Jedis benchmarking on local Redis server - redis

I'm using JMH to test the performance of Jedis on a local Redis server (Jedis version 2.9.0, Redis version 6.2.6, CPU Quad-Core Intel Core i5). I use 200 threads to send SET command within a connection pool.
#State(Scope.Benchmark)
public class CommonClientBenchmark {
private JedisPool jedisPool;
private final String host = "127.0.0.1";
private final int port = 6379;
#Setup
public void setup() {
JedisPoolConfig jedisPoolConfig = new JedisPoolConfig();
jedisPoolConfig.setMaxTotal(200);
jedisPoolConfig.setMaxIdle(200);
jedisPool = new JedisPool(jedisPoolConfig, host, port, 30000);
}
#TearDown
public void tearDown() {
jedisPool.close();
}
#Threads(200)
#Fork(1)
#Benchmark
#BenchmarkMode(Mode.Throughput)
#Warmup(iterations = 1, time = 30, timeUnit = TimeUnit.SECONDS)
#Measurement(iterations = 2, time = 30, timeUnit = TimeUnit.SECONDS)
public void jedisSet() {
try (Jedis jedis = jedisPool.getResource()) {
jedis.set("jedis", "jedis");
}
}
public static void main(String[] args) throws IOException, RunnerException {
CommonClientBenchmark commonClientBenchmark = new CommonClientBenchmark();
commonClientBenchmark.setup();
org.openjdk.jmh.Main.main(args);
}
}
With the code above, I obtain about 25000+ QPS. However, when I decrease the maxTotal and maxIdle parameter of the connection pool from 200 to 100, the result QPS is even much higher - it reaches about 75000. Could anyone explain the phenomenon? Thanks a lot!
EDIT: I've change the version of Jedis to 4.1.1 and run multiple benchmarking tests, the result is similar. When the size of connection pool is set to 100 (both maxTotal and maxIdle), I obtain about 25000 ~ 50000 QPS. When I increase the size (both maxTotal and maxIdle) to 200, the QPS rise to 60000 ~ 75000.
I've also use iostat 1 to monitor the usage of CPU while running the tests. And I found that when the pool size is set to 200, the %system is often much higher than when it is set to 100.
connection pool size set to 200:
disk0 cpu load average
KB/t tps MB/s us sy id 1m 5m 15m
4.09 929 3.71 7 87 6 39.58 14.44 8.06
4.00 902 3.52 6 89 5 39.58 14.44 8.06
4.50 8 0.04 5 88 6 38.33 14.60 8.15
4.39 145 0.62 6 89 6 38.33 14.60 8.15
28.00 11 0.30 6 88 5 38.33 14.60 8.15
8.00 1 0.01 5 88 6 38.33 14.60 8.15
0.00 0 0.00 5 88 7 38.33 14.60 8.15
4.00 5 0.02 5 88 7 38.94 15.12 8.37
0.00 0 0.00 5 89 6 38.94 15.12 8.37
0.00 0 0.00 5 88 7 38.94 15.12 8.37
0.00 0 0.00 5 89 6 38.94 15.12 8.37
8.68 222 1.88 5 88 7 38.94 15.12 8.37
5.60 10 0.05 5 87 8 45.20 16.81 9.01
29.65 46 1.33 11 82 7 45.20 16.81 9.01
52.57 7 0.36 8 85 7 45.20 16.81 9.01
28.00 2 0.05 5 87 8 45.20 16.81 9.01
223.33 6 1.31 6 87 7 45.20 16.81 9.01
4.19 1344 5.49 8 85 7 44.54 17.15 9.17
4.61 952 4.29 6 89 5 44.54 17.15 9.17
4.00 690 2.69 6 89 5 44.54 17.15 9.17
connection pool size set to 100:
disk0 cpu load average
KB/t tps MB/s us sy id 1m 5m 15m
4.31 13 0.05 16 59 26 6.55 7.86 7.49
750.67 3 2.20 30 53 17 6.58 7.85 7.48
9.14 225 2.01 23 54 23 6.58 7.85 7.48
37.00 8 0.29 23 56 21 6.58 7.85 7.48
32.00 6 0.19 18 55 26 6.58 7.85 7.48
145.20 10 1.41 22 56 22 6.58 7.85 7.48
0.00 0 0.00 22 56 22 6.46 7.80 7.47
4.00 2660 10.39 24 58 18 6.46 7.80 7.47
4.00 1952 7.62 19 56 25 6.46 7.80 7.47
4.00 1 0.00 19 56 24 6.46 7.80 7.47
4.00 5 0.02 18 56 27 6.46 7.80 7.47
0.00 0 0.00 15 57 28 6.10 7.71 7.44
0.00 0 0.00 18 57 25 6.10 7.71 7.44
256.00 10 2.50 18 56 25 6.10 7.71 7.44
6.29 7 0.04 20 57 23 6.10 7.71 7.44
4.00 5 0.02 20 56 24 6.10 7.71 7.44
17.71 7 0.12 20 56 24 6.01 7.66 7.42
23.00 4 0.09 20 58 23 6.01 7.66 7.42
5.00 4 0.02 23 55 22 6.01 7.66 7.42
4.00 1 0.00 20 56 24 6.01 7.66 7.42

Related

Depth profile visual

Hi there I was wondering whether someone might assist with combining plots generated using the example provide on this page Depth Profiling visualization where I have analyzed data for salinity and depth, however I have a categorical variable dividing three estuaries based on whether the mouth is "closed", "open", or "semi-closed". I used the code of Depth Profiling Visualization, however each plot has its own salinity legend scale per plot.
Here is the data.
State Distance Depth pH DO Chla Salinity Max.depth
1 Closed 0.60 0.0 8.66 10.64 0.8880000 18.49 -1.3
2 Closed 0.60 0.5 8.68 10.79 1.4800000 18.51 -1.3
3 Closed 0.60 1.3 8.73 11.26 1.1840000 18.51 -1.3
4 Closed 1.00 0.0 8.48 9.07 5.3280000 18.18 -0.8
5 Closed 1.00 0.8 8.47 8.30 2.9600000 18.35 -0.8
6 Closed 1.60 0.0 8.38 9.70 1.1840000 18.38 -2.0
7 Closed 1.60 0.5 8.40 9.33 NA 18.39 -2.0
8 Closed 1.60 1.0 8.40 9.27 1.1840000 18.39 -2.0
9 Closed 1.60 1.5 8.41 9.27 NA 18.41 -2.0
10 Closed 1.60 2.0 8.47 9.23 1.4800000 18.57 -2.0
11 Closed 2.15 0.0 8.40 9.85 2.6640000 18.26 -3.5
12 Closed 2.15 0.5 8.41 9.95 NA 18.27 -3.5
13 Closed 2.15 1.0 8.42 9.16 1.1840000 18.28 -3.5
14 Closed 2.15 2.0 8.42 9.82 NA 18.28 -3.5
15 Closed 2.15 3.5 8.38 9.17 0.5920000 18.30 -3.5
16 Closed 3.50 0.0 8.30 9.82 2.0720000 17.71 -5.0
17 Closed 3.50 0.5 8.31 9.78 NA 17.71 -5.0
18 Closed 3.50 1.0 8.32 9.75 1.4800000 17.72 -5.0
19 Closed 3.50 2.0 8.32 9.73 NA 17.78 -5.0
20 Closed 3.50 3.0 8.30 9.20 NA 17.95 -5.0
21 Closed 3.50 4.0 8.29 8.80 NA 18.00 -5.0
22 Closed 3.50 5.0 8.24 7.47 1.4800000 18.06 -5.0
23 Closed 4.85 0.0 8.21 10.10 2.9600000 17.33 -1.6
24 Closed 4.85 0.5 8.21 9.90 2.0720000 17.33 -1.6
25 Closed 4.85 1.0 8.21 9.73 NA 17.32 -1.6
26 Closed 4.85 1.6 8.22 9.60 1.1840000 17.32 -1.6
27 Closed 6.00 0.0 8.07 9.07 4.4400000 16.65 -1.5
28 Closed 6.00 0.5 8.06 8.98 5.6240000 16.65 -1.5
29 Closed 6.00 1.0 8.06 8.81 NA 16.67 -1.5
30 Closed 6.00 1.5 8.10 8.80 4.1440000 16.67 -1.5
31 Closed 6.70 0.0 7.83 9.25 0.0000000 13.90 -0.5
32 Open 0.60 0.0 7.56 8.42 1.1840000 1.62 -0.5
33 Open 0.60 0.5 7.62 8.40 1.9733333 1.79 -0.5
34 Open 1.00 0.0 7.67 8.55 1.1840000 1.10 -0.4
35 Open 1.00 0.4 7.62 8.49 1.5786667 1.10 -0.4
36 Open 1.60 0.0 7.48 8.40 1.5786667 0.98 -1.0
37 Open 1.60 0.5 7.47 8.33 NA 0.98 -1.0
38 Open 1.60 1.0 7.45 8.33 2.7626667 0.99 -1.0
39 Open 2.15 0.0 7.19 7.99 1.1840000 0.85 -1.0
40 Open 2.15 0.5 7.19 7.96 NA 0.86 -1.0
41 Open 2.15 1.0 7.18 7.98 1.1840000 0.89 -1.0
42 Open 3.50 0.0 7.14 7.56 0.3946667 0.55 -4.8
43 Open 3.50 0.5 7.20 7.50 NA 0.55 -4.8
44 Open 3.50 1.0 7.28 7.38 1.9733333 0.55 -4.8
45 Open 3.50 2.0 7.38 7.10 NA 0.55 -4.8
46 Open 3.50 3.0 7.56 6.15 NA 0.56 -4.8
47 Open 3.50 4.0 7.20 4.43 NA 2.65 -4.8
48 Open 3.50 4.8 6.93 2.25 1.9733333 6.76 -4.8
49 Open 4.85 0.0 6.90 8.29 1.1840000 0.26 -1.2
50 Open 4.85 0.5 6.77 8.20 0.7893333 0.27 -1.2
51 Open 4.85 1.2 6.55 8.20 0.7893333 0.39 -1.2
52 Open 6.00 0.0 6.49 9.53 1.1840000 0.13 -1.0
53 Open 6.00 0.5 6.59 9.53 NA 0.13 -1.0
54 Open 6.00 1.0 6.79 9.53 1.1840000 0.13 -1.0
55 Open 6.70 0.0 6.48 9.48 0.7893333 0.11 -0.5
56 Semi-closed 0.60 0.0 8.05 6.30 19.7300000 18.86 -1.4
57 Semi-closed 0.60 0.5 8.04 5.19 19.7300000 24.07 -1.4
58 Semi-closed 0.60 1.0 8.00 5.98 NA 30.50 -1.4
59 Semi-closed 0.60 1.4 7.87 6.19 5.1300000 31.18 -1.4
60 Semi-closed 1.00 0.0 7.99 5.75 22.8900000 18.81 -0.9
61 Semi-closed 1.00 0.5 7.95 5.10 NA 19.08 -0.9
62 Semi-closed 1.00 0.9 7.86 3.42 11.8400000 26.60 -0.9
63 Semi-closed 1.60 0.0 7.88 6.05 11.4500000 17.29 -1.7
64 Semi-closed 1.60 0.5 7.87 5.78 NA 17.32 -1.7
65 Semi-closed 1.60 1.0 7.86 4.74 8.6800000 17.44 -1.7
66 Semi-closed 1.60 1.5 7.84 3.90 NA 19.65 -1.7
67 Semi-closed 1.60 1.7 7.91 3.75 9.0800000 21.07 -1.7
68 Semi-closed 2.15 0.0 7.91 6.95 22.8900000 16.50 -1.3
69 Semi-closed 2.15 0.5 7.92 6.76 26.4400000 16.50 -1.3
70 Semi-closed 2.15 1.0 7.91 5.99 NA 17.40 -1.3
71 Semi-closed 2.15 1.3 7.97 4.10 7.1000000 18.79 -1.3
72 Semi-closed 3.50 0.0 7.75 6.13 18.5500000 15.86 -4.5
73 Semi-closed 3.50 0.5 7.72 5.90 NA 15.86 -4.5
74 Semi-closed 3.50 1.0 7.65 4.38 9.0800000 16.38 -4.5
75 Semi-closed 3.50 1.5 7.56 1.59 NA 20.09 -4.5
76 Semi-closed 3.50 2.0 7.55 0.38 NA 22.11 -4.5
77 Semi-closed 3.50 3.0 7.53 0.42 NA 30.36 -4.5
78 Semi-closed 3.50 4.0 7.52 0.52 NA 31.50 -4.5
79 Semi-closed 3.50 4.5 7.54 0.68 1.1800000 31.84 -4.5
80 Semi-closed 4.85 0.0 7.66 6.31 21.7100000 15.41 -1.6
81 Semi-closed 4.85 0.5 7.65 6.18 NA 15.44 -1.6
82 Semi-closed 4.85 1.0 7.65 5.57 21.3100000 15.54 -1.6
83 Semi-closed 4.85 1.6 7.52 0.76 6.7100000 22.60 -1.6
84 Semi-closed 6.00 0.0 7.74 8.50 87.6200000 13.11 -1.0
85 Semi-closed 6.00 0.5 7.66 7.38 NA 13.92 -1.0
86 Semi-closed 6.00 1.0 7.60 3.20 7.5000000 15.42 -1.0
87 Semi-closed 6.70 0.0 8.55 6.94 0.0000000 0.25 -0.5
I was hoping someone might be able to assist to unify the scales of the three legends from the three mouth conditions of estuary so that only one legend describing salinity for all plots is possible.

Slicing numpy with condition

I have numpy array with the sape of 178 rows X 14 columns like this:
0 1 2 3 4 5 6 7 8 9 10 11 \
0 1.0 14.23 1.71 2.43 15.6 127.0 2.80 3.06 0.28 2.29 5.64 1.04
1 1.0 13.20 1.78 2.14 11.2 100.0 2.65 2.76 0.26 1.28 4.38 1.05
2 1.0 13.16 2.36 2.67 18.6 101.0 2.80 3.24 0.30 2.81 5.68 1.03
3 1.0 14.37 1.95 2.50 16.8 113.0 3.85 3.49 0.24 2.18 7.80 0.86
4 1.0 13.24 2.59 2.87 21.0 118.0 2.80 2.69 0.39 1.82 4.32 1.04
.. ... ... ... ... ... ... ... ... ... ... ... ...
173 3.0 13.71 5.65 2.45 20.5 95.0 1.68 0.61 0.52 1.06 7.70 0.64
174 3.0 13.40 3.91 2.48 23.0 102.0 1.80 0.75 0.43 1.41 7.30 0.70
175 3.0 13.27 4.28 2.26 20.0 120.0 1.59 0.69 0.43 1.35 10.20 0.59
176 3.0 13.17 2.59 2.37 20.0 120.0 1.65 0.68 0.53 1.46 9.30 0.60
177 3.0 14.13 4.10 2.74 24.5 96.0 2.05 0.76 0.56 1.35 9.20 0.61
12 13
0 3.92 1065.0
1 3.40 1050.0
2 3.17 1185.0
3 3.45 1480.0
4 2.93 735.0
.. ... ...
173 1.74 740.0
174 1.56 750.0
175 1.56 835.0
176 1.62 840.0
177 1.60 560.0
[178 rows x 14 columns]
I tried to print it in dataframe for all the rows and only the first (index 0) column and the output worked like this:
0
0 1.0
1 1.0
2 1.0
3 1.0
4 1.0
.. ...
173 3.0
174 3.0
175 3.0
176 3.0
177 3.0
[178 rows x 1 columns]
using the same logic, I want totake all the rows and only the first column with the value is below 2. I tried to do it like this and it doesn't work:
reduced = data[data[:,0:1]<=2]
I got an
IndexError
like this:
IndexError Traceback (most recent call last)
<ipython-input-159-7eab0abd8f99> in <module>()
----> 1 reduced = data[data[:,0:1]<=2]
IndexError: boolean index did not match indexed array along dimension 1; dimension is 14 but corresponding boolean dimension is 1.
anybody can help me?
thank in advance
Solved it.
It is just as simple as just convert the numpy array to dataframe and then select rows based on condition in dataframe:
reduced = data[data['class'] <= 2]

Replace closest values by average (or min/max) and keep exactly two rows by id if all values by id are equal

I have a dataframe that looks like this:
df_segments =
id seg_length
15 000b994d-1a6b-4698-a270-b0f671b1e612 16.3
11 000b994d-1a6b-4698-a270-b0f671b1e612 1.1
3 000b994d-1a6b-4698-a270-b0f671b1e612 1.1
7 000b994d-1a6b-4698-a270-b0f671b1e612 16.3
31 016490a8-8740-4205-bfe4-c9fe45e853d3 1.0
27 016490a8-8740-4205-bfe4-c9fe45e853d3 1.4
19 016490a8-8740-4205-bfe4-c9fe45e853d3 1.4
23 016490a8-8740-4205-bfe4-c9fe45e853d3 1.0
39 05290fe1-ead2-462b-bbec-a7669eed7883 1.1
35 05290fe1-ead2-462b-bbec-a7669eed7883 1.4
47 05290fe1-ead2-462b-bbec-a7669eed7883 1.1
43 05290fe1-ead2-462b-bbec-a7669eed7883 1.4
63 0537a9e3-09c4-459c-a6e4-25694cfbacbd 1.1
59 0537a9e3-09c4-459c-a6e4-25694cfbacbd 1.4
51 0537a9e3-09c4-459c-a6e4-25694cfbacbd 1.4
55 0537a9e3-09c4-459c-a6e4-25694cfbacbd 1.1
71 05577c2e-da7d-4753-bba6-66762385e159 1.0
67 05577c2e-da7d-4753-bba6-66762385e159 5.4
79 05577c2e-da7d-4753-bba6-66762385e159 1.0
75 05577c2e-da7d-4753-bba6-66762385e159 5.4
1475 5a104c86-327e-466f-b14a-6953cacddcbb 0.5
1479 5a104c86-327e-466f-b14a-6953cacddcbb 0.5
1487 5a104c86-327e-466f-b14a-6953cacddcbb 0.5
1483 5a104c86-327e-466f-b14a-6953cacddcbb 0.5
2287 8e853797-a7f3-4605-8848-f6b211f9b055 2.1
2283 8e853797-a7f3-4605-8848-f6b211f9b055 2.1
2279 8e853797-a7f3-4605-8848-f6b211f9b055 2.1
2275 8e853797-a7f3-4605-8848-f6b211f9b055 2.1
3351 c1120018-c626-4c1b-81a5-476ce38f346b 0.6
3347 c1120018-c626-4c1b-81a5-476ce38f346b 1.2
3359 c1120018-c626-4c1b-81a5-476ce38f346b 0.5
3355 c1120018-c626-4c1b-81a5-476ce38f346b 1.2
All id have four row. For most id, dropping duplicates results in two rows. But for a few ids one of two things can happen:
Either all rows are equal, in which case drop_duplicates() will result in a single row.
drop_duplicates() with result in three or for rows because all values of seg_length are different.
However, all seg_length are the length of the sides in a rectangle (or very close to it) and squares. So, what I would like to do are the following things:
A. If all rows by id have the same seg_length value, keep two rows.
B. Replace the two largest (resp. smallest) values (by id) with their average. In other words:
df_segments =
id seg_length
3351 c1120018-c626-4c1b-81a5-476ce38f346b 0.6
3347 c1120018-c626-4c1b-81a5-476ce38f346b 1.2
3359 c1120018-c626-4c1b-81a5-476ce38f346b 0.5
3355 c1120018-c626-4c1b-81a5-476ce38f346b 1.2
would become:
df_segments =
id seg_length
3351 c1120018-c626-4c1b-81a5-476ce38f346b 0.55
3347 c1120018-c626-4c1b-81a5-476ce38f346b 1.2
3359 c1120018-c626-4c1b-81a5-476ce38f346b 0.55
3355 c1120018-c626-4c1b-81a5-476ce38f346b 1.2
Alternatively, use min/max if it is easier:
df_segments =
id seg_length
3351 c1120018-c626-4c1b-81a5-476ce38f346b 0.6
3347 c1120018-c626-4c1b-81a5-476ce38f346b 1.2
3359 c1120018-c626-4c1b-81a5-476ce38f346b 0.6
3355 c1120018-c626-4c1b-81a5-476ce38f346b 1.2
I have tried to use np.where and define conditions but without any luck. I also tried to create a separate dataframe with the ids whose count was not 2 after dropping duplicates from the original dataframe, df_segments but I ended up in the same situation.
If anyone has an idea, I would be thankful for insights.
If I understand well, you want to average values 2 by 2 within each id. This also happens to do what you want when it’s 4 times the same value.
>>> averages = df.groupby('id')['seg_length'].apply(
... lambda s: s.sort_values().groupby([0, 0, 1, 1]).mean()
... )
>>> averages
id
000b994d-1a6b-4698-a270-b0f671b1e612 0 1.10
1 16.30
016490a8-8740-4205-bfe4-c9fe45e853d3 0 1.00
1 1.40
05290fe1-ead2-462b-bbec-a7669eed7883 0 1.10
1 1.40
0537a9e3-09c4-459c-a6e4-25694cfbacbd 0 1.10
1 1.40
05577c2e-da7d-4753-bba6-66762385e159 0 1.00
1 5.40
5a104c86-327e-466f-b14a-6953cacddcbb 0 0.50
1 0.50
8e853797-a7f3-4605-8848-f6b211f9b055 0 2.10
1 2.10
c1120018-c626-4c1b-81a5-476ce38f346b 0 0.55
1 1.20
Name: seg_length, dtype: float64
If you want to keep the original shape, you can use transform (on both groupbys):
>>> replaced_seglengths = df.groupby('id')['seg_length'].transform(
... lambda s: s.sort_values().groupby([0, 0, 1, 1]).transform('mean')
... )
>>> replaced_seglengths
15 1.10
11 1.10
3 16.30
7 16.30
31 1.00
27 1.00
19 1.40
23 1.40
39 1.10
35 1.10
47 1.40
43 1.40
63 1.10
59 1.10
51 1.40
55 1.40
71 1.00
67 1.00
79 5.40
75 5.40
1475 0.50
1479 0.50
1487 0.50
1483 0.50
2287 2.10
2283 2.10
2279 2.10
2275 2.10
3351 0.55
3347 0.55
3359 1.20
3355 1.20
Finally replace the column in the dataframe:
>>> df['seg_length'] = replaced_seglengths
>>> df
id seg_length
15 000b994d-1a6b-4698-a270-b0f671b1e612 1.10
11 000b994d-1a6b-4698-a270-b0f671b1e612 1.10
3 000b994d-1a6b-4698-a270-b0f671b1e612 16.30
7 000b994d-1a6b-4698-a270-b0f671b1e612 16.30
31 016490a8-8740-4205-bfe4-c9fe45e853d3 1.00
27 016490a8-8740-4205-bfe4-c9fe45e853d3 1.00
19 016490a8-8740-4205-bfe4-c9fe45e853d3 1.40
23 016490a8-8740-4205-bfe4-c9fe45e853d3 1.40
39 05290fe1-ead2-462b-bbec-a7669eed7883 1.10
35 05290fe1-ead2-462b-bbec-a7669eed7883 1.10
47 05290fe1-ead2-462b-bbec-a7669eed7883 1.40
43 05290fe1-ead2-462b-bbec-a7669eed7883 1.40
63 0537a9e3-09c4-459c-a6e4-25694cfbacbd 1.10
59 0537a9e3-09c4-459c-a6e4-25694cfbacbd 1.10
51 0537a9e3-09c4-459c-a6e4-25694cfbacbd 1.40
55 0537a9e3-09c4-459c-a6e4-25694cfbacbd 1.40
71 05577c2e-da7d-4753-bba6-66762385e159 1.00
67 05577c2e-da7d-4753-bba6-66762385e159 1.00
79 05577c2e-da7d-4753-bba6-66762385e159 5.40
75 05577c2e-da7d-4753-bba6-66762385e159 5.40
1475 5a104c86-327e-466f-b14a-6953cacddcbb 0.50
1479 5a104c86-327e-466f-b14a-6953cacddcbb 0.50
1487 5a104c86-327e-466f-b14a-6953cacddcbb 0.50
1483 5a104c86-327e-466f-b14a-6953cacddcbb 0.50
2287 8e853797-a7f3-4605-8848-f6b211f9b055 2.10
2283 8e853797-a7f3-4605-8848-f6b211f9b055 2.10
2279 8e853797-a7f3-4605-8848-f6b211f9b055 2.10
2275 8e853797-a7f3-4605-8848-f6b211f9b055 2.10
3351 c1120018-c626-4c1b-81a5-476ce38f346b 0.55
3347 c1120018-c626-4c1b-81a5-476ce38f346b 0.55
3359 c1120018-c626-4c1b-81a5-476ce38f346b 1.20
3355 c1120018-c626-4c1b-81a5-476ce38f346b 1.20
use np.select([conditions],[solutions])
conditons
condition1=df2.groupby('id')['seg_length'].apply(lambda x:x.duplicated(keep=False))
condition2=df2.groupby('id')['seg_length'].apply(lambda x:~x.duplicated(keep=False))
Solution
sol1=df2['seg_length']
sol2=(df2.loc[condition2,'seg_length'].sum(0))/2
df2['newseg_length']=np.select([condition1,condition2],[sol1,sol2])
id seg_length newseg_length
3351 c1120018-c626-4c1b-81a5-476ce38f346b 0.6 0.55
3347 c1120018-c626-4c1b-81a5-476ce38f346b 1.2 1.20
3359 c1120018-c626-4c1b-81a5-476ce38f346b 0.5 0.55
3355 c1120018-c626-4c1b-81a5-476ce38f346b 1.2 1.20

Is pow(x, 2.0) fast as x * x in GLSL? [duplicate]

Which is faster in GLSL:
pow(x, 3.0f);
or
x*x*x;
?
Does exponentiation performance depend on hardware vendor or exponent value?
I wrote a small benchmark, because I was interested in the results.
In my personal case, I was most interested in exponent = 5.
Benchmark code (running in Rem's Studio / LWJGL):
package me.anno.utils.bench
import me.anno.gpu.GFX
import me.anno.gpu.GFX.flat01
import me.anno.gpu.RenderState
import me.anno.gpu.RenderState.useFrame
import me.anno.gpu.framebuffer.Frame
import me.anno.gpu.framebuffer.Framebuffer
import me.anno.gpu.hidden.HiddenOpenGLContext
import me.anno.gpu.shader.Renderer
import me.anno.gpu.shader.Shader
import me.anno.utils.types.Floats.f2
import org.lwjgl.opengl.GL11.*
import java.nio.ByteBuffer
import kotlin.math.roundToInt
fun main() {
fun createShader(code: String) = Shader(
"", null, "" +
"attribute vec2 attr0;\n" +
"void main(){\n" +
" gl_Position = vec4(attr0*2.0-1.0, 0.0, 1.0);\n" +
" uv = attr0;\n" +
"}", "varying vec2 uv;\n", "" +
"void main(){" +
code +
"}"
)
fun repeat(code: String, times: Int): String {
return Array(times) { code }.joinToString("\n")
}
val size = 512
val warmup = 50
val benchmark = 1000
HiddenOpenGLContext.setSize(size, size)
HiddenOpenGLContext.createOpenGL()
val buffer = Framebuffer("", size, size, 1, 1, true, Framebuffer.DepthBufferType.NONE)
println("Power,Multiplications,GFlops-multiplication,GFlops-floats,GFlops-ints,GFlops-power,Speedup")
useFrame(buffer, Renderer.colorRenderer) {
RenderState.blendMode.use(me.anno.gpu.blending.BlendMode.ADD) {
for (power in 2 until 100) {
// to reduce the overhead of other stuff
val repeats = 100
val init = "float x1 = dot(uv, vec2(1.0)),x2,x4,x8,x16,x32,x64;\n"
val end = "gl_FragColor = vec4(x1,x1,x1,x1);\n"
val manualCode = StringBuilder()
for (bit in 1 until 32) {
val p = 1.shl(bit)
val h = 1.shl(bit - 1)
if (power == p) {
manualCode.append("x1=x$h*x$h;")
break
} else if (power > p) {
manualCode.append("x$p=x$h*x$h;")
} else break
}
if (power.and(power - 1) != 0) {
// not a power of two, so the result isn't finished yet
manualCode.append("x1=")
var first = true
for (bit in 0 until 32) {
val p = 1.shl(bit)
if (power.and(p) != 0) {
if (!first) {
manualCode.append('*')
} else first = false
manualCode.append("x$p")
}
}
manualCode.append(";\n")
}
val multiplications = manualCode.count { it == '*' }
// println("$power: $manualCode")
val shaders = listOf(
// manually optimized
createShader(init + repeat(manualCode.toString(), repeats) + end),
// can be optimized
createShader(init + repeat("x1=pow(x1,$power.0);", repeats) + end),
// can be optimized, int as power
createShader(init + repeat("x1=pow(x1,$power);", repeats) + end),
// slightly different, so it can't be optimized
createShader(init + repeat("x1=pow(x1,${power}.01);", repeats) + end),
)
for (shader in shaders) {
shader.use()
}
val pixels = ByteBuffer.allocateDirect(4)
Frame.bind()
glClearColor(0f, 0f, 0f, 1f)
glClear(GL_COLOR_BUFFER_BIT or GL_DEPTH_BUFFER_BIT)
for (i in 0 until warmup) {
for (shader in shaders) {
shader.use()
flat01.draw(shader)
}
}
val flops = DoubleArray(shaders.size)
val avg = 10 // for more stability between runs
for (j in 0 until avg) {
for (index in shaders.indices) {
val shader = shaders[index]
GFX.check()
val t0 = System.nanoTime()
for (i in 0 until benchmark) {
shader.use()
flat01.draw(shader)
}
// synchronize
glReadPixels(0, 0, 1, 1, GL_RGBA, GL_UNSIGNED_BYTE, pixels)
GFX.check()
val t1 = System.nanoTime()
// the first one may be an outlier
if (j > 0) flops[index] += multiplications * repeats.toDouble() * benchmark.toDouble() * size * size / (t1 - t0)
GFX.check()
}
}
for (i in flops.indices) {
flops[i] /= (avg - 1.0)
}
println(
"" +
"$power,$multiplications," +
"${flops[0].roundToInt()}," +
"${flops[1].roundToInt()}," +
"${flops[2].roundToInt()}," +
"${flops[3].roundToInt()}," +
(flops[0] / flops[3]).f2()
)
}
}
}
}
The sampler function is run 9x 512² pixels * 1000 times, and evaluates the function 100 times each.
I run this code on my RX 580, 8GB from Gigabyte, and collected the following results:
Power
#Mult
GFlops*
GFlopsFp
GFlopsInt
GFlopsPow
Speedup
2
1
1246
1429
1447
324
3.84
3
2
2663
2692
2708
651
4.09
4
2
2682
2679
2698
650
4.12
5
3
2766
972
974
973
2.84
6
3
2785
978
974
976
2.85
7
4
2830
1295
1303
1299
2.18
8
3
2783
2792
2809
960
2.90
9
4
2836
1298
1301
1302
2.18
10
4
2833
1291
1302
1298
2.18
11
5
2858
1623
1629
1623
1.76
12
4
2824
1302
1295
1303
2.17
13
5
2866
1628
1624
1626
1.76
14
5
2869
1614
1623
1611
1.78
15
6
2886
1945
1943
1953
1.48
16
4
2821
1305
1300
1305
2.16
17
5
2868
1615
1625
1619
1.77
18
5
2858
1620
1625
1624
1.76
19
6
2890
1949
1946
1949
1.48
20
5
2871
1618
1627
1625
1.77
21
6
2879
1945
1947
1943
1.48
22
6
2886
1944
1949
1952
1.48
23
7
2901
2271
2269
2268
1.28
24
5
2872
1621
1628
1624
1.77
25
6
2886
1942
1943
1942
1.49
26
6
2880
1949
1949
1953
1.47
27
7
2891
2273
2263
2266
1.28
28
6
2883
1949
1946
1953
1.48
29
7
2910
2279
2281
2279
1.28
30
7
2899
2272
2276
2277
1.27
31
8
2906
2598
2595
2596
1.12
32
5
2872
1621
1625
1622
1.77
33
6
2901
1953
1942
1949
1.49
34
6
2895
1948
1939
1944
1.49
35
7
2895
2274
2266
2268
1.28
36
6
2881
1937
1944
1948
1.48
37
7
2894
2277
2270
2280
1.27
38
7
2902
2275
2264
2273
1.28
39
8
2910
2602
2594
2603
1.12
40
6
2877
1945
1947
1945
1.48
41
7
2892
2276
2277
2282
1.27
42
7
2887
2271
2272
2273
1.27
43
8
2912
2599
2606
2599
1.12
44
7
2910
2278
2284
2276
1.28
45
8
2920
2597
2601
2600
1.12
46
8
2920
2600
2601
2590
1.13
47
9
2925
2921
2926
2927
1.00
48
6
2885
1935
1955
1956
1.47
49
7
2901
2271
2279
2288
1.27
50
7
2904
2281
2276
2278
1.27
51
8
2919
2608
2594
2607
1.12
52
7
2902
2282
2270
2273
1.28
53
8
2903
2598
2602
2598
1.12
54
8
2918
2602
2602
2604
1.12
55
9
2932
2927
2924
2936
1.00
56
7
2907
2284
2282
2281
1.27
57
8
2920
2606
2604
2610
1.12
58
8
2913
2593
2597
2587
1.13
59
9
2925
2923
2924
2920
1.00
60
8
2930
2614
2606
2613
1.12
61
9
2932
2946
2946
2947
1.00
62
9
2926
2935
2937
2947
0.99
63
10
2958
3258
3192
3266
0.91
64
6
2902
1957
1956
1959
1.48
65
7
2903
2274
2267
2273
1.28
66
7
2909
2277
2276
2286
1.27
67
8
2908
2602
2606
2599
1.12
68
7
2894
2272
2279
2276
1.27
69
8
2923
2597
2606
2606
1.12
70
8
2910
2596
2599
2600
1.12
71
9
2926
2921
2927
2924
1.00
72
7
2909
2283
2273
2273
1.28
73
8
2909
2602
2602
2599
1.12
74
8
2914
2602
2602
2603
1.12
75
9
2924
2925
2927
2933
1.00
76
8
2904
2608
2602
2601
1.12
77
9
2911
2919
2917
2909
1.00
78
9
2927
2921
2917
2935
1.00
79
10
2929
3241
3246
3246
0.90
80
7
2903
2273
2276
2275
1.28
81
8
2916
2596
2592
2589
1.13
82
8
2913
2600
2597
2598
1.12
83
9
2925
2931
2926
2913
1.00
84
8
2917
2598
2606
2597
1.12
85
9
2920
2916
2918
2927
1.00
86
9
2942
2922
2944
2936
1.00
87
10
2961
3254
3259
3268
0.91
88
8
2934
2607
2608
2612
1.12
89
9
2918
2939
2931
2916
1.00
90
9
2927
2928
2920
2924
1.00
91
10
2940
3253
3252
3246
0.91
92
9
2924
2933
2926
2928
1.00
93
10
2940
3259
3237
3251
0.90
94
10
2928
3247
3247
3264
0.90
95
11
2933
3599
3593
3594
0.82
96
7
2883
2282
2268
2269
1.27
97
8
2911
2602
2595
2600
1.12
98
8
2896
2588
2591
2587
1.12
99
9
2924
2939
2936
2938
1.00
As you can see, a power() call takes exactly as long as 9 multiplication instructions. Therefore every manual rewriting of a power with less than 9 multiplications is faster.
Only the cases 2, 3, 4, and 8 are optimized by my driver. The optimization is independent of whether you use the .0 suffix for the exponent.
In the case of exponent = 2, my implementation seems to have lower performance than the driver. I am not sure, why.
The speedup is the manual implementation compared to pow(x,exponent+0.01), which cannot be optimized by the compiler.
Because the multiplications and the speedup align so perfectly, I created a graph to show the relationship. This relationship kind of shows that my benchmark is trustworthy :).
Operating System: Windows 10 Personal
GPU: RX 580 8GB from Gigabyte
Processor: Ryzen 5 2600
Memory: 16 GB DDR4 3200
GPU Driver: 21.6.1 from 17th June 2021
LWJGL: Version 3.2.3 build 13
While this can definitely be hardware/vendor/compiler dependent, advanced mathematical functions like pow() tend to be considerably more expensive than basic operations.
The best approach is of course to try both, and benchmark. But if there is a simple replacement for an advanced mathematical functions, I don't think you can go very wrong by using it.
If you write pow(x, 3.0), the best you can probably hope for is that the compiler will recognize the special case, and expand it. But why take the risk, if the replacement is just as short and easy to read? C/C++ compilers don't always replace pow(x, 2.0) by a simple multiplication, so I wouldn't necessarily count on all GLSL compilers to do that.

Extracting the second last line from a table using a specific number followed by an asterisk (e.g. xy.z*)

I'm looking to extract and print a specific line from a table I have in a long log file. It looks something like this:
******************************************************************************
XSCALE (VERSION July 4, 2012) 4-Jun-2013
******************************************************************************
Author: Wolfgang Kabsch
Copy licensed until 30-Jun-2013 to
academic users for non-commercial applications
No redistribution.
******************************************************************************
CONTROL CARDS
******************************************************************************
MAXIMUM_NUMBER_OF_PROCESSORS=16
RESOLUTION_SHELLS= 20 10 6 4 3 2.5 2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.8
MINIMUM_I/SIGMA=4.0
OUTPUT_FILE=fae-ip.ahkl
INPUT_FILE= /dls/sci-scratch/Sam/FC59251/fr6_1/XDS_ASCII.HKL
THE DATA COLLECTION STATISTICS REPORTED BELOW ASSUMES:
SPACE_GROUP_NUMBER= 97
UNIT_CELL_CONSTANTS= 128.28 128.28 181.47 90.000 90.000 90.000
***** 16 EQUIVALENT POSITIONS IN SPACE GROUP # 97 *****
If x',y',z' is an equivalent position to x,y,z, then
x'=x*ML(1)+y*ML( 2)+z*ML( 3)+ML( 4)/12.0
y'=x*ML(5)+y*ML( 6)+z*ML( 7)+ML( 8)/12.0
z'=x*ML(9)+y*ML(10)+z*ML(11)+ML(12)/12.0
# 1 2 3 4 5 6 7 8 9 10 11 12
1 1 0 0 0 0 1 0 0 0 0 1 0
2 -1 0 0 0 0 -1 0 0 0 0 1 0
3 -1 0 0 0 0 1 0 0 0 0 -1 0
4 1 0 0 0 0 -1 0 0 0 0 -1 0
5 0 1 0 0 1 0 0 0 0 0 -1 0
6 0 -1 0 0 -1 0 0 0 0 0 -1 0
7 0 -1 0 0 1 0 0 0 0 0 1 0
8 0 1 0 0 -1 0 0 0 0 0 1 0
9 1 0 0 6 0 1 0 6 0 0 1 6
10 -1 0 0 6 0 -1 0 6 0 0 1 6
11 -1 0 0 6 0 1 0 6 0 0 -1 6
12 1 0 0 6 0 -1 0 6 0 0 -1 6
13 0 1 0 6 1 0 0 6 0 0 -1 6
14 0 -1 0 6 -1 0 0 6 0 0 -1 6
15 0 -1 0 6 1 0 0 6 0 0 1 6
16 0 1 0 6 -1 0 0 6 0 0 1 6
ALL DATA SETS WILL BE SCALED TO /dls/sci-scratch/Sam/FC59251/fr6_1/XDS_ASCII.HKL
******************************************************************************
READING INPUT REFLECTION DATA FILES
******************************************************************************
DATA MEAN REFLECTIONS INPUT FILE NAME
SET# INTENSITY ACCEPTED REJECTED
1 0.1358E+03 1579957 0 /dls/sci-scratch/Sam/FC59251/fr6_1/XDS_ASCII.HKL
******************************************************************************
CORRECTION FACTORS AS FUNCTION OF IMAGE NUMBER & RESOLUTION
******************************************************************************
RECIPROCAL CORRECTION FACTORS FOR INPUT DATA SETS MERGED TO
OUTPUT FILE: fae-ip.ahkl
THE CALCULATIONS ASSUME FRIEDEL'S_LAW= TRUE
TOTAL NUMBER OF CORRECTION FACTORS DEFINED 720
DEGREES OF FREEDOM OF CHI^2 FIT 357222.9
CHI^2-VALUE OF FIT OF CORRECTION FACTORS 1.024
NUMBER OF CYCLES CARRIED OUT 4
CORRECTION FACTORS for visual inspection by XDS-Viewer DECAY_001.cbf
XMIN= 0.6 XMAX= 1799.3 NXBIN= 36
YMIN= 0.00049 YMAX= 0.44483 NYBIN= 20
NUMBER OF REFLECTIONS USED FOR DETERMINING CORRECTION FACTORS 396046
******************************************************************************
CORRECTION FACTORS AS FUNCTION OF X (fast) & Y(slow) IN THE DETECTOR PLANE
******************************************************************************
RECIPROCAL CORRECTION FACTORS FOR INPUT DATA SETS MERGED TO
OUTPUT FILE: fae-ip.ahkl
THE CALCULATIONS ASSUME FRIEDEL'S_LAW= TRUE
TOTAL NUMBER OF CORRECTION FACTORS DEFINED 7921
DEGREES OF FREEDOM OF CHI^2 FIT 356720.6
CHI^2-VALUE OF FIT OF CORRECTION FACTORS 1.023
NUMBER OF CYCLES CARRIED OUT 3
CORRECTION FACTORS for visual inspection by XDS-Viewer MODPIX_001.cbf
XMIN= 5.4 XMAX= 2457.6 NXBIN= 89
YMIN= 40.0 YMAX= 2516.7 NYBIN= 89
NUMBER OF REFLECTIONS USED FOR DETERMINING CORRECTION FACTORS 396046
******************************************************************************
CORRECTION FACTORS AS FUNCTION OF IMAGE NUMBER & DETECTOR SURFACE POSITION
******************************************************************************
RECIPROCAL CORRECTION FACTORS FOR INPUT DATA SETS MERGED TO
OUTPUT FILE: fae-ip.ahkl
THE CALCULATIONS ASSUME FRIEDEL'S_LAW= TRUE
TOTAL NUMBER OF CORRECTION FACTORS DEFINED 468
DEGREES OF FREEDOM OF CHI^2 FIT 357286.9
CHI^2-VALUE OF FIT OF CORRECTION FACTORS 1.022
NUMBER OF CYCLES CARRIED OUT 3
CORRECTION FACTORS for visual inspection by XDS-Viewer ABSORP_001.cbf
XMIN= 0.6 XMAX= 1799.3 NXBIN= 36
DETECTOR_SURFACE_POSITION= 1232 1278
DETECTOR_SURFACE_POSITION= 1648 1699
DETECTOR_SURFACE_POSITION= 815 1699
DETECTOR_SURFACE_POSITION= 815 858
DETECTOR_SURFACE_POSITION= 1648 858
DETECTOR_SURFACE_POSITION= 2174 1673
DETECTOR_SURFACE_POSITION= 1622 2230
DETECTOR_SURFACE_POSITION= 841 2230
DETECTOR_SURFACE_POSITION= 289 1673
DETECTOR_SURFACE_POSITION= 289 884
DETECTOR_SURFACE_POSITION= 841 326
DETECTOR_SURFACE_POSITION= 1622 326
DETECTOR_SURFACE_POSITION= 2174 884
NUMBER OF REFLECTIONS USED FOR DETERMINING CORRECTION FACTORS 396046
******************************************************************************
CORRECTION PARAMETERS FOR THE STANDARD ERROR OF REFLECTION INTENSITIES
******************************************************************************
The variance v0(I) of the intensity I obtained from counting statistics is
replaced by v(I)=a*(v0(I)+b*I^2). The model parameters a, b are chosen to
minimize the discrepancies between v(I) and the variance estimated from
sample statistics of symmetry related reflections. This model implicates
an asymptotic limit ISa=1/SQRT(a*b) for the highest I/Sigma(I) that the
experimental setup can produce (Diederichs (2010) Acta Cryst D66, 733-740).
Often the value of ISa is reduced from the initial value ISa0 due to systematic
errors showing up by comparison with other data sets in the scaling procedure.
(ISa=ISa0=-1 if v0 is unknown for a data set.)
a b ISa ISa0 INPUT DATA SET
1.086E+00 1.420E-03 25.46 29.00 /dls/sci-scratch/Sam/FC59251/fr6_1/XDS_ASCII.HKL
FACTOR TO PLACE ALL DATA SETS TO AN APPROXIMATE ABSOLUTE SCALE 0.4178E+04
(ASSUMING A PROTEIN WITH 50% SOLVENT)
******************************************************************************
STATISTICS OF SCALED OUTPUT DATA SET : fae-ip.ahkl
FILE TYPE: XDS_ASCII MERGE=FALSE FRIEDEL'S_LAW=TRUE
186 OUT OF 1579957 REFLECTIONS REJECTED
1579771 REFLECTIONS ON OUTPUT FILE
******************************************************************************
DEFINITIONS:
R-FACTOR
observed = (SUM(ABS(I(h,i)-I(h))))/(SUM(I(h,i)))
expected = expected R-FACTOR derived from Sigma(I)
COMPARED = number of reflections used for calculating R-FACTOR
I/SIGMA = mean of intensity/Sigma(I) of unique reflections
(after merging symmetry-related observations)
Sigma(I) = standard deviation of reflection intensity I
estimated from sample statistics
R-meas = redundancy independent R-factor (intensities)
Diederichs & Karplus (1997), Nature Struct. Biol. 4, 269-275.
CC(1/2) = percentage of correlation between intensities from
random half-datasets. Correlation significant at
the 0.1% level is marked by an asterisk.
Karplus & Diederichs (2012), Science 336, 1030-33
Anomal = percentage of correlation between random half-sets
Corr of anomalous intensity differences. Correlation
significant at the 0.1% level is marked.
SigAno = mean anomalous difference in units of its estimated
standard deviation (|F(+)-F(-)|/Sigma). F(+), F(-)
are structure factor estimates obtained from the
merged intensity observations in each parity class.
Nano = Number of unique reflections used to calculate
Anomal_Corr & SigAno. At least two observations
for each (+ and -) parity are required.
SUBSET OF INTENSITY DATA WITH SIGNAL/NOISE >= -3.0 AS FUNCTION OF RESOLUTION
RESOLUTION NUMBER OF REFLECTIONS COMPLETENESS R-FACTOR R-FACTOR COMPARED I/SIGMA R-meas CC(1/2) Anomal SigAno Nano
LIMIT OBSERVED UNIQUE POSSIBLE OF DATA observed expected Corr
20.00 557 66 74 89.2% 2.7% 3.0% 557 58.75 2.9% 100.0* 45 1.674 25
10.00 5018 417 417 100.0% 2.4% 3.1% 5018 75.34 2.6% 100.0* 2 0.812 276
6.00 18352 1583 1584 99.9% 2.8% 3.3% 18351 65.55 2.9% 100.0* 11* 0.914 1248
4.00 59691 4640 4640 100.0% 3.2% 3.5% 59690 64.96 3.4% 100.0* 4 0.857 3987
3.00 112106 8821 8822 100.0% 4.4% 4.4% 112102 50.31 4.6% 99.9* -3 0.844 7906
2.50 147954 11023 11023 100.0% 8.7% 8.6% 147954 29.91 9.1% 99.8* 0 0.829 10096
2.00 332952 24698 24698 100.0% 21.4% 21.6% 332949 14.32 22.3% 99.2* 1 0.804 22992
1.90 106645 8382 8384 100.0% 56.5% 57.1% 106645 5.63 58.8% 94.7* -2 0.767 7886
1.80 138516 10342 10343 100.0% 86.8% 87.0% 138516 3.64 90.2% 87.9* -2 0.762 9741
1.70 175117 12897 12899 100.0% 140.0% 140.1% 175116 2.15 145.4% 69.6* -2 0.732 12188
1.60 209398 16298 16304 100.0% 206.1% 208.5% 209397 1.35 214.6% 48.9* -2 0.693 15466
1.50 273432 20770 20893 99.4% 333.4% 342.1% 273340 0.80 346.9% 23.2* -1 0.644 19495
1.40 33 27 27248 0.1% 42.6% 112.7% 12 0.40 60.3% 88.2 0 0.000 0
1.30 0 0 36205 0.0% -99.9% -99.9% 0 -99.00 -99.9% 0.0 0 0.000 0
1.20 0 0 49238 0.0% -99.9% -99.9% 0 -99.00 -99.9% 0.0 0 0.000 0
1.10 0 0 68746 0.0% -99.9% -99.9% 0 -99.00 -99.9% 0.0 0 0.000 0
1.00 0 0 98884 0.0% -99.9% -99.9% 0 -99.00 -99.9% 0.0 0 0.000 0
0.90 0 0 147505 0.0% -99.9% -99.9% 0 -99.00 -99.9% 0.0 0 0.000 0
0.80 0 0 230396 0.0% -99.9% -99.9% 0 -99.00 -99.9% 0.0 0 0.000 0
total 1579771 119964 778303 15.4% 12.8% 13.1% 1579647 14.33 13.4% 99.9* -1 0.755 111306
========== STATISTICS OF INPUT DATA SET ==========
R-FACTORS FOR INTENSITIES OF DATA SET /dls/sci-scratch/Sam/FC59251/fr6_1/XDS_ASCII.HKL
RESOLUTION R-FACTOR R-FACTOR COMPARED
LIMIT observed expected
20.00 2.7% 3.0% 557
10.00 2.4% 3.1% 5018
6.00 2.8% 3.3% 18351
4.00 3.2% 3.5% 59690
3.00 4.4% 4.4% 112102
2.50 8.7% 8.6% 147954
2.00 21.4% 21.6% 332949
1.90 56.5% 57.1% 106645
1.80 86.8% 87.0% 138516
1.70 140.0% 140.1% 175116
1.60 206.1% 208.5% 209397
1.50 333.4% 342.1% 273340
1.40 42.6% 112.7% 12
1.30 -99.9% -99.9% 0
1.20 -99.9% -99.9% 0
1.10 -99.9% -99.9% 0
1.00 -99.9% -99.9% 0
0.90 -99.9% -99.9% 0
0.80 -99.9% -99.9% 0
total 12.8% 13.1% 1579647
******************************************************************************
WILSON STATISTICS OF SCALED DATA SET: fae-ip.ahkl
******************************************************************************
Data is divided into resolution shells and a straight line
A - 2*B*SS is fitted to log<I>, where
RES = mean resolution (Angstrom) in shell
SS = mean of (sin(THETA)/LAMBDA)**2 in shell
<I> = mean reflection intensity in shell
BO = (A - log<I>)/(2*SS)
# = number of reflections in resolution shell
WILSON LINE (using all data) : A= 14.997 B= 29.252 CORRELATION= 0.99
# RES SS <I> log(<I>) BO
1667 8.445 0.004 2.3084E+06 14.652 49.2
2798 5.260 0.009 1.5365E+06 14.245 41.6
3547 4.106 0.015 2.0110E+06 14.514 16.3
4147 3.480 0.021 1.2910E+06 14.071 22.4
4688 3.073 0.026 7.3586E+05 13.509 28.1
5154 2.781 0.032 4.6124E+05 13.042 30.3
5568 2.560 0.038 3.1507E+05 12.661 30.6
5966 2.384 0.044 2.4858E+05 12.424 29.2
6324 2.240 0.050 1.8968E+05 12.153 28.5
6707 2.119 0.056 1.3930E+05 11.844 28.3
7030 2.016 0.062 9.1378E+04 11.423 29.0
7331 1.926 0.067 5.4413E+04 10.904 30.4
7664 1.848 0.073 3.5484E+04 10.477 30.9
7934 1.778 0.079 2.4332E+04 10.100 31.0
8193 1.716 0.085 1.8373E+04 9.819 30.5
8466 1.660 0.091 1.4992E+04 9.615 29.7
8743 1.609 0.097 1.1894E+04 9.384 29.1
9037 1.562 0.102 9.4284E+03 9.151 28.5
9001 1.520 0.108 8.3217E+03 9.027 27.6
HIGHER ORDER MOMENTS OF WILSON DISTRIBUTION OF CENTRIC DATA
AS COMPARED WITH THEORETICAL VALUES. (EXPECTED: 1.00)
# RES <I**2>/ <I**3>/ <I**4>/
3<I>**2 15<I>**3 105<I>**4
440 8.445 0.740 0.505 0.294
442 5.260 0.762 0.733 0.735
442 4.106 0.888 0.788 0.717
439 3.480 1.339 1.733 2.278
438 3.073 1.168 1.259 1.400
440 2.781 1.215 1.681 2.269
438 2.560 1.192 1.603 2.405
450 2.384 1.117 1.031 0.891
432 2.240 1.214 1.567 2.173
438 2.119 0.972 0.992 0.933
445 2.016 1.029 1.019 0.986
441 1.926 1.603 1.701 1.554
440 1.848 1.544 1.871 2.076
436 1.778 0.927 0.661 0.435
444 1.716 1.134 1.115 1.197
440 1.660 1.271 1.618 2.890
436 1.609 1.424 1.045 0.941
448 1.562 1.794 1.447 1.423
426 1.520 2.517 1.496 2.099
8355 overall 1.253 1.255 1.455
HIGHER ORDER MOMENTS OF WILSON DISTRIBUTION OF ACENTRIC DATA
AS COMPARED WITH THEORETICAL VALUES. (EXPECTED: 1.00)
# RES <I**2>/ <I**3>/ <I**4>/
2<I>**2 6<I>**3 24<I>**4
1227 8.445 1.322 1.803 2.340
2356 5.260 1.167 1.420 1.789
3105 4.106 1.010 1.046 1.100
3708 3.480 1.055 1.262 1.592
4250 3.073 0.999 1.083 1.375
4714 2.781 1.061 1.232 1.591
5130 2.560 1.049 1.178 1.440
5516 2.384 1.025 1.117 1.290
5892 2.240 1.001 1.058 1.230
6269 2.119 1.060 1.140 1.233
6585 2.016 1.109 1.344 1.709
6890 1.926 1.028 1.100 1.222
7224 1.848 1.060 1.150 1.348
7498 1.778 1.143 1.309 1.655
7749 1.716 1.182 1.299 1.549
8026 1.660 1.286 1.376 1.538
8307 1.609 1.419 1.481 1.707
8589 1.562 1.663 1.750 2.119
8575 1.520 2.271 2.172 5.088
111610 overall 1.253 1.354 1.804
======= CUMULATIVE INTENSITY DISTRIBUTION =======
DEFINITIONS:
<I> = mean reflection intensity
Na(Z)exp = expected number of acentric reflections with I <= Z*<I>
Na(Z)obs = observed number of acentric reflections with I <= Z*<I>
Nc(Z)exp = expected number of centric reflections with I <= Z*<I>
Nc(Z)obs = observed number of centric reflections with I <= Z*<I>
Nc(Z)obs/Nc(Z)exp versus resolution and Z (0.1-1.0)
# RES 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
440 8.445 0.75 0.95 0.98 1.00 0.98 0.99 1.00 1.00 1.02 1.02
442 5.260 1.18 1.11 1.09 1.09 1.07 1.08 1.08 1.08 1.07 1.06
442 4.106 0.97 1.01 0.98 0.97 0.96 0.94 0.92 0.91 0.92 0.94
439 3.480 0.91 0.88 0.91 0.91 0.89 0.90 0.90 0.89 0.89 0.93
438 3.073 0.92 0.92 0.90 0.93 0.94 0.99 1.02 0.99 0.96 0.96
440 2.781 0.98 1.01 1.02 1.05 1.04 1.03 1.04 1.02 1.01 1.01
438 2.560 1.02 1.10 1.05 1.03 1.01 1.03 1.04 1.01 1.04 1.02
450 2.384 0.78 0.93 0.92 0.93 0.89 0.89 0.92 0.95 0.96 0.95
432 2.240 0.69 0.82 0.84 0.86 0.91 0.92 0.93 0.94 0.95 0.95
438 2.119 0.75 0.87 0.95 1.02 1.09 1.09 1.12 1.12 1.10 1.08
445 2.016 0.86 0.86 0.87 0.90 0.91 0.93 0.98 0.99 1.00 1.00
441 1.926 0.88 0.79 0.79 0.81 0.82 0.84 0.85 0.85 0.86 0.86
440 1.848 1.00 0.89 0.85 0.83 0.85 0.85 0.88 0.90 0.90 0.92
436 1.778 1.03 0.87 0.79 0.79 0.80 0.84 0.85 0.87 0.90 0.92
444 1.716 1.09 0.85 0.81 0.78 0.80 0.80 0.81 0.81 0.84 0.85
440 1.660 1.27 1.01 0.93 0.88 0.85 0.84 0.84 0.85 0.88 0.91
436 1.609 1.34 1.00 0.89 0.83 0.80 0.80 0.80 0.81 0.80 0.83
448 1.562 1.39 1.09 0.93 0.86 0.81 0.78 0.77 0.79 0.78 0.78
426 1.520 1.38 1.03 0.88 0.83 0.82 0.80 0.78 0.76 0.75 0.74
8355 overall 1.01 0.95 0.92 0.91 0.91 0.91 0.92 0.92 0.93 0.93
Na(Z)obs/Na(Z)exp versus resolution and Z (0.1-1.0)
# RES 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
1227 8.445 1.10 1.22 1.21 1.21 1.14 1.10 1.12 1.10 1.11 1.09
2356 5.260 1.15 1.10 1.09 1.03 1.03 1.03 1.01 1.01 1.01 1.00
3105 4.106 0.91 0.96 0.99 1.01 1.02 1.00 1.00 0.99 0.99 1.00
3708 3.480 0.93 0.97 1.00 1.06 1.05 1.04 1.04 1.04 1.04 1.05
4250 3.073 0.94 1.02 1.01 1.00 1.01 1.00 1.00 1.01 1.02 1.02
4714 2.781 1.11 1.04 1.02 1.02 1.02 1.01 1.01 1.01 1.00 1.00
5130 2.560 1.00 1.10 1.06 1.03 1.01 1.02 1.01 1.01 1.01 1.02
5516 2.384 1.09 1.08 1.05 1.04 1.04 1.02 1.01 1.01 1.01 1.01
5892 2.240 0.98 0.99 1.00 1.01 1.01 1.01 1.00 1.00 1.00 1.00
6269 2.119 1.14 1.04 1.02 1.00 1.00 1.00 1.01 1.02 1.02 1.01
6585 2.016 1.17 1.02 1.01 1.02 1.02 1.03 1.02 1.02 1.02 1.02
6890 1.926 1.35 1.07 1.00 0.99 1.00 1.01 1.01 1.00 1.00 1.01
7224 1.848 1.52 1.11 1.01 0.97 0.96 0.98 0.98 0.98 0.98 0.99
7498 1.778 1.80 1.22 1.03 0.97 0.95 0.94 0.95 0.95 0.95 0.96
7749 1.716 2.01 1.28 1.07 0.99 0.94 0.92 0.92 0.92 0.93 0.93
8026 1.660 2.31 1.41 1.13 1.01 0.95 0.92 0.90 0.89 0.89 0.89
8307 1.609 2.62 1.54 1.19 1.04 0.95 0.90 0.88 0.87 0.86 0.87
8589 1.562 2.94 1.69 1.29 1.10 1.00 0.93 0.89 0.86 0.85 0.85
8575 1.520 3.14 1.78 1.34 1.13 1.01 0.93 0.88 0.85 0.83 0.83
111610 overall 1.73 1.24 1.09 1.03 0.99 0.97 0.96 0.96 0.96 0.96
List of 33 reflections *NOT* obeying Wilson distribution (Z> 10.0)
h k l RES Z Intensity Sigma
72 11 61 1.52 17.34 0.2886E+06 0.2367E+05 "alien"
67 53 6 1.50 15.85 0.2638E+06 0.1128E+06 "alien"
35 10 25 3.17 14.39 0.2118E+08 0.2364E+06 "alien"
46 17 99 1.50 14.16 0.2357E+06 0.9588E+05 "alien"
34 32 2 2.75 13.44 0.1239E+08 0.1279E+06 "alien"
79 6 15 1.60 13.10 0.3117E+06 0.2477E+05 "alien"
61 20 33 1.88 12.54 0.8900E+06 0.3054E+05 "alien"
44 4 48 2.30 12.38 0.4695E+07 0.6072E+05 "alien"
66 25 19 1.79 11.89 0.5788E+06 0.2739E+05 "alien"
66 25 11 1.81 11.88 0.5781E+06 0.2771E+05 "alien"
60 43 61 1.50 11.77 0.1959E+06 0.9769E+05 "alien"
72 11 17 1.74 11.64 0.4278E+06 0.2619E+05 "alien"
80 24 26 1.50 11.41 0.1899E+06 0.9793E+05 "alien"
41 21 26 2.59 11.09 0.6988E+07 0.7945E+05 "alien"
44 18 20 2.59 11.08 0.6982E+07 0.7839E+05 "alien"
23 3 62 2.59 11.06 0.6971E+07 0.9154E+05 "alien"
69 7 22 1.80 11.06 0.5383E+06 0.2564E+05 "alien"
73 10 15 1.72 10.98 0.4036E+06 0.2356E+05 "alien"
70 17 35 1.68 10.96 0.3286E+06 0.2415E+05 "alien"
57 24 41 1.88 10.91 0.7746E+06 0.2842E+05 "alien"
82 24 6 1.50 10.74 0.1787E+06 0.1019E+06 "alien"
69 25 62 1.50 10.67 0.1775E+06 0.8689E+05 "alien"
24 20 44 2.91 10.45 0.9641E+07 0.1017E+06 "alien"
66 43 5 1.63 10.37 0.2468E+06 0.2294E+05 "alien"
81 4 29 1.53 10.36 0.1725E+06 0.2364E+05 "alien"
60 40 26 1.72 10.32 0.3792E+06 0.2578E+05 "alien"
39 18 57 2.18 10.24 0.3885E+07 0.5573E+05 "alien"
70 41 15 1.57 10.19 0.1922E+06 0.2281E+05 "alien"
55 36 41 1.79 10.16 0.4942E+06 0.2967E+05 "alien"
37 4 81 1.88 10.15 0.7202E+06 0.3357E+05 "alien"
56 27 5 2.06 10.14 0.1854E+07 0.3569E+05 "alien"
44 39 29 2.06 10.09 0.1844E+07 0.3805E+05 "alien"
65 46 29 1.56 10.06 0.1898E+06 0.2270E+05 "alien"
List of 33 reflections *NOT* obeying Wilson distribution (sorted by resolution)
Ice rings could occur at (Angstrom):
3.897,3.669,3.441, 2.671,2.249,2.072, 1.948,1.918,1.883,1.721
h k l RES Z Intensity Sigma
82 24 6 1.50 10.74 0.1787E+06 0.1019E+06
67 53 6 1.50 15.85 0.2638E+06 0.1128E+06
80 24 26 1.50 11.41 0.1899E+06 0.9793E+05
60 43 61 1.50 11.77 0.1959E+06 0.9769E+05
69 25 62 1.50 10.67 0.1775E+06 0.8689E+05
46 17 99 1.50 14.16 0.2357E+06 0.9588E+05
72 11 61 1.52 17.34 0.2886E+06 0.2367E+05
81 4 29 1.53 10.36 0.1725E+06 0.2364E+05
65 46 29 1.56 10.06 0.1898E+06 0.2270E+05
70 41 15 1.57 10.19 0.1922E+06 0.2281E+05
79 6 15 1.60 13.10 0.3117E+06 0.2477E+05
66 43 5 1.63 10.37 0.2468E+06 0.2294E+05
70 17 35 1.68 10.96 0.3286E+06 0.2415E+05
73 10 15 1.72 10.98 0.4036E+06 0.2356E+05
60 40 26 1.72 10.32 0.3792E+06 0.2578E+05
72 11 17 1.74 11.64 0.4278E+06 0.2619E+05
66 25 19 1.79 11.89 0.5788E+06 0.2739E+05
55 36 41 1.79 10.16 0.4942E+06 0.2967E+05
69 7 22 1.80 11.06 0.5383E+06 0.2564E+05
66 25 11 1.81 11.88 0.5781E+06 0.2771E+05
61 20 33 1.88 12.54 0.8900E+06 0.3054E+05
57 24 41 1.88 10.91 0.7746E+06 0.2842E+05
37 4 81 1.88 10.15 0.7202E+06 0.3357E+05
56 27 5 2.06 10.14 0.1854E+07 0.3569E+05
44 39 29 2.06 10.09 0.1844E+07 0.3805E+05
39 18 57 2.18 10.24 0.3885E+07 0.5573E+05
44 4 48 2.30 12.38 0.4695E+07 0.6072E+05
44 18 20 2.59 11.08 0.6982E+07 0.7839E+05
41 21 26 2.59 11.09 0.6988E+07 0.7945E+05
23 3 62 2.59 11.06 0.6971E+07 0.9154E+05
34 32 2 2.75 13.44 0.1239E+08 0.1279E+06
24 20 44 2.91 10.45 0.9641E+07 0.1017E+06
35 10 25 3.17 14.39 0.2118E+08 0.2364E+06
cpu time used by XSCALE 25.9 sec
elapsed wall-clock time 28.1 sec
I would like to extract the second last line where the 11th column has a number followed by an asterisk (xy.z*). E.g. in this table the line I'm looking for would contain "23.2*" from the 11th column (CC(1/2)). I would like the second last because the last would be the line that starts with total, and this was a lot easier to extract with a simple grep command.
So the expected output for the code in this case would be to print the line:
1.50 273432 20770 20893 99.4% 333.4% 342.1% 273340 0.80 346.9% 23.2* -1 0.644 19495
In a different file the second last value in the 11th with an asterisk after may correspond to 1.6 in the first column so the expected output would be:
1.60 216910 5769 5769 100.0% 207.5% 214.7% 216910 1.72 210.4% 26.0* -3 0.654 5204
And so on for all the different possible positions of the asterisk in the table.
I've tried using things like grep "[0-9, 0-9, ., 0-9*]" file.name and various other grep and fgrep things but I'm pretty new to this and can't get it to work.
Any help would be greatly appreciated.
Sam
GNU sed
(for your updated script)
sed -n '/LIMIT/,/=/{/^\s*\(\S*\s*\)\{10\}[0-9.-]*\*/H;x;s/^.*\n\(.*\n.*\)$/\1/;x;/=/{x;P;q}}' file
.. output is:
1.50 273432 20770 20893 99.4% 333.4% 342.1% 273340 0.80 346.9% 23.2* -1 0.644 19495
To print the entire second last line which matches that regex, you can do something like this:
awk '$11~/[0-9.]+\*/{secondlast=last;last=$0}END{print secondlast}' logFile
This one liner can do it:
$ awk '{if ($11 ~ /\*/) {i++; a[i]=$0}} END {print a[i -1]}' file
1.50 274090 20781 20874 99.6% 333.7% 341.9% 274015 0.80 347.1% 24.8* 0 0.645 19516
Explanation
It add to the array a[] all lines that contain * the 11th field. Then prints not the last but the previous one.
Update
Since your log is very big and asterisks appear all around, I update my code to:
$ awk '{if ($11 == /[0-9]*.[0-9]*\*/) {i++; a[i]=$0}} END {print a[i -1]}' a
0.90 0 0 147505 0.0% -99.9% -99.9% 0 -99.00 -99.9% 0.0 0 0.000 0
so it looks for lines with NNN.XXX* format.
awk '$11~/^[0-9.]+\*$/ {prev=val; val=$11+0} END {print prev}' log
I add 0 to the value of $11 to convert the string "23.2*" to the number 23.2.
Alternately, when I hear "nth from the end", I think: reverse it and take the nth from the top:
tac log | awk '$11~/^[0-9.]+\*$/ && ++n == 2 {print $11+0; exit}'