Error in Wham output
1,290 views
Skip to first unread message
vidhya sankar
Jan 9, 2012, 11:25:27 AM1/9/12
to PLUMED users
Dear plumed user
i Have successfully run
umbrella .sampling using plumed plugin in gromacs environment
But when i extract the free energy using wham 2d utility i have got
the following error
Also how to give input for aperiodic parameter .In my case umbrella
pulling, the value of restraint is 4.426498
# /root/wham/wham2d/wham-2d Px=0 -4.337968 4.337968 100 Py=0 -4.337968
4.337968 100 0.001 300 0 meta datafile free_ene.dat >out_wham
#Number of windows = 676
# Dumping simulation biases, in the metadata file order
# Window F (free energy units)
# 0 -nan
# 1 -nan
# 2 -nan
# 3 -nan
# 4 -nan
# 5 -nan
# 6 -nan
# 7 -nan
# 8 -nan
# 9 -nan
# 10 -nan
# 11 -nan
# 12 -nan
# 13 -nan
# 14 -nan
# 15 -nan
free_ene.dat file output
#X Y Free +/- Pro +/-
-4.157219 -4.157219 -nan -nan -nan -nan
-4.157219 -3.795722 -nan -nan -nan -nan
-4.157219 -3.434225 -nan -nan -nan -nan
-4.157219 -3.072727 -nan -nan -nan -nan
-4.157219 -2.711230 -nan -nan -nan -nan
-4.157219 -2.349733 -nan -nan -nan -nan
-4.157219 -1.988235 -nan -nan -nan -nan
-4.157219 -1.626738 -nan -nan -nan -nan
-4.157219 -1.265241 -nan -nan -nan -nan
-4.157219 -0.903743 -nan -nan -nan -nan
-4.157219 -0.542246 -nan -nan -nan -nan
-4.157219 -0.180749 -nan -nan -nan -nan
-4.157219 0.180749 -nan -nan -nan -nan
What could be wrong in the simulation? i would be thankful if anyone
assists me
i Have successfully run
umbrella .sampling using plumed plugin in gromacs environment
But when i extract the free energy using wham 2d utility i have got
the following error
Also how to give input for aperiodic parameter .In my case umbrella
pulling, the value of restraint is 4.426498
# /root/wham/wham2d/wham-2d Px=0 -4.337968 4.337968 100 Py=0 -4.337968
4.337968 100 0.001 300 0 meta datafile free_ene.dat >out_wham
#Number of windows = 676
# Dumping simulation biases, in the metadata file order
# Window F (free energy units)
# 0 -nan
# 1 -nan
# 2 -nan
# 3 -nan
# 4 -nan
# 5 -nan
# 6 -nan
# 7 -nan
# 8 -nan
# 9 -nan
# 10 -nan
# 11 -nan
# 12 -nan
# 13 -nan
# 14 -nan
# 15 -nan
free_ene.dat file output
#X Y Free +/- Pro +/-
-4.157219 -4.157219 -nan -nan -nan -nan
-4.157219 -3.795722 -nan -nan -nan -nan
-4.157219 -3.434225 -nan -nan -nan -nan
-4.157219 -3.072727 -nan -nan -nan -nan
-4.157219 -2.711230 -nan -nan -nan -nan
-4.157219 -2.349733 -nan -nan -nan -nan
-4.157219 -1.988235 -nan -nan -nan -nan
-4.157219 -1.626738 -nan -nan -nan -nan
-4.157219 -1.265241 -nan -nan -nan -nan
-4.157219 -0.903743 -nan -nan -nan -nan
-4.157219 -0.542246 -nan -nan -nan -nan
-4.157219 -0.180749 -nan -nan -nan -nan
-4.157219 0.180749 -nan -nan -nan -nan
What could be wrong in the simulation? i would be thankful if anyone
assists me
Dejun Lin
Jan 9, 2012, 11:55:17 AM1/9/12
to plumed...@googlegroups.com, vidhy...@gmail.com
Hi,
Are you using WHAM-2D code by Alan Grossfield? If so, does your windows cover most of the 2D CV space? Can you also paste the metadata input for wham?
-Dejun
2012/1/9 vidhya sankar <vidhy...@gmail.com>
--
You received this message because you are subscribed to the Google Groups "PLUMED users" group.
To post to this group, send email to plumed...@googlegroups.com.
To unsubscribe from this group, send email to plumed-users...@googlegroups.com.
For more options, visit this group at http://groups.google.com/group/plumed-users?hl=en.
Davide Branduardi
Jan 9, 2012, 12:07:01 PM1/9/12
to plumed...@googlegroups.com
Hi Vidhya
check that your distributions from umbrella sampling are overlapping first otherwise
wham algorithm cannot determine the weights. If you have some "holes" then please fill it with other simulations.
HTH
check that your distributions from umbrella sampling are overlapping first otherwise
wham algorithm cannot determine the weights. If you have some "holes" then please fill it with other simulations.
HTH
D
> --
> You received this message because you are subscribed to the Google Groups "PLUMED users" group.
> To post to this group, send email to plumed...@googlegroups.com.
> To unsubscribe from this group, send email to plumed-users...@googlegroups.com.
> For more options, visit this group at http://groups.google.com/group/plumed-users?hl=en.
>
*********************************************
Davide Branduardi
Postdoc
Theoretical Molecular Biophysics Group
Max Planck Institute for Biophysics
Max-von-Laue-Straße 3
60438 Frankfurt am Main
Phone: +49 069 63031503
Fax: +49 069 63031502
davide.b...@biophys.mpg.de
davide.b...@gmail.com
*********************************************
vidhya sankar
Jan 9, 2012, 12:35:30 PM1/9/12
to PLUMED users
Thanks to Davide Branduardi and Dejun Lin
As lin mailed me i have pasted the meta data
file as follows also how to check distribution is overlapped or not
it means how to find out hole
BY mere plotting metadata file or is there is any method available
CV_1 -4.426498 -4.426498 1000 1000
CV_2 -4.426498 -4.072378 1000 1000
CV_3 -4.426498 -3.718258 1000 1000
CV_4 -4.426498 -3.364138 1000 1000
CV_5 -4.426498 -3.010019 1000 1000
CV_6 -4.426498 -2.655899 1000 1000
CV_7 -4.426498 -2.301779 1000 1000
CV_8 -4.426498 -1.947659 1000 1000
CV_9 -4.426498 -1.593539 1000 1000
CV_10 -4.426498 -1.239419 1000 1000
CV_11 -4.426498 -0.885300 1000 1000
CV_12 -4.426498 -0.531180 1000 1000
CV_13 -4.426498 -0.177060 1000 1000
CV_14 -4.426498 0.177060 1000 1000
CV_15 -4.426498 0.531180 1000 1000
CV_16 -4.426498 0.885300 1000 1000
CV_17 -4.426498 1.239419 1000 1000
CV_18 -4.426498 1.593539 1000 1000
CV_19 -4.426498 1.947659 1000 1000
CV_20 -4.426498 2.301779 1000 1000
CV_21 -4.426498 2.655899 1000 1000
CV_22 -4.426498 3.010019 1000 1000
CV_23 -4.426498 3.364138 1000 1000
CV_24 -4.426498 3.718258 1000 1000
CV_25 -4.426498 4.072378 1000 1000
CV_26 -4.426498 4.426498 1000 1000
CV_27 -4.072378 4.426498 1000 1000
CV_28 -4.072378 4.072378 1000 1000
CV_29 -4.072378 3.718258 1000 1000
CV_30 -4.072378 3.364138 1000 1000
CV_31 -4.072378 3.010019 1000 1000
CV_32 -4.072378 2.655899 1000 1000
CV_33 -4.072378 2.301779 1000 1000
CV_34 -4.072378 1.947659 1000 1000
CV_35 -4.072378 1.593539 1000 1000
CV_36 -4.072378 1.239419 1000 1000
CV_37 -4.072378 0.885300 1000 1000
CV_38 -4.072378 0.531180 1000 1000
CV_39 -4.072378 0.177060 1000 1000
CV_40 -4.072378 -0.177060 1000 1000
CV_41 -4.072378 -0.531180 1000 1000
CV_42 -4.072378 -0.885300 1000 1000
CV_43 -4.072378 -1.239419 1000 1000
CV_44 -4.072378 -1.593539 1000 1000
CV_45 -4.072378 -1.947659 1000 1000
CV_46 -4.072378 -2.301779 1000 1000
CV_47 -4.072378 -2.655899 1000 1000
CV_48 -4.072378 -3.010019 1000 1000
CV_49 -4.072378 -3.364138 1000 1000
CV_50 -4.072378 -3.718258 1000 1000
CV_51 -4.072378 -4.072378 1000 1000
CV_52 -4.072378 -4.426498 1000 1000
CV_53 -3.718258 -4.426498 1000 1000
CV_54 -3.718258 -4.072378 1000 1000
CV_55 -3.718258 -3.718258 1000 1000
CV_56 -3.718258 -3.364138 1000 1000
CV_57 -3.718258 -3.010019 1000 1000
CV_58 -3.718258 -2.655899 1000 1000
CV_59 -3.718258 -2.301779 1000 1000
CV_60 -3.718258 -1.947659 1000 1000
CV_61 -3.718258 -1.593539 1000 1000
CV_62 -3.718258 -1.239419 1000 1000
CV_63 -3.718258 -0.885300 1000 1000
CV_64 -3.718258 -0.531180 1000 1000
CV_65 -3.718258 -0.177060 1000 1000
CV_66 -3.718258 0.177060 1000 1000
CV_67 -3.718258 0.531180 1000 1000
CV_68 -3.718258 0.885300 1000 1000
CV_69 -3.718258 1.239419 1000 1000
CV_70 -3.718258 1.593539 1000 1000
CV_71 -3.718258 1.947659 1000 1000
CV_72 -3.718258 2.301779 1000 1000
CV_73 -3.718258 2.655899 1000 1000
CV_74 -3.718258 3.010019 1000 1000
CV_75 -3.718258 3.364138 1000 1000
CV_76 -3.718258 3.718258 1000 1000
CV_77 -3.718258 4.072378 1000 1000
CV_78 -3.718258 4.426498 1000 1000
CV_79 -3.364138 4.426498 1000 1000
CV_80 -3.364138 4.072378 1000 1000
CV_81 -3.364138 3.718258 1000 1000
CV_82 -3.364138 3.364138 1000 1000
CV_83 -3.364138 3.010019 1000 1000
CV_84 -3.364138 2.655899 1000 1000
CV_85 -3.364138 2.301779 1000 1000
CV_86 -3.364138 1.947659 1000 1000
CV_87 -3.364138 1.593539 1000 1000
CV_88 -3.364138 1.239419 1000 1000
CV_89 -3.364138 0.885300 1000 1000
CV_90 -3.364138 0.531180 1000 1000
CV_91 -3.364138 0.177060 1000 1000
CV_92 -3.364138 -0.177060 1000 1000
CV_93 -3.364138 -0.531180 1000 1000
CV_94 -3.364138 -0.885300 1000 1000
CV_95 -3.364138 -1.239419 1000 1000
CV_96 -3.364138 -1.593539 1000 1000
CV_97 -3.364138 -1.947659 1000 1000
CV_98 -3.364138 -2.301779 1000 1000
CV_99 -3.364138 -2.655899 1000 1000
CV_100 -3.364138 -3.010019 1000 1000
CV_101 -3.364138 -3.364138 1000 1000
CV_102 -3.364138 -3.718258 1000 1000
CV_103 -3.364138 -4.072378 1000 1000
CV_104 -3.364138 -4.426498 1000 1000
CV_105 -3.010019 -4.426498 1000 1000
CV_106 -3.010019 -4.072378 1000 1000
CV_107 -3.010019 -3.718258 1000 1000
CV_108 -3.010019 -3.364138 1000 1000
CV_109 -3.010019 -3.010019 1000 1000
CV_110 -3.010019 -2.655899 1000 1000
CV_111 -3.010019 -2.301779 1000 1000
CV_112 -3.010019 -1.947659 1000 1000
CV_113 -3.010019 -1.593539 1000 1000
CV_114 -3.010019 -1.239419 1000 1000
CV_115 -3.010019 -0.885300 1000 1000
CV_116 -3.010019 -0.531180 1000 1000
CV_117 -3.010019 -0.177060 1000 1000
CV_118 -3.010019 0.177060 1000 1000
CV_119 -3.010019 0.531180 1000 1000
CV_120 -3.010019 0.885300 1000 1000
CV_121 -3.010019 1.239419 1000 1000
CV_122 -3.010019 1.593539 1000 1000
CV_123 -3.010019 1.947659 1000 1000
CV_124 -3.010019 2.301779 1000 1000
CV_125 -3.010019 2.655899 1000 1000
CV_126 -3.010019 3.010019 1000 1000
CV_127 -3.010019 3.364138 1000 1000
CV_128 -3.010019 3.718258 1000 1000
CV_129 -3.010019 4.072378 1000 1000
CV_130 -3.010019 4.426498 1000 1000
CV_131 -2.655899 4.426498 1000 1000
CV_132 -2.655899 4.072378 1000 1000
CV_133 -2.655899 3.718258 1000 1000
CV_134 -2.655899 3.364138 1000 1000
CV_135 -2.655899 3.010019 1000 1000
CV_136 -2.655899 2.655899 1000 1000
CV_137 -2.655899 2.301779 1000 1000
CV_138 -2.655899 1.947659 1000 1000
CV_139 -2.655899 1.593539 1000 1000
CV_140 -2.655899 1.239419 1000 1000
CV_141 -2.655899 0.885300 1000 1000
CV_142 -2.655899 0.531180 1000 1000
CV_143 -2.655899 0.177060 1000 1000
CV_144 -2.655899 -0.177060 1000 1000
CV_145 -2.655899 -0.531180 1000 1000
CV_146 -2.655899 -0.885300 1000 1000
CV_147 -2.655899 -1.239419 1000 1000
CV_148 -2.655899 -1.593539 1000 1000
CV_149 -2.655899 -1.947659 1000 1000
CV_150 -2.655899 -2.301779 1000 1000
CV_151 -2.655899 -2.655899 1000 1000
CV_152 -2.655899 -3.010019 1000 1000
CV_153 -2.655899 -3.364138 1000 1000
CV_154 -2.655899 -3.718258 1000 1000
CV_155 -2.655899 -4.072378 1000 1000
CV_156 -2.655899 -4.426498 1000 1000
CV_157 -2.301779 -4.426498 1000 1000
CV_158 -2.301779 -4.072378 1000 1000
CV_159 -2.301779 -3.718258 1000 1000
CV_160 -2.301779 -3.364138 1000 1000
CV_161 -2.301779 -3.010019 1000 1000
CV_162 -2.301779 -2.655899 1000 1000
CV_163 -2.301779 -2.301779 1000 1000
CV_164 -2.301779 -1.947659 1000 1000
CV_165 -2.301779 -1.593539 1000 1000
CV_166 -2.301779 -1.239419 1000 1000
CV_167 -2.301779 -0.885300 1000 1000
CV_168 -2.301779 -0.531180 1000 1000
CV_169 -2.301779 -0.177060 1000 1000
CV_170 -2.301779 0.177060 1000 1000
CV_171 -2.301779 0.531180 1000 1000
CV_172 -2.301779 0.885300 1000 1000
CV_173 -2.301779 1.239419 1000 1000
CV_174 -2.301779 1.593539 1000 1000
CV_175 -2.301779 1.947659 1000 1000
CV_176 -2.301779 2.301779 1000 1000
CV_177 -2.301779 2.655899 1000 1000
CV_178 -2.301779 3.010019 1000 1000
CV_179 -2.301779 3.364138 1000 1000
CV_180 -2.301779 3.718258 1000 1000
CV_181 -2.301779 4.072378 1000 1000
CV_182 -2.301779 4.426498 1000 1000
CV_183 -1.947659 4.426498 1000 1000
CV_184 -1.947659 4.072378 1000 1000
CV_185 -1.947659 3.718258 1000 1000
CV_186 -1.947659 3.364138 1000 1000
CV_187 -1.947659 3.010019 1000 1000
CV_188 -1.947659 2.655899 1000 1000
CV_189 -1.947659 2.301779 1000 1000
CV_190 -1.947659 1.947659 1000 1000
CV_191 -1.947659 1.593539 1000 1000
CV_192 -1.947659 1.239419 1000 1000
CV_193 -1.947659 0.885300 1000 1000
CV_194 -1.947659 0.531180 1000 1000
CV_195 -1.947659 0.177060 1000 1000
CV_196 -1.947659 -0.177060 1000 1000
CV_197 -1.947659 -0.531180 1000 1000
CV_198 -1.947659 -0.885300 1000 1000
CV_199 -1.947659 -1.239419 1000 1000
CV_200 -1.947659 -1.593539 1000 1000
CV_201 -1.947659 -1.947659 1000 1000
CV_202 -1.947659 -2.301779 1000 1000
CV_203 -1.947659 -2.655899 1000 1000
CV_204 -1.947659 -3.010019 1000 1000
CV_205 -1.947659 -3.364138 1000 1000
CV_206 -1.947659 -3.718258 1000 1000
CV_207 -1.947659 -4.072378 1000 1000
CV_208 -1.947659 -4.426498 1000 1000
CV_209 -1.593539 -4.426498 1000 1000
CV_210 -1.593539 -4.072378 1000 1000
CV_211 -1.593539 -3.718258 1000 1000
CV_212 -1.593539 -3.364138 1000 1000
CV_213 -1.593539 -3.010019 1000 1000
CV_214 -1.593539 -2.655899 1000 1000
CV_215 -1.593539 -2.301779 1000 1000
CV_216 -1.593539 -1.947659 1000 1000
CV_217 -1.593539 -1.593539 1000 1000
CV_218 -1.593539 -1.239419 1000 1000
CV_219 -1.593539 -0.885300 1000 1000
CV_220 -1.593539 -0.531180 1000 1000
CV_221 -1.593539 -0.177060 1000 1000
CV_222 -1.593539 0.177060 1000 1000
CV_223 -1.593539 0.531180 1000 1000
CV_224 -1.593539 0.885300 1000 1000
CV_225 -1.593539 1.239419 1000 1000
CV_226 -1.593539 1.593539 1000 1000
CV_227 -1.593539 1.947659 1000 1000
CV_228 -1.593539 2.301779 1000 1000
CV_229 -1.593539 2.655899 1000 1000
CV_230 -1.593539 3.010019 1000 1000
CV_231 -1.593539 3.364138 1000 1000
CV_232 -1.593539 3.718258 1000 1000
CV_233 -1.593539 4.072378 1000 1000
CV_234 -1.593539 4.426498 1000 1000
CV_235 -1.239419 4.426498 1000 1000
CV_236 -1.239419 4.072378 1000 1000
CV_237 -1.239419 3.718258 1000 1000
CV_238 -1.239419 3.364138 1000 1000
CV_239 -1.239419 3.010019 1000 1000
CV_240 -1.239419 2.655899 1000 1000
CV_241 -1.239419 2.301779 1000 1000
CV_242 -1.239419 1.947659 1000 1000
CV_243 -1.239419 1.593539 1000 1000
CV_244 -1.239419 1.239419 1000 1000
CV_245 -1.239419 0.885300 1000 1000
CV_246 -1.239419 0.531180 1000 1000
CV_247 -1.239419 0.177060 1000 1000
CV_248 -1.239419 -0.177060 1000 1000
CV_249 -1.239419 -0.531180 1000 1000
CV_250 -1.239419 -0.885300 1000 1000
CV_251 -1.239419 -1.239419 1000 1000
CV_252 -1.239419 -1.593539 1000 1000
CV_253 -1.239419 -1.947659 1000 1000
CV_254 -1.239419 -2.301779 1000 1000
CV_255 -1.239419 -2.655899 1000 1000
CV_256 -1.239419 -3.010019 1000 1000
CV_257 -1.239419 -3.364138 1000 1000
CV_258 -1.239419 -3.718258 1000 1000
CV_259 -1.239419 -4.072378 1000 1000
CV_260 -1.239419 -4.426498 1000 1000
CV_261 -0.885300 -4.426498 1000 1000
CV_262 -0.885300 -4.072378 1000 1000
CV_263 -0.885300 -3.718258 1000 1000
CV_264 -0.885300 -3.364138 1000 1000
CV_265 -0.885300 -3.010019 1000 1000
CV_266 -0.885300 -2.655899 1000 1000
CV_267 -0.885300 -2.301779 1000 1000
CV_268 -0.885300 -1.947659 1000 1000
CV_269 -0.885300 -1.593539 1000 1000
CV_270 -0.885300 -1.239419 1000 1000
CV_271 -0.885300 -0.885300 1000 1000
CV_272 -0.885300 -0.531180 1000 1000
CV_273 -0.885300 -0.177060 1000 1000
CV_274 -0.885300 0.177060 1000 1000
CV_275 -0.885300 0.531180 1000 1000
CV_276 -0.885300 0.885300 1000 1000
CV_277 -0.885300 1.239419 1000 1000
CV_278 -0.885300 1.593539 1000 1000
CV_279 -0.885300 1.947659 1000 1000
CV_280 -0.885300 2.301779 1000 1000
CV_281 -0.885300 2.655899 1000 1000
CV_282 -0.885300 3.010019 1000 1000
CV_283 -0.885300 3.364138 1000 1000
CV_284 -0.885300 3.718258 1000 1000
CV_285 -0.885300 4.072378 1000 1000
CV_286 -0.885300 4.426498 1000 1000
CV_287 -0.531180 4.426498 1000 1000
CV_288 -0.531180 4.072378 1000 1000
CV_289 -0.531180 3.718258 1000 1000
CV_290 -0.531180 3.364138 1000 1000
CV_291 -0.531180 3.010019 1000 1000
CV_292 -0.531180 2.655899 1000 1000
CV_293 -0.531180 2.301779 1000 1000
CV_294 -0.531180 1.947659 1000 1000
CV_295 -0.531180 1.593539 1000 1000
CV_296 -0.531180 1.239419 1000 1000
CV_297 -0.531180 0.885300 1000 1000
CV_298 -0.531180 0.531180 1000 1000
CV_299 -0.531180 0.177060 1000 1000
CV_300 -0.531180 -0.177060 1000 1000
CV_301 -0.531180 -0.531180 1000 1000
CV_302 -0.531180 -0.885300 1000 1000
CV_303 -0.531180 -1.239419 1000 1000
CV_304 -0.531180 -1.593539 1000 1000
CV_305 -0.531180 -1.947659 1000 1000
CV_306 -0.531180 -2.301779 1000 1000
CV_307 -0.531180 -2.655899 1000 1000
CV_308 -0.531180 -3.010019 1000 1000
CV_309 -0.531180 -3.364138 1000 1000
CV_310 -0.531180 -3.718258 1000 1000
CV_311 -0.531180 -4.072378 1000 1000
CV_312 -0.531180 -4.426498 1000 1000
CV_313 -0.177060 -4.426498 1000 1000
CV_314 -0.177060 -4.072378 1000 1000
CV_315 -0.177060 -3.718258 1000 1000
CV_316 -0.177060 -3.364138 1000 1000
CV_317 -0.177060 -3.010019 1000 1000
CV_318 -0.177060 -2.655899 1000 1000
CV_319 -0.177060 -2.301779 1000 1000
CV_320 -0.177060 -1.947659 1000 1000
CV_321 -0.177060 -1.593539 1000 1000
CV_322 -0.177060 -1.239419 1000 1000
CV_323 -0.177060 -0.885300 1000 1000
CV_324 -0.177060 -0.531180 1000 1000
CV_325 -0.177060 -0.177060 1000 1000
CV_326 -0.177060 0.177060 1000 1000
CV_327 -0.177060 0.531180 1000 1000
CV_328 -0.177060 0.885300 1000 1000
CV_329 -0.177060 1.239419 1000 1000
CV_330 -0.177060 1.593539 1000 1000
CV_331 -0.177060 1.947659 1000 1000
CV_332 -0.177060 2.301779 1000 1000
CV_333 -0.177060 2.655899 1000 1000
CV_334 -0.177060 3.010019 1000 1000
CV_335 -0.177060 3.364138 1000 1000
CV_336 -0.177060 3.718258 1000 1000
CV_337 -0.177060 4.072378 1000 1000
CV_338 -0.177060 4.426498 1000 1000
CV_339 0.177060 4.426498 1000 1000
CV_340 0.177060 4.072378 1000 1000
CV_341 0.177060 3.718258 1000 1000
CV_342 0.177060 3.364138 1000 1000
CV_343 0.177060 3.010019 1000 1000
CV_344 0.177060 2.655899 1000 1000
CV_345 0.177060 2.301779 1000 1000
CV_346 0.177060 1.947659 1000 1000
CV_347 0.177060 1.593539 1000 1000
CV_348 0.177060 1.239419 1000 1000
CV_349 0.177060 0.885300 1000 1000
CV_350 0.177060 0.531180 1000 1000
CV_351 0.177060 0.177060 1000 1000
CV_352 0.177060 -0.177060 1000 1000
CV_353 0.177060 -0.531180 1000 1000
CV_354 0.177060 -0.885300 1000 1000
CV_355 0.177060 -1.239419 1000 1000
CV_356 0.177060 -1.593539 1000 1000
CV_357 0.177060 -1.947659 1000 1000
CV_358 0.177060 -2.301779 1000 1000
CV_359 0.177060 -2.655899 1000 1000
CV_360 0.177060 -3.010019 1000 1000
CV_361 0.177060 -3.364138 1000 1000
CV_362 0.177060 -3.718258 1000 1000
CV_363 0.177060 -4.072378 1000 1000
CV_364 0.177060 -4.426498 1000 1000
CV_365 0.531180 -4.426498 1000 1000
CV_366 0.531180 -4.072378 1000 1000
CV_367 0.531180 -3.718258 1000 1000
CV_368 0.531180 -3.364138 1000 1000
CV_369 0.531180 -3.010019 1000 1000
CV_370 0.531180 -2.655899 1000 1000
CV_371 0.531180 -2.301779 1000 1000
CV_372 0.531180 -1.947659 1000 1000
CV_373 0.531180 -1.593539 1000 1000
CV_374 0.531180 -1.239419 1000 1000
CV_375 0.531180 -0.885300 1000 1000
CV_376 0.531180 -0.531180 1000 1000
CV_377 0.531180 -0.177060 1000 1000
CV_378 0.531180 0.177060 1000 1000
CV_379 0.531180 0.531180 1000 1000
CV_380 0.531180 0.885300 1000 1000
CV_381 0.531180 1.239419 1000 1000
CV_382 0.531180 1.593539 1000 1000
CV_383 0.531180 1.947659 1000 1000
CV_384 0.531180 2.301779 1000 1000
CV_385 0.531180 2.655899 1000 1000
CV_386 0.531180 3.010019 1000 1000
CV_387 0.531180 3.364138 1000 1000
CV_388 0.531180 3.718258 1000 1000
CV_389 0.531180 4.072378 1000 1000
CV_390 0.531180 4.426498 1000 1000
CV_391 0.885300 4.426498 1000 1000
CV_392 0.885300 4.072378 1000 1000
CV_393 0.885300 3.718258 1000 1000
CV_394 0.885300 3.364138 1000 1000
CV_395 0.885300 3.010019 1000 1000
CV_396 0.885300 2.655899 1000 1000
CV_397 0.885300 2.301779 1000 1000
CV_398 0.885300 1.947659 1000 1000
CV_399 0.885300 1.593539 1000 1000
CV_400 0.885300 1.239419 1000 1000
CV_401 0.885300 0.885300 1000 1000
CV_402 0.885300 0.531180 1000 1000
CV_403 0.885300 0.177060 1000 1000
CV_404 0.885300 -0.177060 1000 1000
CV_405 0.885300 -0.531180 1000 1000
CV_406 0.885300 -0.885300 1000 1000
CV_407 0.885300 -1.239419 1000 1000
CV_408 0.885300 -1.593539 1000 1000
CV_409 0.885300 -1.947659 1000 1000
CV_410 0.885300 -2.301779 1000 1000
CV_411 0.885300 -2.655899 1000 1000
CV_412 0.885300 -3.010019 1000 1000
CV_413 0.885300 -3.364138 1000 1000
CV_414 0.885300 -3.718258 1000 1000
CV_415 0.885300 -4.072378 1000 1000
CV_416 0.885300 -4.426498 1000 1000
CV_417 1.239419 -4.426498 1000 1000
CV_418 1.239419 -4.072378 1000 1000
CV_419 1.239419 -3.718258 1000 1000
CV_420 1.239419 -3.364138 1000 1000
CV_421 1.239419 -3.010019 1000 1000
CV_422 1.239419 -2.655899 1000 1000
CV_423 1.239419 -2.301779 1000 1000
CV_424 1.239419 -1.947659 1000 1000
CV_425 1.239419 -1.593539 1000 1000
CV_426 1.239419 -1.239419 1000 1000
CV_427 1.239419 -0.885300 1000 1000
CV_428 1.239419 -0.531180 1000 1000
CV_429 1.239419 -0.177060 1000 1000
CV_430 1.239419 0.177060 1000 1000
CV_431 1.239419 0.531180 1000 1000
CV_432 1.239419 0.885300 1000 1000
CV_433 1.239419 1.239419 1000 1000
CV_434 1.239419 1.593539 1000 1000
CV_435 1.239419 1.947659 1000 1000
CV_436 1.239419 2.301779 1000 1000
CV_437 1.239419 2.655899 1000 1000
CV_438 1.239419 3.010019 1000 1000
CV_439 1.239419 3.364138 1000 1000
CV_440 1.239419 3.718258 1000 1000
CV_441 1.239419 4.072378 1000 1000
CV_442 1.239419 4.426498 1000 1000
CV_443 1.593539 4.426498 1000 1000
CV_444 1.593539 4.072378 1000 1000
CV_445 1.593539 3.718258 1000 1000
CV_446 1.593539 3.364138 1000 1000
CV_447 1.593539 3.010019 1000 1000
CV_448 1.593539 2.655899 1000 1000
CV_449 1.593539 2.301779 1000 1000
CV_450 1.593539 1.947659 1000 1000
CV_451 1.593539 1.593539 1000 1000
CV_452 1.593539 1.239419 1000 1000
CV_453 1.593539 0.885300 1000 1000
CV_454 1.593539 0.531180 1000 1000
CV_455 1.593539 0.177060 1000 1000
CV_456 1.593539 -0.177060 1000 1000
CV_457 1.593539 -0.531180 1000 1000
CV_458 1.593539 -0.885300 1000 1000
CV_459 1.593539 -1.239419 1000 1000
CV_460 1.593539 -1.593539 1000 1000
CV_461 1.593539 -1.947659 1000 1000
CV_462 1.593539 -2.301779 1000 1000
CV_463 1.593539 -2.655899 1000 1000
CV_464 1.593539 -3.010019 1000 1000
CV_465 1.593539 -3.364138 1000 1000
CV_466 1.593539 -3.718258 1000 1000
CV_467 1.593539 -4.072378 1000 1000
CV_468 1.593539 -4.426498 1000 1000
CV_469 1.947659 -4.426498 1000 1000
CV_470 1.947659 -4.072378 1000 1000
CV_471 1.947659 -3.718258 1000 1000
CV_472 1.947659 -3.364138 1000 1000
CV_473 1.947659 -3.010019 1000 1000
CV_474 1.947659 -2.655899 1000 1000
CV_475 1.947659 -2.301779 1000 1000
CV_476 1.947659 -1.947659 1000 1000
CV_477 1.947659 -1.593539 1000 1000
CV_478 1.947659 -1.239419 1000 1000
CV_479 1.947659 -0.885300 1000 1000
CV_480 1.947659 -0.531180 1000 1000
CV_481 1.947659 -0.177060 1000 1000
CV_482 1.947659 0.177060 1000 1000
CV_483 1.947659 0.531180 1000 1000
CV_484 1.947659 0.885300 1000 1000
CV_485 1.947659 1.239419 1000 1000
CV_486 1.947659 1.593539 1000 1000
CV_487 1.947659 1.947659 1000 1000
CV_488 1.947659 2.301779 1000 1000
CV_489 1.947659 2.655899 1000 1000
CV_490 1.947659 3.010019 1000 1000
CV_491 1.947659 3.364138 1000 1000
CV_492 1.947659 3.718258 1000 1000
CV_493 1.947659 4.072378 1000 1000
CV_494 1.947659 4.426498 1000 1000
CV_495 2.301779 4.426498 1000 1000
CV_496 2.301779 4.072378 1000 1000
CV_497 2.301779 3.718258 1000 1000
CV_498 2.301779 3.364138 1000 1000
CV_499 2.301779 3.010019 1000 1000
CV_500 2.301779 2.655899 1000 1000
CV_501 2.301779 2.301779 1000 1000
CV_502 2.301779 1.947659 1000 1000
CV_503 2.301779 1.593539 1000 1000
CV_504 2.301779 1.239419 1000 1000
CV_505 2.301779 0.885300 1000 1000
CV_506 2.301779 0.531180 1000 1000
CV_507 2.301779 0.177060 1000 1000
CV_508 2.301779 -0.177060 1000 1000
CV_509 2.301779 -0.531180 1000 1000
CV_510 2.301779 -0.885300 1000 1000
CV_511 2.301779 -1.239419 1000 1000
CV_512 2.301779 -1.593539 1000 1000
CV_513 2.301779 -1.947659 1000 1000
CV_514 2.301779 -2.301779 1000 1000
CV_515 2.301779 -2.655899 1000 1000
CV_516 2.301779 -3.010019 1000 1000
CV_517 2.301779 -3.364138 1000 1000
CV_518 2.301779 -3.718258 1000 1000
CV_519 2.301779 -4.072378 1000 1000
CV_520 2.301779 -4.426498 1000 1000
CV_521 2.655899 -4.426498 1000 1000
CV_522 2.655899 -4.072378 1000 1000
CV_523 2.655899 -3.718258 1000 1000
CV_524 2.655899 -3.364138 1000 1000
CV_525 2.655899 -3.010019 1000 1000
CV_526 2.655899 -2.655899 1000 1000
CV_527 2.655899 -2.301779 1000 1000
CV_528 2.655899 -1.947659 1000 1000
CV_529 2.655899 -1.593539 1000 1000
CV_530 2.655899 -1.239419 1000 1000
CV_531 2.655899 -0.885300 1000 1000
CV_532 2.655899 -0.531180 1000 1000
CV_533 2.655899 -0.177060 1000 1000
CV_534 2.655899 0.177060 1000 1000
CV_535 2.655899 0.531180 1000 1000
CV_536 2.655899 0.885300 1000 1000
CV_537 2.655899 1.239419 1000 1000
CV_538 2.655899 1.593539 1000 1000
CV_539 2.655899 1.947659 1000 1000
CV_540 2.655899 2.301779 1000 1000
CV_541 2.655899 2.655899 1000 1000
CV_542 2.655899 3.010019 1000 1000
CV_543 2.655899 3.364138 1000 1000
CV_544 2.655899 3.718258 1000 1000
CV_545 2.655899 4.072378 1000 1000
CV_546 2.655899 4.426498 1000 1000
CV_547 3.010019 4.426498 1000 1000
CV_548 3.010019 4.072378 1000 1000
CV_549 3.010019 3.718258 1000 1000
CV_550 3.010019 3.364138 1000 1000
CV_551 3.010019 3.010019 1000 1000
CV_552 3.010019 2.655899 1000 1000
CV_553 3.010019 2.301779 1000 1000
CV_554 3.010019 1.947659 1000 1000
CV_555 3.010019 1.593539 1000 1000
CV_556 3.010019 1.239419 1000 1000
CV_557 3.010019 0.885300 1000 1000
CV_558 3.010019 0.531180 1000 1000
CV_559 3.010019 0.177060 1000 1000
CV_560 3.010019 -0.177060 1000 1000
CV_561 3.010019 -0.531180 1000 1000
CV_562 3.010019 -0.885300 1000 1000
CV_563 3.010019 -1.239419 1000 1000
CV_564 3.010019 -1.593539 1000 1000
CV_565 3.010019 -1.947659 1000 1000
CV_566 3.010019 -2.301779 1000 1000
CV_567 3.010019 -2.655899 1000 1000
CV_568 3.010019 -3.010019 1000 1000
CV_569 3.010019 -3.364138 1000 1000
CV_570 3.010019 -3.718258 1000 1000
CV_571 3.010019 -4.072378 1000 1000
CV_572 3.010019 -4.426498 1000 1000
CV_573 3.364138 -4.426498 1000 1000
CV_574 3.364138 -4.072378 1000 1000
CV_575 3.364138 -3.718258 1000 1000
CV_576 3.364138 -3.364138 1000 1000
CV_577 3.364138 -3.010019 1000 1000
CV_578 3.364138 -2.655899 1000 1000
CV_579 3.364138 -2.301779 1000 1000
CV_580 3.364138 -1.947659 1000 1000
CV_581 3.364138 -1.593539 1000 1000
CV_582 3.364138 -1.239419 1000 1000
CV_583 3.364138 -0.885300 1000 1000
CV_584 3.364138 -0.531180 1000 1000
CV_585 3.364138 -0.177060 1000 1000
CV_586 3.364138 0.177060 1000 1000
CV_587 3.364138 0.531180 1000 1000
CV_588 3.364138 0.885300 1000 1000
CV_589 3.364138 1.239419 1000 1000
CV_590 3.364138 1.593539 1000 1000
CV_591 3.364138 1.947659 1000 1000
CV_592 3.364138 2.301779 1000 1000
CV_593 3.364138 2.655899 1000 1000
CV_594 3.364138 3.010019 1000 1000
CV_595 3.364138 3.364138 1000 1000
CV_596 3.364138 3.718258 1000 1000
CV_597 3.364138 4.072378 1000 1000
CV_598 3.364138 4.426498 1000 1000
CV_599 3.718258 4.426498 1000 1000
CV_600 3.718258 4.072378 1000 1000
CV_601 3.718258 3.718258 1000 1000
CV_602 3.718258 3.364138 1000 1000
CV_603 3.718258 3.010019 1000 1000
CV_604 3.718258 2.655899 1000 1000
CV_605 3.718258 2.301779 1000 1000
CV_606 3.718258 1.947659 1000 1000
CV_607 3.718258 1.593539 1000 1000
CV_608 3.718258 1.239419 1000 1000
CV_609 3.718258 0.885300 1000 1000
CV_610 3.718258 0.531180 1000 1000
CV_611 3.718258 0.177060 1000 1000
CV_612 3.718258 -0.177060 1000 1000
CV_613 3.718258 -0.531180 1000 1000
CV_614 3.718258 -0.885300 1000 1000
CV_615 3.718258 -1.239419 1000 1000
CV_616 3.718258 -1.593539 1000 1000
CV_617 3.718258 -1.947659 1000 1000
CV_618 3.718258 -2.301779 1000 1000
CV_619 3.718258 -2.655899 1000 1000
CV_620 3.718258 -3.010019 1000 1000
CV_621 3.718258 -3.364138 1000 1000
CV_622 3.718258 -3.718258 1000 1000
CV_623 3.718258 -4.072378 1000 1000
CV_624 3.718258 -4.426498 1000 1000
CV_625 4.072378 -4.426498 1000 1000
CV_626 4.072378 -4.072378 1000 1000
CV_627 4.072378 -3.718258 1000 1000
CV_628 4.072378 -3.364138 1000 1000
CV_629 4.072378 -3.010019 1000 1000
CV_630 4.072378 -2.655899 1000 1000
CV_631 4.072378 -2.301779 1000 1000
CV_632 4.072378 -1.947659 1000 1000
CV_633 4.072378 -1.593539 1000 1000
CV_634 4.072378 -1.239419 1000 1000
CV_635 4.072378 -0.885300 1000 1000
CV_636 4.072378 -0.531180 1000 1000
CV_637 4.072378 -0.177060 1000 1000
CV_638 4.072378 0.177060 1000 1000
CV_639 4.072378 0.531180 1000 1000
CV_640 4.072378 0.885300 1000 1000
CV_641 4.072378 1.239419 1000 1000
CV_642 4.072378 1.593539 1000 1000
CV_643 4.072378 1.947659 1000 1000
CV_644 4.072378 2.301779 1000 1000
CV_645 4.072378 2.655899 1000 1000
CV_646 4.072378 3.010019 1000 1000
CV_647 4.072378 3.364138 1000 1000
CV_648 4.072378 3.718258 1000 1000
CV_649 4.072378 4.072378 1000 1000
CV_650 4.072378 4.426498 1000 1000
CV_651 4.426498 4.426498 1000 1000
CV_652 4.426498 4.072378 1000 1000
CV_653 4.426498 3.718258 1000 1000
CV_654 4.426498 3.364138 1000 1000
CV_655 4.426498 3.010019 1000 1000
CV_656 4.426498 2.655899 1000 1000
CV_657 4.426498 2.301779 1000 1000
CV_658 4.426498 1.947659 1000 1000
CV_659 4.426498 1.593539 1000 1000
CV_660 4.426498 1.239419 1000 1000
CV_661 4.426498 0.885300 1000 1000
CV_662 4.426498 0.531180 1000 1000
CV_663 4.426498 0.177060 1000 1000
CV_664 4.426498 -0.177060 1000 1000
CV_665 4.426498 -0.531180 1000 1000
CV_666 4.426498 -0.885300 1000 1000
CV_667 4.426498 -1.239419 1000 1000
CV_668 4.426498 -1.593539 1000 1000
CV_669 4.426498 -1.947659 1000 1000
CV_670 4.426498 -2.301779 1000 1000
CV_671 4.426498 -2.655899 1000 1000
CV_672 4.426498 -3.010019 1000 1000
CV_673 4.426498 -3.364138 1000 1000
CV_674 4.426498 -3.718258 1000 1000
CV_675 4.426498 -4.072378 1000 1000
CV_676 4.426498 -4.426498 1000 1000
On Jan 9, 10:07 pm, Davide Branduardi <davide.brandua...@gmail.com>
wrote:
> > For more options, visit this group athttp://groups.google.com/group/plumed-users?hl=en.
> davide.brandua...@gmail.com
> *********************************************
As lin mailed me i have pasted the meta data
file as follows also how to check distribution is overlapped or not
it means how to find out hole
BY mere plotting metadata file or is there is any method available
CV_1 -4.426498 -4.426498 1000 1000
CV_2 -4.426498 -4.072378 1000 1000
CV_3 -4.426498 -3.718258 1000 1000
CV_4 -4.426498 -3.364138 1000 1000
CV_5 -4.426498 -3.010019 1000 1000
CV_6 -4.426498 -2.655899 1000 1000
CV_7 -4.426498 -2.301779 1000 1000
CV_8 -4.426498 -1.947659 1000 1000
CV_9 -4.426498 -1.593539 1000 1000
CV_10 -4.426498 -1.239419 1000 1000
CV_11 -4.426498 -0.885300 1000 1000
CV_12 -4.426498 -0.531180 1000 1000
CV_13 -4.426498 -0.177060 1000 1000
CV_14 -4.426498 0.177060 1000 1000
CV_15 -4.426498 0.531180 1000 1000
CV_16 -4.426498 0.885300 1000 1000
CV_17 -4.426498 1.239419 1000 1000
CV_18 -4.426498 1.593539 1000 1000
CV_19 -4.426498 1.947659 1000 1000
CV_20 -4.426498 2.301779 1000 1000
CV_21 -4.426498 2.655899 1000 1000
CV_22 -4.426498 3.010019 1000 1000
CV_23 -4.426498 3.364138 1000 1000
CV_24 -4.426498 3.718258 1000 1000
CV_25 -4.426498 4.072378 1000 1000
CV_26 -4.426498 4.426498 1000 1000
CV_27 -4.072378 4.426498 1000 1000
CV_28 -4.072378 4.072378 1000 1000
CV_29 -4.072378 3.718258 1000 1000
CV_30 -4.072378 3.364138 1000 1000
CV_31 -4.072378 3.010019 1000 1000
CV_32 -4.072378 2.655899 1000 1000
CV_33 -4.072378 2.301779 1000 1000
CV_34 -4.072378 1.947659 1000 1000
CV_35 -4.072378 1.593539 1000 1000
CV_36 -4.072378 1.239419 1000 1000
CV_37 -4.072378 0.885300 1000 1000
CV_38 -4.072378 0.531180 1000 1000
CV_39 -4.072378 0.177060 1000 1000
CV_40 -4.072378 -0.177060 1000 1000
CV_41 -4.072378 -0.531180 1000 1000
CV_42 -4.072378 -0.885300 1000 1000
CV_43 -4.072378 -1.239419 1000 1000
CV_44 -4.072378 -1.593539 1000 1000
CV_45 -4.072378 -1.947659 1000 1000
CV_46 -4.072378 -2.301779 1000 1000
CV_47 -4.072378 -2.655899 1000 1000
CV_48 -4.072378 -3.010019 1000 1000
CV_49 -4.072378 -3.364138 1000 1000
CV_50 -4.072378 -3.718258 1000 1000
CV_51 -4.072378 -4.072378 1000 1000
CV_52 -4.072378 -4.426498 1000 1000
CV_53 -3.718258 -4.426498 1000 1000
CV_54 -3.718258 -4.072378 1000 1000
CV_55 -3.718258 -3.718258 1000 1000
CV_56 -3.718258 -3.364138 1000 1000
CV_57 -3.718258 -3.010019 1000 1000
CV_58 -3.718258 -2.655899 1000 1000
CV_59 -3.718258 -2.301779 1000 1000
CV_60 -3.718258 -1.947659 1000 1000
CV_61 -3.718258 -1.593539 1000 1000
CV_62 -3.718258 -1.239419 1000 1000
CV_63 -3.718258 -0.885300 1000 1000
CV_64 -3.718258 -0.531180 1000 1000
CV_65 -3.718258 -0.177060 1000 1000
CV_66 -3.718258 0.177060 1000 1000
CV_67 -3.718258 0.531180 1000 1000
CV_68 -3.718258 0.885300 1000 1000
CV_69 -3.718258 1.239419 1000 1000
CV_70 -3.718258 1.593539 1000 1000
CV_71 -3.718258 1.947659 1000 1000
CV_72 -3.718258 2.301779 1000 1000
CV_73 -3.718258 2.655899 1000 1000
CV_74 -3.718258 3.010019 1000 1000
CV_75 -3.718258 3.364138 1000 1000
CV_76 -3.718258 3.718258 1000 1000
CV_77 -3.718258 4.072378 1000 1000
CV_78 -3.718258 4.426498 1000 1000
CV_79 -3.364138 4.426498 1000 1000
CV_80 -3.364138 4.072378 1000 1000
CV_81 -3.364138 3.718258 1000 1000
CV_82 -3.364138 3.364138 1000 1000
CV_83 -3.364138 3.010019 1000 1000
CV_84 -3.364138 2.655899 1000 1000
CV_85 -3.364138 2.301779 1000 1000
CV_86 -3.364138 1.947659 1000 1000
CV_87 -3.364138 1.593539 1000 1000
CV_88 -3.364138 1.239419 1000 1000
CV_89 -3.364138 0.885300 1000 1000
CV_90 -3.364138 0.531180 1000 1000
CV_91 -3.364138 0.177060 1000 1000
CV_92 -3.364138 -0.177060 1000 1000
CV_93 -3.364138 -0.531180 1000 1000
CV_94 -3.364138 -0.885300 1000 1000
CV_95 -3.364138 -1.239419 1000 1000
CV_96 -3.364138 -1.593539 1000 1000
CV_97 -3.364138 -1.947659 1000 1000
CV_98 -3.364138 -2.301779 1000 1000
CV_99 -3.364138 -2.655899 1000 1000
CV_100 -3.364138 -3.010019 1000 1000
CV_101 -3.364138 -3.364138 1000 1000
CV_102 -3.364138 -3.718258 1000 1000
CV_103 -3.364138 -4.072378 1000 1000
CV_104 -3.364138 -4.426498 1000 1000
CV_105 -3.010019 -4.426498 1000 1000
CV_106 -3.010019 -4.072378 1000 1000
CV_107 -3.010019 -3.718258 1000 1000
CV_108 -3.010019 -3.364138 1000 1000
CV_109 -3.010019 -3.010019 1000 1000
CV_110 -3.010019 -2.655899 1000 1000
CV_111 -3.010019 -2.301779 1000 1000
CV_112 -3.010019 -1.947659 1000 1000
CV_113 -3.010019 -1.593539 1000 1000
CV_114 -3.010019 -1.239419 1000 1000
CV_115 -3.010019 -0.885300 1000 1000
CV_116 -3.010019 -0.531180 1000 1000
CV_117 -3.010019 -0.177060 1000 1000
CV_118 -3.010019 0.177060 1000 1000
CV_119 -3.010019 0.531180 1000 1000
CV_120 -3.010019 0.885300 1000 1000
CV_121 -3.010019 1.239419 1000 1000
CV_122 -3.010019 1.593539 1000 1000
CV_123 -3.010019 1.947659 1000 1000
CV_124 -3.010019 2.301779 1000 1000
CV_125 -3.010019 2.655899 1000 1000
CV_126 -3.010019 3.010019 1000 1000
CV_127 -3.010019 3.364138 1000 1000
CV_128 -3.010019 3.718258 1000 1000
CV_129 -3.010019 4.072378 1000 1000
CV_130 -3.010019 4.426498 1000 1000
CV_131 -2.655899 4.426498 1000 1000
CV_132 -2.655899 4.072378 1000 1000
CV_133 -2.655899 3.718258 1000 1000
CV_134 -2.655899 3.364138 1000 1000
CV_135 -2.655899 3.010019 1000 1000
CV_136 -2.655899 2.655899 1000 1000
CV_137 -2.655899 2.301779 1000 1000
CV_138 -2.655899 1.947659 1000 1000
CV_139 -2.655899 1.593539 1000 1000
CV_140 -2.655899 1.239419 1000 1000
CV_141 -2.655899 0.885300 1000 1000
CV_142 -2.655899 0.531180 1000 1000
CV_143 -2.655899 0.177060 1000 1000
CV_144 -2.655899 -0.177060 1000 1000
CV_145 -2.655899 -0.531180 1000 1000
CV_146 -2.655899 -0.885300 1000 1000
CV_147 -2.655899 -1.239419 1000 1000
CV_148 -2.655899 -1.593539 1000 1000
CV_149 -2.655899 -1.947659 1000 1000
CV_150 -2.655899 -2.301779 1000 1000
CV_151 -2.655899 -2.655899 1000 1000
CV_152 -2.655899 -3.010019 1000 1000
CV_153 -2.655899 -3.364138 1000 1000
CV_154 -2.655899 -3.718258 1000 1000
CV_155 -2.655899 -4.072378 1000 1000
CV_156 -2.655899 -4.426498 1000 1000
CV_157 -2.301779 -4.426498 1000 1000
CV_158 -2.301779 -4.072378 1000 1000
CV_159 -2.301779 -3.718258 1000 1000
CV_160 -2.301779 -3.364138 1000 1000
CV_161 -2.301779 -3.010019 1000 1000
CV_162 -2.301779 -2.655899 1000 1000
CV_163 -2.301779 -2.301779 1000 1000
CV_164 -2.301779 -1.947659 1000 1000
CV_165 -2.301779 -1.593539 1000 1000
CV_166 -2.301779 -1.239419 1000 1000
CV_167 -2.301779 -0.885300 1000 1000
CV_168 -2.301779 -0.531180 1000 1000
CV_169 -2.301779 -0.177060 1000 1000
CV_170 -2.301779 0.177060 1000 1000
CV_171 -2.301779 0.531180 1000 1000
CV_172 -2.301779 0.885300 1000 1000
CV_173 -2.301779 1.239419 1000 1000
CV_174 -2.301779 1.593539 1000 1000
CV_175 -2.301779 1.947659 1000 1000
CV_176 -2.301779 2.301779 1000 1000
CV_177 -2.301779 2.655899 1000 1000
CV_178 -2.301779 3.010019 1000 1000
CV_179 -2.301779 3.364138 1000 1000
CV_180 -2.301779 3.718258 1000 1000
CV_181 -2.301779 4.072378 1000 1000
CV_182 -2.301779 4.426498 1000 1000
CV_183 -1.947659 4.426498 1000 1000
CV_184 -1.947659 4.072378 1000 1000
CV_185 -1.947659 3.718258 1000 1000
CV_186 -1.947659 3.364138 1000 1000
CV_187 -1.947659 3.010019 1000 1000
CV_188 -1.947659 2.655899 1000 1000
CV_189 -1.947659 2.301779 1000 1000
CV_190 -1.947659 1.947659 1000 1000
CV_191 -1.947659 1.593539 1000 1000
CV_192 -1.947659 1.239419 1000 1000
CV_193 -1.947659 0.885300 1000 1000
CV_194 -1.947659 0.531180 1000 1000
CV_195 -1.947659 0.177060 1000 1000
CV_196 -1.947659 -0.177060 1000 1000
CV_197 -1.947659 -0.531180 1000 1000
CV_198 -1.947659 -0.885300 1000 1000
CV_199 -1.947659 -1.239419 1000 1000
CV_200 -1.947659 -1.593539 1000 1000
CV_201 -1.947659 -1.947659 1000 1000
CV_202 -1.947659 -2.301779 1000 1000
CV_203 -1.947659 -2.655899 1000 1000
CV_204 -1.947659 -3.010019 1000 1000
CV_205 -1.947659 -3.364138 1000 1000
CV_206 -1.947659 -3.718258 1000 1000
CV_207 -1.947659 -4.072378 1000 1000
CV_208 -1.947659 -4.426498 1000 1000
CV_209 -1.593539 -4.426498 1000 1000
CV_210 -1.593539 -4.072378 1000 1000
CV_211 -1.593539 -3.718258 1000 1000
CV_212 -1.593539 -3.364138 1000 1000
CV_213 -1.593539 -3.010019 1000 1000
CV_214 -1.593539 -2.655899 1000 1000
CV_215 -1.593539 -2.301779 1000 1000
CV_216 -1.593539 -1.947659 1000 1000
CV_217 -1.593539 -1.593539 1000 1000
CV_218 -1.593539 -1.239419 1000 1000
CV_219 -1.593539 -0.885300 1000 1000
CV_220 -1.593539 -0.531180 1000 1000
CV_221 -1.593539 -0.177060 1000 1000
CV_222 -1.593539 0.177060 1000 1000
CV_223 -1.593539 0.531180 1000 1000
CV_224 -1.593539 0.885300 1000 1000
CV_225 -1.593539 1.239419 1000 1000
CV_226 -1.593539 1.593539 1000 1000
CV_227 -1.593539 1.947659 1000 1000
CV_228 -1.593539 2.301779 1000 1000
CV_229 -1.593539 2.655899 1000 1000
CV_230 -1.593539 3.010019 1000 1000
CV_231 -1.593539 3.364138 1000 1000
CV_232 -1.593539 3.718258 1000 1000
CV_233 -1.593539 4.072378 1000 1000
CV_234 -1.593539 4.426498 1000 1000
CV_235 -1.239419 4.426498 1000 1000
CV_236 -1.239419 4.072378 1000 1000
CV_237 -1.239419 3.718258 1000 1000
CV_238 -1.239419 3.364138 1000 1000
CV_239 -1.239419 3.010019 1000 1000
CV_240 -1.239419 2.655899 1000 1000
CV_241 -1.239419 2.301779 1000 1000
CV_242 -1.239419 1.947659 1000 1000
CV_243 -1.239419 1.593539 1000 1000
CV_244 -1.239419 1.239419 1000 1000
CV_245 -1.239419 0.885300 1000 1000
CV_246 -1.239419 0.531180 1000 1000
CV_247 -1.239419 0.177060 1000 1000
CV_248 -1.239419 -0.177060 1000 1000
CV_249 -1.239419 -0.531180 1000 1000
CV_250 -1.239419 -0.885300 1000 1000
CV_251 -1.239419 -1.239419 1000 1000
CV_252 -1.239419 -1.593539 1000 1000
CV_253 -1.239419 -1.947659 1000 1000
CV_254 -1.239419 -2.301779 1000 1000
CV_255 -1.239419 -2.655899 1000 1000
CV_256 -1.239419 -3.010019 1000 1000
CV_257 -1.239419 -3.364138 1000 1000
CV_258 -1.239419 -3.718258 1000 1000
CV_259 -1.239419 -4.072378 1000 1000
CV_260 -1.239419 -4.426498 1000 1000
CV_261 -0.885300 -4.426498 1000 1000
CV_262 -0.885300 -4.072378 1000 1000
CV_263 -0.885300 -3.718258 1000 1000
CV_264 -0.885300 -3.364138 1000 1000
CV_265 -0.885300 -3.010019 1000 1000
CV_266 -0.885300 -2.655899 1000 1000
CV_267 -0.885300 -2.301779 1000 1000
CV_268 -0.885300 -1.947659 1000 1000
CV_269 -0.885300 -1.593539 1000 1000
CV_270 -0.885300 -1.239419 1000 1000
CV_271 -0.885300 -0.885300 1000 1000
CV_272 -0.885300 -0.531180 1000 1000
CV_273 -0.885300 -0.177060 1000 1000
CV_274 -0.885300 0.177060 1000 1000
CV_275 -0.885300 0.531180 1000 1000
CV_276 -0.885300 0.885300 1000 1000
CV_277 -0.885300 1.239419 1000 1000
CV_278 -0.885300 1.593539 1000 1000
CV_279 -0.885300 1.947659 1000 1000
CV_280 -0.885300 2.301779 1000 1000
CV_281 -0.885300 2.655899 1000 1000
CV_282 -0.885300 3.010019 1000 1000
CV_283 -0.885300 3.364138 1000 1000
CV_284 -0.885300 3.718258 1000 1000
CV_285 -0.885300 4.072378 1000 1000
CV_286 -0.885300 4.426498 1000 1000
CV_287 -0.531180 4.426498 1000 1000
CV_288 -0.531180 4.072378 1000 1000
CV_289 -0.531180 3.718258 1000 1000
CV_290 -0.531180 3.364138 1000 1000
CV_291 -0.531180 3.010019 1000 1000
CV_292 -0.531180 2.655899 1000 1000
CV_293 -0.531180 2.301779 1000 1000
CV_294 -0.531180 1.947659 1000 1000
CV_295 -0.531180 1.593539 1000 1000
CV_296 -0.531180 1.239419 1000 1000
CV_297 -0.531180 0.885300 1000 1000
CV_298 -0.531180 0.531180 1000 1000
CV_299 -0.531180 0.177060 1000 1000
CV_300 -0.531180 -0.177060 1000 1000
CV_301 -0.531180 -0.531180 1000 1000
CV_302 -0.531180 -0.885300 1000 1000
CV_303 -0.531180 -1.239419 1000 1000
CV_304 -0.531180 -1.593539 1000 1000
CV_305 -0.531180 -1.947659 1000 1000
CV_306 -0.531180 -2.301779 1000 1000
CV_307 -0.531180 -2.655899 1000 1000
CV_308 -0.531180 -3.010019 1000 1000
CV_309 -0.531180 -3.364138 1000 1000
CV_310 -0.531180 -3.718258 1000 1000
CV_311 -0.531180 -4.072378 1000 1000
CV_312 -0.531180 -4.426498 1000 1000
CV_313 -0.177060 -4.426498 1000 1000
CV_314 -0.177060 -4.072378 1000 1000
CV_315 -0.177060 -3.718258 1000 1000
CV_316 -0.177060 -3.364138 1000 1000
CV_317 -0.177060 -3.010019 1000 1000
CV_318 -0.177060 -2.655899 1000 1000
CV_319 -0.177060 -2.301779 1000 1000
CV_320 -0.177060 -1.947659 1000 1000
CV_321 -0.177060 -1.593539 1000 1000
CV_322 -0.177060 -1.239419 1000 1000
CV_323 -0.177060 -0.885300 1000 1000
CV_324 -0.177060 -0.531180 1000 1000
CV_325 -0.177060 -0.177060 1000 1000
CV_326 -0.177060 0.177060 1000 1000
CV_327 -0.177060 0.531180 1000 1000
CV_328 -0.177060 0.885300 1000 1000
CV_329 -0.177060 1.239419 1000 1000
CV_330 -0.177060 1.593539 1000 1000
CV_331 -0.177060 1.947659 1000 1000
CV_332 -0.177060 2.301779 1000 1000
CV_333 -0.177060 2.655899 1000 1000
CV_334 -0.177060 3.010019 1000 1000
CV_335 -0.177060 3.364138 1000 1000
CV_336 -0.177060 3.718258 1000 1000
CV_337 -0.177060 4.072378 1000 1000
CV_338 -0.177060 4.426498 1000 1000
CV_339 0.177060 4.426498 1000 1000
CV_340 0.177060 4.072378 1000 1000
CV_341 0.177060 3.718258 1000 1000
CV_342 0.177060 3.364138 1000 1000
CV_343 0.177060 3.010019 1000 1000
CV_344 0.177060 2.655899 1000 1000
CV_345 0.177060 2.301779 1000 1000
CV_346 0.177060 1.947659 1000 1000
CV_347 0.177060 1.593539 1000 1000
CV_348 0.177060 1.239419 1000 1000
CV_349 0.177060 0.885300 1000 1000
CV_350 0.177060 0.531180 1000 1000
CV_351 0.177060 0.177060 1000 1000
CV_352 0.177060 -0.177060 1000 1000
CV_353 0.177060 -0.531180 1000 1000
CV_354 0.177060 -0.885300 1000 1000
CV_355 0.177060 -1.239419 1000 1000
CV_356 0.177060 -1.593539 1000 1000
CV_357 0.177060 -1.947659 1000 1000
CV_358 0.177060 -2.301779 1000 1000
CV_359 0.177060 -2.655899 1000 1000
CV_360 0.177060 -3.010019 1000 1000
CV_361 0.177060 -3.364138 1000 1000
CV_362 0.177060 -3.718258 1000 1000
CV_363 0.177060 -4.072378 1000 1000
CV_364 0.177060 -4.426498 1000 1000
CV_365 0.531180 -4.426498 1000 1000
CV_366 0.531180 -4.072378 1000 1000
CV_367 0.531180 -3.718258 1000 1000
CV_368 0.531180 -3.364138 1000 1000
CV_369 0.531180 -3.010019 1000 1000
CV_370 0.531180 -2.655899 1000 1000
CV_371 0.531180 -2.301779 1000 1000
CV_372 0.531180 -1.947659 1000 1000
CV_373 0.531180 -1.593539 1000 1000
CV_374 0.531180 -1.239419 1000 1000
CV_375 0.531180 -0.885300 1000 1000
CV_376 0.531180 -0.531180 1000 1000
CV_377 0.531180 -0.177060 1000 1000
CV_378 0.531180 0.177060 1000 1000
CV_379 0.531180 0.531180 1000 1000
CV_380 0.531180 0.885300 1000 1000
CV_381 0.531180 1.239419 1000 1000
CV_382 0.531180 1.593539 1000 1000
CV_383 0.531180 1.947659 1000 1000
CV_384 0.531180 2.301779 1000 1000
CV_385 0.531180 2.655899 1000 1000
CV_386 0.531180 3.010019 1000 1000
CV_387 0.531180 3.364138 1000 1000
CV_388 0.531180 3.718258 1000 1000
CV_389 0.531180 4.072378 1000 1000
CV_390 0.531180 4.426498 1000 1000
CV_391 0.885300 4.426498 1000 1000
CV_392 0.885300 4.072378 1000 1000
CV_393 0.885300 3.718258 1000 1000
CV_394 0.885300 3.364138 1000 1000
CV_395 0.885300 3.010019 1000 1000
CV_396 0.885300 2.655899 1000 1000
CV_397 0.885300 2.301779 1000 1000
CV_398 0.885300 1.947659 1000 1000
CV_399 0.885300 1.593539 1000 1000
CV_400 0.885300 1.239419 1000 1000
CV_401 0.885300 0.885300 1000 1000
CV_402 0.885300 0.531180 1000 1000
CV_403 0.885300 0.177060 1000 1000
CV_404 0.885300 -0.177060 1000 1000
CV_405 0.885300 -0.531180 1000 1000
CV_406 0.885300 -0.885300 1000 1000
CV_407 0.885300 -1.239419 1000 1000
CV_408 0.885300 -1.593539 1000 1000
CV_409 0.885300 -1.947659 1000 1000
CV_410 0.885300 -2.301779 1000 1000
CV_411 0.885300 -2.655899 1000 1000
CV_412 0.885300 -3.010019 1000 1000
CV_413 0.885300 -3.364138 1000 1000
CV_414 0.885300 -3.718258 1000 1000
CV_415 0.885300 -4.072378 1000 1000
CV_416 0.885300 -4.426498 1000 1000
CV_417 1.239419 -4.426498 1000 1000
CV_418 1.239419 -4.072378 1000 1000
CV_419 1.239419 -3.718258 1000 1000
CV_420 1.239419 -3.364138 1000 1000
CV_421 1.239419 -3.010019 1000 1000
CV_422 1.239419 -2.655899 1000 1000
CV_423 1.239419 -2.301779 1000 1000
CV_424 1.239419 -1.947659 1000 1000
CV_425 1.239419 -1.593539 1000 1000
CV_426 1.239419 -1.239419 1000 1000
CV_427 1.239419 -0.885300 1000 1000
CV_428 1.239419 -0.531180 1000 1000
CV_429 1.239419 -0.177060 1000 1000
CV_430 1.239419 0.177060 1000 1000
CV_431 1.239419 0.531180 1000 1000
CV_432 1.239419 0.885300 1000 1000
CV_433 1.239419 1.239419 1000 1000
CV_434 1.239419 1.593539 1000 1000
CV_435 1.239419 1.947659 1000 1000
CV_436 1.239419 2.301779 1000 1000
CV_437 1.239419 2.655899 1000 1000
CV_438 1.239419 3.010019 1000 1000
CV_439 1.239419 3.364138 1000 1000
CV_440 1.239419 3.718258 1000 1000
CV_441 1.239419 4.072378 1000 1000
CV_442 1.239419 4.426498 1000 1000
CV_443 1.593539 4.426498 1000 1000
CV_444 1.593539 4.072378 1000 1000
CV_445 1.593539 3.718258 1000 1000
CV_446 1.593539 3.364138 1000 1000
CV_447 1.593539 3.010019 1000 1000
CV_448 1.593539 2.655899 1000 1000
CV_449 1.593539 2.301779 1000 1000
CV_450 1.593539 1.947659 1000 1000
CV_451 1.593539 1.593539 1000 1000
CV_452 1.593539 1.239419 1000 1000
CV_453 1.593539 0.885300 1000 1000
CV_454 1.593539 0.531180 1000 1000
CV_455 1.593539 0.177060 1000 1000
CV_456 1.593539 -0.177060 1000 1000
CV_457 1.593539 -0.531180 1000 1000
CV_458 1.593539 -0.885300 1000 1000
CV_459 1.593539 -1.239419 1000 1000
CV_460 1.593539 -1.593539 1000 1000
CV_461 1.593539 -1.947659 1000 1000
CV_462 1.593539 -2.301779 1000 1000
CV_463 1.593539 -2.655899 1000 1000
CV_464 1.593539 -3.010019 1000 1000
CV_465 1.593539 -3.364138 1000 1000
CV_466 1.593539 -3.718258 1000 1000
CV_467 1.593539 -4.072378 1000 1000
CV_468 1.593539 -4.426498 1000 1000
CV_469 1.947659 -4.426498 1000 1000
CV_470 1.947659 -4.072378 1000 1000
CV_471 1.947659 -3.718258 1000 1000
CV_472 1.947659 -3.364138 1000 1000
CV_473 1.947659 -3.010019 1000 1000
CV_474 1.947659 -2.655899 1000 1000
CV_475 1.947659 -2.301779 1000 1000
CV_476 1.947659 -1.947659 1000 1000
CV_477 1.947659 -1.593539 1000 1000
CV_478 1.947659 -1.239419 1000 1000
CV_479 1.947659 -0.885300 1000 1000
CV_480 1.947659 -0.531180 1000 1000
CV_481 1.947659 -0.177060 1000 1000
CV_482 1.947659 0.177060 1000 1000
CV_483 1.947659 0.531180 1000 1000
CV_484 1.947659 0.885300 1000 1000
CV_485 1.947659 1.239419 1000 1000
CV_486 1.947659 1.593539 1000 1000
CV_487 1.947659 1.947659 1000 1000
CV_488 1.947659 2.301779 1000 1000
CV_489 1.947659 2.655899 1000 1000
CV_490 1.947659 3.010019 1000 1000
CV_491 1.947659 3.364138 1000 1000
CV_492 1.947659 3.718258 1000 1000
CV_493 1.947659 4.072378 1000 1000
CV_494 1.947659 4.426498 1000 1000
CV_495 2.301779 4.426498 1000 1000
CV_496 2.301779 4.072378 1000 1000
CV_497 2.301779 3.718258 1000 1000
CV_498 2.301779 3.364138 1000 1000
CV_499 2.301779 3.010019 1000 1000
CV_500 2.301779 2.655899 1000 1000
CV_501 2.301779 2.301779 1000 1000
CV_502 2.301779 1.947659 1000 1000
CV_503 2.301779 1.593539 1000 1000
CV_504 2.301779 1.239419 1000 1000
CV_505 2.301779 0.885300 1000 1000
CV_506 2.301779 0.531180 1000 1000
CV_507 2.301779 0.177060 1000 1000
CV_508 2.301779 -0.177060 1000 1000
CV_509 2.301779 -0.531180 1000 1000
CV_510 2.301779 -0.885300 1000 1000
CV_511 2.301779 -1.239419 1000 1000
CV_512 2.301779 -1.593539 1000 1000
CV_513 2.301779 -1.947659 1000 1000
CV_514 2.301779 -2.301779 1000 1000
CV_515 2.301779 -2.655899 1000 1000
CV_516 2.301779 -3.010019 1000 1000
CV_517 2.301779 -3.364138 1000 1000
CV_518 2.301779 -3.718258 1000 1000
CV_519 2.301779 -4.072378 1000 1000
CV_520 2.301779 -4.426498 1000 1000
CV_521 2.655899 -4.426498 1000 1000
CV_522 2.655899 -4.072378 1000 1000
CV_523 2.655899 -3.718258 1000 1000
CV_524 2.655899 -3.364138 1000 1000
CV_525 2.655899 -3.010019 1000 1000
CV_526 2.655899 -2.655899 1000 1000
CV_527 2.655899 -2.301779 1000 1000
CV_528 2.655899 -1.947659 1000 1000
CV_529 2.655899 -1.593539 1000 1000
CV_530 2.655899 -1.239419 1000 1000
CV_531 2.655899 -0.885300 1000 1000
CV_532 2.655899 -0.531180 1000 1000
CV_533 2.655899 -0.177060 1000 1000
CV_534 2.655899 0.177060 1000 1000
CV_535 2.655899 0.531180 1000 1000
CV_536 2.655899 0.885300 1000 1000
CV_537 2.655899 1.239419 1000 1000
CV_538 2.655899 1.593539 1000 1000
CV_539 2.655899 1.947659 1000 1000
CV_540 2.655899 2.301779 1000 1000
CV_541 2.655899 2.655899 1000 1000
CV_542 2.655899 3.010019 1000 1000
CV_543 2.655899 3.364138 1000 1000
CV_544 2.655899 3.718258 1000 1000
CV_545 2.655899 4.072378 1000 1000
CV_546 2.655899 4.426498 1000 1000
CV_547 3.010019 4.426498 1000 1000
CV_548 3.010019 4.072378 1000 1000
CV_549 3.010019 3.718258 1000 1000
CV_550 3.010019 3.364138 1000 1000
CV_551 3.010019 3.010019 1000 1000
CV_552 3.010019 2.655899 1000 1000
CV_553 3.010019 2.301779 1000 1000
CV_554 3.010019 1.947659 1000 1000
CV_555 3.010019 1.593539 1000 1000
CV_556 3.010019 1.239419 1000 1000
CV_557 3.010019 0.885300 1000 1000
CV_558 3.010019 0.531180 1000 1000
CV_559 3.010019 0.177060 1000 1000
CV_560 3.010019 -0.177060 1000 1000
CV_561 3.010019 -0.531180 1000 1000
CV_562 3.010019 -0.885300 1000 1000
CV_563 3.010019 -1.239419 1000 1000
CV_564 3.010019 -1.593539 1000 1000
CV_565 3.010019 -1.947659 1000 1000
CV_566 3.010019 -2.301779 1000 1000
CV_567 3.010019 -2.655899 1000 1000
CV_568 3.010019 -3.010019 1000 1000
CV_569 3.010019 -3.364138 1000 1000
CV_570 3.010019 -3.718258 1000 1000
CV_571 3.010019 -4.072378 1000 1000
CV_572 3.010019 -4.426498 1000 1000
CV_573 3.364138 -4.426498 1000 1000
CV_574 3.364138 -4.072378 1000 1000
CV_575 3.364138 -3.718258 1000 1000
CV_576 3.364138 -3.364138 1000 1000
CV_577 3.364138 -3.010019 1000 1000
CV_578 3.364138 -2.655899 1000 1000
CV_579 3.364138 -2.301779 1000 1000
CV_580 3.364138 -1.947659 1000 1000
CV_581 3.364138 -1.593539 1000 1000
CV_582 3.364138 -1.239419 1000 1000
CV_583 3.364138 -0.885300 1000 1000
CV_584 3.364138 -0.531180 1000 1000
CV_585 3.364138 -0.177060 1000 1000
CV_586 3.364138 0.177060 1000 1000
CV_587 3.364138 0.531180 1000 1000
CV_588 3.364138 0.885300 1000 1000
CV_589 3.364138 1.239419 1000 1000
CV_590 3.364138 1.593539 1000 1000
CV_591 3.364138 1.947659 1000 1000
CV_592 3.364138 2.301779 1000 1000
CV_593 3.364138 2.655899 1000 1000
CV_594 3.364138 3.010019 1000 1000
CV_595 3.364138 3.364138 1000 1000
CV_596 3.364138 3.718258 1000 1000
CV_597 3.364138 4.072378 1000 1000
CV_598 3.364138 4.426498 1000 1000
CV_599 3.718258 4.426498 1000 1000
CV_600 3.718258 4.072378 1000 1000
CV_601 3.718258 3.718258 1000 1000
CV_602 3.718258 3.364138 1000 1000
CV_603 3.718258 3.010019 1000 1000
CV_604 3.718258 2.655899 1000 1000
CV_605 3.718258 2.301779 1000 1000
CV_606 3.718258 1.947659 1000 1000
CV_607 3.718258 1.593539 1000 1000
CV_608 3.718258 1.239419 1000 1000
CV_609 3.718258 0.885300 1000 1000
CV_610 3.718258 0.531180 1000 1000
CV_611 3.718258 0.177060 1000 1000
CV_612 3.718258 -0.177060 1000 1000
CV_613 3.718258 -0.531180 1000 1000
CV_614 3.718258 -0.885300 1000 1000
CV_615 3.718258 -1.239419 1000 1000
CV_616 3.718258 -1.593539 1000 1000
CV_617 3.718258 -1.947659 1000 1000
CV_618 3.718258 -2.301779 1000 1000
CV_619 3.718258 -2.655899 1000 1000
CV_620 3.718258 -3.010019 1000 1000
CV_621 3.718258 -3.364138 1000 1000
CV_622 3.718258 -3.718258 1000 1000
CV_623 3.718258 -4.072378 1000 1000
CV_624 3.718258 -4.426498 1000 1000
CV_625 4.072378 -4.426498 1000 1000
CV_626 4.072378 -4.072378 1000 1000
CV_627 4.072378 -3.718258 1000 1000
CV_628 4.072378 -3.364138 1000 1000
CV_629 4.072378 -3.010019 1000 1000
CV_630 4.072378 -2.655899 1000 1000
CV_631 4.072378 -2.301779 1000 1000
CV_632 4.072378 -1.947659 1000 1000
CV_633 4.072378 -1.593539 1000 1000
CV_634 4.072378 -1.239419 1000 1000
CV_635 4.072378 -0.885300 1000 1000
CV_636 4.072378 -0.531180 1000 1000
CV_637 4.072378 -0.177060 1000 1000
CV_638 4.072378 0.177060 1000 1000
CV_639 4.072378 0.531180 1000 1000
CV_640 4.072378 0.885300 1000 1000
CV_641 4.072378 1.239419 1000 1000
CV_642 4.072378 1.593539 1000 1000
CV_643 4.072378 1.947659 1000 1000
CV_644 4.072378 2.301779 1000 1000
CV_645 4.072378 2.655899 1000 1000
CV_646 4.072378 3.010019 1000 1000
CV_647 4.072378 3.364138 1000 1000
CV_648 4.072378 3.718258 1000 1000
CV_649 4.072378 4.072378 1000 1000
CV_650 4.072378 4.426498 1000 1000
CV_651 4.426498 4.426498 1000 1000
CV_652 4.426498 4.072378 1000 1000
CV_653 4.426498 3.718258 1000 1000
CV_654 4.426498 3.364138 1000 1000
CV_655 4.426498 3.010019 1000 1000
CV_656 4.426498 2.655899 1000 1000
CV_657 4.426498 2.301779 1000 1000
CV_658 4.426498 1.947659 1000 1000
CV_659 4.426498 1.593539 1000 1000
CV_660 4.426498 1.239419 1000 1000
CV_661 4.426498 0.885300 1000 1000
CV_662 4.426498 0.531180 1000 1000
CV_663 4.426498 0.177060 1000 1000
CV_664 4.426498 -0.177060 1000 1000
CV_665 4.426498 -0.531180 1000 1000
CV_666 4.426498 -0.885300 1000 1000
CV_667 4.426498 -1.239419 1000 1000
CV_668 4.426498 -1.593539 1000 1000
CV_669 4.426498 -1.947659 1000 1000
CV_670 4.426498 -2.301779 1000 1000
CV_671 4.426498 -2.655899 1000 1000
CV_672 4.426498 -3.010019 1000 1000
CV_673 4.426498 -3.364138 1000 1000
CV_674 4.426498 -3.718258 1000 1000
CV_675 4.426498 -4.072378 1000 1000
CV_676 4.426498 -4.426498 1000 1000
On Jan 9, 10:07 pm, Davide Branduardi <davide.brandua...@gmail.com>
wrote:
>
> *********************************************
> Davide Branduardi
> Postdoc
> Theoretical Molecular Biophysics Group
> Max Planck Institute for Biophysics
> Max-von-Laue-Straße 3
> 60438 Frankfurt am Main
> Phone: +49 069 63031503
> Fax: +49 069 63031502
> davide.brandua...@biophys.mpg.de
> *********************************************
> Davide Branduardi
> Postdoc
> Theoretical Molecular Biophysics Group
> Max Planck Institute for Biophysics
> Max-von-Laue-Straße 3
> 60438 Frankfurt am Main
> Phone: +49 069 63031503
> Fax: +49 069 63031502
> davide.brandua...@gmail.com
> *********************************************
Davide Branduardi
Jan 9, 2012, 1:14:20 PM1/9/12
to plumed...@googlegroups.com
dear Vidhya
do something like this in gnuplot
plot "CV_1" u 2:3,"CV_2" u 2:3,"CV_3" u 2:3,"CV_4" u 2:3 etc....
if you see some overlapping clouds then the problem is not what I mentioned.
If you see clouds which are disconnected from all the other then you have the problem I mentioned.
D
> For more options, visit this group at http://groups.google.com/group/plumed-users?hl=en.
>
*********************************************
Davide Branduardi
Postdoc
Theoretical Molecular Biophysics Group
Max Planck Institute for Biophysics
Max-von-Laue-Straße 3
60438 Frankfurt am Main
Phone: +49 069 63031503
Fax: +49 069 63031502
davide.b...@biophys.mpg.de
davide.b...@gmail.com
*********************************************
Dejun Lin
Jan 9, 2012, 3:04:36 PM1/9/12
to plumed...@googlegroups.com, vidhy...@gmail.com
First of all, are both the force constants and CVs in Gromacs units? Note that the WHAM code uses different units from gromacs' and please make sure they are right.
I think another problem, which kinda similar to what Davide mentioned, is that there are bare patches in the 2D CV space with no data points. However, it's reasonable to avoid sampling some regions which are irrelevant to the problem your are addressing to save computer time. In that case, as soon as all the regions sampled are connected, I think all you need is just a way to mask out the bare patches to prevent the numerical errors ( which is the 'nan' ) from "spreading out" to the regions sampled.
-Dejun
I think another problem, which kinda similar to what Davide mentioned, is that there are bare patches in the 2D CV space with no data points. However, it's reasonable to avoid sampling some regions which are irrelevant to the problem your are addressing to save computer time. In that case, as soon as all the regions sampled are connected, I think all you need is just a way to mask out the bare patches to prevent the numerical errors ( which is the 'nan' ) from "spreading out" to the regions sampled.
-Dejun
ramar...@gmail.com
Feb 9, 2013, 3:04:19 AM2/9/13
to plumed...@googlegroups.com
hi,
Can u please elaborate, as am also facing the same problem
-ram
Can u please elaborate, as am also facing the same problem
-ram
ramar...@gmail.com
Feb 9, 2013, 3:08:47 AM2/9/13
to plumed...@googlegroups.com
hi
Can u elaborate the filling the holes thing as am also facing the same problem
-ram
Can u elaborate the filling the holes thing as am also facing the same problem
-ram
Kartheekpitta(PhD), IIIT-H.
Apr 17, 2017, 12:14:08 PM4/17/17
to PLUMED users
Dear experts,
I have been using wham program to generate PMF from plumed generated COLVAR files. I have done umbrella sampling in plumed with two reaction coordinates. In case of 1D, I am able to generate pmf by extracting $1(Time) ,$2(position of CV),$4 (restraint-psi.bias) from COLVAR* files and parsing it through a respective metadata file. However, for 2d-wham I am not able to obtain PMF, where COLVAR files are used as time series files without any postprocessing. I have been searching for the solution but no success, please help me.
"wham-2d Px=pi -3.1416 3.1416 50 Py=pi -3.1416 3.1416 50 0.001 300 0 2ddatfile free2d 1"
I have been using wham program to generate PMF from plumed generated COLVAR files. I have done umbrella sampling in plumed with two reaction coordinates. In case of 1D, I am able to generate pmf by extracting $1(Time) ,$2(position of CV),$4 (restraint-psi.bias) from COLVAR* files and parsing it through a respective metadata file. However, for 2d-wham I am not able to obtain PMF, where COLVAR files are used as time series files without any postprocessing. I have been searching for the solution but no success, please help me.
"wham-2d Px=pi -3.1416 3.1416 50 Py=pi -3.1416 3.1416 50 0.001 300 0 2ddatfile free2d 1"
Reply all
Reply to author
Forward
0 new messages