Very weird issue with npm - node.js

Hi I do not know what it's happening but I run the command:
npm run watch
and it displays
/public/js/app.js 1.94 MiB /public/js/app [emitted] /public/js/app
0.js 40.4 KiB 0 [emitted]
1.js 690 KiB 1 [emitted]
10.js 47.6 KiB 10 [emitted]
100.js 31.2 KiB 100 [emitted]
101.js 26.6 KiB 101 [emitted]
102.js 15.4 KiB 102 [emitted]
103.js 15.8 KiB 103 [emitted]
104.js 30 KiB 104 [emitted]
105.js 23.6 KiB 105 [emitted]
106.js 17.8 KiB 106 [emitted]
107.js 19.3 KiB 107 [emitted]
108.js 64.6 KiB 108 [emitted]
109.js 21.4 KiB 109 [emitted]
11.js 44.7 KiB 11 [emitted]
110.js 7.95 KiB 110 [emitted]
111.js 21 KiB 111 [emitted]
12.js 44.5 KiB 12 [emitted]
13.js 32.7 KiB 13 [emitted]
14.js 44.7 KiB 14 [emitted]
15.js 47 KiB 15 [emitted]
16.js 29.9 KiB 16 [emitted]
17.js 57.1 KiB 17 [emitted]
18.js 37.8 KiB 18 [emitted]
19.js 40.6 KiB 19 [emitted]
2.js 29.1 KiB 2 [emitted]
20.js 53.6 KiB 20 [emitted]
21.js 30.6 KiB 21 [emitted]
22.js 46.9 KiB 22 [emitted]
23.js 44.5 KiB 23 [emitted]
24.js 30.5 KiB 24 [emitted]
25.js 42.7 KiB 25 [emitted]
26.js 28.6 KiB 26 [emitted]
27.js 37.7 KiB 27 [emitted]
28.js 41.1 KiB 28 [emitted]
29.js 74.1 KiB 29 [emitted]
3.js 45.6 KiB 3 [emitted]
30.js 39.5 KiB 30 [emitted]
31.js 57.2 KiB 31 [emitted]
32.js 28.6 KiB 32 [emitted]
33.js 31.7 KiB 33 [emitted]
34.js 30.7 KiB 34 [emitted]
35.js 39 KiB 35 [emitted]
36.js 28.2 KiB 36 [emitted]
37.js 62 KiB 37 [emitted]
38.js 30.1 KiB 38 [emitted]
39.js 47.8 KiB 39 [emitted]
4.js 72.7 KiB 4 [emitted]
40.js 36.3 KiB 40 [emitted]
41.js 41.7 KiB 41 [emitted]
42.js 35.7 KiB 42 [emitted]
43.js 40 KiB 43 [emitted]
44.js 62.2 KiB 44 [emitted]
45.js 29.2 KiB 45 [emitted]
46.js 45.5 KiB 46 [emitted]
47.js 35.5 KiB 47 [emitted]
48.js 30.9 KiB 48 [emitted]
49.js 45 KiB 49 [emitted]
5.js 61 KiB 5 [emitted]
50.js 35.2 KiB 50 [emitted]
51.js 37.9 KiB 51 [emitted]
52.js 30.3 KiB 52 [emitted]
53.js 57.1 KiB 53 [emitted]
54.js 39.1 KiB 54 [emitted]
55.js 41.9 KiB 55 [emitted]
56.js 23.2 KiB 56 [emitted]
57.js 29.9 KiB 57 [emitted]
58.js 24.9 KiB 58 [emitted]
59.js 25.2 KiB 59 [emitted]
6.js 65 KiB 6 [emitted]
60.js 39 KiB 60 [emitted]
61.js 41 KiB 61 [emitted]
62.js 35.4 KiB 62 [emitted]
63.js 49.5 KiB 63 [emitted]
64.js 72.9 KiB 64 [emitted]
65.js 57.1 KiB 65 [emitted]
66.js 60.7 KiB 66 [emitted]
67.js 39.4 KiB 67 [emitted]
68.js 29.9 KiB 68 [emitted]
69.js 72.7 KiB 69 [emitted]
7.js 62.4 KiB 7 [emitted]
70.js 53.4 KiB 70 [emitted]
71.js 50.9 KiB 71 [emitted]
72.js 79.2 KiB 72 [emitted]
73.js 38.8 KiB 73 [emitted]
74.js 56.6 KiB 74 [emitted]
75.js 50.6 KiB 75 [emitted]
76.js 68 KiB 76 [emitted]
77.js 34 KiB 77 [emitted]
78.js 45.9 KiB 78 [emitted]
79.js 37.9 KiB 79 [emitted]
8.js 77.1 KiB 8 [emitted]
80.js 32 KiB 80 [emitted]
81.js 40.3 KiB 81 [emitted]
82.js 56.6 KiB 82 [emitted]
83.js 50.3 KiB 83 [emitted]
84.js 45 KiB 84 [emitted]
85.js 48 KiB 85 [emitted]
86.js 42.6 KiB 86 [emitted]
87.js 62.4 KiB 87 [emitted]
88.js 29.4 KiB 88 [emitted]
89.js 29.3 KiB 89 [emitted]
9.js 73.3 KiB 9 [emitted]
90.js 49.5 KiB 90 [emitted]
91.js 20 KiB 91 [emitted]
92.js 8.3 KiB 92 [emitted]
93.js 8.09 KiB 93 [emitted]
94.js 35.9 KiB 94 [emitted]
95.js 30.6 KiB 95 [emitted]
96.js 28.7 KiB 96 [emitted]
97.js 40.2 KiB 97 [emitted]
98.js 17.6 KiB 98 [emitted]
99.js 14.3 KiB 99 [emitted]
public/css/app.css 193 KiB /public/js/app [emitted] /public/js/app
and it is OK but the thing is that I do not why when I compile it and it says Ok it does not compile in public/ because if I go to public_html and I check it displays this:
console
So it is out of the folder public/ and I wonder why is it happening?
My webpack.mix is like this one:
const mix = require('laravel-mix');
mix.js('resources/js/app.js', 'public/js')
.sass('resources/sass/app.scss', 'public/css');
so I wonder why is it out of public folder if it says that it will compile in public folder?
Thanks

When running hot or watch, no actual files are generated in the /public directory. Instead, a mini server is created to serve these files, normally on port 8080.
Until you run npm run production you will see the actual files on the folder.
The mix() helper on the view automatically handles this difference and serves either the files or the ones generated on port 8080

Related

How to input cvs and get min and max values python

Read in the same hsv_2020_climo_data.csv file into Pandas DataFrame with the Date column as the index (similar to examples before).
Answer the following questions with formatted print statements.
What are the data types of each column? (i.e. 'MaxTemperature' dtype is int64)
What is the highest maximum temperature for the entire year?
What is the lowest minimum temperature for the entire year?
How much total rain did we get for 2020? (Hint you will need to handle the "Trace" values first)
Make a plot with Maximum Temperature, Minimum Temperature, and Average Temperature with labels and title.
Data below
Date MaxTemperature MinTemperature AvgTemperature Precipitation Snowfall SnowDepth
2020-01-01 53 31 42.0 0.00 0.0 0
2020-01-02 54 45 49.5 3.42 0.0 0
2020-01-03 59 53 56.0 0.32 0.0 0
2020-01-04 56 31 43.5 0.08 0.0 0
2020-01-05 55 29 42.0 0.00 0.0 0
2020-01-06 60 35 47.5 0.03 0.0 0
2020-01-07 55 35 45.0 T 0.0 0
2020-01-08 61 30 45.5 0.00 0.0 0
2020-01-09 60 36 48.0 0.00 0.0 0
2020-01-10 65 58 61.5 T 0.0 0
2020-01-11 72 50 61.0 1.08 0.0 0
2020-01-12 59 46 52.5 0.00 0.0 0
2020-01-13 62 43 52.5 0.08 0.0 0
2020-01-14 64 60 62.0 0.96 0.0 0
2020-01-15 67 60 63.5 0.61 0.0 0
2020-01-16 60 42 51.0 0.03 0.0 0
2020-01-17 59 40 49.5 0.00 0.0 0
2020-01-18 57 45 51.0 0.17 0.0 0
2020-01-19 45 27 36.0 0.00 0.0 0
2020-01-20 28 22 25.0 T T 0
2020-01-21 37 23 30.0 0.00 0.0 0
2020-01-22 46 20 33.0 0.00 0.0 0
2020-01-23 47 39 43.0 0.72 0.0 0
2020-01-24 54 40 47.0 0.19 0.0 0
2020-01-25 41 33 37.0 T 0.0 0
2020-01-26 49 29 39.0 0.03 0.0 0
2020-01-27 58 37 47.5 0.01 0.0 0
2020-01-28 60 31 45.5 0.00 0.0 0
2020-01-29 50 44 47.0 0.03 0.0 0
2020-01-30 60 37 48.5 0.00 0.0 0
2020-01-31 52 45 48.5 T 0.0 0
2020-02-01 50 37 43.5 0.06 0.0 0
2020-02-02 68 31 49.5 0.00 0.0 0
2020-02-03 71 40 55.5 T 0.0 0
2020-02-04 67 55 61.0 0.18 0.0 0
2020-02-05 68 62 65.0 1.16 0.0 0
2020-02-06 64 36 50.0 1.49 0.0 0
2020-02-07 41 33 37.0 T T 0
2020-02-08 53 32 42.5 0.10 T 0
2020-02-09 61 33 47.0 T 0.0 0
2020-02-10 57 49 53.0 1.78 0.0 0
2020-02-11 66 45 55.5 1.12 0.0 0
2020-02-12 70 44 57.0 1.31 0.0 0
2020-02-13 60 36 48.0 0.38 0.0 0
2020-02-14 41 26 33.5 0.00 0.0 0
2020-02-15 54 22 38.0 0.00 0.0 0
2020-02-16 58 42 50.0 0.00 0.0 0
2020-02-17 56 38 47.0 0.00 0.0 0
2020-02-18 62 46 54.0 1.32 0.0 0
2020-02-19 52 43 47.5 T 0.0 0
2020-02-20 47 33 40.0 0.85 T 0
2020-02-21 44 26 35.0 0.00 0.0 0
2020-02-22 56 24 40.0 0.00 0.0 0
2020-02-23 53 34 43.5 0.01 0.0 0
2020-02-24 55 44 49.5 0.62 0.0 0
2020-02-25 62 45 53.5 0.00 0.0 0
2020-02-26 48 36 42.0 0.04 T 0
2020-02-27 46 31 38.5 T T 0
2020-02-28 51 34 42.5 T 0.0 0
2020-02-29 55 36 45.5 0.00 0.0 0
2020-03-01 66 34 50.0 0.00 0.0 0
2020-03-02 60 52 56.0 0.44 0.0 0
2020-03-03 69 55 62.0 0.33 0.0 0
2020-03-04 60 51 55.5 0.04 0.0 0
2020-03-05 59 42 50.5 0.15 0.0 0
2020-03-06 56 37 46.5 0.00 0.0 0
2020-03-07 58 30 44.0 0.00 0.0 0
2020-03-08 65 35 50.0 0.00 0.0 0
2020-03-09 68 45 56.5 T 0.0 0
2020-03-10 70 56 63.0 0.27 0.0 0
2020-03-11 66 51 58.5 0.12 0.0 0
2020-03-12 76 57 66.5 0.26 0.0 0
2020-03-13 67 54 60.5 0.14 0.0 0
2020-03-14 74 50 62.0 0.56 0.0 0
2020-03-15 61 44 52.5 1.07 0.0 0
2020-03-16 59 44 51.5 0.02 0.0 0
2020-03-17 74 53 63.5 0.24 0.0 0
2020-03-18 77 52 64.5 0.00 0.0 0
2020-03-19 78 64 71.0 T 0.0 0
2020-03-20 72 60 66.0 1.11 0.0 0
2020-03-21 59 43 51.0 0.01 0.0 0
2020-03-22 66 43 54.5 0.07 0.0 0
2020-03-23 64 57 60.5 1.80 0.0 0
2020-03-24 76 57 66.5 2.96 0.0 0
2020-03-25 66 51 58.5 0.00 0.0 0
2020-03-26 81 47 64.0 0.00 0.0 0
2020-03-27 85 63 74.0 0.00 0.0 0
2020-03-28 82 66 74.0 0.00 0.0 0
2020-03-29 75 53 64.0 0.41 0.0 0
2020-03-30 70 52 61.0 0.02 0.0 0
2020-03-31 56 43 49.5 0.65 0.0 0
2020-04-01 62 39 50.5 0.00 0.0 0
2020-04-02 70 38 54.0 0.00 0.0 0
2020-04-03 75 42 58.5 0.00 0.0 0
2020-04-04 78 54 66.0 0.00 0.0 0
2020-04-05 81 54 67.5 0.00 0.0 0
2020-04-06 83 52 67.5 0.00 0.0 0
2020-04-07 74 62 68.0 0.00 0.0 0
2020-04-08 80 63 71.5 0.24 0.0 0
2020-04-09 71 57 64.0 0.32 0.0 0
2020-04-10 60 40 50.0 0.00 0.0 0
2020-04-11 71 37 54.0 0.00 0.0 0
2020-04-12 66 54 60.0 3.02 0.0 0
2020-04-13 66 42 54.0 T 0.0 0
2020-04-14 59 39 49.0 0.00 0.0 0
2020-04-15 61 34 47.5 0.00 0.0 0
2020-04-16 69 36 52.5 0.00 0.0 0
2020-04-17 76 45 60.5 0.07 0.0 0
2020-04-18 62 45 53.5 0.21 0.0 0
2020-04-19 63 46 54.5 1.41 0.0 0
2020-04-20 72 51 61.5 0.11 0.0 0
2020-04-21 76 50 63.0 0.00 0.0 0
2020-04-22 68 42 55.0 0.29 0.0 0
2020-04-23 70 54 62.0 0.92 0.0 0
2020-04-24 73 56 64.5 0.01 0.0 0
2020-04-25 74 53 63.5 0.21 0.0 0
2020-04-26 61 41 51.0 0.00 0.0 0
2020-04-27 72 38 55.0 0.00 0.0 0
2020-04-28 77 53 65.0 T 0.0 0
2020-04-29 72 53 62.5 0.13 0.0 0
2020-04-30 71 48 59.5 T 0.0 0
2020-05-01 76 43 59.5 0.00 0.0 0
2020-05-02 83 51 67.0 0.00 0.0 0
2020-05-03 85 58 71.5 0.00 0.0 0
2020-05-04 84 60 72.0 T 0.0 0
2020-05-05 76 56 66.0 T 0.0 0
2020-05-06 65 44 54.5 0.00 0.0 0
2020-05-07 71 39 55.0 0.00 0.0 0
2020-05-08 61 48 54.5 0.86 0.0 0
2020-05-09 64 40 52.0 0.00 0.0 0
2020-05-10 73 39 56.0 0.00 0.0 0
2020-05-11 68 43 55.5 0.00 0.0 0
2020-05-12 66 48 57.0 T 0.0 0
2020-05-13 79 55 67.0 0.05 0.0 0
2020-05-14 85 61 73.0 0.00 0.0 0
2020-05-15 84 67 75.5 0.00 0.0 0
2020-05-16 86 62 74.0 0.00 0.0 0
2020-05-17 79 65 72.0 0.23 0.0 0
2020-05-18 82 60 71.0 T 0.0 0
2020-05-19 71 54 62.5 T 0.0 0
2020-05-20 77 54 65.5 0.25 0.0 0
2020-05-21 79 57 68.0 0.00 0.0 0
2020-05-22 82 63 72.5 1.52 0.0 0
2020-05-23 86 64 75.0 0.28 0.0 0
2020-05-24 88 65 76.5 T 0.0 0
2020-05-25 88 67 77.5 0.00 0.0 0
2020-05-26 76 67 71.5 0.31 0.0 0
2020-05-27 79 61 70.0 0.74 0.0 0
2020-05-28 83 62 72.5 0.21 0.0 0
2020-05-29 83 64 73.5 0.18 0.0 0
2020-05-30 84 63 73.5 0.00 0.0 0
2020-05-31 83 59 71.0 0.00 0.0 0
2020-06-01 85 53 69.0 0.00 0.0 0
2020-06-02 89 67 78.0 0.00 0.0 0
2020-06-03 88 71 79.5 0.06 0.0 0
2020-06-04 87 68 77.5 T 0.0 0
2020-06-05 90 69 79.5 0.41 0.0 0
2020-06-06 91 68 79.5 0.00 0.0 0
2020-06-07 91 71 81.0 0.00 0.0 0
2020-06-08 84 75 79.5 0.43 0.0 0
2020-06-09 87 75 81.0 0.11 0.0 0
2020-06-10 92 65 78.5 0.00 0.0 0
2020-06-11 85 61 73.0 0.00 0.0 0
2020-06-12 88 61 74.5 0.00 0.0 0
2020-06-13 90 58 74.0 0.00 0.0 0
2020-06-14 92 62 77.0 0.00 0.0 0
2020-06-15 83 61 72.0 0.00 0.0 0
2020-06-16 81 60 70.5 0.00 0.0 0
2020-06-17 80 63 71.5 0.00 0.0 0
2020-06-18 85 61 73.0 0.00 0.0 0
2020-06-19 91 64 77.5 0.00 0.0 0
2020-06-20 94 66 80.0 0.00 0.0 0
2020-06-21 88 69 78.5 0.14 0.0 0
2020-06-22 88 68 78.0 0.14 0.0 0
2020-06-23 87 70 78.5 0.25 0.0 0
2020-06-24 75 68 71.5 0.70 0.0 0
2020-06-25 85 70 77.5 0.00 0.0 0
2020-06-26 75 68 71.5 0.15 0.0 0
2020-06-27 82 68 75.0 0.30 0.0 0
2020-06-28 90 73 81.5 0.25 0.0 0
2020-06-29 90 71 80.5 0.01 0.0 0
2020-06-30 88 70 79.0 0.84 0.0 0
2020-07-01 82 68 75.0 0.73 0.0 0
2020-07-02 89 68 78.5 0.00 0.0 0
2020-07-03 94 71 82.5 0.00 0.0 0
2020-07-04 92 71 81.5 0.00 0.0 0
2020-07-05 93 71 82.0 0.47 0.0 0
2020-07-06 88 71 79.5 0.00 0.0 0
2020-07-07 90 73 81.5 0.07 0.0 0
2020-07-08 87 73 80.0 T 0.0 0
2020-07-09 90 71 80.5 0.00 0.0 0
2020-07-10 92 73 82.5 0.00 0.0 0
2020-07-11 92 67 79.5 0.00 0.0 0
2020-07-12 82 68 75.0 1.38 0.0 0
2020-07-13 89 69 79.0 0.00 0.0 0
2020-07-14 91 70 80.5 0.00 0.0 0
2020-07-15 93 70 81.5 0.00 0.0 0
2020-07-16 91 70 80.5 0.00 0.0 0
2020-07-17 94 73 83.5 0.00 0.0 0
2020-07-18 95 73 84.0 0.00 0.0 0
2020-07-19 95 73 84.0 0.00 0.0 0
2020-07-20 95 73 84.0 0.00 0.0 0
2020-07-21 94 74 84.0 T 0.0 0
2020-07-22 92 73 82.5 0.19 0.0 0
2020-07-23 92 71 81.5 0.00 0.0 0
2020-07-24 90 73 81.5 0.00 0.0 0
2020-07-25 94 72 83.0 0.07 0.0 0
2020-07-26 94 71 82.5 0.00 0.0 0
2020-07-27 91 73 82.0 T 0.0 0
2020-07-28 90 72 81.0 T 0.0 0
2020-07-29 92 73 82.5 0.02 0.0 0
2020-07-30 90 74 82.0 0.14 0.0 0
2020-07-31 92 74 83.0 0.25 0.0 0
2020-08-01 87 70 78.5 T 0.0 0
2020-08-02 86 66 76.0 0.00 0.0 0
2020-08-03 91 67 79.0 0.00 0.0 0
2020-08-04 90 70 80.0 0.01 0.0 0
2020-08-05 92 68 80.0 0.00 0.0 0
2020-08-06 92 71 81.5 0.00 0.0 0
2020-08-07 94 69 81.5 0.00 0.0 0
2020-08-08 97 68 82.5 0.00 0.0 0
2020-08-09 96 71 83.5 0.00 0.0 0
2020-08-10 98 74 86.0 0.00 0.0 0
2020-08-11 95 73 84.0 0.49 0.0 0
2020-08-12 93 74 83.5 0.01 0.0 0
2020-08-13 94 71 82.5 T 0.0 0
2020-08-14 90 74 82.0 T 0.0 0
2020-08-15 92 71 81.5 0.00 0.0 0
2020-08-16 93 67 80.0 T 0.0 0
2020-08-17 91 67 79.0 0.00 0.0 0
2020-08-18 93 64 78.5 0.24 0.0 0
2020-08-19 91 68 79.5 1.24 0.0 0
2020-08-20 87 67 77.0 T 0.0 0
2020-08-21 82 68 75.0 0.10 0.0 0
2020-08-22 85 64 74.5 0.00 0.0 0
2020-08-23 88 68 78.0 0.00 0.0 0
2020-08-24 88 72 80.0 T 0.0 0
2020-08-25 82 72 77.0 0.15 0.0 0
2020-08-26 85 70 77.5 1.83 0.0 0
2020-08-27 91 75 83.0 0.22 0.0 0
2020-08-28 86 72 79.0 0.92 0.0 0
2020-08-29 90 74 82.0 0.02 0.0 0
2020-08-30 91 71 81.0 0.23 0.0 0
2020-08-31 87 71 79.0 0.94 0.0 0
2020-09-01 89 71 80.0 0.05 0.0 0
2020-09-02 89 74 81.5 0.00 0.0 0
2020-09-03 89 73 81.0 0.00 0.0 0
2020-09-04 90 67 78.5 T 0.0 0
2020-09-05 88 59 73.5 0.00 0.0 0
2020-09-06 86 57 71.5 0.00 0.0 0
2020-09-07 86 60 73.0 0.00 0.0 0
2020-09-08 87 64 75.5 0.00 0.0 0
2020-09-09 88 65 76.5 0.00 0.0 0
2020-09-10 90 66 78.0 0.00 0.0 0
2020-09-11 93 68 80.5 0.00 0.0 0
2020-09-12 90 73 81.5 0.01 0.0 0
2020-09-13 91 71 81.0 T 0.0 0
2020-09-14 90 69 79.5 0.00 0.0 0
2020-09-15 83 69 76.0 0.00 0.0 0
2020-09-16 74 68 71.0 0.12 0.0 0
2020-09-17 87 70 78.5 0.00 0.0 0
2020-09-18 79 61 70.0 0.00 0.0 0
2020-09-19 76 59 67.5 0.00 0.0 0
2020-09-20 81 58 69.5 0.00 0.0 0
2020-09-21 76 53 64.5 0.00 0.0 0
2020-09-22 75 51 63.0 T 0.0 0
2020-09-23 71 53 62.0 0.79 0.0 0
2020-09-24 66 55 60.5 2.65 0.0 0
2020-09-25 73 64 68.5 T 0.0 0
2020-09-26 76 62 69.0 0.00 0.0 0
2020-09-27 83 61 72.0 0.00 0.0 0
2020-09-28 82 56 69.0 0.42 0.0 0
2020-09-29 70 49 59.5 0.00 0.0 0
2020-09-30 77 47 62.0 0.00 0.0 0
2020-10-01 76 51 63.5 0.00 0.0 0
2020-10-02 69 44 56.5 0.00 0.0 0
2020-10-03 71 40 55.5 0.00 0.0 0
2020-10-04 76 50 63.0 0.00 0.0 0
2020-10-05 76 48 62.0 0.00 0.0 0
2020-10-06 80 48 64.0 0.00 0.0 0
2020-10-07 82 52 67.0 0.00 0.0 0
2020-10-08 82 49 65.5 0.00 0.0 0
2020-10-09 73 63 68.0 0.47 0.0 0
2020-10-10 74 64 69.0 1.35 0.0 0
2020-10-11 75 68 71.5 0.23 0.0 0
2020-10-12 80 64 72.0 0.00 0.0 0
2020-10-13 76 52 64.0 0.00 0.0 0
2020-10-14 82 45 63.5 0.00 0.0 0
2020-10-15 80 54 67.0 0.00 0.0 0
2020-10-16 66 39 52.5 0.01 0.0 0
2020-10-17 68 37 52.5 0.00 0.0 0
2020-10-18 76 50 63.0 0.00 0.0 0
2020-10-19 80 56 68.0 0.00 0.0 0
2020-10-20 81 59 70.0 0.00 0.0 0
2020-10-21 81 58 69.5 0.00 0.0 0
2020-10-22 83 62 72.5 0.00 0.0 0
2020-10-23 83 63 73.0 0.03 0.0 0
2020-10-24 66 55 60.5 0.44 0.0 0
2020-10-25 69 55 62.0 0.00 0.0 0
2020-10-26 75 58 66.5 0.00 0.0 0
2020-10-27 75 58 66.5 T 0.0 0
2020-10-28 74 69 71.5 2.87 0.0 0
2020-10-29 72 48 60.0 0.58 0.0 0
2020-10-30 57 42 49.5 0.00 0.0 0
2020-10-31 68 40 54.0 0.00 0.0 0
2020-11-01 68 43 55.5 0.00 0.0 0
2020-11-02 57 33 45.0 0.00 0.0 0
2020-11-03 66 34 50.0 0.00 0.0 0
2020-11-04 71 39 55.0 0.00 0.0 0
2020-11-05 70 44 57.0 0.00 0.0 0
2020-11-06 76 46 61.0 0.00 0.0 0
2020-11-07 75 48 61.5 T 0.0 0
2020-11-08 79 59 69.0 0.00 0.0 0
2020-11-09 78 62 70.0 0.00 0.0 0
2020-11-10 74 65 69.5 T 0.0 0
2020-11-11 77 58 67.5 0.04 0.0 0
2020-11-12 68 44 56.0 0.00 0.0 0
2020-11-13 71 42 56.5 0.00 0.0 0
2020-11-14 73 41 57.0 0.00 0.0 0
2020-11-15 69 40 54.5 0.02 0.0 0
2020-11-16 63 32 47.5 0.00 0.0 0
2020-11-17 62 34 48.0 0.00 0.0 0
2020-11-18 64 31 47.5 0.00 0.0 0
2020-11-19 66 38 52.0 0.00 0.0 0
2020-11-20 72 40 56.0 0.00 0.0 0
2020-11-21 73 42 57.5 0.00 0.0 0
2020-11-22 69 46 57.5 0.02 0.0 0
2020-11-23 57 35 46.0 0.00 0.0 0
2020-11-24 65 32 48.5 0.00 0.0 0
2020-11-25 65 53 59.0 0.48 0.0 0
2020-11-26 62 40 51.0 0.00 0.0 0
2020-11-27 66 38 52.0 0.58 0.0 0
2020-11-28 57 41 49.0 T 0.0 0
2020-11-29 55 39 47.0 0.73 0.0 0
2020-11-30 44 30 37.0 0.08 T 0
2020-12-01 41 25 33.0 0.00 0.0 0
2020-12-02 52 20 36.0 0.00 0.0 0
2020-12-03 58 25 41.5 0.16 0.0 0
2020-12-04 48 35 41.5 0.82 0.0 0
2020-12-05 56 28 42.0 0.00 0.0 0
2020-12-06 59 30 44.5 T 0.0 0
2020-12-07 47 28 37.5 0.00 0.0 0
2020-12-08 49 25 37.0 0.00 0.0 0
2020-12-09 64 28 46.0 0.00 0.0 0
2020-12-10 71 35 53.0 0.00 0.0 0
2020-12-11 66 37 51.5 0.00 0.0 0
2020-12-12 63 46 54.5 0.30 0.0 0
2020-12-13 60 34 47.0 0.80 0.0 0
2020-12-14 44 35 39.5 0.81 0.0 0
2020-12-15 48 30 39.0 T 0.0 0
2020-12-16 50 35 42.5 0.26 0.0 0
2020-12-17 43 26 34.5 0.00 0.0 0
2020-12-18 50 23 36.5 0.00 0.0 0
2020-12-19 53 27 40.0 0.03 0.0 0
2020-12-20 51 40 45.5 0.16 0.0 0
2020-12-21 61 38 49.5 0.00 0.0 0
2020-12-22 60 31 45.5 0.00 0.0 0
2020-12-23 61 35 48.0 0.12 0.0 0
2020-12-24 52 28 40.0 1.13 T 0
2020-12-25 32 20 26.0 0.00 0.0 0
2020-12-26 50 18 34.0 0.00 0.0 0
2020-12-27 60 26 43.0 0.00 0.0 0
2020-12-28 57 37 47.0 0.00 0.0 0
2020-12-29 61 33 47.0 0.00 0.0 0
2020-12-30 69 41 55.0 0.00 0.0 0
2020-12-31 59 50 54.5 0.03 0.0 0
First of all, notice that some columns contain the value "T". To solve some of the questions, you'll have to replace those:
df = pd.read_csv(r"C:\users\....\DATA.csv", sep=";")
df.replace('T',0, inplace = True)
To get the datatypes:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 366 entries, 0 to 365
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 366 non-null object
1 MaxTemperature 366 non-null int64
2 MinTemperature 366 non-null int64
3 AvgTemperature 366 non-null float64
4 Precipitation 366 non-null object
5 Snowfall 366 non-null object
6 SnowDepth 366 non-null int64
dtypes: float64(1), int64(3), object(3)
memory usage: 20.1+ KB
To get all the information you ask, you need to transform object values to float. They are string because you hade "T" values instead of numeric:
df['Precipitation'] = df.Precipitation.astype(float)
df['Snowfall'] = df.Snowfall.astype(float)
df['SnowDepth'] = df.SnowDepth.astype(float)
Note now that
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 366 entries, 0 to 365
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 366 non-null object
1 MaxTemperature 366 non-null int64
2 MinTemperature 366 non-null int64
3 AvgTemperature 366 non-null float64
4 Precipitation 366 non-null float64
5 Snowfall 366 non-null float64
6 SnowDepth 366 non-null float64
dtypes: float64(4), int64(2), object(1)
memory usage: 20.1+ KB
Now to answer all the questions:
df.describe()
returns:
MaxTemperature MinTemperature AvgTemperature Precipitation \
count 366.000000 366.000000 366.000000 366.000000
mean 73.021858 52.229508 62.625683 0.192678
std 14.496086 14.966696 14.317651 0.471406
min 28.000000 18.000000 25.000000 0.000000
25% 61.250000 40.000000 51.000000 0.000000
50% 74.000000 53.000000 63.250000 0.000000
75% 86.000000 67.000000 75.500000 0.140000
max 98.000000 75.000000 86.000000 3.420000
Snowfall SnowDepth
count 366.0 366.0
mean 0.0 0.0
std 0.0 0.0
min 0.0 0.0
25% 0.0 0.0
50% 0.0 0.0
75% 0.0 0.0
max 0.0 0.0
you have the max, min, .... for all variables.
As for the plot
lines = df.plot.line()

process.memoryUsage returns unvalide values

I have an express API and I want to show the api memory usage, to test the solution, I create a global variable in the module and for each get request I PUSH a data to the array to see if memory increase or no
let array = [];
const randomData = `150 216 86 129 25 116 72 20 155 148 91 30 187 152 136 204 125 182 250
217 97 45 161 48 223 1 55 163 236 240 179 118 234 175 71 56 44 221 245 59 145 66 173 1
12 197 88 146 140 111 223 59 169 53 68 123 0 252 68 96 215 132 236 245 128 43 98 16 110
22 5 179 12 177 87 162 5 134 64 226 250 70 238 114 215 135 147 85 218 140 194 244 107 62
87 19 169 227 2 97 184 215 164 30 34 229 35 46 71 127 120 75 243 68 143 40 203 132 211 212
253 136 159 24 149 241 58 167 199 247 140 229 151 85 65 25 44 106 199 216 110 61 72 135
103 251 122 103 128 134 9 25 38 61 136 159 158 225 137 44 246 176 217 202 149 73 111 73 22
41 151 217 59 69 10 143 85 181 10 194 64 23 244 243 179 240 150 25 111 162 60 221 197 36 191
138 217 185 1 127 226 152 75 7 250 72 147 242 184 70 158 211 154 225 165 130 57 24 50 97 192
125 88 131 183 171 67 209 251 151 64 11 59 206 75 143 17 34 203 95 97 57 209 212 112 99 180
136 142 230 163 82 172 232 134 135 50 101 144 75 94 145 236 206 182 124 120 95 225 144 31 79
75 27 214 115 37 25 122 6 106 26 66 145 135 73 22 53 13 57 202 129 61 42 207 138 143 170 241
`;
exports.getAPIHealth = function (req, res) {
console.log(process.memoryUsage())
const memoryUsedMB = process.memoryUsage().heapUsed / (1024 * 1024);
console.log(memoryUsedMB);
array.push(randomData.toString());
res.status(200).json({
live: true,
memoryUsage: memoryUsedMB.toFixed(2) + ' MB'
})
};
In fact when I run this example I got strange values:
{ rss: 57921536,
heapTotal: 57929728,
heapUsed: 18556128,
external: 655899 }
17.73406219482422
{ rss: 58200064,
heapTotal: 57929728,
heapUsed: 18717496,
external: 655899 }
17.854393005371094
{ rss: 58200064,
heapTotal: 57929728,
heapUsed: 18760720,
external: 655899 }
17.894927978515625
{ rss: 58200064,
heapTotal: 57929728,
heapUsed: 18797824,
external: 655899 }
17.930313110351562
{ rss: 58220544,
heapTotal: 57929728,
heapUsed: 18834928,
external: 655899 }
17.965728759765625
{ rss: 41418752,
heapTotal: 25423872,
heapUsed: 18512640,
external: 153782 }
17.659194946289062
memory increase for each request (and this is normal) but at certain point it decrease,
can any one tell me why I have this behavior ?
NB: I notice that memory usage never reach 18MB (even after many requests)

Pascals find the sum

According to above rules what is the maximum sum of below input? It means please take this pyramid as an input (as file or constants directly inside the code) for your implementation and solve by using it.
215
193 124
117 237 442
218 935 347 235
320 804 522 417 345
229 601 723 835 133 124
248 202 277 433 207 263 257
359 464 504 528 516 716 871 182
461 441 426 656 863 560 380 171 923
381 348 573 533 447 632 387 176 975 449
223 711 445 645 245 543 931 532 937 541 444
330 131 333 928 377 733 017 778 839 168 197 197
131 171 522 137 217 224 291 413 528 520 227 229 928
223 626 034 683 839 053 627 310 713 999 629 817 410 121
924 622 911 233 325 139 721 218 253 223 107 233 230 124 233
Note that, each node has only two children here (except the most bottom ones). As an example, you can walk from 215 to 124 (because 193 is a prime) then from 124 to either 237 or 442. From 124 you cannot go to 117 because it’s not a direct child of 124.

Memory leaks in Gluon/JavaFXPorts running on iOS

After porting our app with Gluon to iOS we noticed that is runs very slowly in the simulator as well as on real devices (in my case iPad 3 running iOS7).
We used profiling tools provided by XCode to examine possible reasons and found some memory leaks.
Then we tried a basic Gluon project (Single View, empty) and found the same memory leaks.
I am neither an expert with RoboVM nor with JavaFXPorts/Gluon so I don't know where to look. But I could provide more information when you tell me what you need.
I appreciate any help and any other suggestions how to make the app more responsive and faster because the memory leaks seem to be just a part of the problem.
Here is the memory leak stacktrace provided by XCode for a basic HelloWorld Application:
Bytes Used # Leaks Symbol Name\
8.72 KB 86.5% 62 start\
8.72 KB 86.5% 62 main\
8.72 KB 86.5% 62 rvmRun\
8.72 KB 86.5% 62 rvmCallVoidClassMethod\
8.72 KB 86.5% 62 rvmCallVoidClassMethodA\
8.72 KB 86.5% 62 callVoidMethod\
8.72 KB 86.5% 62 _call0\
8.72 KB 86.5% 62 [J]org.javafxports.jfxmobile.ios.BasicLauncher.main([Ljava/lang/String;)V\
8.72 KB 86.5% 62 [j]org.robovm.apple.uikit.UIApplication.main([Ljava/lang/String;Ljava/lang/Class;Ljava/lang/Class;)V[clinit]\
8.72 KB 86.5% 62 [J]org.robovm.apple.uikit.UIApplication.main([Ljava/lang/String;Ljava/lang/Class;Ljava/lang/Class;)V\
8.72 KB 86.5% 62 [J]org.robovm.apple.uikit.UIApplication.main(ILorg/robovm/rt/bro/ptr/BytePtr$BytePtrPtr;Ljava/lang/String;Ljava/lang/String;)I\
8.72 KB 86.5% 62 UIApplicationMain\
8.72 KB 86.5% 62 GSEventRunModal\
8.72 KB 86.5% 62 CFRunLoopRunInMode\
8.72 KB 86.5% 62 CFRunLoopRunSpecific\
8.72 KB 86.5% 62 __CFRunLoopRun\
8.72 KB 86.5% 62 __CFRunLoopDoSources0\
8.72 KB 86.5% 62 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION__\
8.72 KB 86.5% 62 __NSThreadPerformPerform\
8.72 KB 86.5% 62 -[GlassRunnable run]\
8.72 KB 86.5% 62 CallVoidMethod\
8.72 KB 86.5% 62 rvmCallVoidInstanceMethodA\
8.72 KB 86.5% 62 callVoidMethod\
8.72 KB 86.5% 62 _call0\
8.72 KB 86.5% 62 [J]com.sun.javafx.application.PlatformImpl$$Lambda$6.run()V\
8.72 KB 86.5% 62 [j]com.sun.javafx.application.PlatformImpl.lambda$runLater$174(Ljava/lang/Runnable;Ljava/security/AccessControlContext;)V[clinit]\
8.72 KB 86.5% 62 [J]com.sun.javafx.application.PlatformImpl.lambda$runLater$174(Ljava/lang/Runnable;Ljava/security/AccessControlContext;)V\
8.72 KB 86.5% 62 [j]java.security.AccessController.doPrivileged(Ljava/security/PrivilegedAction;Ljava/security/AccessControlContext;)Ljava/lang/Object;[clinit]\
8.72 KB 86.5% 62 [J]java.security.AccessController.doPrivileged(Ljava/security/PrivilegedAction;Ljava/security/AccessControlContext;)Ljava/lang/Object;\
8.72 KB 86.5% 62 [J]com.sun.javafx.application.PlatformImpl$$Lambda$19.run()Ljava/lang/Object;\
8.72 KB 86.5% 62 [j]com.sun.javafx.application.PlatformImpl.lambda$null$173(Ljava/lang/Runnable;)Ljava/lang/Void;[clinit]\
8.72 KB 86.5% 62 [J]com.sun.javafx.application.PlatformImpl.lambda$null$173(Ljava/lang/Runnable;)Ljava/lang/Void;\
8.72 KB 86.5% 62 [J]com.sun.javafx.application.PlatformImpl$$Lambda$7.run()V\
8.72 KB 86.5% 62 [j]com.sun.javafx.application.PlatformImpl.lambda$runAndWait$175(Ljava/lang/Runnable;Ljava/util/concurrent/CountDownLatch;)V[clinit]\
8.72 KB 86.5% 62 [J]com.sun.javafx.application.PlatformImpl.lambda$runAndWait$175(Ljava/lang/Runnable;Ljava/util/concurrent/CountDownLatch;)V\
8.72 KB 86.5% 62 [J]com.sun.javafx.application.LauncherImpl$$Lambda$9.run()V\
8.72 KB 86.5% 62 [j]com.sun.javafx.application.LauncherImpl.lambda$launchApplication1$162(Ljava/util/concurrent/atomic/AtomicBoolean;Ljavafx/application/Application;)V[clinit]\
8.72 KB 86.5% 62 [J]com.sun.javafx.application.LauncherImpl.lambda$launchApplication1$162(Ljava/util/concurrent/atomic/AtomicBoolean;Ljavafx/application/Application;)V\
8.72 KB 86.5% 62 [J]com.gluonhq.charm.glisten.application.MobileApplication.start(Ljavafx/stage/Stage;)V\
8.72 KB 86.5% 62 [j]java.lang.String.format(Ljava/lang/String;[Ljava/lang/Object;)Ljava/lang/String;[clinit]\
8.72 KB 86.5% 62 [J]java.lang.String.format(Ljava/lang/String;[Ljava/lang/Object;)Ljava/lang/String;\
8.72 KB 86.5% 62 [J]java.lang.String.format(Ljava/util/Locale;Ljava/lang/String;[Ljava/lang/Object;)Ljava/lang/String;\
8.72 KB 86.5% 62 [J]java.util.Formatter.format(Ljava/lang/String;[Ljava/lang/Object;)Ljava/util/Formatter;\
8.72 KB 86.5% 62 [J]java.util.Formatter.format(Ljava/util/Locale;Ljava/lang/String;[Ljava/lang/Object;)Ljava/util/Formatter;\
8.72 KB 86.5% 62 [j]libcore.icu.LocaleData.get(Ljava/util/Locale;)Llibcore/icu/LocaleData;[clinit]\
8.72 KB 86.5% 62 _bcInitializeClass\
8.72 KB 86.5% 62 rvmInitialize\
8.72 KB 86.5% 62 _call0\
8.72 KB 86.5% 62 [J]libcore.icu.LocaleData.<clinit>()V\
8.72 KB 86.5% 62 [J]libcore.icu.LocaleData.get(Ljava/util/Locale;)Llibcore/icu/LocaleData;\
8.72 KB 86.5% 62 [J]libcore.icu.LocaleData.initLocaleData(Ljava/util/Locale;)Llibcore/icu/LocaleData;\
7.31 KB 72.5% 52 [j]libcore.icu.ICU.getBestDateTimePattern(Ljava/lang/String;Ljava/lang/String;)Ljava/lang/String;[clinit]\
7.31 KB 72.5% 52 [J]libcore.icu.ICU.getBestDateTimePattern(Ljava/lang/String;Ljava/lang/String;)Ljava/lang/String;\
7.31 KB 72.5% 52 [J]libcore.icu.ICU.getBestDateTimePatternNative(Ljava/lang/String;Ljava/lang/String;)Ljava/lang/String;\
7.31 KB 72.5% 52 Java_libcore_icu_ICU_getBestDateTimePatternNative\
7.31 KB 72.5% 52 icu_51::DateTimePatternGenerator::createInstance(icu_51::Locale const&, UErrorCode&)\
7.31 KB 72.5% 52 icu_51::DateTimePatternGenerator::DateTimePatternGenerator(icu_51::Locale const&, UErrorCode&)\
6.47 KB 64.1% 46 icu_51::DateTimePatternGenerator::initData(icu_51::Locale const&, UErrorCode&)\
6.47 KB 64.1% 46 icu_51::DateTimePatternGenerator::addICUPatterns(icu_51::Locale const&, UErrorCode&)\
6.47 KB 64.1% 46 icu_51::DateFormat::create(icu_51::DateFormat::EStyle, icu_51::DateFormat::EStyle, icu_51::Locale const&)\
6.47 KB 64.1% 46 icu_51::SimpleDateFormat::SimpleDateFormat(icu_51::DateFormat::EStyle, icu_51::DateFormat::EStyle, icu_51::Locale const&, UErrorCode&)\
6.47 KB 64.1% 46 icu_51::SimpleDateFormat::construct(icu_51::DateFormat::EStyle, icu_51::DateFormat::EStyle, icu_51::Locale const&, UErrorCode&)\
6.47 KB 64.1% 46 icu_51::SimpleDateFormat::initialize(icu_51::Locale const&, UErrorCode&)\
6.47 KB 64.1% 46 icu_51::NumberFormat::makeInstance(icu_51::Locale const&, UNumberFormatStyle, signed char, UErrorCode&)\
6.47 KB 64.1% 46 icu_51::DecimalFormatSymbols::DecimalFormatSymbols(icu_51::Locale const&, UErrorCode&)\
6.47 KB 64.1% 46 icu_51::DecimalFormatSymbols::initialize(icu_51::Locale const&, UErrorCode&, signed char)\
6.47 KB 64.1% 46 ures_open_51\
6.47 KB 64.1% 46 malloc\
6.47 KB 64.1% 46 malloc_zone_malloc\
864 Bytes 8.3% 6 icu_51::DateTimePatternGenerator::setDecimalSymbols(icu_51::Locale const&, UErrorCode&)\
864 Bytes 8.3% 6 icu_51::DecimalFormatSymbols::DecimalFormatSymbols(icu_51::Locale const&, UErrorCode&)\
864 Bytes 8.3% 6 icu_51::DecimalFormatSymbols::initialize(icu_51::Locale const&, UErrorCode&, signed char)\
864 Bytes 8.3% 6 ures_open_51\
864 Bytes 8.3% 6 malloc\
864 Bytes 8.3% 6 malloc_zone_malloc\
1.41 KB 13.9% 10 [j]libcore.icu.ICU.initLocaleDataNative(Ljava/lang/String;Llibcore/icu/LocaleData;)Z[clinit]\
1.41 KB 13.9% 10 [J]libcore.icu.ICU.initLocaleDataNative(Ljava/lang/String;Llibcore/icu/LocaleData;)Z\
1.41 KB 13.9% 10 Java_libcore_icu_ICU_initLocaleDataNative\
1.12 KB 11.1% 8 icu_51::NumberFormat::makeInstance(icu_51::Locale const&, UNumberFormatStyle, signed char, UErrorCode&)\
1.12 KB 11.1% 8 icu_51::DecimalFormatSymbols::DecimalFormatSymbols(icu_51::Locale const&, UErrorCode&)\
1.12 KB 11.1% 8 icu_51::DecimalFormatSymbols::initialize(icu_51::Locale const&, UErrorCode&, signed char)\
1.12 KB 11.1% 8 ures_open_51\
1.12 KB 11.1% 8 malloc\
1.12 KB 11.1% 8 malloc_zone_malloc\
288 Bytes 2.7% 2 icu_51::DecimalFormatSymbols::DecimalFormatSymbols(icu_51::Locale const&, UErrorCode&)\
288 Bytes 2.7% 2 icu_51::DecimalFormatSymbols::initialize(icu_51::Locale const&, UErrorCode&, signed char)\
288 Bytes 2.7% 2 ures_open_51\
288 Bytes 2.7% 2 malloc\
1.36 KB 13.4% 5 thread_start\
1.36 KB 13.4% 5 _pthread_start\
1.36 KB 13.4% 5 _pthread_body\
1.36 KB 13.4% 5 GC_start_routine\
1.36 KB 13.4% 5 GC_call_with_stack_base\
1.36 KB 13.4% 5 GC_inner_start_routine\
1.36 KB 13.4% 5 startThreadEntryPoint\
1.36 KB 13.4% 5 rvmCallVoidInstanceMethodA\
1.36 KB 13.4% 5 callVoidMethod\
1.36 KB 13.4% 5 _call0\
1.36 KB 13.4% 5 [J]java.lang.Thread.run()V\
1.36 KB 13.4% 5 [J]com.sun.javafx.tk.quantum.QuantumRenderer$PipelineRunnable.run()V\
1.36 KB 13.4% 5 [J]java.util.concurrent.ThreadPoolExecutor$Worker.run()V\
1.36 KB 13.4% 5 [J]java.util.concurrent.ThreadPoolExecutor.runWorker(Ljava/util/concurrent/ThreadPoolExecutor$Worker;)V\
1.36 KB 13.4% 5 [J]com.sun.javafx.tk.RenderJob.run()V\
1.36 KB 13.4% 5 [J]java.util.concurrent.FutureTask.runAndReset()Z\
1.36 KB 13.4% 5 [J]java.util.concurrent.Executors$RunnableAdapter.call()Ljava/lang/Object;\
1.36 KB 13.4% 5 [J]com.sun.javafx.tk.quantum.QuantumRenderer$$Lambda$2.run()V\
1.36 KB 13.4% 5 [j]com.sun.javafx.tk.quantum.QuantumRenderer.lambda$createResourceFactory$414()V[clinit]\
1.36 KB 13.4% 5 [J]com.sun.javafx.tk.quantum.QuantumRenderer.lambda$createResourceFactory$414()V\
1.36 KB 13.4% 5 [j]com.sun.prism.GraphicsPipeline.getDefaultResourceFactory()Lcom/sun/prism/ResourceFactory;[clinit]\
1.36 KB 13.4% 5 [J]com.sun.prism.GraphicsPipeline.getDefaultResourceFactory()Lcom/sun/prism/ResourceFactory;\
1.36 KB 13.4% 5 [J]com.sun.prism.es2.ES2Pipeline.getDefaultResourceFactory(Ljava/util/List;)Lcom/sun/prism/ResourceFactory;\
1.36 KB 13.4% 5 [J]com.sun.prism.es2.ES2Pipeline.findDefaultResourceFactory(Ljava/util/List;)Lcom/sun/prism/es2/ES2ResourceFactory;\
1.36 KB 13.4% 5 [J]com.sun.prism.es2.ES2Pipeline.getES2ResourceFactory(ILcom/sun/glass/ui/Screen;)Lcom/sun/prism/es2/ES2ResourceFactory;\
1.36 KB 13.4% 5 [J]com.sun.prism.es2.ES2ResourceFactory.<init>(Lcom/sun/glass/ui/Screen;)V\
1.36 KB 13.4% 5 [J]com.sun.prism.es2.ES2Context.<init>(Lcom/sun/glass/ui/Screen;Lcom/sun/prism/ps/ShaderFactory;)V\
1.36 KB 13.4% 5 [J]com.sun.prism.es2.IOSGLFactory.createGLContext(Lcom/sun/prism/es2/GLDrawable;Lcom/sun/prism/es2/GLPixelFormat;Lcom/sun/prism/es2/GLContext;Z)Lcom/sun/prism/es2/GLContext;\
1.36 KB 13.4% 5 [J]com.sun.prism.es2.IOSGLContext.<init>(Lcom/sun/prism/es2/GLDrawable;Lcom/sun/prism/es2/GLPixelFormat;Lcom/sun/prism/es2/GLContext;Z)V\
1.36 KB 13.4% 5 [J]com.sun.prism.es2.IOSGLContext.nInitialize(JJJZ)J\
1.36 KB 13.4% 5 Java_com_sun_prism_es2_IOSGLContext_nInitialize\
1.08 KB 10.6% 4 strdup\
1.08 KB 10.6% 4 malloc\
288 Bytes 2.7% 1 malloc\

Average Row/Column Data in a Text File

I need to average three columns of data that are in a row in a text file.
Data:
VALID_TIME STN CDF05 CDF10 CDF20 CDF30 CDF40 CDF50 CDF60 CDF70 CDF80 CDF90 CDF95 MEAN SD
2015031018 KMGM 50.3 51.5 52.9 54.0 54.9 55.8 56.7 57.6 58.6 60.1 61.3 55.8 3.3
2015031106 KMGM 75.7 76.8 78.2 79.2 80.0 80.8 81.6 82.4 83.4 84.8 85.9 81.0 4.0
2015031118 KMGM 54.0 55.1 56.5 57.5 58.4 59.3 60.1 61.0 62.1 63.6 64.8 59.3 3.9
2015031206 KMGM 71.1 72.3 73.9 75.1 76.1 77.0 77.9 78.9 80.1 81.6 82.9 77.0 4.4
2015031218 KMGM 55.5 56.8 58.4 59.5 60.5 61.5 62.4 63.5 64.7 66.3 67.7 61.5 4.4
The columns that I'm interested in averaging together are CDF80, CDF90, and CDF95. The end format should be, for example:
VALID_TIME STN CDF05 CDF10 CDF20 CDF30 CDF40 CDF50 CDF60 CDF70 CDF80 CDF90 CDF95 MEAN NEWAVG
2015031018 KMGM 50.3 51.5 52.9 54.0 54.9 55.8 56.7 57.6 58.6 60.1 61.3 55.8 xx.x
2015031106 KMGM 75.7 76.8 78.2 79.2 80.0 80.8 81.6 82.4 83.4 84.8 85.9 81.0 xx.x
$ cat test.txt | awk -v OFS='\t' 'NR==1{$16="NEWAVG"}; NR!=1{$16=($11+$12+$13)/3};{print $0}'
VALID_TIME STN CDF05 CDF10 CDF20 CDF30 CDF40 CDF50 CDF60 CDF70 CDF80 CDF90 CDF95 MEAN SD NEWAVG
2015031018 KMGM 50.3 51.5 52.9 54.0 54.9 55.8 56.7 57.6 58.6 60.1 61.3 55.8 3.3 60
2015031106 KMGM 75.7 76.8 78.2 79.2 80.0 80.8 81.6 82.4 83.4 84.8 85.9 81.0 4.0 84.7
2015031118 KMGM 54.0 55.1 56.5 57.5 58.4 59.3 60.1 61.0 62.1 63.6 64.8 59.3 3.9 63.5
2015031206 KMGM 71.1 72.3 73.9 75.1 76.1 77.0 77.9 78.9 80.1 81.6 82.9 77.0 4.4 81.5333
2015031218 KMGM 55.5 56.8 58.4 59.5 60.5 61.5 62.4 63.5 64.7 66.3 67.7 61.5 4.4 66.2333
Averaging the columns is trivial, and you can retain most of the original formatting using printf:
$ awk 'NR==1 { printf "%s %8s\n", $0,"NEWAVG"} NR>1 { printf "%s %7.2f\n", $0,($11+$12+$13)/3 }' /tmp/data
VALID_TIME STN CDF05 CDF10 CDF20 CDF30 CDF40 CDF50 CDF60 CDF70 CDF80 CDF90 CDF95 MEAN SD NEWAVG
2015031018 KMGM 50.3 51.5 52.9 54.0 54.9 55.8 56.7 57.6 58.6 60.1 61.3 55.8 3.3 60.00
2015031106 KMGM 75.7 76.8 78.2 79.2 80.0 80.8 81.6 82.4 83.4 84.8 85.9 81.0 4.0 84.70
2015031118 KMGM 54.0 55.1 56.5 57.5 58.4 59.3 60.1 61.0 62.1 63.6 64.8 59.3 3.9 63.50
2015031206 KMGM 71.1 72.3 73.9 75.1 76.1 77.0 77.9 78.9 80.1 81.6 82.9 77.0 4.4 81.53
2015031218 KMGM 55.5 56.8 58.4 59.5 60.5 61.5 62.4 63.5 64.7 66.3 67.7 61.5 4.4 66.23

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