ctfWeightMapCreation
From EosPedia
ctfWeightMapCreation is Eos's Command.
Contents
List of option
Main option
| Option | Essential/Optional | Description | Default |
|---|---|---|---|
| -o | Optional | Output: ASCII | stdout |
| -kV | Optional | Acceleration voltage: [kV] | 200 |
| -Cs | Optional | Spherical aberration coefficient: [mm] | 1.7 |
| -df | Essential | Defocus: [A] (+ is Underfocus) | 2.7 |
| -white | Optional | White Noise(Noise/Signal) | 1.0 |
| -noise | Optional | Noise Power Spectrum | NULL |
| -splusn | Optional | Signal+Noise Power Spectrum | NULL |
| -dRinv | Essential | dRinv[A] | 1024*5/3 |
| -dRmaxInv | Essential | dRmaxInv[A] | 10.0 |
| -SinWin | Optional | (RminInv[A], RmaxInv[A]) | (20.0, 10.0) |
| -c | Optional | ConfigurationFile | NULL |
| -m | Optional | Mode | 0 |
| -h | Optional | Help |
-m details
| Value | Description |
|---|---|
| 0 | No correction : F = G |
| 1 | Phase contrast : F = G*(H/abs(H))(Only 1 or -1) |
| 2 | Phase contrast : F = G*H (Winer filter (S/N << 1)) |
| 3 | Phase contrast : F = G/H (Inverse filter) |
| 4 | Phase contrast : F = G*(H/(H*H + N*N)) (Winer filter (white noise : Required Option -white)) |
| 5 | Phase contrast : F = G*(H/abs(H)*abs(G*G-N*N)/(G*G)) (Winer filter: Required Option -noise, -splusn)) |
Execution example
Case: Options only essential
Case: df=2.7, dRinv=1024*5/3, dRmaxInv=10.0
0.000000 1.000000 1.000000
0.000977 1.000000 -0.000000
0.001953 1.000000 -0.000001
0.002930 1.000000 -0.000002
0.003906 1.000000 -0.000003
0.004883 1.000000 -0.000005
0.005859 1.000000 -0.000007
0.006836 1.000000 -0.000009
0.007812 1.000000 -0.000011
0.008789 1.000000 -0.000014
...
0.090820 1.000000 0.026869
0.091797 1.000000 0.028082
0.092773 1.000000 0.029335
0.093750 1.000000 0.030629
0.094727 1.000000 0.031964
0.095703 1.000000 0.033343
0.096680 1.000000 0.034765
0.097656 1.000000 0.036232
0.098633 1.000000 0.037744
0.099609 1.000000 0.039302
In the following, Setting at df=2.7, dRinv=1024*5/3, dRmaxInv=10.0
Option -kV
Case: kV=100
0.000000 1.000000 1.000000
0.000977 1.000000 -0.000000
0.001953 1.000000 -0.000001
0.002930 1.000000 -0.000003
0.003906 1.000000 -0.000004
0.004883 1.000000 -0.000007
0.005859 1.000000 -0.000009
0.006836 1.000000 -0.000012
0.007812 1.000000 -0.000014
0.008789 1.000000 -0.000016
...
0.090820 1.000000 0.089324
0.091797 1.000000 0.093274
0.092773 1.000000 0.097352
0.093750 1.000000 0.101561
0.094727 1.000000 0.105902
0.095703 1.000000 0.110378
0.096680 1.000000 0.114993
0.097656 1.000000 0.119748
0.098633 1.000000 0.124646
0.099609 1.000000 0.129690
Option -Cs
Case: Cs=2
0.000000 1.000000 1.000000
0.000977 1.000000 -0.000000
0.001953 1.000000 -0.000001
0.002930 1.000000 -0.000002
0.003906 1.000000 -0.000003
0.004883 1.000000 -0.000005
0.005859 1.000000 -0.000007
0.006836 1.000000 -0.000009
0.007812 1.000000 -0.000011
0.008789 1.000000 -0.000013
...
0.090820 1.000000 0.031918
0.091797 1.000000 0.033352
0.092773 1.000000 0.034832
0.093750 1.000000 0.036361
0.094727 1.000000 0.037939
0.095703 1.000000 0.039567
0.096680 1.000000 0.041247
0.097656 1.000000 0.042979
0.098633 1.000000 0.044765
0.099609 1.000000 0.046605
Option -m
Case: m=1
0.000000 1.000000 1.000000
0.000977 -1.000000 -0.000000
0.001953 -1.000000 -0.000001
0.002930 -1.000000 -0.000002
0.003906 -1.000000 -0.000003
0.004883 -1.000000 -0.000005
0.005859 -1.000000 -0.000007
0.006836 -1.000000 -0.000009
0.007812 -1.000000 -0.000011
0.008789 -1.000000 -0.000014
...
0.090820 1.000000 0.026869
0.091797 1.000000 0.028082
0.092773 1.000000 0.029335
0.093750 1.000000 0.030629
0.094727 1.000000 0.031964
0.095703 1.000000 0.033343
0.096680 1.000000 0.034765
0.097656 1.000000 0.036232
0.098633 1.000000 0.037744
0.099609 1.000000 0.039302
Case: m=2
0.000000 1.000000 1.000000
0.000977 -0.000000 -0.000000
0.001953 -0.000001 -0.000001
0.002930 -0.000002 -0.000002
0.003906 -0.000003 -0.000003
0.004883 -0.000005 -0.000005
0.005859 -0.000007 -0.000007
0.006836 -0.000009 -0.000009
0.007812 -0.000011 -0.000011
0.008789 -0.000014 -0.000014
...
0.090820 0.026869 0.026869
0.091797 0.028082 0.028082
0.092773 0.029335 0.029335
0.093750 0.030629 0.030629
0.094727 0.031964 0.031964
0.095703 0.033343 0.033343
0.096680 0.034765 0.034765
0.097656 0.036232 0.036232
0.098633 0.037744 0.037744
0.099609 0.039302 0.039302
Case: m=3
Case: No other settings
0.000000 1.000000 1.000000
0.000977 0.000000 -0.000000
0.001953 0.000000 -0.000001
0.002930 0.000000 -0.000002
0.003906 0.000000 -0.000003
0.004883 0.000000 -0.000005
0.005859 0.000000 -0.000007
0.006836 0.000000 -0.000009
0.007812 0.000000 -0.000011
0.008789 0.000000 -0.000014
...
0.090820 0.000000 0.026869
0.091797 0.000000 0.028082
0.092773 0.000000 0.029335
0.093750 0.000000 0.030629
0.094727 0.000000 0.031964
0.095703 0.000000 0.033343
0.096680 0.000000 0.034765
0.097656 0.000000 0.036232
0.098633 0.000000 0.037744
0.099609 0.000000 0.039302
Case: df=2.7, dRinv=64*5/3, dRmaxInv=1.0
0.000000 1.000000 1.000000
0.015625 0.000000 -0.000027
0.031250 0.000000 0.000194
0.046875 0.000000 0.001564
0.062500 0.000000 0.005590
0.078125 0.000000 0.014376
0.093750 0.000000 0.030629
0.109375 0.000000 0.057639
0.125000 0.000000 0.099237
0.140625 6.263639 0.159652
0.156250 4.112921 0.243136
0.171875 2.831935 0.353115
...
0.843750 -1.971417 -0.507249
0.859375 0.000000 0.005251
0.875000 1.009738 0.990356
0.890625 1.608352 0.621754
0.906250 1.289093 0.775739
0.921875 1.170360 0.854438
0.937500 -1.052999 -0.949668
0.953125 1.005029 0.994997
0.968750 -3.063165 -0.326460
0.984375 -1.204873 -0.829963
1.000000 -2.392987 -0.417888
Case: m=4
Case: No other settings
0.000000 1.000000 1.000000
0.000977 -4940297.000000 -0.000000
0.001953 -1242119.125000 -0.000001
0.002930 -557351.375000 -0.000002
0.003906 -317780.156250 -0.000003
0.004883 -207004.218750 -0.000005
0.005859 -146954.187500 -0.000007
0.006836 -110884.648438 -0.000009
0.007812 -87629.039062 -0.000011
0.008789 -71859.500000 -0.000014
...
0.090820 37.217903 0.026869
0.091797 35.610458 0.028082
0.092773 34.089443 0.029335
0.093750 32.649300 0.030629
0.094727 31.284903 0.031964
0.095703 29.991486 0.033343
0.096680 28.764643 0.034765
0.097656 27.600285 0.036232
0.098633 26.494612 0.037744
0.099609 25.444090 0.039302
Case: SinWin=(15, 5)
0.000000 1.000000 1.000000
0.000977 -4940297.000000 -0.000000
0.001953 -1242119.125000 -0.000001
0.002930 -557351.375000 -0.000002
0.003906 -317780.156250 -0.000003
0.004883 -207004.218750 -0.000005
0.005859 -146954.187500 -0.000007
0.006836 -110884.648438 -0.000009
0.007812 -87629.039062 -0.000011
0.008789 -71859.500000 -0.000014
...
0.090820 34.284809 0.026869
0.091797 32.579319 0.028082
0.092773 30.965157 0.029335
0.093750 29.436731 0.030629
0.094727 27.988871 0.031964
0.095703 26.616741 0.033343
0.096680 25.315859 0.034765
0.097656 24.082047 0.036232
0.098633 22.911407 0.037744
0.099609 21.800304 0.039302
Case: m=5, -noise, -splusn
Case: df=2.7, dRinv=1024*5/3, dRmaxInv=32
-noiseのfile's image
 |
Min Max |
-3.42794 (71, 0, 0) 3.56337 (70, 30, 0) |
-splusnのfile's image
 |
Min Max |
-18651.7 (10, 1, 0) 52942.7 (24, 39, 0) |
Output
This mode will ignore -dRmax and -dR options
0.000000 1.000000 1.000000
0.012500 -1.000000 -0.000023
0.025000 1.000000 0.000031
0.037500 1.000000 0.000533
0.050000 1.000000 0.002098
0.062500 1.000000 0.005590
0.075000 1.000000 0.012116
0.087500 1.000000 0.023034
0.100000 1.000000 0.039938
0.112500 1.000000 0.064660
0.125000 1.000000 0.099237
0.137500 1.000000 0.145855
0.150000 1.000000 0.206721
0.162500 1.000000 0.283817
0.175000 1.000000 0.378439
0.187500 1.000000 0.490411
0.200000 1.000000 0.616825
0.212500 1.000000 0.750193
0.225000 1.000000 0.876038
0.237500 1.000000 0.970358
0.250000 1.000000 0.998231
0.262500 1.000000 0.916192
0.275000 1.000000 0.682602
0.287500 1.000000 0.280724
0.300000 -1.000000 -0.244868
0.312500 -1.000000 -0.751460
0.325000 -1.000000 -0.999174
0.337500 -1.000000 -0.750135
0.350000 1.000000 0.004719
0.362500 1.000000 0.815910
0.375000 1.000000 0.906149
0.387500 -1.000000 -0.030004
0.400000 -1.000000 -0.966833
