174762,328/174762,494/174762,253/174762,357/174762,179/174762,263/174762,348/174762,180/174762,273/174762,130/174762,193/174762,133/174762,157/174762,209/174762,100/174762,138/174762,63/174762,91/174762,130/174762,58/174762,79/174762,40/174762,67/174762,58/174762,53/174762,77/174762,40/174762,53/174762,24/174762,32/174762,48/174762,22/174762,35/174762,20/174762,25/174762,28/174762,14/174762,17/174762,11/174762,15/174762,5/174762,8/174762,17/174762,7/174762,8/174762,2/174762,2/174762,4/174762,4/174762,8/174762,2/174762,2/174762,0/174762,1/174762,3/174762,1/174762,3/174762,0/174762,0/174762,0/174762,0/174762,0/174762,0/174762,2/174762,0/174762,0/174762,0/174762,0/174762,0/174762,0/174762,0/174762,0/174762,0/174762,0/174762,0/174762,0/174762,1/174762,1/174762}
P_8(x)={ 10/699050,34/699050,90/699050,176/699050,350/699050,577/699050,871/699050,1436/699050,1657/699050,2534/699050,2966/699050,3764/699050,5358/699050,4882/699050,6698/699050,6791/699050,7702/699050,9604/699050,8231/699050,10947/699050,8918/699050,10913/699050,12958/699050,10509/699050,13828/699050,9786/699050,12794/699050,13566/699050,11358/699050,14039/699050,9970/699050,13110/699050,10714/699050,11362/699050,14112/699050,9696/699050,12748/699050,8299/699050,10977/699050,11669/699050,9297/699050,11941/699050,8009/699050,10755/699050,10446/699050,9502/699050,12403/699050,8392/699050,11276/699050,7651/699050,10182/699050,12325/699050,9126/699050,12174/699050,8067/699050,10627/699050,10042/699050,9292/699050,12137/699050,7975/699050,10516/699050,6945/699050,8760/699050,11373/699050,7282/699050,9587/699050,5943/699050,7862/699050,8529/699050,6461/699050,8474/699050,5139/699050,6852/699050,4337/699050,5341/699050,6935/699050,4055/699050,5350/699050,3019/699050,4024/699050,4776/699050,3023/699050,4058/699050,2251/699050,3039/699050,1851/699050,2247/699050,3039/699050,1720/699050,2289/699050,1249/699050,1742/699050,2273/699050,1290/699050,1830/699050,987/699050,1372/699050,972/699050,978/699050,1372/699050,767/699050,1038/699050,538/699050,722/699050,994/699050,529/699050,710/699050,367/699050,535/699050,509/699050,393/699050,589/699050,316/699050,401/699050,226/699050,297/699050,389/699050,197/699050,290/699050,143/699050,202/699050,226/699050,147/699050,211/699050,103/699050,140/699050,79/699050,101/699050,142/699050,67/699050,106/699050,51/699050,67/699050,75/699050,46/699050,65/699050,32/699050,45/699050,36/699050,42/699050,55/699050,24/699050,34/699050,11/699050,12/699050,25/699050,9/699050,21/699050,8/699050,17/699050,10/699050,9/699050,11/699050,5/699050,14/699050,5/699050,7/699050,6/699050,3/699050,7/699050,4/699050,5/699050,7/699050,6/699050,6/699050,3/699050,2/699050,0/699050,1/699050,2/699050,0/699050,2/699050,2/699050,3/699050,2/699050,2/699050,0/699050,0/699050,0/699050,0/699050,1/699050,1/699050,0/699050,0/699050,0/699050,1/699050,2/699050,1/699050,2/699050,0/699050,0/699050,0/699050,0/699050,1/699050,1/699050,2/699050}
P_9(x)={ 11/2796202,41/2796202,118/2796202,251/2796202,532/2796202,924/2796202,1500/2796202,2580/2796202,3164/2796202,5018/2796202,6108/2796202,8193/2796202,12030/2796202,11599/2796202,16410/2796202,16947/2796202,20329/2796202,25638/2796202,23361/2796202,31525/2796202,26494/2796202,33543/2796202,39633/2796202,33833/2796202,44866/2796202,33020/2796202,43543/2796202,45974/2796202,41030/2796202,50612/2796202,37388/2796202,49151/2796202,40472/2796202,44081/2796202,54221/2796202,38847/2796202,51156/2796202,33921/2796202,44680/2796202,48127/2796202,38710/2796202,48902/2796202,33436/2796202,44407/2796202,43373/2796202,39062/2796202,50116/2796202,34268/2796202,45652/2796202,30338/2796202,40610/2796202,47920/2796202,36278/2796202,48435/2796202,32356/2796202,42939/2796202,41366/2796202,37675/2796202,49334/2796202,33062/2796202,43626/2796202,29838/2796202,37507/2796202,48140/2796202,31771/2796202,42095/2796202,26739/2796202,35539/2796202,37571/2796202,29438/2796202,38388/2796202,24218/2796202,32092/2796202,21490/2796202,26087/2796202,33871/2796202,20799/2796202,27405/2796202,16238/2796202,21484/2796202,25225/2796202,16608/2796202,22120/2796202,12875/2796202,17174/2796202,10980/2796202,13179/2796202,17537/2796202,10097/2796202,13580/2796202,7732/2796202,10423/2796202,13393/2796202,7912/2796202,10700/2796202,6146/2796202,8242/2796202,6077/2796202,6224/2796202,8464/2796202,4728/2796202,6495/2796202,3578/2796202,4873/2796202,6457/2796202,3565/2796202,4909/2796202,2640/2796202,3644/2796202,3535/2796202,2742/2796202,3829/2796202,2090/2796202,2847/2796202,1587/2796202,2128/2796202,2914/2796202,1549/2796202,2116/2796202,1147/2796202,1600/2796202,1751/2796202,1134/2796202,1596/2796202,838/2796202,1190/2796202,645/2796202,836/2796202,1169/2796202,600/2796202,853/2796202,452/2796202,668/2796202,780/2796202,409/2796202,581/2796202,292/2796202,423/2796202,278/2796202,324/2796202,474/2796202,242/2796202,334/2796202,158/2796202,234/2796202,327/2796202,166/2796202,234/2796202,114/2796202,165/2796202,110/2796202,107/2796202,171/2796202,84/2796202,138/2796202,66/2796202,86/2796202,114/2796202,56/2796202,78/2796202,41/2796202,57/2796202,70/2796202,49/2796202,63/2796202,26/2796202,49/2796202,25/2796202,36/2796202,51/2796202,14/2796202,22/2796202,12/2796202,18/2796202,23/2796202,20/2796202,20/2796202,6/2796202,11/2796202,4/2796202,10/2796202,15/2796202,9/2796202,10/2796202,5/2796202,8/2796202,11/2796202,6/2796202,10/2796202,6/2796202,9/2796202,5/2796202,8/2796202,14/2796202,7/2796202,14/2796202,7/2796202,7/2796202,5/2796202,1/2796202,5/2796202,1/2796202,2/2796202,4/2796202,1/2796202,3/2796202,3/2796202,3/2796202,1/2796202,3/2796202,3/2796202,0/2796202,1/2796202,0/2796202,0/2796202,1/2796202,1/2796202,1/2796202,0/2796202,0/2796202,0/2796202,1/2796202}
Where, x={1,2,3,4,5,.....{up to the number of given values}
In fact this question is a mathematical question: What mathematical functions do these probabilistic distributions simulate? But since I can not make graphics, I can not answer..I just need help to create graphics.
Endless thanks for any help..
A sample drawing I need:

[Complete reedit] after OP edit putting more graphs to single SVG
Well than you can use SVG for the graph (you just need text editor and SVG viewer like Internet browser). You just need a slight modification of the data and either script or manual construct the SVG path. Compute common scale and adjust transform of each graph/path accordingly. Here is VCL/C++ script for this (can ignore the VCL stuff I only use AnsiString type for text manipulation and Memo for simple save of the text to file):
//---------------------------------------------------------------------------
AnsiString generate_svg(int *P)
{
AnsiString svg,path;
float xm,ym,dm,f; int i,ix,c,d,x,y;
const AnsiString col[]=
{
"aliceblue" ,
"antiquewhite" ,
"aqua" ,
"aquamarine" ,
"azure" ,
"beige" ,
"bisque" ,
"black" ,
"blanchedalmond" ,
"blue" ,
"blueviolet" ,
"brown" ,
"burlywood" ,
"cadetblue" ,
"chartreuse" ,
"chocolate" ,
"coral" ,
"lightpink" ,
"lightsalmon" ,
"lightseagreen" ,
"lightskyblue" ,
"lightslategray" ,
"lightslategrey" ,
"lightsteelblue" ,
"lightyellow" ,
"lime" ,
"limegreen" ,
"linen" ,
"magenta" ,
"maroon" ,
"mediumaquamarine" ,
"mediumblue" ,
"mediumorchid" ,
"cornflowerblue" ,
"cornsilk" ,
"crimson" ,
"cyan" ,
"darkblue" ,
"darkcyan" ,
"darkgoldenrod" ,
"darkgray" ,
"darkgreen" ,
"darkgrey" ,
"darkkhaki" ,
"darkmagenta" ,
"darkolivegreen" ,
"darkorange" ,
"darkorchid" ,
"darkred" ,
"darksalmon" ,
"darkseagreen" ,
"darkslateblue" ,
"darkslategray" ,
"darkslategrey" ,
"darkturquoise" ,
"darkviolet" ,
"deeppink" ,
"deepskyblue" ,
"dimgray" ,
"dimgrey" ,
"dodgerblue" ,
"firebrick" ,
"floralwhite" ,
"forestgreen" ,
"fuchsia" ,
"gainsboro" ,
"ghostwhite" ,
"gold" ,
"goldenrod" ,
"gray" ,
"grey" ,
"green" ,
"greenyellow" ,
"honeydew" ,
"hotpink" ,
"indianred" ,
"indigo" ,
"ivory" ,
"khaki" ,
"lavender" ,
"lavenderblush" ,
"mediumpurple" ,
"mediumseagreen" ,
"mediumslateblue" ,
"mediumspringgreen" ,
"mediumturquoise" ,
"mediumvioletred" ,
"midnightblue" ,
"mintcream" ,
"mistyrose" ,
"moccasin" ,
"navajowhite" ,
"navy" ,
"oldlace" ,
"olive" ,
"olivedrab" ,
"orange" ,
"orangered" ,
"orchid" ,
"palegoldenrod" ,
"palegreen" ,
"paleturquoise" ,
"palevioletred" ,
"papayawhip" ,
"peachpuff" ,
"peru" ,
"pink" ,
"plum" ,
"powderblue" ,
"purple" ,
"red" ,
"rosybrown" ,
"royalblue" ,
"saddlebrown" ,
"salmon" ,
"sandybrown" ,
"seagreen" ,
"seashell" ,
"sienna" ,
"silver" ,
"skyblue" ,
"slateblue" ,
"slategray" ,
"slategrey" ,
"snow" ,
"springgreen" ,
"steelblue" ,
"tan" ,
"teal" ,
"lawngreen" ,
"lemonchiffon" ,
"lightblue" ,
"lightcoral" ,
"lightcyan" ,
"lightgoldenrodyellow",
"lightgray" ,
"lightgreen" ,
"lightgrey" ,
"thistle" ,
"tomato" ,
"turquoise" ,
"violet" ,
"wheat" ,
"white" ,
"whitesmoke" ,
"yellow" ,
"yellowgreen" ,
};
// find max x,y,d (for computation of global transform)
for (xm=0,ym=0,dm=0,i=0,ix=0;P[i]>=0;ix++,i++) // process all graphs in P[]
{
d=P[i]; i++; // divider
if (dm<d) dm=d; // global max divider
for (x=0,y=0;P[i]>=0;i++,x++) // process single graph
if (y<P[i]) y=P[i]; // local max y
f=float(y)/float(d);
if (ym<f) ym=f; // global max y
if (xm<x) xm=x; // global max x
}
// [SVG header]
svg ="<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"-100 -100 1100 1100\">\r\n";
// [support gfx] <0,1000> coordinates
svg+=" <g fill=\"none\" stroke-width=\"1px\">\r\n";
svg+=" <rect stroke=\"black\" x=\"0\" y=\"0\" height=\"1000\" width=\"1000\"/>\r\n";
// [graphs]
for (i=0,ix=0;P[i]>=0;ix++,i++) // process all graphs in P[]
{
f=P[i]; i++; // divider
f*=2.0*ym; // scale
svg+=" <path stroke=\""+col[ix]+"\"";
svg+=AnsiString().sprintf(" transform=\"matrix(%.6f,%.6f,%.6f,%.6f,%.6f,%.6f)\"",1000.0/xm,0.0,0.0,-1000.0/f,0.0,1000.0);
svg+=" d=\"M 0 0"; // Move to (0,0) absolute
for (x=0;P[i]>=0;i++,x++) // process single graph
svg+=AnsiString().sprintf(" L %i %i",x,P[i]); // Line to (x,y) absolute
svg+="\"/>\r\n";
}
svg+=" </g>\r\n";
svg+="</svg>\r\n";
return svg;
}
//---------------------------------------------------------------------------
__fastcall TForm1::TForm1(TComponent* Owner):TForm(Owner)
{
int P[]=
{
// divider,y0,y1,y2,....,-1
42,3,3,4,3,4,5,3,3,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,2,2,1,-1,
170,4,6,8,6,8,11,8,9,6,6,8,4,7,3,5,3,3,3,0,1,0,1,2,2,1,0,1,1,1,2,2,2,2,2,2,2,3,3,4,5,4,6,4,4,1,2,1,0,0,0,0,1,-1,
682,5,9,13,15,20,25,25,34,21,26,24,21,26,19,21,22,14,20,10,13,10,6,11,6,8,3,4,5,5,8,5,9,7,9,10,5,5,5,7,11,11,15,13,16,11,15,18,9,9,3,6,10,5,4,2,2,1,0,1,0,2,1,1,3,1,1,-1,
2730,6,12,22,28,41,55,58,80,68,91,84,72,87,65,82,89,61,80,51,66,49,47,61,39,50,21,31,34,24,40,23,36,34,31,44,27,33,18,25,37,33,50,35,52,40,52,66,45,48,32,34,43,32,37,22,29,21,15,22,10,18,11,15,22,10,13,1,4,4,1,2,1,2,0,1,2,0,1,1,0,0,0,0,0,0,0,1,-1,
10922,7,17,33,47,77,108,126,179,167,226,245,235,308,224,303,300,263,318,216,278,204,238,282,180,229,138,185,211,145,194,122,168,137,136,168,113,150,93,123,132,118,165,124,174,156,167,215,150,204,134,167,208,127,180,107,145,119,97,131,81,100,63,78,99,61,83,43,56,58,35,46,19,26,13,14,18,7,8,4,8,9,3,6,2,6,4,6,11,5,6,2,3,6,2,4,4,2,1,2,2,1,2,-1,
43690,8,22,48,77,133,196,262,391,396,550,607,656,877,689,919,922,904,1104,823,1058,806,919,1120,792,1028,647,876,930,721,876,585,741,621,615,777,528,695,420,544,608,488,656,458,620,588,591,775,545,761,507,666,818,551,735,466,626,560,505,659,387,509,307,401,541,314,438,259,339,361,234,318,154,214,125,161,196,90,125,61,95,114,58,88,39,65,36,43,72,41,49,28,41,50,27,32,16,25,12,20,28,11,13,6,9,13,2,6,2,2,3,3,10,3,7,2,3,4,2,4,0,1,1,1,1,1,0,0,0,1,-1,
174762,9,27,67,118,220,348,486,771,825,1214,1373,1633,2257,1906,2554,2557,2737,3353,2728,3572,2798,3278,3935,3040,3969,2675,3469,3713,2907,3630,2491,3269,2647,2733,3408,2315,3043,1931,2584,2710,2155,2789,1904,2566,2473,2331,3061,2134,2871,1939,2595,3186,2249,3031,1967,2575,2440,2253,2909,1827,2398,1549,1992,2595,1593,2098,1248,1661,1819,1347,1741,991,1320,792,964,1310,710,939,507,694,826,473,651,341,468,264,328,494,253,357,179,263,348,180,273,130,193,133,157,209,100,138,63,91,130,58,79,40,67,58,53,77,40,53,24,32,48,22,35,20,25,28,14,17,11,15,5,8,17,7,8,2,2,4,4,8,2,2,0,1,3,1,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,1,-1,
699050,10,34,90,176,350,577,871,1436,1657,2534,2966,3764,5358,4882,6698,6791,7702,9604,8231,10947,8918,10913,12958,10509,13828,9786,12794,13566,11358,14039,9970,13110,10714,11362,14112,9696,12748,8299,10977,11669,9297,11941,8009,10755,10446,9502,12403,8392,11276,7651,10182,12325,9126,12174,8067,10627,10042,9292,12137,7975,10516,6945,8760,11373,7282,9587,5943,7862,8529,6461,8474,5139,6852,4337,5341,6935,4055,5350,3019,4024,4776,3023,4058,2251,3039,1851,2247,3039,1720,2289,1249,1742,2273,1290,1830,987,1372,972,978,1372,767,1038,538,722,994,529,710,367,535,509,393,589,316,401,226,297,389,197,290,143,202,226,147,211,103,140,79,101,142,67,106,51,67,75,46,65,32,45,36,42,55,24,34,11,12,25,9,21,8,17,10,9,11,5,14,5,7,6,3,7,4,5,7,6,6,3,2,0,1,2,0,2,2,3,2,2,0,0,0,0,1,1,0,0,0,1,2,1,2,0,0,0,0,1,1,2,-1,
2796202,11,41,118,251,532,924,1500,2580,3164,5018,6108,8193,12030,11599,16410,16947,20329,25638,23361,31525,26494,33543,39633,33833,44866,33020,43543,45974,41030,50612,37388,49151,40472,44081,54221,38847,51156,33921,44680,48127,38710,48902,33436,44407,43373,39062,50116,34268,45652,30338,40610,47920,36278,48435,32356,42939,41366,37675,49334,33062,43626,29838,37507,48140,31771,42095,26739,35539,37571,29438,38388,24218,32092,21490,26087,33871,20799,27405,16238,21484,25225,16608,22120,12875,17174,10980,13179,17537,10097,13580,7732,10423,13393,7912,10700,6146,8242,6077,6224,8464,4728,6495,3578,4873,6457,3565,4909,2640,3644,3535,2742,3829,2090,2847,1587,2128,2914,1549,2116,1147,1600,1751,1134,1596,838,1190,645,836,1169,600,853,452,668,780,409,581,292,423,278,324,474,242,334,158,234,327,166,234,114,165,110,107,171,84,138,66,86,114,56,78,41,57,70,49,63,26,49,25,36,51,14,22,12,18,23,20,20,6,11,4,10,15,9,10,5,8,11,6,10,6,9,5,8,14,7,14,7,7,5,1,5,1,2,4,1,3,3,3,1,3,3,0,1,0,0,1,1,1,0,0,0,1,-1,
-1
};
Memo1->Lines->Text=generate_svg(P);
Memo1->Lines->SaveToFile("out.svg");
}
//---------------------------------------------------------------------------
resulting SVG:
<svg xmlns="http://www.w3.org/2000/svg" viewBox="-100 -100 1100 1100">
<g fill="none" stroke-width="1px">
<rect stroke="black" x="0" y="0" height="1000" width="1000"/>
<path stroke="aliceblue" transform="matrix(4.504505,0.000000,0.000000,-100.000000,0.000000,1000.000000)" d="M 0 0 L 0 3 L 1 3 L 2 4 L 3 3 L 4 4 L 5 5 L 6 3 L 7 3 L 8 1 L 9 2 L 10 2 L 11 0 L 12 0 L 13 0 L 14 0 L 15 0 L 16 0 L 17 0 L 18 0 L 19 0 L 20 0 L 21 0 L 22 0 L 23 0 L 24 0 L 25 0 L 26 0 L 27 0 L 28 0 L 29 0 L 30 0 L 31 0 L 32 0 L 33 0 L 34 0 L 35 0 L 36 1 L 37 1 L 38 2 L 39 2 L 40 2 L 41 1"/>
<path stroke="antiquewhite" transform="matrix(4.504505,0.000000,0.000000,-24.705881,0.000000,1000.000000)" d="M 0 0 L 0 4 L 1 6 L 2 8 L 3 6 L 4 8 L 5 11 L 6 8 L 7 9 L 8 6 L 9 6 L 10 8 L 11 4 L 12 7 L 13 3 L 14 5 L 15 3 L 16 3 L 17 3 L 18 0 L 19 1 L 20 0 L 21 1 L 22 2 L 23 2 L 24 1 L 25 0 L 26 1 L 27 1 L 28 1 L 29 2 L 30 2 L 31 2 L 32 2 L 33 2 L 34 2 L 35 2 L 36 3 L 37 3 L 38 4 L 39 5 L 40 4 L 41 6 L 42 4 L 43 4 L 44 1 L 45 2 L 46 1 L 47 0 L 48 0 L 49 0 L 50 0 L 51 1"/>
<path stroke="aqua" transform="matrix(4.504505,0.000000,0.000000,-6.158358,0.000000,1000.000000)" d="M 0 0 L 0 5 L 1 9 L 2 13 L 3 15 L 4 20 L 5 25 L 6 25 L 7 34 L 8 21 L 9 26 L 10 24 L 11 21 L 12 26 L 13 19 L 14 21 L 15 22 L 16 14 L 17 20 L 18 10 L 19 13 L 20 10 L 21 6 L 22 11 L 23 6 L 24 8 L 25 3 L 26 4 L 27 5 L 28 5 L 29 8 L 30 5 L 31 9 L 32 7 L 33 9 L 34 10 L 35 5 L 36 5 L 37 5 L 38 7 L 39 11 L 40 11 L 41 15 L 42 13 L 43 16 L 44 11 L 45 15 L 46 18 L 47 9 L 48 9 L 49 3 L 50 6 L 51 10 L 52 5 L 53 4 L 54 2 L 55 2 L 56 1 L 57 0 L 58 1 L 59 0 L 60 2 L 61 1 L 62 1 L 63 3 L 64 1 L 65 1"/>
<path stroke="aquamarine" transform="matrix(4.504505,0.000000,0.000000,-1.538462,0.000000,1000.000000)" d="M 0 0 L 0 6 L 1 12 L 2 22 L 3 28 L 4 41 L 5 55 L 6 58 L 7 80 L 8 68 L 9 91 L 10 84 L 11 72 L 12 87 L 13 65 L 14 82 L 15 89 L 16 61 L 17 80 L 18 51 L 19 66 L 20 49 L 21 47 L 22 61 L 23 39 L 24 50 L 25 21 L 26 31 L 27 34 L 28 24 L 29 40 L 30 23 L 31 36 L 32 34 L 33 31 L 34 44 L 35 27 L 36 33 L 37 18 L 38 25 L 39 37 L 40 33 L 41 50 L 42 35 L 43 52 L 44 40 L 45 52 L 46 66 L 47 45 L 48 48 L 49 32 L 50 34 L 51 43 L 52 32 L 53 37 L 54 22 L 55 29 L 56 21 L 57 15 L 58 22 L 59 10 L 60 18 L 61 11 L 62 15 L 63 22 L 64 10 L 65 13 L 66 1 L 67 4 L 68 4 L 69 1 L 70 2 L 71 1 L 72 2 L 73 0 L 74 1 L 75 2 L 76 0 L 77 1 L 78 1 L 79 0 L 80 0 L 81 0 L 82 0 L 83 0 L 84 0 L 85 0 L 86 1"/>
<path stroke="azure" transform="matrix(4.504505,0.000000,0.000000,-0.384545,0.000000,1000.000000)" d="M 0 0 L 0 7 L 1 17 L 2 33 L 3 47 L 4 77 L 5 108 L 6 126 L 7 179 L 8 167 L 9 226 L 10 245 L 11 235 L 12 308 L 13 224 L 14 303 L 15 300 L 16 263 L 17 318 L 18 216 L 19 278 L 20 204 L 21 238 L 22 282 L 23 180 L 24 229 L 25 138 L 26 185 L 27 211 L 28 145 L 29 194 L 30 122 L 31 168 L 32 137 L 33 136 L 34 168 L 35 113 L 36 150 L 37 93 L 38 123 L 39 132 L 40 118 L 41 165 L 42 124 L 43 174 L 44 156 L 45 167 L 46 215 L 47 150 L 48 204 L 49 134 L 50 167 L 51 208 L 52 127 L 53 180 L 54 107 L 55 145 L 56 119 L 57 97 L 58 131 L 59 81 L 60 100 L 61 63 L 62 78 L 63 99 L 64 61 L 65 83 L 66 43 L 67 56 L 68 58 L 69 35 L 70 46 L 71 19 L 72 26 L 73 13 L 74 14 L 75 18 L 76 7 L 77 8 L 78 4 L 79 8 L 80 9 L 81 3 L 82 6 L 83 2 L 84 6 L 85 4 L 86 6 L 87 11 L 88 5 L 89 6 L 90 2 L 91 3 L 92 6 L 93 2 L 94 4 L 95 4 L 96 2 L 97 1 L 98 2 L 99 2 L 100 1 L 101 2"/>
<path stroke="beige" transform="matrix(4.504505,0.000000,0.000000,-0.096132,0.000000,1000.000000)" d="M 0 0 L 0 8 L 1 22 L 2 48 L 3 77 L 4 133 L 5 196 L 6 262 L 7 391 L 8 396 L 9 550 L 10 607 L 11 656 L 12 877 L 13 689 L 14 919 L 15 922 L 16 904 L 17 1104 L 18 823 L 19 1058 L 20 806 L 21 919 L 22 1120 L 23 792 L 24 1028 L 25 647 L 26 876 L 27 930 L 28 721 L 29 876 L 30 585 L 31 741 L 32 621 L 33 615 L 34 777 L 35 528 L 36 695 L 37 420 L 38 544 L 39 608 L 40 488 L 41 656 L 42 458 L 43 620 L 44 588 L 45 591 L 46 775 L 47 545 L 48 761 L 49 507 L 50 666 L 51 818 L 52 551 L 53 735 L 54 466 L 55 626 L 56 560 L 57 505 L 58 659 L 59 387 L 60 509 L 61 307 L 62 401 L 63 541 L 64 314 L 65 438 L 66 259 L 67 339 L 68 361 L 69 234 L 70 318 L 71 154 L 72 214 L 73 125 L 74 161 L 75 196 L 76 90 L 77 125 L 78 61 L 79 95 L 80 114 L 81 58 L 82 88 L 83 39 L 84 65 L 85 36 L 86 43 L 87 72 L 88 41 L 89 49 L 90 28 L 91 41 L 92 50 L 93 27 L 94 32 L 95 16 L 96 25 L 97 12 L 98 20 L 99 28 L 100 11 L 101 13 L 102 6 L 103 9 L 104 13 L 105 2 L 106 6 L 107 2 L 108 2 L 109 3 L 110 3 L 111 10 L 112 3 L 113 7 L 114 2 L 115 3 L 116 4 L 117 2 L 118 4 L 119 0 L 120 1 L 121 1 L 122 1 L 123 1 L 124 1 L 125 0 L 126 0 L 127 0 L 128 1"/>
<path stroke="bisque" transform="matrix(4.504505,0.000000,0.000000,-0.024033,0.000000,1000.000000)" d="M 0 0 L 0 9 L 1 27 L 2 67 L 3 118 L 4 220 L 5 348 L 6 486 L 7 771 L 8 825 L 9 1214 L 10 1373 L 11 1633 L 12 2257 L 13 1906 L 14 2554 L 15 2557 L 16 2737 L 17 3353 L 18 2728 L 19 3572 L 20 2798 L 21 3278 L 22 3935 L 23 3040 L 24 3969 L 25 2675 L 26 3469 L 27 3713 L 28 2907 L 29 3630 L 30 2491 L 31 3269 L 32 2647 L 33 2733 L 34 3408 L 35 2315 L 36 3043 L 37 1931 L 38 2584 L 39 2710 L 40 2155 L 41 2789 L 42 1904 L 43 2566 L 44 2473 L 45 2331 L 46 3061 L 47 2134 L 48 2871 L 49 1939 L 50 2595 L 51 3186 L 52 2249 L 53 3031 L 54 1967 L 55 2575 L 56 2440 L 57 2253 L 58 2909 L 59 1827 L 60 2398 L 61 1549 L 62 1992 L 63 2595 L 64 1593 L 65 2098 L 66 1248 L 67 1661 L 68 1819 L 69 1347 L 70 1741 L 71 991 L 72 1320 L 73 792 L 74 964 L 75 1310 L 76 710 L 77 939 L 78 507 L 79 694 L 80 826 L 81 473 L 82 651 L 83 341 L 84 468 L 85 264 L 86 328 L 87 494 L 88 253 L 89 357 L 90 179 L 91 263 L 92 348 L 93 180 L 94 273 L 95 130 L 96 193 L 97 133 L 98 157 L 99 209 L 100 100 L 101 138 L 102 63 L 103 91 L 104 130 L 105 58 L 106 79 L 107 40 L 108 67 L 109 58 L 110 53 L 111 77 L 112 40 L 113 53 L 114 24 L 115 32 L 116 48 L 117 22 L 118 35 L 119 20 L 120 25 L 121 28 L 122 14 L 123 17 L 124 11 L 125 15 L 126 5 L 127 8 L 128 17 L 129 7 L 130 8 L 131 2 L 132 2 L 133 4 L 134 4 L 135 8 L 136 2 L 137 2 L 138 0 L 139 1 L 140 3 L 141 1 L 142 3 L 143 0 L 144 0 L 145 0 L 146 0 L 147 0 L 148 0 L 149 2 L 150 0 L 151 0 L 152 0 L 153 0 L 154 0 L 155 0 L 156 0 L 157 0 L 158 0 L 159 0 L 160 0 L 161 0 L 162 1 L 163 1"/>
<path stroke="black" transform="matrix(4.504505,0.000000,0.000000,-0.006008,0.000000,1000.000000)" d="M 0 0 L 0 10 L 1 34 L 2 90 L 3 176 L 4 350 L 5 577 L 6 871 L 7 1436 L 8 1657 L 9 2534 L 10 2966 L 11 3764 L 12 5358 L 13 4882 L 14 6698 L 15 6791 L 16 7702 L 17 9604 L 18 8231 L 19 10947 L 20 8918 L 21 10913 L 22 12958 L 23 10509 L 24 13828 L 25 9786 L 26 12794 L 27 13566 L 28 11358 L 29 14039 L 30 9970 L 31 13110 L 32 10714 L 33 11362 L 34 14112 L 35 9696 L 36 12748 L 37 8299 L 38 10977 L 39 11669 L 40 9297 L 41 11941 L 42 8009 L 43 10755 L 44 10446 L 45 9502 L 46 12403 L 47 8392 L 48 11276 L 49 7651 L 50 10182 L 51 12325 L 52 9126 L 53 12174 L 54 8067 L 55 10627 L 56 10042 L 57 9292 L 58 12137 L 59 7975 L 60 10516 L 61 6945 L 62 8760 L 63 11373 L 64 7282 L 65 9587 L 66 5943 L 67 7862 L 68 8529 L 69 6461 L 70 8474 L 71 5139 L 72 6852 L 73 4337 L 74 5341 L 75 6935 L 76 4055 L 77 5350 L 78 3019 L 79 4024 L 80 4776 L 81 3023 L 82 4058 L 83 2251 L 84 3039 L 85 1851 L 86 2247 L 87 3039 L 88 1720 L 89 2289 L 90 1249 L 91 1742 L 92 2273 L 93 1290 L 94 1830 L 95 987 L 96 1372 L 97 972 L 98 978 L 99 1372 L 100 767 L 101 1038 L 102 538 L 103 722 L 104 994 L 105 529 L 106 710 L 107 367 L 108 535 L 109 509 L 110 393 L 111 589 L 112 316 L 113 401 L 114 226 L 115 297 L 116 389 L 117 197 L 118 290 L 119 143 L 120 202 L 121 226 L 122 147 L 123 211 L 124 103 L 125 140 L 126 79 L 127 101 L 128 142 L 129 67 L 130 106 L 131 51 L 132 67 L 133 75 L 134 46 L 135 65 L 136 32 L 137 45 L 138 36 L 139 42 L 140 55 L 141 24 L 142 34 L 143 11 L 144 12 L 145 25 L 146 9 L 147 21 L 148 8 L 149 17 L 150 10 L 151 9 L 152 11 L 153 5 L 154 14 L 155 5 L 156 7 L 157 6 L 158 3 L 159 7 L 160 4 L 161 5 L 162 7 L 163 6 L 164 6 L 165 3 L 166 2 L 167 0 L 168 1 L 169 2 L 170 0 L 171 2 L 172 2 L 173 3 L 174 2 L 175 2 L 176 0 L 177 0 L 178 0 L 179 0 L 180 1 L 181 1 L 182 0 L 183 0 L 184 0 L 185 1 L 186 2 L 187 1 L 188 2 L 189 0 L 190 0 L 191 0 L 192 0 L 193 1 L 194 1 L 195 2"/>
<path stroke="blanchedalmond" transform="matrix(4.504505,0.000000,0.000000,-0.001502,0.000000,1000.000000)" d="M 0 0 L 0 11 L 1 41 L 2 118 L 3 251 L 4 532 L 5 924 L 6 1500 L 7 2580 L 8 3164 L 9 5018 L 10 6108 L 11 8193 L 12 12030 L 13 11599 L 14 16410 L 15 16947 L 16 20329 L 17 25638 L 18 23361 L 19 31525 L 20 26494 L 21 33543 L 22 39633 L 23 33833 L 24 44866 L 25 33020 L 26 43543 L 27 45974 L 28 41030 L 29 50612 L 30 37388 L 31 49151 L 32 40472 L 33 44081 L 34 54221 L 35 38847 L 36 51156 L 37 33921 L 38 44680 L 39 48127 L 40 38710 L 41 48902 L 42 33436 L 43 44407 L 44 43373 L 45 39062 L 46 50116 L 47 34268 L 48 45652 L 49 30338 L 50 40610 L 51 47920 L 52 36278 L 53 48435 L 54 32356 L 55 42939 L 56 41366 L 57 37675 L 58 49334 L 59 33062 L 60 43626 L 61 29838 L 62 37507 L 63 48140 L 64 31771 L 65 42095 L 66 26739 L 67 35539 L 68 37571 L 69 29438 L 70 38388 L 71 24218 L 72 32092 L 73 21490 L 74 26087 L 75 33871 L 76 20799 L 77 27405 L 78 16238 L 79 21484 L 80 25225 L 81 16608 L 82 22120 L 83 12875 L 84 17174 L 85 10980 L 86 13179 L 87 17537 L 88 10097 L 89 13580 L 90 7732 L 91 10423 L 92 13393 L 93 7912 L 94 10700 L 95 6146 L 96 8242 L 97 6077 L 98 6224 L 99 8464 L 100 4728 L 101 6495 L 102 3578 L 103 4873 L 104 6457 L 105 3565 L 106 4909 L 107 2640 L 108 3644 L 109 3535 L 110 2742 L 111 3829 L 112 2090 L 113 2847 L 114 1587 L 115 2128 L 116 2914 L 117 1549 L 118 2116 L 119 1147 L 120 1600 L 121 1751 L 122 1134 L 123 1596 L 124 838 L 125 1190 L 126 645 L 127 836 L 128 1169 L 129 600 L 130 853 L 131 452 L 132 668 L 133 780 L 134 409 L 135 581 L 136 292 L 137 423 L 138 278 L 139 324 L 140 474 L 141 242 L 142 334 L 143 158 L 144 234 L 145 327 L 146 166 L 147 234 L 148 114 L 149 165 L 150 110 L 151 107 L 152 171 L 153 84 L 154 138 L 155 66 L 156 86 L 157 114 L 158 56 L 159 78 L 160 41 L 161 57 L 162 70 L 163 49 L 164 63 L 165 26 L 166 49 L 167 25 L 168 36 L 169 51 L 170 14 L 171 22 L 172 12 L 173 18 L 174 23 L 175 20 L 176 20 L 177 6 L 178 11 L 179 4 L 180 10 L 181 15 L 182 9 L 183 10 L 184 5 L 185 8 L 186 11 L 187 6 L 188 10 L 189 6 L 190 9 L 191 5 L 192 8 L 193 14 L 194 7 L 195 14 L 196 7 L 197 7 L 198 5 L 199 1 L 200 5 L 201 1 L 202 2 L 203 4 L 204 1 L 205 3 L 206 3 L 207 3 L 208 1 L 209 3 L 210 3 L 211 0 L 212 1 L 213 0 L 214 0 L 215 1 L 216 1 L 217 1 L 218 0 L 219 0 L 220 0 L 221 1"/>
</g>
</svg>
And screenshot:
As you can see I just replaced the /n with nothing, and added n as first number in te sequence for each graph. I also added full list of named colors in SVG , you can play with the order so the darker colors goes first now they ordered alphabetically which is not ideal as you can see many graphs are very close to white making them almost invisible.
Now if you do not want to code you can do this manually too. Just convert your data in notepad by replacing /n with nothing and than add the L (for example by replacing the ,) and add incrementing x coordinate (sadly this one you need to do manually unless you got some macro in text editor). And of coarse add the header and SVG stuff.
From this you just open the SVG in browser and take screenshot. Then in paint you can add what ever you want. If you study the SVG you can even do all the stuff in the SVG directly (too lazy for that) like adding grid, labels, scales legend etc ...
The viewbox gives you scaling/resolution of your graph with some border. The transforms is computed like this:
transform="matrix(1000.0/max(x),0.0,0.0,-1000.0*scale/divider,0.0,1000.0)"
Where x is the max x of all graphs, divider is the n from /n of al lthe values for actual processed graph and scale is the y scale (as distributions probabilities tend to be much less than 1.0 value). Assuming graph starts at (0,0) and 1000.0 is the resolution from viewbox without border. This will ensure that your data do not need to be converted and graph matches standard math notation.
So here is empty SVG:
<svg xmlns="http://www.w3.org/2000/svg" viewBox="-100 -100 1100 1100">
<g fill="none" stroke-width="1px">
<rect stroke="black" x="0" y="0" height="1000" width="1000"/>
<path stroke="***HERE GOES COLOR NAME***" transform="matrix(***HERE COMPUTED TRANSFORM MATRIX***)" d="M 0 0 **HERE GRAPH: L x0 y0 L x1 y1 ... ** "/>
</g>
</svg>
so for each graph add the line <path ...> compute the transform matrix values and add the graph data after d="M 0 0 in form of L 0 P(0) L 1 P(1) ....
You can add/tweak whatever you want it is just text file... However I am no expert in SVG (I just coded decoder/encoder for my vector CAD/CAM SW I needed some years ago for work and using it since but there my knowledge ends) so there might be even simpler ways to do this with SVG (like get rid of the incremental x coordinate). If you could obtain your data in relative manner then you could for example use l 1 dP[x] instead of L x P[x] which would be doable by simple replace in notepad ...

I have encounter the same problem to draw curve properly without using Microsoft Excel charts and I have used charjs that you can find in
https://www.chartjs.org
This tool works very well for my job.
All my curve's data (X/Y coordinate) are put in javascript files that I include at begin of html file. But in your situation, you can simply copy (using Paste/Copy) your variables directly in JavaScript part of html file !
You can even find some javascript code to extract the HTML image in PNG file that you can put in other editor program.
I have seen that you have a computer and it is an android.
So if you will make graphics on internet, I propose you to see the 2 following links
https://webdesign.tutsplus.com/tutorials/how-to-create-a-simple-line-chart-with-chartjs--cms-28129
https://codepen.io/tutsplus/pen/JRVgRB
CodePen site is a web site that allows you to define graphics on the Web knowing HTML, JavaScript and CSS.
But in your case, I think that the better thing is to find some example on Google in searching "CodePen" "chartjs".
I have tested and I find a lot of example of CodePen for Chartjs.
This link display a lof of examples of chartjs graphs.
https://codepen.X/tag/chartjs
make a Paste/Copy of link and replace X by "io" to work around StackOverflow limitation that request some code
I know that you have no experiment in coding but you can perhaps try to modify code using already written code using CodePen user interface.

SAS: Table is sorted correctly, but graph is sorted incorrectly

I thought this would be simple enough to figure out, but I have not been able to address the issue.
My graph is sorted by best_change, but is ascending instead of descending
(despite being pre-sorted in the original excel file). I have tried adding the GROUPORDER argument, but that is not working.
%let gpath=C:\;
%let dpi=200;
ods html close;
ods listing gpath="&gpath" image_dpi=&dpi;
proc print data=TESTCODE (obs=46);
run;
ods html close;
/*--Change in Tumor Size--*/
ods listing style=listing;
ods graphics / reset width=10in height=10in imagename='TumorSize';
title 'Change in Tumor Size';
title2 'ITT Population';
proc sgplot data=TESTCODE nowall noborder;
styleattrs datacolors=(cxbf0000 cx4f4f4f) datacontrastcolors=(black);
vbarparm category= best_change response= best_change / grouporder = DESCENDING group=best_response datalabel=best_response
datalabelattrs=(size=5 weight=bold) groupdisplay=cluster clusterwidth=1;
refline 20 -30 / lineattrs=(pattern=shortdash);
xaxis display=none;
yaxis values=(80 to -100 by -20);
keylegend / title='' location=inside position=topright across=1 border;
run;
Here is my test code that is being processed. The headers of the columns: Best_change = BC, subjid = SJ, PFS_day = Pd, PFS_months = Pm, Censor = C, Best_response = BR
BC SJ Pd Pm C BR
92 23 58 3 PR
91 55 308 11 SD
87 40 383 12 PD
77 45 66 9 1 PR
74 35 56 11 SD
68 2 179 1 PD
67 41 348 3 1 SD
64 25 28 7 PD
61 37 156 5 1 SD
59 10 316 11 SD
56 12 98 9 1 PR
44 19 283 3 PD
44 46 78 10 1 SD
43 34 392 13 1 SD
35 9 214 11 1 PR
35 50 199 9 SD
25 47 302 1 PD
14 52 133 7 SD
11 33 24 4 SD
10 14 343 3 SD
5 31 244 1 SD
1 30 366 7 1 PR
-1 28 369 6 SD
-4 44 146 9 SD
-4 15 109 5 SD
-5 51 203 6 1 SD
-9 38 314 5 SD
-11 43 30 11 1 SD
-18 16 156 5 1 PR
-20 13 36 1 SD
-23 20 190 7 SD
-24 24 332 12 SD
-25 48 309 6 1 SD
-26 17 179 4 SD
-29 54 140 1 1 SD
-30 18 280 9 SD
-31 21 295 3 1 SD
-40 49 114 13 1 SD
-48 5 33 11 PD
-49 40 217 2 SD
-53 53 361 12 SD
-55 26 269 2 SD
-56 42 111 4 1 SD
-59 32 61 2 PR
-60 39 56 4 1 SD
-60 36 388 11 SD

Add "discreteorder=data" to your axis statement
xaxis display=none discreteorder=data ;

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