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combine silent.out file

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combine silent.out file
#1

greetings, 

             am tried to combine silent_1.out to silent_23.out  by using following commands 

------------------------------------------------------------------------

PATH/rosetta_bin_linux_2016.17.58663_bundle/main/source/bin/combine_silent.mpi.linuxgccrelease \
-in:file:silent silent_1.out silent_2.out silent_3.out  silent_4.out silent_5.out silent_6.out  silent_7.out silent_8.out  silent_9.out  silent_10.out  silent_11.out  silent_12.out  silent_13.out  silent_14.out  silent_15.out  silent_16.out  silent_17.out  silent_18.out  silent_19.out  silent_20.out  silent_21.out  silent_22.out silent_23.out  \
-in:file:silent_struct_type binary \
-out:file:silent_struct_type binary \
-out:file:silent  silent.out 
------------------------------------------

got following silent.out  (F_00000006) numerous time why? and after pdb extracted, found six time the same F_00000006 _1 to _6  file ?

EXPECTING YOUR ANWERS 

THANK YOU 

------------------------------------------------------------

SEQUENCE: LPHEMSPALHRKISVQYGDAFHPRPFCMRRVKEMDAYGMVSPFARRPSVNSDLKYIKAKVLREGQARERDKCTIQ
SCORE:     score     fa_atr     fa_rep     fa_sol    fa_intra_rep    fa_elec    pro_close    hbond_sr_bb    hbond_lr_bb    hbond_bb_sc    hbon
d_sc    dslf_fa13       rama      omega     fa_dun    p_aa_pp    yhh_planarity        ref         co    Filter_Stage2_aBefore    Filter_Stage2
_bQuarter    Filter_Stage2_cHalf    Filter_Stage2_dEnd    clashes_bb    clashes_total    silent_score    time description
REMARK BINARY SILENTFILE
SCORE:   364.490   -238.385    337.753    141.980           3.047     -5.128        2.032        -10.709         -0.037         -2.144      -0
.911        0.000     -7.370     11.154    120.696     -0.529            0.013     -3.909     16.937                    0.000                 
    0.000                  0.000                 0.000         0.000            0.000         347.553 272.000  F_00000006
ANNOTATED_SEQUENCE: L[LEU:NtermProteinFull]PHEMSPALH[HIS_D]RKISVQYGDAFHPRPFCMRRVKEMDAYGMVSPFARRPSVNSDLKYIKAKVLREGQARERDKCTIQ[GLN:CtermProteinF
ull] F_00000006
LAAAAAAAAAAAAAAAAP/pu/AAAAAAAAAAAEEJAANCy1+DAAAAABxSNAdvgj/D6Hg1vmbn//cBuC97Jqx5vQiQu/ULwLAcHJQ7vrKPAAp8FyAMUevCwQZC6/Mc0BBMCeikv8iuq+SPUx9r1W
UQp8iuq+SPUx7Ta8D1P8iuq+SPUx7Ta8D1vt8t5/kLoA8bPZc2PN7t3/kMEG5bbNeAwmCLRAVxqT9rS+K5v+6Ju+s+iJAMdcE8vfnI0/0kzyBMcERDwakC3/gT2KAcLgJGwgJoRANBK0As
LmeCwwTrt/UcRBCsCKdqviCYOAZAJEBcGt/bvgqIs/MpllAcrIOzP F_00000006
LL8ox/8QhOAkRRO3Pwum8/kVMpBk/jw3PPFdWAFOD2BEjcL4PV6yeANL8ZCU/+4uPmXRn/UPIECUhqGBQGItd/w7P4A0mRLEQMxJH/wlKu/TyM8/P7TPx/4OcMA0QyT2PGYsu/ggiECUjE
jdPEEq8/w8VaCUreODQEffn+YB8VCEX8JAQUlU7/IWKmAE4PgGQ2LgG+QpnCBkGKEHQImlS/YAaZ9T16iBQwqj4+Wq29/jYCn9P F_00000006
LX0zgAVgS7AElUd9PRwasAdcWAB0p2Z+PnV4xA5eajA0heQwP/6g7AN5ZvA0s+9qP+8XwANV7JA0Bo5DQgaEuAFhnx8zeuSDQkc1jA1zS44jBCdDQelq0Al9o567Q0hCQ4fPkAJNyQ+r/0
zCQ4zWuAF3A++rVgPCQwn0ZA1u1JAUW9cAQFYxuApnOBCUCB4/Pbo/4A179RAUmVWEQqposAZzDgAEPjlHQfBX9A5/oo6bBANCQLbyaA59Fn/LkVwCQxBExA93QbA8t4rBQ F_00000006
Ly4qrARBg6/ToFPsv1u+vA17t2+zDQC9vHcr0A9paiAUYv9BwF0CwApThnBUC7bCwbaRnAVNiv8TLioBwNx7qA1Iz43jO8+GwOM/hAVbKC87FqDJwkO4RANwvW4bgcnIwcGNkAdeFt+7ck
sKwanwjAhFJv/T5nlkvNyX2AF0i58TW4H7vxNQkAxCgR4b/gd+vDvlgA1EEu+jBFZCwwHauA5XIt9jQUrIwOQJxAZcFf8boeRGw F_00000006
La+59AJXBRAkamXEwtQBCBhfBTBUP7HHwV3wABdYGUB0RviKw4mrABJcRRA0wJ2LwqBAIB1KWFBUFuNGwCJtJBlJnHBUGvXAw5FQIBZ5VWCUO1Z6vFWzMBtR74CECmpAwK0XABB+Bp+ThR
SEwSL5ABdkkICUOhfFwizLJBp5oHAkA42HwTDQKBRgj2B0r1KIwIKpHBJDNVAE2qW8vWo/NBBv+7A0BqFAwCYYMBBSdZDkm0Q+vLF5QBl45tCk2TAAwLP6LBxpf4C0w26Ew F_00000006
LFpk/ARlLQCU1KnLwfHW+Atf2VCkeXfOw92KDB9tV2CEubkPwOviDBRUMaDEaQkOwa5CzAlmIkCEhwIPwSI4xARpJsCkTH8QwB46+ANu5pCUflZKwRz1/AlpbuB0c5dPwC/ZtABecLCkTQ
hOwB2KxAhW/ADEYjAOwhaXsAdbHZCE/rPRw F_00000006
HoyJGB5blpCEkzzQw02NKBhHOFDkmjZRw90sHB54ZsDUrSDSwVxXKB5DYGEkFRRSwh24MBtvEoCUCzbSwdewIBtWCHCUZ8uSwAwCGB1na+BU2XaRwfJJGB9t5pCk2FyQw9/ENBdh3ODEkr
nQweiCOBJ4y6C02fUTwyUlQB5cXaCkMKBSwO++FBp30SC06PgTwq9pKBxfIUBEG9ITwXr7BBl86nB0filRwTOVIBl/1OB0em1Qw F_00000006
H68iCB9iTpDUHFXSwJII/Ah8vGEE7k5SwNAW8A10xWEkMmzRw/7P4A9QIoEUKaFSwlCB1AJ+D+DkfLmTwqSpABdrJND0X/NSwwJGCB1A3OEUEMoTw6kpwA1buMEk+l/Tw1TK3ARlnoDEgd
aUwab/vAp/DtD0Ei4Sw F_00000006
Haxl+A1i3QE0k6jQw+Tm8AxR9eEkzi2OwYThDBVirmE0yQnNw1bgDB9pxzE0OkxLwl/41AtTWTE084uMwkioqAlukME0nQnNwZ3KlAJ8iAEEMDWLwsSPkABxRgEEQWaOwyS4ABNLPCEkVZ
ZQwnEK4AN/2sEUwkjPwNKK6AR//EEk9GHMwplC1AFAzdEUjBALwcIOrANuvBE04RUPwU4VaAJVd3DUYH+LwgK1pA15ukD0uz1KwQelkAl25KEExSmJw+keYAdedbEk9gCPwSyojA1vGrEU
VXtMw2+PoAt2ZoE0T6BQw F_00000006
HG3+HBxhfeEUKcoOwo8PNBhwElE05BsNwmfXOBlOY8EE0gYOwk9IRBd9mHF0yQ6MwxnnRB9AzWEEAl4OwBqvRBttsAEkBuuNwhtVVB97riDEHCkOw/EuOBRcvuDUl5zLwvOhUBBGB/Ck97
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EmVMMBNHkDFkIVUQwnHvMBJq0ZF0y3uQwht9IBlkRoF0CW5PwPYfKBhys5FkbeCPwxbmLBBcJcFEtFOSwJhzPBpkZSFULmITwueUOBFAmUF0UOkUwZYVSBxuULFURccVwTV0RBljbJFk87
xWw9kcNBxHOQFEwAYXwQKtVBpzvAFknhfXwfBHKBV/V5EEgs5Qw5f1QBFz7eFkTpiQwxPyHBddoUF0zKdSwf1QLBZyDtFUPReSwv0nTBtDdaF0Kl+SwAMPQBNraBF00H6SwvRiKB5gcMFU
3yvUw9J6NB9uelF0VM0Uw7QwVBNu6FF0AeBVwzXeKBN82WFUdK1Ww3zDNB5TzOFkqCMYwQSEZBxoh7E0xPCXwDaUVBBDV/Ek8xPYw F_00000006
E1/BEBl67gFk4shPwZNy/A5N+tFUpoROwNoW4A9kWgFkPDkMwT2M2AVU1NFUNLNNw9LW5Ax1/5FEgwMQwSVw/ANEQFGk44+QwYFr4AdrXLGUWI9RwzxF/AJThTGEYFwSwn8U4AFKVZG0SM
vTwoAIDBdM8RFU9FEQwZR0BB5bo5FkRB+Mw9EB2AlnmuFUKZ6QwPQqyAJqHBGUW2cPwf1tBBds5KGk6kSQw6gCDB9rPBG0QniRw3750AJRxFGUiYpSw5TayAF4hPGETyZRwdCVBBl2TZGk
dhESwmswCBl3dPGUoPSTwhw38A1SpeG0GnPUw14F1A95CUGEYuYUwJAbyAF8PdGURKQTw F_00000006
E57c0AtkopFEwWYKwoz5rAZ11fFUoc0IwMzeiAB2EvFUDkfIwrH8jAxODBG0SP8Hwq++vAta+YFEwmFEwCZ22AZfxEFkRriEw3fpmAxuTVFUi5SAwqtqwAtrIxE0WwPGwAKf3ABu13F0VZ
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UUVDwiVFtA1e6zEki+EJw F_00000006
EfDoRApZmmFk+ezIwoWh8/sXdzFEu0jIw3caR/Mh2mFEWhcGwt70I/g8jTF00HSHwKZFu/geE7FU00SLwWsBFA1FTFGEnzTMwhtTQAh81WFUeKOJwOW1BAJJsAG0EKpGwCtrs/szgtFEPW
qMwQOu3+AP2AGUmjJLwO7g8/cuEMGkLU/Lw F_00000006
ESRH98qS3xF0GWODw0cij/6lknFUuhRAwhDbHAbWasFUW4yCwtZFLAbIA+FkX/zEw8Hnj/mOJvFEmMpxvOaH4/moXeFEJCbpP2fkn+oC7zFkFPyhvj9z59Iz4AGk/uzCwkvuc/iSUWF0Ni
WAwPC92/Snm9FU04XwvB953/ON/jFUHRn6PupgMAXGobF05C9LvMkHm/SZePF0AbamPtpYm+Q2R5FETMJ2PLIja/I8elF0qcOnvFv7O/UCOAGEAch0v F_00000006
EAGvVAP4scF02uqCwMcblAz3CfF0irEFw1kMuAzGIeFUQQ2Awc+muAX0PPFU1YQ7vIdSnADtqNF0kOpIwAfAyATZOPFk5/DKwtXVzA3vQ+EElONMw6cWsAHDpwE0HtlMwUmU8Az/++Ek3s
nNw3o4RArqxOF0652AwSQ2lA3OouFkEKBHwItAhAXdfOFEA2JKwv7ymAvg99EUAQaHw0oY4AzeMNFkePmIweBpyAfD5eFUEdALw2Op9AT4M0EEcCEPwpx4ABPTTKFEtoPNw F_00000006
EQtM1AzuHuF0IR4AwXcs+AfF5tFkrG/6vlvREBbuDnFk27oAwisGFBrYBuF0X7KFwgkPAB/E9BGUZ93zvnZEFB7WeCG0DnwiPQz1EBL6/7F05Wf7PgJuJBnNbHG0RBfovS3PJB3s88FEpg
EBQCLIOBrm5HGkjsDxPhp5NBDscDGEawn+PjkSSBj96DGUT2nCQsO6zAX3w6FkN8TDwGie9ArMPiF0FLOxv9qc5AHkNEGkd0Xivz6vABzI6HGkmPO8vYvLBB/vN0FkWvN+PQt5JBDz7KGU
7IG6vVZEJBLw61FkQhDFQIUyRBbUyLG0xYamPOfjRBr4CAGkty2FQ F_00000006
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LifIWB7zzVFUkYz6vTEhaBDbkWFkosesvyALaBrpHEFE2CoxPsBTbBvgyGFUjpT+PnV6fBPK7VFEfr54v27mgBPrwqFkFqp+v6yfeB3DT6FExIy8vIddiBfC8qFUzUbCwxt+WBz0SVFUjX
WBw3fPaBrfhlF0ZptiPNy5fBTr9IF07Rv+v6kihBjrkSFEBjlsv F_00000006
LQCnYBPZZxEEgR1dP/UIYBHPceEUaKM3PZ/SSBzhjZEkyNI6PXt+QBDDFIEE9C19PPewaBXHTLEEnF2oPC3uXBDERwEUCSA2vmQLaBnJphEkIMI/PRbXaBjwi6D0Xvq2PO4+eBrTrOEUgf
+dPG4zYBnEOIEU/o2yv F_00000006
LwD0OBbQGpEk8RH4PRhNJBHfZnE0yhP7PmAnIBnSdnEk4zrDQuBoLBzYKyEEHtgGQ319FBDgy5EEg030PDkCABDqv3EEn8I4PYMY6A7xLnEE9/xtPsAn6ALGXGF0ZRc+P3QevAzTSlEE3E
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F2LiPYVgrA7jHQF0aTfCQ6AjhATPdyE0n6h8P F_00000006
L1lsEB7GcbE0+VqFQIEiDB362aEkp+rJQrNV7AXvzfEkzsOKQq2Z0A/rcbEEqRkIQMc9EBzK+EE0jk3KQ6UyKBnFR/DE6urKQspjOBDIVIE0hMYMQRNfNBrF/kDkMgAJQ4QVTBj2wAE0M/
vLQukySBDd+mDElqtJQAMeCBvXeSEkEGJDQ3v6FBTk7mEklhtKQoktCBrADxDEjR1JQz+yDBXVfEEEo79MQumxLBD1zRDkcLzGQ+BFXBjUzEEUPkvMQ0xxVBLlrWDEl+zIQ F_00000006
L3jJ5A/L8oEkqQkMQeAMuAP0prEkzOgNQErboADXOWEUuYsNQ0aFtATEPGEEteiOQbUqvAni61EEAYIQQXQn6AjzPvEkpEjQQG6gABrCkvEkfEjOQPTH5Aj85oEE2ehMQhk6pA3cg2EE5+
IMQi3ZpAHWPwE0cX0QQ3TyuAHtYHF0vjDQQRJX6A3mTfE0J2ARQVlw9AHEV6E0ToTRQh01DBzOHkE0SHtOQi2BCBTc1/E0vDMOQ F_00000006
LgjkcAfEAWEk+v5MQgtQPAT39CEkNJLNQ/021/aI0HEEHavOQUC4Y/62eVEkzD9NQC+UJAvBP0DEhBcKQmzMcALAVmD0siuIQ2uqjAfirADEK84JQ170sArLyzCkPdSIQgafzAfPZTCUpS
6IQyf6xAvJ95BUfbGLQcOn7AHGKKC0s9oGQ02MWATKXjEUr3FMQshVYAvPptDUuNQOQBHfAAbOLGE0ycUJQkus9/G2nYDEImrKQH50jADARAEkP1kIQzHkWAfkwdDUU5dFQAKScA3axlCE
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LEe4v/STE5DUFyeQQpwll+Cp59DUEpSRQnz9b/kcrtDUgOjQQAqL//wtbBEEOlhQQN7cG/WvmiDkcrhSQkCZy/udFCDUPSCSQfx2GAL/+ZDUt7CRQ4ffv/uvY5D0eneQQhPBM+624PEU9G
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HCmLU/Yb+HD0kZ5PQAiz9/grK0CErKfOQEw0FABpGPDEDZGMQYopYAV43PDEiLYLQ2drx/4xMHCUn2iNQ++hs/oO+OBUi4xPQmodB+U8X0AUeoSQQ+AAIA5K0tAklsiQQDw3l8ObD0/zkb
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a+nRQTJvTAdArz+TCsESQtMee/IIeu8TTNxSQ F_00000006
HQI/s/AXLlDUUY4KQ3ih1/gUb+DkSNfIQk35HAhNDTE0UYJJQeeSPAt9WeEUaYtGQvZnu+cFAGEUFkTGQvm8D/6RWgDkuGVDQvf97+0mckDkSboLQBfyDAtsvqDkd6KGQr5xj+WzuNE0v6
nIQ5ZwE/MFsREUCoLDQXfKv/eUs4D0PS8AQ F_00000006
HRWxKApbMXE0tmuLQxQ5YANBToEENaoMQ5UznAdoClEk0lmLQ2nItA5JS0EEd1yKQmPxYAx1mpEkaorPQbe9DAdP7xEEy9bQQA8r6/YWeMFEocuPQ1JWSAJMxbFU+enQQqw9CAtCROEEJb
GNQ6FDTA5rZ3EEZu0LQT/wbAlHzZEkgkQQQaR1iAdcF0E0tKLQQ1nzt/IjMnEEb4KQQccQFAVxRyE0XghRQgOdQAV2lsF0DpWQQHrnRAJk/ZFkB4sRQSB5gAxeCWFk6XQQQ F_00000006
HSbdrAVSFRE034oLQ/9j2AB0FMEkG6/KQKTg5Ax49TE0DgNIQLNYBBJzKZE0LLWHQUkh4ANSNoD0u8KLQCL3BBpiCaD06aYKQ2o/FB9nusD07wKMQA7XLBVQ/gD0s0VLQdgAOBJM69C0dG
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H6GlxA51zUE06t4EQ8dbzANF7aE0J0q+PNSl3ABAyxEEEL49P2/y+AV5a2EULRL3P5lLpA9z3YE0OkL4PbbsqAJ4xZEUMOhvvFlfgApl7VEkGcq5vWkeYARqsAEkeTF3v5ceEANVp0D0vV
/5vwYTu/cZsHEkTSo+veCg8/ATQNDU2Bd3vNMFqAVvHREUfzEGQEfa5ApJMQE049R7PvhSlA9uvJEE0RE6PksrjAVoqlE0RJZ6P8DstA9khpEkSeS0vhebwAdukNEE23p0vkPIVAZwqhEE
CcR2vrI1hAx2ZXEUTyHBw9XIhAl16rDUfM6vvCh03/k8vWEEvfLAwTZpA/0o6CE0v2OAwPxfIAVOs4C0xVUwvI3Ed/0HtDDU43j5v F_00000006
 

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Post Situation: 
Fri, 2016-07-22 05:22
venkatazb

Your question isn't clear to me, but I don't think you're having a problem combining silent files, I think you're either worried that the model IDs are similar (not a problem) or that the model data is identical (a fixable problem).

 

If the question is "why does F_000006 occur repeatedly in the inputs and become F_000006_01, F_000006_02" - that's the way it's supposed to work.  The separate silent files don't know about each other to number themselves uniquely.

 

If the problem is "I have many copies of a file named "F_000006", with or without the _01, _02 decoration, and all of those files produce identical coordinates", then the problem is that in your original run, you had overlapping random number seeds.  There are dozens of other posts on this forum explaining the need for unique random number seeds.  Briefly, if you run Rosetta with identical inputs, you'll get identical outputs, because computers are deterministic.  The random number seed is an input meant to be always different to break this identical output problem by tweaking the random number generator.  The random number seed is taken from an operating system process; sometimes it's different for each Rosetta invocation but sometimes it's just the POSIX timestamp, so many processes started simultaneously get the same random number seed.  If that's what happened, re-do your runs with -constant_seed -jran #####, where ##### is a different value for each invocation.

Fri, 2016-07-22 07:31
smlewis