I have thickness measurements taken on a flat plate. Is there any excel like conditional formatting option in gnuplot? I would like to have four different plots with the values,
Higher than a given number, say, in this case, 0.5
Lower than 0.5
Between a given range, say, 0.5 and 0.51
Only the values 0.5?
How to modify the codes below?
set pm3d map
splot 't.dat' matrix
Here is my data file
0.509 0.510 0.515 0.529 0.521 0.516 0.515
0.511 0.506 0.512 0.528 0.524 0.517 0.512
0.510 0.506 0.506 0.530 0.524 0.522 0.505
0.511 0.509 0.513 0.516 0.511 0.520 0.510
0.524 0.516 0.512 0.511 0.507 0.518 0.492
0.525 0.521 0.515 0.517 0.518 0.522 0.500
0.530 0.521 0.513 0.512 0.511 0.519 0.503
0.562 0.516 0.510 0.516 0.522 0.518 0.508
0.520 0.518 0.512 0.517 0.518 0.518 0.510
0.510 0.509 0.503 0.507 0.523 0.519 0.522
0.506 0.500 0.424 0.507 0.523 0.527 0.519
0.509 0.430 0.500 0.513 0.519 0.528 0.524
0.506 0.503 0.503 0.506 0.513 0.528 0.533
0.506 0.517 0.519 0.524 0.524 0.526 0.528
0.525 0.517 0.499 0.520 0.521 0.524 0.518
0.519 0.518 0.516 0.519 0.521 0.520 0.519
0.521 0.502 0.515 0.518 0.518 0.523 0.522
0.515 0.519 0.519 0.534 0.524 0.525 0.516
0.517 0.510 0.522 0.532 0.533 0.530 0.525
0.520 0.457 0.526 0.530 0.530 0.531 0.524
0.530 0.520 0.531 0.529 0.527 0.526 0.524
Thanks!
You can do conditional plots like this, but the conditional filtering will leave "holes" in your graph.
Without formatting:
set pm3d map
splot "./data" matrix
Plotting only values larger than 0.5:
set pm3d map
splot "./data" matrix u 1:2:($3 > 0.5 ? $3 : 1/0)
You see you have points missing. If you filter even more, you'll have more points missing, to the extreme that you might not have anything to plot (with < 0.5) because there is no way to interpolate. What you can do is substitute points outside of range by a fixed value. For instance, if the value is smaller than 0.5 substitute by 0.5, if it's larger than 0.53 substitute by 0.53:
set pm3d map
splot "./data" matrix u 1:2:($3 < 0.5 ? 0.5 : $3 > 0.53 ? 0.53 : $3)
For nicer display with these small matrices, you can think about interpolation:
set pm3d map interpolate 32,32
splot "./data" matrix u 1:2:($3 < 0.5 ? 0.5 : $3 > 0.53 ? 0.53 : $3)
Related
I am running cProfile on my python script in order to see where i can improve performance and using snakeviz to visualize. The results are pretty vague however; how do I interpret them? Here are the first few lines of it:
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
242059 0.626 0.000 0.914 0.000 pulp.py:585(__init__)
1 0.413 0.413 0.413 0.413 {built-in method _winapi.WaitForSingleObject}
343978/302 0.293 0.000 0.557 0.002 pulp.py:750(addInPlace)
4159617 0.288 0.000 0.288 0.000 pulp.py:165(__hash__)
112 0.282 0.003 0.282 0.003 {method 'read' of '_ssl._SSLSocket' objects}
1913398 0.172 0.000 0.245 0.000 {built-in method builtins.isinstance}
1 0.171 0.171 0.185 0.185 Betfair_Run_Sheet.pyx:243(betfairFinalArray)
377866 0.168 0.000 0.293 0.000 pulp.py:637(addterm)
2255 0.161 0.000 0.161 0.000 mps_lp.py:249(<listcomp>)
1 0.148 0.148 0.570 0.570 mps_lp.py:174(writeMPS)
117214 0.139 0.000 0.444 0.000 pulp.py:820(__mul__)
2 0.136 0.068 0.196 0.098 pulp.py:1465(variablesDict)
5 0.135 0.027 0.135 0.027 {method 'do_handshake' of '_ssl._SSLSocket' objects}
427 0.111 0.000 0.129 0.000 <frozen importlib._bootstrap_external>:914(get_data)
71 0.108 0.002 0.108 0.002 {built-in method _imp.create_dynamic}
2093 0.102 0.000 0.102 0.000 {built-in method nt.stat}
I am using Pulp so aware that takes the lion's share of the time, but the specifics of the setup is not clear from the above, e.g. for the first line of output it seems to be alluding to a line 585 in my script but that is not where I have called or set up the PULP part in it at all.
Same with the <listcomp> 9th one down, there is no list comprehension on that line of my script.
Other things like {method 'do_handshake' of '_ssl._SSLSocket' objects} I don't have a clue what they mean.
I have a complex excel table lookup formula that I need help with. I have tried creating an index match match formula but this needs to look up on the same row, not using column headings in the match formula. Essentially need pull a number based on two parameters from the attached sample table. I'll just throw out an example to make the most sense.
Example 1
Parameter 1: B010, Parameter 2: 60.3
Based on the table I would need the formula to pull in 0.05
B010 part is pretty simple. 60.3 is greater than 60 so it goes to the column with 60.6. And the formula needs to pull in the number BELOW the number that it finds in the row. Result = 0.05 image link
Example 2
Parameter 1: B010, Parameter 2: 45, result: 0.075
Example 3
Parameter 1: B012, Parameter 2: 65, result: 0.033
Is this even possible to do in excel?
B010 48.5 51.6 54.3 57.3 60 60.6 61.1 61.8 62.3 62.8
0.075 0.07 0.065 0.634 0.055 0.05 0.045 0.04 0.035 0.033
B012 48.5 51.6 54.3 57.3 60 60.6 61.1 61.8 62.3 62.8
0.075 0.07 0.065 0.06 0.055 0.05 0.045 0.04 0.035 0.033
B013 48.5 51.6 54.3 57.3 60 60.6 61.1 61.8 62.3 62.8
0.075 0.07 0.065 0.06 0.055 0.05 0.045 0.04 0.035 0.033
H010 48.5 51.6 54.3 57.3 60 61 62.1 63 64 64.9
0.065 0.06 0.055 0.05 0.045 0.042 0.04 0.038 0.035 0.033
H020 48.5 51.6 54.3 57.3 60 61.6 63.1 64.6 66.3 67.7
0.125 0.119 0.113 0.107 0.105 0.1 0.095 0.09 0.085 0.08
Enter as an array formula (ctrl+shift+enter):
=INDEX(B1:K10,MATCH(N2,A1:A10,0)+1,MATCH(TRUE,INDEX(B1:K10,MATCH(N2,A1:A10,0),)>N3,0))
The data are organized where the first column are year and next are monthly average, I need plot x-->month and y--> monthly average and the data base is organized first column= year next columns are monthly mean (12 values) corresponding to year, like that:
1871 -0.107 0.004 -0.503 -0.650 -0.379 0.025 0.317 0.027 -0.732 -0.685 0.037 0.566
1872 0.376 -0.241 -0.904 -1.019 0.367 0.282 -0.061 0.597 0.779 0.818 1.070 1.203
1873 0.831 0.762 0.379 -0.028 0.014 0.349 0.189 0.428 -0.170 0.643 0.859 0.317
1874 0.063 0.125 -0.068 -0.124 0.365 0.535 0.693 1.298 0.554 0.566 0.889 0.185
1875 -0.369 -0.764 -1.238 0.111 0.683 0.696 0.505 1.008 1.210 0.945 -0.307 -0.184
Similar to this graph:
I have a data file:
0.4 -0.97
0.41 -0.96
0.42 -0.95
0.43 -0.93
0.44 -0.92
0.45 -0.91
0.46 -0.90
0.47 -0.88
0.48 -0.87
0.49 -0.86
0.5 -0.84
0.51 -0.83
0.52 -0.82
0.53 -0.81
0.54 -0.80
0.55 -0.78
0.56 -0.77
0.57 -0.76
0.58 -0.74
0.59 -0.73
0.6 -0.72
0.61 -0.71
0.62 -0.70
0.63 -0.69
0.64 -0.67
0.65 -0.66
0.66 -0.65
0.67 -0.64
0.68 -0.62
0.69 -0.61
0.7 -0.60
0.71 -0.59
0.72 -0.58
0.73 -0.56
0.74 -0.55
0.75 -0.54
0.76 -0.53
0.77 -0.52
0.78 -0.51
0.79 -0.50
0.8 -0.49
0.81 -0.47
0.82 -0.47
0.83 -0.46
0.84 -0.44
0.85 -0.43
0.86 -0.42
0.87 -0.41
0.88 -0.40
0.89 -0.39
0.9 -0.38
0.91 -0.49
0.92 -0.48
0.93 -0.47
0.94 -0.46
0.95 -0.44
0.96 -0.43
0.97 -0.42
0.98 -0.41
0.99 -0.40
1.0 -0.39
1.01 -0.38
1.02 -0.37
1.03 -0.36
1.04 -0.35
1.05 -0.34
1.06 -0.33
1.07 -0.32
1.08 -0.31
1.09 -0.30
1.1 -0.30
1.11 -0.29
1.12 -0.28
1.13 -0.27
1.14 -0.26
1.15 -0.25
1.16 -0.24
1.17 -0.24
1.18 -0.23
1.19 -0.22
1.2 -0.21
1.21 -0.20
1.22 -0.20
1.23 -0.19
1.24 -0.18
1.25 -0.17
1.26 -0.17
1.27 -0.16
1.28 -0.15
1.29 -0.14
1.3 -0.13
1.31 -0.12
1.32 -0.11
1.33 -0.11
1.34 -0.10
1.35 -0.09
1.36 -0.08
1.37 -0.08
1.38 -0.07
1.39 -0.06
1.4 -0.05
1.41 -0.04
1.42 -0.03
1.43 -0.03
1.44 -0.02
1.45 -0.01
1.46 -0.01
1.47 -0.00
1.48 0.00
1.49 0.01
1.5 0.02
1.51 0.03
1.52 0.04
1.53 0.04
1.54 0.05
1.55 0.06
1.56 0.06
1.57 0.07
1.58 0.08
1.59 0.08
1.6 0.09
1.61 0.09
1.62 0.10
1.63 0.10
1.64 0.10
1.65 0.11
1.66 0.11
1.67 0.12
1.68 0.12
1.69 0.13
1.7 0.14
1.71 0.14
1.72 0.14
1.73 0.15
1.74 0.15
1.75 0.16
1.76 0.16
1.77 0.17
1.78 0.17
1.79 0.18
1.8 0.19
1.81 0.20
1.82 0.20
1.83 0.21
1.84 0.21
1.85 0.22
1.86 0.22
1.87 0.23
1.88 0.24
1.89 0.24
1.9 0.25
1.91 0.25
1.92 0.26
1.93 0.26
1.94 0.26
1.95 0.27
1.96 0.28
1.97 0.28
1.98 0.28
1.99 0.29
2.0 0.29
2.01 0.29
2.02 0.29
2.03 0.30
2.04 0.30
2.05 0.30
2.06 0.31
2.07 0.32
2.08 0.32
2.09 0.33
2.1 0.33
2.11 0.33
2.12 0.34
2.13 0.34
2.14 0.34
2.15 0.35
2.16 0.35
2.17 0.36
2.18 0.36
2.19 0.36
2.2 0.37
2.21 0.37
2.22 0.37
2.23 0.38
2.24 0.38
2.25 0.38
2.26 0.38
2.27 0.39
2.28 0.39
2.29 0.39
2.3 0.40
2.31 0.40
2.32 0.40
2.33 0.40
2.34 0.41
2.35 0.41
2.36 0.42
2.37 0.42
2.38 0.43
2.39 0.43
2.4 0.43
2.41 0.43
2.42 0.44
2.43 0.44
2.44 0.44
2.45 0.44
2.46 0.45
2.47 0.45
2.48 0.45
2.49 0.45
2.5 0.46
2.51 0.46
2.52 0.46
2.53 0.47
2.54 0.47
2.55 0.47
2.56 0.48
2.57 0.48
2.58 0.49
2.59 0.36
2.6 0.36
2.61 0.36
2.62 0.36
2.63 0.37
2.64 0.37
2.65 0.37
2.66 0.37
2.67 0.38
2.68 0.38
2.69 0.38
2.7 0.38
2.71 0.38
2.72 0.38
2.73 0.38
2.74 0.38
2.75 0.38
2.76 0.38
2.77 0.38
2.78 0.38
2.79 0.39
2.8 0.39
2.81 0.39
2.82 0.39
2.83 0.39
2.84 0.39
2.85 0.28
2.86 0.28
2.87 0.28
2.88 0.28
2.89 0.28
2.9 0.28
2.91 0.28
2.92 0.28
2.93 0.29
2.94 0.29
2.95 0.29
2.96 0.29
2.97 0.29
2.98 0.29
2.99 0.29
3.0 0.19
3.01 0.19
3.02 0.19
3.03 0.19
3.04 0.19
3.05 0.19
3.06 0.19
3.07 0.19
3.08 0.20
3.09 0.20
3.1 0.20
3.11 0.20
3.12 0.20
3.13 0.20
3.14 0.20
3.15 0.20
3.16 0.20
3.17 0.20
3.18 0.21
3.19 0.21
3.2 0.21
3.21 0.21
3.22 0.21
3.23 0.21
3.24 0.21
3.25 0.21
3.26 0.21
3.27 0.21
3.28 0.21
3.29 0.21
3.3 0.21
3.31 0.21
3.32 0.21
3.33 0.21
3.34 0.21
3.35 0.21
3.36 0.21
3.37 0.22
3.38 0.22
3.39 0.22
3.4 0.22
3.41 0.22
3.42 0.22
3.43 0.22
3.44 0.22
3.45 0.22
3.46 0.22
3.47 0.22
3.48 0.22
3.49 0.22
3.5 0.22
3.51 0.23
3.52 0.23
3.53 0.23
3.54 0.23
3.55 0.23
3.56 0.13
3.57 0.13
3.58 0.13
3.59 0.13
3.6 0.13
3.61 0.13
3.62 0.13
3.63 0.13
3.64 0.13
3.65 0.13
3.66 0.13
3.67 0.13
3.68 0.13
3.69 0.13
3.7 0.13
3.71 0.13
3.72 0.14
3.73 0.14
3.74 0.14
3.75 0.14
3.76 0.05
3.77 0.05
3.78 0.05
3.79 0.05
3.8 0.05
3.81 -0.04
3.82 -0.04
3.83 -0.04
3.84 -0.04
3.85 -0.04
3.86 -0.04
3.87 -0.04
3.88 -0.04
3.89 -0.04
3.9 -0.04
3.91 -0.04
3.92 -0.04
3.93 -0.04
3.94 -0.04
3.95 -0.12
3.96 -0.12
3.97 -0.12
3.98 -0.12
3.99 -0.12
4.0 -0.12
4.01 -0.12
4.02 -0.12
4.03 -0.12
4.04 -0.12
4.05 -0.19
4.06 -0.19
4.07 -0.19
4.08 -0.19
4.09 -0.19
4.1 -0.19
4.11 -0.19
4.12 -0.41
4.13 -0.41
4.14 -0.41
4.15 -0.47
4.16 -0.47
4.17 -0.47
4.18 -0.47
4.19 -0.47
4.2 -0.47
4.21 -0.47
4.22 -0.54
4.23 -0.54
4.24 -0.60
4.25 -0.65
4.26 -0.65
4.27 -0.65
4.28 -0.65
4.29 -0.65
4.3 -0.65
4.31 -0.65
4.32 -0.65
4.33 -0.65
4.34 -0.65
4.35 -0.65
4.36 -0.65
4.37 -0.65
4.38 -0.71
4.39 -0.71
4.4 -0.71
4.41 -0.71
4.42 -0.71
4.43 -0.71
4.44 -0.71
4.45 -0.71
4.46 -0.71
4.47 -0.71
4.48 -0.71
4.49 -0.71
4.5 -0.71
4.51 -0.71
4.52 -0.71
4.53 -0.76
4.54 -0.76
4.55 -0.82
4.56 -0.82
4.57 -0.87
4.58 -0.87
4.59 -0.87
4.6 -0.87
4.61 -0.92
4.62 -0.97
4.63 -1.06
4.64 -1.06
4.65 -1.06
4.66 -1.06
4.67 -1.06
4.68 -1.06
4.69 -1.06
4.7 -1.06
4.71 -1.06
4.72 -1.06
4.73 -1.06
4.74 -1.11
4.75 -1.11
4.76 -1.11
4.77 -1.11
4.78 -1.11
4.79 -1.11
4.8 -1.11
4.81 -1.11
4.82 -1.11
4.83 -1.11
4.84 -1.11
4.85 -1.15
4.86 -1.15
4.87 -1.15
4.88 -1.15
I wish to create a "well" smoother curve, so i use
plot "for_gnuplot" lw 3 w l sm b title ""
I get the following image:
This is very nice, but i wish to mark the maximum in some way. I know that with sm b the maximum is not the real maximum of the plot, but i dont know how to mark this new maximum value.
Thanks
You can write the (x,y) data of the smoothed plot to a temporary file, do some statistics on this file, and plot the results:
# Generate the data for the smooth plot
set samples 1000
set table "temp.dat"
plot "for_gnuplot" lw 3 w l sm b title "1"
unset table
# Get maximum values and indices of maximum values:
# A_max_y, A_index_max_y, B_max_y, B_index_max_y
stats "for_gnuplot" prefix "A"
stats "temp.dat" using 1:2 prefix "B"
# Calculate positions from indices.
# We need the x-value (first column) at B_index_max_y. We know that the first
# column of "temp.dat" consists of equidistant x-values. So we just fit a
# linear function to map from index to position. (Could be done analytically.)
pos_from_index(x) = a*x + b
fit pos_from_index(x) "for_gnuplot" using 0:1 via a, b
A_xvalue_max_y = pos_from_index(A_index_max_y)
fit pos_from_index(x) "temp.dat" using 0:1 via a, b
B_xvalue_max_y = pos_from_index(B_index_max_y)
# Make some arrows to indicate maximal values
set arrow 1 from A_xvalue_max_y, graph 0.99 to A_xvalue_max_y, A_max_y fill lw 2
set arrow 2 from B_xvalue_max_y, graph 0.8 to B_xvalue_max_y, B_max_y fill lw 2
set label 1 at A_xvalue_max_y, graph 0.99 "max raw" offset 0.2, -0.3
set label 2 at B_xvalue_max_y, graph 0.8 "max smooth" center offset 0, -0.4
# Finally plot the graphs
set terminal png
set output "graph.png"
plot "for_gnuplot" lw 2 w l title "raw" ,\
"for_gnuplot" lw 2 w l sm b title "smooth"
This produces the following output:
PS: I would be interested if there is a more direct way to access a value from a file at a specific index.
Here is a link: http://www.phyast.pitt.edu/~zov1/gnuplot/html/statistics.html
Scroll to "Determining the position of the minimum and maximum".
I have a real matrix into a prova3hm.txt file, where each row is separated by a blank line. For example:
0.907 0.796 0.796 0.794 0.794 0.793 0.793 0.793 0.793 0.793 0.792 0.792 0.792 0.792 0.792 0.792 0.792 0.792 0.792 0.792 0.791 0.791
0.791 0.791 0.791 0.791 0.791 0.791 0.791 0.791 0.791 0.79 0.79 0.79 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789
0.789 0.788 0.788 0.788 0.788 0.788 0.788 0.788 0.788 0.787 0.787 0.787 0.787 0.787 0.787 0.787 0.784 0.658 0.658 0.658 0.657 0.657
0.657 0.656 0.656 0.656 0.656 0.656 0.656 0.656 0.656 0.655 0.655 0.655 0.655 0.655 0.655 0.654 0.654 0.654 0.653 0.653 0.653 0.653
0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.652 0.652 0.652 0.652 0.652 0.652 0.652
0.652 0.652 0.652 0.652 0.652 0.652 0.652 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651
0.651 0.651 0.651 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.649 0.649
0.649 0.649 0.649 0.649 0.648 0.648 0.648 0.646 0.242 0.241 0.24 0.239 0.239 0.239 0.239 0.239 0.239 0.238 0.238 0.238 0.238 0.238
0.238 0.238 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.236 0.236 0.236 0.236 0.236 0.236 0.236
0.236 0.236 0.236 0.236 0.236 0.236 0.236 0.236 0.236 0.236 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235
0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235
0.234 0.234 0.234 0.234 0.233 0.233 0.233 0.233 0.233 0.233 0.233 0.233 0.233 0.233 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232
0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.231 0.231 0.231 0.231 0.23 0.23 0.23 0.23 0.23 0.23
0.229 0.229 0.165 0.164 0.163 0.162 0.162 0.162 0.162 0.162 0.16 0.16 0.16 0.16 0.16 0.159 0.159 0.159 0.159 0.159 0.159 0.159
0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158
0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157
I need to get a heat map out of this file, with Gnuplot.
I have been trying to use the pm3d package and the splot command, this way:
gnuplot> set pm3d map
gnuplot> splot 'prova3hm.txt' matrix
At this point, I was supposing to get my heat map, that is something like this:
(source: pnas.org)
But, instead, I got an empty image:
Why is my output image empty?
What do I do wrong?
The following script works fine with gnuplot version 4.6.3:
reset
set terminal pngcairo
set output 'test.png'
set autoscale fix
plot 'file.txt' matrix with image
The result is:
To complete is, here the test data:
0.907 0.796 0.796 0.794 0.794 0.793 0.793 0.793 0.793 0.793 0.792 0.792 0.792 0.792 0.792 0.792 0.792 0.792 0.792 0.792 0.791 0.791
0.791 0.791 0.791 0.791 0.791 0.791 0.791 0.791 0.791 0.79 0.79 0.79 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789 0.789
0.789 0.788 0.788 0.788 0.788 0.788 0.788 0.788 0.788 0.787 0.787 0.787 0.787 0.787 0.787 0.787 0.784 0.658 0.658 0.658 0.657 0.657
0.657 0.656 0.656 0.656 0.656 0.656 0.656 0.656 0.656 0.655 0.655 0.655 0.655 0.655 0.655 0.654 0.654 0.654 0.653 0.653 0.653 0.653
0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.653 0.652 0.652 0.652 0.652 0.652 0.652 0.652
0.652 0.652 0.652 0.652 0.652 0.652 0.652 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651 0.651
0.651 0.651 0.651 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.649 0.649
0.649 0.649 0.649 0.649 0.648 0.648 0.648 0.646 0.242 0.241 0.24 0.239 0.239 0.239 0.239 0.239 0.239 0.238 0.238 0.238 0.238 0.238
0.238 0.238 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.237 0.236 0.236 0.236 0.236 0.236 0.236 0.236
0.236 0.236 0.236 0.236 0.236 0.236 0.236 0.236 0.236 0.236 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235
0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.235
0.234 0.234 0.234 0.234 0.233 0.233 0.233 0.233 0.233 0.233 0.233 0.233 0.233 0.233 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232
0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.232 0.231 0.231 0.231 0.231 0.23 0.23 0.23 0.23 0.23 0.23
0.229 0.229 0.165 0.164 0.163 0.162 0.162 0.162 0.162 0.162 0.16 0.16 0.16 0.16 0.16 0.159 0.159 0.159 0.159 0.159 0.159 0.159
0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158
0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157 0.157