From 137f19ba9fbb630b7e9ada7937376c96a457516f Mon Sep 17 00:00:00 2001 From: sjplimp Date: Fri, 22 Jul 2016 19:41:42 +0000 Subject: [PATCH 01/18] git-svn-id: svn://svn.icms.temple.edu/lammps-ro/trunk@15346 f3b2605a-c512-4ea7-a41b-209d697bcdaa --- src/force.cpp | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/src/force.cpp b/src/force.cpp index f668b56b05..aa4ad817ad 100644 --- a/src/force.cpp +++ b/src/force.cpp @@ -244,6 +244,31 @@ Pair *Force::pair_match(const char *word, int exact, int nsub) return NULL; } +/* ---------------------------------------------------------------------- + return style name of Pair class that matches Pair ptr + called by Neighbor::print_neigh_info() + return NULL if no match +------------------------------------------------------------------------- */ + +char *Force::pair_match_ptr(Pair *ptr) +{ + if (ptr == pair) return pair_style; + + if (strstr(pair_style,"hybrid")) { + PairHybrid *hybrid = (PairHybrid *) pair; + for (int i = 0; i < hybrid->nstyles; i++) + if (ptr == hybrid->styles[i]) return hybrid->keywords[i]; + } + + if (strstr(pair_style,"hybrid/overlay")) { + PairHybridOverlay *hybrid = (PairHybridOverlay *) pair; + for (int i = 0; i < hybrid->nstyles; i++) + if (ptr == hybrid->styles[i]) return hybrid->keywords[i]; + } + + return NULL; +} + /* ---------------------------------------------------------------------- create a bond style, called from input script or restart file ------------------------------------------------------------------------- */ From f7c2fdf33543c7794df1bc201048949b67024500 Mon Sep 17 00:00:00 2001 From: sjplimp Date: Fri, 22 Jul 2016 19:41:45 +0000 Subject: [PATCH 02/18] git-svn-id: svn://svn.icms.temple.edu/lammps-ro/trunk@15347 f3b2605a-c512-4ea7-a41b-209d697bcdaa --- src/force.h | 1 + 1 file changed, 1 insertion(+) diff --git a/src/force.h b/src/force.h index d718dbaea3..1873173a74 100644 --- a/src/force.h +++ b/src/force.h @@ -82,6 +82,7 @@ class Force : protected Pointers { void create_pair(const char *, int); class Pair *new_pair(const char *, int, int &); class Pair *pair_match(const char *, int, int nsub=0); + char *pair_match_ptr(Pair *); void create_bond(const char *, int); class Bond *new_bond(const char *, int, int &); From 0f71c66b6fe54bd7c773b7727c81761b87449204 Mon Sep 17 00:00:00 2001 From: sjplimp Date: Fri, 22 Jul 2016 21:19:08 +0000 Subject: [PATCH 03/18] git-svn-id: svn://svn.icms.temple.edu/lammps-ro/trunk@15348 f3b2605a-c512-4ea7-a41b-209d697bcdaa --- src/create_bonds.cpp | 1 + src/delete_atoms.cpp | 1 + 2 files changed, 2 insertions(+) diff --git a/src/create_bonds.cpp b/src/create_bonds.cpp index 309faa7167..7b3ca714a6 100644 --- a/src/create_bonds.cpp +++ b/src/create_bonds.cpp @@ -76,6 +76,7 @@ void CreateBonds::command(int narg, char **arg) neighbor->requests[irequest]->half = 0; neighbor->requests[irequest]->full = 1; neighbor->requests[irequest]->occasional = 1; + neighbor->requests[irequest]->command_style = "create_bonds"; // init entire system since comm->borders and neighbor->build is done // comm::init needs neighbor::init needs pair::init needs kspace::init, etc diff --git a/src/delete_atoms.cpp b/src/delete_atoms.cpp index 5777ed19fb..4ac7c02622 100644 --- a/src/delete_atoms.cpp +++ b/src/delete_atoms.cpp @@ -260,6 +260,7 @@ void DeleteAtoms::delete_overlap(int narg, char **arg) neighbor->requests[irequest]->half = 0; neighbor->requests[irequest]->full = 1; neighbor->requests[irequest]->occasional = 1; + neighbor->requests[irequest]->command_style = "delete_atoms"; // init entire system since comm->borders and neighbor->build is done // comm::init needs neighbor::init needs pair::init needs kspace::init, etc From f9a4b697a49e1b5a62beb4939b4aa12570e4a0cb Mon Sep 17 00:00:00 2001 From: sjplimp Date: Fri, 22 Jul 2016 21:20:38 +0000 Subject: [PATCH 04/18] git-svn-id: svn://svn.icms.temple.edu/lammps-ro/trunk@15349 f3b2605a-c512-4ea7-a41b-209d697bcdaa --- src/neigh_request.h | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/neigh_request.h b/src/neigh_request.h index d0a8077ae7..62e3f61403 100644 --- a/src/neigh_request.h +++ b/src/neigh_request.h @@ -46,6 +46,11 @@ class NeighRequest : protected Pointers { int respaouter; // 1 if a rRESPA outer list int half_from_full; // 1 if half list computed from previous full list + // command_style only set if command = 1 + // allows print_pair_info() to access command name + + const char *command_style; + // ----------------- // optional settings // ----------------- From ff10357ef205cd56cfeb760eb6be3d7c39a187b9 Mon Sep 17 00:00:00 2001 From: sjplimp Date: Fri, 22 Jul 2016 22:10:46 +0000 Subject: [PATCH 05/18] git-svn-id: svn://svn.icms.temple.edu/lammps-ro/trunk@15350 f3b2605a-c512-4ea7-a41b-209d697bcdaa --- src/compute_property_local.cpp | 46 ++++++++++++++++++++++++++++------ src/compute_property_local.h | 2 +- 2 files changed, 39 insertions(+), 9 deletions(-) diff --git a/src/compute_property_local.cpp b/src/compute_property_local.cpp index 864efa4646..60460f271a 100644 --- a/src/compute_property_local.cpp +++ b/src/compute_property_local.cpp @@ -27,6 +27,7 @@ using namespace LAMMPS_NS; enum{NONE,NEIGH,PAIR,BOND,ANGLE,DIHEDRAL,IMPROPER}; +enum{TYPE,RADIUS}; #define DELTA 10000 @@ -39,15 +40,14 @@ ComputePropertyLocal::ComputePropertyLocal(LAMMPS *lmp, int narg, char **arg) : local_flag = 1; nvalues = narg - 3; - if (nvalues == 1) size_local_cols = 0; - else size_local_cols = nvalues; - pack_choice = new FnPtrPack[nvalues]; kindflag = NONE; int i; - for (int iarg = 3; iarg < narg; iarg++) { + nvalues = 0; + int iarg = 3; + while (iarg < narg) { i = iarg-3; if (strcmp(arg[iarg],"natom1") == 0) { @@ -206,8 +206,28 @@ ComputePropertyLocal::ComputePropertyLocal(LAMMPS *lmp, int narg, char **arg) : "Compute property/local cannot use these inputs together"); kindflag = IMPROPER; - } else error->all(FLERR, - "Invalid keyword in compute property/local command"); + } else break; + + nvalues++; + iarg++; + } + + if (nvalues == 1) size_local_cols = 0; + else size_local_cols = nvalues; + + // optional args + + cutstyle = TYPE; + + while (iarg < narg) { + if (strcmp(arg[iarg],"cutoff") == 0) { + if (iarg+2 > narg) + error->all(FLERR,"Illegal compute property/local command"); + if (strcmp(arg[iarg+1],"type") == 0) cutstyle = TYPE; + else if (strcmp(arg[iarg+1],"radius") == 0) cutstyle = RADIUS; + else error->all(FLERR,"Illegal compute property/local command"); + iarg += 2; + } else error->all(FLERR,"Illegal compute property/local command"); } // error check @@ -229,6 +249,8 @@ ComputePropertyLocal::ComputePropertyLocal(LAMMPS *lmp, int narg, char **arg) : if (kindflag == IMPROPER && atom->avec->impropers_allow == 0) error->all(FLERR, "Compute property/local for property that isn't allocated"); + if (cutstyle == RADIUS && !atom->radius_flag) + error->all(FLERR,"Compute property/local requires atom attribute radius"); nmax = 0; vector = NULL; @@ -336,10 +358,11 @@ int ComputePropertyLocal::count_pairs(int allflag, int forceflag) { int i,j,m,ii,jj,inum,jnum,itype,jtype; tagint itag,jtag; - double xtmp,ytmp,ztmp,delx,dely,delz,rsq; + double xtmp,ytmp,ztmp,delx,dely,delz,rsq,radsum; int *ilist,*jlist,*numneigh,**firstneigh; double **x = atom->x; + double *radius = atom->radius; tagint *tag = atom->tag; int *type = atom->type; int *mask = atom->mask; @@ -404,7 +427,14 @@ int ComputePropertyLocal::count_pairs(int allflag, int forceflag) delz = ztmp - x[j][2]; rsq = delx*delx + dely*dely + delz*delz; jtype = type[j]; - if (forceflag && rsq >= cutsq[itype][jtype]) continue; + if (forceflag) { + if (cutstyle == TYPE) { + if (rsq >= cutsq[itype][jtype]) continue; + } else { + radsum = radius[i] + radius[j]; + if (rsq >= radsum*radsum) continue; + } + } if (allflag) { indices[m][0] = i; diff --git a/src/compute_property_local.h b/src/compute_property_local.h index beb84d7d7a..9262bf163f 100644 --- a/src/compute_property_local.h +++ b/src/compute_property_local.h @@ -34,7 +34,7 @@ class ComputePropertyLocal : public Compute { double memory_usage(); private: - int nvalues,kindflag; + int nvalues,kindflag,cutstyle; int nmax; double *vector; From 0659a1fc5a1c5c5efb7d8a0212ad5f5d8aa18cee Mon Sep 17 00:00:00 2001 From: sjplimp Date: Fri, 22 Jul 2016 22:10:50 +0000 Subject: [PATCH 06/18] git-svn-id: svn://svn.icms.temple.edu/lammps-ro/trunk@15351 f3b2605a-c512-4ea7-a41b-209d697bcdaa --- src/compute_pair_local.cpp | 40 ++++++++++++++++++++++++++++++++++---- src/compute_pair_local.h | 3 +-- 2 files changed, 37 insertions(+), 6 deletions(-) diff --git a/src/compute_pair_local.cpp b/src/compute_pair_local.cpp index ad404b3f5e..13dccc5787 100644 --- a/src/compute_pair_local.cpp +++ b/src/compute_pair_local.cpp @@ -31,6 +31,7 @@ using namespace LAMMPS_NS; #define DELTA 10000 enum{DIST,ENG,FORCE,FX,FY,FZ,PN}; +enum{TYPE,RADIUS}; /* ---------------------------------------------------------------------- */ @@ -48,7 +49,8 @@ ComputePairLocal::ComputePairLocal(LAMMPS *lmp, int narg, char **arg) : pindex = new int[nvalues]; nvalues = 0; - for (int iarg = 3; iarg < narg; iarg++) { + int iarg = 3; + while (iarg < narg) { if (strcmp(arg[iarg],"dist") == 0) pstyle[nvalues++] = DIST; else if (strcmp(arg[iarg],"eng") == 0) pstyle[nvalues++] = ENG; else if (strcmp(arg[iarg],"force") == 0) pstyle[nvalues++] = FORCE; @@ -61,9 +63,32 @@ ComputePairLocal::ComputePairLocal(LAMMPS *lmp, int narg, char **arg) : "Invalid keyword in compute pair/local command"); pstyle[nvalues] = PN; pindex[nvalues++] = n-1; - } else error->all(FLERR,"Invalid keyword in compute pair/local command"); + + } else break; + + iarg++; } + // optional args + + cutstyle = TYPE; + + while (iarg < narg) { + if (strcmp(arg[iarg],"cutoff") == 0) { + if (iarg+2 > narg) + error->all(FLERR,"Illegal compute pair/local command"); + if (strcmp(arg[iarg+1],"type") == 0) cutstyle = TYPE; + else if (strcmp(arg[iarg+1],"radius") == 0) cutstyle = RADIUS; + else error->all(FLERR,"Illegal compute pair/local command"); + iarg += 2; + } else error->all(FLERR,"Illegal compute pair/local command"); + } + + // error check + + if (cutstyle == RADIUS && !atom->radius_flag) + error->all(FLERR,"Compute pair/local requires atom attribute radius"); + // set singleflag if need to call pair->single() singleflag = 0; @@ -140,11 +165,12 @@ int ComputePairLocal::compute_pairs(int flag) int i,j,m,n,ii,jj,inum,jnum,itype,jtype; tagint itag,jtag; double xtmp,ytmp,ztmp,delx,dely,delz; - double rsq,eng,fpair,factor_coul,factor_lj; + double rsq,radsum,eng,fpair,factor_coul,factor_lj; int *ilist,*jlist,*numneigh,**firstneigh; double *ptr; double **x = atom->x; + double *radius = atom->radius; tagint *tag = atom->tag; int *type = atom->type; int *mask = atom->mask; @@ -216,7 +242,13 @@ int ComputePairLocal::compute_pairs(int flag) delz = ztmp - x[j][2]; rsq = delx*delx + dely*dely + delz*delz; jtype = type[j]; - if (rsq >= cutsq[itype][jtype]) continue; + + if (cutstyle == TYPE) { + if (rsq >= cutsq[itype][jtype]) continue; + } else { + radsum = radius[i] + radius[j]; + if (rsq >= radsum*radsum) continue; + } if (flag) { if (singleflag) diff --git a/src/compute_pair_local.h b/src/compute_pair_local.h index 663c3f931c..d6c2a12f22 100644 --- a/src/compute_pair_local.h +++ b/src/compute_pair_local.h @@ -34,8 +34,7 @@ class ComputePairLocal : public Compute { double memory_usage(); private: - int nvalues; - int ncount; + int nvalues,ncount,cutstyle; int *pstyle; // style of each requested output int *pindex; // for pI, index of the output (0 to M-1) From 5abac962ed662b59f3262d14d3a24f000e531a72 Mon Sep 17 00:00:00 2001 From: sjplimp Date: Fri, 22 Jul 2016 22:46:14 +0000 Subject: [PATCH 07/18] git-svn-id: svn://svn.icms.temple.edu/lammps-ro/trunk@15352 f3b2605a-c512-4ea7-a41b-209d697bcdaa --- doc/html/_sources/compute_angle_local.txt | 18 +++++-- doc/html/_sources/compute_bond_local.txt | 15 ++++-- doc/html/_sources/compute_dihedral_local.txt | 17 ++++-- doc/html/_sources/compute_improper_local.txt | 18 +++++-- doc/html/_sources/compute_pair_local.txt | 54 +++++++++++++++----- doc/html/_sources/compute_property_local.txt | 44 +++++++++++----- doc/html/_sources/compute_sna_atom.txt | 6 +-- doc/html/compute_angle_local.html | 14 +++-- doc/html/compute_bond_local.html | 12 +++-- doc/html/compute_dihedral_local.html | 14 +++-- doc/html/compute_improper_local.html | 15 ++++-- doc/html/compute_pair_local.html | 52 ++++++++++++++----- doc/html/compute_property_local.html | 43 +++++++++++----- doc/html/compute_sna_atom.html | 6 +-- doc/html/searchindex.js | 2 +- doc/src/compute_angle_local.txt | 16 ++++-- doc/src/compute_bond_local.txt | 15 ++++-- doc/src/compute_dihedral_local.txt | 15 ++++-- doc/src/compute_improper_local.txt | 16 ++++-- doc/src/compute_pair_local.txt | 49 +++++++++++++----- doc/src/compute_property_local.txt | 40 ++++++++++----- 21 files changed, 357 insertions(+), 124 deletions(-) diff --git a/doc/html/_sources/compute_angle_local.txt b/doc/html/_sources/compute_angle_local.txt index f0656219e8..4e40799c7e 100644 --- a/doc/html/_sources/compute_angle_local.txt +++ b/doc/html/_sources/compute_angle_local.txt @@ -8,12 +8,12 @@ Syntax .. parsed-literal:: - compute ID group-ID angle/local input1 input2 ... + compute ID group-ID angle/local value1 value2 ... * ID, group-ID are documented in :doc:`compute ` command * angle/local = style name of this compute command -* one or more keywords may be appended -* keyword = *theta* or *eng* +* one or more values may be appended +* value = *theta* or *eng* .. parsed-literal:: *theta* = tabulate angles @@ -37,6 +37,10 @@ interactions. The number of datums generated, aggregated across all processors, equals the number of angles in the system, modified by the group parameter as explained below. +The value *theta* is the angle for the 3 atoms in the interaction. + +The value *eng* is the interaction energy for the angle. + The local data stored by this command is generated by looping over all the atoms owned on a processor and their angles. An angle will only be included if all 3 atoms in the angle are in the specified compute @@ -55,6 +59,14 @@ For example, angle output from the :doc:`compute property/local ` command in a consistent way. +Here is an example of how to do this: + +.. parsed-literal:: + + compute 1 all property/local atype aatom1 aatom2 aatom3 + compute 2 all angle/local theta eng + dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_1[3] c_1[4] c_2[1] c_2[2] + **Output info:** This compute calculates a local vector or local array depending on the diff --git a/doc/html/_sources/compute_bond_local.txt b/doc/html/_sources/compute_bond_local.txt index cfad7a48dc..a88a1edc07 100644 --- a/doc/html/_sources/compute_bond_local.txt +++ b/doc/html/_sources/compute_bond_local.txt @@ -8,12 +8,12 @@ Syntax .. parsed-literal:: - compute ID group-ID bond/local input1 input2 ... + compute ID group-ID bond/local value1 value2 ... * ID, group-ID are documented in :doc:`compute ` command * bond/local = style name of this compute command -* one or more keywords may be appended -* keyword = *dist* or *eng* +* one or more values may be appended +* value = *dist* or *eng* or *force* .. parsed-literal:: *dist* = bond distance @@ -38,6 +38,13 @@ interactions. The number of datums generated, aggregated across all processors, equals the number of bonds in the system, modified by the group parameter as explained below. +The value *dist* is the length of the bond. + +The value *eng* is the interaction energy for the bond. + +The value *force* is the force acting between the pair of atoms in the +bond. + The local data stored by this command is generated by looping over all the atoms owned on a processor and their bonds. A bond will only be included if both atoms in the bond are in the specified compute group. @@ -60,7 +67,7 @@ Here is an example of how to do this: .. parsed-literal:: - compute 1 all property/local batom1 batom2 btype + compute 1 all property/local btype batom1 batom2 compute 2 all bond/local dist eng dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_1[3] c_2[1] c_2[2] diff --git a/doc/html/_sources/compute_dihedral_local.txt b/doc/html/_sources/compute_dihedral_local.txt index ee95d0c3a8..1fb50f5bda 100644 --- a/doc/html/_sources/compute_dihedral_local.txt +++ b/doc/html/_sources/compute_dihedral_local.txt @@ -8,12 +8,12 @@ Syntax .. parsed-literal:: - compute ID group-ID dihedral/local input1 input2 ... + compute ID group-ID dihedral/local value1 value2 ... * ID, group-ID are documented in :doc:`compute ` command * dihedral/local = style name of this compute command -* one or more keywords may be appended -* keyword = *phi* +* one or more values may be appended +* value = *phi* .. parsed-literal:: *phi* = tabulate dihedral angles @@ -35,6 +35,9 @@ interactions. The number of datums generated, aggregated across all processors, equals the number of angles in the system, modified by the group parameter as explained below. +The value *phi* is the dihedral angle, as defined in the diagram on +the :doc:`dihedral_style ` doc page. + The local data stored by this command is generated by looping over all the atoms owned on a processor and their dihedrals. A dihedral will only be included if all 4 atoms in the dihedral are in the specified @@ -49,6 +52,14 @@ For example, dihedral output from the :doc:`compute property/local ` command in a consistent way. +Here is an example of how to do this: + +.. parsed-literal:: + + compute 1 all property/local dtype datom1 datom2 datom3 datom4 + compute 2 all dihedral/local phi + dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_1[3] c_1[4] c_1[5] c_2[1] + **Output info:** This compute calculates a local vector or local array depending on the diff --git a/doc/html/_sources/compute_improper_local.txt b/doc/html/_sources/compute_improper_local.txt index d2128538c0..53aa475858 100644 --- a/doc/html/_sources/compute_improper_local.txt +++ b/doc/html/_sources/compute_improper_local.txt @@ -8,12 +8,12 @@ Syntax .. parsed-literal:: - compute ID group-ID improper/local input1 input2 ... + compute ID group-ID improper/local value1 value2 ... * ID, group-ID are documented in :doc:`compute ` command * improper/local = style name of this compute command -* one or more keywords may be appended -* keyword = *chi* +* one or more values may be appended +* value = *chi* .. parsed-literal:: *chi* = tabulate improper angles @@ -35,6 +35,10 @@ interactions. The number of datums generated, aggregated across all processors, equals the number of impropers in the system, modified by the group parameter as explained below. +The value *chi* is the improper angle, as defined in the doc pages for +the individual improper styles listed on +:doc:`improper_style ` doc page. + The local data stored by this command is generated by looping over all the atoms owned on a processor and their impropers. An improper will only be included if all 4 atoms in the improper are in the specified @@ -49,6 +53,14 @@ For example, improper output from the :doc:`compute property/local ` command in a consistent way. +Here is an example of how to do this: + +.. parsed-literal:: + + compute 1 all property/local itype iatom1 iatom2 iatom3 iatom4 + compute 2 all improper/local chi + dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_1[3] c_1[4] c_1[5] c_2[1] + **Output info:** This compute calculates a local vector or local array depending on the diff --git a/doc/html/_sources/compute_pair_local.txt b/doc/html/_sources/compute_pair_local.txt index 5c155b6022..eceee7ae69 100644 --- a/doc/html/_sources/compute_pair_local.txt +++ b/doc/html/_sources/compute_pair_local.txt @@ -8,12 +8,12 @@ Syntax .. parsed-literal:: - compute ID group-ID pair/local input1 input2 ... + compute ID group-ID pair/local value1 value2 ... keyword args ... * ID, group-ID are documented in :doc:`compute ` command * pair/local = style name of this compute command -* zero or more keywords may be appended -* keyword = *dist* or *eng* or *force* or *fx* or *fy* or *fz* or *pN* +* one or more values may be appended +* value = *dist* or *eng* or *force* or *fx* or *fy* or *fz* or *pN* .. parsed-literal:: *dist* = pairwise distance @@ -22,6 +22,12 @@ Syntax *fx*\ ,\ *fy*\ ,\ *fz* = components of pairwise force *pN* = pair style specific quantities for allowed N values +* zero or more keyword/arg pairs may be appended +* keyword = *cutoff* +.. parsed-literal:: + + *cutoff* arg = *type* or *radius* + Examples @@ -49,13 +55,13 @@ force cutoff distance for that interaction, as defined by the :doc:`pair_style ` and :doc:`pair_coeff ` commands. -The output *dist* is the distance bewteen the pair of atoms. +The value *dist* is the distance bewteen the pair of atoms. -The output *eng* is the interaction energy for the pair of atoms. +The value *eng* is the interaction energy for the pair of atoms. -The output *force* is the force acting between the pair of atoms, -which is positive for a repulsive force and negative for an attractive -force. The outputs *fx*\ , *fy*\ , and *fz* are the xyz components of +The value *force* is the force acting between the pair of atoms, which +is positive for a repulsive force and negative for an attractive +force. The values *fx*\ , *fy*\ , and *fz* are the xyz components of *force* on atom I. A pair style may define additional pairwise quantities which can be @@ -66,10 +72,18 @@ which calculate the tangential force between two particles and return its components and magnitude acting on atom I for N = 1,2,3,4. See individual pair styles for detils. -The output *dist* will be in distance :doc:`units `. The output -*eng* will be in energy :doc:`units `. The outputs *force*\ , -*fx*\ , *fy*\ , and *fz* will be in force :doc:`units `. The output -*pN* will be in whatever units the pair style defines. +The value *dist* will be in distance :doc:`units `. The value +*eng* will be in energy :doc:`units `. The values *force*\ , *fx*\ , +*fy*\ , and *fz* will be in force :doc:`units `. The values *pN* +will be in whatever units the pair style defines. + +The optional *cutoff* keyword determines how the force cutoff distance +for an interaction is determined. For the default setting of *type*\ , +the pairwise cutoff defined by the :doc:`pair_style ` +command for the types of the two atoms is used. For the *radius* +setting, the sum of the radii of the two particles is used as a +cutoff. For example, this is appropriate for granular particles which +only interact when they are overlapping, as computed by `granular pair styles `_. Note that as atoms migrate from processor to processor, there will be no consistent ordering of the entries within the local vector or array @@ -80,6 +94,14 @@ For example, pair output from the :doc:`compute property/local ` command in a consistent way. +Here is an example of how to do this: + +.. parsed-literal:: + + compute 1 all property/local patom1 patom2 + compute 2 all pair/local dist eng force + dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_2[1] c_2[2] c_2[3] + .. note:: For pairs, if two atoms I,J are involved in 1-2, 1-3, 1-4 @@ -110,7 +132,8 @@ options. The output for *dist* will be in distance :doc:`units `. The output for *eng* will be in energy :doc:`units `. The output for -*force* will be in force :doc:`units `. +*force*\ , *fx*\ , *fy*\ , and *fz* will be in force :doc:`units `. +The outpur for *pN* will be in whatever units the pair style defines. Restrictions """""""""""" @@ -121,7 +144,10 @@ Related commands :doc:`dump local `, :doc:`compute property/local ` -**Default:** none +Default +""""""" + +The keyword default is cutoff = type. .. _lws: http://lammps.sandia.gov diff --git a/doc/html/_sources/compute_property_local.txt b/doc/html/_sources/compute_property_local.txt index 7dffc590a6..6dbe4c6da8 100644 --- a/doc/html/_sources/compute_property_local.txt +++ b/doc/html/_sources/compute_property_local.txt @@ -8,19 +8,19 @@ Syntax .. parsed-literal:: - compute ID group-ID property/local input1 input2 ... + compute ID group-ID property/local attribute1 attribute2 ... keyword args ... * ID, group-ID are documented in :doc:`compute ` command * property/local = style name of this compute command -* input = one or more attributes +* one or more attributes may be appended .. parsed-literal:: - possible attributes = natom1 natom2 ntype1 ntype2 - patom1 patom2 ptype1 ptype2 - batom1 batom2 btype - aatom1 aatom2 aatom3 atype - datom1 datom2 datom3 dtype - iatom1 iatom2 iatom3 itype + possible attributes = natom1 natom2 ntype1 ntype2 + patom1 patom2 ptype1 ptype2 + batom1 batom2 btype + aatom1 aatom2 aatom3 atype + datom1 datom2 datom3 dtype + iatom1 iatom2 iatom3 itype .. parsed-literal:: @@ -37,6 +37,12 @@ Syntax iatom1, iatom2, iatom3, iatom4 = IDs of 4 atoms in each improper itype = improper type of each improper +* zero or more keyword/arg pairs may be appended +* keyword = *cutoff* +.. parsed-literal:: + + *cutoff* arg = *type* or *radius* + Examples @@ -56,9 +62,9 @@ to bond information, then the number of datums generated, aggregated across all processors, equals the number of bonds in the system. Ditto for pairs, angles, etc. -If multiple input attributes are specified then they must all generate -the same amount of information, so that the resulting local array has -the same number of rows for each column. This means that only bond +If multiple attributes are specified then they must all generate the +same amount of information, so that the resulting local array has the +same number of rows for each column. This means that only bond attributes can be specified together, or angle attributes, etc. Bond and angle attributes can not be mixed in the same compute property/local command. @@ -74,6 +80,15 @@ force cutoff distance for that pair to be included, as defined by the :doc:`pair_style ` and :doc:`pair_coeff ` commands. +The optional *cutoff* keyword determines how the force cutoff distance +for an interaction is determined for the *patom1* and *patom2* +attributes. For the default setting of *type*\ , the pairwise cutoff +defined by the :doc:`pair_style ` command for the types of +the two atoms is used. For the *radius* setting, the sum of the radii +of the two particles is used as a cutoff. For example, this is +appropriate for granular particles which only interact when they are +overlapping, as computed by `granular pair styles `_. + If the inputs are bond, angle, etc attributes, the local data is generated by looping over all the atoms owned on a processor and extracting bond, angle, etc info. For bonds, info about an individual @@ -122,6 +137,8 @@ The attributes that start with "a", "d", "i", refer to similar values for :doc:`angles `, :doc:`dihedrals `, and :doc:`impropers `. +The optional *cutoff* keyword + **Output info:** This compute calculates a local vector or local array depending on the @@ -145,7 +162,10 @@ Related commands :doc:`dump local `, :doc:`compute reduce ` -**Default:** none +Default +""""""" + +The keyword default is cutoff = type. .. _lws: http://lammps.sandia.gov diff --git a/doc/html/_sources/compute_sna_atom.txt b/doc/html/_sources/compute_sna_atom.txt index 94ac690891..da548a0fb4 100644 --- a/doc/html/_sources/compute_sna_atom.txt +++ b/doc/html/_sources/compute_sna_atom.txt @@ -14,9 +14,9 @@ Syntax .. parsed-literal:: - compute ID group-ID sna/atom ntypes rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ... - compute ID group-ID snad/atom ntypes rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ... - compute ID group-ID snav/atom ntypes rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ... + compute ID group-ID sna/atom rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ... + compute ID group-ID snad/atom rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ... + compute ID group-ID snav/atom rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ... * ID, group-ID are documented in :doc:`compute ` command * sna/atom = style name of this compute command diff --git a/doc/html/compute_angle_local.html b/doc/html/compute_angle_local.html index 3cb8c9f1e7..221ec3ba67 100644 --- a/doc/html/compute_angle_local.html +++ b/doc/html/compute_angle_local.html @@ -128,14 +128,14 @@

compute angle/local command

Syntax

-
compute ID group-ID angle/local input1 input2 ...
+
compute ID group-ID angle/local value1 value2 ...
 

 

 
    
 
  • ID, group-ID are documented in compute command
    • 
 
  • angle/local = style name of this compute command
    • 
-
  • one or more keywords may be appended
    • 
-
  • keyword = theta or eng
    • 
+
  • one or more values may be appended
    • 
+
  • value = theta or eng
    • 
 

 
 theta = tabulate angles
@@ -155,6 +155,8 @@
 interactions.  The number of datums generated, aggregated across all
 processors, equals the number of angles in the system, modified by the
 group parameter as explained below.

+
The value theta is the angle for the 3 atoms in the interaction.

+
The value eng is the interaction energy for the angle.

 
The local data stored by this command is generated by looping over all
 the atoms owned on a processor and their angles.  An angle will only
 be included if all 3 atoms in the angle are in the specified compute
@@ -171,6 +173,12 @@ same for local vectors or arrays generated by other compute commands.
 For example, angle output from the compute property/local command can be combined
 with data from this command and output by the dump local
 command in a consistent way.

+
Here is an example of how to do this:

+
compute 1 all property/local atype aatom1 aatom2 aatom3
+compute 2 all angle/local theta eng
+dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_1[3] c_1[4] c_2[1] c_2[2]
+

+

 
Output info:

 
This compute calculates a local vector or local array depending on the
 number of keywords.  The length of the vector or number of rows in the
diff --git a/doc/html/compute_bond_local.html b/doc/html/compute_bond_local.html
index d1def02d18..5bde75437f 100644
--- a/doc/html/compute_bond_local.html
+++ b/doc/html/compute_bond_local.html
@@ -128,14 +128,14 @@
 
compute bond/local command

 

 
Syntax

-
compute ID group-ID bond/local input1 input2 ...
+
compute ID group-ID bond/local value1 value2 ...
 

 

 
    
 
  • ID, group-ID are documented in compute command
    • 
 
  • bond/local = style name of this compute command
    • 
-
  • one or more keywords may be appended
    • 
-
  • keyword = dist or eng
    • 
+
  • one or more values may be appended
    • 
+
  • value = dist or eng or force
    • 
 

 
 dist = bond distance
@@ -156,6 +156,10 @@
 interactions.  The number of datums generated, aggregated across all
 processors, equals the number of bonds in the system, modified
 by the group parameter as explained below.

+
The value dist is the length of the bond.

+
The value eng is the interaction energy for the bond.

+
The value force is the force acting between the pair of atoms in the
+bond.

 
The local data stored by this command is generated by looping over all
 the atoms owned on a processor and their bonds.  A bond will only be
 included if both atoms in the bond are in the specified compute group.
@@ -173,7 +177,7 @@ For example, bond output from the dump local
 command in a consistent way.

 
Here is an example of how to do this:

-
compute 1 all property/local batom1 batom2 btype
+
compute 1 all property/local btype batom1 batom2
 compute 2 all bond/local dist eng
 dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_1[3] c_2[1] c_2[2]
 

diff --git a/doc/html/compute_dihedral_local.html b/doc/html/compute_dihedral_local.html
index aee9a984c2..22ac63b16f 100644
--- a/doc/html/compute_dihedral_local.html
+++ b/doc/html/compute_dihedral_local.html
@@ -128,14 +128,14 @@
 
compute dihedral/local command

 

 
Syntax

-
compute ID group-ID dihedral/local input1 input2 ...
+
compute ID group-ID dihedral/local value1 value2 ...
 

 

 
    
 
  • ID, group-ID are documented in compute command
    • 
 
  • dihedral/local = style name of this compute command
    • 
-
  • one or more keywords may be appended
    • 
-
  • keyword = phi
    • 
+
  • one or more values may be appended
    • 
+
  • value = phi
    • 
 

 
 phi = tabulate dihedral angles
@@ -153,6 +153,8 @@
 interactions.  The number of datums generated, aggregated across all
 processors, equals the number of angles in the system, modified by the
 group parameter as explained below.

+
The value phi is the dihedral angle, as defined in the diagram on
+the dihedral_style doc page.

 
The local data stored by this command is generated by looping over all
 the atoms owned on a processor and their dihedrals.  A dihedral will
 only be included if all 4 atoms in the dihedral are in the specified
@@ -165,6 +167,12 @@ same for local vectors or arrays generated by other compute commands.
 For example, dihedral output from the compute property/local command can be combined
 with data from this command and output by the dump local
 command in a consistent way.

+
Here is an example of how to do this:

+
compute 1 all property/local dtype datom1 datom2 datom3 datom4
+compute 2 all dihedral/local phi
+dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_1[3] c_1[4] c_1[5] c_2[1]
+

+

 
Output info:

 
This compute calculates a local vector or local array depending on the
 number of keywords.  The length of the vector or number of rows in the
diff --git a/doc/html/compute_improper_local.html b/doc/html/compute_improper_local.html
index fb4faf7bd1..d44d0c1326 100644
--- a/doc/html/compute_improper_local.html
+++ b/doc/html/compute_improper_local.html
@@ -128,14 +128,14 @@
 
compute improper/local command

 

 
Syntax

-
compute ID group-ID improper/local input1 input2 ...
+
compute ID group-ID improper/local value1 value2 ...
 

 

 
    
 
  • ID, group-ID are documented in compute command
    • 
 
  • improper/local = style name of this compute command
    • 
-
  • one or more keywords may be appended
    • 
-
  • keyword = chi
    • 
+
  • one or more values may be appended
    • 
+
  • value = chi
    • 
 

 
 chi = tabulate improper angles
@@ -153,6 +153,9 @@
 interactions.  The number of datums generated, aggregated across all
 processors, equals the number of impropers in the system, modified by
 the group parameter as explained below.

+
The value chi is the improper angle, as defined in the doc pages for
+the individual improper styles listed on
+improper_style doc page.

 
The local data stored by this command is generated by looping over all
 the atoms owned on a processor and their impropers.  An improper will
 only be included if all 4 atoms in the improper are in the specified
@@ -165,6 +168,12 @@ same for local vectors or arrays generated by other compute commands.
 For example, improper output from the compute property/local command can be combined
 with data from this command and output by the dump local
 command in a consistent way.

+
Here is an example of how to do this:

+
compute 1 all property/local itype iatom1 iatom2 iatom3 iatom4
+compute 2 all improper/local chi
+dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_1[3] c_1[4] c_1[5] c_2[1]
+

+

 
Output info:

 
This compute calculates a local vector or local array depending on the
 number of keywords.  The length of the vector or number of rows in the
diff --git a/doc/html/compute_pair_local.html b/doc/html/compute_pair_local.html
index 922b25d56a..ae12f688ba 100644
--- a/doc/html/compute_pair_local.html
+++ b/doc/html/compute_pair_local.html
@@ -128,14 +128,14 @@
 
compute pair/local command

 

 
Syntax

-
compute ID group-ID pair/local input1 input2 ...
+
compute ID group-ID pair/local value1 value2 ... keyword args ...
 

 

 
    
 
  • ID, group-ID are documented in compute command
    • 
 
  • pair/local = style name of this compute command
    • 
-
  • zero or more keywords may be appended
    • 
-
  • keyword = dist or eng or force or fx or fy or fz or pN
    • 
+
  • one or more values may be appended
    • 
+
  • value = dist or eng or force or fx or fy or fz or pN
    • 
 

 
 dist = pairwise distance
@@ -144,6 +144,13 @@
 fx,fy,fz = components of pairwise force
 pN = pair style specific quantities for allowed N values
 

+
    
+
  • zero or more keyword/arg pairs may be appended
    • 
+
  • keyword = cutoff
    • 
+

+
+cutoff arg = type or radius
+

 

 

 
Examples

@@ -166,11 +173,11 @@ compute group, and if the current pairwise distance is less than the
 force cutoff distance for that interaction, as defined by the
 pair_style and pair_coeff
 commands.

-
The output dist is the distance bewteen the pair of atoms.

-
The output eng is the interaction energy for the pair of atoms.

-
The output force is the force acting between the pair of atoms,
-which is positive for a repulsive force and negative for an attractive
-force.  The outputs fx, fy, and fz are the xyz components of
+
The value dist is the distance bewteen the pair of atoms.

+
The value eng is the interaction energy for the pair of atoms.

+
The value force is the force acting between the pair of atoms, which
+is positive for a repulsive force and negative for an attractive
+force.  The values fx, fy, and fz are the xyz components of
 force on atom I.

 
A pair style may define additional pairwise quantities which can be
 accessed as p1 to pN, where N is defined by the pair style.  Most
@@ -179,10 +186,17 @@ example of ones that do are the units.  The output
-eng will be in energy units.  The outputs force,
-fx, fy, and fz will be in force units.  The output
-pN will be in whatever units the pair style defines.

+
The value dist will be in distance units.  The value
+eng will be in energy units.  The values force, fx,
+fy, and fz will be in force units.  The values pN
+will be in whatever units the pair style defines.

+
The optional cutoff keyword determines how the force cutoff distance
+for an interaction is determined.  For the default setting of type,
+the pairwise cutoff defined by the pair_style
+command for the types of the two atoms is used.  For the radius
+setting, the sum of the radii of the two particles is used as a
+cutoff.  For example, this is appropriate for granular particles which
+only interact when they are overlapping, as computed by granular pair styles.

 
Note that as atoms migrate from processor to processor, there will be
 no consistent ordering of the entries within the local vector or array
 from one timestep to the next.  The only consistency that is
@@ -191,6 +205,12 @@ same for local vectors or arrays generated by other compute commands.
 For example, pair output from the compute property/local command can be combined
 with data from this command and output by the dump local
 command in a consistent way.

+
Here is an example of how to do this:

+
compute 1 all property/local patom1 patom2
+compute 2 all pair/local dist eng force
+dump 1 all local 1000 tmp.dump index c_1[1] c_1[2] c_2[1] c_2[2] c_2[3]
+

+

 

 
Note

 
For pairs, if two atoms I,J are involved in 1-2, 1-3, 1-4
@@ -219,7 +239,8 @@ uses local values from a compute as input.  See 
 
The output for dist will be in distance units.  The
 output for eng will be in energy units.  The output for
-force will be in force units.

+force, fx, fy, and fz will be in force units.
+The outpur for pN will be in whatever units the pair style defines.

 

 

 
Restrictions

@@ -229,7 +250,10 @@ output for eng will be in energy 
 
Related commands

 
dump local, compute property/local

-
Default: none

+

+

+
Default

+
The keyword default is cutoff = type.

 

 

 
diff --git a/doc/html/compute_property_local.html b/doc/html/compute_property_local.html
index a025c296bc..2d5eed0f93 100644
--- a/doc/html/compute_property_local.html
+++ b/doc/html/compute_property_local.html
@@ -128,20 +128,20 @@
 
compute property/local command

 

 
Syntax

-
compute ID group-ID property/local input1 input2 ...
+
compute ID group-ID property/local attribute1 attribute2 ... keyword args ...
 

 

 
    
 
  • ID, group-ID are documented in compute command
    • 
 
  • property/local = style name of this compute command
    • 
-
  • input = one or more attributes
    • 
+
  • one or more attributes may be appended
    • 
 

-
possible attributes = natom1 natom2 ntype1 ntype2
-                   patom1 patom2 ptype1 ptype2
-                      batom1 batom2 btype
-                      aatom1 aatom2 aatom3 atype
-                      datom1 datom2 datom3 dtype
-                      iatom1 iatom2 iatom3 itype
+
possible attributes =  natom1 natom2 ntype1 ntype2
+                    patom1 patom2 ptype1 ptype2
+                       batom1 batom2 btype
+                       aatom1 aatom2 aatom3 atype
+                       datom1 datom2 datom3 dtype
+                       iatom1 iatom2 iatom3 itype
 

 

 
natom1, natom2 = IDs of 2 atoms in each pair (within neighbor cutoff)
@@ -158,6 +158,13 @@
 itype = improper type of each improper
 

 

+
    
+
  • zero or more keyword/arg pairs may be appended
    • 
+
  • keyword = cutoff
    • 
+

+
+cutoff arg = type or radius
+

 

 

 
Examples

@@ -173,9 +180,9 @@ data so it can be accessed by other pair_style and pair_coeff
 commands.

+
The optional cutoff keyword determines how the force cutoff distance
+for an interaction is determined for the patom1 and patom2
+attributes.  For the default setting of type, the pairwise cutoff
+defined by the pair_style command for the types of
+the two atoms is used.  For the radius setting, the sum of the radii
+of the two particles is used as a cutoff.  For example, this is
+appropriate for granular particles which only interact when they are
+overlapping, as computed by granular pair styles.

 
If the inputs are bond, angle, etc attributes, the local data is
 generated by looping over all the atoms owned on a processor and
 extracting bond, angle, etc info.  For bonds, info about an individual
@@ -231,6 +246,7 @@ of bond types is defined in the data file read by the
 
The attributes that start with “a”, “d”, “i”, refer to similar values
 for angles, dihedrals, and
 impropers.

+
The optional cutoff keyword

 
Output info:

 
This compute calculates a local vector or local array depending on the
 number of input values.  The length of the vector or number of rows in
@@ -251,7 +267,10 @@ specified attribute.

 
+

+
Default

+
The keyword default is cutoff = type.

 

 

 
diff --git a/doc/html/compute_sna_atom.html b/doc/html/compute_sna_atom.html
index c8e5883931..f5986941aa 100644
--- a/doc/html/compute_sna_atom.html
+++ b/doc/html/compute_sna_atom.html
@@ -134,9 +134,9 @@
 
compute snav/atom command

 

 
Syntax

-
compute ID group-ID sna/atom ntypes rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ...
-compute ID group-ID snad/atom ntypes rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ...
-compute ID group-ID snav/atom ntypes rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ...
+
compute ID group-ID sna/atom rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ...
+compute ID group-ID snad/atom rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ...
+compute ID group-ID snav/atom rcutfac rfac0 twojmax R_1 R_2 ... w_1 w_2 ... keyword values ...
 

 

 
    
diff --git a/doc/html/searchindex.js b/doc/html/searchindex.js
index 4df19a68d6..70d0952b15 100644
--- a/doc/html/searchindex.js
+++ b/doc/html/searchindex.js
@@ -1 +1 @@
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ed:[498,519],addforc:[],adding:[],addit:[1,2,3,4,6,7,8,9,12,13,14,15,16,17,19,21,28,32,36,37,39,40,41,42,44,50,55,56,65,118,119,124,154,178,179,181,182,186,191,197,198,199,200,201,206,207,208,210,213,221,224,225,229,230,235,237,238,241,242,246,248,249,250,254,257,271,272,273,274,275,276,277,278,279,291,292,294,295,296,298,306,317,320,321,322,324,348,351,352,356,362,368,371,373,378,380,388,390,393,394,395,399,400,402,404,406,408,409,412,414,415,416,417,418,419,423,424,426,427,428,430,431,439,441,442,443,444,445,446,448,450,451,452,453,456,458,459,461,466,467,476,477,478,479,480,481,482,485,489,492,497,498,499,504,505,506,509,511,518,519,520,522,524,527,528,529],addition:[],additionali:204,addres:9,address:[7,8,11,206,253,519],addtorqu:[],adequ:[263,335,348,377,388,506],adher:28,adhikari:257,adiabat:[],adiam:[206,207],adjac:[39,176,178,238,388,448,478,479,480,511,512],adjiman:447,adjust:[2,3,6,15,16,41,61,130,140,156,157,158,161,162,165,166,171,172,177,182,203,206,219,225,229,231,251,254,258,262,267,268,271,272,276,294,298,301,303,304,307,308,309,310,315,317,318,326,335,339,343,348,350,351,352,354,355,357,377,378,386,388,393,395,415,440,441,466,482,507,525],adjust_radiu:326,adjust_radius_factor:326,admiss:276,adof:[158,219],adopt:[316,518],adp:[],adri:[9,313,458,459],adust:172,advanc:[3,15,251,399,492,503],advantag:[1,6,8,11,17,39,40,41,225,393,417,506,511],advect:[3,6,335],advertis:8,advis:[318,388,457,516],afer:3,affect:[1,6,10,14,16,40,62,63,75,96,128,153,162,176,182,206,207,212,219,220,222,223,226,227,228,229,231,232,238,244,250,252,254,260,266,268,272,273,274,275,277,278,279,285,291,292,294,296,317,319,333,347,357,370,377,384,385,386,388,389,390,393,418,440,441,448,494,495,497,499,500,502,505,507,516],affin:[15,16,17,231,393,408],affiniti:393,afile:248,aforement:17,afresh:[305,505,524],afshar:413,after:[],afterrun:505,afterward:3,afterword:41,ag1:177,ag2:177,again:[6,11,12,16,64,152,158,164,172,203,207,231,238,250,303,361,376,388,440,441,492,494,495,497,499,504,511,513,524,527],against:[11,12,13,66,232,388,458,459],aggreg:[6,12,68,71,73,84,102,119,126,250,267,315,317,333,492,525],aggress:[238,250,511],agilio:[9,13],agre:[3,8,199,386,395,428,459],agreement:[5,7],ahd:425,ahead:354,aidan:[0,5,7,9,13,380],aij:13,aim:6,airebo:[],ajaramil:[7,9,13],aka:206,akohlmei:[7,9,13,208,251],aktulga:[7,9,310,459],al2o3:466,al2o3_001:[130,319],al3:177,ala:[257,466],alain:9,alat:[298,443],albe:[456,480,482],albeit:316,albert:9,alchem:[95,172],alcohol:350,alcu:[394,399],alcu_eam:456,alderton:412,alejandr:[271,272],alessandro:13,algebra:446,algorithm:[0,1,6,7,8,9,41,63,207,210,216,225,228,231,238,250,257,259,260,285,300,308,317,321,322,342,343,347,350,355,384,385,386,390,393,414,418,441,446,463,465,492,494,511],alia:[9,15],alias:[1,378],aliceblu:207,align:[6,9,12,28,41,75,180,199,225,252,380,497,500,518],alkali:418,all:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,21,32,36,38,39,40,41,42,44,50,54,55,57,58,59,61,62,63,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,166,171,172,173,174,175,176,177,178,179,180,181,182,184,186,191,197,198,199,200,203,204,205,206,207,208,210,211,212,213,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,234,235,236,237,238,239,240,241,242,243,244,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,263,264,266,267,269,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,289,290,291,292,293,294,295,296,297,298,299,300,302,303,304,305,306,307,308,309,310,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,330,331,332,334,335,336,337,338,339,342,343,344,345,346,347,348,349,350,352,353,354,355,356,357,358,359,360,361,362,366,371,373,375,376,377,378,379,380,383,386,387,388,389,390,392,393,394,395,396,398,399,400,402,403,404,405,406,408,409,412,413,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,435,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,455,456,457,458,459,460,461,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,488,489,490,491,492,494,495,496,497,498,499,500,501,502,504,505,506,507,508,509,510,511,512,514,515,516,518,519,523,524,525,527,528,529],allen:[28,95,412,422],allentildeslei:95,allign:3,allindex:359,alloc:[3,5,6,8,9,11,12,62,244,349,387,389,393,454,459,497,505],allocat:3,alloi:[],allosw:75,allow:[1,2,3,6,8,9,11,12,13,14,15,16,17,21,36,38,39,40,41,55,57,58,59,60,61,63,64,65,81,100,117,119,154,156,157,158,171,176,177,178,180,186,198,199,200,202,203,204,206,207,208,210,211,213,214,215,216,217,219,220,221,222,223,224,225,227,228,229,230,231,232,233,236,237,244,246,247,248,249,251,252,254,257,260,261,266,268,271,272,298,302,303,304,305,306,307,309,311,316,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9,518],coldest:343,coleman8:9,coleman:[9,130,177,319],colin:9,collabor:[7,8,14],collect:[3,6,7,8,9,13,40,42,69,79,86,91,98,103,108,114,116,125,158,166,173,175,178,203,204,207,219,230,260,267,302,312,315,317,358,377,387,389,407,497,504,510,516,529],collid:[236,335,357],colliex:177,collinear:[3,302],collis:[3,257,335,353,357,415,423,489],colllis:335,colloid:[],colombo:39,colon:[208,358,498,516],color1:207,color2:207,color:[3,9,41,203,206,207,225,247,307,312],column:[3,6,9,12,13,42,65,68,69,70,71,73,75,79,81,84,86,87,89,98,100,102,103,114,116,117,119,121,124,125,126,127,128,129,131,152,153,158,166,173,175,176,177,199,203,204,207,210,218,219,220,222,223,260,268,269,307,317,336,337,347,357,395,421,425,458,459,498,512,514,524],columnar:176,colvar:[],colvarmodul:12,com:[],comamnd:231,comand:228,comannd:393,comb3:[],comb:[],comb_1:309,comb_2:309,combiant:410,combin:[3,6,7,9,11,13,35,40,65,68,73,84,95,102,113,119,126,156,157,171,197,203,206,216,222,238,246,251,260,263,271,287,293,300,306,322,339,348,356,359,361,377,378,380,385,393,407,409,410,414,418,419,420,426,438,439,467,477,480,482,483,485,488,500,505,510,518,524,526],come:[],comfort:[12,13],comid:332,comit:519,comm:[0,3,11,12,63,77,205,251,253,254,378,388,393,413,448,455,478,479,499],comm_modifi:[],comm_modift:63,comm_styl:[],comm_world:11,command:[],comment:[2,4,7,11,12,37,56,184,199,203,241,242,255,317,318,347,387,388,394,416,417,419,420,430,443,450,451,452,459,466,467,477,478,479,480,481,482,485,494,495,497,518,519,524],commerci:7,commit:[9,519],commmand:[3,6,12,61,118,295,429,491,492,494,511,528],common:[],commonli:[3,6,9,12,16,24,59,61,115,117,180,203,206,208,372,424,433,467,480,482,497,500,509],commun:[1,3,6,7,8,9,10,11,12,14,15,17,40,41,60,63,64,75,176,181,182,206,207,225,226,227,229,230,231,251,253,257,259,260,261,271,299,306,308,309,310,317,322,335,347,358,375,377,389,390,391,393,414,415,454,490,494,495,506,507,524,527,529],communc:377,comp:[7,205,253,254,287,293,321,378,388,418,448,455,460,474,478,479,481],compact:[65,210,406,476],compani:[5,7],compar:[1,3,4,6,8,12,15,16,39,94,121,130,161,177,186,198,207,235,308,358,360,377,378,386,388,443,466,492,511,512,518,523],comparison:[],compart:382,compass:[7,9,20,21,36,43,44,55,185,186,198,361,362,371,405,476],compat:[3,5,7,8,9,11,12,13,16,17,41,75,128,131,189,203,208,212,218,219,220,222,223,225,238,299,311,339,342,349,352,355,377,393,427,446,448,478,494,495,524],compens:[6,226,227,315,389,418],compet:346,competit:378,compil:[3,7,8,9,12,13,14,15,16,17,18,176,203,205,206,208,251,346,378,393,446,497,498,502,518,524],compl:16,complain:[11,12,16],complement:443,complementari:[7,409,431],complet:[3,6,9,11,12,14,41,61,75,207,225,230,260,300,303,306,335,346,348,359,360,376,388,393,419,463,465,484,492,497,502,505,509,511,514,518,524],complex:[4,6,8,11,12,13,24,40,42,64,100,117,152,154,166,178,179,257,287,330,356,375,388,418,446,478,479,495,497,500,524],compli:[342,346],complic:[6,7,9,12,13,129,217,246,495],complier:12,compon:[3,6,8,12,63,65,69,70,77,86,87,96,97,98,99,100,103,104,107,112,114,115,116,117,118,119,120,121,123,124,128,129,139,142,143,144,145,148,149,150,152,153,155,156,157,158,159,160,161,162,163,164,165,166,167,168,170,171,173,174,175,202,203,204,206,207,213,214,218,219,220,221,222,223,224,228,229,231,232,237,238,244,249,253,254,257,260,262,263,267,268,270,271,272,276,277,278,279,291,292,294,296,297,299,300,301,304,315,317,320,321,323,327,328,331,335,338,339,340,342,349,350,355,356,357,377,380,385,386,387,388,393,413,418,423,440,441,463,465,466,467,497,498,507,515,524,525],componenet:6,componennt:129,composit:[6,217,257,416],compound:[408,418,419,485],compres:[75,125,219],compress:[],compris:[40,356,420,460,484],compton:[130,177],comptu:3,compuat:378,comput:[],computation:[3,6,226,227,347,399],computational:518,compute_arrai:8,compute_inn:8,compute_ke_atom:8,compute_loc:8,compute_modifi:[],compute_peratom:8,compute_sa:[130,319],compute_scalar:8,compute_temp:8,compute_vector:8,compute_xrd:177,concaten:[2,3,528],concav:356,concentr:[75,242,318,416,420,451,479],concept:[6,158,168,219,506],conceptu:[3,6,75,166,229,231,388,409,426,443,502],concern:[6,77,95,205,247],concetr:75,concis:[11,346],conclud:12,concret:8,concurr:[15,378,524],conden:[347,480,482],condens:[6,160,347,395,411,416,431,485],condit:[],conducit:6,conduct:[],cone:500,confer:[15,446],confid:[3,511],config:[12,203,494],configfil:230,configur:[1,2,6,12,14,15,16,37,61,134,180,199,202,203,206,210,229,230,231,232,236,246,253,254,285,300,308,346,375,386,388,395,399,417,443,446,477,480,482,485,492,497,499,500,511,515,519],confin:[497,511],confirm:516,conflict:[3,12,40,204,448,495,519],conform:[3,6,13,61,228,229,270,316,323,346,370,388,418,509],confus:[3,485],conjuct:[9,413],conjug:[7,8,254,385,418,458,459],conjunct:[6,7,75,94,95,125,161,166,172,178,182,207,211,212,254,257,261,285,303,304,308,309,310,312,317,322,335,343,350,355,377,378,388,400,402,406,409,413,418,425,431,448,453,461,483,497,500,504,518,529],connect:[3,6,9,95,163,181,228,251,302,317,321,331,382,388,410,423,458,459,475,482,494,495,497,501,518],conner:395,connor:395,conput:3,consecut:[3,11,12,39,75,178,207,211,212,232,251,252,409,431,435,492,498,500],consequ:[1,6,217,347,430,511],conserv:[3,9,28,210,217,228,235,236,247,250,254,256,257,261,267,269,271,285,287,317,318,321,322,338,339,343,350,351,355,388,412,413,414,423,437,466,506,511],consid:[6,74,75,82,95,126,129,160,163,164,181,203,204,207,211,212,218,220,225,227,228,232,258,272,299,317,342,343,346,347,350,378,406,418,426,459,460,466,475,492,493,495,498,499,500,502,505,507,515,518,524],consider:[6,8,254,255,338,339,340,393,459,506],consist:[3,6,8,9,11,12,40,42,68,73,84,102,114,119,122,123,126,129,158,161,163,178,190,202,208,213,214,219,231,232,235,237,244,247,254,255,256,268,271,273,274,275,276,277,278,279,280,281,283,284,285,286,289,290,291,292,294,295,296,304,307,312,314,315,316,317,318,338,339,340,341,351,377,378,380,382,387,388,393,395,399,401,407,409,418,422,426,429,440,441,443,446,448,450,451,460,463,465,466,478,479,486,495,497,498,499,500,501,502,509,518,524],consistent_fe_initi:216,consit:317,constant:[],constantli:238,constitu:[3,6,260,317,352,356,407,460],constitut:[463,465],constrain:[3,6,8,9,155,156,157,158,159,161,164,165,166,167,168,170,171,210,219,232,238,246,247,252,260,263,265,287,293,302,303,315,317,321,333,343,350,386,387,418,502,509,518],constraint:[],construct:[6,8,12,37,54,56,63,66,70,74,76,77,81,100,117,130,152,177,229,271,299,316,318,356,389,393,412,446,448,475,477,478,479,500,501,516,524],constructor:[8,11],consult:[15,459],consum:[1,312,454,524],consumpt:375,contact:[],contact_stiff:[462,464],contain:[0,1,2,3,4,6,8,9,11,12,13,16,17,18,37,40,41,56,65,95,99,127,130,152,154,158,166,176,177,178,180,184,186,198,199,203,204,206,207,208,210,211,212,216,218,219,220,222,223,225,230,232,237,241,242,248,252,253,254,257,269,285,298,299,302,303,305,306,307,310,314,317,318,319,324,335,342,346,347,356,357,360,376,378,387,388,391,392,394,395,396,399,408,409,412,416,417,418,420,426,427,443,446,450,451,452,456,457,458,459,467,477,478,479,480,481,482,483,485,492,493,494,495,497,498,499,500,502,504,506,509,511,512,514,515,518,519,524,527,529],content:[4,8,9,12,17,459,513,515],context:[3,6,8,12,15,16,127,128,207,226,227,232,302,314,351,385,488,497,504,513,523,524,525],contibut:74,contigu:494,contin:15,continu:[0,2,3,5,6,9,12,13,41,75,87,113,114,174,207,210,211,212,217,219,220,221,222,223,225,228,229,230,231,232,238,246,247,248,250,251,252,254,255,256,262,268,269,271,273,274,275,276,277,278,279,291,292,294,295,296,301,303,306,307,317,319,323,334,335,337,344,345,347,353,356,360,376,392,393,399,413,415,433,436,458,459,460,463,465,481,492,495,497,499,500,505,511,512,514,515,524,527],continuum:[6,7,9,216,347,463,465],contour_integr:216,contract:[61,229,231,271,304,317],contradictori:3,contrain:321,contraint:285,contrari:[248,255],contrast:[1,6,42,55,66,160,163,204,231,358,463,465,488,528],contrib:347,contribut:[3,4,5,6,7,8,9,12,13,16,65,67,69,71,72,74,75,78,79,81,83,85,86,92,95,96,97,98,99,101,103,112,114,116,118,119,120,121,123,125,128,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,212,217,218,219,220,222,223,229,246,254,257,260,261,266,272,294,295,302,303,311,314,317,319,321,349,377,386,388,396,413,415,416,418,426,440,441,446,448,458,459,466,509,515,518,519],contributor:12,control:[3,5,6,7,8,9,11,13,15,26,28,41,95,99,134,152,187,203,204,206,210,216,217,225,229,230,231,250,251,254,255,271,273,274,275,276,277,278,279,304,309,317,318,325,326,338,339,340,347,351,375,377,390,418,422,446,458,459,462,464,477,482,492,494,506,512,513],control_typ:216,controlfil:459,convect:99,conveni:[6,12,28,203,208,223,319,380,467,483,516,519,524],convent:[3,8,28,189,196,198,204,207,316,331,359,416,418,524],converg:[3,6,41,96,203,204,206,208,213,225,228,229,237,244,276,287,293,307,309,312,316,321,384,385,386,388,408,409,431,466,492,504,511,516],convers:[3,8,152,206,207,217,220,304,377,409,410,411,418,431,435,439,453,495,511,523],convert:[2,3,4,5,6,7,8,12,13,19,20,23,27,31,34,35,61,65,75,99,178,185,203,206,207,220,223,269,303,306,358,361,363,367,370,380,388,394,416,446,480,482,489,495,497,498,499,504,514,518,523,524,527,529],convex:[42,356],convinc:[7,12],cook:9,cooki:7,cool:[7,168,238,250,315],cooordin:[203,204],cooper:[5,7],coord123:125,coord1:[3,125,219],coord2:[3,125,219],coord3:[3,125,219],coord:[],coordb:466,coordbb:466,coordiat:386,coordin:[1,3,4,6,7,8,11,13,14,16,40,41,42,61,63,64,65,69,71,75,78,79,81,86,87,95,97,98,103,113,114,116,124,125,127,129,146,152,161,167,173,175,176,178,182,202,203,204,205,206,207,208,210,213,218,219,222,225,226,227,228,229,230,231,232,235,237,238,239,244,246,249,250,251,252,253,254,255,268,270,271,273,274,275,277,278,279,294,297,298,299,302,303,304,314,315,317,320,321,323,328,331,332,333,334,335,337,345,346,347,354,355,357,358,380,386,387,388,393,394,395,398,417,466,492,497,498,499,500,502,505,507,511,518,524,525],coordn:[125,219],coords:466,copi:[0,3,4,8,9,11,12,14,16,40,131,206,347,388,406,458,495,519],copper:489,coproccesor:[],coprocessor:[1,7,9,15,16,393,510],coproprocessor:16,copy_arrai:8,copyright:[7,8,302],coral:207,core:[],core_shel:160,cores:518,coreshel:[4,6,9],coreshell:[],cornel:[6,184,509],corner123i:124,corner123x:124,corner123z:124,corner1i:124,corner1x:124,corner1z:124,corner2i:124,corner2x:124,corner2z:124,corner3i:124,corner3x:124,corner3z:124,corner:[3,6,40,124,206,356,357,380,484,497,519],cornflowerblu:207,cornsilk:207,corp:9,corpor:15,corr:408,correct:[3,6,9,11,12,16,61,86,95,96,112,113,118,121,127,160,165,172,184,204,206,231,238,246,248,254,271,272,294,302,304,307,346,352,356,377,388,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,410,411,412,413,415,416,417,418,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,453,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,484,485,487,488,489,490,497,512,515,518,519],correction_max_iter:216,correctli:[3,8,11,16,75,87,112,113,114,155,156,157,159,161,163,164,165,166,167,170,171,174,203,207,213,232,237,244,255,265,271,272,310,317,321,331,334,353,356,388,389,393,411,441,446,494,495,497,507,519,523,525],correl:[],correpond:[241,242],correspond:[1,2,4,6,8,9,10,11,12,19,20,21,22,23,24,25,26,27,28,29,30,31,34,37,40,42,43,44,45,46,47,48,49,51,53,54,56,74,75,95,106,107,123,124,125,126,129,130,131,139,142,143,144,145,146,148,149,150,152,155,156,165,172,176,177,184,185,186,187,188,189,190,192,193,195,196,197,199,202,203,204,206,207,211,212,213,219,221,222,224,227,229,231,238,239,242,244,245,249,254,257,258,267,268,269,271,273,274,275,276,277,278,279,280,281,285,289,291,292,294,296,298,299,300,304,309,310,317,318,320,321,342,352,353,355,356,357,359,361,362,363,364,366,367,370,372,378,383,385,387,388,394,395,397,400,401,402,403,404,405,406,407,408,409,412,413,416,417,418,419,420,421,422,423,424,425,426,429,431,432,433,434,435,436,437,438,439,440,443,444,446,448,449,451,452,453,455,456,458,459,460,461,466,467,468,477,478,479,480,481,482,483,485,487,488,489,492,494,495,497,498,500,510,511,512,514,515,518,524],correspondingli:[440,441,506],corrupt:3,cosin:[],cosineshift:26,cosmo:[248,253],cossq:[],cost:[1,6,10,11,12,16,39,41,75,120,130,153,177,206,207,219,225,226,227,243,271,309,318,347,377,378,391,409,431,435,446,448,477,494,506],costheta0:[477,480,482,485],costheta:456,costli:[11,15,96,248,389],couett:4,coul:[],could:[2,3,6,9,11,12,16,32,41,50,61,69,75,79,86,95,98,103,114,116,120,123,125,158,168,173,175,191,203,204,206,207,211,212,219,220,225,231,244,253,306,307,308,312,315,317,320,335,336,342,346,347,348,352,356,358,360,368,374,376,384,386,389,393,395,396,421,425,426,429,458,459,493,494,495,497,499,501,504,505,512,513,518,524,525],coulomb:[3,5,6,7,8,9,10,12,14,17,76,96,118,119,127,153,179,183,308,310,348,377,378,386,393,400,402,403,404,405,408,409,410,411,412,418,423,424,426,431,435,439,442,448,453,458,459,461,466,476,482,483,485,488,502,509,515,518,523],coulommb:6,cound:3,count:[1,3,6,8,11,12,41,65,71,81,99,125,127,128,166,176,182,184,213,214,217,219,222,224,225,232,237,243,246,252,271,285,303,320,321,338,339,356,378,386,387,388,390,393,421,425,448,515,516,524],counter:[3,353,492,503,505,511],counteract:246,counterbal:[238,250],counterbalanc:28,counterclockwis:184,counterpart:[203,492],counterproduct:17,coupl:[],couple:[4,6,11,12],courant:324,cours:[3,8,15,138,140,172,203,211,212,247,316,331,346,352,354,355,357,358,378,440,468,494,497,510,518,524,527],courtesi:380,cov:466,coval:[6,9,28,418,443,466,518],covari:248,cover:[6,12,75,199,207,216,257,418,484],coverag:75,cpc:253,cpp:[1,3,6,8,9,11,12,13,95,203,211,212,244,321],cpu:[1,3,9,10,12,14,15,16,17,65,75,207,221,235,255,348,375,378,393,406,476,492,510,511,514,515,516,524],cpuinfo:15,cpuremain:515,cpus:12,cr2:177,cr3:177,crack:[4,389],crada:[5,7],crai:[5,7,13,17,203],crash:[3,12,389,518],craympi:393,creat:[],create_atom:[],create_bond:[],create_box:[],create_elementset:216,create_faceset:216,create_group:205,create_nodeset:216,createatom:[],creation:[],crimson:207,critchlei:302,criteria:[3,127,179,206,207,226,227,228,266,386,455,484,499,502,524],criterion:[12,41,133,178,181,217,225,228,246,285,309,324,353,358,386,388,408,418,423,466,502,511,512],criterioni:511,critic:[6,48,49,269,342,347,386],crmax:75,crmin:75,cross:[3,12,15,21,75,97,129,156,176,186,203,204,206,218,227,231,268,270,294,317,327,331,334,343,350,362,380,388,404,413,415,416,424,425,426,431,433,435,456,461,463,465,480,482,489,497,501,507,527],crossov:1,crossterm:497,crozier:[0,7,13],crucial:307,crystal:[3,4,6,13,15,77,117,298,299,345,380,389,497,501,515,518],crystallin:[6,113,299,380,481,518],crystallis:342,crystallogr:[130,177],crystallograph:[380,515],crystallographi:[130,177,380],cs1:177,cs_chunk:6,cs_im:[40,497],cs_re:[40,497],csanyi:[152,457,467],cscl:443,csequ:6,csh:[11,12,15,406],cshrc:[11,12],csic:[417,477,480,482,485],csinfo:6,csisi:[417,477,480,482,485],csld:[],cst:416,cstherm:6,cstyle:494,csvr:[],ctcm:[394,416],ctemp_core:235,cterm:323,ctr:9,ctype:11,cu1:177,cu2:177,cu3au:443,cube:[6,176,181,235,356,380,518],cubes:[41,225],cubic:[],cuda:[1,3,7,9,12,14,16,393,510,524],cuda_arch:[9,14],cuda_get:14,cuda_home:[9,14],cuda_prec:[9,14],cuh:399,cummul:[3,6,223,226,227,228,230,243,248,254,256,335,338,339,340,341,343,350,425,515],cumul:[6,217,219,222,236,246,254,269,271,276,285,317,319,388],curli:2,current:[0,1,3,5,6,7,8,9,10,11,12,13,14,15,16,17,40,41,42,57,61,63,65,75,77,87,95,113,119,127,128,142,153,158,166,168,174,176,179,182,202,203,204,205,206,207,208,211,212,216,219,223,225,226,227,228,229,230,231,232,236,237,238,244,246,248,251,252,254,260,266,268,271,272,277,278,279,285,287,291,292,293,294,296,302,303,308,309,311,314,315,316,317,318,321,322,323,324,325,326,327,328,330,332,333,334,335,338,339,340,346,347,350,351,352,353,354,355,357,358,360,375,376,377,378,381,382,383,385,386,387,388,393,399,406,408,412,416,418,419,423,426,427,430,440,441,443,444,445,448,456,458,459,462,463,464,465,468,480,482,483,486,492,493,494,495,497,498,499,500,501,503,504,505,507,509,511,512,514,515,519,524,525,526,527,528,529],curv:[6,178,246,287,293,299],curvatur:[422,460,489],custom:[],cut0:495,cut1:506,cut2:506,cut:[],cuthi:[298,310],cutinn:[401,440,441],cutlo:[298,310],cutmax:456,cutoff1:[405,412,431,435,439,442,453,461],cutoff2:[400,402,403,405,411,412,431,435,439,442,453,461,483],cutoff:[3,6,8,10,15,17,39,45,46,54,55,63,74,76,77,81,95,100,117,119,126,127,152,176,179,181,182,227,228,233,241,242,298,307,308,310,312,314,317,322,335,348,352,356,358,375,377,378,386,389,390,391,393,394,395,396,397,398,399,400,401,402,403,404,405,407,409,410,411,412,413,414,415,416,417,418,419,420,421,422,424,425,426,427,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,458,459,460,461,466,467,468,469,470,471,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,495,499,502,506,518,524],cutoffa:417,cutoffc:417,cuu3:416,cval:177,cvd:342,cvel:323,cvff:[],cwiggl:[3,268,352,355,357,524],cyan:[2,206,207],cycl:[3,246,269,271,272,276],cyclic:[3,184,199],cygwin:12,cylind:[3,4,75,206,219,252,293,303,353,356,382,500],cylinder_d:382,cylindr:[6,252,331,353],cypress:393,cyrot:399,cyrstal:299,d3q15:257,d3q19:257,d_double_double:14,d_e:347,d_flag2:306,d_flag:306,d_name:[124,203,306,337,507],d_single_double:14,d_single_single:14,d_sx:306,d_sy:306,d_sz:306,daan:345,dai:12,daili:12,daivi:294,damag:[],dammak:312,damp:[3,6,210,215,254,255,256,261,271,272,276,293,304,307,308,312,317,338,339,351,353,354,385,386,388,400,402,404,409,412,418,423,431,439,453,461,476,483,511,518],damp_com:255,damp_drud:255,dampen:[317,518],dampflag:[353,423],dan:16,danger:[3,12,246,358,413,515],dangl:181,daniel:9,darden:[378,412],darkblu:207,darkcyan:207,darken:206,darkgoldenrod:207,darkgrai:207,darkgreen:207,darkkhaki:207,darkmagenta:207,darkolivegreen:207,darkorang:207,darkorchid:207,darkr:207,darksalmon:207,darkseagreen:207,darkslateblu:207,darkslategrai:207,darkturquois:207,darkviolet:207,dasgupta:308,dash:[423,514],dat:[6,99,199,216,493],data2xmovi:[],data:[],data_atom:8,data_atom_hybrid:8,data_bodi:8,data_vel:8,data_vel_hybrid:8,databas:[],datafil:[12,13,319],dataset:319,datatyp:3,date:[0,4,6,12,13,202,458,459,524],datom1:126,datom2:126,datom3:126,datom4:126,datum:[3,6,42,68,71,73,84,102,119,126,203,220],davenport:442,davi:352,david:[9,18,377,378,480,482],daw:[416,456],dcd:[3,6,7,203,205,206,207,208,300,498,502],dcs:9,ddim:202,deactiv:439,dealt:253,debug:[6,7,11,12,13,16,57,130,134,177,178,300,305,375,377,427,448,486,495,496,504,507,514,524],deby:[],decai:[100,409,489],decid:[3,6,12,15,75,268,306,317,348,512,519],decipher:380,deck:306,declar:205,declin:335,decod:206,decompos:[95,467],decomposit:[3,5,7,17,64,216,300,322],decoupl:[6,518],decreas:[3,15,203,204,213,214,221,228,231,237,238,244,246,254,346,377],decrement:323,dedic:[15,393],deepli:374,deeppink:207,deepskyblu:207,def:[12,13,495],defaul:63,defect:[6,74,176,446],defgrad:[],defin:[2,3,5,6,7,8,11,12,16,19,20,21,22,23,24,25,26,27,28,29,30,31,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,51,53,54,55,56,57,58,59,60,61,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,184,185,186,187,188,189,190,192,193,195,196,197,198,199,200,201,202,203,204,205,206,207,210,211,212,213,214,215,216,217,218,219,220,222,223,224,225,226,227,228,229,231,232,235,236,237,240,242,244,245,246,249,252,253,254,255,256,257,266,268,270,271,272,273,274,275,276,277,278,279,281,282,283,286,289,290,291,292,294,295,296,298,299,300,302,303,304,306,308,310,315,317,318,319,320,321,324,328,332,333,335,337,338,339,340,341,343,344,345,347,349,350,352,353,354,355,356,357,358,360,361,362,363,364,365,366,367,370,371,372,373,375,377,378,380,383,385,386,387,388,389,390,391,392,393,395,396,397,398,400,401,402,403,404,405,406,407,409,410,412,413,414,415,417,418,420,421,422,423,424,425,426,427,429,431,432,433,434,435,436,437,438,439,440,441,442,446,447,448,449,450,451,452,453,455,456,458,459,460,461,462,463,464,465,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,494,495,497,498,500,501,502,503,506,507,508,509,511,512,514,515,518,520,521,522,523,524,525,526],definit:[2,3,6,8,12,13,82,85,127,152,207,219,220,221,222,223,231,240,252,276,319,337,349,352,355,357,359,371,375,387,396,399,407,414,418,429,456,463,465,467,484,495,497,499,506,508,523,524],defint:515,deform:[],deg2theta:177,deg:518,degener:[3,302],degrad:[8,17,299,378,506],degre:[3,6,8,19,20,23,27,28,31,34,35,37,68,84,100,102,104,106,107,109,111,112,117,123,155,156,157,158,159,160,161,162,163,164,165,166,167,168,170,171,177,178,184,185,188,189,196,197,199,202,206,219,228,235,246,248,249,254,255,260,271,272,276,277,278,279,291,292,294,296,300,302,316,317,321,338,339,340,345,361,363,367,370,372,386,412,416,425,507,515,518,525],degrees:199,degress:[158,219],del:511,delai:[3,6,12,389,415,515],deleg:426,delet:[2,3,7,8,12,54,59,62,65,176,181,182,210,219,220,222,223,226,228,243,246,271,319,338,339,358,360,376,387,389,392,448,475,497,498,499,500,508,509,514,519,520,522,524,525],delete_atom:[],delete_bond:[],delete_el:216,deli:202,delimit:[318,495,524],dellago:[6,238],deloc:[272,418,466],delr:443,delt_lo:511,delta:[],delta_1:399,delta_3:399,delta_7:399,delta_conf:3,delta_ij:[443,456],delta_pi:399,delta_r:456,delta_sigma:399,deltah_f:242,delx:202,delz:202,demand:312,demo:11,demon:297,demonstr:[307,443],den:303,dendrim:425,dendtrit:382,denniston:[9,257,259,260,261,299],denomin:[7,183],denot:[130,235,238,255,299,310,312,409,424,426,459,463,465],dens:[75,228,318,418],densiti:[3,6,7,9,17,40,41,61,110,127,138,152,164,176,178,211,212,216,219,225,231,244,257,260,264,265,299,303,304,306,308,347,352,380,383,387,394,395,399,416,443,444,445,450,451,456,460,466,470,472,473,474,497,506,507,515,523],density_continuity:465,density_summation:465,dent:382,depart:[0,7],departur:[269,307],deped:8,depend:[1,2,3,6,8,9,11,12,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,34,37,39,40,41,43,44,45,46,47,48,49,51,53,54,56,63,65,68,71,73,74,75,84,100,102,113,119,123,124,125,126,129,131,152,154,155,161,165,166,172,178,179,184,185,186,187,188,189,190,192,193,195,196,198,199,202,203,204,206,207,210,211,212,213,214,217,219,221,222,223,224,225,227,229,231,237,239,242,245,248,249,250,252,254,255,257,259,260,268,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,298,309,310,312,314,317,318,320,321,322,328,335,338,339,340,342,344,346,347,349,352,355,356,357,358,360,361,362,363,364,366,367,370,372,378,380,386,387,389,390,391,393,394,395,397,398,399,400,401,402,403,404,405,406,407,408,409,410,412,413,416,417,418,419,420,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,443,444,446,447,449,450,451,452,453,455,456,458,459,460,461,466,467,468,476,477,478,480,481,482,483,485,487,488,489,492,494,497,499,500,503,507,509,511,514,515,517,524,525],dependend:6,depflags:12,dephas:[492,511],depos:232,deposit:[],deprec:[3,308,458],deprect:219,depth:[51,156,206,347,420,422,460],dequidt:9,der:[95,118,407,408,439,458,459,488],deriv:[6,7,8,9,37,56,65,95,152,172,199,220,229,231,238,246,254,268,271,273,274,275,276,277,278,279,298,304,308,312,344,345,347,352,353,356,385,387,395,399,407,412,418,419,424,433,437,438,443,446,458,459,476,478,479,488,518],derjagin:488,derlet:298,descend:207,descent:[7,385],descib:[40,206,308],describ:[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,37,39,40,41,42,56,64,65,71,74,75,77,96,121,124,127,130,142,152,153,156,157,158,162,163,166,169,171,172,176,177,178,180,181,190,195,199,203,204,205,210,211,212,219,220,221,222,223,225,228,229,230,231,232,234,235,241,242,247,248,251,252,253,254,255,256,257,259,260,261,266,270,271,272,276,284,295,298,300,305,306,307,308,309,310,317,318,323,331,335,336,337,338,339,340,341,342,343,344,345,350,352,353,355,360,377,378,380,384,385,386,387,388,392,395,396,398,400,401,402,404,405,406,407,408,409,412,416,418,419,420,422,423,424,426,429,431,432,433,434,435,436,437,438,439,440,441,442,443,446,447,450,451,455,456,457,458,459,460,461,466,467,468,475,476,477,478,479,480,481,482,483,485,487,488,489,490,492,494,495,497,498,499,500,501,507,510,511,514,519,524,525,527],descript:[],descriptor:[152,203,427],deserno:378,design:[0,3,6,7,8,11,13,14,16,130,160,163,177,216,228,234,235,271,272,298,299,319,342,347,396,397,398,401,404,409,411,418,439,440,441,444,445,456,459,478],desir:[2,3,6,7,9,11,12,14,15,32,40,50,61,75,96,99,123,128,153,160,178,191,202,219,223,229,231,244,246,247,254,255,256,260,271,294,302,303,304,305,308,312,317,320,321,335,338,339,340,341,346,353,368,374,377,378,380,384,386,387,388,413,416,425,440,441,477,480,482,493,494,495,497,501,506,511,512,514,515,516,524,525,527],desk:7,desktop:[4,6,7,10,12,206],despit:518,destabil:399,destre:370,destroi:[9,11,39,226,227],detail:[1,2,3,4,6,7,8,9,11,12,13,14,15,16,17,18,21,36,40,41,42,55,65,69,70,71,75,79,82,86,98,99,103,114,116,118,120,121,122,123,125,128,129,131,152,153,155,156,157,158,161,171,172,173,175,178,179,182,183,186,198,202,203,204,206,207,210,211,212,216,219,220,221,222,223,225,227,228,229,230,231,232,238,244,246,247,248,249,251,252,254,256,257,261,268,269,270,271,272,273,274,275,276,277,278,279,283,285,291,292,294,295,296,299,302,303,304,306,307,309,310,311,317,321,322,335,338,339,340,341,342,343,345,346,347,348,349,350,351,358,360,362,371,377,378,381,386,387,389,390,393,394,395,396,398,399,401,403,404,405,406,407,408,409,412,413,418,419,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,441,442,443,447,448,450,451,455,458,459,460,466,467,468,476,484,487,488,490,495,497,498,499,500,502,503,506,507,509,512,515,516,519,524,525,529],detect:[2,3,12,42,63,65,94,100,245,303,346,388,408,425,430,492,494,497,508,511,524],determin:[1,3,6,8,9,12,14,15,39,40,42,51,59,60,61,63,64,71,75,95,112,118,123,129,130,131,139,153,166,167,176,177,178,202,203,204,206,207,208,209,213,214,215,218,219,220,221,222,223,224,225,229,231,232,235,237,242,246,249,250,252,254,255,260,266,268,269,271,277,278,279,291,292,294,296,298,300,303,304,307,314,315,316,317,318,319,320,324,326,328,335,338,339,340,342,348,349,352,353,354,355,356,357,358,371,377,378,380,387,389,390,393,395,396,403,408,412,415,416,420,421,423,426,427,435,443,446,448,451,459,460,466,475,478,479,483,488,494,497,498,500,502,504,507,511,513,514,516,523,524,525],determinist:322,detil:119,devan:[9,461],devanathan:482,develop:[0,3,5,6,7,8,9,11,12,14,15,16,17,18,42,251,276,302,307,308,311,395,399,418,445,446,466,485,499],devemi:9,deviat:[269,276,298,421],deviator:9,devic:[1,3,12,14,16,251,393],device_typ:393,devin:[309,408],devis:445,dfactor:206,dff:518,dfft_fftw2:12,dfft_fftw3:12,dfft_fftw:12,dfft_none:12,dfft_single:[3,12,378],dfft_xxx:12,dfftw:12,dfftw_size:12,dft:[9,311,446],dhi:[61,202,231,303],dhug:[269,307],dhugoniot:[269,307],dia:443,diag:318,diagnost:[],diagon:[3,6,91,129,152,153,154,229,271,304,317,350,463,465],diagonalstyl:467,diagram:[41,130,177,198,225,300],diallo:425,diam:[206,207,303,387],diamet:[3,6,40,42,124,178,203,204,206,207,211,212,254,303,306,317,335,337,351,353,387,407,422,423,433,460,484,488,497,498,507],diamond:[380,418,443],diamter:[40,303],dick:6,dicsuss:268,dictat:[217,269],did:[3,12,386,413,415,416,423,448,480,482,505],didn:3,die:17,diel:[],dielectr:[],diff:[3,6,12,15,174,349,377],differ:[1,2,3,4,6,7,8,9,11,12,14,15,16,17,21,36,37,39,41,42,54,55,56,63,66,71,74,75,95,100,104,106,107,132,152,154,155,156,157,158,159,161,164,165,166,167,168,170,171,172,178,179,181,186,198,199,202,203,206,207,210,212,215,217,219,222,225,226,227,228,229,230,231,235,238,241,245,246,247,248,249,250,251,254,255,257,268,271,272,273,274,275,277,278,279,281,283,286,289,290,291,292,296,298,300,302,304,307,308,309,312,315,317,321,323,331,333,335,338,339,340,343,344,345,347,350,351,352,353,356,360,361,371,374,376,377,378,380,381,384,385,387,388,390,391,392,393,394,395,399,403,404,406,407,408,413,414,416,418,420,422,423,424,426,429,431,432,434,435,442,443,444,445,447,448,449,450,451,452,456,458,459,460,461,462,463,465,466,467,468,476,477,478,479,480,482,484,485,488,491,492,494,495,497,499,500,501,502,505,506,507,509,511,512,514,515,516,518,523,524,525,527],differenti:[1,3,6,28,199,318,377,409,456,481],difficult:[229,300,393,425,506,516],difficulti:[100,117,321,458],diffract:[7,9,130,177,319],diffus:[],diffuse:[4,6],digit:[2,3,207,360,446,524],dih_table1:199,dih_table2:199,dihedr:[],dihedral_coeff:[],dihedral_cosine_shift_exp:26,dihedral_styl:[],dihedralcoeff:[3,200],dihedraltyp:227,dihydrid:418,dij:321,dilat:[],dim1:3,dim2:3,dim:[3,61,75,155,159,160,161,164,165,166,167,168,170,178,202,231,252,353,380,443,500,523,524,525],dimdim:524,dimems:299,dimens:[],dimensinon:75,dimension:[3,39,100,123,130,152,155,158,159,160,161,164,165,166,167,168,170,177,201,219,270,299,347,380,384,388,456,497,507],dimensionless:[115,133,134,136,139,141,143,148,152,347,378,467,488],dimensions:319,diment:3,dimentionless:147,dimer:[6,317,443],dimgrai:207,dimstr:[41,225],dinola:[304,338],dintel_offload_noaffinity:15,dipol:[],dipolar:[4,28,40,203,204,337,518],dir1:508,dir2:508,dir:[1,3,4,8,10,11,12,269,298,307,334,456,458,459,495,508,524],dirac:152,direc:456,direct:[],directli:[3,6,8,9,11,12,95,124,129,152,154,203,204,205,206,213,237,248,252,257,299,318,319,339,351,353,354,355,356,380,385,393,394,395,400,402,403,409,412,416,418,431,435,448,453,461,475,495,507,508,509,515,524],directori:[],disabl:[3,12,15,347,393,430,495,510,524],disadvantag:[6,225],disallow:[203,231,271],disappear:499,discard:[2,3,41,75,221,225,348,356,494,499,500],discontinu:[199,386,437],discourag:443,discov:[13,348],discret:[6,8,40,42,206,207,254,257,293],discuss:[],disk:[6,92,93,100,171,201,232,246,303,495],disloc:[74,446],disord:[39,74,446],disp:[],dispar:460,disperion:[412,435],dispers:[3,6,7,9,176,299,377,378,403,412,435,440,448,459,478,485],displac:[],displace_atom:[],displace_box:61,displacemet:500,displai:[11,13,21,36,44,55,186,198,203,206,362,371,406,476],dispters:3,disregard:446,dissip:[6,9,40,88,89,240,247,254,299,322,344,345,401,413,414,423,440,441,476],dissolut:226,dist:[6,73,99,119,128,203,300,316,415,475,492,525],distanc:[],distinct:[6,235,314,377,460],distinguish:[6,94,152,260,418,496,524],distort:[176,394],distrbut:394,distribut:[],distro:[122,406,455,456],ditto:[8,12,14,16,17,42,126,227,317,489,495],div:[8,15],divd:128,diverg:[3,12,39,317,345,395,499,518,527],divid:[3,6,15,41,99,123,128,138,140,153,175,176,186,198,207,219,220,222,225,231,298,343,350,355,377,386,388,419,459,466,485,506,514,524],divis:[6,197,257,399,429,439,494,515,524],dl_poly:[6,7],dlambda:172,dlammps_async_imd:251,dlammps_bigbig:[12,39],dlammps_ffmpeg:[3,12,206],dlammps_gzip:[3,12,203,206,346,497,498,502],dlammps_jpeg:[3,12,206],dlammps_longlong_to_long:12,dlammps_memalign:[9,12,15],dlammps_png:[3,12,206],dlammps_smallbig:12,dlammps_smallsmall:12,dlammps_xdr:[12,203],dlen:507,dlmp_intel_offload:[9,15],dlo:[61,202,231,303],dlopen:6,dlvo:[7,407,488],dm_lb:257,dmax:[335,384],dmpvtk:204,dmpvtp:204,dna:7,doc:[0,1,2,3,4,6,7,8,9,10,11,12,13,14,16,17,21,36,40,42,55,59,61,65,69,71,79,86,95,98,103,114,115,116,118,120,121,122,123,125,128,129,131,153,156,157,158,171,173,175,178,179,180,186,198,202,203,204,205,206,207,208,210,211,212,217,218,219,220,221,222,223,232,246,254,255,266,271,272,277,278,279,283,291,292,294,295,296,303,306,317,331,335,338,339,340,349,353,356,358,360,362,371,376,386,387,388,393,394,395,398,406,408,409,416,417,419,425,426,428,429,443,444,445,448,452,455,456,457,467,476,477,480,482,484,485,495,497,498,499,500,502,505,506,507,514,515,519,524,525,527,528],docuement:460,dodd:42,dodgerblu:207,doe:[0,1,2,3,5,6,7,8,9,11,12,14,16,17,32,37,39,41,42,50,54,56,61,64,65,70,74,75,76,95,96,99,114,118,121,127,128,130,154,156,158,160,161,166,168,172,177,178,179,180,182,184,186,191,198,199,202,203,204,205,206,207,210,216,217,219,224,225,227,228,229,231,235,237,238,241,242,243,246,247,250,252,254,255,257,260,267,271,272,273,274,275,277,278,279,291,292,293,294,295,296,304,305,306,310,312,315,317,335,338,340,342,343,347,350,351,352,355,356,357,358,363,364,365,367,368,370,376,377,378,379,380,387,388,389,394,395,396,397,398,399,401,403,404,405,407,408,409,410,412,413,415,416,417,418,420,421,422,423,424,426,427,428,429,430,433,434,436,437,438,440,441,443,444,445,446,448,450,451,456,457,458,459,460,462,463,464,465,466,467,468,469,470,471,472,473,474,475,477,478,479,480,481,482,483,484,485,487,488,489,490,492,493,494,495,497,498,499,500,501,504,505,507,508,509,510,511,514,515,518,519,524,529],doegenomestolif:7,doesn:[3,7,8,12,178,203,204,217,331,387,389,393,395,408,417,428,458,459,477,480,481,482,485,497,499],dof:[3,8,123,156,157,158,171,219,317,525],dof_per_atom:[158,219],dof_per_chunk:[158,219],doff:[387,497],doi:[6,230,298],domain:[3,6,7,12,13,17,39,41,42,60,63,64,75,130,167,177,180,202,205,206,207,210,217,225,229,231,232,250,253,257,271,272,300,312,317,322,347,352,353,377,378,388,393,415,448,492,494,497,501,514],domin:[1,418,511],don:[0,8,9,12,13,15,127,181,213,237,255,356,443,466,495,497,519],donadio:339,done:[1,3,6,7,8,12,14,16,17,37,39,41,56,61,64,75,172,175,178,181,197,199,203,206,207,216,217,219,221,222,223,225,226,227,228,229,231,232,241,242,244,246,251,252,254,255,262,271,277,278,279,291,292,294,296,297,299,300,301,303,306,314,317,319,321,335,338,339,340,342,344,345,358,360,376,377,378,386,388,389,392,393,395,403,416,426,427,428,435,441,442,443,448,450,451,458,475,478,479,484,492,493,494,495,498,501,502,505,515,516,518,519,524,525],donor:425,dot:[153,174,213,237,249,270],doti:[399,456],doubl:[1,2,3,6,8,9,11,12,14,15,16,39,95,184,231,244,305,356,360,376,378,392,393,399,419,424,446,458,459,493,497,501,505,510,524,525],dover:216,down:[3,6,7,8,11,39,75,229,246,254,335,351,418,448,466,496,516],downhil:[384,385],download:[5,7,8,9,11,12,13,16,251,427,457],downsid:6,downward:314,dozen:[8,9,12,118,210,458,459],dpack_array:12,dpack_memcpy:12,dpack_pointer:12,dpd:[],dpde:264,dpdtheta:[40,88,89,414],dpdtheta_i:[240,241,242],dproduct:396,dr_ewald:[130,319],drag:[],dragforc:257,drai:[206,269,307],drain:[250,351,386],dramat:[9,61,202,226,227,228,229,231,271,335,338,339,378,448,466,494],drautz:399,draw:206,drawback:306,drawn:[42,203,206,207,247,492],drayleigh:[269,307],dreid:[],dreiding:[],drfourth:115,drho:[124,394,416,450,451],drift:[6,113,115,238,247,248,250,254,255,267,315,335,506,514,518],drive:[11,12,214,229,231,249,271,298,304,317,354,388],driven:[6,190],driver:[6,12,14,210,244,251],drop:[3,207,413],droplet:426,drsquar:115,drude:[],drudes:[255,518],dry:243,dsecriptor:427,dsf:[],dsmc:[],dstyle:303,dt_collis:257,dt_lb:257,dt_md:257,dt_srd:335,dtilt:[61,231],dtneb:511,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518],especi:[8,15,166,178,210,217,225,246,307,312,315,316,393,494,516],espresso:[9,311],essenti:[11,12,26,96,140,159,160,161,164,165,166,167,168,170,187,220,299,351,378,395,409,431,482,502,515],essential:[8,276],essex:28,establish:[95,250],estim:[1,3,6,10,12,37,41,56,99,153,216,225,236,269,318,335,342,377,378,384,448,459,478,479,511,515],estimat:3,esu:523,esub:443,eta:[6,257,271,307,308,310,351,417,419,422,456,481,485,523],eta_dot:271,eta_ij:456,eta_ji:419,etag:[40,497],etail:515,etap:271,etap_dot:271,etc:[1,2,3,4,6,7,8,9,10,11,12,13,14,15,39,40,42,54,63,71,97,98,99,104,120,121,124,126,129,153,155,158,159,160,161,162,164,165,166,167,168,170,172,178,180,181,182,191,203,204,206,207,210,211,216,217,218,219,222,223,226,227,231,232,244,246,247,254,271,303,314,319,347,348,356,360,376,377,386,387,388,389,391,416,417,426,439,441,446,454,458,459,477,480,482,485,492,495,497,498,499,504,506,507,511,512,513,514,515,516,518,523,524,527,529],ethernet:17,etol:[386,388,492,511],etot0:307,etot:[6,104,106,107,121,153,164,207,235,255,269,307,514,515],eu2:177,eu3:177,euler:[318,386,388],eulerian:216,euqat:469,europhi:257,ev_tal:8,evalu:[2,3,9,11,12,37,56,75,95,96,99,118,128,131,152,154,158,168,176,178,184,202,203,204,206,207,211,212,213,214,216,218,219,220,221,222,223,224,231,237,247,249,250,252,253,254,255,299,305,308,318,320,324,328,338,339,340,349,352,355,357,358,360,384,386,446,448,450,451,456,462,464,466,478,479,492,493,495,499,500,502,504,505,506,507,511,513,515,524,525],evaluat:[377,378,524],evalut:[360,495],evan:[166,294],evanseck:[6,19,184,404,509],evapor:[],evaul:[8,386],evdwl:[118,154,458,459,515],even:[3,6,8,12,14,15,16,17,33,39,41,52,57,59,61,63,65,74,75,96,118,131,176,179,180,194,197,199,203,204,207,210,211,212,217,218,219,222,223,225,226,227,229,231,232,235,238,252,255,269,271,272,299,303,312,314,317,319,330,335,343,347,350,352,356,358,369,377,384,386,388,393,398,418,419,423,426,429,448,460,485,486,490,497,498,500,502,503,504,506,507,509,512,514,515,516,518,529],evenli:[3,41,57,153,199,225,257,429,486],event:[],eventu:[3,6,12,14,180,511],eventual:308,ever:[9,54,56,253,335],everaer:[407,422,460,476],everi:[0,1,2,3,6,8,9,11,12,14,15,39,41,75,76,99,124,131,140,166,181,203,204,205,206,207,208,210,211,212,213,216,217,218,219,220,221,222,223,224,225,226,227,228,229,231,232,236,238,243,244,246,248,250,251,252,257,258,267,271,272,276,287,293,297,298,299,303,304,305,306,307,308,309,310,312,314,315,317,318,319,321,323,332,335,337,338,339,340,341,342,343,346,347,348,349,350,358,360,376,378,388,389,390,393,413,415,426,439,458,459,466,472,491,492,493,497,499,501,502,504,505,506,511,512,513,515,516,519,524,529],everyth:[8,118,519],everywher:[127,433],eviri:418,evolut:[238,248,257,300,492],evolv:[257,300,348],ewald:[2,3,5,6,7,8,9,12,96,121,130,153,348,377,378,386,400,402,403,409,412,418,431,435,453,461,476,478,483],ewald_disp:412,ewalddisp:3,exact:[21,41,44,75,134,172,181,186,225,228,247,248,254,255,256,303,312,313,335,347,362,377,406,499,504,511,524,527,529],exactli:[3,6,12,16,37,41,42,56,61,75,76,99,127,156,162,169,178,199,211,212,222,225,231,236,241,247,254,255,256,272,284,285,295,299,306,307,335,340,341,354,393,406,413,416,423,426,440,448,450,451,478,479,499,500,507,511,524],exager:518,examin:[6,8,16,228,299,318],examp:[495,524],exampl:[],exce:[3,6,15,16,17,41,60,75,180,218,219,222,223,225,229,231,236,243,271,299,314,319,325,326,335,386,393,497,524],exceed:[3,41,61,225,231,271,335,459,505],excel:418,except:[1,2,5,6,8,10,11,15,19,20,21,22,23,24,25,26,27,28,29,30,31,34,36,37,40,41,43,44,45,46,47,48,49,51,53,54,55,56,61,62,65,75,97,98,119,123,128,153,155,156,157,158,159,160,161,162,164,165,166,167,168,169,170,171,178,182,184,185,186,187,188,189,190,192,193,195,196,198,199,202,203,204,207,210,213,219,220,222,224,225,229,231,239,245,246,249,252,254,256,271,272,273,274,275,276,277,278,279,280,281,284,285,289,291,292,294,295,296,300,309,310,317,320,321,331,335,341,347,355,358,359,360,361,362,363,364,366,367,370,371,372,377,378,380,383,387,388,389,391,392,393,394,395,397,400,401,402,403,404,405,406,407,408,409,411,412,413,416,417,418,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,441,444,449,452,453,455,458,459,460,461,468,476,477,478,480,481,482,483,485,487,488,489,492,494,495,497,499,500,502,505,506,507,508,509,511,515,518,523,524,525,528],exception:495,excess:[221,418],exchang:[2,3,6,8,63,64,210,216,217,238,246,250,254,309,317,343,347,350,377,393,418,512],exchange:377,excit:418,excite:418,exclud:[3,6,9,12,15,16,65,75,112,123,129,152,158,165,166,182,203,219,226,227,258,267,302,315,317,342,353,358,386,387,389,401,423,426,429,440,441,448,475,509],exclude:389,exclus:[1,3,12,15,95,184,408,446,448,506,516],excurs:[271,492],exectubl:12,execut:[1,2,3,4,6,8,9,11,12,16,62,179,206,251,311,360,376,379,392,493,495,505,508,511,524],exempl:466,exemplari:247,exemplifi:418,exert:[6,252,255,287,312,354,355,356,378],exhaust:[216,392,524],exhibit:[100,238,250,271,385,418,506],exist:[3,6,7,8,11,12,13,36,55,57,61,71,74,134,178,179,198,205,206,207,210,215,224,227,229,232,246,302,303,305,306,358,359,361,363,364,365,367,371,381,387,414,426,458,475,486,493,495,497,498,499,508,509,510,519,524,525,527],exit:[2,3,11,12,41,59,203,225,376,392,495,496,505,514,515,516,524],exlanatori:3,exp6:[],exp:[],expand:[],expans:[12,152,197,203,508,524],expect:[1,3,8,12,13,14,15,16,17,18,41,42,75,112,159,170,176,199,225,237,246,248,268,298,304,306,307,312,317,358,378,389,406,443,446,448,492,495,497,499,502,506,511,524],expens:[6,75,207,298,302,317,347,358,377,378,389,393,495],experi:[6,13,14,224,232,251,260,270,304,316,317,384,388,413,448,506,511],experienc:[6,12,15,259,260],experiment:[16,246,377,393,511],expert:12,expertis:7,expir:[515,516],explain:[1,3,6,8,9,11,12,17,41,61,65,68,71,73,75,76,77,80,81,84,94,102,129,158,166,199,203,204,206,207,210,219,220,223,225,227,229,231,271,287,298,306,317,331,358,360,376,377,380,387,388,392,398,416,429,466,468,484,495,498,499,502,504,507,518,519,524,529],explan:[3,6,61,124,129,152,203,204,219,270,298,426,491,494,495,497,506],explanatori:[3,8,128,203,204,218,219,222,317,387,494,524],explantori:[3,313],explic:447,explicit:[6,9,11,21,44,81,95,124,127,172,186,211,212,231,318,325,326,362,377,383,395,396,399,404,406,416,418,430,440,483,491,494,498,501,519],explicitli:[3,6,8,12,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,43,45,46,47,48,49,51,53,54,56,75,123,155,165,168,176,178,184,185,187,188,189,190,192,193,195,196,199,203,204,207,213,224,231,239,245,247,249,254,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,306,307,309,310,317,320,321,340,341,347,355,361,363,364,366,367,370,372,387,393,394,395,397,400,401,402,403,404,405,406,407,408,409,410,412,413,415,416,417,419,420,421,422,423,424,425,426,430,431,432,433,434,435,436,437,438,439,440,444,446,448,449,450,451,452,453,455,459,460,461,468,469,470,471,472,473,474,476,477,478,479,480,481,482,483,485,487,488,489,497,499,500,506,507,509,510,516,518,519],explictli:[15,510],exploit:[14,16,300],explor:[130,177],expon:[3,308,310,318,416,420,422,425,439,447,461],exponenti:[95,318,420,456,477,485,489,511,524],expos:11,exposit:[216,413,415],express:[6,152,164,178,211,212,229,268,298,308,318,347,353,360,399,416,418,433,443,466,467,476,524],expression:360,expressiont:399,extend:[],extens:[3,6,9,15,16,44,45,46,53,55,65,67,72,83,90,91,92,95,96,99,101,104,107,108,118,120,128,131,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,204,210,213,214,217,223,224,230,233,237,238,244,245,246,248,249,250,252,254,256,269,271,276,285,298,299,315,316,317,320,323,328,331,332,334,338,339,340,341,342,344,345,347,349,352,356,357,422,443,446,459,460,467,514,515,523],extensiv:[65,210,515],extent:[1,3,41,42,45,59,75,176,180,203,215,225,252,354,357,377,380,395,462,464,478,479,494,497,500],exterior:[3,6,176,356],extern:[],extra:[3,6,8,11,12,15,16,40,41,46,63,75,112,120,121,123,130,153,155,156,157,159,161,164,165,166,167,168,170,171,177,178,179,180,207,222,225,227,271,305,306,307,317,335,386,387,390,391,393,412,423,426,429,443,448,494,495,497,500,509,518,524],extract:[3,6,11,13,35,65,67,72,83,95,101,118,126,128,131,211,212,310,388,409,419,443,467,495,502,514],extract_atom:11,extract_comput:[11,495],extract_fix:11,extract_glob:11,extract_vari:11,extramake:[12,14],extrapol:1,extrem:[1,3,6,16,60,206,221,229,231,271,345,418,481,518],extrema:439,extreme:418,extrins:216,f77:[5,7,12],f90:[5,7,12],f_1:6,f_5:[174,349],f_a:[480,481,482],f_ave:128,f_c:481,f_f:482,f_fix_id:307,f_foo:524,f_harm:345,f_i:[28,456],f_id:[6,75,128,131,203,204,210,218,219,220,221,222,223,266,337,349,515,524],f_ij:456,f_indent:223,f_int:344,f_j:28,f_jj:99,f_k:456,f_langevin:347,f_max:[307,312],f_msst:269,f_r:[255,480,481,482],f_sigma:399,f_solid:345,f_ss:6,face:[3,6,59,61,75,166,176,180,215,352,354,355,356,357,380,422,443,460,497,500],face_threshold:176,facet:176,facil:[0,12],facilit:[6,13,42],fact:[6,8,15,248,303,335,345,423,458,466,509],factor:[1,3,6,12,17,23,27,31,34,35,39,41,46,47,59,60,61,76,95,99,112,119,126,127,130,152,172,177,180,184,195,202,203,206,207,211,212,220,225,229,231,232,238,246,251,254,256,257,269,271,272,276,300,304,308,316,318,321,324,326,335,339,343,350,351,352,356,367,378,380,387,393,395,396,399,400,402,404,409,410,411,413,414,418,423,426,430,431,443,446,448,450,451,452,453,459,461,468,477,483,494,497,500,501,506,509,511,512,515,518,523,524],factori:[3,495],factoriz:377,fail:[3,9,11,12,61,182,229,232,377,386,388,411,459,495],failur:[133,463,496,524],fairli:[11,448,506,511,519],faken:77,falcon:251,fall:[3,6,207,222,303,495,524],fals:[94,358,466,524],fame:8,famili:[485,494],familiar:[0,11,519],fan:456,far:[3,6,12,16,59,61,63,94,203,204,207,208,225,226,227,229,232,271,298,316,317,335,352,363,367,384,388,389,484,495,497,502,515],farago:254,farrel:[480,482],farther:203,fashion:[6,8,41,75,178,207,210,211,212,217,225,227,232,246,248,252,268,269,271,273,274,275,276,277,278,279,288,291,292,294,295,296,306,307,309,317,323,327,334,337,345,347,351,352,353,355,357,388,426,440,500,509,524,528],fasolino:428,fast:[6,7,9,12,13,15,16,39,203,204,282,307,318,348,377,378,401,440,441,446,476,478,479,499,504,506,515,525,529],faster:[1,6,9,11,12,14,15,16,17,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,41,43,45,46,47,48,49,51,53,54,56,63,65,115,123,155,165,184,185,187,188,189,190,192,193,195,196,199,203,204,207,213,224,225,231,239,245,249,253,254,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,304,308,309,310,317,320,321,335,342,344,347,351,355,361,363,364,366,367,370,372,377,378,390,391,393,394,395,397,399,400,401,402,403,404,405,406,407,408,409,412,413,416,417,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,444,449,452,453,455,459,460,461,468,476,477,478,480,481,482,483,485,487,488,489,492,500,506,510,518],fastest:[1,6,16,166,347,348,393,494],fatal:[3,514],fault:[74,459],faulti:12,fava:422,favor:228,favorit:7,fbmc:342,fcc:[],fcm:[288,524],fcold:238,fdirect:235,fdotr:427,fdt:[],fdti:95,fe2:177,fe3:177,fe_md_boundari:216,featu:8,featur:[],fecr:416,feedback:[7,251],feel:[7,251,252,260,298,356,358,388,448],fehlberg:318,felling:445,felt:356,femtosecond:523,fene:[],fennel:[409,431],fep:[],ferguson:[6,184,509],fermi:[1,9,10,12,14,164,393,482],fermion:418,fernando:442,ferrand:[9,13],few:[1,3,4,5,6,7,9,10,11,12,13,17,39,65,208,218,219,220,222,223,238,255,271,303,306,308,321,349,377,386,387,388,395,466,494,497,502,506,508,516,527],fewer:[1,3,11,14,15,63,260,506],fewest:3,fextern:244,feynman:300,fff:495,ffield:[408,419,458,459,466],fflag1:206,fflag2:206,ffmpeg:[3,12,206],ffplai:206,fft:[1,3,7,9,11,12,14,96,120,121,153,299,377,378,506],fft_inc:[12,378],fft_lib:12,fft_path:12,fftbench:[377,516],fftw2:12,fftw3:12,fftw:[9,12],fhg:[7,9],fhot:238,ficiti:475,fictiti:[6,213,214,237,244,248,300,316,409,431,435,475],field1:[498,502],field2:498,field:[],fifth:[6,331,420,452],figshar:298,figur:[1,3,8,12,15,307,494,495,519],fij:412,file0:298,file1:[11,13,242,298,346,360,387,502,504,508],file2:[11,13,242,346,360,387,502,504,508],file:[],file_from:205,filen:387,filenam:[3,12,13,15,37,41,56,199,203,204,206,207,208,216,219,220,221,222,223,225,230,241,242,298,302,305,308,309,310,313,314,317,318,319,346,347,374,375,376,387,388,394,395,399,409,416,417,419,420,428,443,444,445,450,451,452,456,457,458,459,466,467,477,478,479,480,481,482,485,493,494,495,498,499,504,508,515,524,527,528,529],filennam:504,filep:[3,203,204,207,499,504,529],filepo:314,fill:[7,9,178,206,303,347,380,389,399,446,459,500,519],filter:[207,216],final_integr:8,final_integrate_respa:8,finchham:[6,160,411],find:[0,3,4,6,7,8,9,11,12,13,15,37,39,56,63,75,77,95,128,181,199,208,217,228,229,241,242,243,246,270,298,303,304,312,316,384,386,388,389,409,426,431,435,443,450,451,466,476,478,479,518,519,524],find_custom:8,fine:[15,16,182,213,237,345,389,393,519,524],finer:[152,178,524],finest:377,finger:[178,202,268,500],finish:[6,11,41,225,360,374,376,377,390,392,393,484,502,524,525],finit:[],finni:[7,416,476],finverse:235,fiorin:[9,230],fire:[],firebrick:207,first:[0,1,2,3,5,6,8,9,10,11,12,14,15,16,20,37,39,41,42,45,46,54,56,59,61,63,64,75,87,96,99,113,114,115,117,123,127,128,139,142,145,146,150,153,163,166,172,174,176,177,179,180,181,185,199,203,204,205,206,207,208,210,211,219,220,222,223,225,228,231,241,242,246,247,252,257,268,269,270,271,298,300,305,306,307,309,314,317,321,323,331,332,333,335,336,337,344,345,346,347,349,358,360,361,368,380,386,387,388,389,392,393,394,395,398,399,400,402,404,406,408,409,416,418,419,420,423,424,426,427,428,429,430,431,435,440,441,443,445,446,448,450,451,452,456,458,459,466,467,475,477,478,479,480,481,482,485,489,492,493,494,495,497,498,499,502,504,506,509,510,511,514,515,518,519,524,525,526,527,529],fischer:[6,9,18,19,184,404,509],fit:[3,6,9,12,37,56,199,241,316,335,395,399,428,443,448,450,451,471,478,479,481,505,519,524],five:[77,164,307,387,399,444,497,511],fix:[],fix_deposit:3,fix_flux:216,fix_heat:238,fix_id:[3,229,269,271,273,274,275,276,277,278,279,304,307],fix_manifoldforc:519,fix_modifi:[],fix_nh:8,fix_nv:519,fix_poem:3,fix_pour:3,fix_qeq:3,fix_rattl:321,fix_reax_bond:458,fix_saed_vtk:319,fix_setforc:8,fix_shak:321,fix_srd:3,fixedpoint:[229,271],fixextern:244,fixid:[206,216],fji:412,flag1:[234,391],flag2:[234,391],flag:[3,8,9,11,12,14,15,16,38,40,58,69,78,79,86,87,94,97,98,103,113,114,116,129,130,173,177,181,200,203,204,206,207,208,223,228,230,234,251,254,258,260,267,268,299,306,317,331,334,335,342,346,355,358,373,375,378,387,391,392,393,395,425,430,443,446,475,490,492,494,495,497,498,499,501,502,503,507,519,524],flag_buck:403,flag_coul:[403,412,435],flag_lj:[412,435],flagfld:[401,440,441],flaghi:[3,401,440,441],flaglog:[401,440,441],flagn:234,flagvf:[401,440,441],flat:[6,15,347,352,353,357],flavor:[2,7,12],fld:[352,440,441],flen:396,flex_press:396,flexibl:[3,6,8,179,206,219,230,248,272,343,350,418,481,515],flip:[3,6,231,271,354,355],floor:524,flop:12,floralwhit:207,flow:[],fluctuat:[6,66,95,229,246,247,254,257,271,276,298,299,304,322,345,347,370,414],fluid:[],fluid_veloc:261,flush:[3,207,514],flux:[],flv:206,fly:[7,9,12,41,206,210,216,221,232,235,317,321,348,399,446,515,518],fmackai:9,fmag:233,fmass:300,fmax:[386,515],fmomentum:235,fmsec:[2,207,254,255,268,271,304,317,338,339,506,517,523,525],fname:376,fno:[9,15],fnorm:[386,515],fnpt:235,fnve:238,fnvt:235,foce:426,fock:396,focu:321,fogarti:[9,310,459],foil:[152,298,467],fold:[15,333,506],folk:7,follow:[0,1,2,3,6,7,8,9,10,11,12,13,14,15,16,17,18,19,22,23,24,25,26,27,28,29,30,31,34,35,37,40,41,42,43,45,46,47,48,49,51,53,54,56,61,65,66,74,75,77,88,99,100,106,107,117,124,127,128,131,152,153,156,157,158,164,166,171,174,176,178,179,184,187,188,189,190,192,193,195,196,197,199,203,205,206,207,210,216,217,218,219,220,221,222,223,225,230,231,232,235,236,240,241,242,244,246,247,248,251,253,254,255,257,260,269,271,276,277,278,279,291,292,294,296,299,300,302,305,306,307,308,310,312,314,316,317,318,319,321,337,338,339,340,343,344,345,346,347,349,350,358,359,363,364,365,366,367,370,372,375,380,383,386,387,388,393,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,410,411,412,413,414,415,416,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,447,448,449,450,451,452,453,455,456,457,458,459,460,461,463,465,466,467,468,469,470,471,472,473,474,475,477,478,479,480,481,482,483,484,485,487,488,489,490,492,494,495,497,498,500,502,504,505,506,509,511,512,513,518,519,524,525,528],foo:[4,8,11,12,203,206,244,318,495,508,524],foo_species:318,foot:6,footprint:[12,393],fopenmp:[9,17],forc:[],force_uvm:16,forceatom:260,forcefield:[316,425],forcegroup:257,forcezero:384,ford:412,forestgreen:207,forev:75,forget:[255,518],forgiv:271,fork:[],form:[2,3,6,8,12,18,21,44,54,57,65,69,78,79,81,86,87,95,97,98,100,103,113,114,116,127,152,153,172,173,182,186,207,210,211,212,227,247,248,254,256,260,268,294,299,310,312,316,317,318,347,352,356,361,362,365,370,383,385,387,388,395,396,399,406,414,416,418,421,425,426,430,442,443,445,446,450,451,452,453,456,458,459,460,466,467,468,476,478,480,481,482,488,491,494,495,497,502,507,514,518,524],formal:[6,82,85,99,247,248,254,271,300,335,343,466],format:[2,3,6,7,8,9,12,13,21,37,41,44,56,57,71,81,186,199,203,204,205,206,207,208,219,222,223,225,227,241,242,299,302,306,308,310,313,317,318,319,330,346,347,358,359,362,383,387,388,394,395,399,406,416,419,420,430,443,445,450,451,457,458,459,461,467,478,479,485,486,494,495,497,498,499,502,513,514,515,524,526,527],formation:242,former:[6,12,39,41,207,225,347,351,399,401,503,509,524],formerli:[7,13],formul:[1,6,9,40,66,153,213,237,254,271,294,308,310,316,321,346,377,395,399,416,418,422,443,455],formula:[2,3,6,7,13,20,21,36,44,54,55,74,77,95,97,98,99,104,106,107,116,123,130,131,153,155,156,157,158,159,160,161,162,164,165,166,167,168,169,170,171,177,178,183,185,186,198,202,203,211,212,213,214,219,220,221,222,223,224,231,237,249,250,252,254,255,268,298,299,305,314,320,328,333,335,338,339,340,349,352,353,355,357,358,360,361,362,364,371,380,387,395,396,398,399,404,405,406,407,412,413,416,417,422,423,424,425,427,430,431,433,434,435,437,438,440,441,443,447,448,449,460,461,468,476,477,478,480,481,482,485,487,488,493,497,500,507,514,515,523,524,525],forth:[1,6,11,12,13,14,392,495,500,504],fortran:[3,6,9,11,12,13,244,416,426,443,458,459],fortun:8,forward:[3,8,95,376,388,393],foster:[399,455,456],foul:181,found:[3,6,9,12,77,172,203,228,230,238,246,251,257,263,299,342,348,360,376,389,406,409,412,492,498,499,514],four:[6,11,54,87,113,114,152,174,269,347,370,387,388,446,492],fourier:[],fourth:[6,15,115,316,331,342,404,420,452,466],fox:[6,130,184,474,509],fphi:[37,56,478,479],fpic:12,fplo:[37,56,478,479],fprime:[478,479],fqdn:253,fqq:412,fraction:[1,3,6,8,12,15,39,41,85,120,153,181,202,206,207,217,226,227,228,229,269,303,307,314,315,318,335,340,341,380,388,393,399,401,420,423,440,441,502,507],fragment:[42,251,314],fraig:42,frame:[91,152,207,216,269,307,354,422],framer:[206,207],framework:[5,248,394,467],franc:9,frattl:238,fraunhof:9,free:[5,6,7,9,13,28,62,65,74,95,172,211,212,298,335,344,345,346,347,385,388,396,418,439,446,449,456,488,494],freedom:[3,6,8,104,106,107,109,111,112,123,155,156,157,158,159,160,161,162,163,164,165,166,167,168,170,171,219,228,235,246,248,254,255,260,271,272,276,277,278,279,291,292,294,296,300,302,317,321,338,339,340,345,386,412,515,518,525],freeli:[0,6,7,12,156,157,171,176,206],freez:[],frenkel:[6,238,246,345],freq:215,frequenc:[3,6,15,39,113,207,221,285,299,300,307,312,318,375,413,418,459,466,492,506,511,524,528],frequent:[3,66,70,74,76,77,81,96,100,117,152,207,226,227,238,243,343,350,448,483,504,516],fri:[269,307],friction:[4,5,6,9,10,42,210,248,254,307,312,317,347,351,353,423,507],friedrich:324,from:[],front:[269,307,354],frontend:[206,311],frozen:[6,123,182,245,247,255,389,421],fs2:[6,99],fscale:251,fstr:524,fstring:495,fstyle:353,ftol:[386,388,492,511],fuchsia:207,fuction:409,fudg:321,fugac:246,fugacity_coeff:246,fulfil:6,full:[1,2,3,6,9,12,15,16,37,39,40,99,206,220,221,230,238,257,298,318,377,378,393,399,416,418,419,422,459,48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,357,358,360,377,381,385,386,388,395,396,412,413,415,446,450,451,457,458,459,466,478,479,492,494,495,497,499,500,502,504,505,507,511,512,515,518,524,525,527,529],initial:[2,3,6,87,113,180,524],initial_integr:8,initial_integrate_respa:8,initialis:457,initialt:347,inlclud:11,inlin:495,inner2:[404,424],inner:[3,8,15,57,203,252,360,376,384,385,386,388,392,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,410,412,413,415,416,417,418,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,453,455,457,458,459,460,461,466,467,468,469,470,471,472,473,474,475,477,478,479,480,481,482,484,485,486,487,488,489,490,506,511,524],inner_distance_cutoff:425,innergroup:260,innermost:[37,56,75,391,478,479,506],innernod:260,innner:437,inordin:348,inorgan:[6,485],inp:[230,360,467,485],input1:[68,71,73,84,102,119,124,125,126,128,129,131,337],input2:[68,71,73,84,102,119,124,125,126,128,129,131,337],input:[],input_doubl:3,inquir:324,insensit:12,insert:[3,5,7,8,9,12,61,178,210,232,246,252,303,377,467,475,495,501,518],insertion:[3,232,246,303],insid:[2,3,6,8,11,75,129,141,147,178,203,204,207,218,232,233,238,243,246,252,257,260,303,317,335,352,354,355,356,357,358,375,380,495,496,497,499,500,507,524],inside:[257,433,495,511],insight:[6,13],instabl:[257,412,465],instal:[1,3,6,8,9,11,12,14,15,16,176,203,205,206,208,378,388,403,404,427,431,435,439,446,492,498,499,504,512,519,529],install:[],instanc:[6,9,11,211,230,248,354,421,426,448,456,495,518],instantan:[6,65,228,229,247,248,271,276,299,304,307,312,314,317,342,503,515],instanti:[6,11,12,216,426,494],instead:[1,3,6,8,11,12,13,15,16,17,40,41,61,63,65,74,75,98,113,128,156,160,182,184,199,203,204,212,219,222,223,225,229,230,246,254,257,260,261,299,305,306,315,317,337,355,375,377,378,381,389,393,402,403,416,430,432,439,443,446,449,459,476,483,492,500,504,511,513,518,524],institut:[9,251,302],instruct:[3,4,6,8,9,10,11,12,13,15,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,43,45,46,47,48,49,51,53,54,56,123,155,165,176,184,185,187,188,189,190,192,193,195,196,199,201,206,213,224,231,239,245,249,254,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,309,310,317,320,321,355,361,363,364,366,367,370,372,378,393,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,444,449,452,453,455,459,460,461,468,477,478,480,481,482,483,485,487,488,489,500,506,518],instruction:11,insuffici:[3,6,12],insult:271,insur:[3,6,11,12,16,39,40,63,77,112,114,178,179,199,203,204,206,207,213,226,227,232,237,239,243,244,246,249,254,267,305,306,315,317,335,347,352,356,357,358,360,376,387,389,393,407,422,426,454,460,478,490,494,495,497,498,502,505,506,514,515,524,525],insure:16,int_max:3,inteatom:10,integ:[3,6,8,11,12,39,40,42,66,71,74,75,117,124,126,128,131,152,176,178,181,182,184,188,189,193,197,199,202,203,206,207,217,219,226,227,228,232,234,244,246,247,248,251,254,255,256,257,299,302,303,306,307,312,317,318,335,337,339,342,346,347,366,377,380,401,413,414,415,429,443,458,459,463,465,467,492,494,495,496,497,505,506,507,511,514,524,525],integer:[3,497],integr:[],integral:[300,450,451],integrate_ulsph:[],intel:[],intel_coprocessor:15,intel_cpu:12,intel_cpu_intelmpi:15,intel_cpu_mpich:15,intel_cpu_openpmi:15,intel_phi:12,intend:[3,6,8,12,13,35,221,247,457,497],intens:[1,3,6,9,65,69,78,79,86,94,97,98,99,103,113,114,115,116,123,125,127,128,130,131,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,173,174,175,177,210,219,220,222,223,225,226,227,228,236,243,250,260,269,271,276,314,317,319,335,343,347,349,350,514,515,523],intensiv:[65,210,515],intepol:524,inter:[17,42,63,64,158,181,182,203,228,254,256,270,309,317,377,388,399,507,518,524,527,529],interact:[1,3,6,7,8,9,10,11,12,14,16,21,28,32,33,36,39,40,42,44,50,52,54,55,57,59,63,65,68,73,76,81,84,95,96,100,102,117,118,119,121,123,126,127,134,135,136,137,139,140,141,142,143,144,145,147,148,149,150,152,153,154,156,157,171,172,176,179,180,181,182,183,184,186,190,191,194,197,198,203,204,210,211,212,226,227,228,245,246,251,252,254,256,260,285,298,300,302,303,308,310,316,317,318,325,326,335,336,342,347,351,352,353,356,357,362,363,364,365,367,369,371,377,378,386,387,388,389,390,391,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,451,453,454,455,456,459,460,461,463,465,466,467,468,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,492,495,497,499,501,502,506,507,509,514,515,518,527],interatom:[3,4,7,9,10,178,203,270,344,345,394,399,416,418,427,443,446,466,481,524],intercept:130,interchang:6,interconnect:17,interconvert:418,intereract:39,interesect:356,interest:[1,5,7,8,9,11,13,75,177,300,342,345,378,417,441,458,459,495,524],interf:[393,519],interfac:[],interfer:[12,271,395],interg:[6,518],intergr:506,interi:441,interior:[3,6,41,356,500],interlac:443,interleav:[6,178,505],intermedi:[6,12,61,206,270,298,370,388,495,496,505,509],intermix:492,intermolecular:[154,395],intern:[],internal:[3,206,212,303,524],internal_element_set:216,internal_quadratur:216,internat:[15,130,177,216],internet:253,interpenetr:443,interpentr:[470,471,473],interpol:[6,14,37,56,110,199,206,207,216,241,242,257,298,377,378,388,399,448,450,451,459,472,478,479,480],interpret:[2,6,11,197,206,222,423,468,492,495,511,524],interrupt:307,intersect:[3,6,130,207,356,358,500],intersert:356,interspers:386,interstiti:[176,446],intertia:[3,103],interv:[3,6,99,205,220,254,271,307,312,313,326,466,472,492,511,524],intestieti:130,intial:[6,393,395],intiial:[41,502],intiti:[3,334],intra:317,intra_energi:246,intramolecular:[28,154,246],introduc:[6,9,100,117,206,238,271,307,312,317,370,377,394,409,418,431,435,439,477,511,524],introduct:[],intuit:380,inv:[130,177,319],invalid:[3,12,75,97,181,285,388,440,441,499],invari:[117,145,150,152],invent:321,invers:[],inverse:518,invert:[1,6,182,299],invis:356,invoc:[176,228,393,463,465,495],invok:[1,3,6,7,8,9,11,12,13,15,16,17,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,41,43,45,46,47,48,49,51,53,54,56,57,61,65,69,75,78,79,86,87,95,96,97,98,103,113,114,116,121,122,123,128,131,155,165,172,173,176,178,179,181,182,184,185,187,188,189,190,192,193,195,196,199,203,204,206,207,208,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,231,232,233,234,236,237,238,239,243,244,245,246,247,249,250,251,252,254,255,256,257,258,259,260,261,262,263,264,265,267,268,270,271,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,301,302,303,304,305,306,308,309,310,311,312,313,314,315,317,319,320,321,323,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,361,363,364,366,367,370,372,376,377,378,379,380,386,388,393,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,419,421,422,423,424,425,426,427,431,432,433,434,435,436,437,438,439,440,441,444,448,449,452,453,455,459,460,461,463,465,467,468,477,478,480,481,482,483,485,486,487,488,489,491,494,495,496,498,500,502,505,506,508,509,511,514,515,518,524,525],invokd:3,invoke:[3,12,16],involv:[3,6,7,8,12,65,119,126,127,128,158,182,210,217,226,246,257,302,305,310,335,377,385,386,388,398,415,422,429,477,480,482,493,494,500,502,506,511,512],ioff:[387,497],ion:[6,7,9,160,297,331,347,378,399,410,419,421,443,446,466,476,482,489,497,518],ionic:[6,9,400,402,410,418,419,453,466,518],ioniz:[408,418],ionocoval:9,iparam:[3,227],ipi:[],ipp:[],ir3:177,ir4:177,irregular:[6,41,60,225,229,231,271,317],irrelev:452,irrespect:[440,441],irrevers:[235,318],is_act:[3,524],is_avail:[3,524],is_defin:[3,524],isbn:488,isele:[377,378],isenthalp:[9,271,272,273,274,275],ismail:[377,378,403,435],isn:[3,8,11,12,238,250],iso:[3,229,235,255,271,272,273,274,275,276,277,278,279,304,312,317,518],isobar:[9,271,272,277,278,279,322,414],isodem:418,isoenerget:[9,322,414],isoenthalp:[322,414],isol:[3,181,358,423],isomorph:300,isotherm:[9,246,271,272,277,278,279,304,322,414],isotop:306,isotrop:[6,100,254,304,377,378,401,422,440,441],isovolum:319,isralewitz:323,issu:[1,3,6,9,11,12,13,14,16,17,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,43,45,46,47,48,49,51,53,54,56,61,75,77,87,113,123,155,165,184,185,187,188,189,190,192,193,195,196,199,206,213,224,228,229,231,232,237,238,239,245,246,249,254,269,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,300,304,306,309,310,317,320,321,334,338,339,340,345,355,357,360,361,363,364,366,367,370,372,378,387,388,391,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,419,421,422,423,424,425,431,432,433,434,435,436,437,438,439,440,444,449,452,453,455,459,460,461,468,475,477,478,480,481,482,483,485,487,488,489,497,499,500,506,509,514,515,516,524,525],issue:216,ital:[458,459],itali:13,item:[6,7,8,41,203,207,225],iter:[3,6,12,39,41,65,205,213,214,224,225,229,235,237,244,252,287,293,299,308,309,317,321,332,342,358,360,376,384,385,386,388,392,466,492,502,506,511,515,516,524],iterat:[12,386],ith:[75,128,131,218,219,220,221,222,223,337,349,515,524],itself:[2,3,6,7,8,9,11,12,13,17,42,61,99,118,169,203,205,206,207,208,220,221,230,235,255,270,311,317,347,358,360,387,388,409,419,422,426,427,450,451,478,479,495,501,504,505,509,524,529],itset:507,ityp:[3,57,126,127,178,215,227,310,486],itype1:127,itype2:127,itypen:127,ivector:8,ivori:207,ixcm:317,ixi:[42,317,387],ixx:[42,103,317,387,524],ixy:[103,387,524],ixz:[42,103,317,387,524],iycm:317,iyi:[42,103,317,387,524],iyz:[42,103,317,387,524],izcm:317,izrailev:323,izumi:481,izz:[42,103,317,387,524],j0jt:99,j20:220,j_m:152,jackson:447,jacobi:3,jacobsen:385,jacs:[6,184,509],jagreat:13,jame:[9,18],janssen:298,januari:443,jaramillo:[7,9,13,418],jarzynski:323,jatempl:9,jcc:9,jcp:352,jec:13,jeff:13,jeffer:15,jello:271,jensen:[254,377],jeremi:[9,445],jerom:9,jewett:13,jiang:[255,518],jiao:[9,13],jiht:[7,9],jik:399,jim:7,jku:[7,9],jmake:12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415,509],reconstruct:[3,230,466],record:[208,230,323],recov:[229,238,271],recreat:359,rectangl:[41,225,380],rectangular:[7,41,64,180,225,246,380,497,499,501],rectilinear:[130,177,204],rector:53,recurs:[41,225,399,484],recust:41,recv:494,red:[2,206,207,228,300],redefin:[3,499,505,524],redirect:12,redo:12,reduc:[],reduct:[17,18,128,130,177,269,307,377],redund:419,ree:471,reed:[269,307],rees:[7,9,13],ref:[344,345,385],refactor:6,refer:[],referenc:[3,6,12,65,71,75,125,131,203,204,210,220,246,306,349,378,409,425,452,460,495,515,524],reflect:[],refman:9,reformat:7,refresh:216,reg:500,regard:[6,61,268,321,322,327,455,459],regardless:[14,75,178,181,202,222,231,254,271,273,274,275,277,278,279,304,317,328,335,393,494,500,507],regim:[6,343,350,410,506],region:[],region_spher:8,region_styl:356,regist:[8,127,154,330],regoin:6,regress:524,regspher:178,regstrip:358,regul:6,regular:[1,3,41,64,96,176,180,203,206,217,225,246,306,347,378,410,429,494,497,499,501],reigon:524,reinder:15,reinhardt:[344,345],reject:[178,228,458,512],rel:[1,6,26,35,41,61,75,100,113,134,142,152,156,160,161,163,178,187,207,210,217,225,231,232,235,238,246,252,255,267,268,294,298,303,312,314,315,318,323,331,335,337,342,343,347,354,358,377,378,386,418,422,423,440,441,443,460,488,498,506,511,515,518,525],relat:[],relationship:[6,241,308,360,377,466,488,518,524],relax:[],releas:[0,5,7,8,13,226],relect:[3,448],reles:42,relev:[2,6,12,41,82,85,122,140,178,182,207,211,212,215,216,217,218,219,220,221,222,223,225,226,227,231,232,236,238,239,243,245,246,247,250,251,257,258,259,261,262,263,264,265,267,268,270,280,281,282,283,284,285,286,287,288,289,290,293,297,301,302,303,305,306,311,313,314,315,319,321,328,333,335,336,337,342,343,346,347,348,349,350,351,352,353,354,355,357,358,377,382,386,396,397,401,407,409,410,412,413,415,418,421,422,423,424,425,430,432,433,434,436,437,438,440,441,448,449,450,451,455,460,468,475,478,479,487,488,489,494,510,525],reli:[3,12,309,418,459,489,497,507],reloc:12,reltol:318,remain:[7,12,32,36,41,50,55,61,75,95,114,158,159,160,161,165,166,167,168,170,181,191,197,198,199,203,211,212,217,219,220,229,231,254,255,262,271,272,277,278,279,291,292,294,296,301,302,326,335,338,339,340,346,347,358,360,368,371,387,399,418,426,439,446,448,476,492,497,498,502,507,509,511,515,516,518,519,524,525],remaina:399,remaind:[9,178,203,232,303,335,348,482,497],remap:[3,6,12,61,63,75,161,178,202,231,252,268,294,377,497,498,499],remedi:[6,518],rememb:[2,519],remot:519,remov:[2,3,6,8,13,54,75,76,81,100,117,125,127,152,156,157,158,159,160,161,165,166,167,168,170,171,178,181,182,204,210,219,226,243,254,255,260,263,267,269,271,277,278,279,291,292,294,296,302,308,317,318,319,321,335,338,339,340,342,358,377,388,412,441,446,497,500,508,509,519,524,525],remove_bia:8,remove_bias_al:8,remove_molecul:216,remove_sourc:216,remove_speci:216,ren:177,renam:[12,359,508,519],render:[12,13,203,206,207],rendon:[271,272],reneighbor:[3,8,12,39,59,75,225,246,335,348,358,413,514,515],renssela:302,renumb:75,reorder:[3,12,39,494],rep:512,repeat:[2,6,206,207,228,229,246,318,327,380,399,480,482,484,492,511],repeatedli:2,repel:252,repes:203,replac:[2,3,6,11,12,41,65,97,98,128,155,156,157,158,159,160,161,164,165,166,167,168,170,171,203,204,206,207,208,219,220,222,223,225,228,232,254,276,305,306,312,314,353,393,395,409,423,433,450,451,478,498,499,504,505,515,524,525,527,529],replic:[],replica:[],replica_fil:12,report:[],repositori:[],reprens:347,repres:[1,3,6,8,9,12,14,40,41,42,61,70,75,98,124,127,129,190,199,203,204,206,219,220,221,222,223,229,235,242,247,249,254,257,271,300,302,304,312,317,318,319,323,331,332,347,349,356,378,388,394,399,422,429,439,440,441,443,444,445,453,456,458,459,483,484,492,494,497,507,509,512,518,524,527],represent:[3,6,8,9,59,61,146,180,203,247,248,300,347,399,418,422,446,460,497,500,518],reprocess:502,reproduc:[3,15,271,353,409,416,423],repul:443,repuls:[6,7,35,40,45,46,119,252,308,352,353,356,395,399,407,409,413,418,420,423,425,439,443,447,466,475,482,485,488,489,507],reqir:[308,310],request:[3,6,8,12,41,113,181,199,203,251,257,315,322,335,337,375,377,448,458,459,492,502,507,511,516,519,524,525,527],requir:[],rerun:[],rescal:[],research:[5,7,88,89,240,257,261,322,446,492,511],resembl:[312,519],resepct:6,reserv:[12,251,518],reservoir:[99,238,246,250,254,347],reset:[],reset_atomic_reference_posit:216,reset_dt:8,reset_target:8,reset_tim:216,reset_timestep:[],resid:13,residu:251,residue1:389,resist:[6,251],resolut:[221,466,478,479],resolv:[229,300,335,441,519],resort:3,resourc:[7,394,416],respa:[3,15,213,214,224,233,236,237,249,251,252,266,271,298,309,316,320,323,331,332,333,334,351,352,356,391,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,410,412,413,415,416,417,418,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,440,441,442,443,444,445,446,447,449,450,451,453,455,457,458,459,460,461,466,467,468,469,470,471,472,473,474,475,477,478,479,480,481,482,484,485,487,488,489,490,505,506,516,517,524],respecifi:446,respect:[1,6,9,13,14,16,19,20,22,23,24,25,26,27,28,29,30,31,34,37,42,43,45,46,47,48,49,51,53,54,56,61,74,75,95,97,106,107,123,130,134,154,155,160,163,165,172,176,177,184,185,187,188,189,190,192,193,195,196,199,204,206,207,213,222,224,227,228,229,231,238,239,245,249,252,254,255,257,271,273,274,275,276,277,278,279,280,281,287,289,291,292,294,296,308,309,310,317,319,321,323,331,334,347,352,355,361,363,364,366,367,370,372,375,377,378,383,386,387,392,393,394,395,397,399,400,401,402,403,404,405,407,408,409,412,413,416,417,419,420,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,443,444,446,448,449,452,453,455,459,460,461,465,466,467,468,477,478,480,481,482,483,485,487,488,489,494,498,507,510,516,518,524,526,527,529],respon:9,respond:[6,7,161,231,418,455],respons:[6,7,269,343,350],resquar:[],rest:[6,8,12,38,58,200,306,310,316,373,399,441,443,490,514,515,518],restart1:300,restart2:300,restart2data:[],restart:[],restartfil:[12,13],restor:[3,8,62,63,113,178,211,212,266,306,323,331,332,337,359,514,515],restore_bia:8,restore_bias_al:8,restrain:[],restraint:[9,230,269,316,334,430],restratin:316,restrict:[],result:[1,2,3,6,7,9,10,11,12,13,14,15,17,19,20,22,23,24,25,26,27,28,29,30,31,34,37,39,40,41,43,45,46,47,48,49,51,53,54,56,65,66,69,70,75,79,86,95,98,99,103,113,114,116,121,123,125,126,127,128,130,131,153,155,158,161,165,172,173,175,177,178,181,184,185,187,188,189,190,192,193,195,196,199,202,203,204,206,207,210,213,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,235,238,239,241,245,246,247,249,254,255,257,261,269,271,273,274,275,276,277,278,279,280,281,289,291,292,294,295,296,299,300,308,309,310,314,315,317,318,320,321,335,343,344,345,347,348,349,352,353,355,357,360,361,363,364,366,367,370,372,377,378,388,391,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,418,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,441,443,444,446,448,449,450,451,452,453,455,459,460,461,468,477,478,479,480,481,482,483,485,487,488,489,492,494,497,499,500,502,506,507,508,509,511,523,524,525],resum:524,retain:[2,226,227,395,399,446,494],retart:[32,50,191,368],retir:[3,458],retreiv:8,retriev:[6,8,244,444,445,524],reus:[3,509],rev:[6,13,66,74,100,117,121,152,153,166,217,248,254,256,269,271,272,276,294,299,309,312,317,323,335,339,342,344,345,350,385,399,407,408,409,412,416,417,418,422,423,428,433,440,441,442,443,445,450,451,456,460,467,477,480,481,482,485,492],revers:[2,6,8,95,189,228,252,271,297,298,308,327,343,344,350,388,439,506,518],review:[152,308,323,342,446,457,467,492,511,518,519],rewind:376,rewrap:203,rewrit:[5,12],rewritten:18,rezwanur:455,rfac0:[152,467],rfactor:335,rfile:317,rg0:333,rgb:207,rh3:177,rh4:177,rhaphson:3,rheolog:6,rhi:[450,451,478,479],rho0:[443,463,465,473,474],rho0_meam:443,rho:[],rho_0:[473,474],rho_alpha_beta:416,rho_bkgd:443,rho_colloid:352,rho_e:347,rho_fin:346,rho_i:[444,445],rho_initi:346,rho_ref_meam:443,rho_wal:352,rhodo:[9,10],rhodopsin:[1,10,15],rhohi:303,rholo:303,rhosum:[],rhot:238,rhs:318,ribier:385,richard:9,richardson:317,richi:[9,18],rick:[308,309,408,466],rick_and_stuart:309,ridg:[9,18],right:[3,6,11,12,41,154,178,196,198,202,204,225,228,252,257,268,297,298,318,360,380,409,497,500,507,519,524],rightmost:[41,225],rigid:[],rigidid:129,rigidifi:317,rii:[97,98],rij:[100,117,226,227,298,413,414,450,451,475],rin:[425,436,437],ring:[],rino:[77,485],rinv:377,rirj:[353,423],risi:[152,467],risk:[8,316,506],rix:[97,98],rjk:[226,227],rjone:[7,9,13],rk4:318,rkf45:318,rlo:[450,451,478,479],rmask:[3,524],rmass:[3,306],rmax:[75,179,226],rmdir:508,rmin0:[152,467],rmin:[75,179,227,433],rms:[318,377,378],rmsd:346,rnemd:6,robert:9,robin:207,robust:[384,385,386,459],rock:443,rockett:456,rod:317,rodata:12,rodnei:312,roi:7,role:[6,342],roll:12,ronchetti:117,room:[59,61],root:[11,95,97,98,205,342,346,416,504],rosati:39,rose:443,ross:443,rosski:300,rosybrown:207,rot:[6,99,300,316,342,525],rotat:[],rotate:327,rotation:117,rotaton:500,rough:[6,178,206,357],roughli:[7,10,12,41,161,206,221,246,254,255,263,270,271,285,304,317,335,338,339,342,378,388,393,462,464,499,506],round:[1,3,12,19,20,22,23,24,25,26,27,28,29,30,31,34,37,39,40,42,43,45,46,47,48,49,51,53,54,56,75,123,155,165,184,185,187,188,189,190,192,193,195,196,199,207,213,224,231,239,245,249,254,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,309,310,317,320,321,355,361,363,364,366,367,370,372,378,391,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,419,421,422,423,424,425,429,431,432,433,434,435,436,437,438,439,440,444,449,450,451,452,453,455,459,460,461,468,477,478,480,481,482,483,485,487,488,489,499,500,506,523,524],rous:247,rout:[95,425,439],routin:[5,6,8,11,14,15,37,39,56,96,182,257,446,457,478,479,510],roux:[6,235,255,483,518],row:[6,68,69,71,73,79,84,86,98,102,103,114,116,119,125,126,127,129,131,158,166,173,175,176,177,219,220,222,223,260,317,347,349,357,418],royalblu:207,rozero:443,rperp:[268,327],rpi:302,rpm:12,rrespa:[1,3,5,7,8,15,211,212,268,271,389,394,395,396,397,398,400,401,402,403,404,405,407,408,40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Documentation","5. Accelerating LAMMPS performance","3. Commands","12. Errors","7. Example problems","13. Future and history","6. How-to discussions","1. Introduction","10. Modifying & extending LAMMPS","4. Packages","8. Performance & scalability","11. Python interface to LAMMPS","2. Getting Started","9. Additional tools","5.GPU package","5.USER-INTEL package","5.KOKKOS package","5.USER-OMP package","5.OPT package","angle_style charmm command","angle_style class2 command","angle_coeff command","angle_style cosine command","angle_style cosine/delta command","angle_style cosine/periodic command","angle_style cosine/shift command","angle_style cosine/shift/exp command","angle_style cosine/squared command","angle_style dipole command","angle_style fourier command","angle_style fourier/simple command","angle_style harmonic command","angle_style hybrid command","angle_style none command","angle_style quartic command","angle_style sdk command","angle_style command","angle_style table command","angle_style zero command","atom_modify command","atom_style command","balance command","Body particles","bond_style class2 command","bond_coeff command","bond_style fene command","bond_style fene/expand command","bond_style harmonic command","bond_style harmonic/shift command","bond_style harmonic/shift/cut command","bond_style hybrid command","bond_style morse command","bond_style none command","bond_style nonlinear command","bond_style quartic command","bond_style command","bond_style table command","bond_write command","bond_style zero command","boundary command","box command","change_box command","clear command","comm_modify command","comm_style command","compute command","compute ackland/atom command","compute angle command","compute angle/local command","compute angmom/chunk command","compute basal/atom command","compute body/local command","compute bond command","compute bond/local command","compute centro/atom command","compute chunk/atom command","compute cluster/atom command","compute cna/atom command","compute com command","compute com/chunk command","compute contact/atom command","compute coord/atom command","compute damage/atom command","compute dihedral command","compute dihedral/local command","compute dilatation/atom command","compute dipole/chunk command","compute displace/atom command","compute dpd command","compute dpd/atom command","compute erotate/asphere command","compute erotate/rigid command","compute erotate/sphere command","compute erotate/sphere/atom command","compute event/displace command","compute fep command","compute group/group command","compute gyration command","compute gyration/chunk command","compute heat/flux command","compute hexorder/atom command","compute improper command","compute improper/local command","compute inertia/chunk command","compute ke command","compute ke/atom command","compute ke/atom/eff command","compute ke/eff command","compute ke/rigid command","compute meso/e/atom command","compute meso/rho/atom command","compute meso/t/atom command","compute_modify command","compute msd command","compute msd/chunk command","compute msd/nongauss command","compute omega/chunk command","compute orientorder/atom command","compute pair command","compute pair/local command","compute pe command","compute pe/atom command","compute plasticity/atom command","compute pressure command","compute property/atom command","compute property/chunk command","compute property/local command","compute rdf command","compute reduce command","compute rigid/local command","compute saed command","compute slice command","compute smd/contact/radius command","compute smd/damage command","compute smd/hourglass/error command","compute smd/internal/energy command","compute smd/plastic/strain command","compute smd/plastic/strain/rate command","compute smd/rho command","compute smd/tlsph/defgrad command","compute smd/tlsph/dt command","compute smd/tlsph/num/neighs command","compute smd/tlsph/shape command","compute smd/tlsph/strain command","compute smd/tlsph/strain/rate command","compute smd/tlsph/stress command","compute smd/triangle/mesh/vertices","compute smd/ulsph/num/neighs command","compute smd/ulsph/strain command","compute smd/ulsph/strain/rate command","compute smd/ulsph/stress command","compute smd/vol command","compute sna/atom command","compute stress/atom command","compute force/tally command","compute temp command","compute temp/asphere command","compute temp/body command","compute temp/chunk command","compute temp/com command","compute temp/cs command","compute temp/deform command","compute temp/deform/eff command","compute temp/drude command","compute temp/eff command","compute temp/partial command","compute temp/profile command","compute temp/ramp command","compute temp/region command","compute temp/region/eff command","compute temp/rotate command","compute temp/sphere command","compute ti command","compute torque/chunk command","compute vacf command","compute vcm/chunk command","compute voronoi/atom command","compute xrd command","create_atoms command","create_bonds command","create_box command","delete_atoms command","delete_bonds command","dielectric command","dihedral_style charmm command","dihedral_style class2 command","dihedral_coeff command","dihedral_style cosine/shift/exp command","dihedral_style fourier command","dihedral_style harmonic command","dihedral_style helix command","dihedral_style hybrid command","dihedral_style multi/harmonic command","dihedral_style nharmonic command","dihedral_style none command","dihedral_style opls command","dihedral_style quadratic command","dihedral_style spherical command","dihedral_style command","dihedral_style table command","dihedral_style zero command","dimension command","displace_atoms command","dump command","dump custom/vtk command","dump h5md command","dump image command","dump_modify command","dump molfile command","echo command","fix command","fix adapt command","fix adapt/fep command","fix addforce command","fix addtorque command","fix append/atoms command","fix atc command","fix atom/swap command","fix ave/atom command","fix ave/chunk command","fix ave/correlate command","fix ave/correlate/long command","fix ave/histo command","fix ave/time command","fix aveforce command","fix balance command","fix bond/break command","fix bond/create command","fix bond/swap command","fix box/relax command","fix colvars command","fix deform command","fix deposit command","fix drag command","fix drude command","fix drude/transform/direct command","fix dt/reset command","fix efield command","fix ehex command","fix enforce2d command","fix eos/cv command","fix eos/table command","fix eos/table/rx command","fix evaporate command","fix external command","fix freeze command","fix gcmc command","fix gld command","fix gle command","fix gravity command","fix heat command","fix imd command","fix indent command","fix ipi command","fix langevin command","fix langevin/drude command","fix langevin/eff command","fix lb/fluid command","fix lb/momentum command","fix lb/pc command","fix lb/rigid/pc/sphere command","fix lb/viscous command","fix lineforce command","fix manifoldforce command","fix meso command","fix meso/stationary command","fix_modify command","fix momentum command","fix move command","fix msst command","fix neb command","fix nvt command","fix nvt/eff command","fix nph/asphere command","fix nph/body command","fix nph/sphere command","fix nphug command","fix npt/asphere command","fix npt/body command","fix npt/sphere command","fix nve command","fix nve/asphere command","fix nve/asphere/noforce command","fix nve/body command","fix nve/eff command","fix nve/limit command","fix nve/line command","fix nve/manifold/rattle command","fix nve/noforce command","fix nve/sphere command","fix nve/tri command","fix nvt/asphere command","fix nvt/body command","fix nvt/manifold/rattle command","fix nvt/sllod command","fix nvt/sllod/eff command","fix nvt/sphere command","fix oneway command","fix orient/fcc command","fix phonon command","fix pimd command","fix planeforce command","fix poems","fix pour command","fix press/berendsen command","fix print command","fix property/atom command","fix qbmsst command","fix qeq/point command","fix qeq/comb command","fix qeq/reax command","fix qmmm command","fix qtb command","fix reax/bonds command","fix reax/c/species command","fix recenter command","fix restrain command","fix rigid command","fix rx command","fix saed/vtk command","fix setforce command","fix shake command","fix shardlow command","fix smd command","fix smd/adjust_dt command","fix smd/integrate_tlsph command","fix smd/integrate_ulsph command","fix smd/move_tri_surf command","fix smd/setvel command","<no title>","fix smd/wall_surface command","fix spring command","fix spring/chunk command","fix spring/rg command","fix spring/self command","fix srd command","fix store/force command","fix store/state command","fix temp/berendsen command","fix temp/csvr command","fix temp/rescale command","fix temp/rescale/eff command","fix tfmc command","fix thermal/conductivity command","fix ti/rs command","fix ti/spring command","fix tmd command","fix ttm command","fix tune/kspace command","fix vector command","fix viscosity command","fix viscous command","fix wall/lj93 command","fix wall/gran command","fix wall/piston command","fix wall/reflect command","fix wall/region command","fix wall/srd command","group command","group2ndx command","if command","improper_style class2 command","improper_coeff command","improper_style cossq command","improper_style cvff command","improper_style distance command","improper_style fourier command","improper_style harmonic command","improper_style hybrid command","improper_style none command","improper_style ring command","improper_style command","improper_style umbrella command","improper_style zero command","include command","info command","jump command","kspace_modify command","kspace_style command","label command","lattice command","log command","Description","mass command","min_modify command","min_style command","minimize command","molecule command","neb command","neigh_modify command","neighbor command","newton command","next command","package command","pair_style adp command","pair_style airebo command","pair_style awpmd/cut command","pair_style beck command","pair_style body command","pair_style bop command","pair_style born command","pair_style brownian command","pair_style buck command","pair_style buck/long/coul/long command","pair_style lj/charmm/coul/charmm command","pair_style lj/class2 command","pair_coeff command","pair_style colloid command","pair_style comb command","pair_style coul/cut command","pair_style coul/diel command","pair_style born/coul/long/cs command","pair_style lj/cut/dipole/cut command","pair_style dpd command","pair_style dpd/fdt command","pair_style dsmc command","pair_style eam command","pair_style edip command","pair_style eff/cut command","pair_style eim command","pair_style exp6/rx command","pair_style gauss command","pair_style gayberne command","pair_style gran/hooke command","pair_style lj/gromacs command","pair_style hbond/dreiding/lj command","pair_style hybrid command","pair_style kim command","pair_style lcbop command","pair_style line/lj command","pair_style list command","pair_style lj/cut command","pair_style lj96/cut command","pair_style lj/cubic command","pair_style lj/expand command","pair_style lj/long/coul/long command","pair_style lj/sf command","pair_style lj/smooth command","pair_style lj/smooth/linear command","pair_style lj/cut/soft command","pair_style lubricate command","pair_style lubricateU command","pair_style lj/mdf command","pair_style meam command","pair_style meam/spline","pair_style meam/sw/spline","pair_style mgpt command","pair_style mie/cut command","pair_modify command","pair_style morse command","pair_style multi/lucy command","pair_style multi/lucy/rx command","pair_style nb3b/harmonic command","pair_style nm/cut command","pair_style none command","pair_style peri/pmb command","pair_style polymorphic command","pair_style quip command","pair_style reax command","pair_style reax/c command","pair_style resquared command","pair_style lj/sdk command","pair_style smd/hertz command","pair_style smd/tlsph command","pair_style smd/tri_surface command","pair_style smd/ulsph command","pair_style smtbq command","pair_style snap command","pair_style soft command","pair_style sph/heatconduction command","pair_style sph/idealgas command","pair_style sph/lj command","pair_style sph/rhosum command","pair_style sph/taitwater command","pair_style sph/taitwater/morris command","pair_style srp command","pair_style command","pair_style sw command","pair_style table command","pair_style table/rx command","pair_style tersoff command","pair_style tersoff/mod command","pair_style tersoff/zbl command","pair_style thole command","pair_style tri/lj command","pair_style vashishta command","pair_write command","pair_style yukawa command","pair_style yukawa/colloid command","pair_style zbl command","pair_style zero command","partition command","prd command","print command","processors command","python command","quit command","read_data command","read_dump command","read_restart command","region command","replicate command","rerun command","reset_timestep command","restart command","run command","run_style command","set command","shell command","special_bonds command","suffix command","tad command","temper command","thermo command","thermo_modify command","thermo_style command","timer command","timestep command","Tutorial for Thermalized Drude oscillators in LAMMPS","LAMMPS GitHub tutorial","uncompute command","undump command","unfix command","units command","variable command","velocity command","write_coeff command","write_data command","write_dump command","write_restart command"],titleterms:{"break":226,"default":[36,39,40,55,59,60,61,63,64,75,95,96,100,112,113,115,117,118,130,134,135,152,158,166,167,171,177,178,181,183,198,201,202,203,204,206,207,208,209,211,212,213,215,216,217,219,223,226,227,229,230,231,232,236,243,246,247,252,254,255,256,257,258,260,266,269,271,272,276,294,295,299,300,303,304,305,306,307,309,312,314,315,317,319,335,337,342,343,344,345,348,350,352,354,358,371,375,377,378,380,381,384,385,387,389,390,391,393,396,399,401,418,440,441,446,448,458,459,475,476,492,493,494,497,498,500,502,504,505,506,509,511,513,514,515,516,517,523,525,527,528],"function":524,"long":[221,400,402,403,404,405,409,411,412,431,435,439,453,461,483],"new":8,"static":12,acceler:1,account:519,ackland:66,acknowledg:7,adapt:[211,212],addforc:213,adding:519,addition:[12,13,519],addtorqu:214,adiabat:6,adjust_dt:324,adp:394,after:519,airebo:395,alloi:416,amber2lmp:13,amber:6,angl:[8,67,68],angle_coeff:21,angle_styl:[2,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],angmom:69,append:215,arrai:6,aspher:[6,90,156,273,277,281,282,291],asphere:9,atc:[9,216],atom:[6,7,8,66,70,74,75,76,77,80,81,82,85,87,89,93,100,105,106,109,110,111,117,121,122,124,152,153,176,215,217,218,306,524],atom_modifi:39,atom_styl:40,attract:5,aveforc:224,awpmd:[9,396],axel:519,balanc:[41,225],barostat:6,basal:70,bcc:298,beck:397,berendsen:[304,338],between:6,binary2txt:13,bodi:[6,8,42,71,157,274,278,283,292,398],body:9,bond:[8,13,72,73,226,227,228,313],bond_coeff:44,bond_styl:[2,43,45,46,47,48,49,50,51,52,53,54,55,56,58],bond_writ:57,bop:399,born:[400,411],boundari:[7,59],box:[6,60,229],branch:519,brownian:401,buck:[402,403,411,442],bug:3,build:[11,12],calcul:6,call:12,categori:2,centro:74,ch2lmp:13,chain:13,chang:519,change_box:61,charmm:[6,19,184,404,439],chunk:[6,69,75,79,86,98,103,114,116,125,158,173,175,219,332],citat:7,class2:[9,20,43,185,361,405],clear:62,cluster:76,cmm:9,cna:77,code:6,coeffici:6,colloid:[9,352,407,488],colvar:[13,230],colvars:9,com:[78,79,159],comb3:408,comb:[309,408],come:5,comm_modifi:63,comm_styl:64,command:[2,6,8,12,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,520,521,522,523,524,525,526,527,528,529],common:3,comparison:1,compos:6,compress:9,comput:[2,6,8,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,524],compute_modifi:112,condit:7,conduct:[6,343],constant:[6,524],constraint:7,contact:[80,132],coord:81,core:6,coreshell:9,correl:[220,221],cosin:[22,23,24,25,26,27,187],cossq:363,coul:[400,402,403,404,405,409,410,411,424,431,435,439,453,461],coupl:6,creat:227,create_atom:178,create_bond:179,create_box:180,createatom:13,creation:7,csld:339,csvr:339,cubic:433,custom:[8,203,204],cut:[49,396,402,405,409,412,418,421,431,432,439,447,453,483],cvff:364,damag:[82,133],data2xmovi:13,data:6,databas:13,deby:[409,43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to_nodeset:216,added:[498,519],addforc:[],adding:[],addit:[1,2,3,4,6,7,8,9,12,13,14,15,16,17,19,21,28,32,36,37,39,40,41,42,44,50,55,56,65,118,119,124,154,178,179,181,182,186,191,197,198,199,200,201,206,207,208,210,213,221,224,225,229,230,235,237,238,241,242,246,248,249,250,254,257,271,272,273,274,275,276,277,278,279,291,292,294,295,296,298,306,317,320,321,322,324,348,351,352,356,362,368,371,373,378,380,388,390,393,394,395,399,400,402,404,406,408,409,412,414,415,416,417,418,419,423,424,426,427,428,430,431,439,441,442,443,444,445,446,448,450,451,452,453,456,458,459,461,466,467,476,477,478,479,480,481,482,485,489,492,497,498,499,504,505,506,509,511,518,519,520,522,524,527,528,529],addition:[],additionali:204,addres:9,address:[7,8,11,206,253,519],addtorqu:[],adequ:[263,335,348,377,388,506],adher:28,adhikari:257,adiabat:[],adiam:[206,207],adjac:[39,176,178,238,388,448,478,479,480,511,512],adjiman:447,adjust:[2,3,6,15,16,41,61,130,140,156,157,158,161,162,165,166,171,172,177,182,203,206,219,225,229,231,251,254,258,262,267,268,271,272,276,294,298,301,303,304,307,308,309,310,315,317,318,326,335,339,343,348,350,351,352,354,355,357,377,378,386,388,393,395,415,440,441,466,482,507,525],adjust_radiu:326,adjust_radius_factor:326,admiss:276,adof:[158,219],adopt:[316,518],adp:[],adri:[9,313,458,459],adust:172,advanc:[3,15,251,399,492,503],advantag:[1,6,8,11,17,39,40,41,225,393,417,506,511],advect:[3,6,335],advertis:8,advis:[318,388,457,516],afer:3,affect:[1,6,10,14,16,40,62,63,75,96,128,153,162,176,182,206,207,212,219,220,222,223,226,227,228,229,231,232,238,244,250,252,254,260,266,268,272,273,274,275,277,278,279,285,291,292,294,296,317,319,333,347,357,370,377,384,385,386,388,389,390,393,418,440,441,448,494,495,497,499,500,502,505,507,516],affin:[15,16,17,231,393,408],affiniti:393,afile:248,aforement:17,afresh:[305,505,524],afshar:413,after:[],afterrun:505,afterward:3,afterword:41,ag1:177,ag2:177,again:[6,11,12,16,64,152,158,164,172,203,207,231,238,250,303,361,376,388,440,441,492,494,495,497,499,504,511,513,524,527],against:[11,12,13,66,232,388,458,459],aggreg:[6,12,68,71,73,84,102,119,126,250,267,315,317,333,492,525],aggress:[238,250,511],agilio:[9,13],agre:[3,8,199,386,395,428,459],agreement:[5,7],ahd:425,ahead:354,aidan:[0,5,7,9,13,380],aij:13,aim:6,airebo:[],ajaramil:[7,9,13],aka:206,akohlmei:[7,9,13,208,251],aktulga:[7,9,310,459],al2o3:466,al2o3_001:[130,319],al3:177,ala:[257,466],alain:9,alat:[298,443],albe:[456,480,482],albeit:316,albert:9,alchem:[95,172],alcohol:350,alcu:[394,399],alcu_eam:456,alderton:412,alejandr:[271,272],alessandro:13,algebra:446,algorithm:[0,1,6,7,8,9,41,63,207,210,216,225,228,231,238,250,257,259,260,285,300,308,317,321,322,342,343,347,350,355,384,385,386,390,393,414,418,441,446,463,465,492,494,511],alia:[9,15],alias:[1,378],aliceblu:207,align:[6,9,12,28,41,75,180,199,225,252,380,497,500,518],alkali:418,all:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,21,32,36,38,39,40,41,42,44,50,54,55,57,58,59,61,62,63,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,166,171,172,173,174,175,176,177,178,179,180,181,182,184,186,191,197,198,199,200,203,204,205,206,207,208,210,211,212,213,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,234,235,236,237,238,239,240,241,242,243,244,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,263,264,266,267,269,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,289,290,291,292,293,294,295,296,297,298,299,300,302,303,304,305,306,307,308,309,310,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,330,331,332,334,335,336,337,338,339,342,343,344,345,346,347,348,349,350,352,353,354,355,356,357,358,359,360,361,362,366,371,373,375,376,377,378,379,380,383,386,387,388,389,390,392,393,394,395,396,398,399,400,402,403,404,405,406,408,409,412,413,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,435,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,455,456,457,458,459,460,461,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,488,489,490,491,492,494,495,496,497,498,499,500,501,502,504,505,506,507,508,509,510,511,512,514,515,516,518,519,523,524,525,527,528,529],allen:[28,95,412,422],allentildeslei:95,allign:3,allindex:359,alloc:[3,5,6,8,9,11,12,62,244,349,387,389,393,454,459,497,505],allocat:3,alloi:[],allosw:75,allow:[1,2,3,6,8,9,11,12,13,14,15,16,17,21,36,38,39,40,41,55,57,58,59,60,61,63,64,65,81,100,117,119,154,156,157,158,171,176,177,178,180,186,198,199,200,202,203,204,206,207,208,210,211,213,214,215,216,217,219,220,221,222,223,224,225,227,228,229,230,231,232,233,236,237,244,246,247,248,249,251,252,254,257,260,261,266,268,271,272,298,302,303,304,305,30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,515],cellbeta:[6,515],cellc:[6,515],cellgamma:[6,515],center:[3,6,24,42,65,69,75,78,79,86,94,97,98,108,113,114,115,125,127,129,130,158,159,160,163,166,170,173,175,178,206,207,210,211,212,214,219,222,229,231,232,233,235,246,247,252,254,255,260,267,271,277,278,279,291,292,294,296,303,308,314,315,317,319,323,331,332,333,335,337,338,339,340,342,343,345,352,356,361,380,382,387,398,417,418,422,423,429,440,441,443,444,445,458,459,477,480,481,482,484,485,500,507,518,524],centimet:523,centr:238,central:[3,63,74,80,81,100,117,127,134,152,176,260,298,321,333,365,387,446,452,458,459,485,497],centro:[],centroid:[3,300,484,507],cerda:377,ceriotti2:248,ceriotti:[13,248,253],certain:[1,2,3,6,8,12,16,39,75,124,128,131,182,203,204,206,218,219,220,222,223,228,244,245,317,320,336,349,360,368,376,389,426,448,459,483,499,503,518,524],certainli:252,cerutti:378,cfg:[3,6,7,9,13,203,205,206,207,208],cfile:459,cfl:[140,324],cfor:323,cg_type:461,ch2:321,ch2lmp:[],ch3:321,ch5md:205,chain3:389,chain:[],challeng:[6,323],chalopin:312,champaign:[251,377,378,440],chan:446,chandler:[394,416],chandrasekhar:[6,431],chang:[],change_box:[],changeabl:203,channel:[4,213],chapter:[300,378],charact:[2,3,6,12,37,41,56,65,199,203,204,206,207,208,210,225,241,242,306,314,318,360,387,392,418,420,430,450,451,456,458,459,466,478,479,494,495,499,504,505,524,527,528,529],character:[6,70,74,117,127,152,467,492,511],characterist:[255,335,344],charg:[1,3,4,5,6,7,9,11,14,40,86,95,96,124,130,177,178,203,204,208,210,211,212,217,232,237,246,306,308,309,310,314,337,350,377,378,387,400,402,408,409,411,412,416,418,419,426,431,435,439,453,458,459,466,476,482,483,485,486,488,489,497,498,502,507,509,518,523,524],charmm2lammp:13,charmm:[],chartreus:207,cheap:335,cheaper:[236,422,460],check:[3,6,8,9,11,12,14,16,38,39,41,58,75,99,199,200,217,225,226,227,232,243,246,252,253,303,316,321,335,343,345,350,358,360,373,376,386,387,388,389,390,393,415,427,430,448,459,490,492,494,495,497,505,511,514,515,516,524],checkf:199,checkout:519,checkqeq:459,checku:199,chem:[6,13,19,20,24,39,40,43,45,46,95,96,117,123,153,184,185,195,221,230,235,238,247,248,255,257,270,271,272,294,295,300,304,307,309,317,322,323,335,338,339,342,343,344,345,352,361,370,372,377,378,385,388,395,400,404,405,408,409,410,412,413,414,418,421,422,424,425,431,435,436,439,443,447,448,450,451,453,466,475,483,506,509,511,518],chemic:[9,40,88,89,130,172,177,203,204,216,217,246,308,313,314,342,378,420,451,458,459,471,479],chemistri:[307,308,310,395,399,418,458,459],chen:347,cheng:408,chenoweth:[458,459],chenoweth_2008:[458,459],chi:[102,167,202,298,308,310,373,419,422,525],chiefli:457,child:8,chip:[7,9,16,17,393,510],chipot:230,chiral:370,chmod:[11,12],cho:443,chocol:[7,207],choic:[3,6,9,12,14,15,17,40,41,54,95,153,156,157,171,182,197,199,219,225,228,231,232,248,254,257,269,271,300,304,308,317,342,353,371,378,384,385,388,390,393,426,439,448,454,459,497,506,507,510,511,517,518,523],choos:[1,3,6,7,8,9,12,16,17,28,39,54,95,128,168,169,206,226,227,228,229,232,243,254,257,269,271,273,274,275,276,277,278,279,303,304,322,335,339,353,377,378,385,486,492,494,506,512],chose:[480,482],chosen:[2,3,6,12,16,152,178,181,190,199,206,212,217,229,232,243,246,247,255,257,269,271,276,300,303,314,335,339,342,343,348,350,351,357,378,379,385,393,414,418,423,430,433,461,480,492,506,511,512,518],chri:176,christian:[7,9,16],christoph:7,chunk:[],chunkid:[69,79,86,98,103,114,116,125,158,173,175,219,332],chute:[4,9,10,249],ciccotti:321,cieplak:[6,184,509],cii:220,cij:220,circl:[42,75,330],circular:[3,6,156,201],circumst:17,circumv:[100,117,312],citat:[],cite:[3,7,8,12,254,466],civ:9,cl1:177,clarendon:[28,412],clarifi:[7,480,482],clariti:360,clark:453,class2:[],classic:[0,3,5,6,7,8,9,15,244,300,307,312,347,372,418],classifi:[476,485],claus:495,clean:[6,12,14,16,505],cleanli:[496,515,528],clear:[],clearli:7,clebsch:152,clermont:[9,13],clever:501,click:[2,11,21,36,44,55,178,186,198,206,251,362,371,388,406,476,519],client:[251,253],climb:[270,388,511],clinic:[7,13],clo:[167,202,525],clock:[12,492,511,516],clockwis:[184,353],clone:519,close:[3,6,11,12,13,39,41,70,75,153,181,203,227,228,229,248,255,257,271,287,294,317,321,353,356,376,378,381,384,385,388,393,395,399,409,410,429,443,448,450,451,458,462,464,478,482,501,507,518,519,521],closer:[3,10,41,127,176,202,203,225,229,233,344,388],closest:[227,298,317,350,422,460,475,486],cloud:[466,518],clovertown:17,clsuter:76,clump1:[302,317],clump2:[302,317],clump3:[302,317],clump:317,cluster:[],clutter:3,cmap:497,cmatrix:248,cmax:443,cmd:[11,12,300,508],cmdarg:11,cmin:443,cmm:[],cmoput:145,cn1:220,cn2:220,cna:[],cnn:220,cnr:13,cnrs:9,cnt:[426,501],co2:[40,177,318,321,387,420],coars:[7,9,10,28,35,40,54,190,242,302,317,318,335,420,424,451,461,479,506,509],coarser:[378,524],coarsest:152,code:[],coeff:[3,7,8,12,20,21,32,44,50,184,185,186,191,197,361,362,368,406,426,430,448,463,465,468,497,526],coeffcient:497,coeffici:[],coefficienct:413,coefficient0:416,coefficient1:416,coeffieci:[6,397,429],coeffincientn:416,coexist:[246,418],cohes:[6,419,443],coincid:[134,356,404,440,441,492],colberg:205,cold:[6,163,246,250,389,518],coldest:343,coleman8:9,coleman:[9,130,177,319],colin:9,collabor:[7,8,14],collect:[3,6,7,8,9,13,40,42,69,79,86,91,98,103,108,114,116,125,158,166,173,175,178,203,204,207,219,230,260,267,302,312,315,317,358,377,387,389,407,497,504,510,516,529],collid:[236,335,357],colliex:177,collinear:[3,302],collis:[3,257,335,353,357,415,423,489],colllis:335,colloid:[],colombo:39,colon:[208,358,498,516],color1:207,color2:207,color:[3,9,41,203,206,207,225,247,307,312],column:[3,6,9,12,13,42,65,68,69,70,71,73,75,79,81,84,86,87,89,98,100,102,103,114,116,117,119,121,124,125,126,127,128,129,131,152,153,158,166,173,175,176,177,199,203,204,207,210,218,219,220,222,223,260,268,269,307,317,336,337,347,357,395,421,425,458,459,498,512,514,524],columnar:176,colvar:[],colvarmodul:12,com:[],comamnd:231,comand:228,comannd:393,comb3:[],comb:[],comb_1:309,comb_2:309,combiant:410,combin:[3,6,7,9,11,13,35,40,65,68,73,84,95,102,113,119,126,156,157,171,197,203,206,216,222,238,246,251,260,263,271,287,293,300,306,322,339,348,356,359,361,377,378,380,385,393,407,409,410,414,418,419,420,426,438,439,467,477,480,482,483,485,488,500,505,510,518,524,526],come:[],comfort:[12,13],comid:332,comit:519,comm:[0,3,11,12,63,77,205,251,253,254,378,388,393,413,448,455,478,479,499],comm_modifi:[],comm_modift:63,comm_styl:[],comm_world:11,command:[],comment:[2,4,7,11,12,37,56,184,199,203,241,242,255,317,318,347,387,388,394,416,417,419,420,430,443,450,451,452,459,466,467,477,478,479,480,481,482,485,494,495,497,518,519,524],commerci:7,commit:[9,519],commmand:[3,6,12,61,118,295,429,491,492,494,511,528],common:[],commonli:[3,6,9,12,16,24,59,61,115,117,180,203,206,208,372,424,433,467,480,482,497,500,509],commun:[1,3,6,7,8,9,10,11,12,14,15,17,40,41,60,63,64,75,176,181,182,206,207,225,226,227,229,230,231,251,253,257,259,260,261,271,299,306,308,309,310,317,322,335,347,358,375,377,389,390,391,393,414,415,454,490,494,495,506,507,524,527,529],communc:377,comp:[7,205,253,254,287,293,321,378,388,418,448,455,460,474,478,479,481],compact:[65,210,406,476],compani:[5,7],compar:[1,3,4,6,8,12,15,16,39,94,121,130,161,177,186,198,207,235,308,358,360,377,378,386,388,443,466,492,511,512,518,523],comparison:[],compart:382,compass:[7,9,20,21,36,43,44,55,185,186,198,361,362,371,405,476],compat:[3,5,7,8,9,11,12,13,16,17,41,75,128,131,189,203,208,212,218,219,220,222,223,225,238,299,311,339,342,349,352,355,377,393,427,446,448,478,494,495,524],compens:[6,226,227,315,389,418],compet:346,competit:378,compil:[3,7,8,9,12,13,14,15,16,17,18,176,203,205,206,208,251,346,378,393,446,497,498,502,518,524],compl:16,complain:[11,12,16],complement:443,complementari:[7,409,431],complet:[3,6,9,11,12,14,41,61,75,207,225,230,260,300,303,306,335,346,348,359,360,376,388,393,419,463,465,484,492,497,502,505,509,511,514,518,524],complex:[4,6,8,11,12,13,24,40,42,64,100,117,152,154,166,178,179,257,287,330,356,375,388,418,446,478,479,495,497,500,524],compli:[342,346],complic:[6,7,9,12,13,129,217,246,495],complier:12,compon:[3,6,8,12,63,65,69,70,77,86,87,96,97,98,99,100,103,104,107,112,114,115,116,117,118,119,120,121,123,124,128,129,139,142,143,144,145,148,149,150,152,153,155,156,157,158,159,160,161,162,163,164,165,166,167,168,170,171,173,174,175,202,203,204,206,207,213,214,218,219,220,221,222,223,224,228,229,231,232,237,238,244,249,253,254,257,260,262,263,267,268,270,271,272,276,277,278,279,291,292,294,296,297,299,300,301,304,315,317,320,321,323,327,328,331,335,338,339,340,342,349,350,355,356,357,377,380,385,386,387,388,393,413,418,423,440,441,463,465,466,467,497,498,507,515,524,525],componenet:6,componennt:129,composit:[6,217,257,416],compound:[408,418,419,485],compres:[75,125,219],compress:[],compris:[40,356,420,460,484],compton:[130,177],comptu:3,compuat:378,comput:[],computation:[3,6,226,227,347,399],computational:518,compute_arrai:8,compute_inn:8,compute_ke_atom:8,compute_loc:8,compute_modifi:[],compute_peratom:8,compute_sa:[130,319],compute_scalar:8,compute_temp:8,compute_vector:8,compute_xrd:177,concaten:[2,3,528],concav:356,concentr:[75,242,318,416,420,451,479],concept:[6,158,168,219,506],conceptu:[3,6,75,166,229,231,388,409,426,443,502],concern:[6,77,95,205,247],concetr:75,concis:[11,346],conclud:12,concret:8,concurr:[15,378,524],conden:[347,480,482],condens:[6,160,347,395,411,416,431,485],condit:[],conducit:6,conduct:[],cone:500,confer:[15,446],confid:[3,511],config:[12,203,494],configfil:230,configur:[1,2,6,12,14,15,16,37,61,134,180,199,202,203,206,210,229,230,231,232,236,246,253,254,285,300,308,346,375,386,388,395,399,417,443,446,477,480,482,485,492,497,499,500,511,515,519],confin:[497,511],confirm:516,conflict:[3,12,40,204,448,495,519],conform:[3,6,13,61,228,229,270,316,323,346,370,388,418,509],confus:[3,485],conjuct:[9,413],conjug:[7,8,254,385,418,458,459],conjunct:[6,7,75,94,95,125,161,166,172,178,182,207,211,212,254,257,261,285,303,304,308,309,310,312,317,322,335,343,350,355,377,378,388,400,402,406,409,413,418,425,431,448,453,461,483,497,500,504,518,529],connect:[3,6,9,95,163,181,228,251,302,317,321,331,382,388,410,423,458,459,475,482,494,495,497,501,518],conner:395,connor:395,conput:3,consecut:[3,11,12,39,75,178,207,211,212,232,251,252,409,431,435,492,498,500],consequ:[1,6,217,347,430,511],conserv:[3,9,28,210,217,228,235,236,247,250,254,256,257,261,267,269,271,285,287,317,318,321,322,338,339,343,350,351,355,388,412,413,414,423,437,466,506,511],consid:[6,74,75,82,95,126,129,160,163,164,181,203,204,207,211,212,218,220,225,227,228,232,258,272,299,317,342,343,346,347,350,378,406,418,426,459,460,466,475,492,493,495,498,499,500,502,505,507,515,518,524],consider:[6,8,254,255,338,339,340,393,459,506],consist:[3,6,8,9,11,12,40,42,68,73,84,102,114,119,122,123,126,129,158,161,163,178,190,202,208,213,214,219,231,232,235,237,244,247,254,255,256,268,271,273,274,275,276,277,278,279,280,281,283,284,285,286,289,290,291,292,294,295,296,304,307,312,314,315,316,317,318,338,339,340,341,351,377,378,380,382,387,388,393,395,399,401,407,409,418,422,426,429,440,441,443,446,448,450,451,460,463,465,466,478,479,486,495,497,498,499,500,501,502,509,518,524],consistent_fe_initi:216,consit:317,constant:[],constantli:238,constitu:[3,6,260,317,352,356,407,460],constitut:[463,465],constrain:[3,6,8,9,155,156,157,158,159,161,164,165,166,167,168,170,171,210,219,232,238,246,247,252,260,263,265,287,293,302,303,315,317,321,333,343,350,386,387,418,502,509,518],constraint:[],construct:[6,8,12,37,54,56,63,66,70,74,76,77,81,100,117,130,152,177,229,271,299,316,318,356,389,393,412,446,448,475,477,478,479,500,501,516,524],constructor:[8,11],consult:[15,459],consum:[1,312,454,524],consumpt:375,contact:[],contact_stiff:[462,464],contain:[0,1,2,3,4,6,8,9,11,12,13,16,17,18,37,40,41,56,65,95,99,127,130,152,154,158,166,176,177,178,180,184,186,198,199,203,204,206,207,208,210,211,212,216,218,219,220,222,223,225,230,232,237,241,242,248,252,253,254,257,269,285,298,299,302,303,305,306,307,310,314,317,318,319,324,335,342,346,347,356,357,360,376,378,387,388,391,392,394,395,396,399,408,409,412,416,417,418,420,426,427,443,446,450,451,452,456,457,458,459,467,477,478,479,480,481,482,483,485,492,493,494,495,497,498,499,500,502,504,506,509,511,512,514,515,518,519,524,527,529],content:[4,8,9,12,17,459,513,515],context:[3,6,8,12,15,16,127,128,207,226,227,232,302,314,351,385,488,497,504,513,523,524,525],contibut:74,contigu:494,contin:15,continu:[0,2,3,5,6,9,12,13,41,75,87,113,114,174,207,210,211,212,217,219,220,221,222,223,225,228,229,230,231,232,238,246,247,248,250,251,252,254,255,256,262,268,269,271,273,274,275,276,277,278,279,291,292,294,295,296,301,303,306,307,317,319,323,334,335,337,344,345,347,353,356,360,376,392,393,399,413,415,433,436,458,459,460,463,465,481,492,495,497,499,500,505,511,512,514,515,524,527],continuum:[6,7,9,216,347,463,465],contour_integr:216,contract:[61,229,231,271,304,317],contradictori:3,contrain:321,contraint:285,contrari:[248,255],contrast:[1,6,42,55,66,160,163,204,231,358,463,465,488,528],contrib:347,contribut:[3,4,5,6,7,8,9,12,13,16,65,67,69,71,72,74,75,78,79,81,83,85,86,92,95,96,97,98,99,101,103,112,114,116,118,119,120,121,123,125,128,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,212,217,218,219,220,222,223,229,246,254,257,260,261,266,272,294,295,302,303,311,314,317,319,321,349,377,386,388,396,413,415,416,418,426,440,441,446,448,458,459,466,509,515,518,519],contributor:12,control:[3,5,6,7,8,9,11,13,15,26,28,41,95,99,134,152,187,203,204,206,210,216,217,225,229,230,231,250,251,254,255,271,273,274,275,276,277,278,279,304,309,317,318,325,326,338,339,340,347,351,375,377,390,418,422,446,458,459,462,464,477,482,492,494,506,512,513],control_typ:216,controlfil:459,convect:99,conveni:[6,12,28,203,208,223,319,380,467,483,516,519,524],convent:[3,8,28,189,196,198,204,207,316,331,359,416,418,524],converg:[3,6,41,96,203,204,206,208,213,225,228,229,237,244,276,287,293,307,309,312,316,321,384,385,386,388,408,409,431,466,492,504,511,516],convers:[3,8,152,206,207,217,220,304,377,409,410,411,418,431,435,439,453,495,511,523],convert:[2,3,4,5,6,7,8,12,13,19,20,23,27,31,34,35,61,65,75,99,178,185,203,206,207,220,223,269,303,306,358,361,363,367,370,380,388,394,416,446,480,482,489,495,497,498,499,504,514,518,523,524,527,529],convex:[42,356],convinc:[7,12],cook:9,cooki:7,cool:[7,168,238,250,315],cooordin:[203,204],cooper:[5,7],coord123:125,coord1:[3,125,219],coord2:[3,125,219],coord3:[3,125,219],coord:[],coordb:466,coordbb:466,coordiat:386,coordin:[1,3,4,6,7,8,11,13,14,16,40,41,42,61,63,64,65,69,71,75,78,79,81,86,87,95,97,98,103,113,114,116,124,125,127,129,146,152,161,167,173,175,176,178,182,202,203,204,205,206,207,208,210,213,218,219,222,225,226,227,228,229,230,231,232,235,237,238,239,244,246,249,250,251,252,253,254,255,268,270,271,273,274,275,277,278,279,294,297,298,299,302,303,304,314,315,317,320,321,323,328,331,332,333,334,335,337,345,346,347,354,355,357,358,380,386,387,388,393,394,395,398,417,466,492,497,498,499,500,502,505,507,511,518,524,525],coordn:[125,219],coords:466,copi:[0,3,4,8,9,11,12,14,16,40,131,206,347,388,406,458,495,519],copper:489,coproccesor:[],coprocessor:[1,7,9,15,16,393,510],coproprocessor:16,copy_arrai:8,copyright:[7,8,302],coral:207,core:[],core_shel:160,cores:518,coreshel:[4,6,9],coreshell:[],cornel:[6,184,509],corner123i:124,corner123x:124,corner123z:124,corner1i:124,corner1x:124,corner1z:124,corner2i:124,corner2x:124,corner2z:124,corner3i:124,corner3x:124,corner3z:124,corner:[3,6,40,124,206,356,357,380,484,497,519],cornflowerblu:207,cornsilk:207,corp:9,corpor:15,corr:408,correct:[3,6,9,11,12,16,61,86,95,96,112,113,118,121,127,160,165,172,184,204,206,231,238,246,248,254,271,272,294,302,304,307,346,352,356,377,388,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,410,411,412,413,415,416,417,418,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,453,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,484,485,487,488,489,490,497,512,515,518,519],correction_max_iter:216,correctli:[3,8,11,16,75,87,112,113,114,155,156,157,159,161,163,164,165,166,167,170,171,174,203,207,213,232,237,244,255,265,271,272,310,317,321,331,334,353,356,388,389,393,411,441,446,494,495,497,507,519,523,525],correl:[],correpond:[241,242],correspond:[1,2,4,6,8,9,10,11,12,19,20,21,22,23,24,25,26,27,28,29,30,31,34,37,40,42,43,44,45,46,47,48,49,51,53,54,56,74,75,95,106,107,123,124,125,126,129,130,131,139,142,143,144,145,146,148,149,150,152,155,156,165,172,176,177,184,185,186,187,188,189,190,192,193,195,196,197,199,202,203,204,206,207,211,212,213,219,221,222,224,227,229,231,238,239,242,244,245,249,254,257,258,267,268,269,271,273,274,275,276,277,278,279,280,281,285,289,291,292,294,296,298,299,300,304,309,310,317,318,320,321,342,352,353,355,356,357,359,361,362,363,364,366,367,370,372,378,383,385,387,388,394,395,397,400,401,402,403,404,405,406,407,408,409,412,413,416,417,418,419,420,421,422,423,424,425,426,429,431,432,433,434,435,436,437,438,439,440,443,444,446,448,449,451,452,453,455,456,458,459,460,461,466,467,468,477,478,479,480,481,482,483,485,487,488,489,492,494,495,497,498,500,510,511,512,514,515,518,524],correspondingli:[440,441,506],corrupt:3,cosin:[],cosineshift:26,cosmo:[248,253],cossq:[],cost:[1,6,10,11,12,16,39,41,75,120,130,153,177,206,207,219,225,226,227,243,271,309,318,347,377,378,391,409,431,435,446,448,477,494,506],costheta0:[477,480,482,485],costheta:456,costli:[11,15,96,248,389],couett:4,coul:[],could:[2,3,6,9,11,12,16,32,41,50,61,69,75,79,86,95,98,103,114,116,120,123,125,158,168,173,175,191,203,204,206,207,211,212,219,220,225,231,244,253,306,307,308,312,315,317,320,335,336,342,346,347,348,352,356,358,360,368,374,376,384,386,389,393,395,396,421,425,426,429,458,459,493,494,495,497,499,501,504,505,512,513,518,524,525],coulomb:[3,5,6,7,8,9,10,12,14,17,76,96,118,119,127,153,179,183,308,310,348,377,378,386,393,400,402,403,404,405,408,409,410,411,412,418,423,424,426,431,435,439,442,448,453,458,459,461,466,476,482,483,485,488,502,509,515,518,523],coulommb:6,cound:3,count:[1,3,6,8,11,12,41,65,71,81,99,125,127,128,166,176,182,184,213,214,217,219,222,224,225,232,237,243,246,252,271,285,303,320,321,338,339,356,378,386,387,388,390,393,421,425,448,515,516,524],counter:[3,353,492,503,505,511],counteract:246,counterbal:[238,250],counterbalanc:28,counterclockwis:184,counterpart:[203,492],counterproduct:17,coupl:[],couple:[4,6,11,12],courant:324,cours:[3,8,15,138,140,172,203,211,212,247,316,331,346,352,354,355,357,358,378,440,468,494,497,510,518,524,527],courtesi:380,cov:466,coval:[6,9,28,418,443,466,518],covari:248,cover:[6,12,75,199,207,216,257,418,484],coverag:75,cpc:253,cpp:[1,3,6,8,9,11,12,13,95,203,211,212,244,321],cpu:[1,3,9,10,12,14,15,16,17,65,75,207,221,235,255,348,375,378,393,406,476,492,510,511,514,515,516,524],cpuinfo:15,cpuremain:515,cpus:12,cr2:177,cr3:177,crack:[4,389],crada:[5,7],crai:[5,7,13,17,203],crash:[3,12,389,518],craympi:393,creat:[],create_atom:[],create_bond:[],create_box:[],create_elementset:216,create_faceset:216,create_group:205,create_nodeset:216,createatom:[],creation:[],crimson:207,critchlei:302,criteria:[3,127,179,206,207,226,227,228,266,386,455,484,499,502,524],criterion:[12,41,133,178,181,217,225,228,246,285,309,324,353,358,386,388,408,418,423,466,502,511,512],criterioni:511,critic:[6,48,49,269,342,347,386],crmax:75,crmin:75,cross:[3,12,15,21,75,97,129,156,176,186,203,204,206,218,227,231,268,270,294,317,327,331,334,343,350,362,380,388,404,413,415,416,424,425,426,431,433,435,456,461,463,465,480,482,489,497,501,507,527],crossov:1,crossterm:497,crozier:[0,7,13],crucial:307,crystal:[3,4,6,13,15,77,117,298,299,345,380,389,497,501,515,518],crystallin:[6,113,299,380,481,518],crystallis:342,crystallogr:[130,177],crystallograph:[380,515],crystallographi:[130,177,380],cs1:177,cs_chunk:6,cs_im:[40,497],cs_re:[40,497],csanyi:[152,457,467],cscl:443,csequ:6,csh:[11,12,15,406],cshrc:[11,12],csic:[417,477,480,482,485],csinfo:6,csisi:[417,477,480,482,485],csld:[],cst:416,cstherm:6,cstyle:494,csvr:[],ctcm:[394,416],ctemp_core:235,cterm:323,ctr:9,ctype:11,cu1:177,cu2:177,cu3au:443,cube:[6,176,181,235,356,380,518],cubes:[41,225],cubic:[],cuda:[1,3,7,9,12,14,16,393,510,524],cuda_arch:[9,14],cuda_get:14,cuda_home:[9,14],cuda_prec:[9,14],cuh:399,cummul:[3,6,223,226,227,228,230,243,248,254,256,335,338,339,340,341,343,350,425,515],cumul:[6,217,219,222,236,246,254,269,271,276,285,317,319,388],curli:2,current:[0,1,3,5,6,7,8,9,10,11,12,13,14,15,16,17,40,41,42,57,61,63,65,75,77,87,95,113,119,127,128,142,153,158,166,168,174,176,179,182,202,203,204,205,206,207,208,211,212,216,219,223,225,226,227,228,229,230,231,232,236,237,238,244,246,248,251,252,254,260,266,268,271,272,277,278,279,285,287,291,292,293,294,296,302,303,308,309,311,314,315,316,317,318,321,322,323,324,325,326,327,328,330,332,333,334,335,338,339,340,346,347,350,351,352,353,354,355,357,358,360,375,376,377,378,381,382,383,385,386,387,388,393,399,406,408,412,416,418,419,423,426,427,430,440,441,443,444,445,448,456,458,459,462,463,464,465,468,480,482,483,486,492,493,494,495,497,498,499,500,501,503,504,505,507,509,511,512,514,515,519,524,525,526,527,528,529],curv:[6,178,246,287,293,299],curvatur:[422,460,489],custom:[],cut0:495,cut1:506,cut2:506,cut:[],cuthi:[298,310],cutinn:[401,440,441],cutlo:[298,310],cutmax:456,cutoff1:[405,412,431,435,439,442,453,461],cutoff2:[400,402,403,405,411,412,431,435,439,442,453,461,483],cutoff:[3,6,8,10,15,17,39,45,46,54,55,63,74,76,77,81,95,100,117,119,126,127,152,176,179,181,182,227,228,233,241,242,298,307,308,310,312,314,317,322,335,348,352,356,358,375,377,378,386,389,390,391,393,394,395,396,397,398,399,400,401,402,403,404,405,407,409,410,411,412,413,414,415,416,417,418,419,420,421,422,424,425,426,427,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,458,459,460,461,466,467,468,469,470,471,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,495,499,502,506,518,524],cutoffa:417,cutoffc:417,cuu3:416,cval:177,cvd:342,cvel:323,cvff:[],cwiggl:[3,268,352,355,357,524],cyan:[2,206,207],cycl:[3,246,269,271,272,276],cyclic:[3,184,199],cygwin:12,cylind:[3,4,75,206,219,252,293,303,353,356,382,500],cylinder_d:382,cylindr:[6,252,331,353],cypress:393,cyrot:399,cyrstal:299,d3q15:257,d3q19:257,d_double_double:14,d_e:347,d_flag2:306,d_flag:306,d_name:[124,203,306,337,507],d_single_double:14,d_single_single:14,d_sx:306,d_sy:306,d_sz:306,daan:345,dai:12,daili:12,daivi:294,damag:[],dammak:312,damp:[3,6,210,215,254,255,256,261,271,272,276,293,304,307,308,312,317,338,339,351,353,354,385,386,388,400,402,404,409,412,418,423,431,439,453,461,476,483,511,518],damp_com:255,damp_drud:255,dampen:[317,518],dampflag:[353,423],dan:16,danger:[3,12,246,358,413,515],dangl:181,daniel:9,darden:[378,412],darkblu:207,darkcyan:207,darken:206,darkgoldenrod:207,darkgrai:207,darkgreen:207,darkkhaki:207,darkmagenta:207,darkolivegreen:207,darkorang:207,darkorchid:207,darkr:207,darksalmon:207,darkseagreen:207,darkslateblu:207,darkslategrai:207,darkturquois:207,darkviolet:207,dasgupta:308,dash:[423,514],dat:[6,99,199,216,493],data2xmovi:[],data:[],data_atom:8,data_atom_hybrid:8,data_bodi:8,data_vel:8,data_vel_hybrid:8,databas:[],datafil:[12,13,319],dataset:319,datatyp:3,date:[0,4,6,12,13,202,458,459,524],datom1:[84,126],datom2:[84,126],datom3:[84,126],datom4:[84,126],datum:[3,6,42,68,71,73,84,102,119,126,203,220],davenport:442,davi:352,david:[9,18,377,378,480,482],daw:[416,456],dcd:[3,6,7,203,205,206,207,208,300,498,502],dcs:9,ddim:202,deactiv:439,dealt:253,debug:[6,7,11,12,13,16,57,130,134,177,178,300,305,375,377,427,448,486,495,496,504,507,514,524],deby:[],decai:[100,409,489],decid:[3,6,12,15,75,268,306,317,348,512,519],decipher:380,deck:306,declar:205,declin:335,decod:206,decompos:[95,467],decomposit:[3,5,7,17,64,216,300,322],decoupl:[6,518],decreas:[3,15,203,204,213,214,221,228,231,237,238,244,246,254,346,377],decrement:323,dedic:[15,393],deepli:374,deeppink:207,deepskyblu:207,def:[12,13,495],defaul:63,defect:[6,74,176,446],defgrad:[],defin:[2,3,5,6,7,8,11,12,16,19,20,21,22,23,24,25,26,27,28,29,30,31,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,51,53,54,55,56,57,58,59,60,61,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,184,185,186,187,188,189,190,192,193,195,196,197,198,199,200,201,202,203,204,205,206,207,210,211,212,213,214,215,216,217,218,219,220,222,223,224,225,226,227,228,229,231,232,235,236,237,240,242,244,245,246,249,252,253,254,255,256,257,266,268,270,271,272,273,274,275,276,277,278,279,281,282,283,286,289,290,291,292,294,295,296,298,299,300,302,303,304,306,308,310,315,317,318,319,320,321,324,328,332,333,335,337,338,339,340,341,343,344,345,347,349,350,352,353,354,355,356,357,358,360,361,362,363,364,365,366,367,370,371,372,373,375,377,378,380,383,385,386,387,388,389,390,391,392,393,395,396,397,398,400,401,402,403,404,405,406,407,409,410,412,413,414,415,417,418,420,421,422,423,424,425,426,427,429,431,432,433,434,435,436,437,438,439,440,441,442,446,447,448,449,450,451,452,453,455,456,458,459,460,461,462,463,464,465,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,494,495,497,498,500,501,502,503,506,507,508,509,511,512,514,515,518,520,521,522,523,524,525,526],definit:[2,3,6,8,12,13,82,85,127,152,207,219,220,221,222,223,231,240,252,276,319,337,349,352,355,357,359,371,375,387,396,399,407,414,418,429,456,463,465,467,484,495,497,499,506,508,523,524],defint:515,deform:[],deg2theta:177,deg:518,degener:[3,302],degrad:[8,17,299,378,506],degre:[3,6,8,19,20,23,27,28,31,34,35,37,68,84,100,102,104,106,107,109,111,112,117,123,155,156,157,158,159,160,161,162,163,164,165,166,167,168,170,171,177,178,184,185,188,189,196,197,199,202,206,219,228,235,246,248,249,254,255,260,271,272,276,277,278,279,291,292,294,296,300,302,316,317,321,338,339,340,345,361,363,367,370,372,386,412,416,425,507,515,518,525],degrees:199,degress:[158,219],del:511,delai:[3,6,12,389,415,515],deleg:426,delet:[2,3,7,8,12,54,59,62,65,176,181,182,210,219,220,222,223,226,228,243,246,271,319,338,339,358,360,376,387,389,392,448,475,497,498,499,500,508,509,514,519,520,522,524,525],delete_atom:[],delete_bond:[],delete_el:216,deli:202,delimit:[318,495,524],dellago:[6,238],deloc:[272,418,466],delr:443,delt_lo:511,delta:[],delta_1:399,delta_3:399,delta_7:399,delta_conf:3,delta_ij:[443,456],delta_pi:399,delta_r:456,delta_sigma:399,deltah_f:242,delx:202,delz:202,demand:312,demo:11,demon:297,demonstr:[307,443],den:303,dendrim:425,dendtrit:382,denniston:[9,257,259,260,261,299],denomin:[7,183],denot:[130,235,238,255,299,310,312,409,424,426,459,463,465],dens:[75,228,318,418],densiti:[3,6,7,9,17,40,41,61,110,127,138,152,164,176,178,211,212,216,219,225,231,244,257,260,264,265,299,303,304,306,308,347,352,380,383,387,394,395,399,416,443,444,445,450,451,456,460,466,470,472,473,474,497,506,507,515,523],density_continuity:465,density_summation:465,dent:382,depart:[0,7],departur:[269,307],deped:8,depend:[1,2,3,6,8,9,11,12,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,34,37,39,40,41,43,44,45,46,47,48,49,51,53,54,56,63,65,68,71,73,74,75,84,100,102,113,119,123,124,125,126,129,131,152,154,155,161,165,166,172,178,179,184,185,186,187,188,189,190,192,193,195,196,198,199,202,203,204,206,207,210,211,212,213,214,217,219,221,222,223,224,225,227,229,231,237,239,242,245,248,249,250,252,254,255,257,259,260,268,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,298,309,310,312,314,317,318,320,321,322,328,335,338,339,340,342,344,346,347,349,352,355,356,357,358,360,361,362,363,364,366,367,370,372,378,380,386,387,389,390,391,393,394,395,397,398,399,400,401,402,403,404,405,406,407,408,409,410,412,413,416,417,418,419,420,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,443,444,446,447,449,450,451,452,453,455,456,458,459,460,461,466,467,468,476,477,478,480,481,482,483,485,487,488,489,492,494,497,499,500,503,507,509,511,514,515,517,524,525],dependend:6,depflags:12,dephas:[492,511],depos:232,deposit:[],deprec:[3,308,458],deprect:219,depth:[51,156,206,347,420,422,460],dequidt:9,der:[95,118,407,408,439,458,459,488],deriv:[6,7,8,9,37,56,65,95,152,172,199,220,229,231,238,246,254,268,271,273,274,275,276,277,278,279,298,304,308,312,344,345,347,352,353,356,385,387,395,399,407,412,418,419,424,433,437,438,443,446,458,459,476,478,479,488,518],derjagin:488,derlet:298,descend:207,descent:[7,385],descib:[40,206,308],describ:[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,37,39,40,41,42,56,64,65,71,74,75,77,96,121,124,127,130,142,152,153,156,157,158,162,163,166,169,171,172,176,177,178,180,181,190,195,199,203,204,205,210,211,212,219,220,221,222,223,225,228,229,230,231,232,234,235,241,242,247,248,251,252,253,254,255,256,257,259,260,261,266,270,271,272,276,284,295,298,300,305,306,307,308,309,310,317,318,323,331,335,336,337,338,339,340,341,342,343,344,345,350,352,353,355,360,377,378,380,384,385,386,387,388,392,395,396,398,400,401,402,404,405,406,407,408,409,412,416,418,419,420,422,423,424,426,429,431,432,433,434,435,436,437,438,439,440,441,442,443,446,447,450,451,455,456,457,458,459,460,461,466,467,468,475,476,477,478,479,480,481,482,483,485,487,488,489,490,492,494,495,497,498,499,500,501,507,510,511,514,519,524,525,527],descript:[],descriptor:[152,203,427],deserno:378,design:[0,3,6,7,8,11,13,14,16,130,160,163,177,216,228,234,235,271,272,298,299,319,342,347,396,397,398,401,404,409,411,418,439,440,441,444,445,456,459,478],desir:[2,3,6,7,9,11,12,14,15,32,40,50,61,75,96,99,123,128,153,160,178,191,202,219,223,229,231,244,246,247,254,255,256,260,271,294,302,303,304,305,308,312,317,320,321,335,338,339,340,341,346,353,368,374,377,378,380,384,386,387,388,413,416,425,440,441,477,480,482,493,494,495,497,501,506,511,512,514,515,516,524,525,527],desk:7,desktop:[4,6,7,10,12,206],despit:518,destabil:399,destre:370,destroi:[9,11,39,226,227],detail:[1,2,3,4,6,7,8,9,11,12,13,14,15,16,17,18,21,36,40,41,42,55,65,69,70,71,75,79,82,86,98,99,103,114,116,118,120,121,122,123,125,128,129,131,152,153,155,156,157,158,161,171,172,173,175,178,179,182,183,186,198,202,203,204,206,207,210,211,212,216,219,220,221,222,223,225,227,228,229,230,231,232,238,244,246,247,248,249,251,252,254,256,257,261,268,269,270,271,272,273,274,275,276,277,278,279,283,285,291,292,294,295,296,299,302,303,304,306,307,309,310,311,317,321,322,335,338,339,340,341,342,343,345,346,347,348,349,350,351,358,360,362,371,377,378,381,386,387,389,390,393,394,395,396,398,399,401,403,404,405,406,407,408,409,412,413,418,419,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,441,442,443,447,448,450,451,455,458,459,460,466,467,468,476,484,487,488,490,495,497,498,499,500,502,503,506,507,509,512,515,516,519,524,525,529],detect:[2,3,12,42,63,65,94,100,245,303,346,388,408,425,430,492,494,497,508,511,524],determin:[1,3,6,8,9,12,14,15,39,40,42,51,59,60,61,63,64,71,75,95,112,118,119,123,126,129,130,131,139,153,166,167,176,177,178,202,203,204,206,207,208,209,213,214,215,218,219,220,221,222,223,224,225,229,231,232,235,237,242,246,249,250,252,254,255,260,266,268,269,271,277,278,279,291,292,294,296,298,300,303,304,307,314,315,316,317,318,319,320,324,326,328,335,338,339,340,342,348,349,352,353,354,355,356,357,358,371,377,378,380,387,389,390,393,395,396,403,408,412,415,416,420,421,423,426,427,435,443,446,448,451,459,460,466,475,478,479,483,488,494,497,498,500,502,504,507,511,513,514,516,523,524,525],determinist:322,detil:119,devan:[9,461],devanathan:482,develop:[0,3,5,6,7,8,9,11,12,14,15,16,17,18,42,251,276,302,307,308,311,395,399,418,445,446,466,485,499],devemi:9,deviat:[269,276,298,421],deviator:9,devic:[1,3,12,14,16,251,393],device_typ:393,devin:[309,408],devis:445,dfactor:206,dff:518,dfft_fftw2:12,dfft_fftw3:12,dfft_fftw:12,dfft_none:12,dfft_single:[3,12,378],dfft_xxx:12,dfftw:12,dfftw_size:12,dft:[9,311,446],dhi:[61,202,231,303],dhug:[269,307],dhugoniot:[269,307],dia:443,diag:318,diagnost:[],diagon:[3,6,91,129,152,153,154,229,271,304,317,350,463,465],diagonalstyl:467,diagram:[41,84,130,177,198,225,300],diallo:425,diam:[206,207,303,387],diamet:[3,6,40,42,124,178,203,204,206,207,211,212,254,303,306,317,335,337,351,353,387,407,422,423,433,460,484,488,497,498,507],diamond:[380,418,443],diamter:[40,303],dick:6,dicsuss:268,dictat:[217,269],did:[3,12,386,413,415,416,423,448,480,482,505],didn:3,die:17,diel:[],dielectr:[],diff:[3,6,12,15,174,349,377],differ:[1,2,3,4,6,7,8,9,11,12,14,15,16,17,21,36,37,39,41,42,54,55,56,63,66,71,74,75,95,100,104,106,107,132,152,154,155,156,157,158,159,161,164,165,166,167,168,170,171,172,178,179,181,186,198,199,202,203,206,207,210,212,215,217,219,222,225,226,227,228,229,230,231,235,238,241,245,246,247,248,249,250,251,254,255,257,268,271,272,273,274,275,277,278,279,281,283,286,289,290,291,292,296,298,300,302,304,307,308,309,312,315,317,321,323,331,333,335,338,339,340,343,344,345,347,350,351,352,353,356,360,361,371,374,376,377,378,380,381,384,385,387,388,390,391,392,393,394,395,399,403,404,406,407,408,413,414,416,418,420,422,423,424,426,429,431,432,434,435,442,443,444,445,447,448,449,450,451,452,456,458,459,460,461,462,463,465,466,467,468,476,477,478,479,480,482,484,485,488,491,492,494,495,497,499,500,501,502,505,506,507,509,511,512,514,515,516,518,523,524,525,527],differenti:[1,3,6,28,199,318,377,409,456,481],difficult:[229,300,393,425,506,516],difficulti:[100,117,321,458],diffract:[7,9,130,177,319],diffus:[],diffuse:[4,6],digit:[2,3,207,360,446,524],dih_table1:199,dih_table2:199,dihedr:[],dihedral_coeff:[],dihedral_cosine_shift_exp:26,dihedral_styl:[],dihedralcoeff:[3,200],dihedraltyp:227,dihydrid:418,dij:321,dilat:[],dim1:3,dim2:3,dim:[3,61,75,155,159,160,161,164,165,166,167,168,170,178,202,231,252,353,380,443,500,523,524,525],dimdim:524,dimems:299,dimens:[],dimensinon:75,dimension:[3,39,100,123,130,152,155,158,159,160,161,164,165,166,167,168,170,177,201,219,270,299,347,380,384,388,456,497,507],dimensionless:[115,133,134,136,139,141,143,148,152,347,378,467,488],dimensions:319,diment:3,dimentionless:147,dimer:[6,317,443],dimgrai:207,dimstr:[41,225],dinola:[304,338],dintel_offload_noaffinity:15,dipol:[],dipolar:[4,28,40,203,204,337,518],dir1:508,dir2:508,dir:[1,3,4,8,10,11,12,269,298,307,334,456,458,459,495,508,524],dirac:152,direc:456,direct:[],directli:[3,6,8,9,11,12,95,124,129,152,154,203,204,205,206,213,237,248,252,257,299,318,319,339,351,353,354,355,356,380,385,393,394,395,400,402,403,409,412,416,418,431,435,448,453,461,475,495,507,508,509,515,524],directori:[],disabl:[3,12,15,347,393,430,495,510,524],disadvantag:[6,225],disallow:[203,231,271],disappear:499,discard:[2,3,41,75,221,225,348,356,494,499,500],discontinu:[199,386,437],discourag:443,discov:[13,348],discret:[6,8,40,42,206,207,254,257,293],discuss:[],disk:[6,92,93,100,171,201,232,246,303,495],disloc:[74,446],disord:[39,74,446],disp:[],dispar:460,disperion:[412,435],dispers:[3,6,7,9,176,299,377,378,403,412,435,440,448,459,478,485],displac:[],displace_atom:[],displace_box:61,displacemet:500,displai:[11,13,21,36,44,55,186,198,203,206,362,371,406,476],dispters:3,disregard:446,dissip:[6,9,40,88,89,240,247,254,299,322,344,345,401,413,414,423,440,441,476],dissolut:226,dist:[6,73,99,119,128,203,300,316,415,475,492,525],distanc:[],distinct:[6,235,314,377,460],distinguish:[6,94,152,260,418,496,524],distort:[176,394],distrbut:394,distribut:[],distro:[122,406,455,456],ditto:[8,12,14,16,17,42,126,227,317,489,495],div:[8,15],divd:128,diverg:[3,12,39,317,345,395,499,518,527],divid:[3,6,15,41,99,123,128,138,140,153,175,176,186,198,207,219,220,222,225,231,298,343,350,355,377,386,388,419,459,466,485,506,514,524],divis:[6,197,257,399,429,439,494,515,524],dl_poly:[6,7],dlambda:172,dlammps_async_imd:251,dlammps_bigbig:[12,39],dlammps_ffmpeg:[3,12,206],dlammps_gzip:[3,12,203,206,346,497,498,502],dlammps_jpeg:[3,12,206],dlammps_longlong_to_long:12,dlammps_memalign:[9,12,15],dlammps_png:[3,12,206],dlammps_smallbig:12,dlammps_smallsmall:12,dlammps_xdr:[12,203],dlen:507,dlmp_intel_offload:[9,15],dlo:[61,202,231,303],dlopen:6,dlvo:[7,407,488],dm_lb:257,dmax:[335,384],dmpvtk:204,dmpvtp:204,dna:7,doc:[0,1,2,3,4,6,7,8,9,10,11,12,13,14,16,17,21,36,40,42,55,59,61,65,69,71,79,84,86,95,98,102,103,114,115,116,118,120,121,122,123,125,128,129,131,153,156,157,158,171,173,175,178,179,180,186,198,202,203,204,205,206,207,208,210,211,212,217,218,219,220,221,222,223,232,246,254,255,266,271,272,277,278,279,283,291,292,294,295,296,303,306,317,331,335,338,339,340,349,353,356,358,360,362,371,376,386,387,388,393,394,395,398,406,408,409,416,417,419,425,426,428,429,443,444,445,448,452,455,456,457,467,476,477,480,482,484,485,495,497,498,499,500,502,505,506,507,514,515,519,524,525,527,528],docuement:460,dodd:42,dodgerblu:207,doe:[0,1,2,3,5,6,7,8,9,11,12,14,16,17,32,37,39,41,42,50,54,56,61,64,65,70,74,75,76,95,96,99,114,118,121,127,128,130,154,156,158,160,161,166,168,172,177,178,179,180,182,184,186,191,198,199,202,203,204,205,206,207,210,216,217,219,224,225,227,228,229,231,235,237,238,241,242,243,246,247,250,252,254,255,257,260,267,271,272,273,274,275,277,278,279,291,292,293,294,295,296,304,305,306,310,312,315,317,335,338,340,342,343,347,350,351,352,355,356,357,358,363,364,365,367,368,370,376,377,378,379,380,387,388,389,394,395,396,397,398,399,401,403,404,405,407,408,409,410,412,413,415,416,417,418,420,421,422,423,424,426,427,428,429,430,433,434,436,437,438,440,441,443,444,445,446,448,450,451,456,457,458,459,460,462,463,464,465,466,467,468,469,470,471,472,473,474,475,477,478,479,480,481,482,483,484,485,487,488,489,490,492,493,494,495,497,498,499,500,501,504,505,507,508,509,510,511,514,515,518,519,524,529],doegenomestolif:7,doesn:[3,7,8,12,178,203,204,217,331,387,389,393,395,408,417,428,458,459,477,480,481,482,485,497,499],dof:[3,8,123,156,157,158,171,219,317,525],dof_per_atom:[158,219],dof_per_chunk:[158,219],doff:[387,497],doi:[6,230,298],domain:[3,6,7,12,13,17,39,41,42,60,63,64,75,130,167,177,180,202,205,206,207,210,217,225,229,231,232,250,253,257,271,272,300,312,317,322,347,352,353,377,378,388,393,415,448,492,494,497,501,514],domin:[1,418,511],don:[0,8,9,12,13,15,127,181,213,237,255,356,443,466,495,497,519],donadio:339,done:[1,3,6,7,8,12,14,16,17,37,39,41,56,61,64,75,172,175,178,181,197,199,203,206,207,216,217,219,221,222,223,225,226,227,228,229,231,232,241,242,244,246,251,252,254,255,262,271,277,278,279,291,292,294,296,297,299,300,301,303,306,314,317,319,321,335,338,339,340,342,344,345,358,360,376,377,378,386,388,389,392,393,395,403,416,426,427,428,435,441,442,443,448,450,451,458,475,478,479,484,492,493,494,495,498,501,502,505,515,516,518,519,524,525],donor:425,dot:[153,174,213,237,249,270],doti:[399,456],doubl:[1,2,3,6,8,9,11,12,14,15,16,39,95,184,231,244,305,356,360,376,378,392,393,399,419,424,446,458,459,493,497,501,505,510,524,525],dover:216,down:[3,6,7,8,11,39,75,229,246,254,335,351,418,448,466,496,516],downhil:[384,385],download:[5,7,8,9,11,12,13,16,251,427,457],downsid:6,downward:314,dozen:[8,9,12,118,210,458,459],dpack_array:12,dpack_memcpy:12,dpack_pointer:12,dpd:[],dpde:264,dpdtheta:[40,88,89,414],dpdtheta_i:[240,241,242],dproduct:396,dr_ewald:[130,319],drag:[],dragforc:257,drai:[206,269,307],drain:[250,351,386],dramat:[9,61,202,226,227,228,229,231,271,335,338,339,378,448,466,494],drautz:399,draw:206,drawback:306,drawn:[42,203,206,207,247,492],drayleigh:[269,307],dreid:[],dreiding:[],drfourth:115,drho:[124,394,416,450,451],drift:[6,113,115,238,247,248,250,254,255,267,315,335,506,514,518],drive:[11,12,214,229,231,249,271,298,304,317,354,388],driven:[6,190],driver:[6,12,14,210,244,251],drop:[3,207,413],droplet:426,drsquar:115,drude:[],dru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96,ernst:9,eror:3,eros:443,erose_form:443,erot:[],errata:[480,482],erratum:352,erron:3,error:[],erta:423,ervel:[124,204,497],escap:[232,518],especi:[8,15,166,178,210,217,225,246,307,312,315,316,393,494,516],espresso:[9,311],essenti:[11,12,26,96,140,159,160,161,164,165,166,167,168,170,187,220,299,351,378,395,409,431,482,502,515],essential:[8,276],essex:28,establish:[95,250],estim:[1,3,6,10,12,37,41,56,99,153,216,225,236,269,318,335,342,377,378,384,448,459,478,479,511,515],estimat:3,esu:523,esub:443,eta:[6,257,271,307,308,310,351,417,419,422,456,481,485,523],eta_dot:271,eta_ij:456,eta_ji:419,etag:[40,497],etail:515,etap:271,etap_dot:271,etc:[1,2,3,4,6,7,8,9,10,11,12,13,14,15,39,40,42,54,63,71,97,98,99,104,120,121,124,126,129,153,155,158,159,160,161,162,164,165,166,167,168,170,172,178,180,181,182,191,203,204,206,207,210,211,216,217,218,219,222,223,226,227,231,232,244,246,247,254,271,303,314,319,347,348,356,360,376,377,386,387,388,389,391,416,417,426,439,441,446,454,458,459,477,480,482,485,492,495,497,498,499,504,506,507,511,512,513,514,515,516,518,523,524,527,529],ethernet:17,etol:[386,388,492,511],etot0:307,etot:[6,104,106,107,121,153,164,207,235,255,269,307,514,515],eu2:177,eu3:177,euler:[318,386,388],eulerian:216,euqat:469,europhi:257,ev_tal:8,evalu:[2,3,9,11,12,37,56,75,95,96,99,118,128,131,152,154,158,168,176,178,184,202,203,204,206,207,211,212,213,214,216,218,219,220,221,222,223,224,231,237,247,249,250,252,253,254,255,299,305,308,318,320,324,328,338,339,340,349,352,355,357,358,360,384,386,446,448,450,451,456,462,464,466,478,479,492,493,495,499,500,502,504,505,506,507,511,513,515,524,525],evaluat:[377,378,524],evalut:[360,495],evan:[166,294],evanseck:[6,19,184,404,509],evapor:[],evaul:[8,386],evdwl:[118,154,458,459,515],even:[3,6,8,12,14,15,16,17,33,39,41,52,57,59,61,63,65,74,75,96,118,131,176,179,180,194,197,199,203,204,207,210,211,212,217,218,219,222,223,225,226,227,229,231,232,235,238,252,255,269,271,272,299,303,312,314,317,319,330,335,343,347,350,352,356,358,369,377,384,386,388,393,398,418,419,423,426,429,448,460,485,486,490,497,498,500,502,503,504,506,507,509,512,514,515,516,518,529],evenli:[3,41,57,153,199,225,257,429,486],event:[],eventu:[3,6,12,14,180,511],eventual:308,ever:[9,54,56,253,335],everaer:[407,422,460,476],everi:[0,1,2,3,6,8,9,11,12,14,15,39,41,75,76,99,124,131,140,166,181,203,204,205,206,207,208,210,211,212,213,216,217,218,219,220,221,222,223,224,225,226,227,228,229,231,232,236,238,243,244,246,248,250,251,252,257,258,267,271,272,276,287,293,297,298,299,303,304,305,306,307,308,309,310,312,314,315,317,318,319,321,323,332,335,337,338,339,340,341,342,343,346,347,348,349,350,358,360,376,378,388,389,390,393,413,415,426,439,458,459,466,472,491,492,493,497,499,501,502,504,505,506,511,512,513,515,516,519,524,529],everyth:[8,118,519],everywher:[127,433],eviri:418,evolut:[238,248,257,300,492],evolv:[257,300,348],ewald:[2,3,5,6,7,8,9,12,96,121,130,153,348,377,378,386,400,402,403,409,412,418,431,435,453,461,476,478,483],ewald_disp:412,ewalddisp:3,exact:[21,41,44,75,134,172,181,186,225,228,247,248,254,255,256,303,312,313,335,347,362,377,406,499,504,511,524,527,529],exactli:[3,6,12,16,37,41,42,56,61,75,76,99,127,156,162,169,178,199,211,212,222,225,231,236,241,247,254,255,256,272,284,285,295,299,306,307,335,340,341,354,393,406,413,416,423,426,440,448,450,451,478,479,499,500,507,511,524],exager:518,examin:[6,8,16,228,299,318],examp:[495,524],exampl:[],exce:[3,6,15,16,17,41,60,75,180,218,219,222,223,225,229,231,236,243,271,299,314,319,325,326,335,386,393,497,524],exceed:[3,41,61,225,231,271,335,459,505],excel:418,except:[1,2,5,6,8,10,11,15,19,20,21,22,23,24,25,26,27,28,29,30,31,34,36,37,40,41,43,44,45,46,47,48,49,51,53,54,55,56,61,62,65,75,97,98,119,123,128,153,155,156,157,158,159,160,161,162,164,165,166,167,168,169,170,171,178,182,184,185,186,187,188,189,190,192,193,195,196,198,199,202,203,204,207,210,213,219,220,222,224,225,229,231,239,245,246,249,252,254,256,271,272,273,274,275,276,277,278,279,280,281,284,285,289,291,292,294,295,296,300,309,310,317,320,321,331,335,341,347,355,358,359,360,361,362,363,364,366,367,370,371,372,377,378,380,383,387,388,389,391,392,393,394,395,397,400,401,402,403,404,405,406,407,408,409,411,412,413,416,417,418,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,441,444,449,452,453,455,458,459,460,461,468,476,477,478,480,481,482,483,485,487,488,489,492,494,495,497,499,500,502,505,506,507,508,509,511,515,518,523,524,525,528],exception:495,excess:[221,418],exchang:[2,3,6,8,63,64,210,216,217,238,246,250,254,309,317,343,347,350,377,393,418,512],exchange:377,excit:418,excite:418,exclud:[3,6,9,12,15,16,65,75,112,123,129,152,158,165,166,182,203,219,226,227,258,267,302,315,317,342,353,358,386,387,389,401,423,426,429,440,441,448,475,509],exclude:389,exclus:[1,3,12,15,95,184,408,446,448,506,516],excurs:[271,492],exectubl:12,execut:[1,2,3,4,6,8,9,11,12,16,62,179,206,251,311,360,376,379,392,493,495,505,508,511,524],exempl:466,exemplari:247,exemplifi:418,exert:[6,252,255,287,312,354,355,356,378],exhaust:[216,392,524],exhibit:[100,238,250,271,385,418,506],exist:[3,6,7,8,11,12,13,36,55,57,61,71,74,134,178,179,198,205,206,207,210,215,224,227,229,232,246,302,303,305,306,358,359,361,363,364,365,367,371,381,387,414,426,458,475,486,493,495,497,498,499,508,509,510,519,524,525,527],exit:[2,3,11,12,41,59,203,225,376,392,495,496,505,514,515,516,524],exlanatori:3,exp6:[],exp:[],expand:[],expans:[12,152,197,203,508,524],expect:[1,3,8,12,13,14,15,16,17,18,41,42,75,112,159,170,176,199,225,237,246,248,268,298,304,306,307,312,317,358,378,389,406,443,446,448,492,495,497,499,502,506,511,524],expens:[6,75,207,298,302,317,347,358,377,378,389,393,495],experi:[6,13,14,224,232,251,260,270,304,316,317,384,388,413,448,506,511],experienc:[6,12,15,259,260],experiment:[16,246,377,393,511],expert:12,expertis:7,expir:[515,516],explain:[1,3,6,8,9,11,12,17,41,61,65,68,71,73,75,76,77,80,81,84,94,102,129,158,166,199,203,204,206,207,210,219,220,223,225,227,229,231,271,287,298,306,317,331,358,360,376,377,380,387,388,392,398,416,429,466,468,484,495,498,499,502,504,507,518,519,524,529],explan:[3,6,61,124,129,152,203,204,219,270,298,426,491,494,495,497,506],explanatori:[3,8,128,203,204,218,219,222,317,387,494,524],explantori:[3,313],explic:447,explicit:[6,9,11,21,44,81,95,124,127,172,186,211,212,231,318,325,326,362,377,383,395,396,399,404,406,416,418,430,440,483,491,494,498,501,519],explicitli:[3,6,8,12,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,43,45,46,47,48,49,51,53,54,56,75,123,155,165,168,176,178,184,185,187,188,189,190,192,193,195,196,199,203,204,207,213,224,231,239,245,247,249,254,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,306,307,309,310,317,320,321,340,341,347,355,361,363,364,366,367,370,372,387,393,394,395,397,400,401,402,403,404,405,406,407,408,409,410,412,413,415,416,417,419,420,421,422,423,424,425,426,430,431,432,433,434,435,436,437,438,439,440,444,446,448,449,450,451,452,453,455,459,460,461,468,469,470,471,472,473,474,476,477,478,479,480,481,482,483,485,487,488,489,497,499,500,506,507,509,510,516,518,519],explictli:[15,510],exploit:[14,16,300],explor:[130,177],expon:[3,308,310,318,416,420,422,425,439,447,461],exponenti:[95,318,420,456,477,485,489,511,524],expos:11,exposit:[216,413,415],express:[6,152,164,178,211,212,229,268,298,308,318,347,353,360,399,416,418,433,443,466,467,476,524],expression:360,expressiont:399,extend:[],extens:[3,6,9,15,16,44,45,46,53,55,65,67,72,83,90,91,92,95,96,99,101,104,107,108,118,120,128,131,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,204,210,213,214,217,223,224,230,233,237,238,244,245,246,248,249,250,252,254,256,269,271,276,285,298,299,315,316,317,320,323,328,331,332,334,338,339,340,341,342,344,345,347,349,352,356,357,422,443,446,459,460,467,514,515,523],extensiv:[65,210,515],extent:[1,3,41,42,45,59,75,176,180,203,215,225,252,354,357,377,380,395,462,464,478,479,494,497,500],exterior:[3,6,176,356],extern:[],extra:[3,6,8,11,12,15,16,40,41,46,63,75,112,120,121,123,130,153,155,156,157,159,161,164,165,166,167,168,170,171,177,178,179,180,207,222,225,227,271,305,306,307,317,335,386,387,390,391,393,412,423,426,429,443,448,494,495,497,500,509,518,524],extract:[3,6,11,13,35,65,67,72,83,95,101,118,126,128,131,211,212,310,388,409,419,443,467,495,502,514],extract_atom:11,extract_comput:[11,495],extract_fix:11,extract_glob:11,extract_vari:11,extramake:[12,14],extrapol:1,extrem:[1,3,6,16,60,206,221,229,231,271,345,418,481,518],extrema:439,extreme:418,extrins:216,f77:[5,7,12],f90:[5,7,12],f_1:6,f_5:[174,349],f_a:[480,481,482],f_ave:128,f_c:481,f_f:482,f_fix_id:307,f_foo:524,f_harm:345,f_i:[28,456],f_id:[6,75,128,131,203,204,210,218,219,220,221,222,223,266,337,349,515,524],f_ij:456,f_indent:223,f_int:344,f_j:28,f_jj:99,f_k:456,f_langevin:347,f_max:[307,312],f_msst:269,f_r:[255,480,481,482],f_sigma:399,f_solid:345,f_ss:6,face:[3,6,59,61,75,166,176,180,215,352,354,355,356,357,380,422,443,460,497,500],face_threshold:176,facet:176,facil:[0,12],facilit:[6,13,42],fact:[6,8,15,248,303,335,345,423,458,466,509],factor:[1,3,6,12,17,23,27,31,34,35,39,41,46,47,59,60,61,76,95,99,112,119,126,127,130,152,172,177,180,184,195,202,203,206,207,211,212,220,225,229,231,232,238,246,251,254,256,257,269,271,272,276,300,304,308,316,318,321,324,326,335,339,343,350,351,352,356,367,378,380,387,393,395,396,399,400,402,404,409,410,411,413,414,418,423,426,430,431,443,446,448,450,451,452,453,459,461,468,477,483,494,497,500,501,506,509,511,512,515,518,523,524],factori:[3,495],factoriz:377,fail:[3,9,11,12,61,182,229,232,377,386,388,411,459,495],failur:[133,463,496,524],fairli:[11,448,506,511,519],faken:77,falcon:251,fall:[3,6,207,222,303,495,524],fals:[94,358,466,524],fame:8,famili:[485,494],familiar:[0,11,519],fan:456,far:[3,6,12,16,59,61,63,94,203,204,207,208,225,226,227,229,232,271,298,316,317,335,352,363,367,384,388,389,484,495,497,502,515],farago:254,farrel:[480,482],farther:203,fashion:[6,8,41,75,178,207,210,211,212,217,225,227,232,246,248,252,268,269,271,273,274,275,276,277,278,279,288,291,292,294,295,296,306,307,309,317,323,327,334,337,345,347,351,352,353,355,357,388,426,440,500,509,524,528],fasolino:428,fast:[6,7,9,12,13,15,16,39,203,204,282,307,318,348,377,378,401,440,441,446,476,478,479,499,504,506,515,525,529],faster:[1,6,9,11,12,14,15,16,17,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,41,43,45,46,47,48,49,51,53,54,56,63,65,115,123,155,165,184,185,187,188,189,190,192,193,195,196,199,203,204,207,213,224,225,231,239,245,249,253,254,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,304,308,309,310,317,320,321,335,342,344,347,351,355,361,363,364,366,367,370,372,377,378,390,391,393,394,395,397,399,400,401,402,403,404,405,406,407,408,409,412,413,416,417,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,444,449,452,453,455,459,460,461,468,476,477,478,480,481,482,483,485,487,488,489,492,500,506,510,518],fastest:[1,6,16,166,347,348,393,494],fatal:[3,514],fault:[74,459],faulti:12,fava:422,favor:228,favorit:7,fbmc:342,fcc:[],fcm:[288,524],fcold:238,fdirect:235,fdotr:427,fdt:[],fdti:95,fe2:177,fe3:177,fe_md_boundari:216,featu:8,featur:[],fecr:416,feedback:[7,251],feel:[7,251,252,260,298,356,358,388,448],fehlberg:318,felling:445,felt:356,femtosecond:523,fene:[],fennel:[409,431],fep:[],ferguson:[6,184,509],fermi:[1,9,10,12,14,164,393,482],fermion:418,fernando:442,ferrand:[9,13],few:[1,3,4,5,6,7,9,10,11,12,13,17,39,65,208,218,219,220,222,223,238,255,271,303,306,308,321,349,377,386,387,388,395,466,494,497,502,506,508,516,527],fewer:[1,3,11,14,15,63,260,506],fewest:3,fextern:244,feynman:300,fff:495,ffield:[408,419,458,459,466],fflag1:206,fflag2:206,ffmpeg:[3,12,206],ffplai:206,fft:[1,3,7,9,11,12,14,96,120,121,153,299,377,378,506],fft_inc:[12,378],fft_lib:12,fft_path:12,fftbench:[377,516],fftw2:12,fftw3:12,fftw:[9,12],fhg:[7,9],fhot:238,ficiti:475,fictiti:[6,213,214,237,244,248,300,316,409,431,435,475],field1:[498,502],field2:498,field:[],fifth:[6,331,420,452],figshar:298,figur:[1,3,8,12,15,307,494,495,519],fij:412,file0:298,file1:[11,13,242,298,346,360,387,502,504,508],file2:[11,13,242,346,360,387,502,504,508],file:[],file_from:205,filen:387,filenam:[3,12,13,15,37,41,56,199,203,204,206,207,208,216,219,220,221,222,223,225,230,241,242,298,302,305,308,309,310,313,314,317,318,319,346,347,374,375,376,387,388,394,395,399,409,416,417,419,420,428,443,444,445,450,451,452,456,457,458,459,466,467,477,478,479,480,481,482,485,493,494,495,498,499,504,508,515,524,527,528,529],filennam:504,filep:[3,203,204,207,499,504,529],filepo:314,fill:[7,9,178,206,303,347,380,389,399,446,459,500,519],filter:[207,216],final_integr:8,final_integrate_respa:8,finchham:[6,160,411],find:[0,3,4,6,7,8,9,11,12,13,15,37,39,56,63,75,77,95,128,181,199,208,217,228,229,241,242,243,246,270,298,303,304,312,316,384,386,388,389,409,426,431,435,443,450,451,466,476,478,479,518,519,524],find_custom:8,fine:[15,16,182,213,237,345,389,393,519,524],finer:[152,178,524],finest:377,finger:[178,202,268,500],finish:[6,11,41,225,360,374,376,377,390,392,393,484,502,524,525],finit:[],finni:[7,416,476],finverse:235,fiorin:[9,230],fire:[],firebrick:207,first:[0,1,2,3,5,6,8,9,10,11,12,14,15,16,20,37,39,41,42,45,46,54,56,59,61,63,64,75,87,96,99,113,114,115,117,123,127,128,139,142,145,146,150,153,163,166,172,174,176,177,179,180,181,185,199,203,204,205,206,207,208,210,211,219,220,222,223,225,228,231,241,242,246,247,252,257,268,269,270,271,298,300,305,306,307,309,314,317,321,323,331,332,333,335,336,337,344,345,346,347,349,358,360,361,368,380,386,387,388,389,392,393,394,395,398,399,400,402,404,406,408,409,416,418,419,420,423,424,426,427,428,429,430,431,435,440,441,443,445,446,448,450,451,452,456,458,459,466,467,475,477,478,479,480,481,482,485,489,492,493,494,495,497,498,499,502,504,506,509,510,511,514,515,518,519,524,525,526,527,529],fischer:[6,9,18,19,184,404,509],fit:[3,6,9,12,37,56,199,241,316,335,395,399,428,443,448,450,451,471,478,479,481,505,519,524],five:[77,164,307,387,399,444,497,511],fix:[],fix_deposit:3,fix_flux:216,fix_heat:238,fix_id:[3,229,269,271,273,274,275,276,277,278,279,304,307],fix_manifoldforc:519,fix_modifi:[],fix_nh:8,fix_nv:519,fix_poem:3,fix_pour:3,fix_qeq:3,fix_rattl:321,fix_reax_bond:458,fix_saed_vtk:319,fix_setforc:8,fix_shak:321,fix_srd:3,fixedpoint:[229,271],fixextern:244,fixid:[206,216],fji:412,flag1:[234,391],flag2:[234,391],flag:[3,8,9,11,12,14,15,16,38,40,58,69,78,79,86,87,94,97,98,103,113,114,116,129,130,173,177,181,200,203,204,206,207,208,223,228,230,234,251,254,258,260,267,268,299,306,317,331,334,335,342,346,355,358,373,375,378,387,391,392,393,395,425,430,443,446,475,490,492,494,495,497,498,499,501,502,503,507,519,524],flag_buck:403,flag_coul:[403,412,435],flag_lj:[412,435],flagfld:[401,440,441],flaghi:[3,401,440,441],flaglog:[401,440,441],flagn:234,flagvf:[401,440,441],flat:[6,15,347,352,353,357],flavor:[2,7,12],fld:[352,440,441],flen:396,flex_press:396,flexibl:[3,6,8,179,206,219,230,248,272,343,350,418,481,515],flip:[3,6,231,271,354,355],floor:524,flop:12,floralwhit:207,flow:[],fluctuat:[6,66,95,229,246,247,254,257,271,276,298,299,304,322,345,347,370,414],fluid:[],fluid_veloc:261,flush:[3,207,514],flux:[],flv:206,fly:[7,9,12,41,206,210,216,221,232,235,317,321,348,399,446,515,518],fmackai:9,fmag:233,fmass:300,fmax:[386,515],fmomentum:235,fmsec:[2,207,254,255,268,271,304,317,338,339,506,517,523,525],fname:376,fno:[9,15],fnorm:[386,515],fnpt:235,fnve:238,fnvt:235,foce:426,fock:396,focu:321,fogarti:[9,310,459],foil:[152,298,467],fold:[15,333,506],folk:7,follow:[0,1,2,3,6,7,8,9,10,11,12,13,14,15,16,17,18,19,22,23,24,25,26,27,28,29,30,31,34,35,37,40,41,42,43,45,46,47,48,49,51,53,54,56,61,65,66,74,75,77,88,99,100,106,107,117,124,127,128,131,152,153,156,157,158,164,166,171,174,176,178,179,184,187,188,189,190,192,193,195,196,197,199,203,205,206,207,210,216,217,218,219,220,221,222,223,225,230,231,232,235,236,240,241,242,244,246,247,248,251,253,254,255,257,260,269,271,276,277,278,279,291,292,294,296,299,300,302,305,306,307,308,310,312,314,316,317,318,319,321,337,338,339,340,343,344,345,346,347,349,350,358,359,363,364,365,366,367,370,372,375,380,383,386,387,388,393,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,410,411,412,413,414,415,416,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,447,448,449,450,451,452,453,455,456,457,458,459,460,461,463,465,466,467,468,469,470,471,472,473,474,475,477,478,479,480,481,482,483,484,485,487,488,489,490,492,494,495,497,498,500,502,504,505,506,509,511,512,513,518,519,524,525,528],foo:[4,8,11,12,203,206,244,318,495,508,524],foo_species:318,foot:6,footprint:[12,393],fopenmp:[9,17],forc:[],force_uvm:16,forceatom:260,forcefield:[316,425],forcegroup:257,forcezero:384,ford:412,forestgreen:207,forev:75,forget:[255,518],forgiv:271,fork:[],form:[2,3,6,8,12,18,21,44,54,57,65,69,78,79,81,86,87,95,97,98,100,103,113,114,116,127,152,153,172,173,182,186,207,210,211,212,227,247,248,254,256,260,268,294,299,310,312,316,317,318,347,352,356,361,362,365,370,383,385,387,388,395,396,399,406,414,416,418,421,425,426,430,442,443,445,446,450,451,452,453,456,458,459,460,466,467,468,476,478,480,481,482,488,491,494,495,497,502,507,514,518,524],formal:[6,82,85,99,247,248,254,271,300,335,343,466],format:[2,3,6,7,8,9,12,13,21,37,41,44,56,57,71,81,186,199,203,204,205,206,207,208,219,222,223,225,227,241,242,299,302,306,308,310,313,317,318,319,330,346,347,358,359,362,383,387,388,394,395,399,406,416,419,420,430,443,445,450,451,457,458,459,461,467,478,479,485,486,494,495,497,498,499,502,513,514,515,524,526,527],formation:242,former:[6,12,39,41,207,225,347,351,399,401,503,509,524],formerli:[7,13],formul:[1,6,9,40,66,153,213,237,254,271,294,308,310,316,321,346,377,395,399,416,418,422,443,455],formula:[2,3,6,7,13,20,21,36,44,54,55,74,77,95,97,98,99,104,106,107,116,123,130,131,153,155,156,157,158,159,160,161,162,164,165,166,167,168,169,170,171,177,178,183,185,186,198,202,203,211,212,213,214,219,220,221,222,223,224,231,237,249,250,252,254,255,268,298,299,305,314,320,328,333,335,338,339,340,349,352,353,355,357,358,360,361,362,364,371,380,387,395,396,398,399,404,405,406,407,412,413,416,417,422,423,424,425,427,430,431,433,434,435,437,438,440,441,443,447,448,449,460,461,468,476,477,478,480,481,482,485,487,488,493,497,500,507,514,515,523,524,525],forth:[1,6,11,12,13,14,392,495,500,504],fortran:[3,6,9,11,12,13,244,416,426,443,458,459],fortun:8,forward:[3,8,95,376,388,393],foster:[399,455,456],foul:181,found:[3,6,9,12,77,172,203,228,230,238,246,251,257,263,299,342,348,360,376,389,406,409,412,492,498,499,514],four:[6,11,54,87,113,114,152,174,269,347,370,387,388,446,492],fourier:[],fourth:[6,15,115,316,331,342,404,420,452,466],fox:[6,130,184,474,509],fphi:[37,56,478,479],fpic:12,fplo:[37,56,478,479],fprime:[478,479],fqdn:253,fqq:412,fraction:[1,3,6,8,12,15,39,41,85,120,153,181,202,206,207,217,226,227,228,229,269,303,307,314,315,318,335,340,341,380,388,393,399,401,420,423,440,441,502,507],fragment:[42,251,314],fraig:42,frame:[91,152,207,216,269,307,354,422],framer:[206,207],framework:[5,248,394,467],franc:9,frattl:238,fraunhof:9,free:[5,6,7,9,13,28,62,65,74,95,172,211,212,298,335,344,345,346,347,385,388,396,418,439,446,449,456,488,494],freedom:[3,6,8,104,106,107,109,111,112,123,155,156,157,158,159,160,161,162,163,164,165,166,167,168,170,171,219,228,235,246,248,254,255,260,271,272,276,277,278,279,291,292,294,296,300,302,317,321,338,339,340,345,386,412,515,518,525],freeli:[0,6,7,12,156,157,171,176,206],freez:[],frenkel:[6,238,246,345],freq:215,frequenc:[3,6,15,39,113,207,221,285,299,300,307,312,318,375,413,418,459,466,492,506,511,524,528],frequent:[3,66,70,74,76,77,81,96,100,117,152,207,226,227,238,243,343,350,448,483,504,516],fri:[269,307],friction:[4,5,6,9,10,42,210,248,254,307,312,317,347,351,353,423,507],friedrich:324,from:[],front:[269,307,354],frontend:[206,311],frozen:[6,123,182,245,247,255,389,421],fs2:[6,99],fscale:251,fstr:524,fstring:495,fstyle:353,ftol:[386,388,492,511],fuchsia:207,fuction:409,fudg:321,fugac:246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388,426,495,518,524],ilmenau:7,ilya:[7,9],imag:[],image2pip:206,image:[3,12,203,206,319],imageint:3,imagemagick:[4,206],imagin:[331,346,399,417,426,427,429,444,445,452,456,477,480,481,482,485,509],imaginari:[6,100,246,300,497],imbal:[1,12,41,225,318,393,516],imbalanc:[41,225],imbu:335,imd:[],img:206,immedi:[0,2,3,8,12,178,226,227,232,318,321,336,337,354,494,495,497,499,511,524,528],immediat:[330,524],immens:3,immers:[257,317],impact:[1,4,6,8,9,236,342,395,516],impart:[3,6,249,335,357],impei:[6,431],implement:[1,3,6,8,9,12,16,17,26,82,88,89,95,117,130,160,166,177,178,186,187,198,219,230,234,238,240,248,250,251,254,257,259,260,261,269,271,294,297,299,300,306,307,310,311,312,318,321,322,323,335,342,347,351,370,376,377,378,386,388,393,394,396,399,408,409,411,413,414,416,417,418,426,431,435,439,443,455,458,459,460,480,482,494,495,506,511,518,524,526,527],impli:[3,6,40,61,95,153,202,206,211,212,213,231,237,254,316,338,340,341,377,380,406,495],implicit:[],implicitli:8,implict:410,imporop:387,important:345,impos:[2,6,75,123,167,202,210,213,214,224,237,238,239,244,249,252,261,262,270,285,298,301,320,328,331,332,334,335,342,343,344,345,350,351,352,355,356,357,386,388,390,491,505,516],impose:249,imposs:[1,3,306,497],improp:[],improper:[3,8,15,362,368,370,371,387,497],improper_coeff:[],improper_styl:[],impropercoeff:[3,373],impropertyp:227,imprortant:107,improtant:[211,212],improv:[0,1,9,15,39,41,207,225,238,250,271,299,393,425,431,446,448,459,477,481],imrop:369,in3:177,inaccur:[1,3,6,181,269,377],inaccuraci:356,inact:[425,515,516],inappropri:178,incid:[130,177,232],includ:[],include:[12,374],includelink:9,includig:[360,376],inclus:[],inclusion:12,incom:251,incompat:[3,11,263,287,293,427],incomplet:[3,11,497],incompress:[272,418],inconsist:[3,182,228,498],inconveni:380,incorpor:[197,307,399,410,519],incorrect:[3,161,254,443],incorrectli:[3,380,423,524],increas:[1,3,6,15,17,37,56,59,61,120,130,153,199,203,204,206,207,221,226,227,228,231,238,241,242,246,254,304,315,316,317,318,343,346,350,377,378,388,393,395,418,422,450,451,459,478,479,481,495,506,511,524],increasingli:418,increment:[3,11,140,213,214,224,225,232,237,243,271,303,323,324,358,376,392,429,466,492,495,509,511,524],incur:[16,219,243,347,494],inde:161,indefatig:7,indefinit:344,indent:[],independ:[4,6,9,11,12,16,41,61,65,86,99,128,131,164,178,202,210,218,219,220,222,223,225,228,229,230,231,232,247,249,254,255,257,260,271,299,304,308,312,317,319,323,334,345,347,380,423,446,492,495,514,525],indetermin:[203,204,207],index:[0,3,6,8,11,12,37,39,40,56,57,68,71,73,84,102,119,128,129,131,176,199,203,207,218,220,241,242,251,253,300,319,347,358,359,360,383,392,448,450,451,458,459,478,479,486,497,512,524],indianr:207,indic:[2,3,6,8,11,12,15,20,40,42,54,67,70,72,78,83,87,88,89,96,97,99,101,113,115,123,126,128,153,155,156,157,158,159,160,161,162,163,165,166,167,168,169,170,171,174,185,199,203,206,211,212,229,271,299,317,347,358,361,370,377,387,389,408,415,419,426,443,446,448,452,458,459,466,480,482,494,496,497,499,515,519,524],indigo:207,indirectli:[6,306,524],indistinguish:254,indium:467,individu:[],induc:[],industri:7,ineffici:[3,6,40,66,70,74,76,77,81,100,117,152,166,206,231,271,299,377,390],inelig:217,inerti:441,inertia:[],inertiai:129,inertiax:129,inertiaz:129,inexpens:[248,506],inf:[2,3,12,350,500],infer:[3,104,106,107,172,213,214,225,226,227,237,251,302,335,343,350,380,406,419,497,509,515],infil:[3,13,317,494],infin:[3,386,502,515],infininti:206,infinit:[3,232,245,252,254,257,299,335,347,353,354,378,380,418,497,501,523],infinitesim:6,inflect:[410,433,476],influenc:[3,9,41,85,160,268,303,377,378,448,480,481,482],inform:[0,1,2,3,6,7,8,9,11,12,13,14,15,16,39,41,42,61,63,64,65,71,96,126,128,129,130,177,178,184,203,204,205,206,207,208,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,235,236,237,238,239,243,244,245,246,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,267,270,280,281,282,283,284,285,286,287,288,289,290,293,297,298,299,301,302,303,304,305,306,308,309,310,311,312,313,314,315,316,317,319,320,321,324,331,332,333,335,336,338,339,340,341,342,343,344,346,349,350,351,352,354,355,356,357,359,375,377,378,381,385,386,387,388,389,391,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,412,413,415,416,417,418,419,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,449,450,451,453,455,456,457,458,459,460,461,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,484,485,487,488,489,490,494,495,497,498,499,501,502,504,509,511,512,514,516,524,526,527,528,529],informat:[8,12,498],infrequ:[6,271,413,492,511,516],infti:[410,440,441],ingtegr:399,inher:[377,386,448],inherit:[6,483],inhomogen:[17,347,448],inidividu:386,init:[3,8,315,466],init_fil:347,init_list:8,init_on:8,init_styl:8,initi:[2,3,4,6,7,8,11,12,13,37,39,40,41,56,59,61,64,75,85,87,94,95,113,114,142,174,179,180,199,202,203,204,206,207,208,211,212,215,216,220,225,227,228,229,231,239,241,242,246,247,251,252,253,254,255,257,262,267,268,269,270,271,276,285,287,299,300,301,306,307,312,315,316,317,318,320,322,332,334,335,337,342,344,345,346,347,348,349,352,353,354,355,357,358,360,377,381,385,386,388,395,396,412,413,415,446,450,451,457,458,459,466,478,479,492,494,495,497,499,500,502,504,505,507,511,512,515,518,524,525,527,529],initial:[2,3,6,87,113,180,524],initial_integr:8,initial_integrate_respa:8,initialis:457,initialt:347,inlclud:11,inlin:495,inner2:[404,424],inner:[3,8,15,57,203,252,360,376,384,385,386,388,392,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,410,412,413,415,416,417,418,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,453,455,457,458,459,460,461,466,467,468,469,470,471,472,473,474,475,477,478,479,480,481,482,484,485,486,487,488,489,490,506,511,524],inner_distance_cutoff:425,innergroup:260,innermost:[37,56,75,391,478,479,506],innernod:260,innner:437,inordin:348,inorgan:[6,485],inp:[230,360,467,485],input1:[71,124,125,128,129,131,337],input2:[71,124,125,128,129,131,337],input:[],input_doubl:3,inquir:324,insensit:12,insert:[3,5,7,8,9,12,61,178,210,232,246,252,303,377,467,475,495,501,518],insertion:[3,232,246,303],insid:[2,3,6,8,11,75,129,141,147,178,203,204,207,218,232,233,238,243,246,252,257,260,303,317,335,352,354,355,356,357,358,375,380,495,496,497,499,500,507,524],inside:[257,433,495,511],insight:[6,13],instabl:[257,412,465],instal:[1,3,6,8,9,11,12,14,15,16,176,203,205,206,208,378,388,403,404,427,431,435,439,446,492,498,499,504,512,519,529],install:[],instanc:[6,9,11,211,230,248,354,421,426,448,456,495,518],instantan:[6,65,228,229,247,248,271,276,299,304,307,312,314,317,342,503,515],instanti:[6,11,12,216,426,494],instead:[1,3,6,8,11,12,13,15,16,17,40,41,61,63,65,74,75,98,113,128,156,160,182,184,199,203,204,212,219,222,223,225,229,230,246,254,257,260,261,299,305,306,315,317,337,355,375,377,378,381,389,393,402,403,416,430,432,439,443,446,449,459,476,483,492,500,504,511,513,518,524],institut:[9,251,302],instruct:[3,4,6,8,9,10,11,12,13,15,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,43,45,46,47,48,49,51,53,54,56,123,155,165,176,184,185,187,188,189,190,192,193,195,196,199,201,206,213,224,231,239,245,249,254,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,309,310,317,320,321,355,361,363,364,366,367,370,372,378,393,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,444,449,452,453,455,459,460,461,468,477,478,480,481,482,483,485,487,488,489,500,506,518],instruction:11,insuffici:[3,6,12],insult:271,insur:[3,6,11,12,16,39,40,63,77,112,114,178,179,199,203,204,206,207,213,226,227,232,237,239,243,244,246,249,254,267,305,306,315,317,335,347,352,356,357,358,360,376,387,389,393,407,422,426,454,460,478,490,494,495,497,498,502,505,506,514,515,524,525],insure:16,int_max:3,inteatom:10,integ:[3,6,8,11,12,39,40,42,66,71,74,75,117,124,126,128,131,152,176,178,181,182,184,188,189,193,197,199,202,203,206,207,217,219,226,227,228,232,234,244,246,247,248,251,254,255,256,257,299,302,303,306,307,312,317,318,335,337,339,342,346,347,366,377,380,401,413,414,415,429,443,458,459,463,465,467,492,494,495,496,497,505,506,507,511,514,524,525],integer:[3,497],integr:[],integral:[300,450,451],integrate_ulsph:[],intel:[],intel_coprocessor:15,intel_cpu:12,intel_cpu_intelmpi:15,intel_cpu_mpich:15,intel_cpu_openpmi:15,intel_phi:12,intend:[3,6,8,12,13,35,221,247,457,497],intens:[1,3,6,9,65,69,78,79,86,94,97,98,99,103,113,114,115,116,123,125,127,128,130,131,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,173,174,175,177,210,219,220,222,223,225,226,227,228,236,243,250,260,269,271,276,314,317,319,335,343,347,349,350,514,515,523],intensiv:[65,210,515],intepol:524,inter:[17,42,63,64,158,181,182,203,228,254,256,270,309,317,377,388,399,507,518,524,527,529],interact:[1,3,6,7,8,9,10,11,12,14,16,21,28,32,33,36,39,40,42,44,50,52,54,55,57,59,63,65,68,73,76,81,84,95,96,100,102,117,118,119,121,123,126,127,134,135,136,137,139,140,141,142,143,144,145,147,148,149,150,152,153,154,156,157,171,172,176,179,180,181,182,183,184,186,190,191,194,197,198,203,204,210,211,212,226,227,228,245,246,251,252,254,256,260,285,298,300,302,303,308,310,316,317,318,325,326,335,336,342,347,351,352,353,356,357,362,363,364,365,367,369,371,377,378,386,387,388,389,390,391,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,451,453,454,455,456,459,460,461,463,465,466,467,468,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,492,495,497,499,501,502,506,507,509,514,515,518,527],interatom:[3,4,7,9,10,178,203,270,344,345,394,399,416,418,427,443,446,466,481,524],intercept:130,interchang:6,interconnect:17,interconvert:418,intereract:39,interesect:356,interest:[1,5,7,8,9,11,13,75,177,300,342,345,378,417,441,458,459,495,524],interf:[393,519],interfac:[],interfer:[12,271,395],interg:[6,518],intergr:506,interi:441,interior:[3,6,41,356,500],interlac:443,interleav:[6,178,505],intermedi:[6,12,61,206,270,298,370,388,495,496,505,509],intermix:492,intermolecular:[154,395],intern:[],internal:[3,206,212,303,524],internal_element_set:216,internal_quadratur:216,internat:[15,130,177,216],internet:253,interpenetr:443,interpentr:[470,471,473],interpol:[6,14,37,56,110,199,206,207,216,241,242,257,298,377,378,388,399,448,450,451,459,472,478,479,480],interpret:[2,6,11,197,206,222,423,468,492,495,511,524],interrupt:307,intersect:[3,6,130,207,356,358,500],intersert:356,interspers:386,interstiti:[176,446],intertia:[3,103],interv:[3,6,99,205,220,254,271,307,312,313,326,466,472,492,511,524],intestieti:130,intial:[6,393,395],intiial:[41,502],intiti:[3,334],intra:317,intra_energi:246,intramolecular:[28,154,246],introduc:[6,9,100,117,206,238,271,307,312,317,370,377,394,409,418,431,435,439,477,511,524],introduct:[],intuit:380,inv:[130,177,319],invalid:[3,12,75,97,181,285,388,440,441,499],invari:[117,145,150,152],invent:321,invers:[],inverse:518,invert:[1,6,182,299],invis:356,invoc:[176,228,393,463,465,495],invok:[1,3,6,7,8,9,11,12,13,15,16,17,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,41,43,45,46,47,48,49,51,53,54,56,57,61,65,69,75,78,79,86,87,95,96,97,98,103,113,114,116,121,122,123,128,131,155,165,172,173,176,178,179,181,182,184,185,187,188,189,190,192,193,195,196,199,203,204,206,207,208,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,231,232,233,234,236,237,238,239,243,244,245,246,247,249,250,251,252,254,255,256,257,258,259,260,261,262,263,264,265,267,268,270,271,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,301,302,303,304,305,306,308,309,310,311,312,313,314,315,317,319,320,321,323,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,361,363,364,366,367,370,372,376,377,378,379,380,386,388,393,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,419,421,422,423,424,425,426,427,431,432,433,434,435,436,437,438,439,440,441,444,448,449,452,453,455,459,460,461,463,465,467,468,477,478,480,481,482,483,485,486,487,488,489,491,494,495,496,498,500,502,505,506,508,509,511,514,515,518,524,525],invokd:3,invoke:[3,12,16],involv:[3,6,7,8,12,65,119,126,127,128,158,182,210,217,226,246,257,302,305,310,335,377,385,386,388,398,415,422,429,477,480,482,493,494,500,502,506,511,512],ioff:[387,497],ion:[6,7,9,160,297,331,347,378,399,410,419,421,443,446,466,476,482,489,497,518],ionic:[6,9,400,402,410,418,419,453,466,518],ioniz:[408,418],ionocoval:9,iparam:[3,227],ipi:[],ipp:[],ir3:177,ir4:177,irregular:[6,41,60,225,229,231,271,317],irrelev:452,irrespect:[440,441],irrevers:[235,318],is_act:[3,524],is_avail:[3,524],is_defin:[3,524],isbn:488,isele:[377,378],isenthalp:[9,271,272,273,274,275],ismail:[377,378,403,435],isn:[3,8,11,12,238,250],iso:[3,229,235,255,271,272,273,274,275,276,277,278,279,304,312,317,518],isobar:[9,271,272,277,278,279,322,414],isodem:418,isoenerget:[9,322,414],isoenthalp:[322,414],isol:[3,181,358,423],isomorph:300,isotherm:[9,246,271,272,277,278,279,304,322,414],isotop:306,isotrop:[6,100,254,304,377,378,401,422,440,441],isovolum:319,isralewitz:323,issu:[1,3,6,9,11,12,13,14,16,17,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,43,45,46,47,48,49,51,53,54,56,61,75,77,87,113,123,155,165,184,185,187,188,189,190,192,193,195,196,199,206,213,224,228,229,231,232,237,238,239,245,246,249,254,269,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,300,304,306,309,310,317,320,321,334,338,339,340,345,355,357,360,361,363,364,366,367,370,372,378,387,388,391,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,419,421,422,423,424,425,431,432,433,434,435,436,437,438,439,440,444,449,452,453,455,459,460,461,468,475,477,478,480,481,482,483,485,487,488,489,497,499,500,506,509,514,515,516,524,525],issue:216,ital:[458,459],itali:13,item:[6,7,8,41,203,207,225],iter:[3,6,12,39,41,65,205,213,214,224,225,229,235,237,244,252,287,293,299,308,309,317,321,332,342,358,360,376,384,385,386,388,392,466,492,502,506,511,515,516,524],iterat:[12,386],ith:[75,128,131,218,219,220,221,222,223,337,349,515,524],itself:[2,3,6,7,8,9,11,12,13,17,42,61,99,118,169,203,205,206,207,208,220,221,230,235,255,270,311,317,347,358,360,387,388,409,419,422,426,427,450,451,478,479,495,501,504,505,509,524,529],itset:507,ityp:[3,57,102,126,127,178,215,227,310,486],itype1:127,itype2:127,itypen:127,ivector:8,ivori:207,ixcm:317,ixi:[42,317,387],ixx:[42,103,317,387,524],ixy:[103,387,524],ixz:[42,103,317,387,524],iycm:317,iyi:[42,103,317,387,524],iyz:[42,103,317,387,524],izcm:317,izrailev:323,izumi:481,izz:[42,103,317,387,524],j0jt:99,j20:220,j_m:152,jackson:447,jacobi:3,jacobsen:385,jacs:[6,184,509],jagreat:13,jame:[9,18],janssen:298,januari:443,jaramillo:[7,9,13,418],jarzynski:323,jatempl:9,jcc:9,jcp:352,jec:13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80],princip:[3,129,251,466],principl:[6,9,11,251,272,308,418,427,446,477,494,519],prinicp:[42,317,387],print:[],printabl:2,printflag:427,printfluid:257,prior:[176,201,322,379,528],priori:506,prism:[3,6,166,180,500],priveleg:3,privileg:[11,12,251],prob:[226,227],probab:468,probabl:[3,8,12,40,75,168,181,182,197,217,225,226,227,228,232,246,255,271,303,352,358,386,448,450,451,478,492,511,518],probe:524,problem:[],problemat:246,proc:[1,3,8,11,12,14,15,124,203,204,376,494],proce:[41,54,182,225,236,388,446,504,512,515],procedur:[6,12,39,41,207,217,225,246,254,255,256,271,273,274,275,276,277,278,279,291,292,294,295,296,299,338,339,340,341,344,345,386,388,395,401,498,518],proceed:[12,446],procesor:[41,494],process:[],processor:[],processsor:[41,225,494],procp1:[203,204],procsessor:516,procssor:506,produc:[1,3,4,6,7,12,13,14,15,16,19,20,22,23,24,25,26,27,28,29,30,31,34,37,40,41,42,43,45,46,47,48,49,51,53,54,56,65,68,71,73,75,84,102,119,121,123,124,125,126,128,129,131,153,155,165,176,184,185,187,188,189,190,192,193,195,196,199,203,204,206,207,210,213,218,219,220,222,223,224,225,228,231,239,244,245,247,248,249,254,255,256,266,268,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,303,307,308,309,310,312,317,318,319,320,321,336,337,347,348,349,352,355,360,361,363,364,366,367,370,372,378,386,388,391,393,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,418,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,444,449,452,453,455,456,459,460,461,468,477,478,479,480,481,482,483,485,487,488,489,492,494,499,500,502,503,506,511,512,515,524,525],product:[6,15,16,17,152,176,231,294,308,318,348,393,396,418,420,451,459,479,494,524],proessor:393,prof:302,profi:167,profil:[],program:[3,4,6,7,9,11,12,13,15,16,154,203,206,207,208,210,230,244,251,257,311,416,495,496,508,524],programm:[13,16],progress:[1,41,225,251,269,307,385,386,388,515,518],prohibit:507,project:[6,7,13,385,476,519],promis:7,promot:399,prompt:[8,11,12,251,508],proni:[3,247,248],pronounc:250,proofread:8,prop:[6,306],propag:[4,9,215,271,307,324,418,426],propens:6,proper:[228,298,443,495,519],properati:306,properli:[213,237,317,322,330,387,388,414,495,525],properti:[],propoerti:335,proport:[6,39,41,95,113,114,174,225,254,255,256,307,318,343,350,351,423],proportion:254,propos:[6,152,217,229,246,271,294,312,431,445,481,483],prospect:7,protect:335,protein:[7,10,15,178,315,317,333,497,505],protocol:251,proton:[482,489,523],prototyp:[42,455],prouduc:[223,349],prove:[88,89,240,257,322],proven:294,provid:[1,3,4,6,7,8,9,10,11,12,13,14,15,16,17,28,40,42,63,70,74,130,151,154,172,176,177,178,205,206,208,211,218,219,223,228,229,230,231,244,246,251,253,257,261,269,271,299,306,307,308,311,312,317,322,323,342,344,345,348,349,360,375,377,378,384,388,393,395,399,401,406,408,409,413,417,418,423,425,428,430,439,440,443,445,446,456,457,458,459,466,467,475,476,477,480,481,482,485,494,499,505,507,510,511,515,516,519,524],proxim:202,psa:355,pscreen:[3,12,506],pscrozi:[0,7,13],psec:[207,231,250,254,255,271,304,317,338,339,517,523],psend:494,pseudo:[418,492,497,502],pseudodynam:342,pseudopotenti:[9,446],psf:6,psi:[419,488],psi_ij:419,pstart:[3,271,272,276,304,317],pstop:[3,271,272,276,304,317],pstyle:[95,118,211,212],psu:[458,459],psuedo:502,psxevar:15,pt2:177,pt4:177,ptarget:229,pthread:[12,15,16],ptr:[6,11,244,495],ptype1:126,ptype2:126,pu3:177,pu4:177,pu6:177,publicli:5,publish:[7,257,261,287,293,308,382,409,443,446,480,482],pull:[323,331,519],puls:347,pump:[440,441],punctuat:[2,492,511],purchas:206,purdu:[9,13],pure:[11,335,426,444,445,480,482,506],purg:[3,498],purpl:[2,207],purport:11,purpos:[3,6,7,9,12,42,63,75,130,140,146,161,162,177,178,180,182,199,203,223,228,229,254,298,300,303,305,316,335,377,403,429,435,446,448,484,490,497,500,504,507,509,510,524,529],push:[3,8,213,224,231,252,270,298,315,323,386,423,468,519],pushd:252,put:[3,6,8,11,12,13,39,61,166,178,203,232,236,354,355,358,380,458,495,497,501],putenv:[508,524],px1:506,px2:506,pxx:[229,271,304,317,377,378,514,515],pxy:[3,6,515],pxz:[3,6,515],py1:506,py2:506,pydir:11,pyi:[229,271,304,317,377,378,515],pymol:[7,11,13],pymol_aspher:[],pympi:11,pypar:11,python:[],pythonpath:11,pyz:[3,6,515],pz1:506,pz2:506,pzz:[229,269,271,304,307,317,377,378,515],q_1:466,q_2:466,q_3:466,q_d:518,q_i:[419,439],q_j:439,qbmsst:[],qcore:308,qdamp:308,qdist:[409,431,435,439],qeq1:308,qeq2:308,qeq:[],qeqall:466,qeqallparallel:466,qfile:[308,409],qin:250,qinitmod:466,qmin:385,qmmm:[],qmol:311,qoffload:15,qopenmp:15,qout:250,qoverrid:15,qstep:308,qtb:[],quad:[12,17,393,494],quadrant:15,quadrat:[],quadratur:[95,216],quadrupl:394,quadruplet:[194,198,200,361,363,364,365,367,369,370,371,373],qualifi:[3,253],qualiti:[7,206,207,395],quantic:466,quantit:[78,87,113,114,115,174,423],quantiti:[],quantum:[6,9,152,244,248,300,307,311,312,395,399,418,446,466,476],quantum_temperatur:307,quartic:[],quartic_spher:216,quartz:[307,312],quasi:300,quat:[3,507],quaternion:[3,6,40,90,124,129,142,156,157,178,273,274,277,278,281,282,283,291,292,42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,497,504,505,518],recomend:6,recommed:318,recommend:[7,12,15,206,207,238,307,345,377,418,426,440,441,446,459,460,463,465,466,506,516,519],recompil:[1,3,9,12,208,321],recomput:[112,140,182,236,323,415,509],reconstruct:[3,230,466],record:[208,230,323],recov:[229,238,271],recreat:359,rectangl:[41,225,380],rectangular:[7,41,64,180,225,246,380,497,499,501],rectilinear:[130,177,204],rector:53,recurs:[41,225,399,484],recust:41,recv:494,red:[2,206,207,228,300],redefin:[3,499,505,524],redirect:12,redo:12,reduc:[],reduct:[17,18,128,130,177,269,307,377],redund:419,ree:471,reed:[269,307],rees:[7,9,13],ref:[344,345,385],refactor:6,refer:[],referenc:[3,6,12,65,71,75,125,131,203,204,210,220,246,306,349,378,409,425,452,460,495,515,524],reflect:[],refman:9,reformat:7,refresh:216,reg:500,regard:[6,61,268,321,322,327,455,459],regardless:[14,75,178,181,202,222,231,254,271,273,274,275,277,278,279,304,317,328,335,393,494,500,507],regim:[6,343,350,410,506],region:[],region_spher:8,region_styl:356,regist:[8,127,154,330],regoin:6,regress:524,regspher:178,regstrip:358,regul:6,regular:[1,3,41,64,96,176,180,203,206,217,225,246,306,347,378,410,429,494,497,499,501],reigon:524,reinder:15,reinhardt:[344,345],reject:[178,228,458,512],rel:[1,6,26,35,41,61,75,100,113,134,142,152,156,160,161,163,178,187,207,210,217,225,231,232,235,238,246,252,255,267,268,294,298,303,312,314,315,318,323,331,335,337,342,343,347,354,358,377,378,386,418,422,423,440,441,443,460,488,498,506,511,515,518,525],relat:[],relationship:[6,241,308,360,377,466,488,518,524],relax:[],releas:[0,5,7,8,13,226],relect:[3,448],reles:42,relev:[2,6,12,41,82,85,122,140,178,182,207,211,212,215,216,217,218,219,220,221,222,223,225,226,227,231,232,236,238,239,243,245,246,247,250,251,257,258,259,261,262,263,264,265,267,268,270,280,281,282,283,284,285,286,287,288,289,290,293,297,301,302,303,305,306,311,313,314,315,319,321,328,333,335,336,337,342,343,346,347,348,349,350,351,352,353,354,355,357,358,377,382,386,396,397,401,407,409,410,412,413,415,418,421,422,423,424,425,430,432,433,434,436,437,438,440,441,448,449,450,451,455,460,468,475,478,479,487,488,489,494,510,525],reli:[3,12,309,418,459,489,497,507],reloc:12,reltol:318,remain:[7,12,32,36,41,50,55,61,75,95,114,158,159,160,161,165,166,167,168,170,181,191,197,198,199,203,211,212,217,219,220,229,231,254,255,262,271,272,277,278,279,291,292,294,296,301,302,326,335,338,339,340,346,347,358,360,368,371,387,399,418,426,439,446,448,476,492,497,498,502,507,509,511,515,516,518,519,524,525],remaina:399,remaind:[9,178,203,232,303,335,348,482,497],remap:[3,6,12,61,63,75,161,178,202,231,252,268,294,377,497,498,499],remedi:[6,518],rememb:[2,519],remot:519,remov:[2,3,6,8,13,54,75,76,81,100,117,125,127,152,156,157,158,159,160,161,165,166,167,168,170,171,178,181,182,204,210,219,226,243,254,255,260,263,267,269,271,277,278,279,291,292,294,296,302,308,317,318,319,321,335,338,339,340,342,358,377,388,412,441,446,497,500,508,509,519,524,525],remove_bia:8,remove_bias_al:8,remove_molecul:216,remove_sourc:216,remove_speci:216,ren:177,renam:[12,359,508,519],render:[12,13,203,206,207],rendon:[271,272],reneighbor:[3,8,12,39,59,75,225,246,335,348,358,413,514,515],renssela:302,renumb:75,reorder:[3,12,39,494],rep:512,repeat:[2,6,206,207,228,229,246,318,327,380,399,480,482,484,492,511],repeatedli:2,repel:252,repes:203,replac:[2,3,6,11,12,41,65,97,98,128,155,156,157,158,159,160,161,164,165,166,167,168,170,171,203,204,206,207,208,219,220,222,223,225,228,232,254,276,305,306,312,314,353,393,395,409,423,433,450,451,478,498,499,504,505,515,524,525,527,529],replic:[],replica:[],replica_fil:12,report:[],repositori:[],reprens:347,repres:[1,3,6,8,9,12,14,40,41,42,61,70,75,98,124,127,129,190,199,203,204,206,219,220,221,222,223,229,235,242,247,249,254,257,271,300,302,304,312,317,318,319,323,331,332,347,349,356,378,388,394,399,422,429,439,440,441,443,444,445,453,456,458,459,483,484,492,494,497,507,509,512,518,524,527],represent:[3,6,8,9,59,61,146,180,203,247,248,300,347,399,418,422,446,460,497,500,518],reprocess:502,reproduc:[3,15,271,353,409,416,423],repul:443,repuls:[6,7,35,40,45,46,119,252,308,352,353,356,395,399,407,409,413,418,420,423,425,439,443,447,466,475,482,485,488,489,507],reqir:[308,310],request:[3,6,8,12,41,113,181,199,203,251,257,315,322,335,337,375,377,448,458,459,492,502,507,511,516,519,524,525,527],requir:[],rerun:[],rescal:[],research:[5,7,88,89,240,257,261,322,446,492,511],resembl:[312,519],resepct:6,reserv:[12,251,518],reservoir:[99,238,246,250,254,347],reset:[],reset_atomic_reference_posit:216,reset_dt:8,reset_target:8,reset_tim:216,reset_timestep:[],resid:13,residu:251,residue1:389,resist:[6,251],resolut:[221,466,478,479],resolv:[229,300,335,441,519],resort:3,resourc:[7,394,416],respa:[3,15,213,214,224,233,236,237,249,251,252,266,271,298,309,316,320,323,331,332,333,334,351,352,356,391,394,395,396,397,398,399,400,401,402,403,404,405,407,408,409,410,412,413,415,416,417,418,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,440,441,442,443,444,445,446,447,449,450,451,453,455,457,458,459,460,461,466,467,468,469,470,471,472,473,474,475,477,478,479,480,481,482,484,485,487,488,489,490,505,506,516,517,524],respecifi:446,respect:[1,6,9,13,14,16,19,20,22,23,24,25,26,27,28,29,30,31,34,37,42,43,45,46,47,48,49,51,53,54,56,61,74,75,95,97,106,107,123,130,134,154,155,160,163,165,172,176,177,184,185,187,188,189,190,192,193,195,196,199,204,206,207,213,222,224,227,228,229,231,238,239,245,249,252,254,255,257,271,273,274,275,276,277,278,279,280,281,287,289,291,292,294,296,308,309,310,317,319,321,323,331,334,347,352,355,361,363,364,366,367,370,372,375,377,378,383,386,387,392,393,394,395,397,399,400,401,402,403,404,405,407,408,409,412,413,416,417,419,420,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,443,444,446,448,449,452,453,455,459,460,461,465,466,467,468,477,478,480,481,482,483,485,487,488,489,494,498,507,510,516,518,524,526,527,529],respon:9,respond:[6,7,161,231,418,455],respons:[6,7,269,343,350],resquar:[],rest:[6,8,12,38,58,200,306,310,316,373,399,441,443,490,514,515,518],restart1:300,restart2:300,restart2data:[],restart:[],restartfil:[12,13],restor:[3,8,62,63,113,178,211,212,266,306,323,331,332,337,359,514,515],restore_bia:8,restore_bias_al:8,restrain:[],restraint:[9,230,269,316,334,430],restratin:316,restrict:[],result:[1,2,3,6,7,9,10,11,12,13,14,15,17,19,20,22,23,24,25,26,27,28,29,30,31,34,37,39,40,41,43,45,46,47,48,49,51,53,54,56,65,66,69,70,75,79,86,95,98,99,103,113,114,116,121,123,125,126,127,128,130,131,153,155,158,161,165,172,173,175,177,178,181,184,185,187,188,189,190,192,193,195,196,199,202,203,204,206,207,210,213,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,235,238,239,241,245,246,247,249,254,255,257,261,269,271,273,274,275,276,277,278,279,280,281,289,291,292,294,295,296,299,300,308,309,310,314,315,317,318,320,321,335,343,344,345,347,348,349,352,353,355,357,360,361,363,364,366,367,370,372,377,378,388,391,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,418,419,421,422,423,424,425,426,431,432,433,434,435,436,437,438,439,440,441,443,444,446,448,449,450,451,452,453,455,459,460,461,468,477,478,479,480,481,482,483,485,487,488,489,492,494,497,499,500,502,506,507,508,509,511,523,524,525],resum:524,retain:[2,226,227,395,399,446,494],retart:[32,50,191,368],retir:[3,458],retreiv:8,retriev:[6,8,244,444,445,524],reus:[3,509],rev:[6,13,66,74,100,117,121,152,153,166,217,248,254,256,269,271,272,276,294,299,309,312,317,323,335,339,342,344,345,350,385,399,407,408,409,412,416,417,418,422,423,428,433,440,441,442,443,445,450,451,456,460,467,477,480,481,482,485,492],revers:[2,6,8,95,189,228,252,271,297,298,308,327,343,344,350,388,439,506,518],review:[152,308,323,342,446,457,467,492,511,518,519],rewind:376,rewrap:203,rewrit:[5,12],rewritten:18,rezwanur:455,rfac0:[152,467],rfactor:335,rfile:317,rg0:333,rgb:207,rh3:177,rh4:177,rhaphson:3,rheolog:6,rhi:[450,451,478,479],rho0:[443,463,465,473,474],rho0_meam:443,rho:[],rho_0:[473,474],rho_alpha_beta:416,rho_bkgd:443,rho_colloid:352,rho_e:347,rho_fin:346,rho_i:[444,445],rho_initi:346,rho_ref_meam:443,rho_wal:352,rhodo:[9,10],rhodopsin:[1,10,15],rhohi:303,rholo:303,rhosum:[],rhot:238,rhs:318,ribier:385,richard:9,richardson:317,richi:[9,18],rick:[308,309,408,466],rick_and_stuart:309,ridg:[9,18],right:[3,6,11,12,41,154,178,196,198,202,204,225,228,252,257,268,297,298,318,360,380,409,497,500,507,519,524],rightmost:[41,225],rigid:[],rigidid:129,rigidifi:317,rii:[97,98],rij:[100,117,226,227,298,413,414,450,451,475],rin:[425,436,437],ring:[],rino:[77,485],rinv:377,rirj:[353,423],risi:[152,467],risk:[8,316,506],rix:[97,98],rjk:[226,227],rjone:[7,9,13],rk4:318,rkf45:318,rlo:[450,451,478,479],rmask:[3,524],rmass:[3,306],rmax:[75,179,226],rmdir:508,rmin0:[152,467],rmin:[75,179,227,433],rms:[318,377,378],rmsd:346,rnemd:6,robert:9,robin:207,robust:[384,385,386,459],rock:443,rockett:456,rod:317,rodata:12,rodnei:312,roi:7,role:[6,342],roll:12,ronchetti:117,room:[59,61],root:[11,95,97,98,205,342,346,416,504],rosati:39,rose:443,ross:443,rosski:300,rosybrown:207,rot:[6,99,300,316,342,525],rotat:[],rotate:327,rotation:117,rotaton:500,rough:[6,178,206,357],roughli:[7,10,12,41,161,206,221,246,254,255,263,270,271,285,304,317,335,338,339,342,378,388,393,462,464,499,506],round:[1,3,12,19,20,22,23,24,25,26,27,28,29,30,31,34,37,39,40,42,43,45,46,47,48,49,51,53,54,56,75,123,155,165,184,185,187,188,189,190,192,193,195,196,199,207,213,224,231,239,245,249,254,271,273,274,275,276,277,278,279,280,281,289,291,292,294,296,309,310,317,320,321,355,361,363,364,366,367,370,372,378,391,394,395,397,400,401,402,403,404,405,407,408,409,412,413,416,417,419,421,422,423,424,425,429,431,432,433,434,435,436,437,438,439,440,444,449,450,451,452,453,455,459,460,461,468,477,478,480,481,482,483,485,487,488,489,499,500,506,523,524],rous:247,rout:[95,425,439],routin:[5,6,8,11,14,15,37,39,56,96,182,257,446,457,478,479,510],roux:[6,235,255,483,518],row:[6,68,69,71,73,79,84,86,98,102,103,114,116,119,125,126,127,129,131,158,166,173,175,176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489],z_meam:443,zachari:13,zannoni:422,zbl:[],zblcut:482,zblcutinn:467,zblcutout:467,zblexpscal:482,zblz:467,zcm:317,zcylind:353,zepeda:217,zero:[],zeta:[3,257,308,419],zfactor:206,zflag:[165,166,258,260,267,317,342],zhang:[317,343,423],zhi:[3,6,180,203,215,346,352,355,357,497,500,515],zhi_bound:[6,203],zhou:[13,399,419,456,480,482],zhu:474,ziegenhain:13,ziegler:[302,443,476,482,489],zimmerman2004:216,zimmerman2010:216,zimmerman:[9,74,216,399],zlat:[231,252,515],zlib:[9,203],zlim:466,zlo:[3,6,180,203,215,346,352,354,355,357,497,500,515],zlo_bound:[6,203],zmax:[215,257,524],zmid:232,zmin:[257,524],zn2:177,zone:[130,319],zoom:[3,203,206,207],zorig:75,zplane:353,zr4:177,zrest:334,zsu:[3,203,204,337,498],zwall:352,zwall_veloc:257,zxy:494,zybin:459,zyx:494},titles:["LAMMPS Documentation","5. Accelerating LAMMPS performance","3. Commands","12. Errors","7. Example problems","13. Future and history","6. How-to discussions","1. Introduction","10. Modifying & extending LAMMPS","4. Packages","8. Performance & scalability","11. Python interface to LAMMPS","2. Getting Started","9. Additional tools","5.GPU package","5.USER-INTEL package","5.KOKKOS package","5.USER-OMP package","5.OPT package","angle_style charmm command","angle_style class2 command","angle_coeff command","angle_style cosine command","angle_style cosine/delta command","angle_style cosine/periodic command","angle_style cosine/shift command","angle_style cosine/shift/exp command","angle_style cosine/squared command","angle_style dipole command","angle_style fourier command","angle_style fourier/simple command","angle_style harmonic command","angle_style hybrid command","angle_style none command","angle_style quartic command","angle_style sdk command","angle_style command","angle_style table command","angle_style zero command","atom_modify command","atom_style command","balance command","Body particles","bond_style class2 command","bond_coeff command","bond_style fene command","bond_style fene/expand command","bond_style harmonic command","bond_style harmonic/shift command","bond_style harmonic/shift/cut command","bond_style hybrid command","bond_style morse command","bond_style none command","bond_style nonlinear command","bond_style quartic command","bond_style command","bond_style table command","bond_write command","bond_style zero command","boundary command","box command","change_box command","clear command","comm_modify command","comm_style command","compute command","compute ackland/atom command","compute angle command","compute angle/local command","compute angmom/chunk command","compute basal/atom command","compute body/local command","compute bond command","compute bond/local command","compute centro/atom command","compute chunk/atom command","compute cluster/atom command","compute cna/atom command","compute com command","compute com/chunk command","compute contact/atom command","compute coord/atom command","compute damage/atom command","compute dihedral command","compute dihedral/local command","compute dilatation/atom command","compute dipole/chunk command","compute displace/atom command","compute dpd command","compute dpd/atom command","compute erotate/asphere command","compute erotate/rigid command","compute erotate/sphere command","compute erotate/sphere/atom command","compute event/displace command","compute fep command","compute group/group command","compute gyration command","compute gyration/chunk command","compute heat/flux command","compute hexorder/atom command","compute improper command","compute improper/local command","compute inertia/chunk command","compute ke command","compute ke/atom command","compute ke/atom/eff command","compute ke/eff command","compute ke/rigid command","compute meso/e/atom command","compute meso/rho/atom command","compute meso/t/atom command","compute_modify command","compute msd command","compute msd/chunk command","compute msd/nongauss command","compute omega/chunk command","compute orientorder/atom command","compute pair command","compute pair/local command","compute pe command","compute pe/atom command","compute plasticity/atom command","compute pressure command","compute property/atom command","compute property/chunk command","compute property/local command","compute rdf command","compute reduce command","compute rigid/local command","compute saed command","compute slice command","compute smd/contact/radius command","compute smd/damage command","compute smd/hourglass/error command","compute smd/internal/energy command","compute smd/plastic/strain command","compute smd/plastic/strain/rate command","compute smd/rho command","compute smd/tlsph/defgrad command","compute smd/tlsph/dt command","compute smd/tlsph/num/neighs command","compute smd/tlsph/shape command","compute smd/tlsph/strain command","compute smd/tlsph/strain/rate command","compute smd/tlsph/stress command","compute smd/triangle/mesh/vertices","compute smd/ulsph/num/neighs command","compute smd/ulsph/strain command","compute smd/ulsph/strain/rate command","compute smd/ulsph/stress command","compute smd/vol command","compute sna/atom command","compute stress/atom command","compute force/tally command","compute temp command","compute temp/asphere command","compute temp/body command","compute temp/chunk command","compute temp/com command","compute temp/cs command","compute temp/deform command","compute temp/deform/eff command","compute temp/drude command","compute temp/eff command","compute temp/partial command","compute temp/profile command","compute temp/ramp command","compute temp/region command","compute temp/region/eff command","compute temp/rotate command","compute temp/sphere command","compute ti command","compute torque/chunk command","compute vacf command","compute vcm/chunk command","compute voronoi/atom command","compute xrd command","create_atoms command","create_bonds command","create_box command","delete_atoms command","delete_bonds command","dielectric command","dihedral_style charmm command","dihedral_style class2 command","dihedral_coeff command","dihedral_style cosine/shift/exp command","dihedral_style fourier command","dihedral_style harmonic command","dihedral_style helix command","dihedral_style hybrid command","dihedral_style multi/harmonic command","dihedral_style nharmonic command","dihedral_style none command","dihedral_style opls command","dihedral_style quadratic command","dihedral_style spherical command","dihedral_style command","dihedral_style table command","dihedral_style zero command","dimension command","displace_atoms command","dump command","dump custom/vtk command","dump h5md command","dump image command","dump_modify command","dump molfile command","echo command","fix command","fix adapt command","fix adapt/fep command","fix addforce command","fix addtorque command","fix append/atoms command","fix atc command","fix atom/swap command","fix ave/atom command","fix ave/chunk command","fix ave/correlate command","fix ave/correlate/long command","fix ave/histo command","fix ave/time command","fix aveforce command","fix balance command","fix bond/break command","fix bond/create command","fix bond/swap command","fix box/relax command","fix colvars command","fix deform command","fix deposit command","fix drag command","fix drude command","fix drude/transform/direct command","fix dt/reset command","fix efield command","fix ehex command","fix enforce2d command","fix eos/cv command","fix eos/table command","fix eos/table/rx command","fix evaporate command","fix external command","fix freeze command","fix gcmc command","fix gld command","fix gle command","fix gravity command","fix heat command","fix imd command","fix indent command","fix ipi command","fix langevin command","fix langevin/drude command","fix langevin/eff command","fix lb/fluid command","fix lb/momentum command","fix lb/pc command","fix lb/rigid/pc/sphere command","fix lb/viscous command","fix lineforce command","fix manifoldforce command","fix meso command","fix meso/stationary command","fix_modify command","fix momentum command","fix move command","fix msst command","fix neb command","fix nvt command","fix nvt/eff command","fix nph/asphere command","fix nph/body command","fix nph/sphere command","fix nphug command","fix npt/asphere command","fix npt/body command","fix npt/sphere command","fix nve command","fix nve/asphere command","fix nve/asphere/noforce command","fix nve/body command","fix nve/eff command","fix nve/limit command","fix nve/line command","fix nve/manifold/rattle command","fix nve/noforce command","fix nve/sphere command","fix nve/tri command","fix nvt/asphere command","fix nvt/body command","fix nvt/manifold/rattle command","fix nvt/sllod command","fix nvt/sllod/eff command","fix nvt/sphere command","fix oneway command","fix orient/fcc command","fix phonon command","fix pimd command","fix planeforce command","fix poems","fix pour command","fix press/berendsen command","fix print command","fix property/atom command","fix qbmsst command","fix qeq/point command","fix qeq/comb command","fix qeq/reax command","fix qmmm command","fix qtb command","fix reax/bonds command","fix reax/c/species command","fix recenter command","fix restrain command","fix rigid command","fix rx command","fix saed/vtk command","fix setforce command","fix shake command","fix shardlow command","fix smd command","fix smd/adjust_dt command","fix smd/integrate_tlsph command","fix smd/integrate_ulsph command","fix smd/move_tri_surf command","fix smd/setvel command","<no title>","fix smd/wall_surface command","fix spring command","fix spring/chunk command","fix spring/rg command","fix spring/self command","fix srd command","fix store/force command","fix store/state command","fix temp/berendsen command","fix temp/csvr command","fix temp/rescale command","fix temp/rescale/eff command","fix tfmc command","fix thermal/conductivity command","fix ti/rs command","fix ti/spring command","fix tmd command","fix ttm command","fix tune/kspace command","fix vector command","fix viscosity command","fix viscous command","fix wall/lj93 command","fix wall/gran command","fix wall/piston command","fix wall/reflect command","fix wall/region command","fix wall/srd command","group command","group2ndx command","if command","improper_style class2 command","improper_coeff command","improper_style cossq command","improper_style cvff command","improper_style distance command","improper_style fourier command","improper_style harmonic command","improper_style hybrid command","improper_style none command","improper_style ring command","improper_style command","improper_style umbrella command","improper_style zero command","include command","info command","jump command","kspace_modify command","kspace_style command","label command","lattice command","log command","Description","mass command","min_modify command","min_style command","minimize command","molecule command","neb command","neigh_modify command","neighbor command","newton command","next command","package command","pair_style adp command","pair_style airebo command","pair_style awpmd/cut command","pair_style beck command","pair_style body command","pair_style bop command","pair_style born command","pair_style brownian command","pair_style buck command","pair_style buck/long/coul/long command","pair_style lj/charmm/coul/charmm command","pair_style lj/class2 command","pair_coeff command","pair_style colloid command","pair_style comb command","pair_style coul/cut command","pair_style coul/diel command","pair_style born/coul/long/cs command","pair_style lj/cut/dipole/cut command","pair_style dpd command","pair_style dpd/fdt command","pair_style dsmc command","pair_style eam command","pair_style edip command","pair_style eff/cut command","pair_style eim command","pair_style exp6/rx command","pair_style gauss command","pair_style gayberne command","pair_style gran/hooke command","pair_style lj/gromacs command","pair_style hbond/dreiding/lj command","pair_style hybrid command","pair_style kim command","pair_style lcbop command","pair_style line/lj command","pair_style list command","pair_style lj/cut command","pair_style lj96/cut command","pair_style lj/cubic command","pair_style lj/expand command","pair_style lj/long/coul/long command","pair_style lj/sf command","pair_style lj/smooth command","pair_style lj/smooth/linear command","pair_style lj/cut/soft command","pair_style lubricate command","pair_style lubricateU command","pair_style lj/mdf command","pair_style meam command","pair_style meam/spline","pair_style meam/sw/spline","pair_style mgpt command","pair_style mie/cut command","pair_modify command","pair_style morse command","pair_style multi/lucy command","pair_style multi/lucy/rx command","pair_style nb3b/harmonic command","pair_style nm/cut command","pair_style none command","pair_style peri/pmb command","pair_style polymorphic command","pair_style quip command","pair_style reax command","pair_style reax/c command","pair_style resquared command","pair_style lj/sdk command","pair_style smd/hertz command","pair_style smd/tlsph command","pair_style smd/tri_surface command","pair_style smd/ulsph command","pair_style smtbq command","pair_style snap command","pair_style soft command","pair_style sph/heatconduction command","pair_style sph/idealgas command","pair_style sph/lj command","pair_style sph/rhosum command","pair_style sph/taitwater command","pair_style sph/taitwater/morris command","pair_style srp command","pair_style command","pair_style sw command","pair_style table command","pair_style table/rx command","pair_style tersoff command","pair_style tersoff/mod command","pair_style tersoff/zbl command","pair_style thole command","pair_style tri/lj command","pair_style vashishta command","pair_write command","pair_style yukawa command","pair_style yukawa/colloid command","pair_style zbl command","pair_style zero command","partition command","prd command","print command","processors command","python command","quit command","read_data command","read_dump command","read_restart command","region command","replicate command","rerun command","reset_timestep command","restart command","run command","run_style command","set command","shell command","special_bonds command","suffix command","tad command","temper command","thermo command","thermo_modify command","thermo_style command","timer command","timestep command","Tutorial for Thermalized Drude oscillators in LAMMPS","LAMMPS GitHub tutorial","uncompute command","undump command","unfix command","units command","variable command","velocity command","write_coeff command","write_data command","write_dump command","write_restart command"],titleterms:{"break":226,"default":[36,39,40,55,59,60,61,63,64,75,95,96,100,112,113,115,117,118,119,126,130,134,135,152,158,166,167,171,177,178,181,183,198,201,202,203,204,206,207,208,209,211,212,213,215,216,217,219,223,226,227,229,230,231,232,236,243,246,247,252,254,255,256,257,258,260,266,269,271,272,276,294,295,299,300,303,304,305,306,307,309,312,314,315,317,319,335,337,342,343,344,345,348,350,352,354,358,371,375,377,378,380,381,384,385,387,389,390,391,393,396,399,401,418,440,441,446,448,458,459,475,476,492,493,494,497,498,500,502,504,505,506,509,511,513,514,515,516,517,523,525,527,528],"function":524,"long":[221,400,402,403,404,405,409,411,412,431,435,439,453,461,483],"new":8,"static":12,acceler:1,account:519,ackland:66,acknowledg:7,adapt:[211,212],addforc:213,adding:519,addition:[12,13,519],addtorqu:214,adiabat:6,adjust_dt:324,adp:394,after:519,airebo:395,alloi:416,amber2lmp:13,amber:6,angl:[8,67,68],angle_coeff:21,angle_styl:[2,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],angmom:69,append:215,arrai:6,aspher:[6,90,156,273,277,281,282,291],asphere:9,atc:[9,216],atom:[6,7,8,66,70,74,75,76,77,80,81,82,85,87,89,93,100,105,106,109,110,111,117,121,122,124,152,153,176,215,217,218,306,524],atom_modifi:39,atom_styl:40,attract:5,aveforc:224,awpmd:[9,396],axel:519,balanc:[41,225],barostat:6,basal:70,bcc:298,beck:397,berendsen:[304,338],between:6,binary2txt:13,bodi:[6,8,42,71,157,274,278,283,292,398],body:9,bond:[8,13,72,73,226,227,228,313],bond_coeff:44,bond_styl:[2,43,45,46,47,48,49,50,51,52,53,54,55,56,58],bond_writ:57,bop:399,born:[400,411],boundari:[7,59],box:[6,60,229],branch:519,brownian:401,buck:[402,403,411,442],bug:3,build:[11,12],calcul:6,call:12,categori:2,centro:74,ch2lmp:13,chain:13,chang:519,change_box:61,charmm:[6,19,184,404,439],chunk:[6,69,75,79,86,98,103,114,116,125,158,173,175,219,332],citat:7,class2:[9,20,43,185,361,405],clear:62,cluster:76,cmm:9,cna:77,code:6,coeffici:6,colloid:[9,352,407,488],colvar:[13,230],colvars:9,com:[78,79,159],comb3:408,comb:[309,408],come:5,comm_modifi:63,comm_styl:64,command:[2,6,8,12,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,520,521,522,523,524,525,526,527,528,529],common:3,comparison:1,compos:6,compress:9,comput:[2,6,8,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,524],compute_modifi:112,condit:7,conduct:[6,343],constant:[6,524],constraint:7,contact:[80,132],coord:81,core:6,coreshell:9,correl:[220,221],cosin:[22,23,24,25,26,27,187],cossq:363,coul:[400,402,403,404,405,409,410,411,424,431,435,439,453,461],coupl:6,creat:227,create_atom:178,create_bond:179,create_box:180,createatom:13,creation:7,csld:339,csvr:339,cubic:433,custom:[8,203,204],cut:[49,396,402,405,409,412,418,421,431,432,439,447,453,483],cvff:364,damag:[82,133],data2xmovi:13,data:6,databas:13,deby:[409,431],defgrad:139,deform:[161,162,231],delete_atom:181,delete_bond:182,delta:23,deposit:232,descript:[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,520,521,522,523,524,525,526,527,528,529],diagnost:7,diel:410,dielectr:183,diffraction:9,diffus:6,dihedr:[8,83,84],dihedral_coeff:186,dihedral_styl:[2,184,185,187,188,189,190,191,192,193,194,195,196,197,198,199,200],dilat:85,dimens:201,dipol:[6,28,86,412],dipole:9,direct:235,directori:4,discuss:6,disp:6,displac:[87,94],displace_atom:202,distanc:365,distribut:[7,12],document:0,dpd:[9,88,89,413,414],drag:233,dreid:425,dreiding:6,drude:[6,9,163,234,235,255,518],dsf:[409,431],dsmc:415,dump:[6,8,203,204,205,206,208],dump_modifi:207,dynam:308,eam:[13,416],echo:209,edip:417,eff:[9,13,106,107,162,164,169,256,272,284,295,341,418],efield:237,ehex:238,eim:419,elastic:6,emac:13,energi:[135,414],enforce2d:239,ensembl:7,erot:[90,91,92,93],error:[3,134],evapor:243,event:94,exampl:[4,6,11,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,330,331,332,333,334,335,336,337,338,339,340,341,342,343,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,510,511,512,513,514,515,516,517,520,521,522,523,524,525,526,527,528,529],exp6:420,exp:[26,187],expand:[46,434],extend:[8,11],extern:244,fcc:298,fdt:414,featur:[7,8,519,524],fene:[45,46],fep:[9,13,95,212],field:[6,7],file:6,finit:6,fire:308,fix:[2,6,8,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234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173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,299,300,301,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,520,521,522,523,524,525,526,527,528,529],system:6,tabl:[0,6,37,56,199,241,242,478,479,480],tad:511,taitwat:[473,474],talli:154,tally:9,temp:[155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,338,339,340,341],temper:512,temperatur:6,tersoff:[480,481,482],test:11,tfmc:342,thermal:[6,343,518],thermo:[6,513,524],thermo_modifi:514,thermo_styl:515,thermodynam:[6,8],thermostat:6,thole:483,time:[6,223],timer:516,timestep:517,tip3p:6,tip4p:[6,409,431,435,439],tip:12,tlsph:[139,140,141,142,143,144,145,463],tmd:346,tool:[12,13],torqu:173,transform:235,tri:[290,484],tri_surfac:464,triangl:146,triclin:6,tstat:413,ttm:347,tune:348,tutori:[518,519],type:7,ulsph:[147,148,149,150,465],umbrella:372,uncomput:520,undump:521,unfix:522,unit:523,uppercas:4,use:448,user:[9,12,15,17],using:[6,11],vacf:174,valu:[6,524],variabl:[6,8,524],variou:1,vashishta:485,vcm:175,vector:[6,349,524],veloc:525,version:[0,5,12],vertic:146,via:12,vim:13,viscos:[6,350],viscou:[261,351],visual:6,vol:151,voronoi:[9,176],vtk:[9,203,204,319],wall:[6,352,353,354,355,356,357],wall_surfac:330,warn:3,water:6,weight:222,what:[7,12],wisdom:519,wolf:[400,409],wrapper:11,write:6,write_coeff:526,write_data:527,write_dump:528,write_restart:529,written:519,xmgrace:13,xmovi:13,xrd:177,your:519,yukawa:[487,488],zbl:[482,489],zero:[38,58,200,373,490]}})
\ No newline at end of file
diff --git a/doc/src/compute_angle_local.txt b/doc/src/compute_angle_local.txt
index 0f31292e4d..0ee1d32d7d 100644
--- a/doc/src/compute_angle_local.txt
+++ b/doc/src/compute_angle_local.txt
@@ -10,12 +10,12 @@ compute angle/local command :h3
 
 [Syntax:]
 
-compute ID group-ID angle/local input1 input2 ... :pre
+compute ID group-ID angle/local value1 value2 ... :pre
 
 ID, group-ID are documented in "compute"_compute.html command :ulb,l
 angle/local = style name of this compute command :l
-one or more keywords may be appended :l
-keyword = {theta} or {eng} :l
+one or more values may be appended :l
+value = {theta} or {eng} :l
   {theta} = tabulate angles
   {eng} = tabulate angle energies :pre
 :ule
@@ -32,6 +32,10 @@ interactions.  The number of datums generated, aggregated across all
 processors, equals the number of angles in the system, modified by the
 group parameter as explained below.
 
+The value {theta} is the angle for the 3 atoms in the interaction.
+
+The value {eng} is the interaction energy for the angle.
+
 The local data stored by this command is generated by looping over all
 the atoms owned on a processor and their angles.  An angle will only
 be included if all 3 atoms in the angle are in the specified compute
@@ -52,6 +56,12 @@ property/local"_compute_property_local.html command can be combined
 with data from this command and output by the "dump local"_dump.html
 command in a consistent way.
 
+Here is an example of how to do this:
+
+compute 1 all property/local atype aatom1 aatom2 aatom3
+compute 2 all angle/local theta eng
+dump 1 all local 1000 tmp.dump index c_1\[1\] c_1\[2\] c_1\[3\] c_1\[4\] c_2\[1\] c_2\[2\] :pre
+
 [Output info:]
 
 This compute calculates a local vector or local array depending on the
diff --git a/doc/src/compute_bond_local.txt b/doc/src/compute_bond_local.txt
index b5f9af059c..4b48405c58 100644
--- a/doc/src/compute_bond_local.txt
+++ b/doc/src/compute_bond_local.txt
@@ -10,12 +10,12 @@ compute bond/local command :h3
 
 [Syntax:]
 
-compute ID group-ID bond/local input1 input2 ... :pre
+compute ID group-ID bond/local value1 value2 ... :pre
 
 ID, group-ID are documented in "compute"_compute.html command :ulb,l
 bond/local = style name of this compute command :l
-one or more keywords may be appended :l
-keyword = {dist} or {eng} :l
+one or more values may be appended :l
+value = {dist} or {eng} or {force} :l
   {dist} = bond distance
   {eng} = bond energy
   {force} = bond force :pre
@@ -33,6 +33,13 @@ interactions.  The number of datums generated, aggregated across all
 processors, equals the number of bonds in the system, modified
 by the group parameter as explained below.
 
+The value {dist} is the length of the bond.
+
+The value {eng} is the interaction energy for the bond.
+
+The value {force} is the force acting between the pair of atoms in the
+bond.
+
 The local data stored by this command is generated by looping over all
 the atoms owned on a processor and their bonds.  A bond will only be
 included if both atoms in the bond are in the specified compute group.
@@ -54,7 +61,7 @@ command in a consistent way.
 
 Here is an example of how to do this:
 
-compute 1 all property/local batom1 batom2 btype 
+compute 1 all property/local btype batom1 batom2
 compute 2 all bond/local dist eng
 dump 1 all local 1000 tmp.dump index c_1\[1\] c_1\[2\] c_1\[3\] c_2\[1\] c_2\[2\] :pre
 
diff --git a/doc/src/compute_dihedral_local.txt b/doc/src/compute_dihedral_local.txt
index b8e79fff40..91370a947a 100644
--- a/doc/src/compute_dihedral_local.txt
+++ b/doc/src/compute_dihedral_local.txt
@@ -10,12 +10,12 @@ compute dihedral/local command :h3
 
 [Syntax:]
 
-compute ID group-ID dihedral/local input1 input2 ... :pre
+compute ID group-ID dihedral/local value1 value2 ... :pre
 
 ID, group-ID are documented in "compute"_compute.html command :ulb,l
 dihedral/local = style name of this compute command :l
-one or more keywords may be appended :l
-keyword = {phi} :l
+one or more values may be appended :l
+value = {phi} :l
   {phi} = tabulate dihedral angles :pre
 :ule
 
@@ -30,6 +30,9 @@ interactions.  The number of datums generated, aggregated across all
 processors, equals the number of angles in the system, modified by the
 group parameter as explained below.
 
+The value {phi} is the dihedral angle, as defined in the diagram on
+the "dihedral_style"_dihedral_style.html doc page.
+
 The local data stored by this command is generated by looping over all
 the atoms owned on a processor and their dihedrals.  A dihedral will
 only be included if all 4 atoms in the dihedral are in the specified
@@ -45,6 +48,12 @@ property/local"_compute_property_local.html command can be combined
 with data from this command and output by the "dump local"_dump.html
 command in a consistent way.
 
+Here is an example of how to do this:
+
+compute 1 all property/local dtype datom1 datom2 datom3 datom4
+compute 2 all dihedral/local phi
+dump 1 all local 1000 tmp.dump index c_1\[1\] c_1\[2\] c_1\[3\] c_1\[4\] c_1\[5\] c_2\[1\] :pre
+
 [Output info:]
 
 This compute calculates a local vector or local array depending on the
diff --git a/doc/src/compute_improper_local.txt b/doc/src/compute_improper_local.txt
index 9289435da6..0c289fbf07 100644
--- a/doc/src/compute_improper_local.txt
+++ b/doc/src/compute_improper_local.txt
@@ -10,12 +10,12 @@ compute improper/local command :h3
 
 [Syntax:]
 
-compute ID group-ID improper/local input1 input2 ... :pre
+compute ID group-ID improper/local value1 value2 ... :pre
 
 ID, group-ID are documented in "compute"_compute.html command :ulb,l
 improper/local = style name of this compute command :l
-one or more keywords may be appended :l
-keyword = {chi} :l
+one or more values may be appended :l
+value = {chi} :l
   {chi} = tabulate improper angles :pre
 :ule
 
@@ -30,6 +30,10 @@ interactions.  The number of datums generated, aggregated across all
 processors, equals the number of impropers in the system, modified by
 the group parameter as explained below.
 
+The value {chi} is the improper angle, as defined in the doc pages for
+the individual improper styles listed on
+"improper_style"_improper_style.html doc page.
+
 The local data stored by this command is generated by looping over all
 the atoms owned on a processor and their impropers.  An improper will
 only be included if all 4 atoms in the improper are in the specified
@@ -45,6 +49,12 @@ property/local"_compute_property_local.html command can be combined
 with data from this command and output by the "dump local"_dump.html
 command in a consistent way.
 
+Here is an example of how to do this:
+
+compute 1 all property/local itype iatom1 iatom2 iatom3 iatom4
+compute 2 all improper/local chi
+dump 1 all local 1000 tmp.dump index c_1\[1\] c_1\[2\] c_1\[3\] c_1\[4\] c_1\[5\] c_2\[1\] :pre
+
 [Output info:]
 
 This compute calculates a local vector or local array depending on the
diff --git a/doc/src/compute_pair_local.txt b/doc/src/compute_pair_local.txt
index 4f33bc1cf9..0d420b2202 100644
--- a/doc/src/compute_pair_local.txt
+++ b/doc/src/compute_pair_local.txt
@@ -10,17 +10,20 @@ compute pair/local command :h3
 
 [Syntax:]
 
-compute ID group-ID pair/local input1 input2 ... :pre
+compute ID group-ID pair/local value1 value2 ... keyword args ... :pre
 
 ID, group-ID are documented in "compute"_compute.html command :ulb,l
 pair/local = style name of this compute command :l
-zero or more keywords may be appended :l
-keyword = {dist} or {eng} or {force} or {fx} or {fy} or {fz} or {pN} :l
+one or more values may be appended :l
+value = {dist} or {eng} or {force} or {fx} or {fy} or {fz} or {pN} :l
   {dist} = pairwise distance
   {eng} = pairwise energy
   {force} = pairwise force
   {fx},{fy},{fz} = components of pairwise force
   {pN} = pair style specific quantities for allowed N values :pre
+zero or more keyword/arg pairs may be appended :l
+keyword = {cutoff} :l
+  {cutoff} arg = {type} or {radius} :pre
 :ule
 
 [Examples:]
@@ -44,13 +47,13 @@ force cutoff distance for that interaction, as defined by the
 "pair_style"_pair_style.html and "pair_coeff"_pair_coeff.html
 commands.
 
-The output {dist} is the distance bewteen the pair of atoms.
+The value {dist} is the distance bewteen the pair of atoms.
 
-The output {eng} is the interaction energy for the pair of atoms.
+The value {eng} is the interaction energy for the pair of atoms.
 
-The output {force} is the force acting between the pair of atoms,
-which is positive for a repulsive force and negative for an attractive
-force.  The outputs {fx}, {fy}, and {fz} are the xyz components of
+The value {force} is the force acting between the pair of atoms, which
+is positive for a repulsive force and negative for an attractive
+force.  The values {fx}, {fy}, and {fz} are the xyz components of
 {force} on atom I.
 
 A pair style may define additional pairwise quantities which can be
@@ -61,10 +64,19 @@ which calculate the tangential force between two particles and return
 its components and magnitude acting on atom I for N = 1,2,3,4.  See
 individual pair styles for detils.
 
-The output {dist} will be in distance "units"_units.html.  The output
-{eng} will be in energy "units"_units.html.  The outputs {force},
-{fx}, {fy}, and {fz} will be in force "units"_units.html.  The output
-{pN} will be in whatever units the pair style defines.
+The value {dist} will be in distance "units"_units.html.  The value
+{eng} will be in energy "units"_units.html.  The values {force}, {fx},
+{fy}, and {fz} will be in force "units"_units.html.  The values {pN}
+will be in whatever units the pair style defines.
+
+The optional {cutoff} keyword determines how the force cutoff distance
+for an interaction is determined.  For the default setting of {type},
+the pairwise cutoff defined by the "pair_style"_pair_style.html
+command for the types of the two atoms is used.  For the {radius}
+setting, the sum of the radii of the two particles is used as a
+cutoff.  For example, this is appropriate for granular particles which
+only interact when they are overlapping, as computed by "granular pair
+styles"_pair_gran.txt.
 
 Note that as atoms migrate from processor to processor, there will be
 no consistent ordering of the entries within the local vector or array
@@ -76,6 +88,12 @@ property/local"_compute_property_local.html command can be combined
 with data from this command and output by the "dump local"_dump.html
 command in a consistent way.
 
+Here is an example of how to do this:
+
+compute 1 all property/local patom1 patom2
+compute 2 all pair/local dist eng force
+dump 1 all local 1000 tmp.dump index c_1\[1\] c_1\[2\] c_2\[1\] c_2\[2\] c_2\[3\] :pre
+
 NOTE: For pairs, if two atoms I,J are involved in 1-2, 1-3, 1-4
 interactions within the molecular topology, their pairwise interaction
 may be turned off, and thus they may not appear in the neighbor list,
@@ -105,7 +123,8 @@ options.
 
 The output for {dist} will be in distance "units"_units.html.  The
 output for {eng} will be in energy "units"_units.html.  The output for
-{force} will be in force "units"_units.html.
+{force}, {fx}, {fy}, and {fz} will be in force "units"_units.html.
+The outpur for {pN} will be in whatever units the pair style defines.
 
 [Restrictions:] none
 
@@ -114,4 +133,6 @@ output for {eng} will be in energy "units"_units.html.  The output for
 "dump local"_dump.html, "compute
 property/local"_compute_property_local.html
 
-[Default:] none
+[Default:]
+
+The keyword default is cutoff = type.
diff --git a/doc/src/compute_property_local.txt b/doc/src/compute_property_local.txt
index 2b04c4f6a2..98428c19a2 100644
--- a/doc/src/compute_property_local.txt
+++ b/doc/src/compute_property_local.txt
@@ -10,17 +10,17 @@ compute property/local command :h3
 
 [Syntax:]
 
-compute ID group-ID property/local input1 input2 ... :pre
+compute ID group-ID property/local attribute1 attribute2 ... keyword args ... :pre
 
 ID, group-ID are documented in "compute"_compute.html command :ulb,l
 property/local = style name of this compute command :l
-input = one or more attributes :l
-  possible attributes = natom1 natom2 ntype1 ntype2
-		        patom1 patom2 ptype1 ptype2
-                        batom1 batom2 btype
-                        aatom1 aatom2 aatom3 atype
-                        datom1 datom2 datom3 dtype
-                        iatom1 iatom2 iatom3 itype :pre
+one or more attributes may be appended :l
+  possible attributes =  natom1 natom2 ntype1 ntype2
+		         patom1 patom2 ptype1 ptype2
+                         batom1 batom2 btype
+                         aatom1 aatom2 aatom3 atype
+                         datom1 datom2 datom3 dtype
+                         iatom1 iatom2 iatom3 itype :pre
 
      natom1, natom2 = IDs of 2 atoms in each pair (within neighbor cutoff)
      ntype1, ntype2 = type of 2 atoms in each pair (within neighbor cutoff)
@@ -34,6 +34,9 @@ input = one or more attributes :l
      dtype = dihedral type of each dihedral
      iatom1, iatom2, iatom3, iatom4 = IDs of 4 atoms in each improper
      itype = improper type of each improper :pre
+zero or more keyword/arg pairs may be appended :l
+keyword = {cutoff} :l
+  {cutoff} arg = {type} or {radius} :pre
 :ule
 
 [Examples:]
@@ -50,9 +53,9 @@ to bond information, then the number of datums generated, aggregated
 across all processors, equals the number of bonds in the system.
 Ditto for pairs, angles, etc.
 
-If multiple input attributes are specified then they must all generate
-the same amount of information, so that the resulting local array has
-the same number of rows for each column.  This means that only bond
+If multiple attributes are specified then they must all generate the
+same amount of information, so that the resulting local array has the
+same number of rows for each column.  This means that only bond
 attributes can be specified together, or angle attributes, etc.  Bond
 and angle attributes can not be mixed in the same compute
 property/local command.
@@ -68,6 +71,15 @@ force cutoff distance for that pair to be included, as defined by the
 "pair_style"_pair_style.html and "pair_coeff"_pair_coeff.html
 commands.
 
+The optional {cutoff} keyword determines how the force cutoff distance
+for an interaction is determined for the {patom1} and {patom2}
+attributes.  For the default setting of {type}, the pairwise cutoff
+defined by the "pair_style"_pair_style.html command for the types of
+the two atoms is used.  For the {radius} setting, the sum of the radii
+of the two particles is used as a cutoff.  For example, this is
+appropriate for granular particles which only interact when they are
+overlapping, as computed by "granular pair styles"_pair_gran.txt.
+
 If the inputs are bond, angle, etc attributes, the local data is
 generated by looping over all the atoms owned on a processor and
 extracting bond, angle, etc info.  For bonds, info about an individual
@@ -117,6 +129,8 @@ The attributes that start with "a", "d", "i", refer to similar values
 for "angles"_angle_style.html, "dihedrals"_dihedral_style.html, and
 "impropers"_improper_style.html.
 
+The optional {cutoff} keyword
+
 [Output info:]
 
 This compute calculates a local vector or local array depending on the
@@ -138,4 +152,6 @@ specified attribute.
 
 "dump local"_dump.html, "compute reduce"_compute_reduce.html
 
-[Default:] none
+[Default:]
+
+The keyword default is cutoff = type.

From e519cfb1dccb2d9554f97b243802c62972372b25 Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 22:50:41 +0000
Subject: [PATCH 08/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15353
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 src/fix_shear_history.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/fix_shear_history.cpp b/src/fix_shear_history.cpp
index 657679ce38..7538d57f1a 100644
--- a/src/fix_shear_history.cpp
+++ b/src/fix_shear_history.cpp
@@ -578,7 +578,7 @@ void FixShearHistory::set_arrays(int i)
 
 int FixShearHistory::pack_reverse_comm_size(int n, int first)
 {
-  int i,j,k,last;
+  int i,last;
 
   int m = 0;
   last = first + n;
@@ -595,7 +595,7 @@ int FixShearHistory::pack_reverse_comm_size(int n, int first)
 
 int FixShearHistory::pack_reverse_comm(int n, int first, double *buf)
 {
-  int i,j,k,last;
+  int i,k,last;
 
   int m = 0;
   last = first + n;
@@ -607,7 +607,7 @@ int FixShearHistory::pack_reverse_comm(int n, int first, double *buf)
   } else if (commflag == PERPARTNER) {
     for (i = first; i < last; i++) {
       buf[m++] = npartner[i];
-      for (int k = 0; k < npartner[i]; k++) {
+      for (k = 0; k < npartner[i]; k++) {
         buf[m++] = partner[i][k];
         memcpy(&buf[m],&shearpartner[i][dnum*k],dnumbytes);
         m += dnum;
@@ -637,7 +637,7 @@ void FixShearHistory::unpack_reverse_comm(int n, int *list, double *buf)
     for (i = 0; i < n; i++) {
       j = list[i];
       ncount = static_cast (buf[m++]);
-      for (int k = 0; k < ncount; k++) {
+      for (k = 0; k < ncount; k++) {
         kk = npartner[j]++;
         partner[j][kk] = static_cast (buf[m++]);
         memcpy(&shearpartner[j][dnum*kk],&buf[m],dnumbytes);

From 87bfe1941b6f4252c34f14c67ac789d693aa87ed Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 22:51:37 +0000
Subject: [PATCH 09/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15354
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 src/SHOCK/fix_wall_piston.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/SHOCK/fix_wall_piston.cpp b/src/SHOCK/fix_wall_piston.cpp
index 443b2670cb..3513d6e759 100644
--- a/src/SHOCK/fix_wall_piston.cpp
+++ b/src/SHOCK/fix_wall_piston.cpp
@@ -51,7 +51,7 @@ FixWallPiston::FixWallPiston(LAMMPS *lmp, int narg, char **arg) :
   rampNL3flag = 0;
   rampNL4flag = 0;
   rampNL5flag = 0;
-  z0 = vz = 0.0;
+  t_target = z0 = vx = vy = vz = 0.0;
   xloflag = xhiflag = yloflag = yhiflag = zloflag = zhiflag = 0;
 
   int iarg = 3;

From cff65b956adcee7cf5c5a6a7868382f9bf17bb41 Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 22:52:03 +0000
Subject: [PATCH 10/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15355
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 src/USER-DPD/fix_eos_table_rx.cpp   |  2 +-
 src/USER-DPD/fix_rx.cpp             | 32 +++--------------------
 src/USER-DPD/fix_rx.h               |  2 +-
 src/USER-DPD/pair_multi_lucy_rx.cpp | 39 +++++++++++------------------
 src/dump_image.cpp                  |  2 +-
 5 files changed, 22 insertions(+), 55 deletions(-)

diff --git a/src/USER-DPD/fix_eos_table_rx.cpp b/src/USER-DPD/fix_eos_table_rx.cpp
index 70199e60ce..1e94c2f87c 100644
--- a/src/USER-DPD/fix_eos_table_rx.cpp
+++ b/src/USER-DPD/fix_eos_table_rx.cpp
@@ -687,7 +687,7 @@ void FixEOStableRX::temperature_lookup(int id, double ui, double &thetai)
   
   // Store the current thetai in t1
   t1 = MAX(thetai,tb->lo);
-  t1 = MIN(thetai,tb->hi);
+  t1 = MIN(t1,tb->hi);
   if(t1==tb->hi) delta = -delta;
 
   // Compute u1 at thetai
diff --git a/src/USER-DPD/fix_rx.cpp b/src/USER-DPD/fix_rx.cpp
index 5639646156..96f7f44422 100644
--- a/src/USER-DPD/fix_rx.cpp
+++ b/src/USER-DPD/fix_rx.cpp
@@ -388,7 +388,7 @@ void FixRX::post_constructor()
 
 /* ---------------------------------------------------------------------- */
 
-int FixRX::initSparse()
+void FixRX::initSparse()
 {
   const int Verbosity = 1;
 
@@ -637,9 +637,8 @@ void FixRX::setup_pre_force(int vflag)
   int nlocal = atom->nlocal;
   int nghost = atom->nghost;
   int *mask = atom->mask;
-  double *dpdTheta = atom->dpdTheta;
   int newton_pair = force->newton_pair;
-  double tmp, theta;
+  double tmp;
   int ii;
 
   if(localTempFlag){
@@ -662,8 +661,6 @@ void FixRX::setup_pre_force(int vflag)
     }
   for (int i = 0; i < nlocal; i++)
     if (mask[i] & groupbit){
-      if(localTempFlag) theta = dpdThetaLocal[i];
-      else theta=dpdTheta[i];
 
       // Set the reaction rate constants to zero:  no reactions occur at step 0
       for(int irxn=0;irxndpdThetaLocal;
   }
 
   TimerType timer_localTemperature = getTimeStamp();
@@ -746,14 +740,7 @@ void FixRX::pre_force(int vflag)
   comm->forward_comm_fix(this);
   if(localTempFlag) delete [] dpdThetaLocal;
 
-  TimerType timer_stop = getTimeStamp();
   double time_ODE = getElapsedTime(timer_localTemperature, timer_ODE);
-//   printf("FixRX::pre_force total= %e Temp= %e ODE= %e Comm= %e nlocal= %d %d %d %d %d\n",
-//                       getElapsedTime(timer_start, timer_stop),
-//                       (localTempFlag) ? getElapsedTime(timer_start, timer_localTemperature) : 0.0,
-//                       time_ODE,
-//                       getElapsedTime(timer_ODE, timer_stop),
-//                       nlocal, nSteps, nIters, nFuncs, nFails);
 
   // Warn the user if a failure was detected in the ODE solver.
   if (nFails > 0){
@@ -904,6 +891,7 @@ void FixRX::read_file(char *file)
         error->all(FLERR,"Illegal fix rx command");
       }
       word = strtok(NULL, " \t\n\r\f");
+      if(word==NULL) error->all(FLERR,"Missing parameters in reaction kinetic equation");
       if(strcmp(word,"=") == 0) sign = 1.0;
       if(strcmp(word,"+") != 0 && strcmp(word,"=") != 0){
         if(word==NULL) error->all(FLERR,"Missing parameters in reaction kinetic equation");
@@ -1034,12 +1022,9 @@ void FixRX::rk4(int id, double *rwork)
 
 void FixRX::rkf45_step (const int neq, const double h, double y[], double y_out[], double rwk[], void* v_param)
 {
-   const double c20=0.25;
    const double c21=0.25;
-   const double c30=0.375;
    const double c31=0.09375;
    const double c32=0.28125;
-   const double c40=0.92307692307692;
    const double c41=0.87938097405553;
    const double c42=-3.2771961766045;
    const double c43=3.3208921256258;
@@ -1047,19 +1032,16 @@ void FixRX::rkf45_step (const int neq, const double h, double y[], double y_out[
    const double c52=-8.0;
    const double c53=7.1734892787524;
    const double c54=-0.20589668615984;
-   const double c60=0.5;
    const double c61=-0.2962962962963;
    const double c62=2.0;
    const double c63=-1.3816764132554;
    const double c64=0.45297270955166;
    const double c65=-0.275;
    const double a1=0.11574074074074;
-   const double a2=0.0;
    const double a3=0.54892787524366;
    const double a4=0.5353313840156;
    const double a5=-0.2;
    const double b1=0.11851851851852;
-   const double b2=0.0;
    const double b3=0.51898635477583;
    const double b4=0.50613149034201;
    const double b5=-0.18;
@@ -1146,10 +1128,6 @@ int FixRX::rkf45_h0 (const int neq, const double t, const double t_stop,
                      const double hmin, const double hmax,
                      double& h0, double y[], double rwk[], void* v_params)
 {
-   const double uround = DBL_EPSILON;
-   const double tdist = fabs(t_stop - t);
-   const double tround = tdist * uround;
-
    // Set lower and upper bounds on h0, and take geometric mean as first trial value.
    // Exit with this value if the bounds cross each other.
 
@@ -1696,7 +1674,7 @@ void FixRX::computeLocalTemperature()
   int newton_pair = force->newton_pair;
 
   // local temperature variables
-  double wij=0.0, fr, fr4;
+  double wij=0.0;
   double *dpdTheta = atom->dpdTheta;
 
   // Initialize the local density and local temperature arrays
@@ -1738,8 +1716,6 @@ void FixRX::computeLocalTemperature()
       if (rsq < pairDPDE->cutsq[itype][jtype]) {
         double rcut = sqrt(pairDPDE->cutsq[itype][jtype]);
         double rij = sqrt(rsq);
-        double tmpFactor = 1.0-rij/rcut;
-        double tmpFactor4 = tmpFactor*tmpFactor*tmpFactor*tmpFactor;
         double ratio = rij/rcut;
 
         // Lucy's Weight Function
diff --git a/src/USER-DPD/fix_rx.h b/src/USER-DPD/fix_rx.h
index f1b6cb5a4b..fc411e265a 100644
--- a/src/USER-DPD/fix_rx.h
+++ b/src/USER-DPD/fix_rx.h
@@ -90,7 +90,7 @@ class FixRX : public Fix {
   // Sparse stoichiometric matrix storage format and methods.
   bool useSparseKinetics;
   //SparseKinetics sparseKinetics;
-  int initSparse(void);
+  void initSparse(void);
   int rhs_sparse(double, const double *, double *, void *) const;
 
   int sparseKinetics_maxReactants; //type;
   int nlocal = atom->nlocal;
   int nghost = atom->nghost;
-  double *special_lj = force->special_lj;
   int newton_pair = force->newton_pair;
 
   double fractionOld1_i,fractionOld1_j;
   double fractionOld2_i,fractionOld2_j;
-  double fraction1_i,fraction1_j;
-  double fraction2_i,fraction2_j;
+  double fraction1_i;
   double *uCG = atom->uCG;
   double *uCGnew = atom->uCGnew;
 
@@ -166,11 +164,9 @@ void PairMultiLucyRX::compute(int eflag, int vflag)
     fractionOld1_i = fractionOld1[i];
     fractionOld2_i = fractionOld2[i];
     fraction1_i = fraction1[i];
-    fraction2_i = fraction2[i];
 
     for (jj = 0; jj < jnum; jj++) {
       j = jlist[jj];
-      factor_lj = special_lj[sbmask(j)];
       j &= NEIGHMASK;
 
       delx = xtmp - x[j][0];
@@ -184,8 +180,6 @@ void PairMultiLucyRX::compute(int eflag, int vflag)
 
         fractionOld1_j = fractionOld1[j];
         fractionOld2_j = fractionOld2[j];
-        fraction1_j = fraction1[j];
-        fraction2_j = fraction2[j];
 
         tb = &tables[tabindex[itype][jtype]];
         if (rho[i]*rho[i] < tb->innersq || rho[j]*rho[j] < tb->innersq){
@@ -205,8 +199,10 @@ void PairMultiLucyRX::compute(int eflag, int vflag)
           }
           A_i = tb->f[itable];
           A_j = tb->f[jtable];
-          fpair = 0.5*(A_i + A_j)*(1.0+3.0*sqrt(rsq)/sqrt(cutsq[itype][jtype]))*(1.0 - sqrt(rsq)/sqrt(cutsq[itype][jtype]))*(1.0 - sqrt(rsq)/sqrt(cutsq[itype][jtype]))*(1.0 - sqrt(rsq)/sqrt(cutsq[itype][jtype]));
-          fpair = fpair/sqrt(rsq);
+
+          const double rfactor = 1.0-sqrt(rsq/cutsq[itype][jtype]);
+          fpair = 0.5*(A_i + A_j)*(4.0-3.0*rfactor)*rfactor*rfactor*rfactor;
+          fpair /= sqrt(rsq);
 
         } else if (tabstyle == LINEAR) {
           itable = static_cast ((rho[i]*rho[i] - tb->innersq) * tb->invdelta);
@@ -232,9 +228,9 @@ void PairMultiLucyRX::compute(int eflag, int vflag)
           A_i = tb->f[itable] + fraction_i*tb->df[itable];
           A_j = tb->f[jtable] + fraction_j*tb->df[jtable];
 
-          fpair = 0.5*(A_i + A_j)*(1.0+3.0*sqrt(rsq)/sqrt(cutsq[itype][jtype]))*(1.0 - sqrt(rsq)/sqrt(cutsq[itype][jtype]))*(1.0 - sqrt(rsq)/sqrt(cutsq[itype][jtype]))*(1.0 - sqrt(rsq)/sqrt(cutsq[itype][jtype]));
-
-          fpair = fpair / sqrt(rsq);
+          const double rfactor = 1.0-sqrt(rsq/cutsq[itype][jtype]);
+          fpair = 0.5*(A_i + A_j)*(4.0-3.0*rfactor)*rfactor*rfactor*rfactor;
+          fpair /= sqrt(rsq);
 
         } else error->one(FLERR,"Only LOOKUP and LINEAR table styles have been implemented for pair multi/lucy/rx");
 
@@ -389,15 +385,11 @@ void PairMultiLucyRX::coeff(int narg, char **arg)
   // insure cutoff is within table
 
   if (tb->ninput <= 1) error->one(FLERR,"Invalid pair table length");
-  double rlo,rhi;
   if (tb->rflag == 0) {
-    rlo = tb->rfile[0];
-    rhi = tb->rfile[tb->ninput-1];
+    rho_0 = tb->rfile[0];
   } else {
-    rlo = tb->rlo;
-    rhi = tb->rhi;
+    rho_0 = tb->rlo;
   }
-  rho_0 = rlo;
 
   tb->match = 0;
   if (tabstyle == LINEAR && tb->ninput == tablength &&
@@ -903,8 +895,8 @@ void PairMultiLucyRX::computeLocalDensity()
       const double delz = ztmp - x[j][2];
       const double rsq = delx*delx + dely*dely + delz*delz;
 
-      if (one_type)
-        if (rsq < cutsq_type11){
+      if (one_type) {
+        if (rsq < cutsq_type11) {
           const double rcut = rcut_type11;
           const double r_over_rcut = sqrt(rsq) / rcut;
           const double tmpFactor = 1.0 - r_over_rcut;
@@ -913,9 +905,7 @@ void PairMultiLucyRX::computeLocalDensity()
           rho_i += factor;
           if (newton_pair || j < nlocal)
             rho[j] += factor;
-        }
-      else
-        if (rsq < cutsq[itype][jtype]){
+        } else if (rsq < cutsq[itype][jtype]) {
           const double rcut = sqrt(cutsq[itype][jtype]);
           const double tmpFactor = 1.0-sqrt(rsq)/rcut;
           const double tmpFactor4 = tmpFactor*tmpFactor*tmpFactor*tmpFactor;
@@ -924,6 +914,7 @@ void PairMultiLucyRX::computeLocalDensity()
           if (newton_pair || j < nlocal)
             rho[j] += factor;
         }
+      }
     }
 
     rho[i] = rho_i;
diff --git a/src/dump_image.cpp b/src/dump_image.cpp
index 5d1154478f..fd3dbed9e9 100644
--- a/src/dump_image.cpp
+++ b/src/dump_image.cpp
@@ -1036,7 +1036,7 @@ void DumpImage::create_image()
   // render objects provided by a fix
 
   if (fixflag) {
-    int tridraw,edgedraw;
+    int tridraw=0,edgedraw=0;
     if (domain->dimension == 3) {
       tridraw = 1;
       edgedraw = 1;

From 87a6c1368f6f1e5771fe6a0b7bccfd99d8203a26 Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 22:56:08 +0000
Subject: [PATCH 11/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15356
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 src/Makefile    |  4 +++-
 src/library.cpp | 29 +++++++++++++++++++++++++++++
 src/library.h   |  2 ++
 3 files changed, 34 insertions(+), 1 deletion(-)

diff --git a/src/Makefile b/src/Makefile
index 7cf9d1730f..4467095a6c 100755
--- a/src/Makefile
+++ b/src/Makefile
@@ -1,6 +1,8 @@
 # LAMMPS multiple-machine -*- Makefile -*-
 
 SHELL = /bin/bash
+PYTHON = python
+
 #.IGNORE:
 
 # Definitions
@@ -198,7 +200,7 @@ mpi-stubs:
 # install LAMMPS shared lib and Python wrapper for Python usage
 
 install-python:
-	@python ../python/install.py
+	@$(PYTHON) ../python/install.py
 
 # Create a tarball of src dir and packages
 
diff --git a/src/library.cpp b/src/library.cpp
index e51813413d..29e00d211d 100644
--- a/src/library.cpp
+++ b/src/library.cpp
@@ -28,6 +28,8 @@
 #include "input.h"
 #include "variable.h"
 #include "modify.h"
+#include "output.h"
+#include "thermo.h"
 #include "compute.h"
 #include "fix.h"
 #include "comm.h"
@@ -149,9 +151,19 @@ void *lammps_extract_global(void *ptr, char *name)
   if (strcmp(name,"xz") == 0) return (void *) &lmp->domain->xz;
   if (strcmp(name,"yz") == 0) return (void *) &lmp->domain->yz;
   if (strcmp(name,"natoms") == 0) return (void *) &lmp->atom->natoms;
+  if (strcmp(name,"nbonds") == 0) return (void *) &lmp->atom->nbonds;
+  if (strcmp(name,"nangles") == 0) return (void *) &lmp->atom->nangles;
+  if (strcmp(name,"ndihedrals") == 0) return (void *) &lmp->atom->ndihedrals;
+  if (strcmp(name,"nimpropers") == 0) return (void *) &lmp->atom->nimpropers;
   if (strcmp(name,"nlocal") == 0) return (void *) &lmp->atom->nlocal;
   if (strcmp(name,"ntimestep") == 0) return (void *) &lmp->update->ntimestep;
 
+  // NOTE: we cannot give access to the thermo "time" data by reference,
+  // as that is a recomputed property. only "atime" can be provided as pointer.
+  // please use lammps_get_thermo() defined below to access all supported
+  // thermo keywords by value
+  if (strcmp(name,"atime") == 0) return (void *) &lmp->update->atime;
+
   return NULL;
 }
 
@@ -383,6 +395,23 @@ int lammps_set_variable(void *ptr, char *name, char *str)
   return err;
 }
 
+/* ----------------------------------------------------------------------
+   return the current value of a thermo keyword as double.
+   unlike lammps_extract_global() this does not give access to the
+   storage of the data in question, and thus needs to be called
+   again to retrieve an updated value. The upshot is that it allows
+   accessing information that is only computed on-the-fly.
+------------------------------------------------------------------------- */
+
+double lammps_get_thermo(void *ptr, char *name)
+{
+  LAMMPS *lmp = (LAMMPS *) ptr;
+  double dval;
+
+  lmp->output->thermo->evaluate_keyword(name,&dval);
+  return dval;
+}
+
 /* ----------------------------------------------------------------------
    return the total number of atoms in the system
    useful before call to lammps_get_atoms() so can pre-allocate vector
diff --git a/src/library.h b/src/library.h
index 3bdeddbac4..8b7dc43f08 100644
--- a/src/library.h
+++ b/src/library.h
@@ -39,7 +39,9 @@ void *lammps_extract_fix(void *, char *, int, int, int, int);
 void *lammps_extract_variable(void *, char *, char *);
 
 int lammps_set_variable(void *, char *, char *);
+double lammps_get_thermo(void *, char *);
 int lammps_get_natoms(void *);
+
 void lammps_gather_atoms(void *, char *, int, int, void *);
 void lammps_scatter_atoms(void *, char *, int, int, void *);
 

From 2f936d5e564bf170377d188bbf415288094a50cf Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 22:57:39 +0000
Subject: [PATCH 12/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15357
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 src/library.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/library.cpp b/src/library.cpp
index 29e00d211d..be407c8bbc 100644
--- a/src/library.cpp
+++ b/src/library.cpp
@@ -159,9 +159,10 @@ void *lammps_extract_global(void *ptr, char *name)
   if (strcmp(name,"ntimestep") == 0) return (void *) &lmp->update->ntimestep;
 
   // NOTE: we cannot give access to the thermo "time" data by reference,
-  // as that is a recomputed property. only "atime" can be provided as pointer.
+  // as that is a recomputed property.  Only "atime" can be provided as pointer.
   // please use lammps_get_thermo() defined below to access all supported
-  // thermo keywords by value
+  // thermo keywords by value.
+
   if (strcmp(name,"atime") == 0) return (void *) &lmp->update->atime;
 
   return NULL;

From 778f4d338c784d4697c1ca70ac1622d02d204b43 Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 22:57:54 +0000
Subject: [PATCH 13/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15358
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 python/.gitignore                     |   1 +
 python/examples/demo.py               |  22 +-
 python/examples/gui.py                |  10 +-
 python/examples/mc.py                 |  19 +-
 python/examples/pizza/dump.py         | 232 ++++++-------
 python/examples/pizza/gl.py           |  31 +-
 python/examples/pizza/gnu.py          |  19 +-
 python/examples/pizza/pdbfile.py      |  33 +-
 python/examples/pizza/vmd.py          |  19 +-
 python/examples/plot.py               |   5 +-
 python/examples/simple.py             |   9 +-
 python/examples/split.py              |  13 +-
 python/examples/trivial.py            |   3 +-
 python/examples/viz_atomeye.py        |   5 +-
 python/examples/viz_gl.py             |  11 +-
 python/examples/viz_pymol.py          |   5 +-
 python/examples/viz_vmd.py            |   7 +-
 python/examples/vizplotgui_atomeye.py |  10 +-
 python/examples/vizplotgui_gl.py      |  15 +-
 python/examples/vizplotgui_pymol.py   |  10 +-
 python/examples/vizplotgui_vmd.py     |  10 +-
 python/lammps.py                      | 451 ++++++++++++++++++++++++--
 22 files changed, 694 insertions(+), 246 deletions(-)

diff --git a/python/.gitignore b/python/.gitignore
index 27ffc2f17f..e069c0ec3b 100644
--- a/python/.gitignore
+++ b/python/.gitignore
@@ -1,2 +1,3 @@
 *.pyc
+__pycache__
 build
diff --git a/python/examples/demo.py b/python/examples/demo.py
index fb212d0e12..958ebcd04b 100755
--- a/python/examples/demo.py
+++ b/python/examples/demo.py
@@ -6,13 +6,14 @@
 # Syntax:  demo.py
 #          uses in.demo as LAMMPS input script
 
+from __future__ import print_function
 import sys
 
 # parse command line
 
 argv = sys.argv
 if len(argv) != 1:
-  print "Syntax: demo.py"
+  print("Syntax: demo.py")
   sys.exit()
 
 from lammps import lammps
@@ -22,29 +23,32 @@ lmp = lammps()
 
 lmp.file("in.demo")
 
-print "\nPython output:"
+print("\nPython output:")
 
 natoms = lmp.extract_global("natoms",0)
 mass = lmp.extract_atom("mass",2)
 x = lmp.extract_atom("x",3)
-print "Natoms, mass, x[0][0] coord =",natoms,mass[1],x[0][0]
+print("Natoms, mass, x[0][0] coord =",natoms,mass[1],x[0][0])
 
 temp = lmp.extract_compute("thermo_temp",0,0)
-print "Temperature from compute =",temp
+print("Temperature from compute =",temp)
 
 eng = lmp.extract_variable("eng",None,0)
-print "Energy from equal-style variable =",eng
+print("Energy from equal-style variable =",eng)
 
 vy = lmp.extract_variable("vy","all",1)
-print "Velocity component from atom-style variable =",vy[1]
+print("Velocity component from atom-style variable =",vy[1])
+
+vol = lmp.get_thermo("vol")
+print("Volume from get_thermo = ",vol)
 
 natoms = lmp.get_natoms()
-print "Natoms from get_natoms =",natoms
+print("Natoms from get_natoms =",natoms)
 
 xc = lmp.gather_atoms("x",1,3)
-print "Global coords from gather_atoms =",xc[0],xc[1],xc[31]
+print("Global coords from gather_atoms =",xc[0],xc[1],xc[31])
 
 xc[0] = xc[0] + 1.0
 lmp.scatter_atoms("x",1,3,xc)
 
-print "Changed x[0][0] via scatter_atoms =",x[0][0]
+print("Changed x[0][0] via scatter_atoms =",x[0][0])
diff --git a/python/examples/gui.py b/python/examples/gui.py
index 0b4981cf57..3fc1d7e36c 100755
--- a/python/examples/gui.py
+++ b/python/examples/gui.py
@@ -10,6 +10,7 @@
 # IMPORTANT: this script cannot yet be run in parallel via Pypar,
 #            because I can't seem to do a MPI-style broadcast in Pypar
 
+from __future__ import print_function
 import sys,time
 
 # methods called by GUI
@@ -31,7 +32,7 @@ def quit():
 
 argv = sys.argv
 if len(argv) != 3:
-  print "Syntax: gui.py in.lammps Nfreq"
+  print("Syntax: gui.py in.lammps Nfreq")
   sys.exit()
 
 infile = sys.argv[1]
@@ -60,7 +61,10 @@ runflag = 0
 temptarget = 1.0
 
 if me == 0:
-  from Tkinter import *
+  try:
+    from Tkinter import *
+  except:
+    from tkinter import *
   tkroot = Tk()
   tkroot.withdraw()
   root = Toplevel(tkroot)
@@ -108,5 +112,5 @@ while 1:
   time.sleep(0.01)
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/mc.py b/python/examples/mc.py
index 8e556f3c52..9e7317218b 100755
--- a/python/examples/mc.py
+++ b/python/examples/mc.py
@@ -6,6 +6,7 @@
 # Syntax:  mc.py in.mc
 #          in.mc = LAMMPS input script
 
+from __future__ import print_function
 import sys,random,math
 
 # set these parameters
@@ -21,7 +22,7 @@ random.seed(27848)
 
 argv = sys.argv
 if len(argv) != 2:
-  print "Syntax: mc.py in.mc"
+  print("Syntax: mc.py in.mc")
   sys.exit()
 
 infile = sys.argv[1]
@@ -50,7 +51,7 @@ lmp.command("variable emin equal $e")
 
 x = lmp.extract_atom("x",3)
 
-for i in xrange(natoms):
+for i in range(natoms):
   x[i][0] += deltaperturb * (2*random.random()-1)
   x[i][1] += deltaperturb * (2*random.random()-1)
 
@@ -68,7 +69,7 @@ estart = lmp.extract_compute("thermo_pe",0,0) / natoms
 elast = estart
 naccept = 0
 
-for i in xrange(nloop):
+for i in range(nloop):
   iatom = random.randrange(0,natoms)
   x0 = x[iatom][0]
   y0 = x[iatom][1]
@@ -100,9 +101,9 @@ nbuild = lmp.extract_variable("nbuild",None,0)
 lmp.command("run 0")
 estop = lmp.extract_compute("thermo_pe",0,0) / natoms
 
-print "MC stats:"
-print "  starting energy =",estart
-print "  final energy =",estop
-print "  minimum energy of perfect lattice =",emin
-print "  accepted MC moves =",naccept
-print "  neighbor list rebuilds =",nbuild
+print("MC stats:")
+print("  starting energy =",estart)
+print("  final energy =",estop)
+print("  minimum energy of perfect lattice =",emin)
+print("  accepted MC moves =",naccept)
+print("  neighbor list rebuilds =",nbuild)
diff --git a/python/examples/pizza/dump.py b/python/examples/pizza/dump.py
index 76e1f3bcee..ec18cba62e 100644
--- a/python/examples/pizza/dump.py
+++ b/python/examples/pizza/dump.py
@@ -6,6 +6,10 @@
 # certain rights in this software.  This software is distributed under 
 # the GNU General Public License.
 
+# for python3 compatibility
+
+from __future__ import print_function
+
 # dump tool
 
 oneline = "Read, write, manipulate dump files and particle attributes"
@@ -179,7 +183,7 @@ d.extra(data)				   extract bond/tri/line list from data
 
 # Imports and external programs
 
-import sys, commands, re, glob, types
+import sys, re, glob, types
 from os import popen
 from math import *             # any function could be used by set()
 
@@ -220,7 +224,7 @@ class dump:
     self.flist = []
     for word in words: self.flist += glob.glob(word)
     if len(self.flist) == 0 and len(list) == 1:
-      raise StandardError,"no dump file specified"
+      raise StandardError("no dump file specified")
     
     if len(list) == 1:
       self.increment = 0
@@ -245,19 +249,19 @@ class dump:
       snap = self.read_snapshot(f)
       while snap:
         self.snaps.append(snap)
-        print snap.time,
+        print(snap.time,end='')
         sys.stdout.flush()
         snap = self.read_snapshot(f)
 
       f.close()
-    print
+    print()
 
     # sort entries by timestep, cull duplicates
 
     self.snaps.sort(self.compare_time)
     self.cull()
     self.nsnaps = len(self.snaps)
-    print "read %d snapshots" % self.nsnaps
+    print("read %d snapshots" % self.nsnaps)
 
     # select all timesteps and atoms
 
@@ -266,36 +270,36 @@ class dump:
     # set default names for atom columns if file wasn't self-describing
     
     if len(self.snaps) == 0:
-      print "no column assignments made"
+      print("no column assignments made")
     elif len(self.names):
-      print "assigned columns:",self.names2str()
+      print("assigned columns:",self.names2str())
     elif self.snaps[0].atoms == None:
-      print "no column assignments made"
+      print("no column assignments made")
     elif len(self.snaps[0].atoms[0]) == 5:
       self.map(1,"id",2,"type",3,"x",4,"y",5,"z")
-      print "assigned columns:",self.names2str()
+      print("assigned columns:",self.names2str())
     elif len(self.snaps[0].atoms[0]) == 8:
       self.map(1,"id",2,"type",3,"x",4,"y",5,"z",6,"ix",7,"iy",8,"iz")
-      print "assigned columns:",self.names2str()
+      print("assigned columns:",self.names2str())
     else:
-      print "no column assignments made"
+      print("no column assignments made")
 
     # if snapshots are scaled, unscale them
 
     if (not self.names.has_key("x")) or \
        (not self.names.has_key("y")) or \
        (not self.names.has_key("z")):
-      print "no unscaling could be performed"
+      print("no unscaling could be performed")
     elif self.nsnaps > 0:
       if self.scaled(self.nsnaps-1): self.unscale()
-      else: print "dump is already unscaled"
+      else: print("dump is already unscaled")
 
   # --------------------------------------------------------------------
   # read next snapshot from list of files
 
   def next(self):
 
-    if not self.increment: raise StandardError,"cannot read incrementally"
+    if not self.increment: raise StandardError("cannot read incrementally")
 
     # read next snapshot in current file using eof as pointer
     # if fail, try next file
@@ -307,15 +311,15 @@ class dump:
       snap = self.read_snapshot(f)
       if not snap:
         self.nextfile += 1
-	if self.nextfile == len(self.flist): return -1
+        if self.nextfile == len(self.flist): return -1
         f.close()
-	self.eof = 0
-	continue
+        self.eof = 0
+        continue
       self.eof = f.tell()
       f.close()
       try:
         self.findtime(snap.time)
-	continue
+        continue
       except: break
 
     # select the new snapshot with all its atoms
@@ -324,7 +328,7 @@ class dump:
     snap = self.snaps[self.nsnaps]
     snap.tselect = 1
     snap.nselect = snap.natoms
-    for i in xrange(snap.natoms): snap.aselect[i] = 1
+    for i in range(snap.natoms): snap.aselect[i] = 1
     self.nsnaps += 1
     self.nselect += 1
 
@@ -370,14 +374,14 @@ class dump:
       if snap.natoms:
         words = f.readline().split()
         ncol = len(words)
-        for i in xrange(1,snap.natoms):
+        for i in range(1,snap.natoms):
           words += f.readline().split()
         floats = map(float,words)
         if oldnumeric: atoms = np.zeros((snap.natoms,ncol),np.Float)
         else: atoms = np.zeros((snap.natoms,ncol),np.float)
         start = 0
         stop = ncol
-        for i in xrange(snap.natoms):
+        for i in range(snap.natoms):
           atoms[i] = floats[start:stop]
           start = stop
           stop += ncol
@@ -413,7 +417,7 @@ class dump:
   
   def map(self,*pairs):
     if len(pairs) % 2 != 0:
-      raise StandardError, "dump map() requires pairs of mappings"
+      raise StandardError("dump map() requires pairs of mappings")
     for i in range(0,len(pairs),2):
       j = i + 1
       self.names[pairs[j]] = pairs[i]-1
@@ -430,15 +434,15 @@ class dump:
         self.nsnaps -= 1
         ndel += 1
       else: i += 1
-    print "%d snapshots deleted" % ndel
-    print "%d snapshots remaining" % self.nsnaps
+    print("%d snapshots deleted" % ndel)
+    print("%d snapshots remaining" % self.nsnaps)
 
   # --------------------------------------------------------------------
   # scale coords to 0-1 for all snapshots or just one
 
   def scale(self,*list):
     if len(list) == 0:
-      print "Scaling dump ..."
+      print("Scaling dump ...")
       x = self.names["x"]
       y = self.names["y"]
       z = self.names["z"]
@@ -466,7 +470,7 @@ class dump:
 
   def unscale(self,*list):
     if len(list) == 0:
-      print "Unscaling dump ..."
+      print("Unscaling dump ...")
       x = self.names["x"]
       y = self.names["y"]
       z = self.names["z"]
@@ -493,7 +497,7 @@ class dump:
   # wrap coords from outside box to inside
 
   def wrap(self):
-    print "Wrapping dump ..."
+    print("Wrapping dump ...")
 
     x = self.names["x"]
     y = self.names["y"]
@@ -515,7 +519,7 @@ class dump:
   # unwrap coords from inside box to outside
 
   def unwrap(self):
-    print "Unwrapping dump ..."
+    print("Unwrapping dump ...")
 
     x = self.names["x"]
     y = self.names["y"]
@@ -537,7 +541,7 @@ class dump:
   # wrap coords to same image as atom ID stored in "other" column
 
   def owrap(self,other):
-    print "Wrapping to other ..."
+    print("Wrapping to other ...")
     
     id = self.names["id"]
     x = self.names["x"]
@@ -554,9 +558,9 @@ class dump:
       zprd = snap.zhi - snap.zlo
       atoms = snap.atoms
       ids = {}
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         ids[atoms[i][id]] = i
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         j = ids[atoms[i][iother]]
         atoms[i][x] += (atoms[i][ix]-atoms[j][ix])*xprd
         atoms[i][y] += (atoms[i][iy]-atoms[j][iy])*yprd
@@ -570,7 +574,7 @@ class dump:
     pairs = self.names.items()
     values = self.names.values()
     str = ""
-    for i in xrange(ncol):
+    for i in range(ncol):
       if i in values: str += pairs[values.index(i)][0] + ' '
     return str
 
@@ -581,12 +585,12 @@ class dump:
 
   def sort(self,*list):
     if len(list) == 0:
-      print "Sorting selected snapshots ..."
+      print("Sorting selected snapshots ...")
       id = self.names["id"]
       for snap in self.snaps:
         if snap.tselect: self.sort_one(snap,id)
     elif type(list[0]) is types.StringType:
-      print "Sorting selected snapshots by %s ..." % list[0]
+      print("Sorting selected snapshots by %s ..." % list[0])
       id = self.names[list[0]]
       for snap in self.snaps:
         if snap.tselect: self.sort_one(snap,id)
@@ -602,7 +606,7 @@ class dump:
     atoms = snap.atoms
     ids = atoms[:,id]
     ordering = np.argsort(ids)
-    for i in xrange(len(atoms[0])):
+    for i in range(len(atoms[0])):
       atoms[:,i] = np.take(atoms[:,i],ordering)
 
   # --------------------------------------------------------------------
@@ -614,33 +618,33 @@ class dump:
     else: f = open(file,"a")
     for snap in self.snaps:
       if not snap.tselect: continue
-      print snap.time,
+      print(snap.time,end='')
       sys.stdout.flush()
 
       if header:
-        print >>f,"ITEM: TIMESTEP"
-        print >>f,snap.time
-        print >>f,"ITEM: NUMBER OF ATOMS"
-        print >>f,snap.nselect
-        print >>f,"ITEM: BOX BOUNDS"
-        print >>f,snap.xlo,snap.xhi
-        print >>f,snap.ylo,snap.yhi
-        print >>f,snap.zlo,snap.zhi
-        print >>f,"ITEM: ATOMS",namestr
+        print("ITEM: TIMESTEP",file=f)
+        print(snap.time,file=f)
+        print("ITEM: NUMBER OF ATOMS",file=f)
+        print(snap.nselect,file=f)
+        print("ITEM: BOX BOUNDS",file=f)
+        print(snap.xlo,snap.xhi,file=f)
+        print(snap.ylo,snap.yhi,file=f)
+        print(snap.zlo,snap.zhi,file=f)
+        print("ITEM: ATOMS",namestr,file=f)
       
       atoms = snap.atoms
       nvalues = len(atoms[0])
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if not snap.aselect[i]: continue
         line = ""
-        for j in xrange(nvalues):
+        for j in range(nvalues):
           if (j < 2):
             line += str(int(atoms[i][j])) + " "
           else:
             line += str(atoms[i][j]) + " "
-        print >>f,line
+        print(line,file=f)
     f.close()
-    print "\n%d snapshots" % self.nselect
+    print("\n%d snapshots" % self.nselect)
 
   # --------------------------------------------------------------------
   # write one dump file per snapshot from current selection
@@ -649,34 +653,34 @@ class dump:
     if len(self.snaps): namestr = self.names2str()
     for snap in self.snaps:
       if not snap.tselect: continue
-      print snap.time,
+      print(snap.time,end='')
       sys.stdout.flush()
       
       file = root + "." + str(snap.time)
       f = open(file,"w")
-      print >>f,"ITEM: TIMESTEP"
-      print >>f,snap.time
-      print >>f,"ITEM: NUMBER OF ATOMS"
-      print >>f,snap.nselect
-      print >>f,"ITEM: BOX BOUNDS"
-      print >>f,snap.xlo,snap.xhi
-      print >>f,snap.ylo,snap.yhi
-      print >>f,snap.zlo,snap.zhi
-      print >>f,"ITEM: ATOMS",namestr
+      print("ITEM: TIMESTEP",file=f)
+      print(snap.time,file=f)
+      print("ITEM: NUMBER OF ATOMS",file=f)
+      print(snap.nselect,file=f)
+      print("ITEM: BOX BOUNDS",file=f)
+      print(snap.xlo,snap.xhi,file=f)
+      print(snap.ylo,snap.yhi,file=f)
+      print(snap.zlo,snap.zhi,file=f)
+      print("ITEM: ATOMS",namestr,file=f)
       
       atoms = snap.atoms
       nvalues = len(atoms[0])
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if not snap.aselect[i]: continue
         line = ""
-        for j in xrange(nvalues):
+        for j in range(nvalues):
           if (j < 2):
             line += str(int(atoms[i][j])) + " "
           else:
             line += str(atoms[i][j]) + " "
-        print >>f,line
+        print(line,file=f)
       f.close()
-    print "\n%d snapshots" % self.nselect
+    print("\n%d snapshots" % self.nselect)
 
   # --------------------------------------------------------------------
   # find min/max across all selected snapshots/atoms for a particular column
@@ -688,7 +692,7 @@ class dump:
     for snap in self.snaps:
       if not snap.tselect: continue
       atoms = snap.atoms
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if not snap.aselect[i]: continue
         if atoms[i][icol] < min: min = atoms[i][icol]
         if atoms[i][icol] > max: max = atoms[i][icol]
@@ -698,7 +702,7 @@ class dump:
   # set a column value via an equation for all selected snapshots
 
   def set(self,eq):
-    print "Setting ..."
+    print("Setting ...")
     pattern = "\$\w*"
     list = re.findall(pattern,eq)
 
@@ -715,14 +719,14 @@ class dump:
 
     for snap in self.snaps:
       if not snap.tselect: continue
-      for i in xrange(snap.natoms):
-        if snap.aselect[i]: exec ceq
+      for i in range(snap.natoms):
+        if snap.aselect[i]: exec(ceq)
           
   # --------------------------------------------------------------------
   # set a column value via an input vec for all selected snapshots/atoms
 
   def setv(self,colname,vec):
-    print "Setting ..."
+    print("Setting ...")
     if not self.names.has_key(colname):
       self.newcolumn(colname)
     icol = self.names[colname]
@@ -730,10 +734,10 @@ class dump:
     for snap in self.snaps:
       if not snap.tselect: continue
       if snap.nselect != len(vec):
-        raise StandardError,"vec length does not match # of selected atoms"
+        raise StandardError("vec length does not match # of selected atoms")
       atoms = snap.atoms
       m = 0
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if snap.aselect[i]:
           atoms[i][icol] = vec[m]
           m += 1
@@ -746,12 +750,12 @@ class dump:
     icol = self.names[col]
     id = self.names["id"]
     ids = {}
-    for i in xrange(self.snaps[istep].natoms):
+    for i in range(self.snaps[istep].natoms):
       ids[self.snaps[istep].atoms[i][id]] = i
     for snap in self.snaps:
       if not snap.tselect: continue
       atoms = snap.atoms
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if not snap.aselect[i]: continue
         j = ids[atoms[i][id]]
         atoms[i][icol] = self.snaps[istep].atoms[j][icol]
@@ -765,14 +769,14 @@ class dump:
     inew = self.names[new]
 
     min,max = self.minmax(old)
-    print "min/max = ",min,max
+    print("min/max = ",min,max)
 
     gap = max - min
     invdelta = n/gap
     for snap in self.snaps:
       if not snap.tselect: continue
       atoms = snap.atoms
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if not snap.aselect[i]: continue
         ivalue = int((atoms[i][iold] - min) * invdelta) + 1
         if ivalue > n: ivalue = n
@@ -796,7 +800,7 @@ class dump:
 
   def atom(self,n,*list):
     if len(list) == 0:
-      raise StandardError, "no columns specified"
+      raise StandardError("no columns specified")
     columns = []
     values = []
     for name in list:
@@ -809,11 +813,11 @@ class dump:
     for snap in self.snaps:
       if not snap.tselect: continue
       atoms = snap.atoms
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if atoms[i][id] == n: break
       if atoms[i][id] != n:
-        raise StandardError, "could not find atom ID in snapshot"
-      for j in xrange(ncol):
+        raise StandardError("could not find atom ID in snapshot")
+      for j in range(ncol):
         values[j][m] = atoms[i][columns[j]]
       m += 1
 
@@ -827,7 +831,7 @@ class dump:
     snap = self.snaps[self.findtime(n)]
     
     if len(list) == 0:
-      raise StandardError, "no columns specified"
+      raise StandardError("no columns specified")
     columns = []
     values = []
     for name in list:
@@ -836,9 +840,9 @@ class dump:
     ncol = len(columns)
 
     m = 0
-    for i in xrange(snap.natoms):
+    for i in range(snap.natoms):
       if not snap.aselect[i]: continue
-      for j in xrange(ncol):
+      for j in range(ncol):
         values[j][m] = snap.atoms[i][columns[j]]
       m += 1
 
@@ -887,7 +891,7 @@ class dump:
   def iterator(self,flag):
     start = 0
     if flag: start = self.iterate + 1
-    for i in xrange(start,self.nsnaps):
+    for i in range(start,self.nsnaps):
       if self.snaps[i].tselect:
         self.iterate = i
         return i,self.snaps[i].time,1
@@ -912,7 +916,7 @@ class dump:
     # need Numeric/Numpy mode here
     
     atoms = []
-    for i in xrange(snap.natoms):
+    for i in range(snap.natoms):
       if not snap.aselect[i]: continue
       atom = snap.atoms[i]
       atoms.append([atom[id],atom[type],atom[x],atom[y],atom[z]])
@@ -927,7 +931,7 @@ class dump:
     bonds = []
     if self.bondflag:
       alist = {}
-      for i in xrange(len(atoms)): alist[int(atoms[i][0])] = i
+      for i in range(len(atoms)): alist[int(atoms[i][0])] = i
       for bond in self.bondlist:
         try:
           i = alist[bond[2]]
@@ -953,9 +957,9 @@ class dump:
   # --------------------------------------------------------------------
 
   def findtime(self,n):
-    for i in xrange(self.nsnaps):
+    for i in range(self.nsnaps):
       if self.snaps[i].time == n: return i
-    raise StandardError, "no step %d exists" % n
+    raise StandardError("no step %d exists" % n)
 
   # --------------------------------------------------------------------
   # return maximum box size across all selected snapshots
@@ -982,7 +986,7 @@ class dump:
     for snap in self.snaps:
       if not snap.tselect: continue
       atoms = snap.atoms
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if not snap.aselect[i]: continue
         if atoms[i][icol] > max: max = atoms[i][icol]
     return int(max)
@@ -1004,11 +1008,11 @@ class dump:
         nbonds = int(f.readline())
         item = f.readline()
         if not re.search("BONDS",item):
-          raise StandardError, "could not read bonds from dump file"
+          raise StandardError("could not read bonds from dump file")
 
         words = f.readline().split()
         ncol = len(words)
-        for i in xrange(1,nbonds):
+        for i in range(1,nbonds):
           words += f.readline().split()
         f.close()
 
@@ -1019,7 +1023,7 @@ class dump:
         ints = [abs(int(value)) for value in words]
         start = 0
         stop = 4
-        for i in xrange(nbonds):
+        for i in range(nbonds):
           bondlist[i] = ints[start:stop]
           start += ncol
           stop += ncol
@@ -1027,7 +1031,7 @@ class dump:
           self.bondflag = 1
           self.bondlist = bondlist
       except:
-        raise StandardError,"could not read from bond dump file"
+        raise StandardError("could not read from bond dump file")
       
     # request bonds from data object
     
@@ -1043,7 +1047,7 @@ class dump:
           self.bondflag = 1
           self.bondlist = bondlist
       except:
-        raise StandardError,"could not extract bonds from data object"
+        raise StandardError("could not extract bonds from data object")
 
     # request tris/lines from cdata object
     
@@ -1057,7 +1061,7 @@ class dump:
           self.lineflag = 1
           self.linelist = lines
       except:
-        raise StandardError,"could not extract tris/lines from cdata object"
+        raise StandardError("could not extract tris/lines from cdata object")
 
     # request tris from mdump object
     
@@ -1066,10 +1070,10 @@ class dump:
         self.triflag = 2
         self.triobj = arg
       except:
-        raise StandardError,"could not extract tris from mdump object"
+        raise StandardError("could not extract tris from mdump object")
 
     else:
-      raise StandardError,"unrecognized argument to dump.extra()"
+      raise StandardError("unrecognized argument to dump.extra()")
       
   # --------------------------------------------------------------------
 
@@ -1103,7 +1107,7 @@ class tselect:
       snap.tselect = 1
     data.nselect = len(data.snaps)
     data.aselect.all()
-    print "%d snapshots selected out of %d" % (data.nselect,data.nsnaps)
+    print("%d snapshots selected out of %d" % (data.nselect,data.nsnaps))
 
   # --------------------------------------------------------------------
 
@@ -1115,7 +1119,7 @@ class tselect:
     data.snaps[i].tselect = 1
     data.nselect = 1
     data.aselect.all()
-    print "%d snapshots selected out of %d" % (data.nselect,data.nsnaps)
+    print("%d snapshots selected out of %d" % (data.nselect,data.nsnaps))
 
   # --------------------------------------------------------------------
 
@@ -1124,7 +1128,7 @@ class tselect:
     for snap in data.snaps:
       snap.tselect = 0
     data.nselect = 0
-    print "%d snapshots selected out of %d" % (data.nselect,data.nsnaps)
+    print("%d snapshots selected out of %d" % (data.nselect,data.nsnaps))
 
   # --------------------------------------------------------------------
 
@@ -1140,7 +1144,7 @@ class tselect:
       snap.tselect = 0
       data.nselect -= 1
     data.aselect.all()
-    print "%d snapshots selected out of %d" % (data.nselect,data.nsnaps)
+    print("%d snapshots selected out of %d" % (data.nselect,data.nsnaps))
   
   # --------------------------------------------------------------------
 
@@ -1149,14 +1153,14 @@ class tselect:
     snaps = data.snaps
     cmd = "flag = " + teststr.replace("$t","snaps[i].time")
     ccmd = compile(cmd,'','single')
-    for i in xrange(data.nsnaps):
+    for i in range(data.nsnaps):
       if not snaps[i].tselect: continue
-      exec ccmd
+      exec(ccmd)
       if not flag:
         snaps[i].tselect = 0
         data.nselect -= 1
     data.aselect.all()
-    print "%d snapshots selected out of %d" % (data.nselect,data.nsnaps)
+    print("%d snapshots selected out of %d" % (data.nselect,data.nsnaps))
 
 # --------------------------------------------------------------------
 # atom selection class
@@ -1173,12 +1177,12 @@ class aselect:
     if len(args) == 0:                           # all selected timesteps
       for snap in data.snaps:
         if not snap.tselect: continue
-        for i in xrange(snap.natoms): snap.aselect[i] = 1
+        for i in range(snap.natoms): snap.aselect[i] = 1
         snap.nselect = snap.natoms
     else:                                        # one timestep
       n = data.findtime(args[0])
       snap = data.snaps[n]
-      for i in xrange(snap.natoms): snap.aselect[i] = 1
+      for i in range(snap.natoms): snap.aselect[i] = 1
       snap.nselect = snap.natoms
 
   # --------------------------------------------------------------------
@@ -1201,29 +1205,29 @@ class aselect:
     if len(args) == 0:                           # all selected timesteps
       for snap in data.snaps:
         if not snap.tselect: continue
-        for i in xrange(snap.natoms):
+        for i in range(snap.natoms):
           if not snap.aselect[i]: continue
-          exec ccmd
+          exec(ccmd)
           if not flag:
             snap.aselect[i] = 0
             snap.nselect -= 1
-      for i in xrange(data.nsnaps):
+      for i in range(data.nsnaps):
         if data.snaps[i].tselect:
-          print "%d atoms of %d selected in first step %d" % \
-                (data.snaps[i].nselect,data.snaps[i].natoms,data.snaps[i].time)
+          print("%d atoms of %d selected in first step %d" % \
+                (data.snaps[i].nselect,data.snaps[i].natoms,data.snaps[i].time))
           break
-      for i in xrange(data.nsnaps-1,-1,-1):
+      for i in range(data.nsnaps-1,-1,-1):
         if data.snaps[i].tselect:
-          print "%d atoms of %d selected in last step %d" % \
-                (data.snaps[i].nselect,data.snaps[i].natoms,data.snaps[i].time)
+          print("%d atoms of %d selected in last step %d" % \
+                (data.snaps[i].nselect,data.snaps[i].natoms,data.snaps[i].time))
           break
 
     else:                                        # one timestep
       n = data.findtime(args[0])
       snap = data.snaps[n]
-      for i in xrange(snap.natoms):
+      for i in range(snap.natoms):
         if not snap.aselect[i]: continue
-        exec ccmd
+        exec(ccmd)
         if not flag:
           snap.aselect[i] = 0
           snap.nselect -= 1
diff --git a/python/examples/pizza/gl.py b/python/examples/pizza/gl.py
index 71db5b63e7..765653c983 100644
--- a/python/examples/pizza/gl.py
+++ b/python/examples/pizza/gl.py
@@ -6,6 +6,9 @@
 # certain rights in this software.  This software is distributed under 
 # the GNU General Public License.
 
+# for python3 compatibility
+from __future__ import print_function
+
 # gl tool
 
 oneline = "3d interactive visualization via OpenGL"
@@ -73,7 +76,7 @@ g.ldef()  			   default = 0 fill
   if atom/bond/tri/line has type > # defined properties, is an error
   
 from vizinfo import colors         access color list
-print colors                       list defined color names and RGB values
+print(colors)                      list defined color names and RGB values
 colors["nickname"] = [R,G,B]       set new RGB values from 0 to 255
 
   140 pre-defined colors: red, green, blue, purple, yellow, black, white, etc
@@ -499,7 +502,7 @@ class gl:
   # add GL-specific info to each bond
   
   def reload(self):
-    print "Loading data into gl tool ..."
+    print("Loading data into gl tool ...")
     data = self.data
 
     self.timeframes = []
@@ -527,9 +530,9 @@ class gl:
       self.triframes.append(tris)
       self.lineframes.append(lines)
       
-      print time,
+      print(time,end='')
       sys.stdout.flush()
-    print
+    print()
 
     self.nframes = len(self.timeframes)
     self.distance = compute_distance(self.boxframes[0])
@@ -651,7 +654,7 @@ class gl:
         self.w.tkRedraw()
         self.save(file)
         
-        print time,
+        print(time,end='')
         sys.stdout.flush()
         i += 1
         n += 1
@@ -690,7 +693,7 @@ class gl:
                           fraction*(self.scale_stop - self.scale_start)
           self.viewupright()
 
-	if n == nstart or self.panflag: self.center = compute_center(box)
+        if n == nstart or self.panflag: self.center = compute_center(box)
 
         if bonds: self.bonds_augment(bonds)
 
@@ -706,11 +709,11 @@ class gl:
         self.w.tkRedraw()
         self.save(file)
 
-        print n,
+        print(n,end='')
         sys.stdout.flush()
         n += 1
 
-    print "\n%d images" % ncount
+    print("\n%d images" % ncount)
 
   # --------------------------------------------------------------------
 
@@ -776,11 +779,11 @@ class gl:
       ncolor = self.vizinfo.nlcolor
       for line in self.linedraw:
         itype = int(line[1])
-        if itype > ncolor: raise StandardError,"line type too big"
+        if itype > ncolor: raise StandardError("line type too big")
         red,green,blue = self.vizinfo.lcolor[itype]
         glColor3f(red,green,blue)
         thick = self.vizinfo.lrad[itype]
-	glLineWidth(thick)
+        glLineWidth(thick)
         glBegin(GL_LINES)
         glVertex3f(line[2],line[3],line[4])
         glVertex3f(line[5],line[6],line[7])
@@ -825,7 +828,7 @@ class gl:
           for bond in self.bonddraw:
             if bond[10] > bound: continue
             itype = int(bond[1])
-            if itype > ncolor: raise StandardError,"bond type too big"
+            if itype > ncolor: raise StandardError("bond type too big")
             red,green,blue = self.vizinfo.bcolor[itype]
             rad = self.vizinfo.brad[itype]
             glPushMatrix()
@@ -848,7 +851,7 @@ class gl:
             ncolor = self.vizinfo.ntcolor
             for tri in self.tridraw:
               itype = int(tri[1])
-              if itype > ncolor: raise StandardError,"tri type too big"
+              if itype > ncolor: raise StandardError("tri type too big")
               red,green,blue = self.vizinfo.tcolor[itype]
               glMaterialfv(GL_FRONT_AND_BACK,GL_EMISSION,[red,green,blue,1.0]);
               glMaterialf(GL_FRONT_AND_BACK,GL_SHININESS,self.shiny);
@@ -906,7 +909,7 @@ class gl:
                    ymin >= ylo and ymax <= yhi and zmin >= zlo and zmax <= zhi:
               if bond[10] > bound: continue
               itype = int(bond[1])
-              if itype > ncolor: raise StandardError,"bond type too big"
+              if itype > ncolor: raise StandardError("bond type too big")
               red,green,blue = self.vizinfo.bcolor[itype]
               rad = self.vizinfo.brad[itype]
               glPushMatrix()
@@ -938,7 +941,7 @@ class gl:
                      ymin >= ylo and ymax <= yhi and \
                      zmin >= zlo and zmax <= zhi:
                 itype = int(tri[1])
-                if itype > ncolor: raise StandardError,"tri type too big"
+                if itype > ncolor: raise StandardError("tri type too big")
                 red,green,blue = self.vizinfo.tcolor[itype]
                 glMaterialfv(GL_FRONT_AND_BACK,GL_EMISSION,
                              [red,green,blue,1.0]);
diff --git a/python/examples/pizza/gnu.py b/python/examples/pizza/gnu.py
index f6f0167330..7d796d5586 100644
--- a/python/examples/pizza/gnu.py
+++ b/python/examples/pizza/gnu.py
@@ -6,6 +6,9 @@
 # certain rights in this software.  This software is distributed under 
 # the GNU General Public License.
 
+# for python3 compatibility
+from __future__ import print_function
+
 # gnu tool
 
 oneline = "Create plots via GnuPlot plotting program"
@@ -87,7 +90,7 @@ g.curve(N,'r')                 set color of curve N
 import types, os
 
 try: from DEFAULTS import PIZZA_GNUPLOT
-except: PIZZA_GNUPLOT = "gnuplot"
+except: PIZZA_GNUPLOT = "gnuplot -p"
 try: from DEFAULTS import PIZZA_GNUTERM
 except: PIZZA_GNUTERM = "x11"
 
@@ -133,7 +136,7 @@ class gnu:
       self.export(file,linear,vectors[0])
       self.figures[self.current-1].ncurves = 1
     else:
-      if len(vectors) % 2: raise StandardError,"vectors must come in pairs"
+      if len(vectors) % 2: raise StandardError("vectors must come in pairs")
       for i in range(0,len(vectors),2):
         file = self.file + ".%d.%d" % (self.current,i/2+1)
         self.export(file,vectors[i],vectors[i+1])
@@ -167,13 +170,13 @@ class gnu:
   def export(self,filename,*vectors):
     n = len(vectors[0])
     for vector in vectors:
-      if len(vector) != n: raise StandardError,"vectors must be same length"
+      if len(vector) != n: raise StandardError("vectors must be same length")
     f = open(filename,'w')
     nvec = len(vectors)
-    for i in xrange(n):
-      for j in xrange(nvec):
-        print >>f,vectors[j][i],
-      print >>f
+    for i in range(n):
+      for j in range(nvec):
+        print(str(vectors[j][i])+" ",file=f,end='')
+      print ("",file=f)
     f.close()
 
   # --------------------------------------------------------------------
@@ -350,7 +353,7 @@ class gnu:
 
     self.__call__("set key off")
     cmd = 'plot '
-    for i in range(fig.ncurves):
+    for i in range(int(fig.ncurves)):
       file = self.file + ".%d.%d" % (self.current,i+1)
       if len(fig.colors) > i and fig.colors[i]:
         cmd += "'" + file + "' using 1:2 with line %d, " % fig.colors[i]
diff --git a/python/examples/pizza/pdbfile.py b/python/examples/pizza/pdbfile.py
index 1713ada043..2ca85b64da 100644
--- a/python/examples/pizza/pdbfile.py
+++ b/python/examples/pizza/pdbfile.py
@@ -6,6 +6,9 @@
 # certain rights in this software.  This software is distributed under 
 # the GNU General Public License.
 
+# for python3 compatibility
+from __future__ import print_function
+
 # pdb tool
 
 oneline = "Read, write PDB files in combo with LAMMPS snapshots"
@@ -83,7 +86,7 @@ class pdbfile:
     elif len(args) == 2:
       filestr = args[0]
       self.data = args[1]
-    else: raise StandardError, "invalid args for pdb()"
+    else: raise StandardError("invalid args for pdb()")
 
     # flist = full list of all PDB input file names
     # append .pdb if needed
@@ -97,14 +100,14 @@ class pdbfile:
       for i in xrange(len(flist)):
         if flist[i][-4:] != ".pdb": flist[i] += ".pdb"
       if len(flist) == 0:
-        raise StandardError,"no PDB file specified"
+        raise StandardError("no PDB file specified")
       self.files = flist
     else: self.files = []
 
     if len(self.files) > 1 and self.data:
-      raise StandardError, "cannot use multiple PDB files with data object"
+      raise StandardError("cannot use multiple PDB files with data object")
     if len(self.files) == 0 and not self.data:
-      raise StandardError, "no input PDB file(s)"
+      raise StandardError("no input PDB file(s)")
 
     # grab PDB file from http://rcsb.org if not a local file
     
@@ -112,7 +115,7 @@ class pdbfile:
       try:
         open(self.files[0],'r').close()
       except:
-        print "downloading %s from http://rcsb.org" % self.files[0]
+        print("downloading %s from http://rcsb.org" % self.files[0])
         fetchstr = "http://www.rcsb.org/pdb/cgi/export.cgi/%s?format=PDB&pdbId=2cpk&compression=None" % self.files[0]
         urllib.urlretrieve(fetchstr,self.files[0])
 
@@ -142,20 +145,20 @@ class pdbfile:
         which,time,flag = self.data.iterator(flag)
         if flag == -1: break
         self.convert(f,which)
-        print >>f,"END"
-        print time,
+        print("END",file=f)
+        print(time,end='')
         sys.stdout.flush()
         n += 1
 
     else:
       for file in self.files:
         f.write(open(file,'r').read())
-        print >>f,"END"
-        print file,
+        print("END",file=f)
+        print(file,end='')
         sys.stdout.flush()
         
     f.close()
-    print "\nwrote %d datasets to %s in PDB format" % (n,file)
+    print("\nwrote %d datasets to %s in PDB format" % (n,file))
 
   # --------------------------------------------------------------------
   # write series of numbered PDB files
@@ -190,7 +193,7 @@ class pdbfile:
         self.convert(f,which)
         f.close()
         
-        print time,
+        print(time,end='')
         sys.stdout.flush()
         n += 1
 
@@ -210,12 +213,12 @@ class pdbfile:
         f = open(file,'w')
         f.write(open(infile,'r').read())
         f.close()
-        print file,
+        print(file,end='')
         sys.stdout.flush()
         
         n += 1
 
-    print "\nwrote %d datasets to %s*.pdb in PDB format" % (n,root)
+    print("\nwrote %d datasets to %s*.pdb in PDB format" % (n,root))
 
   # --------------------------------------------------------------------
   # write a single PDB file
@@ -280,10 +283,10 @@ class pdbfile:
         if self.atomlines.has_key(id):
           (begin,end) = self.atomlines[id]
           line = "%s%8.3f%8.3f%8.3f%s" % (begin,atom[2],atom[3],atom[4],end)
-          print >>f,line,
+          print(line,file=f,end='')
     else:
       for atom in atoms:
         begin = "ATOM %6d %2d   R00     1    " % (atom[0],atom[1])
         middle = "%8.3f%8.3f%8.3f" %  (atom[2],atom[3],atom[4])
         end = "  1.00  0.00    NONE"
-        print >>f,begin+middle+end
+        print(begin+middle+end,file=f)
diff --git a/python/examples/pizza/vmd.py b/python/examples/pizza/vmd.py
index 7a49617b4c..fb5095617a 100644
--- a/python/examples/pizza/vmd.py
+++ b/python/examples/pizza/vmd.py
@@ -6,6 +6,9 @@
 # certain rights in this software.  This software is distributed under 
 # the GNU General Public License.
 
+# for python3 compatibility
+from __future__ import print_function
+
 # vmd tool
 
 # Minimalistic VMD embedding for Pizza.py
@@ -52,7 +55,7 @@ except: PIZZA_VMDDIR = "/usr/local/lib/vmd"
 try: from DEFAULTS import PIZZA_VMDDEV
 except: PIZZA_VMDDEV = "win"
 try: from DEFAULTS import PIZZA_VMDARCH
-except: PIZZA_VMDARCH = "LINUX"
+except: PIZZA_VMDARCH = "LINUXAMD64"
 
 # try these settings for a Mac
 #PIZZA_VMDNAME = "vmd"
@@ -62,8 +65,8 @@ except: PIZZA_VMDARCH = "LINUX"
 
 try: import pexpect
 except: 
-  print "pexpect from http://pypi.python.org/pypi/pexpect", \
-      "is required for vmd tool"
+  print("pexpect from http://pypi.python.org/pypi/pexpect", \
+      "is required for vmd tool")
   raise
 
 # Class definition
@@ -109,7 +112,7 @@ class vmd:
       self.VMD.sendline(command)
       self.VMD.expect('vmd >')
       if self.debugme:
-        print "call+result:"+self.VMD.before
+        print("call+result:"+self.VMD.before)
     return
     
   # --------------------------------------------------------------------
@@ -127,9 +130,9 @@ class vmd:
   # turn on debugging info
   def debug(self,status=True):
     if status and not self.debugme:
-      print 'Turning vmd.py debugging ON.'
+      print('Turning vmd.py debugging ON.')
     if not status and self.debugme:
-      print 'Turning vmd.py debugging OFF.'
+      print('Turning vmd.py debugging OFF.')
     self.debugme = status
 
   # --------------------------------------------------------------------
@@ -141,14 +144,14 @@ class vmd:
       try:
         command = raw_input("vmd > ")
       except EOFError:
-        print "(EOF)"
+        print("(EOF)")
         self.__call__('menu main off')
         return
       if command == "quit" or command == "exit":
         self.__call__('menu main off')
         return
       if command == "gopython":
-        print "gopython not supported here"
+        print("gopython not supported here")
         continue
       self.__call__(command)
 
diff --git a/python/examples/plot.py b/python/examples/plot.py
index a3f465ca19..efb9ecaa68 100755
--- a/python/examples/plot.py
+++ b/python/examples/plot.py
@@ -10,6 +10,7 @@
 #          compute-ID = ID of compute that calculates temperature
 #                       (or any other scalar quantity)
 
+from __future__ import print_function
 import sys
 sys.path.append("./pizza")
 from gnu import gnu
@@ -18,7 +19,7 @@ from gnu import gnu
 
 argv = sys.argv
 if len(argv) != 5:
-  print "Syntax: plot.py in.lammps Nfreq Nsteps compute-ID"
+  print("Syntax: plot.py in.lammps Nfreq Nsteps compute-ID")
   sys.exit()
 
 infile = sys.argv[1]
@@ -71,5 +72,5 @@ while ntimestep < nsteps:
 lmp.command("run 0 pre no post yes")
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/simple.py b/python/examples/simple.py
index 4ca4b81a8d..9315a555fc 100755
--- a/python/examples/simple.py
+++ b/python/examples/simple.py
@@ -11,13 +11,14 @@
 #                  in.lammps = LAMMPS input script
 # also need to uncomment either Pypar or mpi4py sections below
 
+from __future__ import print_function
 import sys
 
 # parse command line
 
 argv = sys.argv
 if len(argv) != 2:
-  print "Syntax: simple.py in.lammps"
+  print("Syntax: simple.py in.lammps")
   sys.exit()
 
 infile = sys.argv[1]
@@ -56,13 +57,13 @@ lmp.scatter_atoms("x",1,3,x)
 lmp.command("run 1");
 
 f = lmp.extract_atom("f",3)
-print "Force on 1 atom via extract_atom: ",f[0][0]
+print("Force on 1 atom via extract_atom: ",f[0][0])
 
 fx = lmp.extract_variable("fx","all",1)
-print "Force on 1 atom via extract_variable:",fx[0]
+print("Force on 1 atom via extract_variable:",fx[0])
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
 
 # uncomment if running in parallel via mpi4py
diff --git a/python/examples/split.py b/python/examples/split.py
index 65cf395581..719877d826 100755
--- a/python/examples/split.py
+++ b/python/examples/split.py
@@ -7,13 +7,14 @@
 # Syntax:  split.py in.lammps
 #          in.lammps = LAMMPS input script
 
+from __future__ import print_function
 import sys
 
 # parse command line
 
 argv = sys.argv
 if len(argv) != 2:
-  print "Syntax: simple.py in.lammps"
+  print("Syntax: simple.py in.lammps")
   sys.exit()
 
 infile = sys.argv[1]
@@ -57,12 +58,12 @@ if color == 0:
   lmp.command("run 1");
 
   f = lmp.extract_atom("f",3)
-  print "Force on 1 atom via extract_atom: ",f[0][0]
+  print("Force on 1 atom via extract_atom: ",f[0][0])
 
   fx = lmp.extract_variable("fx","all",1)
-  print "Force on 1 atom via extract_variable:",fx[0]
-  print "Proc %d out of %d procs has" % (me,nprocs), lmp
-  print "Calculation on partition 0 complete"
+  print("Force on 1 atom via extract_variable:",fx[0])
+  print("Proc %d out of %d procs has" % (me,nprocs), lmp)
+  print("Calculation on partition 0 complete")
 
 else:
   # could run a 2nd calculation on second partition
@@ -71,7 +72,7 @@ else:
   
   import time
   time.sleep(2)
-  print "Calculation on partition 1 complete"
+  print("Calculation on partition 1 complete")
 
 # shutdown mpi4py
   
diff --git a/python/examples/trivial.py b/python/examples/trivial.py
index 7e61b97cb3..0e3d6b91af 100755
--- a/python/examples/trivial.py
+++ b/python/examples/trivial.py
@@ -6,13 +6,14 @@
 # Syntax:  trivial.py in.lammps
 #          in.lammps = LAMMPS input script
 
+from __future__ import print_function
 import sys
 
 # parse command line
 
 argv = sys.argv
 if len(argv) != 2:
-  print "Syntax: trivial.py in.lammps"
+  print("Syntax: trivial.py in.lammps")
   sys.exit()
 
 infile = sys.argv[1]
diff --git a/python/examples/viz_atomeye.py b/python/examples/viz_atomeye.py
index ca0d60f443..3803b2e79c 100755
--- a/python/examples/viz_atomeye.py
+++ b/python/examples/viz_atomeye.py
@@ -8,6 +8,7 @@
 #          Nfreq = dump and viz shapshot every this many steps
 #          Nsteps = run for this many steps
 
+from __future__ import print_function
 import sys,os
 
 # set this to point to AtomEye version 3 executable
@@ -19,7 +20,7 @@ ATOMEYE3 = "/home/sjplimp/tools/atomeye3/A3.i686-20060530 > atomeye.out"
 
 argv = sys.argv
 if len(argv) != 4:
-  print "Syntax: viz_atomeye.py in.lammps Nfreq Nsteps"
+  print("Syntax: viz_atomeye.py in.lammps Nfreq Nsteps")
   sys.exit()
 
 infile = sys.argv[1]
@@ -68,5 +69,5 @@ while ntimestep < nsteps:
 lmp.command("run 0 pre no post yes")
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/viz_gl.py b/python/examples/viz_gl.py
index fcb92e03f2..476509ad43 100755
--- a/python/examples/viz_gl.py
+++ b/python/examples/viz_gl.py
@@ -8,6 +8,7 @@
 #          Nfreq = dump and viz shapshot every this many steps
 #          Nsteps = run for this many steps
 
+from __future__ import print_function
 import sys
 sys.path.append("./pizza")
 
@@ -15,7 +16,7 @@ sys.path.append("./pizza")
 
 argv = sys.argv
 if len(argv) != 4:
-  print "Syntax: viz_gl.py in.lammps Nfreq Nsteps"
+  print("Syntax: viz_gl.py in.lammps Nfreq Nsteps")
   sys.exit()
 
 infile = sys.argv[1]
@@ -48,7 +49,11 @@ ntimestep = 0
 # just proc 0 handles reading of dump file and viz
 
 if me == 0:
-  import Tkinter
+  tkroot = None
+  try: 
+    import Tkinter
+  except:
+    import tkinter as Tkinter
   tkroot = Tkinter.Tk()
   tkroot.withdraw()
 
@@ -79,5 +84,5 @@ while ntimestep < nsteps:
 lmp.command("run 0 pre no post yes")
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/viz_pymol.py b/python/examples/viz_pymol.py
index 17f3d44810..02178a0fb0 100755
--- a/python/examples/viz_pymol.py
+++ b/python/examples/viz_pymol.py
@@ -8,6 +8,7 @@
 #          Nfreq = dump and viz shapshot every this many steps
 #          Nsteps = run for this many steps
 
+from __future__ import print_function
 import sys
 sys.path.append("./pizza")
 
@@ -15,7 +16,7 @@ sys.path.append("./pizza")
 
 argv = sys.argv
 if len(argv) != 4:
-  print "Syntax: viz_pymol.py in.lammps Nfreq Nsteps"
+  print("Syntax: viz_pymol.py in.lammps Nfreq Nsteps")
   sys.exit()
 
 infile = sys.argv[1]
@@ -78,5 +79,5 @@ while ntimestep < nsteps:
 lmp.command("run 0 pre no post yes")
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/viz_vmd.py b/python/examples/viz_vmd.py
index 47f27d26ff..807a8c9d55 100755
--- a/python/examples/viz_vmd.py
+++ b/python/examples/viz_vmd.py
@@ -8,6 +8,7 @@
 #          Nfreq = dump and viz shapshot every this many steps
 #          Nsteps = run for this many steps
 
+from __future__ import print_function
 import sys
 sys.path.append("./pizza")
 
@@ -15,7 +16,7 @@ sys.path.append("./pizza")
 
 argv = sys.argv
 if len(argv) != 4:
-  print "Syntax: viz_vmd.py in.lammps Nfreq Nsteps"
+  print("Syntax: viz_vmd.py in.lammps Nfreq Nsteps")
   sys.exit()
 
 infile = sys.argv[1]
@@ -82,10 +83,10 @@ if me == 0:
   v.flush()
   # uncomment the following, if you want to work with the viz some more.
   #v('menu main on')
-  #print "type quit to terminate."
+  #print("type quit to terminate.")
   #v.enter()
   #v.stop()
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/vizplotgui_atomeye.py b/python/examples/vizplotgui_atomeye.py
index 935123d2b6..71730974df 100755
--- a/python/examples/vizplotgui_atomeye.py
+++ b/python/examples/vizplotgui_atomeye.py
@@ -12,6 +12,7 @@
 # IMPORTANT: this script cannot yet be run in parallel via Pypar,
 #            because I can't seem to do a MPI-style broadcast in Pypar
 
+from __future__ import print_function
 import sys,os,time
 sys.path.append("./pizza")
 
@@ -50,7 +51,7 @@ def update(ntimestep):
 
 argv = sys.argv
 if len(argv) != 4:
-  print "Syntax: vizplotgui_atomeye.py in.lammps Nfreq compute-ID"
+  print("Syntax: vizplotgui_atomeye.py in.lammps Nfreq compute-ID")
   sys.exit()
 
 infile = sys.argv[1]
@@ -97,7 +98,10 @@ if me == 0:
 # display GUI with run/stop buttons and slider for temperature
 
 if me == 0:
-  from Tkinter import *
+  try:
+    from Tkinter import *
+  except:
+    from tkinter import *
   tkroot = Tk()
   tkroot.withdraw()
   root = Toplevel(tkroot)
@@ -161,5 +165,5 @@ while 1:
 lmp.command("run 0 pre no post yes")
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/vizplotgui_gl.py b/python/examples/vizplotgui_gl.py
index ee26487bb2..937670e511 100755
--- a/python/examples/vizplotgui_gl.py
+++ b/python/examples/vizplotgui_gl.py
@@ -12,6 +12,7 @@
 # IMPORTANT: this script cannot yet be run in parallel via Pypar,
 #            because I can't seem to do a MPI-style broadcast in Pypar
 
+from __future__ import print_function
 import sys,time
 sys.path.append("./pizza")
 
@@ -46,7 +47,7 @@ def update(ntimestep):
 
 argv = sys.argv
 if len(argv) != 4:
-  print "Syntax: vizplotgui_gl.py in.lammps Nfreq compute-ID"
+  print("Syntax: vizplotgui_gl.py in.lammps Nfreq compute-ID")
   sys.exit()
 
 infile = sys.argv[1]
@@ -86,7 +87,10 @@ temptarget = 1.0
 # just proc 0 handles reading of dump file and viz
 
 if me == 0:
-  from Tkinter import *
+  try:
+    from Tkinter import *
+  except:
+    from tkinter import *
   tkroot = Tk()
   tkroot.withdraw()
 
@@ -107,7 +111,10 @@ if me == 0:
 # display GUI with run/stop buttons and slider for temperature
 
 if me == 0:
-  from Tkinter import *
+  try:
+    from Tkinter import *
+  except:
+    from tkinter import *
   tkroot = Tk()
   tkroot.withdraw()
   root = Toplevel(tkroot)
@@ -171,5 +178,5 @@ while 1:
 lmp.command("run 0 pre no post yes")
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/vizplotgui_pymol.py b/python/examples/vizplotgui_pymol.py
index c0628a2b1b..c7a8a00c6d 100755
--- a/python/examples/vizplotgui_pymol.py
+++ b/python/examples/vizplotgui_pymol.py
@@ -12,6 +12,7 @@
 # IMPORTANT: this script cannot yet be run in parallel via Pypar,
 #            because I can't seem to do a MPI-style broadcast in Pypar
 
+from __future__ import print_function
 import sys,time
 sys.path.append("./pizza")
 
@@ -48,7 +49,7 @@ def update(ntimestep):
 
 argv = sys.argv
 if len(argv) != 4:
-  print "Syntax: vizplotgui_pymol.py in.lammps Nfreq compute-ID"
+  print("Syntax: vizplotgui_pymol.py in.lammps Nfreq compute-ID")
   sys.exit()
 
 infile = sys.argv[1]
@@ -106,7 +107,10 @@ if me == 0:
 # display GUI with run/stop buttons and slider for temperature
 
 if me == 0:
-  from Tkinter import *
+  try:
+    from Tkinter import *
+  except:
+    from tkinter import *
   tkroot = Tk()
   tkroot.withdraw()
   root = Toplevel(tkroot)
@@ -170,5 +174,5 @@ while 1:
 lmp.command("run 0 pre no post yes")
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/examples/vizplotgui_vmd.py b/python/examples/vizplotgui_vmd.py
index d969666a1a..9eab0865e2 100755
--- a/python/examples/vizplotgui_vmd.py
+++ b/python/examples/vizplotgui_vmd.py
@@ -12,6 +12,7 @@
 # IMPORTANT: this script cannot yet be run in parallel via Pypar,
 #            because I can't seem to do a MPI-style broadcast in Pypar
 
+from __future__ import print_function
 import sys,time
 sys.path.append("./pizza")
 
@@ -47,7 +48,7 @@ def update(ntimestep):
 
 argv = sys.argv
 if len(argv) != 4:
-  print "Syntax: vizplotgui_vmd.py in.lammps Nfreq compute-ID"
+  print("Syntax: vizplotgui_vmd.py in.lammps Nfreq compute-ID")
   sys.exit()
 
 infile = sys.argv[1]
@@ -105,7 +106,10 @@ if me == 0:
 # display GUI with run/stop buttons and slider for temperature
 
 if me == 0:
-  from Tkinter import *
+  try:
+    from Tkinter import *
+  except:
+    from tkinter import *
   tkroot = Tk()
   tkroot.withdraw()
   root = Toplevel(tkroot)
@@ -169,5 +173,5 @@ while 1:
 lmp.command("run 0 pre no post yes")
 
 # uncomment if running in parallel via Pypar
-#print "Proc %d out of %d procs has" % (me,nprocs), lmp
+#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
 #pypar.finalize()
diff --git a/python/lammps.py b/python/lammps.py
index bbc46cf2b9..1931e5e0c7 100644
--- a/python/lammps.py
+++ b/python/lammps.py
@@ -11,17 +11,26 @@
 #   See the README file in the top-level LAMMPS directory.
 # -------------------------------------------------------------------------
 
-# Python wrapper on LAMMPS library via ctypes
+# Python wrappers on LAMMPS library via ctypes
 
+# for python3 compatibility
+from __future__ import print_function
+
+# imports for simple LAMMPS python wrapper module "lammps"
 import sys,traceback,types
 from ctypes import *
 from os.path import dirname,abspath,join
 from inspect import getsourcefile
 
-class lammps:
-  
+# imports for advanced LAMMPS python wrapper modules "PyLammps" and "IPyLammps"
+from collections import namedtuple
+import os
+import select
+import re
+
+class lammps(object):
   # detect if Python is using version of mpi4py that can pass a communicator
-  
+
   has_mpi4py_v2 = False
   try:
     from mpi4py import MPI
@@ -32,11 +41,11 @@ class lammps:
     pass
 
   # create instance of LAMMPS
-  
+
   def __init__(self,name="",cmdargs=None,ptr=None,comm=None):
 
     # determine module location
-    
+
     modpath = dirname(abspath(getsourcefile(lambda:0)))
 
     # load liblammps.so unless name is given.
@@ -62,12 +71,12 @@ class lammps:
     # if ptr, then are embedding Python in LAMMPS input script
     #   ptr is the desired instance of LAMMPS
     #   just convert it to ctypes ptr and store in self.lmp
-    
+
     if not ptr:
       # with mpi4py v2, can pass MPI communicator to LAMMPS
       # need to adjust for type of MPI communicator object
       # allow for int (like MPICH) or void* (like OpenMPI)
-      
+
       if lammps.has_mpi4py_v2 and comm != None:
         if lammps.MPI._sizeof(lammps.MPI.Comm) == sizeof(c_int):
           MPI_Comm = c_int
@@ -112,13 +121,16 @@ class lammps:
           self.lib.lammps_open_no_mpi(0,None,byref(self.lmp))
           # could use just this if LAMMPS lib interface supported it
           # self.lmp = self.lib.lammps_open_no_mpi(0,None)
-          
+
     else:
-      self.opened = 0
-      # magic to convert ptr to ctypes ptr
-      pythonapi.PyCObject_AsVoidPtr.restype = c_void_p
-      pythonapi.PyCObject_AsVoidPtr.argtypes = [py_object]
-      self.lmp = c_void_p(pythonapi.PyCObject_AsVoidPtr(ptr))
+      if isinstance(ptr,lammps):
+        # magic to convert ptr to ctypes ptr
+        pythonapi.PyCObject_AsVoidPtr.restype = c_void_p
+        pythonapi.PyCObject_AsVoidPtr.argtypes = [py_object]
+        self.lmp = c_void_p(pythonapi.PyCObject_AsVoidPtr(ptr))
+      else:
+        self.lmp = None
+        raise TypeError('Unsupported type passed as "ptr"')
 
   def __del__(self):
     if self.lmp and self.opened: self.lib.lammps_close(self.lmp)
@@ -131,15 +143,15 @@ class lammps:
     return self.lib.lammps_version(self.lmp)
 
   def file(self,file):
-    file = file.encode()
+    if file: file = file.encode()
     self.lib.lammps_file(self.lmp,file)
 
   def command(self,cmd):
-    cmd = cmd.encode()
+    if cmd: cmd = cmd.encode()
     self.lib.lammps_command(self.lmp,cmd)
 
   def extract_global(self,name,type):
-    name = name.encode()
+    if name: name = name.encode()
     if type == 0:
       self.lib.lammps_extract_global.restype = POINTER(c_int)
     elif type == 1:
@@ -149,7 +161,7 @@ class lammps:
     return ptr[0]
 
   def extract_atom(self,name,type):
-    name = name.encode()
+    if name: name = name.encode()
     if type == 0:
       self.lib.lammps_extract_atom.restype = POINTER(c_int)
     elif type == 1:
@@ -163,7 +175,7 @@ class lammps:
     return ptr
 
   def extract_compute(self,id,style,type):
-    id = id.encode()
+    if id: id = id.encode()
     if type == 0:
       if style > 0: return None
       self.lib.lammps_extract_compute.restype = POINTER(c_double)
@@ -181,9 +193,9 @@ class lammps:
 
   # in case of global datum, free memory for 1 double via lammps_free()
   # double was allocated by library interface function
-  
+
   def extract_fix(self,id,style,type,i=0,j=0):
-    id = id.encode()
+    if id: id = id.encode()
     if style == 0:
       self.lib.lammps_extract_fix.restype = POINTER(c_double)
       ptr = self.lib.lammps_extract_fix(self.lmp,id,style,type,i,j)
@@ -205,10 +217,10 @@ class lammps:
   # free memory for 1 double or 1 vector of doubles via lammps_free()
   # for vector, must copy nlocal returned values to local c_double vector
   # memory was allocated by library interface function
-  
+
   def extract_variable(self,name,group,type):
-    name = name.encode()
-    group = group.encode()
+    if name: name = name.encode()
+    if group: group = group.encode()
     if type == 0:
       self.lib.lammps_extract_variable.restype = POINTER(c_double)
       ptr = self.lib.lammps_extract_variable(self.lmp,name,group)
@@ -230,21 +242,28 @@ class lammps:
   # set variable value
   # value is converted to string
   # returns 0 for success, -1 if failed
-  
+
   def set_variable(self,name,value):
-    name = name.encode()
-    value = str(value).encode()
+    if name: name = name.encode()
+    if value: value = str(value).encode()
     return self.lib.lammps_set_variable(self.lmp,name,str(value))
 
+  # return current value of thermo keyword
+
+  def get_thermo(self,name):
+    if name: name = name.encode()
+    self.lib.lammps_get_thermo.restype = c_double
+    return self.lib.lammps_get_thermo(self.lmp,name)
+
   # return total number of atoms in system
-  
+
   def get_natoms(self):
     return self.lib.lammps_get_natoms(self.lmp)
 
   # return vector of atom properties gathered across procs, ordered by atom ID
 
   def gather_atoms(self,name,type,count):
-    name = name.encode()
+    if name: name = name.encode()
     natoms = self.lib.lammps_get_natoms(self.lmp)
     if type == 0:
       data = ((count*natoms)*c_int)()
@@ -259,7 +278,379 @@ class lammps:
   # assume vector is of correct type and length, as created by gather_atoms()
 
   def scatter_atoms(self,name,type,count,data):
-    name = name.encode()
+    if name: name = name.encode()
     self.lib.lammps_scatter_atoms(self.lmp,name,type,count,data)
 
 
+################################################################################
+# Alternative Python Wrapper
+# Written by Richard Berger 
+################################################################################
+
+class OutputCapture(object):
+  """ Utility class to capture LAMMPS library output """
+
+  def __init__(self):
+    self.stdout_pipe_read, self.stdout_pipe_write = os.pipe()
+    self.stdout_fd = 1
+
+  def __enter__(self):
+    self.stdout = os.dup(self.stdout_fd)
+    os.dup2(self.stdout_pipe_write, self.stdout_fd)
+    return self
+
+  def __exit__(self, type, value, tracebac):
+    os.dup2(self.stdout, self.stdout_fd)
+
+  # check if we have more to read from the pipe
+  def more_data(self, pipe):
+    r, _, _ = select.select([pipe], [], [], 0)
+    return bool(r)
+
+  # read the whole pipe
+  def read_pipe(self, pipe):
+    out = ""
+    while self.more_data(pipe):
+      out += os.read(pipe, 1024).decode()
+    return out
+
+  @property
+  def output(self):
+    return self.read_pipe(self.stdout_pipe_read)
+
+
+class Variable(object):
+  def __init__(self, lammps_wrapper_instance, name, style, definition):
+    self.wrapper = lammps_wrapper_instance
+    self.name = name
+    self.style = style
+    self.definition = definition.split()
+
+  @property
+  def value(self):
+    if self.style == 'atom':
+      return list(self.wrapper.lmp.extract_variable(self.name, "all", 1))
+    else:
+      value = self.wrapper.lmp_print('"${%s}"' % self.name).strip()
+      try:
+        return float(value)
+      except ValueError:
+        return value
+
+
+class AtomList(object):
+  def __init__(self, lammps_wrapper_instance):
+    self.lmp = lammps_wrapper_instance
+    self.natoms = self.lmp.system.natoms
+
+  def __getitem__(self, index):
+    return Atom(self.lmp, index + 1)
+
+
+class Atom(object):
+  def __init__(self, lammps_wrapper_instance, index):
+    self.lmp = lammps_wrapper_instance
+    self.index = index
+
+  @property
+  def id(self):
+    return int(self.lmp.eval("id[%d]" % self.index))
+
+  @property
+  def type(self):
+    return int(self.lmp.eval("type[%d]" % self.index))
+
+  @property
+  def mol(self):
+    return self.lmp.eval("mol[%d]" % self.index)
+
+  @property
+  def mass(self):
+    return self.lmp.eval("mass[%d]" % self.index)
+
+  @property
+  def position(self):
+    return (self.lmp.eval("x[%d]" % self.index),
+            self.lmp.eval("y[%d]" % self.index),
+            self.lmp.eval("z[%d]" % self.index))
+
+  @property
+  def velocity(self):
+    return (self.lmp.eval("vx[%d]" % self.index),
+            self.lmp.eval("vy[%d]" % self.index),
+            self.lmp.eval("vz[%d]" % self.index))
+
+  @property
+  def force(self):
+    return (self.lmp.eval("fx[%d]" % self.index),
+            self.lmp.eval("fy[%d]" % self.index),
+            self.lmp.eval("fz[%d]" % self.index))
+
+  @property
+  def charge(self):
+    return self.lmp.eval("q[%d]" % self.index)
+
+
+class PyLammps(object):
+  """
+  More Python-like wrapper for LAMMPS (e.g., for iPython)
+  See examples/ipython for usage
+  """
+
+  def __init__(self,name="",cmdargs=None,ptr=None,comm=None):
+    if ptr:
+      if isinstance(ptr,PyLammps):
+        self.lmp = ptr.lmp
+      elif isinstance(ptr,lammps):
+        self.lmp = ptr
+      else:
+        self.lmp = None
+        raise TypeError('Unsupported type passed as "ptr"')
+    else:
+      self.lmp = lammps(name=name,cmdargs=cmdargs,ptr=None,comm=comm)
+    print("LAMMPS output is captured by PyLammps wrapper")
+
+  def __del__(self):
+    if self.lmp: self.lmp.close()
+    self.lmp = None
+
+  def close(self):
+    if self.lmp: self.lmp.close()
+    self.lmp = None
+
+  def version(self):
+    return self.lmp.version()
+
+  def file(self,file):
+    self.lmp.file(file)
+
+  def command(self,cmd):
+    self.lmp.command(cmd)
+
+  @property
+  def atoms(self):
+    return AtomList(self)
+
+  @property
+  def system(self):
+    output = self.info("system")
+    d = self._parse_info_system(output)
+    return namedtuple('System', d.keys())(*d.values())
+
+  @property
+  def communication(self):
+    output = self.info("communication")
+    d = self._parse_info_communication(output)
+    return namedtuple('Communication', d.keys())(*d.values())
+
+  @property
+  def computes(self):
+    output = self.info("computes")
+    return self._parse_element_list(output)
+
+  @property
+  def dumps(self):
+    output = self.info("dumps")
+    return self._parse_element_list(output)
+
+  @property
+  def fixes(self):
+    output = self.info("fixes")
+    return self._parse_element_list(output)
+
+  @property
+  def groups(self):
+    output = self.info("groups")
+    return self._parse_groups(output)
+
+  @property
+  def variables(self):
+    output = self.info("variables")
+    vars = {}
+    for v in self._parse_element_list(output):
+      vars[v['name']] = Variable(self, v['name'], v['style'], v['def'])
+    return vars
+
+  def eval(self, expr):
+    value = self.lmp_print('"$(%s)"' % expr).strip()
+    try:
+      return float(value)
+    except ValueError:
+      return value
+
+  def _split_values(self, line):
+    return [x.strip() for x in line.split(',')]
+
+  def _get_pair(self, value):
+    return [x.strip() for x in value.split('=')]
+
+  def _parse_info_system(self, output):
+    lines = output[6:-2]
+    system = {}
+
+    for line in lines:
+      if line.startswith("Units"):
+        system['units'] = self._get_pair(line)[1]
+      elif line.startswith("Atom style"):
+        system['atom_style'] = self._get_pair(line)[1]
+      elif line.startswith("Atom map"):
+        system['atom_map'] = self._get_pair(line)[1]
+      elif line.startswith("Atoms"):
+        parts = self._split_values(line)
+        system['natoms'] = int(self._get_pair(parts[0])[1])
+        system['ntypes'] = int(self._get_pair(parts[1])[1])
+        system['style'] = self._get_pair(parts[2])[1]
+      elif line.startswith("Kspace style"):
+        system['kspace_style'] = self._get_pair(line)[1]
+      elif line.startswith("Dimensions"):
+        system['dimensions'] = int(self._get_pair(line)[1])
+      elif line.startswith("Orthogonal box"):
+        system['orthogonal_box'] = [float(x) for x in self._get_pair(line)[1].split('x')]
+      elif line.startswith("Boundaries"):
+        system['boundaries'] = self._get_pair(line)[1]
+      elif line.startswith("xlo"):
+        keys, values = [self._split_values(x) for x in self._get_pair(line)]
+        for key, value in zip(keys, values):
+          system[key] = float(value)
+      elif line.startswith("ylo"):
+        keys, values = [self._split_values(x) for x in self._get_pair(line)]
+        for key, value in zip(keys, values):
+          system[key] = float(value)
+      elif line.startswith("zlo"):
+        keys, values = [self._split_values(x) for x in self._get_pair(line)]
+        for key, value in zip(keys, values):
+          system[key] = float(value)
+      elif line.startswith("Molecule type"):
+        system['molecule_type'] = self._get_pair(line)[1]
+      elif line.startswith("Bonds"):
+        parts = self._split_values(line)
+        system['nbonds'] = int(self._get_pair(parts[0])[1])
+        system['nbondtypes'] = int(self._get_pair(parts[1])[1])
+        system['bond_style'] = self._get_pair(parts[2])[1]
+      elif line.startswith("Angles"):
+        parts = self._split_values(line)
+        system['nangles'] = int(self._get_pair(parts[0])[1])
+        system['nangletypes'] = int(self._get_pair(parts[1])[1])
+        system['angle_style'] = self._get_pair(parts[2])[1]
+      elif line.startswith("Dihedrals"):
+        parts = self._split_values(line)
+        system['ndihedrals'] = int(self._get_pair(parts[0])[1])
+        system['nangletypes'] = int(self._get_pair(parts[1])[1])
+        system['dihedral_style'] = self._get_pair(parts[2])[1]
+      elif line.startswith("Impropers"):
+        parts = self._split_values(line)
+        system['nimpropers'] = int(self._get_pair(parts[0])[1])
+        system['nimpropertypes'] = int(self._get_pair(parts[1])[1])
+        system['improper_style'] = self._get_pair(parts[2])[1]
+
+    return system
+
+  def _parse_info_communication(self, output):
+    lines = output[6:-3]
+    comm = {}
+
+    for line in lines:
+      if line.startswith("MPI library"):
+        comm['mpi_version'] = line.split(':')[1].strip()
+      elif line.startswith("Comm style"):
+        parts = self._split_values(line)
+        comm['comm_style'] = self._get_pair(parts[0])[1]
+        comm['comm_layout'] = self._get_pair(parts[1])[1]
+      elif line.startswith("Processor grid"):
+        comm['proc_grid'] = [int(x) for x in self._get_pair(line)[1].split('x')]
+      elif line.startswith("Communicate velocities for ghost atoms"):
+        comm['ghost_velocity'] = (self._get_pair(line)[1] == "yes")
+      elif line.startswith("Nprocs"):
+        parts = self._split_values(line)
+        comm['nprocs'] = int(self._get_pair(parts[0])[1])
+        comm['nthreads'] = int(self._get_pair(parts[1])[1])
+    return comm
+
+  def _parse_element_list(self, output):
+    lines = output[6:-3]
+    elements = []
+
+    for line in lines:
+      element_info = self._split_values(line.split(':')[1].strip())
+      element = {'name': element_info[0]}
+      for key, value in [self._get_pair(x) for x in element_info[1:]]:
+        element[key] = value
+      elements.append(element)
+    return elements
+
+  def _parse_groups(self, output):
+    lines = output[6:-3]
+    groups = []
+    group_pattern = re.compile(r"(?P.+) \((?P.+)\)")
+
+    for line in lines:
+      m = group_pattern.match(line.split(':')[1].strip())
+      group = {'name': m.group('name'), 'type': m.group('type')}
+      groups.append(group)
+    return groups
+
+  def lmp_print(self, s):
+    """ needed for Python2 compatibility, since print is a reserved keyword """
+    return self.__getattr__("print")(s)
+
+  def __getattr__(self, name):
+    def handler(*args, **kwargs):
+      cmd_args = [name] + [str(x) for x in args]
+
+      with OutputCapture() as capture:
+        self.lmp.command(' '.join(cmd_args))
+        output = capture.output
+      lines = output.splitlines()
+
+      if len(lines) > 1:
+        return lines
+      elif len(lines) == 1:
+        return lines[0]
+      return None
+
+    return handler
+
+
+class IPyLammps(PyLammps):
+  """
+  iPython wrapper for LAMMPS which adds embedded graphics capabilities
+  """
+
+  def __init__(self,name="",cmdargs=None,ptr=None,comm=None):
+    super(IPyLammps, self).__init__(name=name,cmdargs=cmdargs,ptr=ptr,comm=comm)
+
+  def image(self, filename="snapshot.png", group="all", color="type", diameter="type",
+            size=None, view=None, center=None, up=None, zoom=1.0):
+    cmd_args = [group, "image", filename, color, diameter]
+
+    if size:
+      width = size[0]
+      height = size[1]
+      cmd_args += ["size", width, height]
+
+    if view:
+      theta = view[0]
+      phi = view[1]
+      cmd_args += ["view", theta, phi]
+
+    if center:
+      flag = center[0]
+      Cx = center[1]
+      Cy = center[2]
+      Cz = center[3]
+      cmd_args += ["center", flag, Cx, Cy, Cz]
+
+    if up:
+      Ux = up[0]
+      Uy = up[1]
+      Uz = up[2]
+      cmd_args += ["up", Ux, Uy, Uz]
+
+    if zoom:
+      cmd_args += ["zoom", zoom]
+
+    cmd_args.append("modify backcolor white")
+
+    self.write_dump(*cmd_args)
+    from IPython.core.display import Image
+    return Image('snapshot.png')

From 952a0d1fb776227b0f22236af43f7920ed020068 Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 22:58:42 +0000
Subject: [PATCH 14/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15359
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 python/lammps.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/lammps.py b/python/lammps.py
index 1931e5e0c7..0a996d0235 100644
--- a/python/lammps.py
+++ b/python/lammps.py
@@ -281,6 +281,9 @@ class lammps(object):
     if name: name = name.encode()
     self.lib.lammps_scatter_atoms(self.lmp,name,type,count,data)
 
+# -------------------------------------------------------------------------
+# -------------------------------------------------------------------------
+# -------------------------------------------------------------------------
 
 ################################################################################
 # Alternative Python Wrapper

From 7dc6873c513cb2953682876c60fccbad00ad9cc6 Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 22:59:39 +0000
Subject: [PATCH 15/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15360
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 python/examples/ipython/interface_usage.ipynb | 1155 +++++++++++++++++
 .../ipython/interface_usage.ipynb.orig        |    0
 .../ipython/interface_usage_bonds.ipynb       |  916 +++++++++++++
 .../ipython/interface_usage_bonds.ipynb.orig  |    0
 python/examples/ipython/simple.ipynb          |  232 ++++
 python/examples/ipython/simple.ipynb.orig     |    0
 6 files changed, 2303 insertions(+)
 create mode 100644 python/examples/ipython/interface_usage.ipynb
 create mode 100644 python/examples/ipython/interface_usage.ipynb.orig
 create mode 100644 python/examples/ipython/interface_usage_bonds.ipynb
 create mode 100644 python/examples/ipython/interface_usage_bonds.ipynb.orig
 create mode 100644 python/examples/ipython/simple.ipynb
 create mode 100644 python/examples/ipython/simple.ipynb.orig

diff --git a/python/examples/ipython/interface_usage.ipynb b/python/examples/ipython/interface_usage.ipynb
new file mode 100644
index 0000000000..24c9e55467
--- /dev/null
+++ b/python/examples/ipython/interface_usage.ipynb
@@ -0,0 +1,1155 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using LAMMPS with iPython and Jupyter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "LAMMPS can be run interactively using iPython easily. This tutorial shows how to set this up."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Download the latest version of LAMMPS into a folder (we will calls this `$LAMMPS_DIR` from now on)\n",
+    "2. Compile LAMMPS as a shared library and enable PNG support\n",
+    "   ```bash\n",
+    "   cd $LAMMPS_DIR/src\n",
+    "   python2 Make.py -m mpi -png -a file\n",
+    "   make mode=shlib auto\n",
+    "   ```\n",
+    "\n",
+    "3. Create a python virtualenv\n",
+    "   ```bash\n",
+    "   virtualenv testing\n",
+    "   source testing/bin/activate\n",
+    "   ```\n",
+    "\n",
+    "4. Inside the virtualenv install the lammps package\n",
+    "   ```\n",
+    "   (testing) cd $LAMMPS_DIR/python\n",
+    "   (testing) python install.py\n",
+    "   (testing) cd   # move to your working directory\n",
+    "   ```\n",
+    "\n",
+    "5. Install jupyter and ipython in the virtualenv\n",
+    "   ```bash\n",
+    "   (testing) pip install ipython jupyter\n",
+    "   ```\n",
+    "\n",
+    "6. Run jupyter notebook\n",
+    "   ```bash\n",
+    "   (testing) jupyter notebook\n",
+    "   ```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from lammps import IPyLammps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LAMMPS output is captured by PyLammps wrapper\n"
+     ]
+    }
+   ],
+   "source": [
+    "L = IPyLammps()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Neighbor list info ...',\n",
+       " '  1 neighbor list requests',\n",
+       " '  update every 20 steps, delay 0 steps, check no',\n",
+       " '  max neighbors/atom: 2000, page size: 100000',\n",
+       " '  master list distance cutoff = 2.8',\n",
+       " '  ghost atom cutoff = 2.8',\n",
+       " '  binsize = 1.4, bins = 5 5 5',\n",
+       " 'Setting up Verlet run ...',\n",
+       " '  Unit style    : lj',\n",
+       " '  Current step  : 0',\n",
+       " '  Time step     : 0.005',\n",
+       " 'Memory usage per processor = 2.04486 Mbytes',\n",
+       " 'Step Temp E_pair E_mol TotEng Press ',\n",
+       " '       0         1.44   -6.7733681            0   -4.6218056   -5.0244179 ',\n",
+       " '      10    1.1298532   -6.3095502            0   -4.6213906   -2.6058175 ',\n",
+       " 'Loop time of 0.001452 on 1 procs for 10 steps with 256 atoms',\n",
+       " '',\n",
+       " 'Performance: 2975206.618 tau/day, 6887.052 timesteps/s',\n",
+       " '0.0% CPU use with 1 MPI tasks x no OpenMP threads',\n",
+       " '',\n",
+       " 'MPI task timing breakdown:',\n",
+       " 'Section |  min time  |  avg time  |  max time  |%varavg| %total',\n",
+       " '---------------------------------------------------------------',\n",
+       " 'Pair    | 0.001326   | 0.001326   | 0.001326   |   0.0 | 91.32',\n",
+       " 'Neigh   | 0          | 0          | 0          |   0.0 |  0.00',\n",
+       " 'Comm    | 6.9e-05    | 6.9e-05    | 6.9e-05    |   0.0 |  4.75',\n",
+       " 'Output  | 8e-06      | 8e-06      | 8e-06      |   0.0 |  0.55',\n",
+       " 'Modify  | 3.2e-05    | 3.2e-05    | 3.2e-05    |   0.0 |  2.20',\n",
+       " 'Other   |            | 1.7e-05    |            |       |  1.17',\n",
+       " '',\n",
+       " 'Nlocal:    256 ave 256 max 256 min',\n",
+       " 'Histogram: 1 0 0 0 0 0 0 0 0 0',\n",
+       " 'Nghost:    1431 ave 1431 max 1431 min',\n",
+       " 'Histogram: 1 0 0 0 0 0 0 0 0 0',\n",
+       " 'Neighs:    9984 ave 9984 max 9984 min',\n",
+       " 'Histogram: 1 0 0 0 0 0 0 0 0 0',\n",
+       " '',\n",
+       " 'Total # of neighbors = 9984',\n",
+       " 'Ave neighs/atom = 39',\n",
+       " 'Neighbor list builds = 0',\n",
+       " 'Dangerous builds not checked']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 3d Lennard-Jones melt\n",
+    "\n",
+    "L.units(\"lj\")\n",
+    "L.atom_style(\"atomic\")\n",
+    "L.atom_modify(\"map array\")\n",
+    "\n",
+    "L.lattice(\"fcc\", 0.8442)\n",
+    "L.region(\"box block\", 0, 4, 0, 4, 0, 4)\n",
+    "L.create_box(1, \"box\")\n",
+    "L.create_atoms(1, \"box\")\n",
+    "L.mass(1, 1.0)\n",
+    "\n",
+    "L.velocity(\"all create\", 1.44, 87287, \"loop geom\")\n",
+    "\n",
+    "L.pair_style(\"lj/cut\", 2.5)\n",
+    "L.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n",
+    "\n",
+    "L.neighbor(0.3, \"bin\")\n",
+    "L.neigh_modify(\"delay 0 every 20 check no\")\n",
+    "\n",
+    "L.fix(\"1 all nve\")\n",
+    "\n",
+    "L.variable(\"fx atom fx\")\n",
+    "\n",
+    "L.run(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BlJifRxiiVMLMTCQNsm1+KraIxaDEy1AcEF7MRtD5kHRc5vN0++3elVeeHRo6Wyw+PTj4\nTwMDPSqq+lbgvcB8Or115coNK1c+ARROnSrt2ydvu00zgYYmAI2LgKeeor6+RfLd/ua71GKabakU\nZTLI5ZDNIp2GbcMwot6L66JSQbUKw0h5nmQpjhBs0PWBjBIINQPdwP/5T/KXPnfex8gABhEZhmNZ\nSKXAf2ihgKYmFAooFJBOR6zDVMR1nwnJMEAk+VV/+ZCKDGIOSL2s3+35+dq14Nprw7Vr51asmCsW\n9xeL31u+vDWXG1KZdHcDGeBQT8/DPT0PAkvK5QnOpNNR1RqaADQuDLZto7a2KN9tdBT798N1cfw4\nPvNDKphmWzpN+TyamtDSgqYm5HJwHADwfVQqmJ+PXkQAUkAgJc+EHSmdxAA2q5J87v0Q1n5i8Sfh\nwbJJZJomLAuOE1V5dcCPLGD8EgJC1P6nlABCJQaNjWANNBD3gi6UJPRCXQva2uj1r3dXrz49PHy6\nv/+JwcG/7+/vVZl0bwR+BZjNZjdfccWGK654EsidOFHZs0e+4Q2aCTQ0AWi8VOzaRdlslO+2dy+O\nHkWlgv378fePIWUazakU5fNobUVnZ6TDyefhOBAC1WrkCbbt2JpLQthCWFJa/E/ATgxgmQPmgR1/\njf67Gp/kq0R9gCAiIvALiAq970dXjTCMJEDsAKhWefaLMGQyCKUMOCMo8YqZQKhLgAVkXmE/iMnJ\n2rXghhvCtWtnR0Zmi8V9xeK/rljRnk4vU9eCPwAc4MDSpZuWLt0EdM3PTx46FG7erKOqNTQBaLwY\n7N1Lto2JCRw9it27cfp0lO+2jShlGHnbTrEQs7MTS5eipwednSgUYNtRC35qCrYNIKrLvo8gsILA\nJDIBg8iU0qyXY6aBtHKQLXwvisQR3hICQRCNeefmkM1CStg2fB+zs5iawuws5udRqcB14XkIAhmG\nvpSBsgEHiTlEzARxUtAr9q2fvBYsWULr17urV58cGjrZ3//I0NA9fX19wNXAeuCtwK8Bk/n801dd\n9aOrrnoayB49Wt29W775zZoJNDQBaDxn6TdNYLF8t4eIOgHLMPKOg1wOLS3o6kJvLy67DF1daG6G\nZcHzMDsL24YQ0dmcj+SWZZqmGYYkhCElT19NVXCZAxxAAAc+D9QPA2SicHtCWEEQub1mZpDNggjl\nMkwTQYC5OUxNYWICMzMRB3gegsANQ1cIT8oGGohfsn4g/MrH6dO1a8HNN4fXXjuzbNnMwMDuYvEb\nK1Z02PaIyqT7I8AE9l1++YOXX/4Q0DE7O81R1XfdpclAQxOARgKPP06dnbV8tx07IlsV57tlAYso\nZZp2KgU24nZ0oLsbPT3o7kZTE0wT1Wp0GC+XMTvbIMUBt3FUtYXK5kzqcHILnipO9veEcMMwy2f/\n2VlMTMAw4Hk1F1i5jJkZTE5G94ByGa4rfL8aEwDgqyl08sUPQ4kHe7UgeS247DJat666atXxwcHj\n/f0PDQ8XenouU1HVvwh8DBhvanrymms2XHPNZiBz+HB1xw689a2aCTQ0Abzm8dhj1NWFSgWnT0dy\nz2S+2w+JOoBIh+M4yGQiEU5LC1pb0dqKQgGGAduOKjKrcSyrNqQlApEEQCQTdYsW6HDeRPSDRF1j\nqagppStlOQhyrutwoefsh3IZ6XSkPuLhM5sU5ua4C1Ty/YoiAF4P4KmXr/4FibpPr9qf4LFj0TfN\nNGn9+uCaaziTbmex+NVVqzqJVqhrwacBCewpFjcWiw8DbVNTM5xJ92u/pslAQxPAaw/PPENdXZif\nj/Ld9u2D6+LUKfzbv6n4e66MRFasw2E5ZjoNjuRkCVCy3AO1fwGQmLgu1GJC6XCsBU2YKuACBlAR\nohwEc57XXiqBG1Weh/n5KPghbjqVSpEFrFKZd91yEFSEqEpZVdXfTbw89QALn+fVizCs/SUGBzmq\n+ujAwFHOpOvq6geuAW4B3gf8FnC6tfXx66/feP31zwiRPnzY3bYN73iHZgJNABqvDezYQU1NmJnB\n8eMYHY3y3Q4fxsaNtSpgApIIRGQYNYl9VNfVVFZKVKtw3Wj66vsIgliHE0gZJnQ4ol6XKRM6HKf+\n8SpAFSApK1KWwtD2PNMwWjjswXWjHFAmABYFVauoVkW1WnLdku+Xw7AchjEHMJ30JAYA3AV6VNGA\nuFTf9v8gChc8hg988IKKUA8dir6abdOttwZr1kwOD08Wi9sGBu5fsaILWKky6d4KBIaxc3Bw4+Dg\no0DrxMTs/v1i61Z89KOaDDQBaPyEIs53O3oUe/fixAmUSti2Ddu21X3sOdyNvVSRBJOLPnddUqmI\nAHg2OzfHB3C4bqzFZB9AwDFBquol5T3n0+HMAVmAAFtKWwgzCOC6IZAPw5TngbfBsN40CPipKp5X\n8f2K71eCoBKGVSkrUhakFIADdKpLRmxIrgK3AXPAvOoIXSTsIhoHDCAFtAKUWF/MkXll4B+JpoEq\n8F8uKBP4fu2rLV9Or3tdeeXKw4ODh/v7f7hsWXN7exFYC6wHPgTcDZxob3+0vX3jjTc+GwSpsTFv\n61a8612aCTQBaPwEIZnvtmsXzp3D3By+9KVFPudRpZYyiAX4sd4/lUIYIpWCECiXMTWFyUlMT2N+\nnsewzAGuEJzJ7Ce0mMl/xsMAo/6P/qSUf0cEDnETgsJQAoGUbhimPS9lWZZhGESSE/+DwAtDNwjc\nIHDDsCqEK0QqDCsAgO7FFsf7anF8EzAPzACfJpp+YYvjXzj2EZ0F8sASwAEslU0hlDDJVY+RB/Lq\nMSaAv7gIrrR9+2pR1bff7l99NWfSPTM4eO+yZd3AapVJ958A17K2L1u2cdmyR4Hm8fH50VGxbp1m\nAk0AGq9mPPMMFQqL5Lvdf/8in+3PEg3E0T0N1Z/D10qlmgusQYdTrcL3q0EQCzH56ySbHkkCYA64\nleihROGbUyXbkBJCCMCX0hUiFQS2YViGERfTUIhACC8MfSFcITwhTCEs4DKiNODEi+PVRcRXk+EU\nkFJqVHYp/xeiSeCzF6L+biaygKVEGaIUh2ZH6+tlFJotpasew1EuOTbKfZJoHPj7i2NOjqOqiWj1\nanHDDaUVKw4NDIz19z8wMtLc0jIIXAesBz4GfBI41tn5SGfnRqDP884eOuRv3oz3vleTgSYAjVcV\nNm2ipUujNV6HDtXy3b7+9cU/zDKu/lJWw7Di+5lKBXNzUfCy6yKXq2n/uQs0PY2ZGZRKqFYDz6uE\noRuGbr0Ix0vkwUVlSIlwTOB+ohJQBTxgBiAgAAxAcqCQlK4QThhaRCZRrZ6qjfCBlE1h6AAFogJR\nxjTThmEljMRCykAIj7+OlLZaFWDUU9F7ie798YrvVqIC0GwYOcPImKbFqyvj7ApeXi+ELYQtpcmB\n1QsEqe8muv9iBlQk5aSZDN1xh3fllePDw+PF4tMDA18YGupJRFW/Gyg7zrMrVmxYseJxoHD6dGnf\nPvn612sm0ASg8YrH00/T0qVRvtv+/di9G56HM2fwne+c9wPMg1NLyqoQlTCc9/10pUJzczXxZbyR\nMZ4KzM2xFEfwJFZJcViLGRNALMWJmSbW5PQCFaAEzAJPAkNAoJpRgZS+EA6Rw1U7EfUp1Jm6TQhr\n4eJ4Lr5SQggjDJ0gcIIgXhzPqUFSjYJjt/B7iL78korvQaIy0EzUYppNlmWxJSLeXy8EB9jZQWCz\nU1oIEoKLsah/Bh94J9HXLklIUaVSuxasWSOuu25++fL9AwMHisXvjYy0FgpDKqr6/wUywOElSx5e\nsuRBoKdSOTc2Fjz5pM6ke9WDpNQ/wp9AbNtGudwi+W4/+MHz/Li/TJQD8kStltXiOK3pdEsuB35l\ns1EWNI9hWYtZqaBSCSqVkuuWPK8UBJUwLEtZkbIiZVltiJwDZoFyotJ5QBn4E9UQLysOeAhoAjJA\nE5BTK1xsIiaAuK3PidM9UjYBBdNs5cXx6XRkSbMsAA2L4+F5XhCUw7AsZVmIcuLBpoFzwBngMPCt\nF/+J2EWUNYx2y2pi5wSrZpPfq1i25HnwPJcfQ4iylPH3ZwaYAs4Bp4FDwL+/TB/M5ma67TZccQWG\nhlAsmoOD2WKxF7gSWA+8DugD5oAtwAbgSeDIyZPlvXvlHXfoMqJvABqvDHC+29QUjh+v5buNjuKx\nx57/U1pR41lHCCsIDNeVRE1CmJz9yYdr3gTAtdV1K55X9ryK79epMKWsAg2vpAafT74FdbqP2zLH\ngV7ABUKVJp0C+PckV80IYAQoAHnTbE8ujs/no2sKEJXdcjl6ETlEAgjDMCRauDi+CegAforoRy+m\n+O4gyhO1WVYTh1c3NUWZqakUTDMKzWbXAmfYEaXi874QnJmaqn+MzpcvrHRmpnYtuO66cO3aueXL\n9xWLo8Xid1asaMtkhlUm3X8FHOBgb+9Dvb2bgO5SaZKjqnUmnSYAjZcN+/ZF+W5HjmDPnlq+2wv8\nz0ssnpHSFsIIQy61fhhmfD9dqdgsxAQgRBCGXhC4vl8NgmoQVMOwGobVhAy/ol5c/cMF1T8EcqZp\ncmqQKnYfBT4PNKneURZIs5xGraGPd40tsji+ra22OD4eVHBmEbsZFlsc76iUunhx/Asvvs8QNRMV\nLKs53l/W3h49RiYDIgRBFGgxMxOZ2qSEEI6UnhB8s4ljU+Po7AJQWDAhv8RI9gY6O+nWW93Vq08N\nDZ3q739saOjzl122VEVVvwW4C5jK5TZfeeWGK698CsgdP17ZvVu+6U2aCTQBaFwq7NlD3J8fH8fh\nw9i5M1LofPnLL+JzOAeYgAAsKUkIDmhzhUgHQco0LdPknVusw/HD0A9DLwxdJcWpSunWV39u71QS\ndT+u/j6QsywjDNnSKqQUQAD8CnAPkAVcIA9klGDGio3KwJuAFFEhXhzf2YklS9DVhbY25HJRZhwn\nHKVStf1lYUhhaIWhKaUZhlZiPYCtjuF5oAr8NNEDz1d8nyRqBlKm2RSvL16yJHqMlhZkMgDgupid\nRSYTzU54m00QWPFjqAtQAwfkz5OZ+rJgfLx2LbjxxvDaa2dGRmYGBvYUi99asaLdcZap8IlPARYw\n2te3qa/vIaBzbm6KM+k++EFNBpoANC4aOOGHx7xjY1G+29QU3vM1/Mv95AHve2FnyUnAUnYtFmIG\nUnpCVMPQNgwr1uEAgj1frPqPE3ikZKNTXPr5JeoP/qyIrwCGbadjWScQSJkGfGAU6ANKahKQUpcA\nEyDgZ/hXTLPgOOB1BUuWRIvjeW+wYUSVl/VL7GVTvmWLp8FE8eL4hsDqlBpEPzccwDaMrGU56TSa\nmtDRgZ4eXHYZlixBW1tNODs1BdOEENEzqB0GpmEYQsTri42ER9oG0oC7IC7pZUfyWrB0Ka1bV129\n+sTg4In+/keGh/9vb2+fyqT7BeCjwESh8OSaNRvWrNkMZI8cqezciZ/9Wc0EmgA0Ligef7yW73bg\nAHbsgO9jfBy//a/RuLUC3Es0qwQ5v3f+mvJXUv4BUahUOqEQPpEnpS2ErYSYBHDKW6TFlDJgwY+U\nnnK6VlXpLynxT4PcxQN+dPnlKJdNwGHTgBC2lHwY/yvgt4AOoAQU1CTAVnOCHGAZRsayLN4b3NZW\nC6zu7EQuB6C2ON51o9SgVCpaHE8KKrCa6osvL46/jejB83+jNhB182PYNjIZNDWhvR3d3ejtRW9v\nRACcXB0nmLKgVi1VJsPg0NT4SYwFj5F5Bb/rTpxQ+VEGrV8frFkzPTIyXSzuKha/vnJlh2kuV9eC\nPwUI2NPf/2B//8NA+/T0zMGD4TPP4EMf0mSgCUDjx8Mzz1Bn5yL5bn/777VSy0qbZmAOmAb+kKhy\nfgfsJPf9VUC/L6XLY1iiqAtPFEsYQymDRPbyUlXf+f+Nkw/4/92SyMOpAsjleK2jE4auEBaRRRS3\nRP4U+BjQDuQSjSAL+ChfBYhSltW4OL6rC52d0eoYXhxfLkd7jBORpXT+3fHJnKLcc37beX2xxfvr\nMxnk89EcoqMDnZ1obYXjwPdhGKhWa8+QWGJM6i6FRDLdwsd4pV0CFkKI2uMVi3TzzZWVK48NDh4r\nFh8cHi50d1+uMul+CfjPwNmWlsfXrt24du0WID025m7fjre9TTOBJgCNF4+dO2v5bvv2YWwM1SrG\nxvDNh43QAO2U5AcAACAASURBVG9mZwdsWplgudM9AXyCaB64Z0Fx+YyUv06UVTTgAWk+mEvJQkxS\nOnqhQt+WAWUgCziqSyNVl5/ph68CNwATgAtsAR746Z/GxAQ3QygIuCFuqKY8vz4NTAKfVhyQVofi\n2uJ4ll3mcuCtxYUC8nlkMpH0PpWq7RNOLo5fkAkq60swN8FSz/fJISLLNCneX5/N1vSy2Ww0eV5s\ncz0P0gVnLqnQbLnYY3Av6FWEw4ejv4Fl0a23BldfzVHVOwYGvrJyZSewArgJWAf8LBACuwYGHhwY\neARonZyc5ajqD39Yk4EmAI0XgD17yHEwMYFjx7BnD06eRKmELVuw61AatuTgAU8IT0pHSks1u43E\nepbTwC8R3beAA84CLYAbE4Aaw/KKx1iHI4C1QBXIAR1KrR/LNOObQRXIAlm1Gn4KWAP85wce+N/r\n1yOVQrUKwzAMg4SIWiKJnrgFfAT4E8BWvSDOdzMNI1oczyU+9n8ptU+0Mp6ZgP9nYnF8WD+RFvX1\nN95f9gaiHy52+v4O0RJA8kSEi7tl1QxoQGz+qkWlBkHdFvtE7FLDCnuReAweUL8aEQS179uyZXTj\njeWVK48MDh7p79+wbFlTR0e/yqT7APAJ4GRb2+M33LDhhhu2hmFqbMx79lm8852aCTQBaJwHnO92\n7hwOH8bu3Srf7YF2ECEnIQTrTFJBUBWCradU74CNO/JvJ2pwP31NyrcT5QBPle9UQocTu7FuBhyg\njShLlDIMTmuAGg/4KvqGpe62qqpxj/vXHnnknquuivshlNjYBUVR/M+PALPAfaoainhrfLw4nuX2\n1SrK5ajaxrp7jq3mAFEhOK80mVad/KdM7A02z38JsOJA6fgxGp6BWYGTlHhpAQdoKz6Q/Bgcm7rg\nYRpys1+sNeGl4QcqtjqZ4PQrF+LP3b+/lkl3223+VVdNLFs20d//7MDAfcuXdwGrVPjE2wHPNHcM\nD28cHn4UaDl3bm50VGzZgo9/XJOBJgANAMCWLdTUhDDE2bNRvtvcHKan8S9bhtBuggP0WffieYbn\nZX2fFf1SCJZaxp9z7s/0Am8m+n79R/1bUr6ZqARUlCPXUWNYAt4NeECeqMkwcqaZiVsccfRNGEoh\nvDC0hbA4+kZKSvS7ucZ9aPv2M8C329ujZkj9ErFkT9wC/h/gnfUl24w9yTxinZ6GlFHznfcGz85i\nbi7OqmMO8OuXBie3xof1BPAcH4/490de3zgbg1eYuW7U/efIPN5dnMhM9RbsLm54jCT/ORftvbSV\naIZLM9AEUHx1U1KuLxBNAx5w94VggjiTDsCqVXTDDaWVK8cGBg739/9gZKSltbWoMuk+AvwucLyj\n49GOjo0337zN91NjY96WLXjPezQTaAJ4DeOhh6i3F9UqzpyJFjq6LiYn8fVj16M3XcseqFZri9oN\nI+15Ub9eiKQDNgvkgRagG3g90ab6D/lhoAMoA4XEJcAEPgIIoMUwWkyzybbrtkJy9E0QwPfJ91NB\nYAaBEYZsKWD6SSpB2e31xomJf83lRL1adOEeMX6zRhVTSk8Ih72+pVJtcTzblYMA8/ONi+NdF77v\nhqGXCKyOY0oXLo6n8y+OF/E0W0o/DG22Sc/NYXIyWpXMD+O60S8yB/DuBN5fL4QnBO8u5vUJfuL0\nHSyYS19wbCOaAQrAEhWdTQuis6vKkjYL/DXROaAK/PUFuovs3h19nVSK7rjDv+oqzqTbMjDwz8PD\nSxJR1e8CKra9bWRkw8jI40DTmTPzo6Pylls0E2gCeI3h6aeptxflMk6exIEDiXw39424vCnamR6G\nqFZRKmFuLumAdaT0fd8n4nFu7DliGmgCWhcIH/dK2UfUBswCBTVG/kOAgJxhtNt2Lp2OZrCZTMQB\n8ZG8WuWmh+W6GSIZBEKIENFcOg14KvagHSgB7yyVPm/bYaIhHtb3Q3iAEaqy60nphmE+3kqfycA0\n4Xk1Fxj/OgdW8yXAdUUQ1PYGn2dxPBLFl4BvEsX9kDvVNyfOTPWEqASBzSQ0PQ3bhpTR+mIieF5E\nTlNTmJ6O1hd7XiUIqonHSAam+urfkeiGGRf6jfQEUQ64jChDZCeXFqjeHYt6qwuis8eBDxN99oL2\no1y35jK76ipx3XXzK1YcGBg4WCz++8hIS1PToMqk+03g94Ej3d0Pd3c/CPRWq+fGxvynnsKdd2oy\n0ATwk45t26i1FbOzUb7bgQNwXRw7hv/Ivx09nWhtRTYLw4DvR8XIcWpRlGFohaGlujGx78mqjx9Y\nuCTruJQmUU9ChyOBrGG02nYum0WhgOZmNDdHCTxsd+KGTNz7BiwgJaOhtJ+oJmlleW0FZuuP4Q0b\nJeORANdKE3CF4MXx6YWL45kAmAXjxfHlMly37PsV3h5TH1aafKG+AdWlinIFuI9oFpgHLgc8wAKq\nQpR9P1utWvPzsO2o6M/NRZuTg6C2v352li8inueVmYekdM/zDBevnu0iCoAuojxRlqOzuXHHvSwp\nfY6tltLld0tCNRDPZj5E9LmLMJNINv8KBbrtNu/KK88ODZ0tFp8eHPyngYEe4EpgHXAr8F5gPp1+\nZuXKDStXPgEUTp0q7d0rb79dM4EmgJ9E7N4d5bsdO4a9e3HsGMpljI7i8Z53YOlS9PSgvb3WdpiZ\niRywPJbktb2+b4WhwWauhP3VTDhg04upzkMpAThEOWADkCEqWFYhtj51dqK9Hc3N0Rmcj+T8ABx9\nw0p/IbigcPSNXZ++kAOagQ/6/v80jGQ/JEz0Q7gGcay0IWVVynIYznmeUy4bycXxHPwQL45nKiqV\nUKmUPK/EBKAWxzcsLXAXLAqWQKciAO6Jcz8kfoyyEOkgcFy3rVSKvuHVapQ+xA7k+DFKJVQqXrU6\nzwQQ769fsMLeW6zjdEHwLJEDtBtGk2kWODqbJaoqOhthyJnVdhiaRAYLBxaLrf4A0Sng+xdtND03\nV7sWXHtteN11c8uXzxWL+4vF7y5f3prLDatrwe8BaeBQT89DPT2bgCXl8gRn0umoak0APyHYt48s\nC+fO4ehR7N6NM2dQKuGfn70CfX3o7kZfH5YuRUdHFD5TKiGVihywXP54EmCahvLxkoofINVasdTB\n/HzWU0/Kx4gygNMQfdPbi+7u6P4BFX0T3wZYB5lQ+pthaCT+UFtxDyuOfkuI/7HYHrH47FkGcgAB\nFSlTYWj7vlmptBAZXPHT6ajrxX90w+J4z2tYHO/Wh5W6qugnQ+taiHzAlTKdWON1hAezUqaFmA9D\n0/OIqFlKgwlgYR51tQrXLfMrCPgZKvWBqS5wPVBJOOniXrwPfJbIAz7+YxTcZ4hyHJ1tWRk2LqTT\nkTuab4pxdLbr2r5Pvg8gbtw1CAc6gArwFqJ/u8jypOS1oL2dbr3VveKK00NDp/v7Hx8a+vvLL+dM\nunXAm4APALPZ7OYrrthwxRVPArkTJyq7d8s3vlEzgSaAVyd27SLHgZQ4exaHD2PXLpXv9ng/ehIO\n2O7uiADCEOl0tLM3l6t9wk0TpkmxC0l9qJLreVlyHp4/By2jom9sTr5sb68l8LS3I5OJ2t+TkzCM\n2iC6UmEFjhkEhhCkrGRJpb+tHGpB/RIx/pcg0Q2fBvJskpLSEsLwfSISQD4M054X6S/j6Dffl75f\nXWxxfJxXmizBYb1Glg+8TZblCmGzkUI9dglIARJwmNJ8n9cX54IgW61GJBT333y/6vtV36/4fpWf\nYUFo9iBgqig6I7GpJo7WKAFzwN8QzQP/7cXX3GeJckDONNscJ82+5dg0Z9tRtyoZnV2pWECaj/xC\nBFL69ZnVHJs6e2k/DhMTtWvBDTeEa9fOjozMDgzsLRa/tXx5ezq9DHgdsA74b4AN7F+6dNPSpQ8B\nXfPzk5xJp6OqNQG8avDoo9TdDdfF2bM4dAg7dkSb2O/f0IaOFDgBv6kJzc2RCZbDZ5gDkkupkg7Y\nBT5YmWCC57Ce/pBoCWDG0TccfdzVhZ6eiACYeObmojYUN77Zi2tZUQBD4izfwD0xDfwx8EnABLJA\nG+ADT6iDOYB9QCt/BSYAQPp+tDje9x3TtE3TIJJSChVW2rA4vipEVfVzGlJLG9xY7GROO44TBFYY\nGkLwOkrmoRQgAFNKQwgKQ07Nq4ZhyfNs07QMDnmrZabyM7hC8MrMuPr3Kn9ctENY/WWjHcKKALLq\nNQ38KdG5F6nGsYG0YbTYdjqbrUVnt7TU7fjkkcnsbJ1wgKOzieKYpmReaSvwRqL/uORJFclrwZIl\ntH69u3r1yaGhk/39jwwNfbavrw+4GlgPvA34MDCZzz919dUbrr76aSB79Gh11y75Mz+jmUATwCsY\njz9O3d2L5Lt95oG0WVAOWMepFfrYgxq7XtXuw+gXlQRzUall0gFrL+aAzQIGkWkYKduOEniamtDS\ngtbWqJqkUvB9ACiXkcnUxTComwcr/aGSDxpuIXECTz6RJOEC64B5FSq3BZhTgkVTSggRBgETQG1x\nPBGvAg6lDMLQF8JTryisVJ27Oaf6msTw2Ve3kA1AAJQApNOG52XZRxaG7HH7eeBHSjBqSMmuLk/K\ndBg6hmEZhkkUP0YgRCBEFJjK24mldKXs5xsGkDOMNFHjIntW47CTbjEb3SeIJoAvvIDiy0sLcpaV\n58lNRweWLEF3N9rbkc/X5udxdHZsYw5D3qNpEiW1AzEN5IDs86XmXWycPl27FqxbF15zzczIyEyx\nuLtY/MbKlR2WNaIy6f4IMIB9l1++6fLLHwI6ZmamOZPurrukJgCNVxC2bq3lu42OYnQUrouTJ/GX\nPzCQIhAhVm7wZi6eNPLIlzv+LHvn8W8YMhkEqpmblFommYB1ltZibwuTo28MwzBNsPafNaDx+kNW\nQCbKPZKBa0Qi0WBZ9AoSjyKWJOxInqrUvDDyOuA0YKjBQHTEBlwhHCFqi+MTYaWBUv1HgdWACywH\n5pTHig/dIhGcVwZ+CmADFPJ5VCpUrWa4Dx6GLN6fUSxF3Cgn8qWsGoZDZKlZCxL76wP1DDxRuByw\ngQ7DKBhGljfIN1B4GPISeUsIttEZifXx8fftF4i+8ZzF9zGiFsA2jILjIJtFa2s0N+rtRWcnCgVY\nFjwvmtwko7Ndlxt3vEjZIDISYU2xfiwF5F8Zn5rkteDyy+nmm6urVh0fHDxeLD40NFTo6bkMuAZY\nD7wL+Bgw3tz85LXXbrj22s1A5vDh6o4deOtbpSYAjZcZO3dSodCY73boEP7sIYRGVK+NeMjJ/iPO\nv4w/ydPTsegwtp5KIXwhYttRg9qSmSDuyTTkz3yDqI/DCeLom2TqDp8WgWTmfi0DR6XfNHDPwitI\n3Au6C/iqYQRKqVldkGR3XA0GpHKEuUQxAZC6IkTeNy6+7B4AlgMlwAZ6lK8NCwxQrPZpAmaA/3nk\nyDngzzo7DdUSscPQBsYBUoJ9btd4gBOGHJrdkJgUnejVb1sOZIlaDKPZtlPxTS45vQgC+L7t+3YQ\nmGFoqBChpI0uNg0sdHE3NH9sopRlpXh039qKzk709EQKgnw+cizH62tYOMAXONM0DMM4T3R2cpHO\ny9IIeg4cPRo9jGnSLbcEa9ZwJt1OzqQjWqGuBZ8GJLC7WHzQ9/9q7146ePC1tbFAE8ArC3G+29Gj\n2Ls3ynf74hflt4iSDtg0V3/W+/PqQZ5/cgQCW0+ZAziCJgjcMPQS9tdgwQuJ7IEG66nDdYcbOHHR\njzfuzs/DMGCakQKV/1xOX/C8mAYC5h5FP+EC+olnwhmg2TQ5yc6V0pYyXt3FT7hdVcBQGbJSUtpE\nkW5dPWStXCpp6XLAAVqJ0kSO2jIfJVerrQaulKmE4IfPuf99fPwM8Jls1uJ+CPDrUn5O7e2KulVx\nr0bNiuOjehjnZgNXAXmiFtNscxyDM0Q5LJoHNvyNVTY6uG7G82QQiHgDT2I87gItQO9zLo+0AcMw\n0hxZyiuLObOao7Pz+ejNwwP8hthq7qfVX+Ya5jfWK3tvQRjWvi1DQ3TjjZVVq44ODh7t798wPNzU\n1dUPXPvww3/f0wPPw9mzOH0ad95JX/iC1ASgcanB+W7j4zhyBLt2YWICc3P40peiAEtuUPhSVsMw\nzZU36YCNXWDczG1wwPp+7IBtSEGIXw3j2cYPkqKfUAgzXnTOeTumyTLT6P4xMRFplRIcENHPArdX\n/C/J6BsLcNJpJwicMLSEYDU6qcmBAG4FHkwsjvc4pkLKKKooEToUn5evAwAUiAqGkTGMtGGYCwxQ\nPCfgCr64Aapc/t+plCEEN0NOAZ1AVf1oeGOBozwW8UUk+RjXAjmigmm2pdMGl2Oe4XN8NABOleAN\nNuwtANJAEAS+lA7gqyXyKSXIaQa6FkvyALCRqB0gIts04ThIpyPtAEuA8nnkclHHKTlM4mZUPLbh\nv0J9bHVMBhYgAOflngS8EBw8GD2ebdPrXx9cffXksmWTa9Zs7elBpYJTp3DgAHbujH4OmgA0Lh02\nb6aWlsXy3f6llj3gAaaUVSEqQZD3PIuP/5z9UCohnY7El3EkWeyA9byS73P2wEL3k5eIQEie8r5L\nFMsQC3HujZRuGGZ50sBBC5YF30cmE9FPMoFHJR8gCKpcXlUrJlgQwtNwtEQ2C8+zfd/gwbKKEoq7\nH2eB5sRBOLk43khUXj593wzYQJNhNJtmPpkdvagBKgyjJfUJyokNUL/uulPA/zIMSHk38FdAXv10\nsgtWV1Kiv8S8lWU1juMYuRxaW9HRgY4OtLUhn4/UOBwgyqkS3BESwhDCEcKLnXQJ3wbrMpuA8vk+\n3kS0MDq7YeQQhtErztDmRD/VuFsYnd2QWW29speXNcD3ZTxsKxQwP48TJ6JlSnxl1QSgcenw8MPU\n09OY7zYxgW98o3aeiqynnD0QBHOu21ouR26jOHuAGwh8OYiPkJXKvOvG1tOk7zTpgG04uUngskRP\nfDwRwFAJgmwy+kaIGv3EyTzT05iaiocQZUU/npqCJmX+/C8NQdBobuYLhEmU8X0RBIEQAZBSUULv\nAb4I5JRKMlMfWE2J9ssbgDzQZJqttp2KDVDs1UoaoFwXnud4nkHEIxOhZuZ+wgDFytG7hfgUEaT8\nMPC3QBmoqqiMVIKHkLiICM6XNoyCbadYi9nZid5e9PSgowOFAmw7SrGemoqMxNwO8n1eIm/yNBgw\nVU8sbsFngPR5DBzsIpbJ6Gx253FsNZd7fquwfCDOrGbhQJxZXR/TFJNBLOS9eJGlG4niJqFMhJWW\n1dv4pfnjdu6kfB7T09Gw7fBhVCo4dAgDA5oANC7h2b+nB6USTp3C/v3Ysweeh9On8d3v1r2ny0Aa\nICkrUpbC0PY8s1xuAqLsAXbAxuWMHbCVSuSA9f2kA9aVspoIHjifA7adyFfF+kR8/5CyHAQZ182W\nStFVOaYfFpBwWZmbw9wcE0BVRd/E9NPAQO6i0TctLfEJ3ZLSUUqepBT9F4EvAPNqcXx6welbAu8E\nCkDeNNscx4mdE5xeF7dc+Jn5ZRiW66bUSIBLf2yAygIFoB0YS3yvDgI9an998hKQHAMI4O1qiXzO\nccDH/+5uLF2Kvj50daGpqdZDYz0VN9nYRud5pu+ztDSWAxn1myPZl9CwOaAWnc0XnTi2mi+YAFIp\nhCHm5zE5GcXV8eoCNbzx1c7n8yU1JdfZX1g8QzQL5IB23r+m/kbJsNISMA98hog3zf3xC2aCvXvJ\ncTA5iaNHo2VK5TKuuy5aUKQJQONSYPt2amlpzHc7ehQ//GHj+3geyAAElKS0wtDwfRCFQD4MbdeN\nehoJ6yk8r3J+B2z8YjOUWFD9Q6DZMPxYhyNlmgMYhCiFoeO6lmk6fESNAxji6KFEEnWlWi15XmlB\n9E1D+g2An0uMBHzg3qeeeu8NN8RJdnYY1mLslCDdBvYCfYoDFl4CJLdcTLPFcZxcDi0tkWWhuRm5\nHCwrMkDFYW2saxIixXwD2Ik99U4iva4N+B0pPwVI4I+Au4F2YE7lZjuJ/fVxIc4CFlHKsmwexra0\nRHp8ztIoFCI1juNEfTwe8KicbVJinOQk1kgIcmIaiHEvUW/8LY1lY3F0tmGgUoHjRC3E6WlMTEQc\noPYWeIm5UbCYcCCof5ILhT1EbPm+DEgR8QpoJHuA6l2UUbefDDAJ3E1UAj7zfDQwOkoAxsejZUoT\nE5idxTveQadOaRmoxqXC7t2UyTTmu+3diyefXORdOK52lBtSWlIaYSg9L3LAep5jWXbCehqEoRcE\nbhBUz+OAjV9VRQCiPv/AAwqOE4Qh/4dlKVMApCxLaYeh6fuoVJqkzDABsIQx3gTgeXDd0PPKrlv2\nPKafihARByTo5xZgDoAS48uEGL8K3P/UU3PK//W7+bzJ8WT1UvT/AUwC/wDMqIWRjkq3N4A/AFJE\necvKZjJRy6W7G93dUc+d1wZw7VtogBJiUQNUnF5XAv4AuBsIgf8KfALort9dHN9FAHyM+cAwHB7G\nxkGqsY0uVuPwLoG4SRXPKmIJViLJA/U2OlFPACKe8Evp8tUwXlrAgjG2AYdhxDesHeBLgOvC96u8\nt0BFlj6HcAAXjgC2EFlAD1GOKG0YNpEVyVClSNgp2E1t1S+RNoEzwPuJvngeDnjqKWprQxBgfByH\nDmHnzmjg8vM//xqtQpoAXh7s20emiXPncOQI9uzBmTOYn8e99573APIXUv6FOv2ZQoAFMFK6YeiY\npmOaSetpyJJ/Xjulon1jB2xc/ctAebHqHwA/6OpCEFi+bwWBEQSzQnA+hMX3DyXAz4VhxnVTyVUw\nQvhB4Pp+NQiqvl8Nw2ocfZMo/VcDZcBKbCNBQozvKTF+EzAHzAB/Nj8/C/ymZTVoEJkMfgH4nOrP\nxMX3z4A0YJtmIW65dHXVgvPy+Ui3ynkVcXJn0gBFZAALDVB2wgAV90N+H5gCPpt4DDsxCbC5SrIa\np8FGF4eysZNjUSddLMU5zxJjSjxbnOfqq8xqV4hKGM57Xp4LPef0VSqRhKwhB0LJt7hzWIvOXiy5\nOslDFwRbifJAs2EUTDNrWZR0nHCsbBiKMIz8cUIYyp2A+u/Pu4nuX8ABjzxCS5agWsXp09GwzfOi\nYduXvkSaADQuBXbuJP6w8w10587ItvXlLz/P9ZNX9wV8WOYqzzkBYcjZA5GjVR2Ukg5YVzmqYgIo\nAWUVPd9Q/X1uwSspeobot4nu8TwJcI40hBC+z/ST8v04gIETeALmnjDk3VuuEMnomzXAFJAC2ojS\nRDbA1i0W+URGByldNVB1EhawvwmCKeBjie9JTAPvAP4BMIAmxQE5wCJKc3QdG6C6utDbi76+mv6d\nxyecnBqPzZUBihYzQMWpeeyN+P+A9yY64+8DPgOcAbKxKjS2m/FXY8MEF7W4rnHXjvv+PINdaKM7\nzy57kbDR8T0gvgRUABcwpazE0dmVisPR2Xz14cju+HLA2oFSCeVy2XVLandCHJ3d8AoTwxt5IXYY\nbCcqELWaZottm45T2zTHaihO4/B9w/MyQWAEAQFSCJnYchokDOTvIPpmggM2b6YlS+qGba6LM2ca\nh22aADQuIvgMEue7bd+OSgVTU/jqV5//XXgmEYHAJd4jigxQQtRZT+sdsHx2cxMilrJ6JbWS8Yen\nAqC9HfPz0VwBSEk5q6iCy3yodnI5QdAQfcPpC2xYi8xcAE+eVwBVoJsobxgZw0jxNpKohEihxPiR\n+UsJ6o16heingY/Vlxv+9fcBVeCrgAl8n0/fSQMUJ6B1dKCzE11dyOUAoFSKYnC46xLLQ+sb7slm\nC9Wn12XrY6sB/BrgA/+oEtyYw/g7LOM2Dhd9npdwIebEDj6Gx5PYxBJ5X+2yD86zRz45E/5zok9K\nOQe0ACYQCQd836xWW4gcIaJVZbHSlBt3iejssu+Xg6DMmdWJ6OwkAch67YD4sat/nqjFstpYqZXL\nRXG2rC+Iv108YXLdFFE0r6r3xyW9EXGkVXLYtm8fDh5EtYqjR/GjH73WI+E0AVw6PPEELVlSZznx\nPIyP41//9QW9Cz8v5ceJAvWB58ruAJFRNhGBINWBMdaxeKqxniSAeDl7w5r4/3j/+zEzU6sOUhpC\nTKnkA1JH9ahSJwIYAPBxLDb9xssFOX4nBTQbRrNl5bi7XS/GN4IgxZluYWgq/xfqR9P8nH8LfKC+\n6MQ9kLcC35byIQ6dNozIAMWGWzZAxTHIXPiSrfYFuXUyEWHUkJwaz129+iWOjHcDU8D3pSSiVPLh\nYxN1rPdPpxGGSKUiQW3Sxa1cFEg4+BaOYUN1L4wXPLQDf040DXTwr0tpC2EGAbmukDIfhjnfJ5YR\nx4lSQdAQnV1l2Zhq3zWEp/oJN0D8FnrJ4Li6gmW18XWtuRktLdGsPumPm5/H3Fz8nknHSi01q08l\ntpwWgE7gdqK/24VMBpOT0bDt+HGUy9i9G08/rQNBNQFcKmzdSh0djZaTEyfw/e+/iHchW0/dRFJ8\nSspIdKjUgQ3W0+QSDzfR/U8e38KEpbYEoKcn2mgY69B9/y+z2U+Wy1nFGdyocYhsFmkATAFsFq0l\nH6jGMecfNBtGq2076XTU9V64OMV14Xlp36cg4FZTchVJUoz/d8AvL6ZGt+KBJJFBFBmg2AMVd9ij\nIamIVUZ1Nqi45ZLIT12YXBSPBL4D3KS0TMl+iFCM+E9qqzDfiqJhA5v10unoIsJqnEoFMzOYmIg4\ngK3UPIxNJHk0EIBfvzXTADpVk2pe3QstKQ0hZBAwc5d9P2VZtmEYhhFFloahp7QDNeGAOvhXgJ7E\nLUeqdyAfKfjn8sBL/Wg8RdQMpEyzxXGiRUOdnejsRFsbCgU4TuSPY9t5rHYTAkLYbI4jii1yDYHV\n/3cvgGjYtns3zp59nmGbJgCNi9L3X2g5GRvDpk0v7o34NSnfRZRPlMJUosvcMA2Ly3rDDaBcX6d+\nBXDrq8m9n/60B/zq+98fXberVbiu6fsT9Qk8TD+2lBYPS1W/VdRbdn3gGqAAFEyz3XGs+CTOF3ze\nI58UBrhBfQAAIABJREFU41cqqFZrp2YhAilTgJfYRsIX/M8B71xMjf5XRDdyTUyOUvnozW2Ecjmq\n+MorFynfk8mpigPOF54aj4UdoAWYXhBxGt8JwsQS+WoYer7vsBpnYiLaIRxrUuNrAc+FSiVUq6Hn\ncYzH+RYI+wvGsE3qV44ABhDyT0cIoUgoFQS2aXJqKdRIKcqsVp5tFg70SjkFdCmDW1Ks5dcvq3kT\n8EdEL0qMz0gDjmEULMuO/XG85bSzE01Ndf44Pi6oNXPwfSsILCIOgGrQa/3iNmQyCAKcOxe569kd\nf//9uvprAriEaLCcnDoV5bu9tK/2FSnfRsQeyFxC+NjggE02TJIH56oqDR8Fquo/zKo6FSTil//x\ni1/kPOTfGxnhg/PfptMfr1aT0TeR6UnKJP2Ievq5hZXaptnqOBbf7tva0NoarRHmMHoufOyBUn0n\nR02w7cQa4diTVQBagK8AP5VQo/MReKmKruN6Z8eHbnaoTU1Bykhuz5rIRQ1QygqwqAEqrB8G8I6a\naaAKTKi//kZVB7lMW1JWpWQ1Tlu5XFtdyesTTDMaAsc2urk5lMvSded9vxwErMZxn0+Nw8iqn8K4\nivg2ACjhAF/drDC0GpJTefOX+rYPSDkOAOgDnMSyGv7KfmJZTUa9ssAE8NtEf/OCOeARoo6kP66l\nJZrVX3YZurvR1BRl3M7MRG8V/jmqwHPTNI0wpIQciM8BV2xCNhuNeXmZUqWCyUl87Wu6+msCuIQY\nHSWiOstJnO/2knEU6ADKynoaKw6TDlihjmlJAuDEhU8AQhksk5/qBglmASgAM8Bfjo5OAH/e3Mw9\nqFY1DGww3y6kH/7TTSBtGE1x/kFXF7q70dUVrRFmQTofe3ncp9oyZhhaKg0/DsCJ7U5poKDOvwuj\nhMLYyitEhq3RsQGKCJVK3bmSDVCcYOq68H33/AYo/uNEYjRtAF2Kls6pJTPJhvg8UAVMgBfZp3zf\nrlYLrMbx/aj/wwQQr3Yol1EuP3348JwS25D6gbqKoX3AUIvsZX1PL60edR3wjLq48IIEH3DD0FHC\nASMhwRIJ4cBSKcuxGJ/I4Tl/YsbD+21cwGGGTuyrMYG7ic4AX3wBNJABTMNIWZYV++Pa29HdjZ6e\niADYH8eRU/xDrB/XQ43r4yPIwBNob0e5HC1TerHDNk0AGhcATz9Nra2LWE7ifLeXPk6QcgVRK1Cq\ndz+Z9TloYX3GPXtWubLniFJEDk9K4+N/rBlVw7Q4D9kBfn9m5izwD5b1viDIA1X1R6fOc//gavXz\nvOnQNAvJPfJ9fejpQXs7crlIizkzg1Qqut0nt5EYhmkYPN4wlCgonr6yTvQp4MNqYwxTXTzP8KSs\nCtHE80NeeGIYtZZLbLidmqrFl7quTCSn8hdZaIBCYvhMQJO6DRiKhpOSmLul/CciEzCldISwfN8g\nkkATq3Hi20AiZPuJAwd8IAt0Jr5gbOyqKiEv08McMF4/knUSNHkOEIonAo7O5hCh+N6m9ufE0dlD\nQAZoYTG+aRqLLasJYjF+Yv6EelHQ8y6r+T7REoCInHhWH0+A+VUogAiWBdeN1swpa3TSLRHtOpVS\nAj1bkc9jbq5xmZL/gK5JmgAuFZ7DcnJh2kpS9hI1K+tpJpE/k0ygjI/hBvDHAJQEM2uaKcOIdsSr\n3MdIghkvQK8PQ2Z8Mgj+zjA+IEQWKC92CWgYLGcAiyhrWQbvkec1wizG7+hANhuNQFkDw935xDYS\nk+WY598h7AAhcA/we0AFmAdm45YL4EpZCYKS5+U4oo777HHLhf/ExQxQlaQBakHD3UtUf/5nrp75\nuJL+PNG3Vfkr8TeT1ThhCN8PAU+IrOelbduI1ThCPHP4cAA0AxkiZtZIX6usf1594yUNTAOmCuzj\n32YlVvi+EfgPIKf4I0pOVR5afqrkvW0NkCVqNc1my7JZjB8vl06I8dkkaAYBCYGEGL9hYv+zRN87\nPweklD/OYn8c/3HxK9YIxMHdiyEZUNq2E44TDdv27sWRI6hUcPAglj4S2Wg0NAFcdCy0nCya7/Zj\n4qSURNQF5BKF2KwPIBPqyPzXQBZoMc0m08wszEMOQwSBHYY2p04+Zx7yHwrxv4BfBWbqe1DJLhD/\n0XcxMbAYn+V9fMHv6kJXV0QArIBkCXwut8gRD+cNP2BDVgi4QBtQAdJAGjihDFBVIcphOO95Trls\nc8uF9e/ccuEDeNIAVanMe15EAEr8vvAl6qfoEsgsGLrw74wj8ucBUy2RJyFEEIRSekJUfN+xLDZS\nADgwPp4DCkQZw0gTWYZRW1gmZaDiVN1E1yWZgnBKaXKSVyULOAl0AJVEdHZqwY+Mf2rrgDxRq2W1\n8Xk8l6s5JGIxPjepqlW4bpqIl9WEQsQbbzLqr98CLDnPogKGoWJKiW1x8U+cxzYsDJMy3o0TDeo5\nqTQRVsrf+dweGEa0TCketmXvxTLAvZhhpZoANGpY1HJy7Ngi+W4/PngJKhFlVARNLAdKFojPsgTT\nNNts2+QEgjhqBojD4/gzlvJ9AmQYxmHICyWYfw78DXAnMFWfwtawBzEdZdEbDocfcEFhJT6/0uno\nRBkfM/mk2SDGr5fhJ4/eppoMFxKDkFGgwIstpXTC0PZ9o1ptIbLjzIN4AJswQIWuW3LdsueVw7Ac\nJxct4IDqArWPABwiPyFVYhF6K9Cryt+0GsaawP/P3nuGyXVV6f7v3uecyl2dlWW1goMcscFjjBM2\nMDDMZYgDmCGMAZucBvCFP5dhyGl8hxkyQ8ZDxhiDGQzOxoCFo7IlWbJl5ZbUqcIJO9wP6+xd+9Sp\nto1h/p/qPPXo0dOWu6ur6uy191rv+3tBfmmKHqOiy5hoNhNghLFBzmueV7S+hHRtVgGlBJvGC9ea\nO7k37pNp5PAVLwG+Z0YmoTk3BFlkKYBnAFXG6mTFolYMhdXkxfj04BxA0Xo+6AU33TkiJtWBBfMH\nlll/nLLNJcsrbTRQKCCOU3uETTmlOY2xR5A/LtkifAWl0mHbpk2pjHbJ99KzF++vdP0C8P/DZflu\nFOhIlpMtW7BunQZwO2OhWQWYM3Slfi4Nad/+p5PNbRkoOviEwKzIVwI1xuqeN1oscrv+Eg/ZDh7N\n1JGyvdIASCkFYyKbP1UB6sAY8CngM8DFQM1BYPoOE9gzYnwvn0biHj5MT7k7isQ5SXTlkChnD26T\naS8BvsUYdTPOBLZReTAGKIqkr0lZieOOAYp+dJKoJAmTpE3cOiEIXuSSU118nuxFTg1My6Xg6JRI\nrjoNPJuxX2r9Acakeb8UkEgZax0oFTA2kCQcGOJ8mMhFdAyi2BaL2EsSSgn2hWCkYTXfqkvuFQOv\nUeqzDsSCA88GfgJUgVY2t8AeAl5A50jOB10xfj6shjjStHXQGlJyK8aX0kowrSerYsJqnsfY1b2y\nCqxlHS6ubmoqlWlRb7DVwtQUjh7FzEwn6VoICBErteuWaBQQIg1T2rAhHbad/CNM/c/ASvsFoH/1\nuFy+W5fl5LeMeUDVWPPRC3zWBGaBzzBGasJP/ImVwJaBwLSDAuAakopyPlwscuLhWB6ylWDaBDE6\n5hsJZmzCeO3S5vKQh4EvA58HZoH3mkCokkO/+Yg1Qz2CGJ/WNRtF4orxTYywdDA4+UB5u8ktA2Vi\nA2itgKa581NyKiC1jqRsJQmRU2kMm5KLhIikJAMU0U+jXuRUeuRLUQL4jOW9SHQUGASmAABT5h1X\nJho+UqoArNYaQJXz0SCoUmQjNV6KxdQkYTfFYYgwZFFUSRINKClF1uwdmZPHYuCNWn8qe1p6GvAz\nYDYnHqMCUCQxfhAUKpWOFrMrrIbi3ug0YJLrO2E1jFFYjZcd0tBpoJn7xH6FsQmjqkphpdYGQbQs\natYRt47i52hjbwS7bSk3XtdcONoZtt13H5IEhw/j3KsxbdS6tgY8QnJyvwD0rz+r7VMuQ6nUcrJp\nU4fvto4xj2SCJoWcObgeO9Mrm/vEqgnfyth/PN7TgL02MlbkvF4oBPauXrQolWBWq5kQR5snIyWk\n5ITgV6rrfra3dA2IjAn2n4AE+Gy2vxxk00g8l0c/Nwd6vUjbRzoc2tzZA76UipimJo1E9hLjc7cR\nxLlUitbEWXPGcg1QFGfmGqCUY4CyM3DSQXWRU9tZcqp09t1twGOMUNVeDhpaBkLgIsbqwHIzp6XC\nX9KaZhg1zoeCoEqMaEIgDAykFZraVjQbn5ujrgvTumhEronpuhQcVPUgMAP8C/DOLDTpHOBaYNrJ\nrqFDwLupbHtezWoxFyzohNUMDKQ6HDJq0FMiJX67Dd93w2osJ8MNq6Ez5YWM3ZQNq4mBQOtIqVDK\nVpJUaPtPcNYoSltPNrNhZgbT05iZoQKQJMnm66cXDqLZxL592L4dW7ciinDgAJ79S8w6Yi0rZPD6\nS1W/APzFLwp0zFtOPvoT3P19NgTUOK8wVuScmDnpukM0N6Uil+fjbGk94G2MTQLffbx7ljsYG2as\n6EowFy7EsmVYsqQjwST2AEVQ2SixKEIce5xzR4LZBcKkA75wqJBvMOvat4DARPcJYz0t0/6OdNzl\nMgA0m2kBoCM/GbIsACdJXPhBV4CwGyNs47GKnpeQfQw4CnhG/gSXnNplgKLjBdHEKK7SCGGjLDm1\naWAPeXLqdZXKXJIw0tSbF6pr+asCV2t9MWN1IyWiYnk+UGas5vv1chn1OkZHUwrC0FBqkiCzAtVI\nh4ocKOVnKQhuA4pOHkdyEAsAFwE/NJ0ZKyAu0aze8zw3rMaK8W1YTRCkJzbS1DphNXkxPnM+zIGJ\nxPkCY/S7X651SAWAxFpSNuK41G5zKnJE6LNGcTsVoEerJaNo642HB8uYncWePdi2rcN3u/hGzGX9\ncdrBXvWvfgH4S1533MEWL+5hOfnwNUiAUcbqnlfzvMCONxkjtpovZVHKopR0J1s1Xr737aoJ/6Sr\nBPiMlYMAVoK5cGEPCSZRaGh1psmb7/fkIfMcD1kCfwBOdm4zBbwQ+CCwxdSDWKlQyjIJ3snFQ/s7\n6wKjr1tHbquFKJJCZGKEe4nxmbPF40DgeYE5srxS6+8Dwjwr6pMUpQwcA1T6UpsukzCB9RabETng\nvPMAAfzGsThII8lntsdl3qa8XLVoyJQvYqwCxEANeAEtwW5iAYVELlqE0dE0tIv23RThYuN8ncbL\nfHEFJBL9JPD6XNPshcC3gENAFagA36JGEGMF38+E1VCA2vAwBgYAIAjSd81Z+m1YDYnxwZh2XoQu\neGoRWA7MAdPAhxgLgQX0n7RuKVVIkiAMh0lsSmWGUnpo+GwDMtvtJAx33Hyo5Hfz3TZuxFvuRsMR\na3X5427p93/6BeAveN17Lxsd7WE5+cR1KAADnA/5ftVKm7tmenFMYupqkvDcTE/Y1ROIcmfnx3Ld\nzNiY5SFbCSZN9hYswOho2oShPV2jkbpssgbL/LftuqVpy5mnQnqMkYLe0zrSuiVENYoKtNB7Xir6\nJFuWHULMzmJmBnNzNOLLiPHnB+BkDvie5znJ6UcMsMySUx8ZXOEihWPgCeZw45t/JoBnG/QNSSpv\nBH41Ph6325gHiG+zugKgDADYDYybEPkCEDBW9ryirdDkklu6ND2iASmqmpp1NDghGzMd0bKRNe6O\nm04elWzTDKYyvRSYAf4baJonBs47Ynyi9dFHgj4VWme8VzTacbbYKven+4LYyvQ54J3mmHKEumcA\nBwpK+ULwKNLAgFIBbQtIrIWMUK0Vxw/fNsmBycl02DY5iUYDz/8vPN0c1MJ5xFr9q18A/mLXxo2s\nWu3mu+3cic/ehiKlkAdBmSjERDan8A3axNkWahgyxspJonrN9KpACAw5asLH/vQKAGPM4zygbZ3l\nIddqHdI61SGXh9xLgpm/n+HwkIuGxpM4QhRudmEcCJVqCTEXx6PNZod4QznyVADocEBK/GYT7fac\nzZFXyjLoY4dpGueiSDQAWhBNM/o1wFcdA1RMc07XAOUsXi459XSAA2Vg2JnYWxdu5LhwnwF8cHIy\nAt5RKDxCXJctlgDWaf0ExgaBN5FMi/MSHdFoRE+hlTR6rVSgFBqN9MBEgxPHlsXscpzddHOn6hSB\nrwPPy+b32hJOu/VbSYlvxfh2oXd1mV1ifBLvumL8bDhB10thJbAFoG5efw7MmnSjQCkPQJKQPaKS\nJEXfL/h+ipg1Kac7fn2oUoESnTAl4ru9+AedfFMr1lLO0q9Mz7B/9QvAX+Dq4rvt24dWK+W7bWas\nzPlwEJQrFdTrGB5OHe0kuySaFTVbiGxOGgytExIFOhHkxaya8G8Y+2+tAdxgUgHcZZcel5m1IOUh\nWwlmnods9ZeUPGV5yMYY3BOGrLLbOlraPgN8KitnulVrmmwzrVskxo9jzvkQY8zmyFstpp09hKEM\nw2YUNZOkJUQ7l0YSOmJ85Pd3dk1E6h07YAxi1gBVyBmgXN/yuQAjZ7WZ2HN3Ym+yyLsm9keAj8bx\nmz2vixuqc1ldT2fs7cAHgHcRhYkxznnRRtbU62lKMH1mymU6F6bd8K7zmbMZ1/Oc0uzJwweqQAA8\nmG3QwxGkdQr8fGJ8OqURNY+EmAaeauMfRFayZV8N5hxNquZEpYG9xh/na820VlLSxKgtRMHzrD9O\nay2U2nb19NJqZ9i2fj3CEEeP4uKfpPwlG3MU5jIkZC6woX/1C8DjuXbuJFJ9t+Xku9/VAO5jbICx\ngSCoUgr52BgWLEhnelQALNCKZnoki1QqkJJWf29+NeFR4FbGSsCIQ3AT2W3p1xk7CqwElhgJprbJ\nVlaH026nEVTU/CEVppVg0s7OpDOKXvezRfB7RgA66tjEyJm8wUBGAyvGjyIS4xejKF3LrO5ICBXH\nnTQSiiJxgiS7HhcCArghe5+7qyH9+TbgCmOAahvdS5cBir7DS4Ap8jRwXjZZ5ARKs5lTNI0oGHWs\n5+xkOfAlKfcDr8vplFxUdRFYDEjg2+ane5z7dESjVqGNSaBjmdaZrkv2mi8bMt94WW6ilZcCDeBA\n7vt0h9WQGofkmCTGp6ns0aMdvZapAVpKin6bL6lGZsF5JefrRwDfwkqVUmYU31aK8HPcEOimb2wt\nHUWrlYYpbdqEOMahQ3jFz9PV3y0AKteTTHPu+le/APw517p1bGSkYznZuDG9TX74w1TsPw4UPK9u\nrTSLFqUzvZGRlEMZRekglLof1Nk0Mz2ulGew5i7ZvGBmeh8EvmkIbtqGrmRX3qrZVVkJpm8jqKx+\nQ8qUsksKHGq+GxYmhFDGYynMjS2d+9ZKMO3ets45rY/WhnY6sIOWIa09pZiUpMWkgXDB83zP44yl\nJHoniiSSMpQy0trNolpueHaew8X8G3PbN83+rkuhr4FLgf8ALLmonCMXAXgF0HIm9gV3Yg94SnlS\nFqQsShlJ2TWx78pq/7/Aq7NrXxfCaMwsjlOApgXONl7cOh3H6fAz33gxBzVpxtePkBJsj2j/2xxZ\njgIKmAZmAQDfYmyJCauJ6Ee7UWUkxictpp3VW3AehdW4s/rcoF7kwmoKjNk+5wXAOuf8IbVOSBen\nVEDKWoABhXXJaI7vtncv/vHXaDopF/aT0DMT6cb+BLhfAP6c61H5blXA57zi+365jIEBjI5mZnqV\nCrROSWSMZfy3YYg45iS7ofDw+Wd6Y77fcRI4me9FJxigabtDWsdK+fm7utHocJhdHrLxWIZSUioI\n3cMz1sE0Tze57nkUIOU5iII5s0SSGJ+Ur5FSrSQhMX4nRtgo8RNS4hv2fQSMm0PGiFn9e3ro5oDP\nHT16GIiBV3HuNqNfCXzBGKCqOXzpWwAO1Dgf9v2andgTLMh14caxlyQViipTSiulnIm9a8X6AnBx\nlhvqvlZDnie0vlWphfQMGVP0+tgiTXkAnNOktzMbp/GviStAbtPdVZ7dlLSuzjt9kZxZHVq41qGU\nURwXu8T4lUrGBkxifHJjRZGg8Eg7qzcT+8R5TZDlCVKEnHUXHjHzFUu7S0c1FPMCLF2viO/28MO4\n//6U77ZjBy67vSP4sdv/yJnZqK6Y6/7VLwCP+7rrrpTvtm8fduzAli2p5eTaa9PV/3rGFgEe5x3Z\n5fBwKqZetAhjY2lLt9GA1qmz0c70nBRyWhO7BI7uTO85SXL9wEDawbf8TqV8s/IyYMbefkqFQlTI\nSUR7f8Ivk7BEiHRbNzWV3tWtFt3Vd87MJKaNAKBmJP+ecYHFwP3OkywUi74QXEpm5EwamDYxwhzQ\nSknGEiBUqsC5LwQd8GkQKZ00EhJ9RlovBjzTkQ8A38QOK9ObiufpyH9RqRZwsbMgvhT4kqHXlR0D\n1EcBbly4FTsnJ6WjDZ9yJvZgrMyYThI3qswu/TStWQR8HXhuL60qgMEgSJRicSwNBSFRqmjzaqzi\n0xLwKTiMTmlOZE1sGy9OGXBrgM5W6Ep2ZaTV9uWMPQWIAF9rK8YvWvkNtX0sOdVOBUxYjQzDhiPW\nooIdO+ETPcNqXMu0n4WVUlEvGmeMx9ipm3We7/a0K3EOUrG/a9cIc2cyYaS6v+5v//sF4HFf69ez\nwcEelpMbbuh8qioUSsd5gWZ6rpiavJ2kulEqw2LrEt7Mo7fpVhMODlojfkGIQAhPSkvl1WZJ8rSm\nBJJqHBcJp0WLfqOR9qDoZEB9oZkZNBpbNm+eBErAkBmW2iFhnkR/InAEiKgSlEo8jitJAkBLSUvb\nw2bpYZZ/oFSRsXyKvTL5i6T3X6v1HFBzPHRBlouZmJ7DfB35A8BXgRc5G+QXAV8CylkDFIiTEQQV\nmtgPDWF4GPV6x4VL9FBa8gyarah1IkRC43oHAVQ2+LMR4Brg7Oz7mP69XA6SRMaxsFNlKYu27X70\naBpUYE0S9MbREc1p04VOYkGitW+qWg0Yz+oCbqHOO2M2p1M4eqqGqeuBE1ZTt5ConmE17TZaLRGG\nRE5tmYFNT3Kqzj26Ah584JBJGUqMK74APPN+kv/g0KHOsG1uDud+D7NZkbQNrGeO4Us5waXN/hLW\nLwCP+yK+W5flZNMm3HlnZk/RAZ+R3sZlmtu1XqlHxZq7XeyeM70CgLEx17VLcBgIoU2KemgkmG2l\nWkLMRtFoq8XpNiZBoSvBbLdJf/mHTZtKwBKgxFjAmOdkxVCSVH7HTXkjywACSzDDhkyUSoB3a/0V\ns+JYMX6B7Li0UhtAkCvGPwGIbEfe94MsF5MpRZiKR+7IUw/kW8DfOUKpVwKfdwxQvwJKjNV8f4Bc\nuHZiT5wMcuHSrpwkvNSgl9Iz9j13J+uWgQGz/PWQKtVqiCLRaFBIZKRUW8qBKGJ0FCPFfbvdmRJR\nR4gaL6YAkEY20ropxIyx8lad/lhivGxN4OnANPBDrV8AJI5VOATqwE5gnE4JFFYjBDNifE4FoCus\nJo4RRe0oaiVJy4zr27lx/SOI8bnp7HOHQ/cjoAokJtziOb9Pk3sOHcqEKZ35o3T1Txzmdmii7txX\n2zYJW042Z//qF4A/7SK+2+Qkdu/u5ru5148ZW0ZyN8YyWHPLOCQltSEPd0eQG9VNT7FNF/jMB55x\n772/ueiiVDVPOnHGioAQgsgwLa1JgtnWuimlnyReuz0IeCTBbDQ65B8hEEWb16+PgDHGaoyVOS8y\nRrg06s8Io4EheJlLrbAN5Xc89NBB4LtjYz4pWc3G/GFgiXFUdVLszXdgptmlnDQSHxjgfNh66Gz+\nH/DYO/K0PVwC/AB4urGDAXgdkABfB35DO03OUxeu5WTYqDJagWZnceRIyo6m9zGOUxeumVJ2ZZEX\nTY3cCJyQXf4kgKEhNJvRoUOxDYkUYi6K6gTGANLJPJlgXdEwPdrtMIpaSTLZak0DA8ASyoGw/THH\nzhaZvKAqMAP8DJgEXuBw/SpADWjlwmoU0TviuBQEPh1StYad1SdJKERI2FTyalDcsSPTOjWXo0lP\n6dq0jHd7F/4a+Jlx2L3+txgbQ7uNgwexYwfWr0/5bqdcjZncWxyaSXvXRsoqf37TX/37BeBxXPfd\nx8iI02U5+f73e3yeClkDJHNneqSeBjrZ1jTTo36uEVNrpUROdSOy9cCd6WHx4g6/E4DWnlIFpWKl\nfK3nHAmmT1s5SsyQspKTYN77wAMBMMZ5nfOa73tZagVTqmA0MEGvHbdydtzPOXz450NDgZQe57Qs\nfkDrjwF1Zz0qZsX47vc5j3TinI8UCpVSCdVqd0fenntoct6rIx+bQJKaCST5BXBWthVzsXHhlny/\naAc2hGFYuhTj4x1QEmm33Il9EBAng3POzNylJ/+nnC3hVAIxOgrPaxuTRFupphCFOPZbrQq9I10V\nmj5FhoLQDsNmkjzQaJSBZYyVGSsy5jPGnf6YMEFmkeHEuSC/a4BDwN8555U5gGfDamhWXxKiEMfp\nVsCA8xIpEyljKSOl0ofZ+x8HHKGBkCPWklmZ8rOAr8VxDDzPNDzt9QCwEPjnOzHYi++2+peYyiKY\nYmfCzLIfJPo3IXC0v5D1C8DjuG69lS1Z0sNy8uMfz7ub6Gx2lCpY7OXMTLqXbLfheenmjmZ6lG5h\nZnop+CynpLaVoGsYgCVLOhHqXVRerV9gpl6cJJiGhxNK2UwSstiQBHPrgQNVYMDzhn2/3KWBAVxq\nhRfHVSHcHbdLxLTbsYump6+vVrnDhnwb8Gmg4gQZFuaJEebUkS8U5u3IW5goTc57deQLztJG/olh\nYB1wqrMTf5114dLAhly41AJavBjj46lkq9kEkKpfchP7fDePZVlJCtgBrHCWpN9cdhkOH4bWTTMa\naWkdKOUnCeOcTBLp7+iaJOIYcSyjqBXHB48caVB/jLGq55U9j9OJM939KiglzNLsA55J63U7JAB+\nAPytqVVTxqlL4DxFBz6l2lIWKJPZNpeUogEMUUhJfhZrvQo4AghgUTYXSJqyF5tO/QAwa1pSDeDZ\nTrrDJcBz16NcxsxMGqa0cyfCEA89hMU34UhO2SkdDqDbALQfyKPA+v72v18A/tTrjjvYkiXdfLdY\nQ0/sAAAgAElEQVRDh3DNNfN+mDpaQKUiKQvUWCdFB238Lc7FnekZ+rFOksiAiGmml2Qtvq7sMr2Z\nFyxI53K0PQxD6oqk3hmtXQmmVkrRvFGpghAB5yTBnJyZodV/tFAoUlyXS62wO27a/HoeoqgMaCEs\nPU04GVg1S61oNn9RLLrP9g3AFUDTEWJaMb6dA7/YdORrpVLakV+4MPXQuR15io+neYaUj9CRD4wu\nqG7QEfaRkjvdqDKbRU4ZWJRVqXWPIHKnxad7IYBskZZAKbtpxdgY9dyOGkOsR0c0KXUcW5NE0fd9\nE5ijaMctRJgkh6enFTDE2JDv132fU2gMPTEzn0CS+EL4ZCuRkhldgMqFVv4UeDrgAQkwa4RDZC9I\nGMuI8Z1RjTTiY2F0n0sBQccRoEDHEUfcRR/mSGs3qoiK9GHgp8BF5rm9cDN8w3fbsgV796LVwvr1\nGLkXh8yHRDsK13xmtV39I+D3/aW/XwAex9WT77Z3L6677pE+T3Qg9akTKsSA3f7TvtWCz2gJc2d6\n7XZnppdLIV8JRL16qQnwpXe/+/Uf/nDXzpQRC0EpZiSYwkKPDQ85kDLgnAawZaDseUOFQrFSSSEE\nLrXCZqbTjtuQ6At0VyuV33GTDnIIeFEUfdP33ZdsFzAONHIRknQPv4I4DcTFdD10bkeeuJj0SlJv\nxHAxPSlpnbLaEtuUt4Ek9wOrzVpDH3rPxZ/ZcT1lllHX2w5yekVZ5QkZef5PAGwH3gQkwDfMAABz\ncx85//wrbr0VpgZASmUUQalJgvOUQmHiCtpzcxqocT7q+zUqWhbMSScAG29J0Z5xDPMdlBPtaZUz\nC4FrgPMBD2l3JTEQ724xvrHjamMtpH9zKtCkFEnGqp5X4tx38URaK6VipWKlAq198ohkU4gZcDOw\nD/jo/dC6m+9243+hDLQB3wH6++bs6Ln7IWf1D4F7+6t/vwA8jmvTpg7fbevW1HLywAO47bZH+TyR\n6tkDQqWaQszmZ3rUrqEDPoV7mJleK46bBD4zSupRgAMjJuq2y/pER+kG8MX3v38GiIAPnHNOZvIM\naMYu1vqHpndkI6hcHvKQlCXG6r5fKZdT/rulVrjnFdpxmzEgzCTAp1vatDtcDUzdyH40Y8rEyn8M\neCMwDswAA1lHLgPKhotZcMnVFEgyNpZyMbs68jQDDwJ4piWvlBsP4NYA6shbWhm9RKnrwrZQ6EhB\n7Tti9lHnnYy4BMkwQ3vhfLcuCFqXC7cCDAERcCnw9csvbwAJ8M6nPW3WnB05bZalTI9oUtK+mxkM\nDsXBezQdCYIanVfq9TStl85D9NEinX6zSR+Got3vK5Uv1RTt+WvgSUAL0EBlHjE+d0c+WtO/eQIQ\nA8OMDXpePQg4FU5LmlIKUnIhSkIUhPCkpHeHSqk7F3nm3ajVIAQmJ1N3PWmSox9gv9EslZy2Yb4A\n2NU/Bjb2l/5+AXh819atLAi6LSff+c5j+jy1KQCdZnpSFuLYa7WqtOJTB9nO9Ch73cz0CHzWJPCZ\nUjWlSDu4hE7TZKEyHXyR4+3UgKPA+26/vQl8ZskSMKYcM8HRnAQzNcFqPa51uuO2WTF2x22pFWHY\nwffnqBVEoqdJL++lgblEiH9nzF0ZPwpMA1eYzPSSmRZ+mCqBy8W0gSTkoaOOfKORlgE6B3R15B1t\nidt9cgNJdgPPBcadToLscuFSwWMspWTTWY1cr1n8WWKMuHlCRp6TscBU7hbQAGaBK2644TDAzRGN\npDuxlEVyyZEG1zRSlNZlIWqMDVjF6shImtZbr6dvEPGTrWLVsDwLWsdKBeSkc6I9rVy1BtwBHAcc\nNr2yhMKB7azeTBHcJvvZxkA3Yo8jRJC2qaJ0HIkixDGPoooQEEJLaQ8QdBxZeBMGBtJhG7nrowhH\njqB6FWIn5TR0nk9g3lMX6J0Au/pLf78APL5r+/ZU1XLoEB56qMN3+8fv4bormXTuXjpBvyX3USOo\nejrTk9JLEptCzqIowxUwM704jttx3HLYZ1AqBpYyVmGsyFjgkLDsoZv8ll27oQCYBF67b9+XBwfd\nBN2Lgf8yTaTY8JBJnTlB1Iog8HqS6C21olRKDzG04yYagc2KyVIrusqAzD2oDLwK+DfAM+eAwHAx\nmfXQ2R3u4GD6IBM1TWXdwHQDLrbkavRqyrsd+eVOV4061EGXC9eNvmo00hxaiz+LYySJdeG60xp3\naK+yLtwh8xEqOo8SsMM4YDtHNCCQ0jcmDDr8VZWi3JhBW60XL06jPYeG0vcoDFNvuZ2OmFLtS+lp\n7eXiKm20J5WrqgH024zSYna1dXtfHKi4x5GBAQwMoFJJ+1HWPk2fGc5ZGJbokyklfZILwKI7MDLS\ng++28OeI3M6+c7v5jBXMCQDAdH/R7xeAP/P6wx8Y2aqI77ZhQ2o5+ZcfmeaJWT1Jx9YAPsfYFBAB\nHzGfv2mgQi5HAz5LZ3pClKOIZnqMwGfOTC9ylNSBlBVgkPOa55U9z7NHaSPtoHZqSgx1qA/uKPJ1\nMzOTwL/5vi0Dh4BB06GKzBTudLvj9n0QtsilVlBWDFErbB53uewOQuejVjCnDBSBd2r9z9n4XPrL\n64EE+BrQAMrGbzxvR540qcrJ87BNefP6PzK22u3Ifxp4k2lzx1pHUla6JvaUUWxnztPTXVFlBEqy\n05o8++wCp8YkwI+AAc4jx7Fs++CTgDQmCdJKUuPF15obZSeAcSDgvOr7vh2PL16M5ctTyGCxmJLa\niCFBOwzHa+JRc6xXcBjVgAjYBRwD7AIWAU1g0KTG58F5moLMGBsMghqJtUZGOsFhhUKqHSB7uZWT\nae2Z6VEgZQBU70W1irk57N2LbduwfTuiCHv2YPVv0r5lz6Vd9Ff8fgH4C163384WLkz5bq7l5MvX\ncNK6S6PJiQyPs+wwZ97B2L9pDeCDWl/BWCovoZkeAW2UapHqhvNUGEc1QCnaRcZSRlrXpKwyNuR5\ng0Hg0QpoZY50mhaCC1FKEl/KlPrgDuWySLI3CPEJxmgBeiXwNSPBbJttXclQK4qkgSEAjrvjJgiw\nUhkMvZsU9hioFRIo5xbHxNkdvxJoAafQPtdtx7sdeZIhkd7JAnlcbLXlYuba8TLrobNIHKroPkXR\nCjEQx7514dJKSvto4mRQF4gaQeTCTZLMxF7rBDgGmAUUnWbMAYjkKBcDP1dqDkiAF5gmFb1xfwP8\nEqiaAkAfrcA0XuhVWkmnPc4rFNZIucEU7blkCYaH0zdrbg6MdcAVbn8se1xjOblqwZxfTwXuAoaB\nFjDg0LPd+ISLgTJjJd9PjyNEPCS9Vr2OQiF1MlPz0B5HhIAQND3yGFMbdMXw3bZuxe7daLexfTsW\n/g4PAMfME6/Wv/oF4C953XUXW7iwh+Xk6usLzE/nXSmeTKkIKGhtGy92G3U5YweAb2ttZ3oUiiqV\nSoCIWrpENqe9Kq1W1EBQKtF6RKkqYyNBMGhzu0jYQwWANP60oYsiP47LRtphCQ1lEx9WB0aBJvB+\nrd9plt0XAFca+Q31dj2k1Aru7rjd1oqbB2n32o4Y5rGT6JMsGzJxAknoOsmJT1EAt10y66GzHXka\nDpJ/wnbkhUAvJpqcx0MXZCf2LSnn4ni41Up193GMRiMdepPtgKLKHBeum1UZAXXTkVjotCaUw2Ow\nHqhZ4BqtJ4EIeKEphw8CS4C2ccmVc5E1ZcBjzPe8YhB05KojIxgdxdgYhofTF8fa1kgXZE9O9hzp\nvDtuJgw3c/KHgCXAicA6YBCYM1QGd1YPoAIEjNWCgFUqGBrC+HiaMr1wIQYHUSiksCnSEdBbaXDW\nnhBcyrn1SWD4bps348ABNJs46UqMAgcABew0qQn9q18A/qeuDRvY4GAPy8mNdw+iwuwOtKiUNMwZ\nD7CgY52FDr6IsYVdsksgkbLAWEEp3ygU4WCcacFaqFSFsaEgGKThJ23Da7VuBCOtQc0mGKPuijAN\naDvTK5nh6gjwYJbH+yzgR6YGvMY87XTHbQ1N1vMVRZ2f21MD0ysCUM5DrbgCuOQR2ZDMhbYTuZqW\nXddDR52ZfEc+iubjYroPld3ztojcp3Vb6xZN7NvtepcL1ypNyXjcbqPdbkVRkyRbSrW1riklgBIw\nylgRIHqS3d0LQ0+iAabb/T8MfBt4KVAC3gJ8FhgCwuym2665xJjyOYcb7UkPGr3S3NVd8W1IPemI\nGCPodM9Dm4UMlsxXbgBOA4YNQ8LO6jnwAYI5e156HKHIo8WLU/nA4GCKsKZBOiV9UkENAvj+1nVz\nngfIbr7bRd/DlKlDidFr9QO8+gXgf+rasoWVSt2Wkw0bcPfkMgybnELqPyQJMWcIdKyV0i65zLGk\n7wQCs9wokx2YDl0dsBqMkloCS7SuMFb1/WGaxI6Opg8bH0ZKRNr52o6QQT4Qh5LcT4FTBqrAEPAp\n4FJn2X0qcDUwY/Qw6UqtdUqtsKTf2VlonZrX7I7bamCEIGpFYqxAPTH0MkutiBz2pM4mdrmcUfLQ\nlanV43bkyU5BKpd8Rz7HxezqOLlnDnrMGW6ar3VBSi9JGGNK6wEpPZrY+9nPQBwnUdSitDIhaGLP\npIyBRYzVOC9zTtIdZoaWwvBKI4NF6oKVcuBHwEHgJcBrgc8CNaCZ23T/DT15xjglxlgunj2iWR+4\nsUS4WtVOtKfzoc0f2uzZiK4Rzh8EhNYTWpPSrGhqQAHgjBU9z7P9qOFhjI5ifBzj4xgcTEHWJNai\nLpA5Vv7+J/vH/Q7fbcOG1BP59z/CbFavRQ20PsKzXwD+h64nrF17KrDr4MFGGOrf/AaNBq783UpU\nKlho9lO2C0FNZ85LcayddEB36a8ZCvy9wCnOTC8GCloXaDLszPQstqwCFDivE4yMWDSLF2Phwo4Q\nkzwEtBQ6vVRGJxKtuZSu6cn6nqpABHwJ+Huz8krg2cC3TZ8qMeONDLWCfiiZfi21ghRRDok+pVbM\nA67IZ8VEDiNM5w5PiZGoUke+FkU+zQ/djjw1EwiYbGFKrRZsQ8YwCWKn5sXZSBZ74Hgl51eTY0Br\nX2smpU4SoXXkuHDJy0roG4oqC+lPKUOlPCkrwBDnA75fpekI7b4BaM2kDAyvNCC9rOmkdTlyJfA1\n4KXAJcC/A1VgILvp9h3jgnVXZRiudIyjWQUZy7NyVZUNdMvrstwByW5gBBgKAkpmP6D1sJRHzXDi\n23QoYSygziHptYjXVKuhWk3bPgBcizLn4Px3/7l5wTja7U6YEg3bXnc15pwTiT2OzAC39ue9/QLw\nF7927CisWXM7sBh4cOHC2xYuvPn88zeE4eEtu3bdcQf+8ZrnIwgAdHbEFH/BGLQumpFAMg8FfgGw\nHjjecWyV7HHeEVPTEnAGUGCs5Ptld5i2bBmWLEmV77SNmprqgOmpIFE7ldpKxvuaV2EWDPLXbr0B\nvMxFMyoVClGgc7rNIclTKygC0Cy4KknSsDAjpMmnAHZRK9rmIXMBitI8PU4eOilLtiNP74LtyLtG\nJ9ORb8cxmahtRz7KFoDI2r6cPrjHWMMoLD2lAFj8WUuIgudZ+AG5cIWd2CsVKVU2E/vhIPBtii/N\nTmyRjmPEsRfHlSRhUpLR18XxuzDLrwAXA5cA/2qE+Ta2TDuLdUdDTGby6Wn4PtrttABQWKONdbO5\nMSbaM3G4Uj1zPWmDrwBWqRBJoihlgzHqdx2jdZleQHscIeeXKw0wVsH0RTAHkbuvuHlBHY1GOmy7\n/35EEfbvx3v+Gw3nLOJOj4rAfzJGr9Lb+5WgXwD+UtfGjcmaNS8HTgHOAZ4KvAJolEp3r11749q1\nf8A//nr//tbWrfrCq9+K2dn0HjONF26Ct3wp/Sxh0dpqmsBeYDg70/N7gc8KFB9GQkzS0o2Pp+1U\nig9zgxttli81KKy0wwgx8w7YAiBNLEniKCMzO24i0VtqhZS9qRW043aoFaFtuTgbbXfH7bb4tcnr\n0Dk6ZmL+E9e6pXXxsXTkjRaoGYbNOG4J0TI44sjJJLFUerfdpE0BmDEDG+5wMkKliqTB5+nkVJvc\n3cQM7QeUqjE26HmjxSIn9RTxSq1Z2tbpVguexxgrEaxUSmkHAw49aRhYCnwfeDbwWuAA8GPj8iu6\n0xGtEykDu/rb/hjFi1LGgzsgCUPEsaaMZROy1sWYEjnGlEfW8XrdLWC0oD8E7NBaMaad6QK6kizp\nor6/yTrd8H9+WC9jZiYNU6Jh24MP4lM3o5Ejn7uGkmXGNPdpxprAv/TLQL8A/PnXoUO46qrtjG1f\nseIXxx8/XK2uAf4KONeEZj+wePFtixffjAt/0God2blTrFuHV3/9HNtjTXGbDgW+S1JdAprGUFN3\nhH2+A67SwDMAjzFuva/VaqedSg/KbScAjpV2dCWIZZc2ZBsvVAOqwF8BUTa0trPjFmI2igabTXRl\nxbjUCkcD04zjZpK07YKb7e/HzlHDVZ4MGfzOkezqL0yy2GPtyJskgI6HzlQj2qW6PHp6iFzHSQKc\nsX1msWM2WpmxotaBUkFuYq/MtGNQqSpQ8bzhYpGTXXl4ONW/26ppQxOpKaQ117qodaK1b8Y2XXP7\nOjAN/BY4EfCB5wHfB44CFXuootwYIQJq1k1Pd/pjlNxL6k8b7Wk8a2laLx3XcnKs2KnW9pOTAqtp\n+eacMVaOYwUIKbUJoKfMz47bi44jnKc1oNXC1BTxbre8+QslH0ePYvdubN2KvXvRbOK++/CdjbwJ\nJXMnEvc4MuIIr6eAf2HsKPAf/TLQLwB//nXkiP72t6OTTz6wevWBFSt+v3r1V485ZilwKnAu8Ezg\nEmCmUrnz5JNvPPnkdXj1PXv2tLds0c/4h3EvSVzQMetFgVfAFNDIzvS68PcBLXmc+zY+zFV3UFch\nSTKizC5pB6CyYnzdy/pUBJZloxynDIm+pVRBCNpx19wdtwURZ3fcDdpxS2lbLlQDwmwEIHJPaYFx\nTnCAQtst1+iTQAPYRv3fXh35gu8HnkfkaqlUIgR15K2HjpLo7YrfduJh2057wT1zgLEPet6npbSu\nXWE5Gc7EvoOfNOjTJUCR88Eg8GlmMz6e6t/Jq0w7cbIRWvKHlJAyMGdHz8ThBs6mgWrAjLP5fREw\nBdxg2kQ+KVaTpBKGPr1BQIrGI+OVW3iMTDYy/TE3rberRZa/fvWiF6XfhD4GADcGLkkEJK0TKxiz\nzUNSDZRKHRPAkSPbXv1Jbvhumzbh8GHMzeETPy40pWxqlXdQC6cOceB9wKfMZNjutN7A2Bf7NaBf\nAP78a9UqHDiAj3wEZ50lzzhj9vjjZycmtk5M/OyEE0aKxWOBs4BzgX8mnuOyZbcsW3YLDm1pzLUf\n3CnvvBPnXcpYbtppmTNVYAtF2jozPT/LQUxhZCTvs1t72+o1u92O6clKO5SCiWmcLz4Mzj3zHuDz\npitSAfZQqrAh0RO1Qmo9ICXPZcXQE4ji2NXAhFq3tW47y24InGka7tIhc1HK6x4gBhaYp7QPaBhq\nI1WLfEe+y0NHBSANJKHBrOnI0+pGNaANLJ0niOoOUwBaZlnfD6jsxL5oVFUd/iWgzcT+eKDIWNn3\nq1aytXgxli3DokUYHu6sfbT6WxhOHCOOGfFKleLZLodLT4qBPwDHmq+XgK/a45rWLaWKQhSiaIQG\nv8SYopwcxtKfRX2nZhPtdhiGjSRpCdFWqiuxPXJObO5oJE2sXLoUU1NdBawgZaRUYu3TSkVCFMmo\nQf9YytR9pjXC8O6//eeBAcgc3+0z15QbWrgOavdQIrOdQxtkj2wVfx1j+4Cf98tAvwD8qdd1jOHL\n2QbFi1+MNWuwZg1WrFj8D08799zwpJP2rVq1b2Li9tWrv7J06TLgNOBc4LnA64AjAwN/PO20G047\n7U68Zs/u3e0Dm4Bndz6y3Nl379R6kLGumR4dAt5sbzY3NtLew4QRtUwCyg4zs19rfxWO91X2ypK0\nR5MiUCe8OxAAU+a+IhI9E4JIc5GUJUOtIAKdkFJIGeV33ErZGO5TgMNGRT5o0HXKLL6RiaWdA/YD\nc8CYeXp7qLertQZ6duRdLqZNpFJaU24aiSxJa7tU62lg2ChnYJnGjgnrSSb9/J212lwcK+ADjH1E\n6wGzDHWTDxzDBxWAoqEnpTMbWwDIjkstu0YDhULGQWYadw49A8woArzs2bGaNawtAvaSOlPrklIF\nKf04ZozVtfbIpUzfnDYNdFyLIkRRM4pacdxKkpZpjoUmJtr9U+aOaxLA4sVpAbPfMI6Z53lSpnN1\nIh4mSZGaPySaCENUKvQ/3vKizy2tp55KClMivtuXrqs2lIiUioC8XqvncaRMhT9rdKd/fBFjN/Zr\nQL8A/ElXqceXSumjWNx/6zaCoLHly885R5x++vRxx01PTGyamPjJ2rVjvn888GTgHOCjAAPuP+aY\nm4855lbo+2dmph94QN59N45c2hkJPJOxGa0BFBizNcBiVax2XmjtW1Uf9VLJSkOzXzpfT093hJhJ\nAiESpXpan+y31VmfZ41zSxB6IXC92fqlWTG045ay4HlWA6PtamuQFTFtJJWKAFpQlgMJsBgoMhaY\nBpc2v5dVuVTMYxo4AkwDY0AEvF3rDwMKODB/R943ubuwm3H6B0Ci9TFaTwEAFjkxhFY5YyuQG0T1\niUajAbze8yTwVuBfgZbJj3yEqLKnADQcLlMBsPSkBQtS2S45YF0frDu2MWxnlt3kusdHOgpsA1Y7\nCLn7jTy/oLUnJUsSqtYVIcphyGk8brbqKkkiIdpxHArRtpgpczbK/5mfyUfAJ97znlngY296U0rb\nJkWW53HOI4OSbUvZSJJCGNYoj5r0AqUSfP+P77lq6XDKd9u+HZs3I45x8CC+cctAUyaUHuzO6uNs\n89AdHSmgyJidnJeM3T0EhoEG8NeM/bpfA/oF4LFfPP8laqmbKCXEMaTU99yDw4exbx/27DnmTe97\nylPCk07as2rVnhUrblmzZmDRouXA6cC5wEuANwOTg4N/OOOMG8844y68dt+DD4br1+P7z0XB/IRY\na8bYlNljFt3tjNaRlL6NDp6aQqEAKVMYGXlfp6Zw+HDG+5okpMR3j9IiK+1Atp1a831LEEJPaoXZ\ncfs5Er1LrYjp1tV6NaCBOmNVxkqMFcz/pc3INDFPz/IzXL8COf7HgA8BbwOeAdzucDSpI18wgSTc\neeM6YkqtlwAaWM5YmbEUnU0RWoaQQfDUnkFUn5HyEsYksBsYA1pAO3sI4E490zSzYSzwPM8O7Yl/\nSVD+gYG0bMdxD1ip4ZWm1FJnzdK5GlBx3rX3ABcBRQMZJEiGAhKl2kKUfD/wPPuySyrVlNZLD5vW\na2qAfbSyXjBbAN4OzAAl4L2f//wU8KULL3Txf6F5ZVpKBUnic844r1pMUxDc9+U7hnvx3a68o94Q\nKfLWlQ9EWZlyl0ckBgqcJ1J2jc3LRj0121+8+wXgT7p67BZcJUMQIIrSgzwJHxuN3Re9CgcO4GeH\nvXvuOu888YQnTB177NTExMaVK3944onjwAnA2cBTgGcBGtg8MXHTxMRv/05vm5qa+eMf2T334LLL\ntL3nGWMVVwauVFvKqivtoD4yWZ/ovqLsMEeJH7pCTKPEj7Pt1K6dJi8UykmipVRSKmBS6wy1QuuE\nsYLW3dQKs5hSVH1iTu6rgQIwxHnN86oWXGoHGNShMs0i205h2X6uBnaZA0r9MXTk4TylJwAhUGOs\nzljFBlFZhNw8QVQs+7J8S+v9wAeA9wJDQNPgqYu5if0zbByVq3+3sFLXl2uJScaFm34x58JV8+jf\nC8ApQAysAwLgecCvLT5Pa60UWbRKShWEIMYUs3wRkx0Wm89GOiAxW/4W0AKeZvRR0uwD7Db8IBAD\no6YIveKmm75z2mmW/deiDpvW9jhC1aiaJEEYbvr5rkoBU1NpmBLx3bZtw483DtDqT9v/tjOrD50T\nic55RG6uVOaSxGfMN0kGdmRiJ+fPYOw3/UNAvwA8lusbjE3kv0pLP2296QhPWvipqS7mjFx2jNrY\nnL0znEuSpy6Lzz67vXbt7lWrdq9YcdOxxw6Mj68AzgDOBV4FvAPYPzz8+zPPvOnMM+9WqrRrV3Tf\nfXjBC9JK8F+MxYCndahUS4hWFFUo2YoWfWt9oj4vwR3p0W4nXTTKXtIO0avTxS1BSKl3AJ83FAfy\n4kaOBqZrx90V/3sKUGVsiPPBICh0EZtdDH2S+EJwIZhSMLG0MsfPeAC4Avgk8F7gU8Bj6cifBUhg\nhPNBzxvwfW7tSBaNME8Qlc6ligvgC8DlwP8BBoFZoJb9ufRD6TCn6LDoRh5aWGm7TYHJqRGXQhnz\n9KQctdStBPaE9GPgFcAZwBxQBGZsdx7QSgnGUsKESd+1/IlMWq9J4o2cPtiZAAdKwIADrXOnNS1g\nDpgG5gAOLAVedt993z3mGGgtgTnAI0UvHUeEUFrTcWT2hr0Bw5EjeOghbNmS8t0+eU09lnJOpqwO\nexxpZ48j7V72QAEwz6MkAy6lOzb3zWmgQowTxm7u14B+AXjUy+u5MpJoj4jq5DkieTX5KqemLAQY\ncRwKQcyZnz7IW7t0U+tn+bjgguS0044ee+zRiYn7Vq783vHHLwBONMeC5wIJ5xtWr75p9erbgaHD\nh+d27FAzn0f5TWAEIxNiLo6DViuw+EmrxHeJbK0WWq04DBsUHyZlW+tZpRKDIKbd66AzKFvvbLdR\nKoGyBEiKrvUhQAJhlkTfvePOUo8E8CRggLFBzxspFrnVrZay/Ayip4UhoqjMmE4S4mdIxzMcAQNA\nCCwGdgD/BHwT+Bjwrl4deTeQ5OlAYJIRq+TCtZHI9AJanl0UceNjskFUXbNEegJfBd4BvBcYNQ4s\nF38Gg6ujJTvd3dO6T54s69gIQ0xPdwY2DpLBxofNh2SA0/kpAQuAIjAFfBc4BMAI9oneEQM0Hqdq\n3ZlVUHUx7S/X9vFEoEi/GmO+Myp3vWk0rSkDFRMOsx9YCDx/9+6fDg9LYMqI3AhVraQUWrxAfFsA\nACAASURBVPO7Q+FDS0xO4sEHsXFjetN8+JrKjIrtcSTKHUfsiSTuNY2IARo8cKWoyNEmgGeRQfRL\n9a9+AXj0S/TkC1oldRiiWk3dsLQKU+/FTF9jBwIcA7TD+r5A4wbM3YCjwGeOV2ed1Vq79sGVKx+c\nmPjNsccOjoxMAE8EzgMuAy4H9oyN3T42dtOTn3xfcunBXbviu++WhVdyL0lYGA4CRfrRxuvbtZy1\njLRjst2mvWE9O/bsSgw+DZgCYuBOAOUyQYR8pSjK8f1afxiomv+rYhrl7o7b3TJL4ClAjbEBzxsp\nlXi1miYH1Oup/AOAHWaQgZkxACWtpRC0yhRMLG3JAIuIn3EQGPK8ROvdSnV15F0u5ssBBZQ5HykU\nqvkgqi7zWquFVosxVopjytS1QVRuK3kIWAhcC/w0qxxNgE+Yn9spgVorpbj9NaenqbKmsBAS5h85\nki6BDj0pNpoluzS7AxvpRMTQPncwi42bNCpJ6RzXqDHSNauQNrHdFLnzgIRI4JzTtMY3BnJlToRd\n0xp3YHMQGASeNjX1g3L59b7/TSHSpB2tFVC5RdDs4+DBNEyJDs/v+3lxRotUr2UZTcY1Ylf/piG+\n6ezePwFaAPmobRszPza3HaELGLulfwjoF4BHvvJKAwCYm0s5MyRjcAnMZKtpNNBqCZcCb2pAF3nm\nWfdD3Y/dwAcKuPDC5NRTD69Zc3hi4u6VK6889thFwEnAU4Czgb8HwiC477jjbjruuN/hpTsPHZrb\ntq21+jlDVSnLURRQW5lOAEopIaIkCZMkTJLDc3OzwACwDCgw5jmyS+ns+KzwpgpMAacCb9u27d+P\nOQa+7wmR4oMYe6fWHzNbMIss7kmtkMAzSMjE+WCxyKvVNJN2fBzDw6jV0ukFIcmsDeoR+RlFc4of\nAFrA86T8VRDcxNjZUg46HXnXQxcDNc6HgqBaLneY+ENDnSAqMsR2BVFRLq7WrgPLqu+pldwERp30\ntwhoAe8Fpk1LLZXAK9UWoko1hg6O1EUkFxiRi6anceSIa8eNhUjpSVl0kltskJ3bV7PVdz0wbhr3\nsXlZCsaI7nrWuhST5wMcWMD5AOdVz/PdhADD7ZFKRUS4M1AplpXiKKANvLjd/rLvTxuaCIDVt+tx\nw3fbsQMbNiBJMDmJf7rWn9HCTo8SJ1IpMsJcWwBU7pnT73jzxARIYkQTiOz67uYOFYBafxXvF4BH\nvZrzFQCSMZBkmxrZdjLcaqHdjsOwGUVNIVpGxhAZVbX7sAO9V8aIr8Ou6/Bp4OST1ZlnNk844YGV\nK3dOTPzquOOGBgdXAmcC5wJvAd4L7F6w4LcLFtyEqfVRNPnAruSPf8Rz3jneEWIaacdks1nulRhs\nVTG01QpzicE+JQbv3v3V8XGK3mVKEUGIsEUuib7QKwNWkrmB84EgKNLiSzEgixdjbCzVwAiRbv/o\nNGDApWksrVIeY57hZ/gOP4NoGRIoFgqBlOs4PyFJ6sZGZzvy7wZKjJU9b9D6sBYtSiPjKYiK0ivz\nQVRSBkr5ZMQl90O2DlHf4/3AF7LxnwNGPJqx41IBIL0/8bppZkMHoC5eabutsri6vPg9ySWfMPOC\n2HX8HOB3QM10z7qOaywLGbQHhWcRp9bzBn2/RNMae7i0Lw7RzoXwpGRSIkc7F07X7g1CfIyx72od\nA8+8C/VefLc3/orNQmon6Nj9Dm4BaDpwQHc+FNP23/mNHsvkvH/1C8CjXO/S+huMdX1xutUaUMqj\nnSP1kR0KvI6idhy3LHbGoQ64nprQMbW625kZz4s26/Ym3db6jIq+8ML4lFMOrVlzaMWKO1et+uaq\nVYuBk4FzgHOAlwHNYvGeE0648YQTfo9XPHjgQPP++/Wyv61S+yIOw2HGBjmvcl7xPObyF7WGUprm\nbLTMzZMY/NrJyUPAt4tFSxD6FPAWYABoAAPZQ4C7r7yYtDGc1yy5esECLF2KZcuwYAHq9RQjSgxn\nF4jWbsP3iZ/Bs/Hx3KkBxK27sNm8qV6vJMlWYCRJRhwfNekjA85rQQAKoqInsHRpGkRFFajR6LCj\nycQUhohjz+TiWmyql+0hlExS8ZWMxabHUjTPbRaIzNC+KUQpDAcajfSjYg1QVPOIiWbSe3QYNuK4\nRUAe2wd3SKVxr02JNrkotlVVBU4Hfg+0SP5k6mJXWKPbr3uBndYUCj7pVu20xpL1aFQThoiiYhyn\nC7FSXU2kGhACC4EGsF/rw8B7NqBUSvlu99+PXbsQhti1C5fegllo5NZ0l0Nl9Ugie85we5g3nHYa\npqcxz8BcOYHPthd0PmN9fHS/ADz6IaBrszAVhrGU5SQhVTXd1Zok1WR/TZLQUOBDo2J2aTNtw5xR\nOSWDVd8z4O6Wbl6L5rU4Hzj9dPmkJzWOP377ypU7Vqy49vjjh2u11YZJ9y6gDOxatOi2RYtuRmNj\nu314505xzzo8/3V+3fe5qz5EhxvBkoQSg7mTGKx6CW9eGkVf8Tz7bD8KvNmhEOepFRqoAB5jRd/3\ni0VUqxgaShPklyzBwoUYGEhHoKSkIj6MkwTCOGecg9Q4zsCTOWVAkT+5VGJASetDWreUWqYUITOv\nAnzGAs6rlENCT4Do2YsWYXAwZdFMT2dycWmq7/vwPCZE6sLt1UoumJdowPMipTrDcIADh4Ai0ZO0\nLggRxDFvtao0cmi3USymGiTLxQxDtNsiDImd15KyRXZcZxdsdw9xLwl8wFhg5iVW9bjNADLrxleY\n54vQO/5yOj953kix6BNnkKY11Wo6LCF2kMX8cQ6g5MTMuT/dNsrGgX8DvrgZnocjR9IwpX370Gzi\nrrvw+s2po5s9hgIgc10maYhVM3SWklJZq2N2fu4m27hRz/2rXwAe5WoAI9mvzCRJbCjwpKqGkVQn\nUlJGFeW2d7AzORFb1Gv1jwFeKFSM+t5O524AZu/B9D3YDHxkSF9wQXzyyQdXrz64YsUdq1d/fcWK\nJcApwLnA04BXAbPl8l0nnXTjSSfdgUt2793b2rq1/bS3rM0kBtN2O4oQhjYxWJrEYLr9Ks7t1wLe\nJOXHGbPP9iPA64HBLLXCHgIup20m5wXPA62/AwMdEObICGo1MIZmM80ScJZ+N0EegHYi0ZGlEBM/\nA+UylLJd+wcYa2p9MjWmGCv6PqMIezLiUiguZZFTEBW5KFwXLnU8jLUN5gkga5SzqpJqEARS+lJy\nykrTWpt9gwaKSvlS8jgGILWuCuGFIdyZjRBIEm1hpUlCjlxrgApzvNKeCTkkfne7VQXg5cDXgHFz\nXHMtC13LbhUoeV4HWjc2hrExjIx0hiU0xJ6Zcac1adIcGbBzxDqinX/wfmiNw4fx4IPYvDnlux3z\nXawEpufpRwmnsRYZ0SeycwtlKkQDuP3MMzE9DSEi4j7NE/Mpc9uIyxlrAp/vnwP6BWC+aypfAIQg\nB2yBctuNA9ZlziTGUxObTZxtYjbNIKsr5olAx+l+lhZipawMhvZ0R4BLplH+GQZ+hlcDZ54pn/jE\n2eOOm1258v6JiWuOP36kXD7WHAveBxSAHUuX3rp06S3YvLnROLpzp7jzTrz6R8/q9Bw4dxODRS4x\n2FooH8oyeBXwcWAa+JBDorcrS8GkEvo2B4rgGeVyKsEsFNIUSVoHLSze/Nml8u5ilzKnG/PMhx66\nbsmSdGzAOQ0Mfqk15eKmQVSlEiqVHkFUWueDqOzzUQaeqsxEUWePI3QOeGa7fV21yhjTUipTRGeN\nHTfQ2lOKSaniONE6lLIURYHn+Z6XWupoYEP0JEInSTkthH2pfaBiql0dGAO25Oy4PyoUGlJyM+N1\neeMvBPYBfwSq2fBI97j2YaDIWNX3K8QsGhtLpzVUKcnpQkNsKgaWPGho51xrj7Eu2vlz70S9DiFw\n6FDKd0vn/T/E4ax7juUaO8JJv2FOkXAPCtT6v/m88zA1hShSTpJBnEsyyCd9kmvhEPAqxr7VrwH9\nAtDz+qTWX/lKZgwwa7Y8gWXOODiBjq3GmGBdHRut/qIXcDgGbj3pJMzNQSnfbqmyu6pzgNuACBDA\nh4DKH1H8IwLgsnF93nnRySfvX7Vq/8TE7atW/efy5UsNk+5/AZcCU7XaH0899cZTT12HV9/y8MPh\n5s36mf/8V1baQYnB9OPIQhk4zW4KsHyX1u/P7qck8Abgc8CsGczambBOuaW8s7Ba3y+tHeRgIFwd\nZSdQLBRl0vY6vLuVwAOkSaRCoYA49oTgUnLGuNbFdJ12cnG7+Kk0trFBVLlQ3HSwaXD2br9OO9wk\nAuehWAwYK5kPgACeDdxksQ20zGtNBaBoDo4psNo5O7bCcA4oAUOOWlc5zRBS654CHAVC4CHz4eFG\n3d91RrHHlCcC1xkBVZdl4Qpaiz2vaoclNK5fvrwzLCHGFJE73WlNFHGa1jjZFfT3k27B4CDCEAcP\npoGOUYSjR7HnqvTUWM655zpzYPMrS+fbdnX/KRfo1gsuwMwMSaes29Fi4/J2d2QbekPmJ76Mse/2\na0C/ADyWa1bryDBn7P5FO9pK4eSMu1FTJGKLc8O3joq5VrOLUU8ZTADsB0ZMIzg0N/N7JuFdhbOv\nwlnA2WfLM86YOe64mYmJLRMTP127djQIjjNMug8CHrBt+fKbly+/Fc/cOjs7RUy617xjwJPS09oz\nicHuLrJoEoM/CLwtmxAC4A3mN/2OqQHpQkk0G7vaWqUs5RaQjY5I9KR/d6ywiWGX2hD5LnypGwie\nYWfaXsFjCaIyo9cMOVVKIqd24KnZgmRbyZ0nUC5D6yIxFQCSD82Z14GOD8IkiHXoSYYdLbXmUTTn\nqHV9V61rMHmR8TrQYwpQwDQwBTD7m2qNXsw4CVwE/ByYzJZqenM9xgqeV6JpzeBgOq1ZvLgzLImi\n1PlopzU20tLGzDHGjI5g1ToMD6PVwr592LGjw3f73LXsKdC17Ky+6yayJwDPWf3zR4Q28PvzzsPM\nDL19rThuOpqLLrt7lEWHWkVDzSk2L2TsJ/0a0C8Aj3rNACXaI2dz27WJbu9KcHVlDG74icpu7q6/\n+GIcPJjOAz3P55xzzo04x+7p/g64yqgs3K4udX63MzZ0d2Fs+wBGR5c17j/nnPDEE/euXr13xYrb\nVq/+4pIly4EnAOcCLwLeCByu1+84/fQbTz/9Trxm7+RD0YYNOOW5nBlxd5fupZg9TXdFOTLghcAL\ngbpb2+yyS0s/CS6lTFeTPD/DLMH2FN+zkyucTi4H8gA1u3OXlpttXbg2iErrNIiKvHv2CSQJpCRy\namIqUN6Rm3kCpVJatukUxZiv9RFH/04x67HWllfq9j0CIUitW2asxJjPuXWTKZPLSB3FvFo3oJmw\nHZlkJY9d4vcLgdXAV43tozMTZiyd1pBXbnAwHdWMjKBeB+dot9MsyezS3z2wATSw+F5Uq5idTflu\nO3YgivDww0iuR5mz24E1Wg/mmOduh8czq3/X9t/eMi3gnjPPxNQUHUSaUdQzdyjKjtB1dvXXjmKK\n3IXPZOy6fg3oF4BHvqZdRZ3jgulq6bhNTCoA8TwyhgiYA1CtolzOyGByEhS6mS8CfgFUgJYjwnkx\nLQ0EHy6VMDCw5+QXYsUqLFjJX/nGc88Vp58+feyx0xMTG1eu/PHatWOcn2COBR8HAGxdseKmFStu\ng9w2PT2zY4fceQ8WXNYjMfhzwMuck3XXrop+WQoUo8z0Dp6IbFBap/4J0sPMzHR8sK0WSTCpD+62\ncZNsYIvKrm4270xnWwTEt+mkopPnlgg89EzoCRw+3AHnhSGSRGaNuF1ZMV25uBy46IEHbly+HEni\nC+EZ09yrtL7StPuIx5B+bLT2TdMGwCKlqowNcl7jvOz7zDap7KlFKeJ0WrWuu+egx7vb7f3Apzh3\nTVI6K3wMgDJwPvBkoAYsAC4Cfk2fXs59apTZaY0BnqcSoCTpjpmzAxtz2qM/RzbqIMDUFB5+GFu3\n4uGH0Wph2zas+ANmgSLnUqmtQEvr0wjRAZQcj4VLgeW91EoJcM9ppyFJMD2NKEpIch3HbQM7ac8/\nOe/ae4msYmoAGOtnBvQLwKNeR62vspcBSuW8MFa61yWxdz0st3z849izp4NI49we6nV2l01/PgX4\nFTDjyPsCkxhcpAJQq6WJwSMj6rbfponBQqx8xrFnn90+8cSHV616eMWKm9asqS9ceIxBVf8D8Dbg\n4NDQ75/0pJue9KS79Gv379oVrV+P6ed3jgLVrJEN2YioJlCyv7vWoZRRHBfb7bSDTIJL4mco1RGW\nOCAEkSRtA1CKsvHxbtVx5aHv3rr104sXu10a24WLpIyTpGDR2cTtIHQSSfLp61NTaTMhDBHHLjk1\n30fuqkDUjLIEfGaoCQw4DAjDKyU7LtGTqK33RCACqowN+349CLh1XVmBkJm1FgmTJyVTyh4082fN\nNyj1yZwKXme7VePGGSCA3zIW03CbnnN+WkOnN6B7WmMihjroJ60lUNmiGcPhw9i9G5s34+BBNJuo\nXIm1QESrrecllBYJrAMaBl7t8rfdAsCcQYgAtqxYAcYwNUVJnxE53m2MgckdCrMZBvQX1SvJIHCa\nnGVzKOmj4voF4JGuyTx4MlsAukT0odGfIffP6L9e/73v4eGHuyJ8U/0J7a0MltI92p8F/AqYAgaB\nsnlLfPLuFwppYrB9kMaRsV0/uw8HDmDvXv+Vrz7/fPGEJxCTbv3ExA/Wrh0H1hom3f8CJGMbV626\nadWq25+ntx89Ort9u7rnHlz3hh56RPsbzRkAg691RDEgVABcfka5nNpuaWNO/Iy5ObRaKopIBU/N\nXBtg8Ag5UBoYAd68f/+/DgwIgzaLgYAqkFKNJBmhJ+D7GR+WfQKUZ2sqUDtJWsbGETkJCo8cRJXZ\nIJvrUuBLhp5EJbPorHExUOV81PfrJFStVFKJFD1POrgY45UXx+Uk0UJYoVFiUiGrZjzQAv4/4H/n\nRiaujW4MCIEHjFEZhCwlua07rbFMWSHSXllX0pwpA6R5m71X0KHu0CE8+CA2bUpr+vLvpx+JdAPh\neYFS6UxLax/4BTALTAFvdQ7WXeEKEXDn4GDA+WSjwY1kWdDknKa+VnUNuPjolpNk0AWPE8AVQCPr\nMCfelOov8P0C8AjX/lzznfcSMifOYtFTxkAJtzfccAP27u1oYIwKRTpd7J6+dg/4a+BKoAp8nb4z\naVJJ90IPizsWIt190yYujsV73oc9e7BpP26ePK5xz5Of3Fq79qGVKx9aseKGY4+tj41NAGcA5wGv\nAd4F7BsZ+d1ZZ9141ln3XnrpgV274nvuwW9f3C1Il8AhgAMh4AFtrVtSFpMkaLfrpLm0/AwqABaM\n3Gqh3ZbtdiOKmg4FIQ/PiLLNDfrLGADg8rm5/cBHPI9WWx8ItG5JWUqSYrtdpVfDCaLqPAESxTab\naLfblIsrZdu0kmkaOQgMOi27lskujoB1NqqTwltMR54eDwMLzPin7RinnwNUaO9PYWFkvBoYSKsj\n0aWsWtfzwJgHFElioFSS9XxZSt10bl4is+KcQaBoPGWe1oETM9cZkjebmJkhg0X6Z7udTmsssMhQ\nS2Oltl0fLiymY96dO7FhQzpbOe3HmHJ+NAc8z6OgYy/rrw6AjwKTQN0gRupAjbEyY2XOi5xPRVEn\nd4gUdzSop06dOS+Sa7ptNBcuOjQPj+uK5vZMTKkCns7Y9f1DQL8AzFcAZrOSas/ZrbifMJnrY8L5\nBxEwCXQ4wISBpEpgVCj5FN+u/N6XAA0gcIQ3nfaxTQymFYS0HAScye7jtkXH4NrG7FXRWNx86lOT\n0047smbNkYmJe1au/O5xxy0ETjRMuucDseetX7PmpjVrbv97/cDkZGP7dvWNc9LnFgFvZew7Wrfp\nvtK6qJQvBI8izVhdKUaLWo6fgSgKDbiU+BmP3Mx129wKGHCWvLdJ+SHG2qbNUlSqIIQXRaAgKnoC\nlvtGgtQoQhjqMExzcYVohOERoATUgcDh4HdlRjaAGeA0oG1ec5Wr2ZcDHwMGs0P7VwBlxqq+P1Qq\noV7HyAjGx1NKXaWS8oJo0z093fFvG9eVnTO7Mi1CpQ4D/xe4JBubzpzm4WXA100B4yZXWWgd21E5\nTWtKJQBotTrDEooadXtlSRILsfHXjYW9+G4X/AzTzjpLS+3T5uZ+Xir9P/beO1yuq7r7/+59zpl2\n53aVq2Lfe9UlW+7gii0TwuskBPwLkFBijG0cWkzCL5S8piYEfhAIIQQCmIRiQjMQv4SOsWzLDWS5\nSZYly7Jkq7dbp522937/WGfv2eecucI4JL9/5jzz+PFjS3fmzpxZa+21vuvzTdpkaciHo92nkZ6c\nR1IWjNNnGmQdW06foQVb9OdAh9qiu1b6beGW95zXXRLuJoCTXLuA+Xr7qWwdAuz4LueQMSgr+t//\n85+jXk9GoFYD2lah5Ku5fFlHoHYBxIyRDt2xRS/U+A6CNnuHwJN5+LCUu+A1bheNX6jzlVq3Tj7/\n+Y01a/aMj+8dG/vZypUDg4PjwHnAJcBbgHcD++fPv3f+/DsuUo+G4bG9e6MHH0TxKjal1DD94kq5\nUnIhWBQJIBSiEkUl33c0PwNSSiGCKErxM4wPVHqD2tfNtPyxo5JWyv+VUq/WRlSeUk4cM8ZsI6pk\n0KLPRiqK/CgyvrhHW60KsBgokWsx6TWNMYBSZrBf0tX3JPCeJ5+cAT7e25sXrb4F+ARQBepAFXgP\nlQ6c9xlKkiFkDA8nG2pEF5+cTE4qlKg0Js8hfbC1dWUbnrSALwCv1O8JcoqgMudXKrVRKWoKJUZg\nUjajqEJw1lIpMRoiSCptbNCxwJrWyDDcfvvU/N6E77ZrF3btQhDg0CG89KeY7YRS50axSv+iZaPm\nlNCwTDiEdugk+2jHSgBI+w6ZeZutuaAEoOZAh36O84aUmSKf6TxUAC5n7I7uIaCbAPJXoH3bKxZj\ny9yaztwyBrPU4wOP3HwzDh5MtitNRCYbmSixQ8qggG0JikxLg0xsIsfgimFMTk4myLOeHnCewIcz\njsFhiDgONHyY6qmfAY3HUX8cs8DriuqFL4zOOOP4ihXHR0e3LFt28/LlI8Dp+ljwJ0CrUHhk9eqN\nq1ffj9fs/euj9SeeUNsva/tAqTiONRi54Dge55xzKNXmZ5hOrkZnd/SkbeXgGcIioFUsWH9dG1F5\n2ohKaCOqYhCQLy4ARevWtIgrRK3REMBCxnrINJhz1zK7F7YcM8fBp8/9rbXa3xYK+Zx9LfCPGlZK\nN0/ZdUvEKB0aSghFS5YkCYCxpOXiOAmiTm9dJYYnpCJlzAbV2Zi8qgUeR1oyJIES51LKplJM99x9\npVpCNKKo3GqxWi3pQfk+yuUkA2Vo540GfP/xjcd7c3y3PXtw9SbU5h6W2P0xlVuu5sAJoN9Cardd\nB5Tilupaam5VBhwUWP0fOYfojhIDLXgrixMHa8m8ewjoJoCTXeTbPqNrQFvDkJcxmNo/BB5717vQ\n04P9+xMSJB3zrcJKWDIYYyNjP0Lr6Ap9iqeZXiBlK44rVP7PzCRaF5q7mg747Gyie9GOwS1aobSG\nruaJIuDGANM/QeMnuB4480x53nn1NWt2j48/NTr649WrB3t7l2lU9duB9wJPL1x498KFd16qtvn+\niT17os2b5aLrkiZDy3E8xlzOs/wMgmfYtrTW9lwTOE9HBPq232HF1paOegXdBukHpgDXMqJSQpAk\ntChEgXN6AcwaJ8ZSNny/Agxz3kuuxTamQssxy0L4RIqm/QylkOtHvS8M/4rzKCcbvQr4pO4pOZyX\nXJfUuhgcxPz5WLQIixdj3jzaKUsoHcargGb4jmP23WjrCrnVX7O08SPgWuCBdJxVwB09Pa0oipWq\naYg0zWObQhSiyPP9ATob0a1CqiSbdt5qodmUrdYTG48UHExMYN8+7NyZ8N1e9rWk6xLofXWklW8x\n8MlmMwBeZylW7Qf9OlP6r4eZybkFrM0UATY5ztewxbzuk44IX3bdhpRS91fziil6G7tXNwGc7LJ9\n2/M5gKeHwyGwd8MG9Pfj0KH2oI9O1rQQW6uh2VS2jcwcGph8YUX/0VHKV6oZx5UgKNfrCW8yDFGr\nJRJMSjnGMbheR6sVEn1eyoQ/3GmLkkCMbwKiRxE8inuAv+1VGzaEp59+dMWKo6Ojm5ct+/L4+GKN\nqt4AXAXUSqWH1q3buG7dr/D6pw8fbu7cKZwXK2KWGcd2qqwTDJE2kafwcRpQ0+6VhndP3/PfBRpI\nyswNWsvhWW2QJ2w5h1ICoDlhgXPbF9eU9jyK+hjr53zA84qkoTIcfFjomzAsxbETxwkH3+Kn2via\nj0hZB96aW5q7DvjjjFqXuJuGkTc0lHKLNNaVlI1MQtKhDbkOBre62H3AhUBND0K30hvieUUpI6Um\nheBAWbu5FZRy49ihaY1SThQl0xoa1xtLyzAMguCZTUe5wvHjeOYZbN+OiQnUanjFN5LRQkdoHUXh\nPwemgUng81K+hrHMZMuoG/YDQ5qZ6OcUdx3BQRl0qOrEDqLD97T1t/LTNRsV1726CeA3ywR0ujew\nLQVsKhQGi8WhcrlSrWJqKqnpyMCdTtZUrTebVFjVwzBlI9NJBqNyO430JWFKtaRsCFEIAtdxEsdg\niiO2AS+xEJpNtFqB79MztoQg9qTxBA+tZ0Tuy7ypBv8HaP4AvwOce64499za6tVPjI/vGhv74erV\ng5XKCs2k+2ugBDy1aNGmRYvuQrS90ZjYuzfevBmjf0ZdemVYC8aTdq3WES205is2NMnXYKVZ4B7g\nBBAA56QdY6Z0w02RaFKpkLE2wIMxY/teFaLNwS+XE1QceUbaHHwa1Pu+R/xXLUSJ9OpDqHdKh4H6\nHGmbvjkO557B5NlSXUo8cZwn05ndK0kPq7BVnToYBWAh4AP9eli9HpgG4HlcCFfKdznOp4So6BmS\nJ6UDsCiSQKSnNa7ZSpNSChHG8WM/ODIwABFl+W6vvqU9p6GWXdTJtnfAGlp8R6kjJfabpAAAIABJ\nREFUwMs7KVaXAnu1BXQGYup0cjLInADmEt2FQA34brHYiOP8grfZ7zNz6a5hQDcBPJfW0IcYGyX4\nImNNKb0ocjhXjPVQyd9spiyWtH9vGASNICAZTNOggJ8dDbhJSDKgqVRBCLejY7CBD4chgkBZjsEt\nIVqaPm8/Ar0xlJ+79umi7E6g/iBmH8QkcP2wuvTS4LTTjixffmRs7P5ly/711FOXAGcAlwBXANcB\n0z09W04/fePpp2/GtfsOHGjt2KFqL25/jZ8H1IAKMI+xIuAxZnuRC8sntqTpFPQ4AdwNnGWFv/36\nbKR0hZj07i2CE4CFSvUCPY4zaDj4AwPo70e1muRpw8GnMxNjADylikpFShUYSyzX04rMIeDTwFXW\nKaoNNKbzh5HqGpce+nRo+kqCXVIEEJ7IYPK0RFjM4XtlBhLv0aA3IxgtAu89fvwE8E/lMmdsAujR\nvTJqaikhBBBK2RKiEIaepp3TsOSJ708tGsjy3SYmcNWtbbt2M7NRafU9RdhqOi4r4N+BP0jjmumx\nGNgNVIBGJ3IcSzd2zNkrzk3dlNX8aQL/UanUo6ijy2Z+wbvbBeomgOdyvU+pz9OYTilPSi4EwlAo\nFQpRoa0oCsf6Ox+SBoZA8HoFqS4lDXtdrTXqAYaAENie/nYJYBYoUR2nlCsEY0wBsVLkGFww7Wyl\nIIQwjsFx3DLCm1y+MdqbjvT5Hs6pCW6XYx+cwPFbceBW/B1w/vninHNmV6+eHRvbOTb2/TVrhorF\nVcD5wMXA+wEP2LV06V1Ll26C2lGrTe7ZI7ZsQfwGjDDWwzmpv13T7qAeEcm9tfuKa03a6bEVOAYs\nAxygBJwA+iwshyEx2IXkMqDEeb/neQaESYrMvr5ECklujsa1mDZgpfSk9JRyrZ0me6e0B6gAXwFe\nolOpAl5oIjVjCmA2p8iodckxnRbT6nWDx7ApdSInNBKdDE8KQG9uIkX/902t1ic870bOPyWlp0tv\nSCk1wIOGJY6VAGbubCwaQKOBw4fx5JPYsQNhiCNHcM2Pks5P01JeCuvmtLdeyp36Nv8BbEhr8+kx\nH3hKr7tXNDnOVtyp9AmAzY0Opej/497eehgGFivUXvMOO2mWulc3ATyXq65bQK5STAilpdYtbSPD\ntY1MLEQigxEiEGImDGOgBAxa84NYqxeo77EemAJCYLe++/93tfrpeh0a4gYhJEnohCiGYcFJGAVK\nKSllLGWoTYMJMhPo5k9GctPSiqPMQb4OVKg3MoeJ2GuBiV/h6l/h74GPjqhLLvFPO+3QsmWHxsbu\nWb78C0uWLNWo6iuBNwETvb2bzzxz45lnbsF1B/bta21+TPz+q3vaLXilmBCeEJ4QBSESAD0F0PS5\nhB6PAkvIj1djkyMdfexFXAY8XwtyekiPTxz8xYuxYAH6+1EqJfxL4uAbAy/i4MexIwS35JiOJSKk\ncbRMq1eZ/jRJklQwkCJbreu6if6S9MG0tKFzQCRlQqnLSYRj61nM3m+Fc+NRk1FPvT2KPuQ4e4Ee\nS1wrpIwYC5UqGNo5YwwoPBAOVLJ8t337cP3taOSUl6HVebd5FV9irDGHc+QPgfNzUw0GfBS4Huix\nDKi9XBco1o0v3qn8N9F/Y39/PQwzRptz4ULnmrJ0r24CeFbXLMDpQKoUAEkqFCkLQnhaXAiNAiYV\nSjMIODBIbCxNi1SaL21sx3v0YxoYB2aAIwDjvKazBacwTwlAShK9GGqp1M8YEbvYqG46rVDmZZf0\n7b23Wo2iSAJSytgSYpetJjiZ3jwNnH0E4rt45rv4G+CSS+KzzppetWp6bGz7+Pj31qyZ57qrNZPu\nwwADdp566l2nnroJM0/MzEwTqvoNH11uuiJuFPVEERMCQijN6893gXcCKwAX2AcMA610FUlB5EXa\ntTjh4JNt/aJFOOUUjIxgYADFIuK4DQ6iYN1skmsxKK1KyTQGmeswZCj8IXAXsNZ6D5POA300GbUu\nQYqo6URqXcoBdA4Iw0QfnBYHm/Ca6WDQ6ylxTjeDsN4oM915pxB/x9hnlZI6agu96GAPSxZvFXm+\n286duGFzIvjxrbqhmRu9xjoBe5wX0jvMNonzPuCMXPxdBHwXuFw31ip6j9pU+rYMl3fyjQmBSeCh\ngQHSVrQ6gUIDS+mg0pql7tVNAM/lOgy45mStVSiBUgVaaLRVKEApDENgEOhhrMSYx7kLkvkpqY26\nDA04AwT2gNOB183OzgegD8Kkr0+6JYwZ+jz0CqUwqhurq26vUDatg7xK25b5AIpFCnAG7OWlm+C9\nwBDwAeDDwLuAAaAJvAKo3IPgHuwEPnGquugif926A8uWHRgdvWvFit6RkVOBs4AXAH8C/DlwrL//\nV+ecs/Gccx7EGw7u3etv24aX3nAqWi3GWDkMJRALQdsP9LyhZjoOAIuAvcBSYAFwAOgFmrlxoiAb\nRccpk2klbeQuWICRESxahMHBpBtTLCYIo9nZNq7Vso1Mlpq0KDOjyCyn+TORXm7y47jXbFdRgmm1\nEnt6kmDSdHV62qh1fb0gEuQoSVEazmr0wSXHkZZBbmgBkHuBYeAdSr0f+HegokMeGdwXNO18/eOS\n+G7PPIMdO3D0KOp1vPjruBSoW9HTjv7oZO0yBdCtmPE4KuobZhZ4DFiTpiUOAgHwS6AJvBDgFoYr\nQ47LCISUlgg/smBBMwxrxHeSsr1kftLRGr3+LhKumwCe4/VFpf5fxmJzClYqooVGMm61Cvw+IQAs\nYKzXcSqOU7BxjEpBSiVEKGUgpaeUQ0DgdKOTvup7rRVKqS1EaOZJz9heodTCm9gS3gSWbyU1mqKc\niIL+5E9Xr0atBiE8KT3LRMxNm4iRAeHHgRuB/w30AzWLn3HxPjj78CTwQY5LL43PPHNq1aqp0dFt\n4+O3rFtHTDo6FlwBKGD7+Pid4+N346VPTk3N7N4tH34YV7+tGCkVSelp/zKbiNCvD2EOsAbYohdx\nzfI2PYiDzwqFNj+1vz/xLiYjFN9vc/Ap7uvQb1itKtcusKeIQh9HpO4zOIAvZTOO60FQpXUN4i2T\nVQ4h80ipRWrdWg2tVpimJHUUB8e53kXRcehOIKOCQqdh9fuBAWAACGi6owfsl21DsagM3+2xx5Jt\nxQ3fQi0Hu20ZHkZu85Y6MPf19NSiyFHKNM0yFDbKlFdaes0mcCcQAb8DuMBdwMPAXwItK5F7aXko\n06G/CexeuDCIomnfNxvmgbVhnpl1xbm5muhGsW4C+K9cR7X834gQiqS2ZszRSPdTlOoBBhyn33VL\nntc2RqdwLQTimMVxMYq8OHaEYFKac0O+8z5hMYhI9FKksbBeGYVOD1KDDWIrfNid3Chdx5keiw+g\nUqF2hGPa8Z2saIuagvkV4F3Au+mIYxkY0EF+m0T1bmfe5mKlt3dF9egFF7TWrt23bNm+0dGNK1f2\nzZ9/KnAucAlwNfB24PDg4P3Pe97G5z3vYfmGI1N740cewRmvZBkndLMVfAQYBA4CnwJer8teomf/\npTYScDiH4afaEHwCFhmVTqdLdnrkFZklXQfQO8yUainVEKIQhm6zWaLBchAkal1KAMa5rNVCs2nU\nuk0haFrTsY+BNJqbVP+ulK6UVHrbYdeYfdaB49oXnkqBMvC/7kaphCDAsWPYswdbtya7yRd+F7Nz\n+B3lm++GunPbwoVoNqkK4Z1MKz39Ln0U+KQl9m0CNeAuYALwgQLwz0AZ6AP6GLuC7nA9FRDAnuFh\ncwKebLUiGnRJGUgZkq7a2jC3ZUt5xWrUDWHdBPBfub6u1FWMVa0RbhL4lKKCZT1QNfJzY5hOoEoK\nCppThiDgQVCJIiWEFKItnE9/CQ8CU1q4GWrnP08/48lXKENr8zaa4yDvAz+98kocO2YMFBMsgQX2\nspvglAboKd4JTAOf06GZXthXaAbrOJVSCb29u0dWQizF1BLvdf+wYUN0xhkTK1dOjI09Oj7+jdWr\nFwJrNXziZUDE+bbly+9YvvxeyN0nTtSefFLufRgL3tqObgQIOgIUgUXAj4DzAGNEVdAwAGaOXBnn\nyDAEY4bG3FZkEgo/Q2ydIw3YjowCaABlgAEtMoaMIub7fUDFVuvSdJ3UumHYVuuS4Ql1MHLtC1/r\n7jNNDDiO6ziOEORRkzF6M9rQKvBHwNeRTHTffD/mzUOrhcOHsXs3HnsMYYjjx3H2f6a2qMxtE+aa\n7zZ3YRLIO0civcNskncFGGIs0umtrA8HBFw6ChwBVmun5R8zVua8xHmBc+J2TPo+fYjCnnXpjpnZ\nMG+lW50ZuWq71dm9ugngv3LtBk7VXmC+7n7QofUioAr0Os4Qyc8JCNzbm6DqgTaYlx6MMcZKYZjc\n1oCnVJ4G/CQwqHWHPb9uhVLkICotPUjs+E2uA+3dVMeB43QEe+UrO9OguA74DDABVIBf0ECP8wo5\nEZIV7cgIli6Nvv5tDA5S833tW174/Oc3167dOz6+d2zstpUr+4eGxjST7o3AO4GD8+bdO2/eHRde\n+Eh0/bG9e8OHHsL0q9tUnEjHlB1AAKzXq8Wpgt12LSb6ghAA0GoljQ/LL8y4FkdzWBZnKPwOsAsY\nBaY1YcYlTF4c05A/iONyGJbSat2YMHm2Wje9HWKngQ1pCnTbsNN1nSjiNKy26JsZzZIEvgocAOYD\n738Ivb2o13HwIJ58Ek88gTDEoUNY81NMd6Kdi07AK+N1WgM2n3YapqdTO8yM2cuTdhooAFcp9R3O\ni0r5+mBni6yOAQ8CZwPQYqpAa5ZIOgHNi5a5DfMwbdBte3Tnj9S3dQcA3QTwX7zuV+pSxgb0QqNR\nobyCQJKOM1AouMSDHB7GvHkYGEC12vYtIbcskp8rBaVcannTw+plGxoazTyrOpp3NC3A3CuUcu6D\nfAO488YbceBAdkk113mwv9X0lb4dWK//75uAy4BX08oSzWBJhENUnHnzsGABFi7E4CAKBUTRji/e\njWPHcPBg8XVvu/zy6IwzTqxYcWJs7KHx8a+tXDkCnKaPBa8EfM97dNWqjatW3feqV+05dqy+a5f6\nxxegBvQ5ThIpgJ1KtYBjz4aDD8D3E9NKM4ylHCBEQNUlEFlTVjsZ2DulJeB5wAlNJ2aAQzDUOI6V\n8oUoRZGn9cEJIkmISAt282pdSgBrAaaFMSxtM0cx7uZjx6aBJvA6N/udZelhdQW4Dvi9bSgWMT2d\n8N2efhqtFvbuxegmTORKh9Bay8qb4oVAHdh82WU4cSLxOALsHWaVG5yYV9LrOL6U1F3MpxYJbAHO\n1ZqlotYsuWlzeakHXTYu1J51NXSZn7cK6Jb/3QTw27k2KfV8xqpA0xqBMqDAedV1S1T5kvpwZATD\nw+jthesijhMsO50GiEUTx4hjTwiHc5oimN636XtQe/QIUNf97rIlfJzLupK+FRnfAmU1f1rAHZ//\nPPbtA2DH/cxX+iRYgnOBe/WP9YHvUBnOmMc5JyJCpdIew9JhyPMSG8JiEYVC8I4bceAAHj7EPv6L\n008nVPVT4+N7Rkd/umrVQH//Mn0seBtwI7BvwYK7Fyy48xK1NQiO790bbd6MP7rOdaUk9E1scfAj\nIbwMB7/VQqkEpdpYZnKu15L8UNP6ojSqL1V9WwNhBxgBSsAR/b6RWpcYFb6UBc5p7dYsiLQNT3QT\nI7CaGMuAClChEx5jtHSiLLUPhTnqd80C34zjo8CrOlnGm3PAH+4A55icxL592LEDhw+j0cCBr6EI\nHLMCq9ALt06nzVtpQRcefcUrcPRo4hyppQdzDU7s/bWS5/E4pnGXMn83vT52H/B8M1rTmqWUEMj6\nW/mJRWZqbUf/APhpt/zvJoDf1rVZqbWMmQnku2k7n/NqsZjg4BcswJIlWLoU8+ejt7cN7id1oJkE\n+D6d6ClM8LSnkskBBWA+sAeYAfqt9ZlMfSSsalFaIgqmA4S09Bib/uM/cODAc8YSeEAvsAFoArNA\nExiiLyrNYGn9lRxxjSmu3ZSnjVn6F0CdeSamp/H9euPb/gLVuPzycP36YytWHBsdfWDZsq8uWzYC\nrAcu0i7HjWLxoTVrNq5Z80u87un6kcbunWr5CxN+QwgQB7/fmAM7DqIo0f4DbR7n9LQBaKswpJ5M\nkBbkRNZ+qUr7jTCgH2DAdk1vZWnDE6MPzqt147RadzXAaa2BsSLnXmZlhJDgWspZsKQyHvAfwBHg\nyjQilLjZH3kCSuH4cTz9NB5/POG73f2NpICwfU8ztHOeC6MBsOVv/gYHD+LwYYQh3TDG3YjybkcK\nmzmRoFgsMKYYk3EsdVaLLA1rHzAf2AhcbHVWHT3rQqdFhCiXADr+yUCf0rpXNwH81q4dSo3oHFAB\nHMaKrusVi6hWE/wA0YAXLkRvbzJ7JItVCkzkqO55xnYcaVf0jPZcAkuBbcA00Gc1gmyfVfP1yxvX\n2IWeD9z/4x/j2LE8liApUXNONWYoCuuFfRL4ENAC+oHH9QxW0fAgP4ClhEdETNq98v3kNKCJCFAq\nVmpX5NV/Iho/VucqdfbZ4rzzaqtX18bHd4+N/WjVqsFqdblGVb8TKAN7R0Y2jYzcBfnYvNaJPXvi\nzZvF897kNKKo7PsF4uBT840EOTSJoalAvW44+HXDT1WKJPmBtU0aWOQf+zhVBRRwEfCAJTpMINWW\nPjjJwcb2XTcxKMesBypAP+c9nFccx7X5oFKC1rylLGhaBs+1aADcArzYStV/thmDg8mYd+9ebNuW\nkC9uuQXI0c49y/EiI0eme6ABPPmxj+HIkbxzJB2YnhWFbWpq0+BgkfIZQNQNW8Na0XsMdwAX6Jfn\nphNSpqtji1aDTrgIszN8e7f87yaA3/p1RCnG2M/oNuW86DhJ34NowENDmDcPw8OoVgGg2UyE4QSC\n1yx4mw2Z35s3bXcJlIDrgI/ocslUSTzdcrXruEyDiDo/j9x6K44f79AEz/RAOpEJbJlHARjQFlpT\negYryJHcRuIQc41WcCkRUultiAg6oESaxEnPuBGoP4z6w5gFrhtQl10WnH76keXLj4yO/nL58i+N\nji4GzgAuBn4XeD0wWy4/eNppG0877Ve4Zt/Bg/VNO2obruaubRpMRoykzyEUaKslybU4inY1m7Fu\nXAAo6t4F6VUGNajDBiiV9Es9oStN00kvKmWrdduntPSe8/lAD2MDjtPnuh6dmei0ZHJnHLtR5Max\nKwQXgiAQNG4V6anPj4CLAAFceTcGB+H7OHIk4buFISYmMHQrrgJawFHgbq3GmYt2rrTG6eBnPoPZ\nWRw/njG5C+PYF8K2mjg5ha0AoFgkC8xAa1jdtMi4pBtc9wBnm0NAzoHDnliEWiOXaXUKS+lwtBuq\nugngv+lSSt1JJR7nnuuCiPDlcgIfrlQSGSi1fQwC3h630rmYAoT2M7KP88w6B9wLvBWQwGf0SqpZ\noXQtuK6Ta+NSKbT1ppvAGA4eRL2OyUlMTNgmYoISgJUD4ty3OuP218uYp5QLXAlMmX6uUkpKRjNY\nqjyJwFOrgTEEAWZnceIEJifbHpaUfrRrppHimMc7pzH9fUx9H9cCz3ueOPdcYtI9MTb2/dWrh8rl\nlZpJ9x6gAOxesmTTkiV34eDj9fqePXviLVtw7afWt514NYXCD4Ldx45N6BDvapDyXJzqZcAs8Ayg\nyNZKwyH+EPi+JvAYwxNbH5xvTMfA5UCFsSHXHaB9BRING3Njg5X1fYRhIQzBmBJCSimsN8f0wecD\nvwDe9wAGOvHdFv8oyeIRsOniizE5+cMdO3qAF6Z3OCiLn/iLv0C1Cs+DUjhwoO0cSXeL7yuyG9J3\nS8ZuyHTM7IsDJAFw49iV0qH+fs4Cs6S3B47r97AjL9qeAahOtDijWZoGHu2W/90E8N93OYBijDHG\nqfdtQrz2yE067Jo4lnjE66YH0j13keu5K93Spf2jDUAEXADEwOv1LkLROss7Ob5uDDz55jejtxf7\n9yc9ENMEn5425/otExMN3cqARd6nZX0X6AV2pw8oFS1GBHDcRAFjYUaBo1hsz2ApAVBWoCOIfvYw\njo1hTtTJNZOQGG8Eogcw+wB+ATywQL3gBcFppx1etuzw2Ni9y5fftHTpUo2qfglwPTBVrW4+44yN\nZ5zxAK7dvX+/v327uuKNp8ZkGBnHOycmKsCSjFewaeVrlWFLByZqnpDH4Sc4b0hpLBsPAfPShifF\nOdS69In8HlBhbNDzBoyHTF9fAqx2XUjZ5hRRp46xgh4JxIzRW2QiJnnFfOhRVDTf7Ykn8NRT8H3s\n3493397+QAPg3++9twUMAR5wD+dDnjdcKs2vVhlxswcGcPhw0jHLOEfqQ9tcHTO7adbhKhQQBMxx\neBzzdIMo430mgGNWkypzPwurMjA/B500SzPAQ93o300A/91X4lRnut7Gb4S+vdTjpq9Qo5GYkFi9\n78gsuViTNNlpmOYBS63K9LtADZgG3p7bD6AG7vEVKxIU/pEjmJ1N5KeGhq/9YO/bs0cCZWCe/rLJ\ntPqwBdSBWeAUoKW5/AAqjsMAJaXUZWColG+8yOv1hLhJ/DXKBORLVathZsacP0QYNvUMtiMPJzRe\nYFb6+fQxTH0PJ76H1wMXXSTOPntm1aqZsbHHx8ZuXbt22PNWafjE3wIO8MQpp9x1yimb8MzO5uwU\nMek2XM+qjJU5L5CrJX2axiuYZKbaK9hJR6uPSDkF/LWOXNcDnwP6LH1wZlBvx6+XARXGelx3sFhM\nPISHhzE0hP7+NjuIoHLT0+1NAimLWj5ErA7P2pR+zXYQ340MHQ8cQLOJHTvw1QfaZ5pYN82bwDTV\n3Uq1hKhHUSHjHFksJgcRg7ZuNtFqKd0xa+olBtOCtx9550gBQG9FMNP27GSBafx/9qfv6swYgHca\nWtiapUlgRzf6dxPAf+v1FcbGzODLNL4z8nNCj1HXxTpHmxzQ7nukHyJtqUF3/Dxr9mUwol8EJoCF\nNBtgbIjzAc8bLJWiZtMztpE0cza9Bd9Hs7nlsccUMKRLYEcjr6Vu5hiVRVk/pgAFTAHTQMl1k8ii\nvSe5Ur6UjTguBUGVXHBtKIIJKJQa63U0m9L361FkV5SBBUUILSgC0mGlonPDe4GD92HbfVgMvGWJ\nuvhif926g8uXHxwdvXvFis8tWnQKcDZwCfAK4C3Aib6+X5199sazz96C6w4+/bT/wGPyJX/ab1xc\nuJSuEEUhIvIKJum6DiUqrV5/L/Aefeq6CrgJqADNnOFJZoxZAQqc9xUKiWZs4UKMjCSrEmT1TK6f\nk5Mgsb8WDZPzV9JCsdRil+8EkPDdHn88GfA7X8cYMI/zBEGoP1D6NA+f3DmSrNPoqaljFgShthtq\nCtGyttgCaxsrmIPCdru1PaD0uMv0PPNDrwIwqH1AC9Z7yK2JhZMu/819G3QL/24C+B9719pr9Cb6\nm8Y3gEYjEf8Qg96MXnXv27fUFPkckPFU4kA/51Qj+6SVtkSB5KUOpZpKeUI4YQjG+pQqUTlpmstS\nIo53b98+TavLjJU5L1q2uqYEjnQ49nLbmy4FZc8jFy1PKXo9DGhKWYhjLwg45xWK+JR+qJK1W9ut\nVuT7DV1RUgLw0xWlr4lmmZgi0rjQQWAEmAHedBDlW9APXM3bqOrR0cfGx7+zdu08ztfoY8FHAQXs\nGBu7c2zsbkzvmp6eJibd9X+7lCgRXhQ5cczimLyCpeUVbGsQ3wPcqAvYVwE3AbMWocjuYtMr/0vt\nWFAmx4LhYYyM4JRTsHgxhoZQqSQYUbIToF4QKYaDAFHkaFYHjZdHHsV4GXGMEyewdy+2b0/WnF/y\nraQQHnJdCcRStgG0SnlAg7qXGedIKRPnSOPcIKUig6M4DozBkbXA3LI0+L7lHJmxD7PObu31sbyJ\nPKw9hjJwSJ8GitaJyu2kWVK6x7W1G/q7CeB/7GqrEaT0hRBh6BgcPOcIQ/T0JF9jc6KfmsLsLNGA\nRRjaMMioU/fDFp4zoM91Ayl9KR3A0agGuo5pLWZBKUdKFscKiKWsxHEpCIquyzSRdNszzwCYx1if\n41Qdp5A3MpSyLERAJbBSXKnMAid93/5sZuYw8O1i0ZWyCXiAUqoopSsEjyLFWKxUNY6577eJeJQm\no0iGYYsecdzSUMzWs7bMjKzuh8GFHgWUDkD/IlHehNImFBi7YVRdeGFr3br9y5btHx29Y8WKvoUL\nTwXOAS4B/hT4C+DowMD955238bzzHlJvOLx3b7B1K668fh4PgjIg41hYNgl24hkCGsDfA2/WH9Ar\n9Tkgb3jCgPfSsYDzsuuiVEr6PwsXYvFiLFmC4eHk1EhWYjStqdUS5ZjrGvwOAxhj4V1quIIgwNGj\n2LMH27ah1cLkJMT38O/ADUAD+GoYTgEBcGOpZH+gs0qRuszLOEfGcTEMXc4dzg2FjQyO7AXmQEo/\njZs1KB6Zc6qJ6CRhrY9Ji7mUcZA3ezAF4BrAB76pNUKF9KyLpx2WnuqG/m4C+B++KFi7VAoJUQ/D\nfqLMO05S9ZfLcBwY62CapGn1S4NKKmNpZIE8A8t2PPU5FYtuHDtxzKVkUirtDCWASaAESMBRiktJ\nosNQKV+IApmIMcYY23fiRBHo5XzAdatmV4sCtD4f0LC6GEVuHDP66tpKdkuGEQAvDYJve96sEB5V\nlEpxKVkcU+Hpx3EpCAquSwFFacc0GsMG5F6pub4m+tthJcw1XgTwCaCepmBWgGlgSg+NjSCnoNSH\nn4bzNFYCz3dw2WXxmWdOrlw5OTa2dXz8W2vWLADWABcBFwEvAQRjjy1bdseyZffgyt0TEw06Frzy\nBtcTwvTcC9YG0wAwm97FfTnwL3S60pu9nrWy5zDGOS95HsplVKvo70+AGfPnY2gIpVLiIE9yKUNq\nspfpGANjtfvkomE0mzhyJMV36//PJGvOs5qEM8BHfH8S+GixSB/oCaUKOefIxDqYc1cvpyQQC1Lo\n6h1mGvzaCcA4DolOjkMBkDQ8pQz1rkl+fcz2v+SW+OcPgB/q4O4yVrRcJKczF2zqAAAgAElEQVS6\nQb+bAP5/vMg6lQMFpZpC1KLIa7UqtHxEG6eEgycZKGnPGw00m6rVqoVhQze+8yhgCl7SYtMnZ+Ry\nGWFYYgxxrABbFPgC4BF93GZKSSkFEEaRr7/SDmOtRqMEVB1n2PPKpRIqFfT0JFGGXjY1fGlS7ftO\nGJajSMWxzJXAtlPYn0bRKqBkAADkVKxUJGVLiKLjEBSBLHGEUoKoOATGUSrQKdDmV5uwkuf6xrl+\nlMFm7AdGtDY8SKOTCsBTig/dV+jdWsXg4Co8ecEFzbVrnx4ff3ps7PYVK/rmzRvTqOrrgHcAh4aH\n7x0evuP88x+J33Bkcm+87RGs/GPY5idmg+lzwGv0JwXgGiAEvg44acMTIvy4nLsErDYfgdENFwoI\nw44Rn/onJBqe2iKGq6jVcOgQdu3Crl0IAhw6hFU/Q02/G/1WyWw2h98dBJPAp1z33cBnpRRG9CVl\nbJwjOXcBbugglAP0DlekjWsyGM6GXpfL2HlGwM8uvxyTkyT27bg+FqfXx8zEazfwBiACvsBYCNyg\nVNyN+N0E8D983c5Y3AnNGAJ/odTNjHHApdX/KOKcK8Z6jIyPcPBKGRowfD8ZpllA4HwO8K2vU4oG\nXK2i2QTJpTV4gOYHRWDa8r2jwi1kjLAEDmMO1aScD3pemRbWyCylWk32pGhJygiEOAfgKkWN/oxT\nWFEjSweBGvAOx/mSEMkyME0dpQyVKkrpCWGgCMp2zSSxjUH75qxoG1ZzwHajDQBmAau5RSjqA44B\nffptNKsSLwKKjJVct5caL8PDu5acgcElUAsLr3vP5ZdHZ5wxsWLFxNjYw+PjX1+1aiGwTjPp/ggI\nXXfrypUbV668D+qp48fru3bJxiVt/DKNo28G/jANzHgFcAzYpOeuN9MhwFYMkx0NBXo6fhlIlKF0\naM/6RDQMHH0w7Mnx3fbswQvvxoTF6ngzcFNuJZCuN8Tx5x1nwhL7xjaFTSnXEsUmHtEWhc2G8Jg9\nibmcIwOahPk+wtA32wPPYn2MAZ6G7zaAGvCPjNWB93VzQDcB/A9c9zBGk8YBfd6UaSxXA7iJMcJt\ncqU8KR0hWBhKpUIhKmFYJKdZYwUjRBhFPj3SQOCWZWrRSlsaqXRVhb4+6uNDStqntz35DgBMY8uE\nLtYKGkswImWJsV7P6zGs5vnzMTyMgYFEfEJNZxLvm5BEHIK0U5ibtuvqA64VYp1uwUNL6UMaOUrp\npqleiX2NtXWcCSgZL3KZZtZP2zPFHAWzDBwHKoCvjQrodXqcV1y3zaletAhLl2Lx4vD//AilEoRg\nL37xunXEpNszPr53bOznq1b1DwyMaybdW4G/BvbPn3/P/Pl3Qj0ahsf27o22bMHUnyZpwNhOmZdU\nAh4E6kDNHGLsit4wq0k5RsmAsFGEqTDoBTItkHLfA/WCxXc7dAjNJjZ8DWdqgZatpyxrmY1MQxoi\n4GohbuL876SsWIjAFIVNT33sHeYovX1mc5gzEikT/Wd0AhAGuGSZX0bpoVfm4sAQ4GtMHnX5PsLY\nceAfu2mgmwD+m65fMuYCw7TLoyOXKYIifetTd/UJLZ10lWJCKNNLjeMC0YA5Z4YGrCdp5GrkayBw\nvvGdd29Phml9fSmMqF6qJF3g64DvAb1pLGhRKVepMaqFHafPaM9HRhJa0dBQCpVcKiUmVnoYwOLY\nFcJRigthr27aVi0hsA04U1eUlH5oJ9ajhVjtYm87vxuubz4BtNIxxf6NvsF5Pf39txWE5FhwHKhp\ndt5bAYcxh/MyGRXQrz9/fqK/HBpCoYA4VnfdhePHcfAgDhwofeDjv/M74fr1x1esOD46umXZspuX\nLx8BTtfHglcBrULh4dWr71i9+v7Xvnbv0aP1J55QH7wM5+lXYl5PUb/4pv5FaArK6ZhIa1aEiQ3D\nJAHMzCSwUu0ejCjac8e+OAZkwnfbvj1Zpn7pNxNLL6HXYrl2ryxxbpqE9vs8CCwCXivlsDbpjfSB\n6TlQ2JrWB5Fp/TeBe08/HfW6Efu2J14669tDr8ylgL5OXsEu8HbGJoCbu2mgmwB+i9c2xkJgSJu5\nFzg3Ar72dqhSZM1I9+UMwKkrrRQAKQQlgIIQBYsGnPS+iTivd+jNMM2upIyUIm9p1KIWEDXog4A5\njhPHFFjN438BP9J+UqHVBO8BPM57PM+hHgiVwEZ9SLyKRqN9FKCJBXWxyClMSmoLUG2Y2d4saipO\nv/aKIb/yolaRcq1ZyrDa7YoymIPrm6koJWPSohDbPGTTC7oa+DQwqyMIBzzHcYnVYex6aPG1vz8J\nvmZhrVDw/+R1OHwYPzvOPvbImWfK886rr1mze3z8qdHRH69ePdjbu0wz6d4OvBd4euHCuxcuvHOj\n2ub7J/bsiX71K9xzrcbgMBYpVQDqevJJrgAlKvxrNUxNJVrhSgWcJ+zSNKtj81d3DknEMY4dw969\neOyxRFP26u/oH5tjdThAkXPTuC+mHeT7gYXAIe0cGVkdM+9ZUNhCS6OFtDzMUNhawG1r16Jej2l9\nLI6bQhihV97/UqR/DjWmeoiUbgne7GPx/8PYrd0c0E0Av5VrC2NlYCHnVc6zZu5SQkolpTFzd/VN\neUBvAzDNK4+1pZHLmKtFe+1Gqvb/CjsN0xrp1octkAiBjTfcgEOH2rIQzhnnTAiWXqo8CvRby2JF\n4DIqoDgvk/ikry8pgUdGsGgRhoeTbbVaLTkH1GoZpzAzEszIUjNp4AAggIrlYWlr4fMBRXSqKP1O\nFmamorzF8xpCzOXa6Oi6+xbgQ8BbgQ/ZXsGOk6ieisW2UbDx7WpHsnZMUaOjOFBv7vZrUbSyGG7Y\nEK5ff3T58qOjo5uXL//y2NhiYD1wMbABuAqolUoPrVu3cd26X15zzTOHDjV37lTei5IxbNutU6lW\nHJcMLYMg4UTLoARAzvJTU5iaQq12z6cfHhnqwHe77lY00swMlmZWe47jUQtONwlt0VQfUNNveANo\nWZZzGV60mgPDGXSyjjHQ2WkAtVorCJpR1IyiFkkefp3YV1r/ckep1BKC0bfPQuDZLKAXM/bzbg7o\nJoD/4vUwY32M9XPewcxdr/iyKCrGsRvHnG5KQCl1A/A1fV9SqzRK04CzMEgDQ9fRP9NLbaXXnVKW\nRqQHpxdm6UMUYC/WvxL4OtCj4ymVdZwx13GKdgk8MIDBQQwOYmAAxSKiKFGgk4u6Dv30RMo810l7\nL2XggKbi+M/CwzLfUgitNJMp/1vALeVyLY4zm9Ii3Xw3KvKlwHeAhgbttTnVeh+ijaq2HRoIUm1m\nsJZNwpYGb/xQ1X+gLgXOPVecd15t1aonxsd3jY39YPXqoUpluWbS/TVQAp5avHjT4sV3QW5vNCb2\n7IlnHkD5umRZuhnHlSAoNhoJn4NIsbR/a9YJZ2dRqz34L5tH+tFo4NAh7N6NHTsQBDhyBH/x46RW\nCC0rx8xF4iuHOmBpB3kDOm0Ae4H5QIM8CTpZzuXHMMHczpGxdhu9fcGCyWYzoN0x0vtaYl97d6xl\n8bTtAyKKxXIYqjiWgCCznZwVzGLgBYzd3c0B3QTwnK9HGOvVZu4OKfOo/qURrgkTQUDKyEoUQQhF\ndahSRwGpeTWxsTRSymXMSdOApTVMywDNW1Y7teMw7Wef+Qz27TNyQPqnytVN9PgD4BagBvQBPboE\ndjkHqQ8JP0kPKoRpW832UjfPYhV3Mj2ZQA5ZWgTeAHwC6ANauWUo272gYwsoPmlFOQG0V6bTIsL8\nyjRNcSLdTzN+Bm1FFjXfZ2YgRGJUYDjVaZuEOM3qEMAPgPqDmH0QM8D1w+rSS4PTTju8fPnhsbH7\nly374qmnLgHOBC4GrgCuA6Z7eh5Yv37j+vWbce3+Awdajz8elV7mFIPAazS4oWXUakkyMFbGzebW\nLz3YX8HsLA4cwK5dCd9t3z7cuBFNq29maBn2Sq0CONnHS8lz7sEmBwhgEbAbGAJqQK89Cch5jpqE\nzedwjhTaOfL7AwOTvk9DLzo322LfzO5Yq5PYNwJQLEKpIhDHMfWyCto3u2j1suYDlzN2RzcHdBPA\nc7t6GCMzd8dwGXt7O1iINBpUEXNqeSsVSVkA3qXUPwBVK1iX0jTgfD0bz5EA8r3UyFgaGeuutCgw\n795FT/Q7wK3ADPA39DNNCZxpbVG4oRhkHrYGMf1EopNNmLJ6Qf8EXAv8M1AFGjkqTmYMYCcApGNK\nhus7A/y8p6ceRaG20+qIC81jM5S1dpTlVJfLUArELAoCzMzgxIlkVVsrFzM2CfnEc90E6rfi2K34\nO+CCC8Q558yuWjU7NrZzbOz/rFkzVCyu0seCDwAu8OTSpXctXboJrcdrtamtT00/+CCu++gKBEGS\nhmlZOooQho/fsr3sYXIS+/e3+W7bt+PvH2ItpJgZvgVgsPNre3OYMabb6Pk00APcBLwMGAEqltGp\nDbEwnxebm8Jmov+3KpWpIKBuJ6mBzaPj+lh+4pUcectlSOmQCM3o0Kx1vKLuZXUtf7sJ4LlfJc4H\nCgWnWk3M3IeHMTjYpjjkzdyl9KQsSBlq0fRbgX/Ua6u+7n27nWCQJ0kAc1katYDbv/IVHDxI4P4E\nKE0blWmGaCZGXwbcqmnSZuUnKz2cnYVSyerpzEwiQEyXwFI/l9AWgB0XOClelIEQeA3wOaAnR8Wx\n1/fNWyHTcvWMyWUAzAJ39vXVw9AoCCOr9RF1aoAwoNdxAiljIUzPzY/jMu1nzM5iYiIp/EkBFYaJ\nhWTaJiG2jQpydsF24rkW8H+Jfb/EXwKXLVIXX+yfdtqh5csPjY7es3z5F5YsWQqcBVwCXAm8CZjo\n7d181lkbzzrrAVx3cN++1mOP4ff/cqX5gJ74yV4nx3d76zd5poFuGikqty8dA+32HdFqdY1sz29o\nKvAx4GZgFngb0KPlszaFTaV1OHkIMyXyz3geA6ajqN3w1O/bXOtjYW4sRO/qbRdeiKkpEry52jfb\n0QolM+0v6CZndxjQTQDP8er1vGKlkjVzr1YTM3ej0kPKzD3hMlKPVanDwIBFAy5Z2jWebuzEJx2m\nZf4YtT4SUSCF5rQnnylvO2JEX2qZQcaAlJJTA4SCIDGLWq2kBCarFsKJWTkg1AwAO/CJdO/FFj4O\nAj3Ae4C3A73a5qmY7iqo9NiWz+1Ffv/ISM3362HY0sJZgwsN58CF0k8oe54bx5EQIeDpznvZeAVT\nt90wq2n0OjuLmZm2UUEYNqPIN+D7dMqZq/M+wPmWIyr8rgq+i7UMF18cn3329MqV02Nj28fHv7d2\n7bDjrNZMug8DDNh56ql3nnrq3fj9nTMzM7t3i74+yDiRez72WELO/vPv8Ga6gW4eUW4eLrQRLqE3\nZbpPaKcBygEV/Wl+ALhRY0UyzpEZ62CW/qSawD9xPiNEamww9/pYUy/Sdzwc+wB6ehLBm+WbzXRP\nNXOIoTTwe4z9pJsDugngN04ANph36VIsWYL581GttutiY+ZuzQmdOOacEyiNAx8C3glUgaZ1iPY6\nlb0ZGYOY4zQd6+J3y8c/juPHkxygWxMijgMhdvu+r/8wfb0LQC9QAoaBrQAzkVqpSMpAiFQJTKpz\nYtCbEphyQK1GEVAa8z+NCI079V7sunKeVjT9A1AH3ql3JoyPsb1JwOcmO/rAwyMjs88CFxp23Jou\nlbwwDMKwCDjaqKAcBBVq5SmV5Dxjk0BHooxXsBAtQ0BLewV3hDUB6HMcys2eUk8q1boHzXtQB645\nVV10UWvdugPLlh0YHb1rxYrekZFTNar6VcANwLH+/l+ee+5G4I6NGyf27MHWrfB9TE7iz2/lzRwt\no9mJlmFaKHcvXYpGQ2jsmuwkmrJn5ia1v10Leb/QyTky09UkdfKHAReY1UJhZLY95vBwb6XnFsKa\nMP/0+utx+LDxyGR6NJUXoXHLNzvsBvJuAngOl1Mqgfo/pIxcsgQLFqBaBWMJSNngPMm0z3WNmbt9\nR34AeIfV98iY7aGTp91cwzT6JtSAze97Hw4dSnnytVrb9+8/AbhAGejVf1dYBDSKv+doOFpIAGel\nmnFcNp5crgshEmCRKYFJXk4lcLOJIGjqjWW795L5J9L99/n6S94L1IDPAm8B6jqaFHMelnk32hDY\nsXRpKwynfb9FCsLfHBeKSgWM+fV6m1MtBHGqS3SYI061GfVTW4y8gn2/4fuNKGrzmnK69blwHcVi\nsRjHvhCOlJyGKIAC/m0fbtsHF7jawQteEJ911tTKlVNjY9vGxr69bh0x6S4ELgJ+D3h1sTjx0EMJ\n3+3Pf8AayNIyWhYtI78yEgCJzbIpwzuNcGyQRpQ701wL+MAs8EOgbB0C6Nd5BdCvZzzTRjg0x7ZH\n5gRgxL4qVxiFdHYx9sg2DUmrHuyLazdscmn+EGNdVkQ3AfyGV7GY2PKZGcDwMHp6gMTINKWLt+9L\nbQRm7rj3A28Hqum+h9vJ045ZgY9llEJACMwCj7zxjThwoO3JNzuLRuPePXsqwCKgyJhrYSrMYm1g\nbc/36HLVUapll8DkNELiE+PbZUrgWo1ca8gApKX9XmxQXb4ENm9Cv9ZpmO7BF4FjwDzgjVYO6Mj1\nDYA9ixZFQtgKwkAIA6D3rQjSsqLwS3TdGgHfAerUQ1CqqW0Sikp5cewwBsZ6leqh3zfDqSav4DBs\n0S8exy0pW2lS6VyJxxxcEmBfFDEN7JN6drIf4MD7BXrvRPVOVIC3LZcXXNBat+6Z8fFnxsY2rljR\nN3/+yJ49ex5/HGGI/ftxw22sbnWfMrSMKMdfM26IideQlJHVJLQbdyI9E/4G8OI51nF3M9ZQioAW\ns8AU8ANgutP6mJO+mUUnrZevW3YsV/6Tauv2j30M+/dn4r4BadBKjbJeHrPaQXcBTcb8LiuimwB+\ng8vzUCx2MHOnZX0T8W0zd2PjnmuwfhB4M9APVHNEeJsU5qQTQKbxvfmKK+C6OHjQ9uR7eOtWACOM\n0aKyZxHWpJ62JaYfVt/2IFAEGNCSsiGEFwSO4xSNU1it1rEERqvVpPV9IUwQDHLU0iAd/elXqHLu\n0q5c+necBT5P7CDGqoxVHKfCecl1C5x7mlatlJpoNmOKXFpBGGpmRpBWEF6qXwC9vUUdRF4DNIHP\nPfEEBZSC5lQ7UiKOyfnEcKpdA74nzWIcB1HkC0HOJ4ZT3UpnnVau/Bem897bS83roknMUsZKFYDr\ngW9pVgc1at7zFApPwWPstY7asCE688yJ88+fPHxYbd+OKMIbfo4aUuJ3OwEE6aGRbWR/3ymniGaz\nrV8y2ydpCZN9bivoDzSf1WwGA41eLwd+rNfHqr/h+lichtPZuk8f+PlXv4r9+1OaN526hLblyevQ\nzEigBCwFjgN/xthN3RzQTQDPrgfkZOr6rJm7Hvy2JZhzyyIl8GFgEvgHTQO2y2E3PUzLR/8WsGP9\nehw/ngD6tSffgzt3VoB+znsdp+I4jv1qSdijF5XJMtDAvJpA0ZTAQjhhyBjrU6pMqYXONLCcAIIg\nNuZ/UdRK7+/Yzn9+Wglqvsk9jsNpGJgmkR3Qf4bWyYQQkVI+8YcpmTGWeIaQK5m2wM3jQs/RiqOh\ntFQxtsrMAeqhGU61UpwcjOM4UsoXohhFnnY+UYCQMibzEykJ1hToo8+v5VQbWOm9l12G2dkEAStl\nQSkaBriAp5QHPA2couMgvVRicN4Uw/kFnNenbsxauoEeWhPUMB1n7ZWRSQBh2Ixjev2hNYONOumm\nTD+9YzMt1vuMtNRiau1LgJ9ZwKWM/dlcCQAnnXjNAG3rbC12gBBG8BbnRGj5XtaQ/uHXM3YC6LIi\nugng111m3ZcWvsgUm24+snDJKSPJzN0oIzvK5N8IRMCXLGFoIT38zDS+fWDz0FBPoSCnpzklJJ2K\nHn3mmT7GBh2n33U928KFXrkGtxXi2ItjRwgmBIVfBdSAoimBhWCAZCxWqhLH5SAouC6nJ1JKChEJ\nEURR0nuxeKX51U165EeLAVBxXVColdIoR2NgCkAGFypEgbEEF2rmhzY2I6cgPJcknkCZsQJjjt0E\n09wb+pPUa3oBsM1KPPSSyLy+EMfkfMLN0JJ4TYTrmZtT3bQ2VzO9F8yb124oCWFr2Ck8vQP4Z83q\nCC23ACoLhtI35my6fxJatouZClpaKyO/Wry42WrR4MSMr8P02nDHeWnQCUB7S6FQF4JpAJS9UPZ8\n4DZrfSxjf6bSQ2mRm3iptJHvLPDQ5z+PY8cSLbIWPUstQotP6pttXlU1LSe9grGfdnNANwGc7KIq\n25i5l0oQggiRaDQSI19aDqKbMo6VEFH6prTbnfZi6p8CDeBK4Oq0oMJsCEfA1zmvct7jOFNB4Mdx\n0XU9xzGWTM8cP15lbNB1BwsFRn0q6lDZrRtNMmBk4QKQyZ9Q6kXAvTQuUyqxf4qiSMqkBHYcEtgp\npUwJHFq9F+r8mBzQtP4pO2nPb1+wAEFQAkQcC03Qo52dS4D7gIoeWadwoRppZwQk0oiXLAHJBdRB\n4rzCWJFzwu3BeAwoRfKbgi66qSCd1omH6YloSIxr7YTMM+B7+lFzc6pbOvJmcB0NAENDySxBIyUc\nzQTk+iO4VrfCgrRgzM0lgGkrtYSdWiiZ8r8JbFyyxPTuEgFrengT5NCbKreIa59pUpQkXcKbaHse\n8HPtimNv/MGqbER6e4Dl/m8A3HfTTWg2cfQoJiaIgGQUz77exQvTexhGhyYtdRAHyjoX9utP7fcZ\n+3E3B3QTwJxXxsyduJiel4hkbDN3fTIlc9T8TRlby022Um0J8J9AHZgBTgA+MAmM6cXLupSGI+07\njqlMGTBVq/Uz1ue6Q8UiqtXExaW3F5UKPC8Rp9KiMhnJcs6AItXOUkaAp1RNByyulCKmilKBlMYp\njOmqXGjzv0gH0xDoqD4MrN63LeEgW3MuZYH84qWk1gfptW1cqKFVewANDAw4Lz8/fCEQA1XG+snH\n2FipkDpQSlcIV4iiEIEQrpRcSqa/8y8BfmQlntjYJBBJO7N7bEnXoxym1GbfZ1QuAXDnJz+JQ4eS\nz0Kf0oxajFkR8LXAF63+iTkELOuUAOxXojq1UIx2/rZFi2o0uievIT05t5FTwRzj6zCnJY076Wsz\nawQu8CLg63ojt5xeH3M6bQ9kep4+8PAHP4j9+5OJFAne9DJ2pJuQZCOTzwGhFf3pKmhQhPFtnu0G\n+G4COMklw5C3WpidTQSRQZCsAQuRqD+Jy6hvShFFLVOVdLopo/RNyYAhzd4iE/NJrfMxUB2hPfmK\nStF012EsDoJ+oOQ4AwTxJ/PYefOyi8qk3aQDgVKQ0tXIUlpUnrJKYKlL4KK2anEAZjOrdbM1Y/5n\nqw+bncQnyf5OpUJ928QILE0i+33gB51woa7xkuzUPr4CUEA/54Ou22tYnmRjSb8yTS/CkIVhKYp4\nHCc9KH2SmNCfi9DPWzCJxzIqUJZ0PdN4yTifIN3fIAQCqlVUKiiV4HmJGRzlcXqkF3H/GPhXoJ4m\nJmWuSSsQxznNGNKspB8tWDDl+60oMr27jNGQ32l8bYL+NcAUMAn4wHHrTCM7TblMJqBzwCuBKeAX\neiHr2ayP0fu27VWvQqmE/fthTPSspqsIgoY1hQo7NbLyvSzP8q0jXtAg8LuM3dY9BHQTQMerHoZ9\nGTN3ski0zdxnZ+lYqqybMnjWN2VvztTCAXbqFpDSlWkRKAhBCYAD84Ei572e55GP1YIFWLw4tahM\nFMmpqWSQS70svT3v6u35Vyv17bRVS6gUqUjduUpgqpStEtjugcg5vLpuu+IKnDhBQCHuODxNIqPf\n+mAnXKjdO0buJzOgh/Mhz6sSyrRaRU9PEmfptyahFD18v8BYEp2lpHf1GuBfLeeTwFLoOpZ0fS5M\naZBzPslML1vAnbfdhgMHknMJZSYiMWi1WL6Ofjnwb+n+Seaa0O8A71RBK6v1f+vg4FSrRXZDgRYv\nBZZ+KTO5yVu3z9ek6BPWIe8nvb21MLTnWxnVgwMIzQF8NVAHfqDdkgvW0Iunxb4RsPf009HTgyNH\nEvs5mpoYF4pWK/L9RhiSi4BvycAyCxk2AQlaZWcjg0paEr2BsTu7OaCbAPJXLQy9Vqtsm7kXCkkC\noNlAs0kP2WrV9U3ZSjdYT35T9qQ99ujLswrYDVQsGVxRqQQipNQialM4TrVQSPbUFi7EkiXtPTWD\nMPM8GHsp2p53ktYDZ4zi74QOaiLTe0lDOo31jegUASkICusP2yVwk8p/a1uCGxiZDrIMuAr4kgZ4\nVX8df1gAVwMVzgc8r0pOBoSw7utDT0/7FzdJWqOei1r+6Gn5zWFgUNe/fidOtcrNLTPLqyzXNDfn\nnp89/DCOHm3792qIHnH0MuJFOw28DPhXKwdkrmPWK+SdGuhU+9/S0zMVBLHlNRRKGejeXYa8FuXG\n13Qq6kvPeOmkElkkVJGedQnd5zTruH8ERMB/WkGWM2bWvxXwU8fp97y+QqGvWMTsbPLBuW4imqJp\nVhgiCFpah0am2S2lWjkFmm+RJOzTjF1t2IZF1W6Y7yaAzgkgirjvK6BCtyANAGwz9yBAEIRB0AgC\n2g9qGmXks1tMLXMO6r+neyZHgSE92Wt78inlAuOAw1jZdblZVJ43L7WoDKDVguchjpMIOPei8jXA\nvwF+vveizf9Yp6I+yvFbZPorZxoULeAX73gHDhwwZjVz2RUw4OXA1zR0zFaRZ1yoBPBnQJmxiuP0\nl0ro68O8eViwAAsWYGgoOQPRiY3ad5ZuihkfY02RvBH4INBrJZ7CHIlH5naXwlzpraxUmtjeNptJ\n9rWAfTipeJGSAc0DTgCl3J3ppxMAy7VQZoHvFovTUSSNaMoIZzuNr/O9O/o5daAnl3oF8OpG47Oe\nZ/gfGQWOTHPl+oAAuIWxuhaqSqUA/Atj9IY3lKLunFAqlLIShkXPc4wbj5QijgPawzA6NCnzLgIZ\nEVpGhpABRTiWE86LGPtF9xDQTQDZFhCZuQOhlJUoKhiFJRL6WxjHZKXWGswAACAASURBVObessSR\nLX2+zkR/keuuCqDkOFIIwVisv0tEzdwAbNSefFVLT30mfe05L7guikVUKomFy9BQ8ujpgVKJlyH1\nnc32vLVAbxaVlS6Bfe0U1tGqRc4RAQMNbc+XwFSEzgCZp0anvof5CX8AfENjMyppJ0J7j7QMFDjv\nLRRQqWBgAAsWkJM75s1LEkAYJgaK1EaIoqSKjCKab3OyI1aKA+8EPtiJ1XFyTrWcg1Md69b//Xfc\nkWhXMsC+OJZaLpxJA3Y+4MA15HWcuzObaQCn+YxioAHc7DgMmKF9Y5LbmuFNJ/JaI7exYXLY1xhr\nWoJdO/FfHUWf4jwvdrARIPQvC/W8hJ5rFvgkY1NAr+2bLaWKY9I7NKOo4DgkeKP9j1jKmERoZg9j\nbt/soNMXLZpDsGQMi7pXNwHkTgDazD0QosNNaZm5B9Z+UL4kmcvaIgRKrkvCEqOKKegG5QXAnZqM\naKJSkSQxjHmOk1pULpcTDSgRivJbylq0lx/ZvQ0g3wI/J9x2OvVeMj2QDKtZWR3wOnDfP/1TMsrT\ni3LILW1meiAvBr5xUlzoxwCPsYLjlEkBNTSEhQuxeDFOOaXdBCMjF3JSJDWU7kHxKOIWQpIefwV8\nAJixONUm8WRaK7E1d+Xp3900f+rA5u98B4cPt118DUk0CEAcPVrHtX5mlNMK07O8LHdnTueAsgHw\nz4DLGMHXOg5vOpLX7N6drRwl9k6RMWFhO8vaUW4QWAC8U8obAQl4+s4ZBBYDD1hxFsCATgAlvYRB\nQ4WDAKdURxuUUkZAoFRRCG9u32yz1m7v4v1a3+xartSAZVhU6Eb6bgLokABoB4rc3q2bktnLQWkz\nd3NTnsTawvRSmgBcN+lIMJbxtSgCzwDz0p58VDFxxhg52Wa2lGkt2fRMbQtD28JFkyBNz+E64NN0\nGLd6L27O+ylTAkfp7jPSua0JbPra13DwYKJ81y/DqIk6rkxLrYQJgW9auFCjIqc3x2GsRGegahX9\n/RgexoIFWLgQCxeipweModkEY/B9zM6meE2cM9vdzIoFfwXcaB0+iunuky1d51bpbQtvDKpv6yc+\ngf372y6+k5O2XDiOIl+IQINUzUZuXi2GtLjTXN8H/hkYAMra3K0EzNDmRG54c3L0pkgf3eze3U+L\nxboQrhBe2muF0gAtVBNXym58+cCFwKxm+m8B+jgPlKL6xklPFA6bF0COeEKQCtnoHWD2P+xelt4B\n9K05PB0v5lrE+wJjDQ0LstVKZjLc3QvrJoBOCQCIyMxdM3ZMVZIsB5FA3jJz93Pn6yhn5k7fxrtO\nOQWNhhvHpDrnOYPWVwH/BgwCdb0ZlNy+uoeTdXEpFBCGyaIyrU0aByvbLsZCgJkQfBXwWWBGB5S5\nSmBzAsDcxDraPLrvW99KbW/q7kdouh9AbLUXhLUnQdHhj4BbgBndC6KX9GV6XtMEK5dRraK3F/39\n6O9PhsD0TabNbePwbhtbMmYrcMz1TuBGoKAPH/b6kpPmHueFN3Toeeg1r0FvL/bvT3wFCNhHdgKk\nFgtDAqkankRoRf+OarF8AigBbwY+o5uEGfJaZmQi504AMt27s123psg7XguCbft4I6GpAu8HvpAj\nDhng6zRwBvD/Sfk+13Wk5DQAt+6WHdbnTuoyEry5+hthHxnN/mBH17yGBUHKFCvN3KAlz4rwusG+\nmwAy16xeDioyVtBm7qyjmbtGIudvSn8OM/cWgEIBQeAQIUdKbkliDMDk5cAXgHlAD/BefVsTR6xo\nQj+tqtGKsr2obHYUzPa8NrHquD3/KuBfgCl94Ch0smqxeaW8k0CTov+WL3852d6cnGw7KUaR0oty\ntpFWpgFivwkvBr7veS0ph4QwvSkwxjh3iNTkeSAGhsFgUNOfsqP9oP+S0yxmSsK/AmaAfwWmLU61\nlwPf85xLQQ14+vzzcexYAlLlPMnNJBWr19FsqrRajAQ5FJEXAgNpU6B7rOicuagY3wMs1vdYz6/T\nL9kJwOhk7OOdsoJ4DfjVggVotRyCV6etg42EhvY2rgW+x3lCnFXKUF3N4wTw3jiuAR/lXFp+ABFw\nCBhJ+2aX9B6GkxHI5UYRYVqF7FtvV2YR77OO0yACdno/X6R/qe7VTQDZTmtkbsrf3My9mba2MGAW\n+mM/v/BCTE4mO7rMCHOyBrYO8HIgAH4IwMRrpUIpe2wb22IRUoJ4ziSAoUVlygFme96Ov5225/8E\n+IxWH1ZyG/xuTn2INN2F9Cc7//7vceBA8sKMZ1mrhTBsxXF7WTr3AqIcipIDJc9DHE8ATSmbSh2j\nrVfbyhho73wFQYLBMNobjV6w5Td2EyyP7aPpqw98E2hZQxGv02w8Bu74v+y9ebRtV1Un/FtrN6e9\n/esTbMGSVEVsC6VAIKl8gIifRkBKgyCgA2lE1AJEQBRERQSCJX0jGkqQRtRIiQkhNDGIIFLREgEt\nNMnLS957tz3n7G413x9zr3Xm3mufm0RiM8Z31jiDEcLjvrP33XvOtX7z14xGa/3++nCI3V0URQ03\n+RRfilPOMlUU06KYkhzXMVikYyKSYyWnkD4UOAAmizVNvw4823FYV5oWs1HX9N4/e2XAHLWM9zkF\nPnrf+2Jvz3PGpCPsttoA1fchsJYkBISS2UbEUq/9iOgM8ExjXi2lF1T3gccAvw+ssO/m21js3jV0\nSQtbHqjFgqMMDaL40843Pbb5pC3XsgE01jbbMaXuFNxytb2LYe6cjVCHuXP43plIk00mmvgvvWwP\n4zQMa3OtdVlGJFQm4/6iwHBYNwAvA/b2KXle+QZwqHr+xwEFvKlpWbpoC8yrfw787VOfiiTBzTfX\n4gNCoggFyvPC1T4OfRwulBMA0rTv00uchz695LU5K/drkhJpCq1rHZyPMmZzCE6/0QH9xrDW+73A\njzmn+3PAi4HUPQC/Qfi7EGtSHiiFojDASKk+GalyP76qMlWVlWVWVRRik2udKJW71BTuCs73rTmw\n6mxCWsuLqH8JeBawBhw07YPCMYB/RG2QNcTTJjLg4w99KM6da29NhBCMsNs6ClxWFNeNRqlSsdbS\nGOGMXVuFWwFPNuZ1QvBB18OBPwpys/m0AAtiZPy71vIv4pOMGfD6OJ65eXvrg0MHLcv1//cGcI49\nlOkCYkxnA8gYPQZd47XrHv1onDlT/69CGCE6+TB8TpW4EhlZWxgz03pSlms0YyS0YTrtECrTx6nn\nvXiyWCBU9u3nh4EZ8J6uLbBsDoep9P/TAx6A8RinT9faY6/enE6JC1+QE5ljcBdBikDZdJjx9w29\nHqxNjSmt/TNrT7IkywFpfX2OMUV1ehCMktyb9BvF6DcV+8XxOQTfFa6w5+HFwGngDPCOXu+tRQEK\nIjdGaE0Qdq5Uv6rSKCLLptpHz8UJ5I4wViplgAuAPkGL3HOJtquOSuANww+aTybfif8S8DSXMxHm\nzfEGYJpm453Y3Scf/3jcdpufl3RSdfk5lZ7MPoDBQJblsKqgVGj46kv2KeDx1r5ZCG+DmgC3AFtA\n5rDHu5WbrRiW1eJiZcBrk2RGyvy7sNtYrmUDaJ8Acna4ThdM2FoPZd6lD2qoQ5/2NJw+7REJYsXo\nLmzah1rQq05ApwQya2daH1RVnGUjEqZRcm9LqOzU88pBz6SfzIHOCJcWnd8C56TMrJ1ZOwO+pcnI\nNMDHe721NF0fDMaDAXZ26phMHlfgvEhnBH1UVStIi2co5s2ZpHGTVfT7UCpRKiZrPLrV1hZ0mXTO\n2N5GHEOpOseGzJqIfuMHIUWBqsq63Pr4bxDNBjBs7j3pj/2/RXGJ+57SWjgfvdyYnlKUJSBdTVfk\npWpMacygLAFsCrEixFDKvpSJdHbXjvBOel1uXCqDBiCaMei/CuwAL3Eaun4wvBGHjq898nPT856H\nW27hU5ND2Lq2RaMcjahntAxfewSiuqHxOnAK+FFrX8NEuU8CXgussdzsXjM323bFph6SIkBv4jlg\nUVpRwR42BO1tuZYNAADOLA5z76RYlO7BupNoC4oQcATNijFzWhEC2rld0ufvgDVSr1ibGhNXlRTC\nAmOyPfDQc1M9nxfFtCwzn+G1QKXcUs/T1x5EEe3mjLWfBA6AvwZWgT6wIcREa1FVVojKmJFSKcmP\nqfQ5cKZw6EdO+b1kRdlM0eJKaV5iNHDt/e+PnR2q3ZHWPMc41zovyz6BYHEMa5HnGAzqFkjTEaLf\n0BQkz7OybJg1sTTjsmkh6Vcv4M9sATNgjxwRiJhrjBaiAmojVZfI5qeXRBUbKaWATSHWomgljiMf\nI+pYvFLrVKlU61LrSGtprbC2hQrS+gVjfropohbAM4BfBuDOAa2sobiLverJSze961249Vbcckv9\nWBJp2Bgu7tWLI7cSAKur9Oy1DF9jRiHtO3PQfeAFwM+yzc0VwOuc+KOTiNxqAGpBioB/0XaAq+LY\nP/BhbjMWSBGXa9kA6vXn1l4kBBeItmh2pivMvZMar5zN5188/em4/XZPiyRWjKfE0Pt2AlgFLnSh\nwZ6ecT/gFtr0WZsYI7VGVWmAtpb9JBE0V7AWxhil6ggXVnxzFmTIK+8i9Xw/jrVSiu3m9oHYxcjI\nZpIixRVELEmxFsrR4FfrQ5zIZsEXoP7XcBASgpCi2NqczkBl2ZvNhAedCAQjFhANIciBeTrFbJa7\nM1DejDLmnvgqKASpEOE2dsOJmAbuMERj+R4LsWlx2LeMSYCxlBtJMkpT9PtzkbY3LnU9O60qIQS0\ntsYQ0aD1ZB4FXmrtT7LKRXXwR4EC+G2HW/KciVbYnLd6/sL112N3tyYvNUnDoVa5ZVzBx8KX3XTT\nNRddRIavqTGFMZG1UZe6haQDBwGaRCS0VYZldeZm666jDJrV/7d6vdyY1naHP2+6K7JiuZYNoL3+\nj7WnhBg2BaJRczalA3IkAnUoVf/PPuMZOH+e0yJ9NhM1ANr1DIA112lap9rPuZ8cWSuYUJlDz2jV\nX5fiMq+/gU6tUz1fAjKOCXyPtaZ3+BzQcwRqYa01RilVGpNpnVZV4mzerFfwG1OSmI5p5fKmVu6h\nLolQuqGCt9J800c/StvzZ29uQoipA8ESY2ZaJ1UV5/m6lIIqfsusiQCoLEOeZ3k+KcsZ8S9pS9js\nATkDwfgZKG5aSJKEdQTsATusIJIQJLU2ceg2zzD4KmsjYCjlZpoOBwOMRlhZwWiEwaA2LmUJz4hj\nZFkiRN9FOoe1aRXYAl4CPKtL2fSDwAR4KvBdbIbvNy5UKM+84x0YDgHgzBns7TVIw+54Sk9OjZIF\n1GHdtNaJAQyHNGwXVRW5ULaQP0rW0Ar4OeB5zQb2WCAD3uOwrJYIXCwwkUYzReCqXm+idWFt0Qyt\n4x/b3HCEXhHLtWwA9TptrRRilQ0D4ibNLmJvV2e0RQH8xbOfjckEZ89idxd7e/SalWWZKZUbU1pr\njJE0BBOCDJmlIwV5LX5h7V5TPW+0VtYWxhymnvcRLndTqHwAIEkSrWOnBoqs/T7gWvfnSaFDsHXP\nRfhKHt7ihXJOwOnLbgY8BDjrkIGkKbjlkqIJsA/82vb2BHh6ml5dlgQ9p8ZEVSWFMMBY615oIVlV\nqKqKfPrKMnPb/6zLqSlvjqDntsZOBhUF29hbgJPu3FCxFF+fYSBYXR5IuZEkw+GwNi7d3MTaWm1c\nai2KAtNp27jUGHItjY1pnUwGLtLk14Ens28rWTlWwPXAKIqOpumx0WiwsYGjR2ux9JEjuPVW9Ps1\neYwYU5ywWxSlUh4r6yQNV02WZ0TTGhoCBYavnfzRUYDACEe++l3gHBMA+hSB1rvGq38J/G6vVxpz\nQDue4GlfFNo8l+Uv17IBdC4yLxRCxOxk3RnmztnE/sH63BVX4PTpWhRKn9lMsfyAiVJDYFOIkZQD\nn2hIDYAnGhpz2trYh/8RacRFuITq+TpC3b3AJUuAypgTpApodnRkueFrvgaTiYyiqGnfvw1YR6Kg\nCTYJ5ciHgODvdpJi4ET2rUAOnAT6QiRkbsFMp33mYuYMZPrALvCbZXmE6o61sTECsFWlrC217leV\nj8yEtcZnWCqVK1U4q74agOraGFq2m/awgHRypFAGNQDOAqtuhDBc4KP3rUBfiFEcrwwGWFnBkSM4\nfhzHjtXhDRQw5x0jHIJXh+dQbrAQrQaQuoPIEHg98ANuS87r7CuAFwCJtbkxM6UGNBQhhVpV1Wo1\noParINKwawCGyZXDZAuq/qaJ4QAAn2p4+6lm4g1X3vaAXwaexsox3Kt0OfBo4AebudktLMtX/wr4\nvTTV1u5rXbkHvujyipixNt8SFnxk6QOxbACHL+vagPdnTpt8tTDM/f9ecgmkxG23NRCJLCvzfFoU\ndahFVa0LsS7lShz3faKh2wYKrROtE6VIH/8rxrzMaRoNFypThItrAHBe81w9H4onQ/CHq+fr3dx8\nL1fv5niSIr08fWtTsjNq3gR/fOEOQg8CKmBFiLEQAyFSd26Aa1o+eD13yQT8td8BJO3NmxaSPaVS\nl2MsXIxlRW3AmDrK2IE/YfVvueH7o5sIPrwNaLdRLdzosiXFeqgzLh0nCYZDbGzMjUuPHsXKCqKo\nLseDQU3n9dqxOCabkFCjlDjyJYnIOjGZBCiAyNrMmGlV9fN8NJnUAxKalnv0iZIt3LzE5PlBVXm1\nGifschdYBJyxTsNXE8RgCNYDxgt0uRHwBuC3gZucH9RzXRuIGFT4/jimF3Nfa+18Tyu2geBucVMm\nzGxN77JldVs2gLvVBqgTtKhBwm2NT586ldJx+OzZupTTi12WpiwzAiWqKlOqLMuxEJtxvJ4kot+v\nEw09kO3+XyjLpCwjpYRSZ4w56tTzFaFSLlxXNsWTuukE2RJPZk0uREs9f+2DH+zVQK3dnGARvpWb\njvacE1nUxX/1DeA7AQEclXJFynEcR81uFxmTaN3XujQm1zqylpgw3Gr0H4DIkUBAVR4gECymXuIv\n30cZ+6biMCju1dFyw+fHoPf0+wdK2WBc6WGWFPgM8JXMsK8f0IVjblxKDcAbl66szI1Lgdo3YjKZ\nG5dGkdBaGNN6CKXriylggD8AHtAcYxKWQg0sM2aqVFIUUsqBr/g+ONMbiuQ5skzl+aQoZo4zxtm6\nfFhSdaUHz+NuFtj8heqWtOnNwO8waYzPuXHULzvdXA9IyJcF2NO6Jcs/5GmfdhHz6E/+6XL7v2wA\n/7xO8BohyAh3XQgqauM4Pj+bDauqlyTEivGIREVYBIESWquyHAuxEccb/X7t7D8aYThsOMnQ7izL\nIKUsij7weuDpSo0ZN7Hn4mKiBeFZoXgya3qncKUCqee5dZr3TfPv8OXAu4Gh+7EDnqTYZBnydJHL\ngBGwFkXrcdz3Eb7UA1BHLFDDS6sqEkJoDWMsc49RwMXA5xxsZZ0irCdEam1sjHcQ80MITfQqFtIZ\nNoBOA+HCX68Q1ulaW2h1BHwz8CngCEvxTRl9JeHGpaNRbVxK8TXHj9fpPbMZABCflTYB3riUhLiB\nd2kLjxoGulZaGWCtnRoTay2rygqhrB0rJfK8livDmchWlS3LjLQaVcXnJXcabeTdkLzhq3FmG3qB\n5ytYE+1MzK7HPMAlwHXAkPEg+m7QEqoETGAWxHNv0LUvKVljWK5lA7jb68DHnzpEglKNMqXSsqTs\nEapHtSCIJlRam6paE2KFqj8Fu29sYH0d43GdQe9VTsRzFwJAbG3P2ldb+zStST3fiTwg8J9QzQZQ\nBFQlw8Cf6x/3ONx22+G7uX8EjjsQaXgXInwfBawAq1G0laYJMWGIBkPRr9bW0IdLr4zKclBVVilt\njGbuMSWw7SpOrQuztkf0G4AAE8FiLOewwAK3PhX0S2+IZtwpKtzGgumY7gv8GXAM2GOSkRh4ljMu\nTeIYaQq66vEYq6tYWakTjAFYW7uWcgDQ5fYcQlHnOqxPARcFmAydbBIgahJ2+0mSEmFXCEuPJaUb\nKVVoTYrlQ/K2yi7O2LWPfCTOnav5o8zwVbPmHRq+SuCdwGVMl8t7AI3cTwNH3JTeSxziQyPbwgYQ\nprZ5Wf6Hltv/ZQP4Z69zQOy4yYRIKAq1MCZRihShoikIqowRSq0CPSnXKNd3c7PeEh45gtVVMj9A\nUWB/fx7s3hwMntZ6w23k765QWR0aY3vNM56B06fraAFn36+bzjka+BHgTcC6cyIbNCHaVgP4YQrg\njqKNXi8ZjbC6ivX1utsRc59wCWLCEJOH9G4swtf70T8AuMFpdP2usLabt1Y6qOpwB7HcBeHye6W5\nYJtyz52fQaeTcOTQ9hcDB8CrmBY39calQkRS1sal9KFCT1XeSa7aucHsW5mQlRDYhCTBrrxyUV8E\nDFqtSTKSa92jWDQpia9FT2aldeWHJSxsPTuUReNvWm066/mjTfpQp+Grn1V0uoD4GPfHAr8LrLin\nlz9pi/Iq+O+6WBBZQb/l88sStmwAX856lbXPF0J7Q11rlTE9Uuo6mxfu9EL1dAtIpRwlSToYYG0N\nR4/OU93X1+tUL3KzIY0rTQKKAmUZVVUs5buj6FFarzTV8505ji0IyC4WKufANuCdO+lEX/L472Yb\n+H7gt4EVF1fQX+xEllC3S5IeJTgSBrK1hbW1moxIVhZ7ezX85WoiSYpqMZGTFKXAHcC6G0uOmucP\nEijgLrj1qeZN4F3wPMs9r5pZYLo5caUiRT/zh4ErAenE0vQrQMu1lCAXYgQQ8EW5waQP98k5PrzB\nWt3VAEzTJiRpAjIauBJ4v/tjghgBWpNfRW24z5hX2o3f5xQalrfFo4M5d97f24IemzyHYzYXZDjI\ngMpF/FEJfKPbjPs5843NmfAjgfcAQ2DmJgGdxtch4KkCXQ7Xi+0Df7Hc/i8bwJe5th0NmRCJ0tr5\nhpRRODwiMbY2BWIpR8QMWVurU90vvBAnTtQNoKpq5MdvjSnvJY4RRUIpKcS7pHyEMeMgyVYuUM9X\nhwqVC2AX+MxTn4o77vCKUKtU4cQEyrUB7qn7CLc7405krSb0c0BPiH4UrRDYtbWFkydx6hSOH8fG\nBvp9WFtb9xD25Sj8qCqpVMws6X0Q7iOBPwBGgYVkfGgeFj8B6GBYzZMsP7a+vl8UtIcNc89VMLH8\netdd3gZUwI8BMd8me2anN0nd24MQ6PVq41LPwW+2gcoYGmC0HrkWbUY6eTbflQshdqy17pRDzbvQ\nmuTKpG8QRJnl0cE+3agrO74IbhcBKbvuBFB6/igbupTNHhCuU67vEih3AHwbMGne4f8K/D6wzwKL\nFh03ubWtWODKRaHNn1hW/2UD+PLX6619qhBD9mwVVOIJkfC2MA5KPgZEQqRRlFKq++oqNjdx9CiO\nHcOJE1hbqzPNo6hONh8O5xixM+mlkcB7hLjU2nEzQr3lVqTdPihiMbOt96EAbnjuc+sIAWbfPyNE\nmNnmhPb9lwOvBTYdXS90IhsACXW7wQCrq3UDuNe9cPIkNjfrJIPJpO4EVCJpR0wOEFK2InzpAh8K\nfACYAGsLhhCHNAAsdsOfAdcdPbqf55wC36mB4rygC91tJGTptwHhihEZfNa7fuL7j0aIIuR57VzN\njUvJu7soUFWUj0g9oLVaxqXSUWO56SyAx7pZPdzX6Du+QOyGJXCiDe1sVlWT7pktCLfgnLEbv/7r\nMZ1WJLd29KFigRFbq+haYNP9nAwYAgNgAOwCNwDngG911/hg4A+AXZaQGi+Q5dumKrNlJ0fV/9PL\n6r9sAPfUuh3YAHKgChCJiKcaOeXwPNWd+D80GKTZIHHDiZbnCSHNREMeZ/h+4IFuAx5GqIOJJ6Ng\nQ+T3vJ9+6lPrBHNyT5tOkec5mdd7N90uT136aT8GVMDbgJgp+OnzFhIoSDmgKajvdidO1A2A3Jtp\nL0zGzsSgdfFevtsJR0L1HJIvAieAaTMOJT4UBFNdbvh+9psBHzlx4iDPW55xnAXf6SS8xdTLpM+6\n3f+l1uZar/mRPsF6SmF/v1aBEf+HtLjMjCFnxqWtv67VhOg/N4EJcN7JD2s/V0fY9SyAnhuW+BMA\nPwjqIDkyZ9U/hMtqf8PJJHdZN5w/GlJIdXMYS7+dNSkLMtIIxJUx8ElgB/g6AMClwDtZbnMrsIgP\nZqIFrtclcA744rL6LxvAPbh+39rvEWLkTrKdAa30xP8neiJpMEg9wMcZ+kRDuDhDPyFsRhu2CDkf\nAA6AH3D2xb3ACbJlBAZG3dsH/uHyy3H6dM03JUHQbFbk+Ty7ijZ0Td+0ggEC9AMfB2wDf+rMXnwi\noACSKIqp29EEeG0N6+vY2MDaGpKkRjyIEcSTHT0ZZoFr4w8DvwEcB/aDQUgnCCYWeMjQH8iAG06e\nPMhzCk7ImGd12bxqE7gGrUlJDTJ1h79bmsal07IceeNSL8Xy4Q3TacO41DHxfRMKG0DZpAMJ4ALg\nLDAF9oE3Sjmx1gA/CPyOy27sIOwyjcWi6OCQRcPhshnw4RMn9rOsJo8q5cmjnbavodlGDqzEMVEb\nauVH8++iu/1p4CuBCHgMkAHvdt58qSMLJV2JdS1Tlhz462XpXzaAf4n1h9ZeJgSpDQ8JaK2xWtrV\n+kQwb+BMPEigdrKkUEMyiaTZIHE2GMnav0hvAPaA5zQx8dAJ0psAf/6bv7nW+t5++/xv5/b9vPqz\nDV3erP62iSbZ0aioqqnWR7X+Q2p+ngbjuf+e7U7nG6Ua5gEh40UI21QS+U7wg0AGXO1iLMM4FOOw\n8kPc8Gnvf+OJE3t5PmUseH7hh7PgV5Ok0LogLYK10o00I3Ji0HpSVb3ZLCbqvVJ1YD01AFJ70Jhn\nMsFsljGbEGq9rdUyLqWvsc5uy88Y80Ih6Ak5DWy6Enyn0cG6OS0vmiya1vY/A969tbWTZeT2SkeW\nud8Gq/7eg9MEDKKPbGygqmKlpNZCaw+W6mYGQwn8NXAf96t8OPC/rCUxZr8rs7NV/QvgC8vSv2wA\n/6LrC8BJYMIwyiSYylpAu3JW7+59oiFhAkVRN4CdHeztNUx6Cuha2QAAIABJREFUHS1nbtIbsDOf\nAxwAFfBmZ7fChcoG+MvV1XGvN0rTGougJkS0k6oqmsmFLfv+vPlK22BD96FTp5BlPWsp7/svtSYh\n1Ty/13kc1bKvoqin3PRp5fd2Rfh2BuY8HPgdoOfoNymrAkmXWx8fgBPl/9NHj+4y1KvewHbZxung\nwjUQ9XrDqoq0Fu7XOvHGpdZOtU6qKsrzdSlj4nS1jEu9FjfPp+7sNT+FBJUrD44g1tkq+CnFz1n7\nHEADT3E2yzkwbkrVOnn0VZNFI5p4PedQbgPbea6MKY2pnNlGy/DVm/BkXfzRCsBohKIQRTEsS0v+\nhky6QV9jBdgATgJ/B3ytNx9l7iyJO3MnzcmwBm5d1v1lA/jXWV+ydksIQiRWAmpmC3BQftfvoWFC\nwweDOttkbw/nzs1T3Z1Jr7f2VEz4zgkqBHQ8EciA88CjgT6wBqxIOY6i3bIsjcmqqlcUpAaiF0nd\nBfv+/wBMmvsyXnH+BMBgwMUK0l2v9aCWv2QifWo990LY3/fxxXW3M6ZWTgQUTP/x84DHAtvAtUDi\n7jw1v2qBG751mMAntrYqrXfyvNC6dEPvQ5yEHwdMmje8An5nf59goielqdXaCHG5tR+h7SpxgpUS\nzrh0WJbzJADAs554962z24zJmWabQ0A2sK8gSMSn1kyBVwE/AijgccBbnUYhjFsRC04A6ILLDIs2\nekuvt1dVimQEXYavnY6zuqm3wNoaJhMAwtqek3ZX1qbMf3sAjIB1YA84DWwx3B/ojMxZrmUD+LdY\n560FcEKIUVMU6s/alQ99NaZUKiX8d3d3zv3v9QDMU909LadJsq4WsFOqJi4cARM/ebbWal2HF2qd\nKkXjWTA1ENn3N+yjrc2BE+6nbbG9lWqy9x4B/Orf/30JvHBzM9Y60pq/7Y2zzt5ebUY/ndaOxNTt\nqA2wEw99E37cOcSSPgHeD+wBT3CxCmlzV8gdp69fXaUL38lzimysXBxj0eWb/SjgrPshm80Md+5c\n/aaynAAF8AQhpg7Ojl14A6nEZ9y4FDCUG6xUqXXuum8NpADUfREwZ1q7aeV6nk+t2QS+xJ6K73HD\ngJUFNEreAMwCuMzz3PaA18TxnlJzBx7XAEL60NQ9lqHj7J8+8pHY2QE9HsYkxiQk+wiSZOi61oDz\nywD3ZQP4d77OWCtdjEy/aWFYkxGtzY2ZVlVK2/80rcVQ+/tI05oNSZSYvT1Py6ko0ZC2qAHJumxy\nn30bOOiy70+lTLT29v1waqDQvv+Ee/d6QMtqTbFR4ch9doCf397eB36h15tTSozJte57HuRgACGQ\n53MVGJ2BqNvR8KMslVI560Y8xl2xuS7vAQOgAt4IfBq4wPnmrwCPBz4gZU+IVEryzd4pCjAZVE2B\nZ1GROatiDwAK4Li7CfOKyW5C5u4AZcW8ztqxC2+IKKON0nvIuFTKRnqPyw2uHPOnYN03I1Fxcy7S\nqv4Fq5Up2zK/GbjcPRuPAt4GjJukqajJCrPBaak1JCiAnxNCCLFP+B61DXdK62wAZXCAmG//t7Zq\nEIyiKJ3sIxJCOm/BmFlwkxbsNLCyrDLLBvDvefkIgbRpaF44+DXTmkx6B2TSS3TAfr9OCPEmvZRo\nOJ2qPJ84k96CkazLJkOxRZWxwA5zzqHtf0nWaS52xjeAln3/V9N4ExhR6fTaUW+w404JKRvBeWeC\n5xbFZY4HWRgzq6q+zx6JIihV215SA6But7tbc+FJheBIqEWTgeobXksiK9wueABcBPwjm5O/Ddin\nyEYn0BOcKspOGC0n4QeRslcICupJSD/l/1+uWxQOskjYHfgiMy611hpjFN0uH97g76dvQsaQ+qyg\nXYKrpGtBA9BNLOWVwKSZutV3PtXvBh7ihvY/DCjgzYw07IGgpCtvK2TR/AywT2HFCwxfWw2gDI4s\n/sz0wV/5Fdx2Wy2PIL6Dk30IejKZ8iMOkmSWa9kA/r0vy9oAHQVyQoSsnRmTKhUXhZCyTxU/y2pa\nDuHCbDBY5PmUmfRmzKQ3Z3Q93YQICOZ+GPBB3wAI3nXhhVGXfT/t5u4DpMC6lGMpB1GUeIcyN+S0\nLmkysTa2NmJO1HDvZ8ks6SmTZDCZIIpqp6PBoM4k8WpnSsuZTuESHBt8fFZfOsn4tln+7nD0D190\nei63wH9b7iSsGWBC9/YSgh3cTYg9SdfdBG0MkX8SR/7hJ7CvBW5lQ2O6sT0COoLwBm8TUrHwnEUN\nQAdSLOm2zF4vnbhtx9DdBP9bfjzwW8A+i9xKAhplq/qXwI8ACbAXSO0W8UczBkjaINx04tF/Un4Q\n95dFiZHmQzRjZHwMw96yuCwbwL/5ukYI3RwG0gvwlOZUig+pXicEZVj0rI21lmVphVixdqSUyLKa\nGYK5Sa8qy4wzc7xD72J6Ip+2vRSYBGqguRy0ixWjgG8BhkKsS7mWJCkpFej9ZAQeUVU9pWr2njFg\nUmd/T3KnA8iMmZAlfRT1iHREMjcahPpul2XIMptlk6KY8kwSd4FFU1sU0jF5hO83Av+bcWMKP/xk\nF26bSmDeAL4bGAqxEUWrcZzQHfBxV+TroFSk1FCpRKlICGEMJbhx+fFnnfjIOJuQnrWpyw2WrfQ0\nfwoJ0nsQNACOpB+4AxBY3ErkeqEG/gy4XxM1egxwO/CR5z9fvOxlA2YmGNpJFcB3ASNg+84MX1sN\nQHXpLbRzYL7+mmtw661zqaPbYVjngYpA9iEc/4fm/Mu1bAD/NuujQihHOhTNGFsah75RiAOgBH42\n4CdMnEtMYq3Uml7mypisqvouP0DQYNCZ9FKcYT0YdCa9ITtFN3Ou6X2jV/pxwDuca3zR5afId3MP\nAsZCrMfxZpoKEiqTRIsXa4qsyvOoLIdVZZUyxmiHiQ/c3ZiRSZm1M2sTraOqQpatWDukLb9vKjQf\nrioURVGWs7LMWPXPnKSoddzpVCHwSKkY+EfgpNuKFmwsfKe+2Y8BhhTUQ6qF4bDWLvDzGckmiiIR\nAkpZwBhjmoD4/YE/B0YOhStdbnA9l3aPx5361rWWaiYnXxVFU2NsAIj51JoRI876v+gjV12F06ft\ns56F06dx5owfwGRFcVBVE2Om1j7J2nXg9i7DV7m4AbTMNtC8wxnwoZtuwu23t2UfpHJv6r8sk93R\n8g7Yy7VsAP/a6wYhesAR9hrwg61ng6wAB8Au8BIhDoCXszawAyQu0dCb9FbWZkqlVZW4A3ANGRM1\nxYcaBiRr7tPSYobQC+nd024GjgKzBWogX4YeDoyFWI3jLUqw8gYVg8Hcl5REatMplW8J9AGtlArY\ne3cAKWCB1NpIayoElbW5Uv2iSAlUAWBtTYPx+b1ExncytNb1ZsH2n7aWVwoxcbgBtYFLgA8DayQ3\nbfpmh07CvopdQQegJFn30uXV1dq5mnS8xGSlmzCdQogE6Lkxcsqcq/vAGWDT2dkXd2Zn3xneEDaA\nym2lc+CqNJ1obbrE0jww4BHerNB5lrzhiitK4JnPeU6n+pq+1WuAJzcNX3t3xh/1imt02bQVwIc/\n/3ns7881Hy5GZi77YD8zTGLwWNDDhLgX8OYlB3TZAP4V1qeFEMBxYCBET4hYiMg93DUVxFryAe0x\nF4QUOA88W4h94C3WAniZtS8RwjhsyABK69LaVIhU6ygw6fW0nDkvM+DYhZlWHoHx1fBZwCuA1aZ9\nf0sN9H3UG6Joo9erM2qOHMHWFtbXMRohjutJ9WSC3d36QGAtKJK+yd4jTPk80Pf5vYA1xlQVZZL0\nqiqRMnLdTrO0HGp4NRmfXSzvdi09kWkGIvryJ4BvAD4JjJhvdpjiySeZPwIMhBjFceMmbG5ibQ3D\nYT2+phT1vT24BgZrU2tLawlYS1iAwaOA9wPjwM4+DnQJi4xLbVcDoGJ60BVUwDuBx4JeAbyKtZbc\nmUa88uUvnwIvvPRSX/dbBfdVwJOcgGAcfHlusKy6zDb8D6Qv/PHPfKYm+3LZB+kBmQO2YnejFSvm\nBX0pcAy4Qoirlj1g2QD+pav/CFiVciTlQMqUgAtX/oyTQRbWJtZGLJqRT7F+QIh3WQtg17smAIbc\nd7VOhYiN4R6NxvcAl+peMUMe3gDUApPe10k5NcZDwz8GvNLt5lqm6vQlh0Aq5WqSxJRScOwYTp7E\niRPY3MR4XMeXE43HZ9Qw9l5MJsOMvfcfgZuZJb01RlPau7Okl27cx7sdJRB0druZS3EJExzLZlwX\n7wH3BT7JKPCH+2aPgJ6Uq0kiRiNsbOD4cZw8WTtXD4eQEkUx93SDS7JUqnautjbSOmIwVAJcClzt\nQslHzX00P351NgDZ9UAWDkm/ejQ6qCoVFErfDPxhqA9sCkFD5sLlCA2AIbAL/NKHPnQOeNW97oWu\n5vpq4HHAhvMc7DVNZ60LRIoWy63JaulTV1+N229HVeHgAOfP11p3p3GBUpUjQXnNh26GBhv2QkXA\nJqDYm7VcywZwz6+bhFgXYp2GgdybE7WHj1Sqp1SqNVGY62FgUJEr4LuFuNraW90mzjqT3oKH+nr7\naEfNDJXxXKBUsHFZS2MZuqc9yfWAMbNuplL4XCAVIo2iMYE/m5s4fhwXXogLLsCRIxiPa8nu3h6S\nZO5gk2UoCkRRFEVSa+noKB6I33HIg/BHJWNSa5Nmt+N8/KppSd9qAFkwVFQu/cp0pZADkMC3AB9y\nPYC7dLRwjJcAqRD9KBpQdMHmJk6cwL3uhVOnsLWFwQAAsqw+APmb4EwsIikjY2oWI7sJMfAg4H8B\n+868urfAjIE3AM2wlNaiG3Lt+vpBUfByqZrhi2gGBqxL2WLuxs2x+bNvvvk8cOVgoJqNRAOvc05T\nKTN8jQK/qaj5nf14bAL83StfiVtvrbcRJH4kB2xKQSiKhgFq03JcNZNkfA+gmLBjwKOE+KNlD1g2\ngHt8/Y0QYyE243iNhoH0IcDUC1iKAkUhynJYVcIPA73BepPR8VAhPmztE4VYYWhv3w0GiSHKd08t\nvkfLpLcIWBae8fLWJJlqbVygud8dPwF4ObDB7Ptjt0OMhRjEsfDQB5k2X3ABjh6t02uJp+QxEOJx\nkmu/EJ3sva8F/oF1OyLOp0L4tJzGLLEZ4VsEud7Trm5HMqg3RdGUBtEBdkx/y4OA9zhd2LCJZXtw\nOQUiKQcUXUANgE4A1AUHgzq6II5RVfVNIB2fiy4QTmEgmnvVCLg/8EFnEzLsSi/gv0HZhaX4tQ/c\neOzYQZYVPnbR0Ye4Vg6tcpkkdFRt+W6iOW59Upa9Io6rpvSavthzgAnwWjfe4KazUZNTACYd+PvL\nL8fmJm69FYNBLXnxGJp3OimKvKoyrQvmwt0Sf1TNKxJAHxgBq8AR4FIhlum+ywZwD+/9x0JsJMla\nv4/xeB7kTTnmZORL6SUkXhWiHoc63KZlZrIGnAS+U4iPWnuFEHmTltNpH92iWBSsARQhhZ9lWtUG\nas43lG+KnwIo4K1A4nZzV9H5XcpeHNekl9VVbGxga6v+DIewtrZu3t+f84KaIea+mnAQZtt9ee0c\nietu59JyBDvuhIRCfgKwAWfGk8o7QQPVRI2/C3itEBdaGzM7BF/CXkc9Sco+OVePx1hbq28CzUL6\nfWiNKEJR1OQon+Tuxqdkfteyp/Ft4AHAH7mDyKDLzt4skGK11o2nTuVZlrNy2WkNIppQGAaDxPtu\nGgNjWsxdf9ufqtSvCFE1HZ+Uayo8/qHftJbzgwEF/J+trdWVFayt4fz5GjwkGhWRgEnkeHBA6UNF\nUUyZ7CMMYChYloB/zLzubwVYBR4ixPXLHrBsAPfUGgqxEsfr/f68Gm5sYGWlNjDwe0AaBtLm1w0D\nY2NiF3CRMpOWVWAFeJgQymXIFCzMK2m+RYeQrP1uqJV0SuTL3+v3J0qprhmaL4iPB2bAe91bJIQQ\nUsaUXd7vYzDAcFiXOWJAGuPVOvOK74u+EKbJQ/Vf7N7A55y1ZJ1J4ksGZbg3QfBFmSTo6otEvX1L\nHE+NCUEDFXhFrEp5ztrC2gus5dtYwkAEEEuZkHM1NcLhEKPRnAtbVQ3eekCbscHl+5olgBj4LuB/\nOviCW0WF4Q1yselNWZZTZ9YdlsuiSyYNAOMx8lwURc3cBTQNXZvKgwy4APhJa18cNBXFetJ/A/aA\n5wETYJ9SIYFNYACsS7kexwdlGWfZ0G+VJpNa5sLxwyxDluV5PqUr0ppYzouixFoeqFz3N16QN7lc\nywbwz1y9KFrlgPiJEzh6tM4xB5DndZATHwZqnWgdO0oMTURj1gboKHAAfMjaBwqxAmQszCRZEHPa\ngoB0l0uXJ1m/bzw+qKoWNKyaVA1fEB8I/JRrJFIISTE1vMr7PFtyLXZpvZy6B0C7/bvpcmw+E3S7\nThJq58UWDvcX7v03LN0pB96YplOtSw+GLLbGE8BKkhRax8bc5hQGF7vvIwBLd4DfBC9/I2kegX5u\n9O3T2+ubEABQNnCujoBHA+eA64EDFuAT2tnzXM/WOnB+0YckL6JZLg2AtbX6coA+PR5CVOyoSs/n\nGFgDjgK/BDztUOk1gDH79d0KDAEBzGjGU1Uiz421Y61RFHMpiY9HLktTFLOynJXlzOUfZM3kCf+p\nOl24mfFGH+gDDxfiT5aHgGUDuEfWKI5TSjE8cgSnTs1D2wkKmM2wvT0fBpI2qixFFEVKSQdw8yjz\nxBl1VcAlQnzc2m9ynnGjwKSXjwE4BCQZydo2U00K4AMbGwdFQQZqrb1b1WwA1AN6wEmqWS5s0g+3\n5/G8aVr7MxN1jyfVuE5QBckE/HMR8FfAOCChdnY7HUBAYkGGF5kS54FZkP+E3FD0+72qko50aIHP\nWjsBbvdVEmin9BBeQRt/8q0jvwoagDMmOw/qCTk5nMPeB17j9svf70YCaVMfIBzMdfP97vcOfJZf\nxMT5RWcusYCXyyIol/Wlra/7ZhYZUx9Vnekm36YMXX5AKwOudawxjuZL3/Os6weRtbExUmtbltra\nQutBWfayLGJi8lLrsqpqzYcLn8iDKLEwfIIrXQQbcfs2sFzLBnAPQUA0DFxbw9YWjh/HBRfg5Els\nbKDXq6uhZ4WTiQ0hwlJKKYXWRHFvDQO5oRWAz1j7NULsdaW6hyRru5hkTdXww0eO7BdFHePenJ4t\nCrMVwM3AcX9EsDYhcqe37KeLpdnvwQG2t+vBHTNtNm4D3rJr5i3hvsCngCEwXaBEa6lhvd1bS1LE\nwa4J8PY0nWlNoQWH5I/PK9dwiDwnLwHPsu25DHft3JAi8uj3ogcafkRR3QC2t2vTOpffC60rykXp\nOniFHPYY2Hd+Bn8gxJoQK3G8EsejNB0lySBNU287YS12d1u/tWlgCnJ4uayjV9bXffZA7bspJQkS\no6ANUNLW24HvXiC9JqC/JwQpn/vAtwFfct8wshZakwFqrnVfqcQZoBJLQpPsgyIoHIu6YJfDeV8q\nIKcqFpPA/cD18hCwbAD3GAREOeZeDXT0KI4dw/p6vSMmSjhNRBck2cKFbrfaABl1XSLEddb+g7Wr\nQoyaDEW/zTduWxrm+qIZ7H7j8eMHec4dNAvHpDwkzdy/ybXcTOsBufZT4ev3YS2m0zq4kdh7FFND\nKh7H3gtNm/mH7sA3Ah8F9lhiWtrV7fwJIByEcvBnClzV69WoMXN6WHXXpZh7BF37dYSDCwFrU6eu\nSIxJHN22FltoPeQZ7mkKrXFwACnnSghPYXTngEJrfgdaowjVpK9EwHFgSk3d2gigckl/e6+q0iiK\npfSa8NZvbeIswVupNZ3lku7YDMDqao285znKMqoq4m7Jpny6ZSv9p8A3AXkTy6Kf+Xs0emHy733X\nSiV5ImlN0ReZ1rGU3knbcpEjC58om6pvr/xA81romX8+OcqxNkBfvrcs28sGcM8sPw4djTAe1x/S\nxErZ8LHipb9rr9TihNBWa+j+zb61ACIhxoyh6FtFFCSd8hFxBcyAzx4/vl8UjRxBthH2/6yb22Hb\nzFwtrc21Xi1LQebM5NWc57UClmTAVBNZA6gce6/VA/zJg4/mvh34ALDNekAcWNLzCF/RNSSgvf+7\ner2p1rkx0hjB/DkEuzM8r2Yf+E7gFz7/+Z+/970JsamnNUJEfBBqTK7UkIJ69vZqv9IsqymMNPmn\n2Q8dAmYzFEXt1+TuQMl0G4s4OSvAGXddtUqOGoAxqZS1TJpqX7CfnTLo//DkRd9QP/riF+PWW9Hv\ne9KqcKRVBLxV/+wZ4MBJr22gNo+l9PJvQmDOAdJ1O7INr0ggaUxCoCi9INx73CfJuBgGP/bnKnfb\ndMAuglETWPdarmUDuCdWFIFYMZzsCNRTLP+hWaibBGKBIol7Wvkn9TeFoNL8s9Zqa4UQnSa9ITPE\np3P83YUXZmW5VxQzihI0Zg4ONE00Ow3Urh+Nfms6LYEYKKydaT0pihWqcUTZnk7R789pr9NpTX7f\n38dspll8edEMhCoWDA8fDLwX6DM6fMxqPWeUyy7XyQJ452BQaD3ROlbKAiOq/kLEzZtTOdVr1lS9\nvuSLXzwHXLm1FSsVGSOtjYSg6hNZm1s703pYFP3pFGkKKWvsi7i/3rma7sBkgiyrypLfhOKuTSMG\nwA4A1xvID4MaQEJeI0wS2NkAyibsM2VquBalqgB46eckLuu2LC1gkH4XGui5nAnDdGH0Y2MpW+Et\nlwIfc8AXHWhKa3tCJC5NSLozcQ33+TSxwP7Iq9zBrsj36f0gI9oweO1hQnxwiQItG8CXu5j9fS34\nms1qZJZAkum0kWGrFHGrQ/CXP6lgZ+17OQrdi4TIAWvtq4VYA57kWA1JF8naAFPg1lOnSqW2Z7OC\nj9GcXaivC/4fVGAWrQH0+9l0WgCSXPu1PqiqZDbrU155WdazjRZ7bzbDbKaz7MCx9zL394b0bRsw\nUv4Q2AMeD0QOa04XS4r4jn4KvL/XO1CqNEZq3QcuoKgWl/M1hxcoVoWSDwKSZQw88/z5HeB1vR7d\n1dxZJ2XGTJVKyzKezWLf9qh6cudqJ/6ofHSBi1DmnJwy4OT4m9ADvh34C1bjlLUlkFqbChGx1Jqw\nkk2aqTWHmILQ4WOHHmZP23VF3wT/GRrJJcDfAP8hsMybAVFXeMtZ16fnBqhO9kGtYo5eupl5Fdgf\nZc7zI+S5EdXtpc14ah0MA5Zr2QC+7EU8ED4RTRIURd0AdnfrcajXsitFMbaVo1eHZia6OQz4H8BP\nOQbeOeAZQhwHYuBNFOMuxEjKURQNoqgXx70oiqX0Ze78bFb5SRqN0fwkrYkLh8jAfDA4GJDRowAS\na1Ot46qSQqwJMfBbfoK8SPns+E45EfhcRk3OHJtbHwQzyVVAAu8AdoBnABnjBSUM/hIMzX9Pr0cz\n2wOtjyk1BUZCrEk5jqJBi69JvzWtK7Koa6peW27DVxTF6+MYQsys7QPC2hndhLKUQqwBCVEYfYY7\nkUGdF3TWvAmLODllIBHQDmc/7wbaPqunR1ENzlGjc026VHJVc5buK3UGfOLtb8fNN/s9Su27ucBx\nM/Td7DcjaAiBue7IkSrLuPybSjAFEI2aso964M9Sg0KjlFYDqJrQn2EeJ+cDYlso+FiuZQP4cpfR\nWnq1Fw0DlaoNYSjHfHsb29s1JSbLUJbQuvCcdMdMb01EOd4au2ro/80tDnulsHZjjKK6QMiA23NZ\nZqBWMeiZ5whyaNh0GajlAEaj/37q1BtPnxZAZG1ijFQKgAZGWg/LMiWlq2PvWa3LqsqrKqsqfuzI\nXEpBHtBRQif6EfuvVwKnXY75TzODBADviyLfBvZd/viFxhTAmhAbcbxGYTU0jOFRLVWFqkqqKiLV\nK6XXOqVCS1j3Q0q9XsoDx8lJiMKolBVCWTvSelgUUTMPxxJ/ke4DM6/O7gInxzuP0onkUuAaYMBk\n0rVBk7MU9JghXwdNlVzm4qBbqkAql3/y3vfittvmj6hj7ioHwS/ykuPDgIcA+8AUKIFP0mDWMR1s\ngFN9ycUwUEE/3AGbNwDf0nTAc+Mq919zUcMt2bMHDJcNYNkA7oGVVdWIAN/d3TqrnQBxoMZGdndr\nRqBzNMyqimJbii5pPn9SwTw4bTPldc/9VzJIqGPcWY6gYCmDnQZqedMutGy+SL46XHPppTh/HmXp\n2RSRtVQulbWF1jNHR6njy71ps9YFfdyxo5O9VwTcbe28j/jXOO/+4eXMpjQVYs+YqKmHos9Iyq04\nXvFK3eGwFhkB9fbccV1kWQ7L0gJGa9301BsBBbABnAR+zJhfB26g2mEtTZWNuwnTOPacHDjn6pIC\nDNwdyIOb4D9VF4UxY+X1NqaS4wTZmF37ZvPJ3HPIvhdJdaoCiStVs7bcxIJ6gPGk1QXMXdU8qp5g\nKRff5DLIOkV/FvgB4F3AOpABq7yrNQ92tmnm6q/IC6e57s+r3F8qxCyQfehlqV42gHt8zZQaEdaf\nJHV2+f5+/c/+ZEDO5tMpsqx0MbZFYGUVsjB9D+gHUq9/BKzDDTRAMQN1mG2onGqO0XwD+AaWG+V/\n+McYyz4HMB5THjdn78Gz96ztaU10lLoB+JgaYu8ZUwZ0FN4DOlMK3irlzBj6SuSS9M3Ap5xXROVB\nA2vjIH3wfsBAyo04XhkOMR5jfR1ra/O8GppSeFlGFEEIYW2P7g8jLKbMoGkd2ANeADzQlRtJeb+U\nXaN1T+tYiFhKz8nRPqvH2pKxGPOAkzPraoEKuBKYuEv7fuCdLjAg64pqEF0NoGTMrpanm09enAEf\nvvLKOu2rxdxVquhibXkCq22CKuthysXZs/vAy9LUdEmgLwP+EBg3YxjiBaxffgKQ7NUIVe4vE2LG\nrC9asmd+HHm7EFUQzrpcywZwN9a0qnpFsTqZ1NgCcQG98QNtMykTajYraRjIxrCdw0A+CaRykwpB\nhanvnBLuDfw9MGKbo9q/l8ZoTQvlVo7gRW7AGDkpqeeBZMBDgT13fH7+T/0Ubr2V0O1zQOT4l5y9\nlzo6ypy9584ch7D3+EAydO1PhEiESJpGSbcD6y42K5SiQPtkAAAgAElEQVSJ0fXen6JaomiNxBlb\nWzh6FEeO1Hk1XpS3v4/d3TqqxRhQCHtXXk3qDJrWgPPALiDcbpoOXpW1qTEpUUUZh9148ITdhGIx\nhdEEADqftQrg4cD7gInThIdRDV/dfDJ33RPVcouzTaz8uhe9CLfcUh9VmXyvcpyl0hj68q0GULIq\nTGvc5GX5xvATZfmLURT6TWngO9wwgAvdeRinaXo62ebPR7P658DLpWyNmoqmSwT/v2wBM+DNQtC9\nev6yEywbwN1dE62johBCrBC2kGX1MJAaAPGC8twPAzkfhj+j/p91MBHVVBAD27gzwJZL1i45iuq4\nFq1x34OBc26rlbB3jBMBfVzlPrADvOiVr5wBr7jkEkj5P+51r5+++WbP3tOevSdE7Hyb4fa/h7P3\niI9YBLi/p44QkBU59Sld9cOAq9ku2FsjePLrZVQWpVxNU/iollOncOIEtrYwGkHK+oi2vY0oqqe1\nLK+mRVj0qlcywBkAfwl8qxNneE5Oj0K+hGhgF862Wrk/FjJYZmz222qBB4B1Nt2+LD0E+CNgb3Fg\nAF/brlsvKpcF8JEnPxk33zw/EhF5t8ncze/CUZXWkDRpXV7cP6P1C4UIcSQAlwLvBsaB0J2Tu5RL\nLpMB8dcyxOzVUna6ROQLXCKOOpetFUq8EWIX+LVlG1g2gLvVAERVESA+qqqUcsyJQ+2swcqyzKoq\nY8PAjPmZ8Me080ktaE8U7Ez/M/AJYOzKCq8IYYz7dwIVcCEwEIL27F5FrN14oGBxlb7TnAOeft11\nv3nxxQjYewOgZ23qLO3anp10SuiKL581mT+tlIJrR6ODqiJaTsQYnzHwncCfAANgdYFXRCJEP477\nZNRMUS0+r8bHFfijADl1k+o1imQXYdG3AbotQ2DbfdWawkiQkbWRA98a18Us+ENSZtEFytNO9k1S\nTp0PMw+u+S/A1cCuw817zV7eOJseWi5z4MaHPQynT9cZzt53czZTeT7xvptubnFXfDf7UhpjtEu5\n4Fd9Cnietc9j/57Lv78PeIvzmBuy/Anh6n4cyD5aJihT4PVSzu7aiIVu9RRYY0+7f61+Wogd4K3L\nNrBsAHepARgDpYwLbe+XZRJFkZRz2Y7WpVKF1oVSrWFgFjypuutJ/eNeb6KUdCWJa/HvC3wGmLIY\n916Aoj4eOADGQhBhtB9FiVerAaAIRmPqEC4WV8n3lc+86aYzwLs3Np6wszNi88OavccSLrEgBNzX\nvplj07fGd56/IaJIKLUoMuXbgWuAA6YT9rvgFIilHFBcgTfnOHECp07hyJE6rmA6rf05vIuR86+u\no1qaeTX8r6ajwJeAr3DAeoPCaG3Ydzt963K390fAdaHqv8c6aOt5EMAjgN9y+9YB04S3VrEgeZGA\n8r+6+GLccUfN3HW+mygKT1ptGckdztylS+hJ6dNJe+5U6q1DTwIvBZ7eZPT773YFMAHe7ygPXOTo\nhe6yKyG5BN4URaW10+aIJVswYvG/kd+XMrfWH5dbHfSxQvzesgcsG8CdNwAS6CtVGZNrnVZV4goJ\nrNXUA2gY2JyIhk/qIhq+L0xkG9dqA18J/A0wcQWx34QFngrkwIYQ61G0kiSRFy0zuXKk1MDFVRK5\nBS4grBU39vCdnRI45urXnbL3wgaQBRk1rZSC60+dwnQqhIC7hwjo298EfLi5C46B59IXoLyaXq/O\nq1lfx8ZG/RkMoDWEqOc0vV5L9Wrp0/xuHub2dpIDN42gsc1wwTTikAaQNa0vDCvNxMn57Tieat1J\nvKGf/0NABrzbfR/6Aq2VLyiXH97aGiSJ2d2VdOE0zVaqJNLqoYJB/zHN0QV1r14UVeTowIC7VsrF\nm4HLXfUPLayfC6wBz3cQXyh057hQCVwVRcraiZtShKq36QJycwGMokiycFbTtJFQLpx1Wd+XDeCw\ntU94tzEl0fClrHPMPRpOk0AXYt4aBnY+qa3Y3oY4kxVE3wa+Bvgr4KC5JYyAFwACWJVyM47HFFdJ\nhc9nbhAhsixRFFFZDquKWPzGxYSF5Ov/C5wB1oAiyM5tvZ+mS79TBXg0529cc9FF2NuDK8Qhf9xf\n+IOA97my4sehQojI59XweE6q9UTNIqq+PwA1Va+2i7Do/2pv1PrNwPXAGMhZZksIxy9qACpwrjZs\n/vFbaTrVuuUW1ykQeSjwgSQZVFXRZXE8bSYvVsDvxPEwirbzvF9VaRzXzF0a2oe+m4y02moAnczd\nCkiiiCR1Hq5sDVFWgD1gCzgb0HLop90GSOAXXST1ELiiaX9rgfdFkYcuD4gq1pwzZWzGXnbZE5FL\nxCCO4cJZdfNpXwFy4JQLZ12W+GUDWNwAAEM0DyFSIWjvI7mjIdFFXChrFchzWn4mrcCTP93cnOX5\nIts4vzO9GPhIkCMogKGUm0kyHgzmcZXD4dyxgEBwMquQUgjRL8vagrF5kCcR1jpwBNgDzjD2Xifu\n1Mnes13sPePOB9vAIqOk1oVT2/se4J3AOff16qgWIQRFtXgrGy/VJps2F87OPfpr1auDXHQXFmcZ\nFvQR4CLgU8AAmHXlNIjFIia7wLuUmv3v9PtTpXjiOVeKGNYFqSH101RLSXDNB5tb6m3383+edt9C\n7HvBoNaJUi3iliLtgtupcN/NvElaNV2IygGAKIq0joyRwTnV86lK4A3AM4Apk6p9zv2o16bpT5fl\nCmMnv5WaqxAeAto3BswmSC8OBz1kxHLD8ePI80PCWQm2OuHCWZdVftkAutcuF+gTibB59DYOz1Vd\nKYbEh6m6APHKIa1mwYfbxlGe+B+6TXEfeD8wEGKNqr/PrV1fx8pK7V5J3I/9faRpTYi0tpUB0uId\nUWTxFHilED9q7ZDhToew97xvs1gwkKTMlr/8ju/A9nYdmcJiilvcQZoKUi7u9wLviOORUle5n0Y3\nPPLhXD5XNoqQJKiqWpPhbfrJnIOrXlnr6ozJjBwl6VLgBcE0Imqa8Zmm4XPo1M2nsu8ajaZVVbji\ne6esGwDo96U7urUwlTcCe8AXnR9yzdzVuu98Nz1xy/KjasDcLZpGcp3M3Qr4+IUXYjp1YKUQgAim\nVqkbpJ9wNfoA2AO+BpgBnwYSKW8BTobhoE7wIYPG2SkSzlz17xyx7AI0EDoknNUn3uwvbeOWDeCQ\ndb7JwkwccUV0PamdMbZlsCP2IqwPXnihnU7VAkuWlm1cAlwPTIBHAp8ABkL043it16vp8BRXeeQI\nVleRprVkYX8f/T6iyHvjQGvyxqGRps8A4TlQA+AXrH058KPAbhMIaiW3aFf15IKUAuIUnQf+9uEP\nx7lzVIvn6tMFCWIc/hqmaRVFf1QUmuXVRDTV9P4ccYyiQByjqnBwUCueSPXqegAptqquyBp/5/lM\n+BRggbcAl7ubMOhyrvYbz8MpjAXw++Mx5TS0KDcl+we+6gCv8RizGR3dWnm31LN33fcnTXgJFMak\nQC0YdFCY8d7LjrRaLWDuoiuWZwbw3GO+wrCjHvAC4BXulaHPNvANwDPz/B1p+oSyXHU9707DQTtP\nAGVwo7js+cYHPhA7O8TQI7tvgq144o2POhg5SfNyLRtA9wlAuaSR/mIWZhhjm7Pc9vCgSnyYeTVc\nECDFqyGBpy8C/hgYAokQ4yQRwyHW13H0KC64ABdcgOPHsbaGJIHWmExw/nzt58ziKqOqiqSUxkhG\nhYyaOVDkz/OrwBOBVQfX9prsPTRNm8OUArpvn/rxH8cdd+DsWYrNsi43ptMiqeWUJwH0+3FVmaKg\nP1xSWAoFVVL1p5wyimarqtq0g6JaSPValpRXUzQzMrl0rgooSUeEIAjiWuB+wTSC16loQYY73/t/\nYGWFePc8wKtlGGcDv6APPeAB2N+vj27WsvwrwJ3ebnPzUnquhs53kwSDgvnf+alPK/+dk1Zt0N1r\n5u43fiN2dvgg3XTBlYIN0leCcGO6sU8ry9dG0ZO1HgE5UwgnXUSDsAHkwYiFy54z4LonPhG33z4P\nZ1UqUop/DdkMZ+27cNbrloeAZQPoPAFwFmba3Aijy9HQb6w0e5rRHFLNgOu/6qswnRZ8Z+raQGhw\n6B1uh/SOCRFH0ShNMRxibQ1Hj9aM+BMn6gZQljRunYuA+n1PiBRKCeczHAaApOw0/evAAfDzQNxl\n2hwvdu2vgAnwhac9DefPY2/Ph0dmh9oPVKwB0OeS7e3rjh6tDg5qmyNjMqVWKK+GnFm9PJtaHR0L\ndnfnDSDPM5dXU3i/vGZYZtXczApgI4q0+738HVBY+3Vu1Nlb4Fwtm1NZugl7wMfG44Oqqv3yjAlF\nTHngFUqPCjY366MboVjBaxkD3wtcDawwLtb8Ke3y3dRBAwgRFdsUD05aw5umfCHMYyEMjVRjra0P\n/edPaP0aKZ9izIGTvPVZZ5VdXyOcM4Xb/xy49pd/GadP18kNLp5PBPTfcNMzWpb5ZQPoXLc2Da16\nARq+6ARgAzTA+6DlwIe+7uswmeRKFbQjZu/kIbZx0kXXSiF6USSpAayuNuIqV1cRRchzwKmifAyI\ny5gVAekIQRvosQr1E4AFfsPVlx6zKou7MmqmwM3PfCamU9xxB/b2vP1AUVUznhvTxMGrAAapr30w\nIIQkAiiq5aAoVsmhmhyZptM6qoXMObzqdTJBlhXOnclHtYRxBapJRrLAsNeDMUrrgjwkgL+39t3A\n85qNMA6S2nzI2gT489XVQqn9qqoFIkQPcwKR/wQcsIbHsUHit7zm6qt/4rLLaql52Z4R+Cr2YOAa\n5rvJ5WOLbNdaxsudiMp8p/LgB+P8+fkcJXAMDQfpMfDfrP2fQhhnv9raHj3bmM8DJx2/eRCMWMIG\nULH2EL56BXAHMH/a3V6HJ96gFjK3GRbUAx4ixPXLQ8CyAbTWp639eiFWgUnzuNrJBqmCiWhrV0X7\nlOsvugiTSeULk7MMKhktZNFU8J+AU4AQIqGxJ9lh+rjK0QijUQ3U0mvA4yp9AhT5EDA3gtZbQT3g\nj4H/7IQ2BvhxQAH/D/CY4DBE9evM93zPfORw+nQdm+UycypnP5A5rVwrMuz+DIs37FD/ln/6JwJS\nhLXzqJYsG/qoFrLqa+XVZBmyLM/zqVe9Bu5MRRDVMoc1RiMoFVdVrFSstdRaGLNj7SucUPmJXfoA\nA2TAZ1ZWiHWzW5akESmsLY0pABKIfLUbGxxjkBqHXAiRnwC/cc0120AJ/NLJk4eA77c435vVBRvq\nFh9JLSatciuhDLjmUY/C2bNkH+3hyqrJqddMxOA/fRKOGaOs7blH2tNvjgFfD7wY+BFgr/lmtU4t\n/hwcNcNB+RC+AD7+yU/izBncfDNP5K6LftOQvDOcNVkeApYNYNH6nLUnhCAoPDS0am1V5KF8mBy4\n4b73xXSq8nxSVZQhxWkhramgDQaDNR9ciIiyKvnHu9WjmVjpsyrdpzOkjGuj/DTvvsCNzSN8DrwP\nGAmxEUXrSbLe768PBslohOEQd9xRM458bozzZKZCTHnFOQsOK4D7ADM3Y4ia/jAeo/hrxwSdWZsY\nE1eVyHNj7ZiormQE7ZMAyhJlaUjy6lSvtT9Hl+S1CvB3DWB9vd56F0WvLKncPMOYN1tLk/m3ASMh\nBkL0iR9MAhGaG5Ul+eVplglB6pB7AwXteYWgUe3cXtSNZwtnTDRwnMXzwE/edtu3dT2c9Jg9GngH\nsOqYu8OgPYdshdJ1PhGoFjz4cw7w2B0IuzOGsi0PT7mgnXUqZeUGEi3V2AqwCbwIeDXwQ01+c9SM\nvYu6XCJ49c+BT33uc9jerrNu/DNPL0IXYNWCraQbCy/XsgF0rzPWCiE2gZHrAS1KjO4KbRdB9f/U\nfe6DyaQoiinbDmdBcm/RxCV4YZK0badTrSfCU1wlVVsqKWRQSgEgxItvsiG1UzDoLjakn5KtAA8E\nJo7OdBMwowu0NjUmVkoUhQXGxgxpMEvBYUCNXJelKsuMqrBSmTdKAnLgK4ApMAS2XJyvZDiSj/PN\nnckdiLZkjNTalqW2ttB6UJa9LItYVEupdUlpLVz16tyZOvNqQh8xHDlSH1+khBA9V+N2tDauZ2gm\nEInJZo5H2zO3OGJe3htIgS0hhlL2hfCZ74YlOpTG5ABlWPrCFwVpMAg0dI8Afs8JrELhQjjaVWww\niy7a0g7w2Yc9DDs7lB9Ad7JYnHdfNXuSBBIpPd/Mk824aoz8Nn4TeAJwh2OgJV0K4aiLZUBzps//\nzd/UcB+l8vlwVq3JB6UFW4XeG5HrAcu1bAALFyElQogIWGk6mcRdWLBoIj8HwBe+8ivVZJJVVVaW\njWrYFGQeYnBY8G2758J7QuTeHqIIsxkAzGZ1TI3PAHGM+NJnVXaxMA2rCxI4zlSX+8DFwBSIAAsk\n1kpjrFIGoNlsryjSKJKuqClySaKP1sTD8dv/o4AM4nyFK53k3U9KpdTaxwHXul9EZC20JopOrnVf\nqURKUr3Wss9Q9UrmHF15NaorqiUHcPw4dnfrZmYMjEmNKY3571L+mjGlA/pJIJJamzi/vPbQ1VoF\nfBuQsQDLPp3YnGWTNCY2pqe11jon2iIgWHCu7bJVCOMbLwHe0+wBCeMs8foeLWbu0sHrhkc/urbU\npsj7oqhPqzTAb45tyq7TKoBIykhKsvxrObCmbpj0BuCxwKuAXeAX3AyjCogG4ZxpBnzpj/+4Pne2\nwlnpaa+qOpzVwVaK8aA864yTIP6rENcuxwDLBnBX2kDKxqGtJ1UET+rfnjypjdmdTsugGrZs43h6\ne2gb9940/XRZ1lRub/JFfP/t7ZoO3+/DWmRZzYb0GSB5jrLUjg3ZIuEsOsg/D7jSZdMTofvzLjYg\ntlYYU9PPjcmUSpt5xfPIFJdXXLpC/BXAUIg1KVcozpdVQ/Luh9Y0gPXVkOfVWJ9XY0ymdSxlzLXZ\npHp1LaQzr8arXtG8w1Qgrnv+82sGrTN8hVKJUsQoPwessvJXj0OYxR6aZJ7/ApTAqpQbUbSWpnWA\nJWFldGohz+qqispypJR0mb3WwReamWv6xXu279yXAwVhdMAoMBMVTeKWDMAfan5/+YhH4OxZn3Lh\n4cq8a47CI+9bB5SoaXIlusTDGngncBmggGe63dKbXIdIghFLBdz++Mfj1CkcO4YzZ+psvjzH/j7O\nn5+Hs+Y5ytLQ025M6bX6hz7ty0q3bAB3ow0AiIRosZg9fFkBn1hdjYQQQpyfzbT3Y6GpoKuGY2vp\nFq+7YsSpIPSC/ZVPb3dc+AogoCPN8zkdnrj/lFtJ/57iKvf2aklUVRELs3By0E4WJppuBOPmLuw2\n997O84qtreMqlYql9JCCZrW49Di4tfemEUIcryWJ8HG+HMSvKlRVXFVxVUk3wzhnrXRvLM+rIdWr\ndKIn6xVPC/JquOrVNEc4Hv3AiRM1v8jNMEhmLLWWxvyiEM+3duCA8lZ6Vzh0bQdY0qC+35+nStCw\ngf6iPB8IYauKHKjolEbj05ZeKZSM+KPbdwHvAc46UCVllsjcd7NFZa6Azz7wgdAaZ8/6lIsWXNmZ\nclE4/XCbxiq9Z0qbZeCJBjoQQgvgCUAGnAF+hg40UXQkTY+NRsP1dWxtYXd3/rT3egDm5q/+ac8y\n4hznLJvvTjnHyxjhZQO4e0tb+2Ihxk4oNBJiSFNBKVMhdspSAsIbcrGqVFk7MCYG+sCqQ3hbQd60\nUd0HvgnYAXIn+UmAgvgwVZWSHipJ6n3QaIQ4hrVzXGh/39Pwc4KeHCpyV9wIBDCSUtL0GLDAo4CP\nubpW4+CkPiXY2jnlEbmo7gEE6FtbAheRfxFpmKkaDocd1ZA+Ug7K0ihlgOcY82prFXOJqfNqgJg1\nAD/0OySvpuXOZJuj0T0Am5ugHND9fc4op0sTQtxs7RF3YhsxXmyLxWiA7wUGQqxTgOXKSm3asbKC\n4RBJAmtRFHPealRD/T1rlVI0BUlchmVrvRR4RnNLywvZQ4GnAD/EiKH8qBqSVr9wn/sginDunD+R\nVIRVVlWuFD02+YKUiyxwsjJuv9JJvGmRzXrNZ8/PNl4I7AEFkFibGzOtqmGW1YwvKvoUzgrACwPp\ngaennU4trmmVjmh3J94by7VsAHdr7bj9PpwrXClEDQoTfOHfClcNN40BsAL0hUhd2JbXavrhZ+Z4\nIEQFOQe8tCwfQMdnazOtJ1WV5vmI4iopCpEqKb3DxIh3B/m8KCaOhr/IjaDoApoHUlpjuIHoWdeK\nrCOE9Cg50l1Lh/rU2gq42FXDNXKvozjf1VUMh42+xeJ8YW2fuqYQZ63VbnLo82oScuoXQjLQ3HsN\nVQHnnRpA1YTX/Z/MgBtuvBFnznQYSkvpdbCvF+JJ1q65wKlBV3qXAX4AGAgxjON1CjDY2sKxYzh6\nFBsbdasm5dr+PnZ2eIBlROIDyrB0Vbu1xs3NbGgkdxx4P7AH7AC3A78aTAU08MnxeBjHvTie7O8T\nYmOt1VrTEIXgyoINb4qulItwjqKBa48fr2YzEzhL20A1lgAfAy4Ousg6cAd9YWtnWverKs3zNXra\ntUaWzfcNFP4zm9UP/GyWUzgr2+60nnMeesP1z8u1bAB3b11p7U8KUTFDdh/e2/INpRfgQmtHwIoQ\nQylp+CmbVBDSu6aOPBc12SAZ/VdrZ8YkSlFc5ZBSn7Ks3qvSVprsH/IceT4lTqRSM3eQ5z0gD94H\nXxkLoE9xr4SqAz3gMcB7XV7xHAenbxsMQj2V+2JfDb1/EcX58mpI84zd3frFpjwDYxJrE2tfIsQL\nrR01fcTqrXfXAFZ1iZ6ypj9Hy0fsHBDKJoCGoTR9rgSeAozdp5N1MwRSKVcowHJ9HceP44ILcPIk\ntrYwHiOKUJY4OKgDLD13lhAwN0GVboLaWn3g7cB3L97MrjF6Qgy80DFEx8CKEGMhhlLulWWudU+p\n2FGSAGgapLOUi9o69P9j783j7LqqO9/fPsMdq25NUkkqWdjGxGAMBPrlNTg4EKaYKTQBQsB0whCa\nNITAC48EOiHt914aMC+BhAbC9IBAAhiIwY3Thth4BptgbGPkUTaeJFlSSVWq4d57xr13/7HO3ned\ns8+VBwh5n+ieT330EVCU6p577lprr/Vb35/WCav6j+3HcuytMV3Gr7bqlLjTQETpiioMKf00FcNh\nj5724bDenDVJKk97xEQWXGjnsjfySTibJIBHcB0BZoCUByYbuxmM5UmAAqaF6Pn+lO837IoWUEhB\npGxK2VQqkTJQyquTggwJBK11Q2s/z4UQGsiU6mZZEMcjFaaxw43TNDZ2ldYDlk+bj22sevXCAuK4\noXWmtTUubgD7gUUjok/qcC7CobR3KRqGoaBouLiIpaVSNCSUG02zrZ1vlhV2vp7na32f1tvZ1mur\nDtI5DtScGFM2OG1661b2w9tuw+pqlSZNgitHMvsh4PWO6sb+Ju8BQiEavt9pNgsDy8VF7NyJE07A\n1q2YmoIQhXyFDCzpxEbj+iDwssyj4oBZQPOLqE3/E3hqXQJQQNehJizbOldrzV0upOSW93aJoRik\n0xDFcbkYx44u5igO5Mql/nmMceJ6JXWEGGgdABpoKBVYc1alOnnesuasplDQWRZnGT3tsfO0V77c\ntpUEJhKgSQJ4JNffa/1qITomGtaaqJwBeCQFCYIpqwOxUhAb79KUtk8rUhDbpN4LBIAia3ilkOdW\nhNNMktD3fabCrBzkregoZrbaUVkOXxFEotEoAKKGIE1/vgX4GNAz9VSn3Ad3maDPA0Ihmr7ftdFw\n27bCzpeioeeNoiERHaIIwyGdacjO0RPi00K8UetZY9Xi3mo42ANLvozNYd+1qyyYBzfdVIAraHuZ\nqQlHNOlyGvgr4Gxg1uQArrqh5l6b/Muo37VlSwFtXVwsHIyHw8LAcnMTnU6FYEPHDjH+Y9lgr6tS\nz37J8yKt7bmTQvCtgDbZotblwjN+98p6PhtsX63LRerIqOgp/fbSUjIckgTTxa/mpmvEyTzKMXVp\nCbGpdcg0x6WnPQi4OWuuVG7NWU3bqvK0WwFYxKr+0tM+uSYJ4JFde4GtBqkYOaSEFwI+SUHCsNNu\nj6QgtMIKUOgfOZgnSRtQea7YJJM+hGtGO0FnCyUldY3iPA99PzSDSquGzKTMmFdlWt45sHVcWleC\npQCaTWRZkOe+lJ4QHCD6OuATQNf0wbl5JN+CpkBJ0bBFdr48Gi4tYevWAl9Bdr5pin6/iIamBc+j\n4YeBNwFTwIAhBPi/O86vxqvzq7FuZZdfcgmWlxFFJTEJ1eMchGBGGraq/RiwDvwJ4LENrL+j2tbz\nGtbAkibAMzOYncXsLDodKAUhiskNPQnMwLLSdKr9WCqgVe5m2PeuMHC3m2hACjwF2G232KzlPc2r\nKG8JQYcD/WAuF0OHHW37gRFg8asJM0qqyI4rptCVM8Q60PS8Q0o17W4KSc6ATOtYygZrW+myOWtm\nelYVc1b7tCdOrSMrrO3JNUkAD+v6rtZPE6JnCLcdB8jV9ry5MOxYJ9vZWfR6aLeLdkeSFHunVgqi\ndZMaL0rxZfonAntsH4PZVcaeRwd5joAfKeLr7CqHpo4b1vnqZcAAIIGm8DzP88hPmPNeXgN8Etgg\nBRQ7BFS6Ma+iRMjtfCkaWkdf8nOnlghFwzC0tbAbDT8EvBHoAtN1cA44taSu43NwiuTVn/gE9u8v\nlFS0VEE5wCjKJZWWDGSdlelPfwgkwN8Y1U0IQAjP8wJCNtGZz37RS+MzhkrzQYiiVy6EGjOftHtV\nu4FfB/aUw1nT9+l9bwApwzAsA3PMj6XF/Fho2OA2x2oTQMXxkYOMvr1jxyCKCL6U1onNsrqe1WOB\nFLjZ/JBrpqbiLDtH6w9LWZgj0dMupW1b+Z43Elk4Kjuaxtkp17BuaCFZ9I8mUWySAH6a6/taP1GI\nKdMNsJXpW4GWEFNBMN1uo9fDli2FFGR2Ft1u0fu2UhDj4g2lAqXIgJvbtoTAmsUVGBpBYnVHbOys\nuCLe0AjSsgcIJQA5xtjgyjPPLPahrKDbsoZMMBzQdLcAACAASURBVP0tY01Vsc3iY4D6aFix85Wy\nGhOPGQ0/BLyO5pkGf+b+u7nh9R/DqiUC/vntb8e+fWg2CxnSYID1dRw9yhXlJChMy4ry1FGU/54Z\nTtDvIMjA0uLJqFtNpz2SspDatc7DshAyMXBT5bIbVS1gDjgVWAPuMUe3hu+nxr09YB7uzwIuBbqm\nU98pp23vwVwuKn4s2kFyDoBhkgxYC76CX03MQnvlOsGsmq8C3wcQhqSDWpFSmyxOmuNUqYYQIUDm\nlFXJmfO0Jw/2tNNbeelkADBJAD/ltVvrXSwHtIBzqD71/R5JQebnsX07du7E9u1YWChaH2la7DES\n0NgMA5Blge97SpGZkSWW7ANOMAf5oo7TumE+5zVimLqDvP1I5OU6zlbNA9LDmMg1ajEbpq7tp78C\n+BSwWrZM4cV4NRrSj6Wyl16sJTkbH48R2EtrHg05wO6vgd8xgMmu41cTjOGI8eg/AHa/4AU4cADr\n6yMfTXJRZr6SQzZCTxi6dZyi/IV0841xysi1mMa8dMhrNJDnxfoSSdftyEFKPkGVYzrUfKt2q9EK\nh8AhYD/g+X4gpU9mWGU50L8HrgaGzOWi4vmMY7pcjLO8p+bPNxcW+oRfJfMDox2Ky+tj7tBiwSys\nNYGnA286evRT09O+lO/x/fdJ2WFtq5E5a53kTBpKnZsABmW+lixTryfXJAH8LIYBWjeFmDFRiQyv\n277fICU4DT/JvWvLlmISGEXodIrVU5p80vAzCLw8t8tHVg3yEuAS5gFCT7nFh3ljXMtzxwQqKn8e\neM8kBi575Stx8KCt9+V4J3cALwc+DSwzFKWtKP/UjYY08bYLUEFQREPCGVHYjaJRUVzWk/AZ7IeB\nNeAc4Ig5B9h6trL1WuFzpMDNu3ZhagpHjiCKRsMYEmLadyGKhknST9NB3f5EraJcm03p3CxJNLiB\nJWmc0rQwMuv3R+0mc9pAnud1vmmVi0v+Z8reWwDO3tz8XLvteR49NoINb6iGmAMiZ5Duml1XEoCq\nAzLbTto/zM9vJAnt39ro7ypw8jrR51TZ+kIAr93c/JtWyxPiMDBb17byxxjI1J5ahuweuk/7RP8z\nSQA/sysxvKDLKAx5XovA/dPThXPLtm3YsQNbthS9736/6D5vbhb7R4RG8CxLZWRmRF9PBb5rJO0P\nhUZQ6wIoywd5yT7J6zSXJiUSo4faMty1AXkDkABfBDaYZUpoRta5AXw2Kna+ZF7Go2G594I8z5Uq\nDWCZrJD+8lYgBz5RtkPhLEke1FLggkZjptE4Ohz2tPbzHFFUyLFgFOVpiiSRpCjPsogU5UxSkpQN\nHWv3YKkTnUjZpbzOiR0bG6MtMMJ1cGRTnYFlNj4BeIZlX9FxvSKK/jYMhfPNHvDrwHnAjONy4T48\n/Ow4bo5CBKHDwHqSxEZvxrUGFb0ZXw2TzMxSO17wb4rjD4Xhez3v3Up1mTlrs87wQJd7+vzUYg8u\nui5vbUxi1iQB/MwvrfXVQvhClIafU1MjHcjsLFqtostBnlbWwsVMPu3qqbtS/wAwD8TMAyRkn2FR\n1+jkHwlVbojrshz+e69/PQ4etFhdPvYc5+JL1eUrgL8WgoA/dBRQFh+vdWqjYb+Po0dJY4rNTQRB\nsclM0dC6OSYJsiwe7yJpS2MBvBFIgCHwAeAXyqp84grMAk1gVoi+UZRnWnezrNVo+Hx/QsoszxMS\nlRO0lSIac3OkP3cB19etFI1OZkpFUmZJEtKMhzBNdOAjtSvdivV1/pJjY2CQMqOIcRoVehNbY2a2\nL8+yv/V9K+zhmeN5wNeAGaDLFtk4d5rXBMJhR6Psx/L5bjdTaiPLaGssYe43nL06LLfgbSCOgJYQ\n0uyN299/EXhzlv2/nncXcIJjzhqMMTyQ4592t/wfAN+dlP+TBPAvd+N8z/MrUhDaACDZH6lf+NiT\nPY6qjvpLX88zZrAVGsG4Ti7/SIhj2mpf8Za34NChIv6mqc7zhNR1deb1LoXmNVp/zfPuM/D93UAK\nBMQvknI6TQNr52vbX1b7T10gKoeNi2Q8xs7XSkqq0VCIO7QmR61NYB1YM1ABZRXlUhLFOs7zZpqS\nohwWYmpp0gwo3dU6NyMH6+F1hsk6fRO2bjQ32SdwQp5vpuk89fSoy0f+zNa/zKIv+n0Mh4khr1ER\n/RDBNU0hyHirZULnNDAHLAGvk/IjrIbgw5sXAp8DFsa7XFhzCM+hh3I/ls80m+tZRltj1vomZc0f\nqzdL6uh7GXC+7w+Voke0aQwDSOW1BfjPSn1WiDdofYy2FcYPrkV5l0KV/eYun0T/SQL4l7sUdXC4\nTZ31byGXV5oNcikIWRqRdRfrvSinEf80o+io0AgqUGL+kZCsjquVw19FNu6WqUt26lK6ANF8DFIx\nADpGMwozLw2AWKmhlJtpOmejIbV9bDRMkqIiNna+aZoOmI2wa+dbWxo3PC9XKtO6aVrGZD/St8tH\nWmulaKUolrLh+4EBcpCwagSy1jpVqqUU3dKtZtsArD1iVYZ9YB043agkfSDSeqBUmGVBFPVsxCdi\nK/1HCzAYDq1v2oB800zHyTYxWpUjJsvffEnbKj67wAywBvwXrf+kbnjjAb8NRMA/AKF5hLjezGVH\n8/UOmqJ/Jgg2pLT2ZzmzeuYJgPCrqLOnj+u8w+hVELhiHnin1n8FvAnoAZvE0SqP/YXTU8oYIbXW\n8iwCrppE/0kC+Be6vi7ENvap87lvF4lMhCDIcEG/MVGvcG6xw0+n3yJZFfPLwEWmVuo4NALe28nr\n6riKWfE1b3hDYeNuCNIpMXUtTsspwJ9ZHhXSn+fl+avDUCullIq0TgBP64bWQynJznf6GNGQBrBR\nlFg7X4NzqbXz1U40tNEkYNGkBRwx9w2ENhKC9CShUsQTFcYh2YIQ5qTUQBfYahwcLZjeUk4TE51b\nJvLeD0xTLtQ6lDLIMk8IBUxL6adpcfijl2xGDjJNh0kSZdmQgZcryhk3AdjHIBCCVDFW7mkD6Axw\ntOwc4Fq/nQWcCfwZ82nh+OhaP5YY+KjnAdhQauT8zjYWUwe/qupOpSnw7V4vTlNfa19Kn7kFhOb3\nmQIi4L8BfwG88ZhtK3sukXVPO8qt/2sn0X+SAP7lLsGHn0r5VvdCe0ZEPgiCEQDHelmYHJAyG+7a\nZXoq4s4C/g7oOQto/Fzs1x3kefSPgBte/nIcPFh0JMjGnZxApORIRarBTzGn9ZAVX5mp+4bAV7Ns\nzbRfKRqGWjekDPLcSxINTEspSHRPpwHrb2PsfCOiOUpZ+LnXEV1ql2ADzyP5Y1CuZ48Ybqs20aoJ\nNKQMyY3SYvvMoHtOKQEsCdE1Do5BnWdZQ2uqW3m9TPY1ntaB1p6UOstyrVOl2llGDIMCEk4WEXke\nk4cluVdKmTDwsp2dzoxJALmhcNOetkVsWvPFGDgH+MNy4w7mSaBvngPOATaAd/r+o6RsMbgTrydy\n4L3mv1ynQ17ZSrMCX4pZ6x/OibNYG/Z93/OKlwDYV2HfO0qr9Gv/BbAB/FeTA5omBwhWBzyUp/3G\nSfSfJIB/0WvUedc6kbJFw09r3UWFP22B0eaRde8yenArBUnLy/S292JLm1cCA+AbQJMp4it2lR47\nLPPDQQrc+Ku/Cq1x6NBDsXFfBAKjcA0Mhb8AWZvaKjZdqXVgFRjyaJjnFEATKdtZ1gqCwGCftYmG\nSZaN7HytmnC8na+qREMbTcrax8cBe4ApFoBahttKNGneSXi01l3rWWYhDYSjUMpTKpSybR0clfLK\n2L795kf5SgFQeU4JYJhlDYM2KlwiaL5iDCzTOgPLqE6oLtkM1jMnGHoVnlkZCcxpoFs+ouWOEcpW\nU3G/V8rbhBhoTROUz5j1Dqs1WCuPCuAcNF38qmLRWbGBUwxcfNJJ6Pc9xztMlL3DQqAFfAR4DZAB\nbwP+HBDmHBCyI0tt24o/7T+ahP5JAvg5XPQxCIxt4VSS+BToif0wHKLdhucVGEgafpKF73AIsmCV\nMmE6kErjO2OKjoKm0mpFUnazrMUwmRUPEC7yy4FNYO9ppxU2IMZhmNu4x8YJJAIWAEmBQIimEOTi\nC8MOs/ZbTaqs2WfykI2GWkMpioaJUkPjImmjYW4HsEol9Kdp/rh2vlmda2ZKtXx5ecLGxENmbplx\nRTlA21I2oT4F6AgxHwSzYSjs3J76NszBEWnayjKPmKyE7TM6lnXAM203TZW+1olSDSnJC94OaSQZ\nl5vx6cjDsuxf5iYAW3Ff1O1uZBk1sOBEdltEfxQ4m41wBBubC6DHfvKHtT4A/B8AgN8Evgh0gLQs\nGA2cmbALX0pMkhZ15X9C+kuzbOh6h8FJA1328L/NgJiiOscbdyEmB26fhP5JAvi5XdQq8YFY62Ge\nbybJ7HA4Qr+Rmx1twNqVKPqKojhNrRSkdviZsDO1rcJEo9HJsiEwVGqLlAET41cmwynwg16vHYat\nIEjW10kAo2jyOcbGfUbrBjDjeVOe1/a8ht3mRbHbRci5RCliD/BP4B2AoGioNfm2UwJoeF7RVzEE\nU+XY+VaiYYXo4vK8Lt6yJYuiQkRbplZ4wC8CN5oduqQOYiqAZwJdiv7cwbHdLuDD1ofZkPsaQug8\nt24/FAGfDvwzN7DUOhUi0bqhFIGXR5nY/h8td5MRbLiBZeXKTfk/TijMjwKhCeL05ZIYuo5g7M+B\ntwMauAvYCQwM8ali1Yvx9L2kbgBr14b7wHef+lSsrsIsCXKpWyUNEPmuyX4yfdv55+PClyMGzisn\nJ58x5iJgZRL6Jwng53xRv0KQd6OUYZYFw+EUkZ/JwpR633z4GUW0evpQhp+VyacG0G5DiBYg8/yg\n1gP6Ai4CFswpflqIrud1PG8tTSMpGwwgqqhQZQBRa1y8qHVHiFnP6wVBkzBtFA1t7z7PQ7JNl9KT\n0tMaStkP/H4gqNj5mukrHSOEYTqOcC4PFg3lmLiDus0JzbSPjwLuBAYMYsq5rS8GOkL0gmDWepbN\nzWFmBt1uiRVB7oOmKdS0nuPkXQM0gKOm4a6Mk3MTaGhdGFjy1kTZNy0tbzBxA8tKAkiNtKnit1Vr\nvNVwHh7N2jKtuuHtXwBvAt4GfBBYAAbAtLN1CKYL4m+EdLbGNJNODYGrX/xiLC+T2k05h7nalxAA\nFwH/u0lgH9NvB777Mn3Xysrm2+9UN96IN795EugnCeD/H1cf6AACGGodKuVnmRBCAVNSesT+JBt0\n21JI0yxNI7N6WkhBlKodftZ7cHc60Jq8YlIKr1oHwLOB79ldGNfG3br4UjVqwPfWxn2n1uThPtdo\niFYL7TZarerqLDF8kqSZZQKAlMriiIFXAd/i0ZA6RSYa+i7GebydLyWAzIStSuu5D5Bty7hQQuHh\nJ8B2U9JyRfmrqc3t+7NE8LcOjmRZ02hAqaJfd/RocZgz5L7CwVEIO3zeD+wwodAaWDasgSXDWFbE\ni5kzQa3dAqMe+pVbtgyiKC+zLccZb/0j8NQ6EoMybaKmUc1OAbPABvB54CXA64APAwvAupFgVjaH\nKzvn3pi14dzMYK997Wtx8KDFPVW8w9zXYo8yTeBXzPv+dfFhEgLQc/JWvSvPm/fck95wA37rtyaZ\nYJIA/lWvP9L640II6i8rJfJcAzkNhNOUS0Ekb7xUhp9AxcArKpsZ8aV2dDqkHw1pMqm1b6SBa+bj\nxJGKtgAv7VKWbdxPBLpCzAbBfKuFbhfT0+j10O2i1SpcDC09bTiE7yOKChKOlLkQhCNuAgeARdN8\noGjY4na+FefkY0bD1GkoW6LLFSeeiH6/lhrEw+KvAhcBW4yi3LYOyMFxKgyDTgczM9i6FUtLWFrC\n4iKmp0f4Nhrjs8xdWCYo5Zl5sg/8LvAFYJo50rTGG1iqYxpYijpLgJj8qK1OrOxUwxdH7CS8ATzG\n3MM72T+dmhQYMtUNSUhjcydfAyTAPzgSTK+swR+3N2Cf0gFw49vehgMHiLMNKbWUmTlCjbMP4+2s\nXWzek5qnYhP4iNhLJvJ/rp8MzCwv9/fsUWeeOckEkwTwM70uF8J+rhTTMwzNE/kHpuE4MJ8Njw8/\npWw6UpDCuYXGnkYKwoefQ4ffWRl+DqkFlCRIU5Flvukz0NcyU0AW6nWtyQrKTQDKAERPoh1R35+j\ninhuDgsLmJ/HzAza7cLIkPohNNymA43WDaVS4yBGLZHXm2hoi/qHFQ0t0HGcnW9C/KI8z1lArISS\n3KRAAfwaEAPfAzqmpH0/pQHfnyJu69xcYVr5qEdh2zb0egXEjV4p7S0TMG44hO975FnGQpUHvAw4\nz/CTp9jMOaiTJ8oxBpaiPOyx1wD4/vbtcRRVLAoqiyOiHD2pm78BnAqsAgnwE+Aznjdg1PGArRG0\ngQuBM8yPeinwaaBlbpptnQXl6Wut/jIFrj/3XBw5guXlEespyxLTbBznHYbyWHtBCHuOTI0hXRvo\nAF1gBXiH+FEM/I3+wOLiFcDOJDl8zz3Zddfht397kgwmCeCRXtcLsQl0gQWzDqqddVDCD3xMiFUz\nAUN5+GmlIAGTghTDTysFMdJPd/g5YOixiu7iype9DMvLhdOI7wujqyOK3GuA84ApFmIqNu6VzWH6\nsVNA0/d7jYZHAZEYdouLmJsrJEzkaU6MMxgj4jz3yUXSsIipe/sK4EsmTcYPR09iy+HMaSjb5YMB\n8L1TTkG/n1iOJosg/O88lDwZuITlAN/zmr7vE7VpZqZw8SUTx16vWN0gJ3cC+FhsH5k4WjMDNny+\n31jFRXXIJrD+iXsC0OUJarUQOfFENRjE3KamTitciZ7zbGGNluO2k4qUSWbtJgFJuTos7wrgtUAE\nfNWYErvyG995Q2ln+O5zzsHBgzh6FEePFmq3OJZZFhnWE418MsduoSJtmguCkUzAPMZcABoAh4DX\nind9Xt8PDJvNGx/3uMse97jvA9MHDw7uuEM/85mTTDBJAA/5ukWIDQNXIScKLn7n66BtA8JsA6vA\nnUYLaIefNFMNy1IQzWy4LUXSXaWhBKDroIkxUACNyWXeRCKikpIYZi+wo2zjXvEsqwSjM4BQiJbv\nd4hjurCAHTuwaxd27MD8PDodAIgirK0hDIu1XqIaJAmyzLr42sjiAfuABSBizXfXRpg3tTKWXFUd\n0cVOFC877TRsbmaco2ngcWmZHyfK2OHLgacBXzCGiA0i9bfb6HbR6xXkvpkZTE9DCPh+4VlmUU7M\ns6wyfKbrPwMfBmadCarvMPsqCcB1sHF0ZkYrTL7tZb+t1FkWob9Ml20S6OtcKQ8D7yoL8K34UgE/\nAk4t56FnAFdqLYQIy2IzzpSmF3XfW99asJ727y924An6TXJnrnYry52zMaiPVrMJKaVShB31ze4Y\nyqdDDfyWeNRX9NuBXwHeCbSBe7dvv2r79iuAHVF05O678x/8AK9//SQZTBLA+Os6IZrAkhAdIVqe\n1zCqFWtBR4HGXQelr3vZ8DMzw8+QSUHAm+/lZXo3AYyz7vr2u96FfftGtlN1MpjfAz5dtnF39Xw8\n/jaAwPPaBLLu9YpyeGkJO3diYQHtNpQqRtkE8iSGj3XxLYvxKaz8PvBBoMeioQ0cos7NMWM8L/cT\nbnf6L3vCE9DvK5JOmeWJpC4gci0N/XM94IcWCCyE7/uFPwG3b6RzFTCyb6QavywudLF99JffAT4K\nzAAbZd3ROGafZucD/g2Va0haYXIpMK+XK4aTcuagv3TMAcXtIv4Z8C72823XiDRCuqw6u5J+iNaf\nFmKBBga+Px+Gc63WQqfj06xoagqdDh54oCSfHQ4xGNDG+6ZRu42TuqXlJbLi6naRZX6ed7LMl1JI\nCVrCcDwMUuCl4sN/fc/nTjppB/Ak4OnAc4DXApvt9vWnn37Z6ad/H5h64IHhbbfp5zxnkgkmCcC5\nZsw6aIdCA2HdtIbWgVJNKZWUMbU7lKI9TB6kDgHSyPbpiWyWh5/FNxvtSu4MP6O6ZXpuZhSRFEcp\nYvfTj6vV1f0m8HfMxr1dVkDyavRFRBewTgbdLmZnMT+PLVsKVUyrhTwv2uIVGwPfB/Hvyv0QGGjz\nh4BeORoGDrvCngC0w/NCGUN/1emnYzBQcbxJ6AileDlZCYgoB0QNdIQQWvcBLQQ4tg+wClekaTHx\njqISuc8gmyqWCcoZO78K+AgwzyaoYdnFXpn39xgGlpVrM02HVipm9rRdrTDKkoEWie4tvafsE/ce\n4I/LOdKeA24FTmUZbpSHgC7gAQ2tB1IGaep73owQAZc7E/iIaChJgjjO45i8FobMPLLyEqjvh3Li\nUQDm5oo3wvOaaar5Ph1rH00BMbANeOPJGy/60MbJJ+856aRvPvax8+32Y4CnAmcCfwI0gJ8sLV25\ntHQlsG0wWL377vy66/CGN0ySwSQBAAC2+P5MGPqc4cwxXlnmZVlRiQC2ErFlyHOAy1jzPeF9D2f4\neYxl+nzM8DMG/uncc7FvX4GQk5IMFF1dHX29GDiP2bi3yoszYC6+Agg8LyQng3a7MDOgrahOB81m\nAbZjTfAR8ZQdQVwV5uuBvwRmmHFmbTTUDF9RwZfa6P/9xz4W/X4Wx4MkGRhsUVT2bEnK4KBKQGyz\nZeZ6z7LNTYQh4riwbaD+db9v7QoKywRL7ivfeXv/XwdkwOfHsJfdBnpFGut2QvomdEZjjLeUcwqU\nQMv3pZT0exJqLTFTkDlgG/BB4I3sjfMBBQRAm0V/fpBaM3xsX+tQKS/PkSRS6ykpO0lSyIXZvojM\nsjhNoyyLsowbh0VlAxlSvrnmATmAhYViZdLzALTYDl2otQXh0Vi4B2wFvvkO3LJV/8qvJKeffuCU\nUw6ceOI1p5zy6V27dgK/CDwdeBHwn4Cj3e51T3ziZU984g+A7t690a236rPOmmSC4zsBzNt10E6n\nWAelaafF5kQRVSLFCVQpoozZKv6XgSuAIZAws/hxKPNaKYga0/1IgE1g5J9l4lEJIWeXjMwPfy5w\nfp2NO+8CCeqHkH8vNbttlOelMf3JrMyL9giTJNbq8V8HDIG/L0dDz5i3BGU3xwq+lF7FEPjxiSfK\nzc2IoglzbqkExKjs26XK0aTlebZ4lEBuU7v18Go0kGVoNguAx9oaVlawtsZzgJYyUSobo2PhQsaz\ngS8A62ZQ9KATVGv7/OOdO7+C/SUVELOp4a+0YrxV4WQQKpUW1hpl+X8XmAU22Runy87DzzC/zAXs\n1zhH6/9OvT6tA6UsLC+RcpBlzTgOfJ/eVmU5H3bPnLB3RvHseoe5faoMwOLiCCCoFJQqVGdKBUbI\nFLI0MA1sAk86jPPPL6z6zjhD/rt/t37qqesnnXTbSSd943GPW2g0TjXHgv8b8IE9u3ZduWvXVcDW\njY2jd98tr78erdYkARyHl50Ekv6dEgB1M6nrbdZBW2YkEJYrkRawD5ivG35W5De89ZGyU/y4ZfoB\ncNWf/RkOH+b4fm2YYq460P7lLOBLjPBVaUm/wxaAFs9ibc1pY5lOQtT8oTjI0o9d7amVpfPVnlcB\nnwdWmZ1vaO5M4JjcgqkJf7hjh1RqbTBI8zyVkjiapCaMHYxaZAD9rg9tDDR8/0t5frLF9llkd7+P\ntbVi+YuY1bQFRjg/8q3klglKuZYJWdkygd7KVwKHgI8BTzBSnMZ4A8sYuGF+XgNHBlUYRJ8QreS0\nw1qF9KdyUBk5cMn8vI7jAtfBdtYsMZv4+38PvMgR4PvADrOI9zLgL4VIgD/VGsCmGfkS64lGWaR2\naxgwH+yaoaGGpJZ8V3YOsC/Bjf5Er8LWraC2p4EyBVL6SvlK+UJw4ZkFSrdYFaVZT3LnTvH0p8en\nn77/0Y/ef9JJV59yyieWlnYBTwbOBF4GvBk40uv94MlPvuzJT77ui1/8ySQBHIdHgHls3YqtWzE/\nX6yDWu8qWgc1lYggDEBdJfJq4LN1w8/a6SvXwHnjl+kj4Mo//mMcOlQy0U2SIUVDZiVYUcKQKum3\ngE8Cy0yawomhCsiFIH2qR6GfbAzW14uKmNSQFA3pX7ctESlzx0FMliX52kSWVwMbwJuA/8BSoxsN\nYe7MVTMzAjgyHJY4msa5JTGbE/ExA6I9aV0+N6eSpNiEov+vlIMs6xK3ldgPSYJut9j8oqY2+TeY\npGstE1LHMsHefNtSF2a+GgI3AH3gUmAaeJfJfEQRvywMG55HWuHVONblsFWcAJRKje1ihZSX1kX/\nDEAYiiwLpPS09qTkok/LjCPR5/nAMx3V7AKQGM7rFLAGvFeIo+YVjdRuSmVax1pb8l0hCiLUh+V8\nWNZTHeojrZM7p8D3PvzhwqzUnr+TxMsyKznjexg2vVECeLYQl5Vv4/79xX/0PHHmmflTnrL2C7+w\ndtJJN5988j+cdtoWz3ss8DTg6cD7AAGcPEkAx99F0pelJWzZgunpkn15ZR00z8M894XwaYKqNWea\nvwL4LDBddu/yy8133gLCGCmIBZp/701vwoED2NzExoYNRjEhPMlAsc6/hTsLvh6IgS+Xbdyti28h\ncFKqZSti7uJLCYCy4MpKqQ1lMlBWtvDNx4jxfaALfB1YA1aBd7JtKSqEz/f9phANzwuEOJokwo7N\nrQsV52jWaWfzOifCBEAYenkemVXYROtIqX6WNaMooGEPCZxo603rojU0GBSOlcNhHsfWs2x029nY\nOWH3nLN3QiHIr2YFAPBe6+ggRAtYl5KwcR7TClcTgJlyx3XYOLepGAHUx/M8z5Oywo62aYCegWkm\nmbWpawZIyuBlqm8OAOvsJheoDylDIUKlPId8N1K7jZE7D51jsZ14YW6uMNUgMS4JtOzWSxkobR+w\n0DCxx11KjW7wySeLM86IHv/4vY9+9N4TT7ziMY/pbdv2qJtu+tFTn4rNTd1u45ZbJgng3+h1jRD4\npJMAdu7Erl0FDyAIkCSldVBStjUaSBKPLqUEE7/bFupvAJ92hp+VZfrcSC9ql+mVkb3/8Dd+Aw88\nUDRkjKgujuM+U8JwTUVattDikr6zgAsb8p5D6QAAIABJREFUjVipXp5TDT6aQmsd5XmLbFuo9gcK\nIbznIctA7r602kPK7jRNyy6+Wd1qjyh/tVjX6w3ADtO6JYubDaWaQoROQCxxNMdEkwFTklS0swMT\nEH+/3T4/inwgIM+yLAvieM7zPIr49OZSAiBFEElQoii1N9wMM209noy/4Ro4PwgGZkvuN4ALAQvz\noVfRMFphf/wmcL9OKzxw2lz29V7ypCdhbW00qCdVKyNIe+WjwDXAO1nzMAMuAV5gFjsqpnJ3mdKk\nWM3TusjihrNd1fiSeWQd6mNYPu9KJqC4+LvfxYEDo9BvVXll3YFmSYu/rgbwq0Jc8WBk0HvuKb4h\nCMQznpE/+cmrv/u7q50O1tawdy9uv32EwZ0kgH9rV82YZ2Gh2AXdtg3T0/A8RFHh6ELdALsWZPZv\nUSabe4zM/jIgAb5hhp/cvUscUwpimS2bwO1nnIGDB0sUNhLVkX+LUlF5EMr1MJU+OM0Yw0bDz/NN\nIFJqqPU1WqeAr3Ws1DDPu3HcGAywtjZy8W02i7/TyYCOIP0+oihPkoHdTqo7f6R1YEvbg24DTwDu\nMicGaaJJQ2sqmWldi0tdj7E88aw6u8oM+DaQAFc88YlYX6fiMaJ7rnVDKT/PBXmWKdWkBEBCRsBK\nvwrPMnJwzPPYSI9qv7RjXEP0Pc/YFRw2JxKrFW5o3SCKFCufqzJQRyo2LNsucrHAEChptMYTpIXJ\nAVPAdlYN0PHiW1qvAynwonI/7STgHvYSCPVBLEK/bCf5oN5hYOck7h1WvASewMrK4OL2Gl/Pyuuy\nTOyHfuW5BnD77cLzsLKC++/Hrbfi4EEMBvi7v9OTBPBv7fq2EDvc/5YmwHNzmJvD1BQA+D6SBK1W\nhQTAyxAOo0fZMJ20mN8A1kzj5SEu08fAHSedhCDAykoxmM1znWUJKeooEtUZKEaOhRbXBX5n1y5E\nkZckbTKuMnM5QfkgzzfSdGE4FNa3nZrjQoxWe4gENxjk/AhSB7IeJ8bnHrCWHMejSYEOpYMR896S\nZY4m/fxnkNsXM3DnwLgh8CJgDTh39+53n3giRcOBESCFNPOgdKsUeZaFvj+yTCARSxnbZ4fPUbkh\nEznbYcUhz/OEUrZZ8Srgy8a0KzUOw2FZK6yd6mSzLBXjajHtdE6+89zn4vBhlKOwqoNmC1Yvb2Fv\nAf0r08A0sAZ8E9gAXmxu+zRwCJg173KnbNr+0Ml3qHsJlH4u3b0bKyujDQwSnhma9Ah+V16C0WUt\nU+PhBITbbxe0/7e8jHvvxS23YHUVm5v40peOL23o8ZIA6lVe1hCKYr3W42oQlD/nlT/taYA6kmcB\nnxEi1/okRw5YYTMQ6O3qqal2EKz0+6Hn+Z4njKaCPBRTUtSZlfpxShjtzEJzoIAcAL7WDa0zrYd0\nK7SOtO5LGaSp53kzgE/eNXQrgMpqT5IkgzQtVnvKFTEX46d1FXFlFPk04J8ZOa4CjRhXTqbAs02V\nR6FHGI2//YbELMGRXeVf3nffYeCcbnfDCt61FkpRakmUioxnmfU+K02ezUGHN3+4ZU1Sd8M5z85e\n9wPbzPy2Y2QCQVkrXHk+N9gCV1z+aaq8Kb0GFAtrzs4a12XpcrgMgDnPyww6kHf/LTT0AmAdeCHQ\nBp4OXAF0zaC4YhvgPVgCSMrjrgpoawXA2lrBkKCCw2YCpTJimLOZRz6Gh+o/5Ghw7bVi61akKZaX\ncffd2L0bdAz+6lePu82A4yUBeIXwsfwGk2eL7beQFpC+0nQkfLQfrbr1K1Veq6FKZNb3M60PKZUY\n5vNTgKaJ/ucCU0BPiK4QI/+WLLP+Ldp4eI10dcY/krr/FRlMMsZCC71e8QINwa0vZcuUw4FSXpZp\nIaTW3TxvJ4mgsZu9LXmemtWe2K4mMTlmrRi/UhFX5pAB8FhgNzA09sLu0nIlmry0zq7SIpisXWXi\n2FU2gPcNBhtAYMWapFYEEqVio2MZmV/S4MGo/msdHG0CqNxtu8FQdCpY2H0z8DGDX54uuxTY17tY\nflzXjmm8ZWvnTeDal74Uy8sk0LILa9LhpEqzb2zfjlcqdWEYpkbe5jOjN366/Qrw60AL+CXgKqDP\n1gwbY1gjlRZQPv4l0LLLdd/6FpaXcfRoreosNW9HVlad2b/wWfdDmgVeIxYXEUU4eBA/+Ql+/GNk\nGY4cwTe+cTzuhR0vCaCWuFIMWmkdlBIAkSCpErH6dynByezl9SubD/hIqhuGBLQKDNDqR8AQ6ANX\nGqcn2qjKlSr8WzwvMPbfxclXqZISxlhoxeXB4MAR1Vk1Eaankab05ee5L+Uh0yolJwMAOk1zpeI8\nb6VpIwh8z/PM/n1ePoIkRgxTewRJy6GfblEEwHEDD4C7mW1L95jLE68Cjm1XmZNkXilOPxXloLNh\nfhrIkUapphANEvUa/B8surXs4JiWDbwq0qOKkPGfpqc30lQ5xjVnA/8fMAUM6qzK3OvoeOMt67o+\nBK4++2wcPlzUK2ZJMLPYQWdtrYKPbjYaDSl9KX1zgHDVtBlwHvDrQBP4ReB7QN+ksVZd2q4sPOox\n6As7q7/2M5/Bvn2IY6yvY3UVq6uF7C1JkGUJwUSVckmoXPdsv54jxKXHnAPfcINYXES/jwcewJ13\n4o47kCQ4cAAXXXScbgUfLwlA1c0nR+ugeV5sAxEPYHUVa2sWakgbWBZEXLG2sGmA8+LR6fhZ1sky\njwGt6NFfNfj+IhhpnQjRsEoYkwCsf5Zkn+fEnKl/gX3M6M/vOpy1S9/8Zhw4gMHAUi48If44CD6b\n55o7GQCZ1rFSzTy3PSjYNpT5So3yh4vTh+WKWLODOf0mn/O8gcGT8XnJrwLfBuZNQOyMIRe9FWgL\nMWftKs1Avoj+UiLPgzwP8jyU0pdSaC20toHM5unbzJhdGwfHltahECGJemsta8x7nTgJIC0PWngv\n2zUqsDfkZcDfGl4Qr6ArRTddq/xZKqOcpFGLXfs7v4OVFQvfz8zGXGpQqdkxY6UPoN0WadrJMuS5\nApTZms7LrNYl4DzgBUADOA24FlgDeuWjm0s9UY7aDeXmTx/44Z/8Ce6/f7SEsb5e9IIGA0SRMksY\ntTDRzEFoiDHZ1F67d4teD+vr2LcPe/bg7rsRx7j3Xlx++fHLhDguEsDfCrFUmwAo+lPhT9zjKCoq\nEat9TBJwrLmDZc+NQo5L7jA1RUCrVprCYCQolP+msV7S5v/bMkoYOxXk2ByLwToN6APCbPZqtjYc\nA88ENs2WzT+TrIKoPmWovRBinVQQZn+HjiBNrRtCBIaHWmSgSkvEOYKQesSiTCvFY+FpblA8FQ3f\nM4BvAgsM2V8hx50D1NtVkl8jjalpSpGmjTSFEFpKN4qlwKOBe4w3um1N0OTZ19ortEewSVqad7bi\no3sMz7IEuHh+fj1JXGKErRJeBHzezFprQR2jo+mDGW/dcPbZI2pFFKk0HdqVaRMrs/KfLnIOU1OI\nIgjRBmSeW7e4hmFIWP/IHcBFwC8DIfBLwLfNu9YupzF7zq6gPirOkSmwDtz2m7+JffuK2RstYdD6\nvfnc9Q0AKra7IOMXX9zRS+W67TbRamF1Ffffj9tvL/DVN92Em246rolAx0UCkOWlTVZorUKI0Too\nzQAqlQitg0pZWQfNyvUIKnXc7Cz6/cJV3DqhGwngYVNe0Q9pW224IxCiz/wvAjEQAjuYW7cuC2Bi\noAf0gTXgl4iBbCE/ZYHgPkDYBFBe7SlA1iYBKJsDjIbd1SYOzYhPsYo4MyWecuaQ9i8+8EIgAi6r\ncyL86DHsKumdsntbNDYUomG3B4SgPN1k4LCDwIKZVCcVapPhvLoGXhUle2Xty/a7qZcdM2pQpVlh\nWdCvMdi4KdP7Cuqa14lTO1vgzw9f+UrEcWG8NRhgMNBlgZZdFLfSrJTRQ0uxstejvCeUIo1AaIzv\nQwY76RjC625gCQiB5wJfBw6a0xu3nKQxT17nHabMvfrxaadhagqHDqHZLM5zpDigtZsoUlHUNwRA\nSxNyeah5+e3QtUUeAGDPHuF5OHwY992HW27BkSPY3MQXvziBwR0fCYA+9oHbHFxdhZQYDksuuMQ0\nNyScPEkGxtfCroMmD1aJYG6uhJEggITBSPwe8HGzl0/xtFkmd9oj/3OAdaANbKH5J+Azk/fc9Klb\n5oc0zee2CfzRBz6wBnz6Wc+qCEjO8bwPKpVXxPjOas+ooi+TeCsJIHbSlZUnfikM+1LWUoO4m/n/\nBnwH8JkT4deALtCxBu7crrLTKQD0rl2lAYeFBsrNwWFPA64BplgCcAcPqHMssPqifLyQcQhcurCw\nkSS2AK8FB9nr1cAXgKNMUlm5orrQGQF3PutZOHiwoHfEMaJIxnGf0VJjdkTjOyKpIwlVNCIy1L9Q\nysAsuvvlRXebRPumdxQCzwe+pbUQYshWzcOyC4VXFrylwMVTU71WS25u+rVLGGmKJEnSdEhmMmwL\nrxaJ6r4idxHshhvE9DSkxOHDuOce3HxzYVpz3nmT6H/cJIBNIK4VCaytIcuKLrnlAZD11XBI66CD\nJBnk+bC8DpqUN0JdwTXm5uxiEfWpCSPhmU/X3cCSCaDdMVPB5wMpsF2Irud1PK/peT6bfypyzlMq\n0bqhdcBUHHzi+urLL//y4x9PQwgrp7sf2GlqqNQV49eFuZxlOy7+gSPaoap5A+DNkIochU8jQ+Ar\nQA68xqCJp4CG7/caDd/aVdKy3txckQBq7Sql9GjqrhS3wLUIhNOBHwIDIGLo1lr5qXsCkGOGsTRs\nv3hxcTNJIrM3wC1rxlUJLwfeCQyA59ZtMPXZv5UDdz/mMQUX4ciR4sVmGdJ0mKYjYKp5RN21tcgJ\nlMqqhKmHliSe73tSekpxlzef3cA2IIE7gZMB3yQtuxAjhLDIv9C5pZvAl4Xoed5Glikgk7KdZa04\nDu1IXyklZZrncZYleU406Zh5CXCeaGwcpF2iXOW68kqxcyeSBAcP4u678eMfI0mwslKgQyfX8ZIA\n3q31Z+u2LvONjYBKfto7p6NoniNNNa2DpmlRiZhzKI/+tZVIoTbp9ex5FkHg+74vpTCuMgL4f4D3\nAHNmAccWpLZ0eiXQBmY8rxcE3SAYLccb/LonZTPPm1ISLpGWj1CnCHr5rbeev30750ifA7wf6JnA\n1HFUfaLc1ckdd5GYGSNrR9s3BL7SbA6kzMrUUncaaVGUKXAhMAAeDTSEaPl+t9nE9DTm57F9O3bt\nwtJS4VYmRL1dJYmdiNhBJBytKyScU4AfA5sGldoaz+7mCUA4EH/uWnPJtm2bvF9hDJ8TBxqBcsti\nCvCAS4FNoF9+Mg8Al4XhVBB0gqAThpubm7SyIIRQWkspMykTu7NG+GW2sBaVlwRT5/mUwMW///s4\ncGA0JfKKGRGYy5tXZkg0jD+M7wQOPUZ7814h5oAWMNDa01pLmQOpUpGUjTS1ume7+JLRBoah3tqS\nK3JkuO7iS2UmfN11YudODIc4cAB33olbb0Wa4tAhXHjhJPoffyqgQd2obWU4tJVIsVhk1q+SLOPr\noHHdp8tdBx3VVtSttkAr+lwJwZUw7wb+DOgBm2U5vAf8ATAlxIzvz4Vh2Gyi1Sp+GjVMCZZLyzJp\n2s4ykef0y4+sjFm83gG88ODB86anuZ76LcBfAV0mxm8620l6jLI7LhvS6vJ8LwK+2OkMsiwpt0Eq\n4g1OIttiDhZT9Gt4XjsI0G4XCWDbNiwt4YQTigRA5i1j7Co9E1Ncahj1Lh4N3Ghsc1x0a+XQo5xW\nBhi4KQEuWVzcIPcr61fDzoj8C06h0GH/YiUBfFGITSklkCoVS9nMstD3/fKOCMXKArzs0FKHLFzy\nKDmKlWOoO1TPW683Lt6ldtBPgJcAKfAMIa56MPbOESA0GjmSnOVap0o1pQw9L/A8r9AJa9IjFIoD\no2U6xhKGdpYw+JzjppvE3Bw2N7F/P/bswZ13Ikmwbx8uvngS/Y/LBNCvK1uOJskwz20lAos1J2sL\nKka05p8ul0TvwtkzYCRYpM+V53Ebd/sMvhP4U2Cm7Cb434ApIXq+v9Bs+uTVRXZdNP+kQQWB6mhZ\nQYiWDdNKZcayhsbLXaAHLAL/YXPzs2HIZSGvA/6GrfZUbNxFXQKwxawN39rxsfny1NQgy2JHume/\n3I/gjBCEGEsoFlu7yqmpgtWxZUvx1WpBynq7Sub4qM0c2158S+NJwOVjdCwob/bVChltuPmnhYW1\nJIlpROT0KyoGXu5z0hTCzleqTUutNZBLSTYGRYPeGFZbz2p6uzkt1d0RkXXy3BTgjJMKREiVCXdg\nytEQaAM7gHXgLODdQpx7zBzwV1q/Rwhr/iWVyoQoFl+EIDuB4imqIGDHL2GkdZKzFLjY/Ca33CI6\nHRw9in37cPvtuP9+RBH27ME110yi//GaAFYBH5itjACyrKhEjK/FqBIxZUhmV3DZp2tgvpSzEZob\nZm8FKaHqPBQ18E5gFfikyQFfpHrc8+aoA05uvXNz6PXQ6cD3i/knYdps50rrpqnuybLGLvS3TKSb\nAV6bZX/teTwW3wEssdUebmfGq11+AnBtvBQTeKwCpXnJMc1sbVE87XmJ1oHWR7UWQhR2lY0GyLLN\ntauk/OraVY4xquSBjHLA04ELgBbQY+0vYVS2ftlBwXOi/xC4eGZmLUlsvyIpzyoj4JSyGJSfhC43\nBl42YVeuDRryA6mUnJZq5aqKbUEXRAdnPj9gA6qKKekmMM7oTY0BCsGcohrAgikXVoE/EmId+NT4\nNLBiljA4EjUk3bNh543wf1q7SxgJ0xxHY/Ra9rm64w4RhlhZwX334bbbjke+2yQB1Fx/ofV7hKgk\ngA1DZi9tA5mPVmUdNC4/iMdYB73y3HOxd28VI+E4KXJ95CuBT5H7NtD0vF4YNjodzM5icRHbt2Pr\n1tL8k7bViNpmSA++lAH51zPXpKBsnpdRe5oJmd4NbAKfc8rhWicDwZbd4Lj4psAycHG7bRNApROS\nlJ1geRE3FYY+TSCl1BTpXLtK9mKLUQ3dYR7I6m6vKpe09uY8E/gA8DRT1VqMRMZaYV55MY2kLP84\nNSWVWkvT3LpfsRW5E4DIjEM9k0StMooeHlra+KSUr/e8oE6ftm5iZYvho0s7IuZxqtBSKy0gPYa9\nfPX73499+wreTp5br2k5xvRYlecBPfYkFFoDIb48Jgd8XOu3CNFhFpiJ1g2SaRnfAlReVFmCxU8A\nFXmCPXd+S+vbbhN0Qj58GPfei5tvLjyev/zlSfQ/7hMAHQIeVUkAJBmkSqRc5fFKpLINFJlzKOqi\n/xBwMRK5VeAwjAQ/lXvA7wL/CWgK0fT9XquF6WksLGD7dpxwAnbswMICOp1i/mnx/YSwpy9ygzLe\nSRVDKGue939p/Tambac/XwJ81lnI4j1xn3FMvTH9kP8xPR3neZ+aIcYKPHLApS4vKAVEo9HJsouj\naDuN0O3hidtVkmqQXixJ4GnGTtQmkwYqu7huJrB3huRG/wwMDGnO2uZUuK30eybA/2g2BbBGsAdW\nIlAf5gQgB6aABTpGWMYci7wUyKaADeAo8AmlBsDr6p5V+39pak2UC8JlC2NzXxlWV+ihw/KERpYF\nWsWOC+055vkIuWP8gtw0wJE7/wV4n6s1EOL8MTlg2cBE6fe0MNHA2XxU7Mzkfu7giA4sTZoIPzTm\nveeegu929Ci+9rVJ9J8kAADA32j9qU+VtrXWTSViTwAPWolEbPWpdh30O+99Lw4eLDm5c6CVI4vM\nmcSwBQRCdKj9beefO3cW889Op/AuJxNz8nGkUMhUHDRVqxhCWRVHewzU/rXAR4FDDFdZkXVXnAx4\n9B8A3+p2N7IsMZuosdYNU73OmbuUsU8y3dIbgRz4zkkn0STDi6JRxNHat3aVtJpHdpXU/Se7Sovr\nMDAcu7Scj1GgKvMSfCM5pTfxEmADWAduBqaA9wEhIIDPAU0hGkIEgC/Eep7zY6JdkTsN2KQmnhAt\nIRoGV4fyukbq4OpC4AjwceDMynSKTXH4jojFZSunq8Mf0bguVtoC5er3vAeHD2N93d46ZZE7jLk2\nzuhNmGRZozUQ4qK6HPAPWr9UiMoSRu3ii3Rkx4l5RbVdR6Ji/Pk12Lq14LvddRd270aW4fBhXHDB\nJPpPEsD4a82KQMwCVK30pbIOmpjnFY41xyaAw4eLk6dxFY+NqawriMxYAiAVvO95rSAYzT8XFrB1\nKxYXCwEMWTaSc0unw/1qqBVeWNaU7QoqHqqfBv4QmCu/wO8DvwdkwGeB2Fnt8R0XX21EkN/udHKl\nNrKMOiE9KSPTWA9YD0SyrTeC4q0Dj6eyzuyj2o90pnUqZZui/+Ym1tbQbI7sKrOsZFe5uVmcA/I8\nqVSyDrRDsMaFXx7D0tdhQAH/pzF326ACnAy8GL5fsxW504AMWBRi2vM6vt/0PEFjCRS4OsWEOoHW\nftl1a9xplZ607hiFbu2KRlKW58Lhx8XAJX/0R1heHnE34xiE3JHSQoQqJAl767goiFqLrtbgWUJc\nXpcDLtD6+UIMyosvgUN/ql3CSOvcs+2H7v3Xo9eb8N0mCeDhX0cdMK+omy/xSiQrP4i6XFtd+eY3\nY3mZu4rnaRrRnNCRxFRcxZuAEML3vDAM0Wyi0xkhEKanMTWFVqvYLGs0KnGf+9Wo8vCTZwKiIjeB\nmfJvHgFnAhvAEHg/cA3weOADZqPY7vfb+5MDFzWbVN6uZ1luOKlNpRSwE6ClZY9Dm5mtY9v0o9rA\nUeBPf/zjdeCju3aNKkqtIynbNO3Y2ECzCQBxjHa7xq7S0IPTLIsYDS2t28UV7G4INoa169PPBi4H\nuhW/GiAs2x/aKuFJQAuY8byZIOiEIeiL3hfqX0np5XmLPKWlFERgdbiblesg0DO62Ao1qFaumjE6\nAk8tlVh5FCi8pmmJmtxG09QSFxKgVrtlo7+9QiFIa9Aoaw1mx8tD7wW2AAOGQmo8WFZLzKENdd+T\nAh/ajVar4LvdcQfuuQdxjHvuwRVXTKL/JAE82HWkvIIr6nSNOeuuyjo9uK2tvnP22UXzhzASg4Gi\n/SAH0pIySItNAB4AITzP82j+WYkmFFC0LgaeFQkHK7TVmOEnWC/oPcAnGbSHTja0kbAG/HvgLuDd\nZfPelhANsjsXwgPWafPAtMu2KaWNw0HL85pG4VfU9UatWCwtO/5oAfCGvXufC5TsKpOk6dpVkpk7\nt6vc3EQUySQZ0NLGePmpC4oJPa8gRjDd1F5gm+lTxc6uAI+/Tyd5le/PNRqNZhPt9kiWSiNr6+uZ\nJL7hbmql7OkhH0Opeh5wgTG3maqD7+vyjl5aZi+73f8U2AB+8OIX49AhDIcF72Q4jIm5Zrym7Qpb\nWrabrqiqXJ+fBoPHpWM+brdrfYIQ887ii+8AEG0p4NXRpO3Gycduhe9jZQV79+K22/DAAxgMcP31\nuOWWSfSfJICHcC075VXtOmhaXgetra0uPessHDzIMRJ5FFmMRIHRdwSRsYnRzzU/UFhdNgpjyOoI\n1A4/rWvNGMsayQpMLuQIjCEURwm1mAyGCrqKeW9irMB9hxe0S+sQmPW8ad/v+r5njyYAtPalDJVq\nS5mQKY3WHkGby1ogZe6JB1AC2EiSheHQI0RHkhS2DRRY6T7TrRgOrV3l0KzsWRgDd6yEa1cphEXf\nWN3Uq4EvmQJ8uuxZxhPA84nr6fvzzWbY6YzOau12AajI82Ib3ODqPCID0qyCcTdbziFAA2cCF5my\nulteWkZZoJUZHYHLj1PMOub6M84orGPo7iXJgIzeysidxNl1d5E7OWG0DX2kMmRqAWcJ8U91h4B9\nWgPYJYSrOKjMgT0HJ4dy9P/sHdAaR47g3ntx660TvtskATz8a39dJeImAFX3IHIBzDKA5WUOtIqI\n05JlQ+MqHh0TaOXz+l1rzxaPw2HhTS8lfL+Yf9KMwc6Z0xR5Lg13IS8bJ7mWNZQDZsMwUyox9LQK\nSgjAFcB2IDaft2Jw5/CCnkDYTs+bDcNGozGy2LQ9EAOuaea5l+eFO4Kdo7Jyj+wqhdZDrRtSBlnm\nRdEMENiSn7hv4+wq7S5uWXtqw5ku9+4U4Huep5Q1cLcz8xcbIN1wTK0KCsq+P9tohLSusbCAhQXM\nzqLbRRhC69Exxd4NrUNDrLOsOoqbUfnJtGXBecCUg48WhuSqWDHuOe4xNlYeBW479VSsrNDZUWdZ\nTEbTzOjNmh5HjtGbcpS7XwvDvpSCWf14bFuY0sAxrr1aN4VwEbA2JPl1W3j2FX3kh+j1kOdYXi74\nbjQn+spXJtF/kgAezrUP6JhPeFiuRLQJmg+6DnrtiSdCCBw9CusqToPfMqQlKi8Sx44ofjT/VKpF\n0b/fx9raCH5ACaDfx8pKMf80ZiDgKg7WF65VcVCM81qtZp43pIxJgG8Moezn/KXAhcA04wW55r1n\nANO0tNxo+O12sa7VbqPRABnNM24l4jg0XolSqVzrpolQHSAB1kzgsHaVAKTWXSk7aSos2trklapd\nJTlWOomWy09VmdghSDllTHi4bup5wNfHFOACeDvQFGIqCNrtNmZmsHUrtm/H9u1YWMD0dIGpoP5V\nszlKWnkOu65B6E3D3axc1wCPAwTwCiAGvloeBnhGilOJlS7FLwYu3bIl9LzBxkbFazqRMjXPJ/ea\nroB3tHPrchidrtYoP1cekx0/X4hvj98OS7QWQhw1oyDumx2Ut/Aq0f+/X4mZGcQxDh0qDB2TBKur\nE77bJAE8ghOA1oFTiQhzX2ykG7cOGgEfuxqHX9qvAq2kTA3NKqkDWkXAk0xcvpIJ2qghQ+6Mxfyz\n1YIQiONiC4zYpWRZwzzL8iyLac5szGyzuvmnqFjWpKlI03aaCikBqDJKKAXOAr5ponPX4QX9GkHt\nfX++2fS7XczMYHYWMzOYmiqiHvVAiK1NxTvQMPL5UIiG1ilrOjWMvaWntacUFbmZ1rGUgzS1NLTC\n11eph2tXWdk+i62zvDMtp1v0TOBvvgAqAAAgAElEQVQCQ8PnBfi76O++P9VogMr/xUWccAJ27sTW\nrUUCSJLR+JpNApCmged5nudrzbmblatbfr9eAPwjsM6wfTxc+g55n/pCX2g0Wr6/EkUjr2mG3EmJ\nJkTtsjFGb+kYjISuJeCa35a0Bq0H+/RxkihXnfEtPC452wQu/AHm5jAc4oEHcNddE77bJAH81FdO\nVYwQzXIlEjrbQBwUkwJfvQXLy9izB+f/9spbPt2FoXRJrbM6oNUJBmLlm0MGTY+fAwxNwPW1TpQa\nSjmVJAHVjzTzHAyKTGCbIevrVgGpTRs3HgMizupoM5idLWYJQrTSVBqDYiITpEbX8WvAN+p4QR5l\nTd+fCcOAoM1bt2LrViwsoNcrAp89xNjevVJgPRAfCIjdbwaw6+YDT3aVtIuXKNUwdpWekeHnBoj2\n0O0qZZmGdtnCQhTHWggthGKnHx7OzgK+UPbwCoAOEAjR9P2GtSugE8DOndi2DVNT8DzEMVqtohFE\nKdCQAT0DnrLSGjcBNIE7gUezb4gbjaGUQ6VO0JqbGbgA1wz4ayHaQmxIGbte0wy5Y43e7Ow3KnOE\nUE6Zual7xuEiNNMaBMCzhbhMP3horjClQ2aSo4AV87/edJPodrGxUfDd7roLSYK9e3HJJZPoP0kA\nP92lTRpolbWPfrk7SZ+uS+5EFGH//uIEmmU4mmWWZ+ICrZYAZbAz9oRR2Q69D2iX55/zw+HIo4bC\nBxmhkHESrYANhzqK+mk6sAmAfZhTNgWFM2PE/PzISkXrplKZsckNtW6YMrNlmiGbBp9JN+cdQFOI\nju932m30etiyBUtLWFrC4iJmZopVteEQa2vF0rJ1R8iyIM99KX2jrOfc+TVAGPmHphOV1onWobGr\ntIsFaoxdpd0bcmloqryRALZ0Nk465QFnA0Pg62Zx7AJqwtC6Bql1e70Srm5qCgCGQ0hZrGu4uLoy\nJ8q9QlNB229qBgE9M/coNdS6D/SBDeA3gA8Ch4E+MEuaVGCD7gmtsJW9ppURbuVlozc3Aag6jlAK\nfLPb3cyyvA4XoYwSyTONoEfwMay9br214Lvt3Yvbb8fevRgOsWcPrr12Ev0nCeBnmgaK+yJE05S6\nGjgbmAVeMWblZCPPUbZRpI/WKUBKcVOIBhCY+EV2u5bhFQO3U/vbzD/DLPOiaJbiZpKg2azOP+MY\ncZyTXw2zA+TwNU7gqXEs2LKF03UC6k1rzSUxVhn5XOBrwJrhBb3PQJu7jQY6naIJvmMHdu3C9u2Y\nnUWjgTwv8hZpeJg7AgjZr5Qw1AqLZ9gKrAJtQBjQTQo0jF1lVS/48O0qc+ZIrg2iQx4zltGteDbw\nFOCD9jgoROD7Ba7ODj8IV0czmzyvx9XRgYNegkHDujGMmGt3ACfbbBSGDa1TpUIhAjOND4EvmtkJ\nbTJn7HCZOF7Tto7hXtNuAhgYyaxrfD8kn5+y1sB1+7FWPz+T6447RBDgyBHcfz9uvRWHDk34bpME\n8HNpDfFr927RbldXTu69F2+5UqxjpGu0GIlTgQawRYiO57U8j0IY4XwJI0Orsw2tQ637Zv451DqU\n0s4/p6RsJknhBGDjdZapLCusoLKM1jgfonPeyDtpbm7Um05TZJmVxFR8VEjX8Vzgy2YoSsaBoe+3\nKQH0epifL7h1O3ZgdrZQrDYaheOmnQwze/qivDXTV5iosQr0mIrJyk8DkyrAenGPwK6SvufKHTuS\n4TBj7CAuncpZILOLr08APkmtCSGE5/m+D0uss7g6GlDbka8l1nHoJp0Uy2cONwFwUoUEEIYBmf+Y\nJB2Uk3Qb2G+QOzn3PR7vNV2bAIZlr93KfbtwZmYjSVKmNeByg8pN83/qj+EttwgqIZaXce+9uOWW\nCd9tkgD+Na7bbhPNZnXl5MYb8dabsY4ajMRTga4QM543HQRNChBmO1RoTZ/klpSxsTBc1rpjet8+\nafXofCBlK00bQRB4nkfJw6g4SMgRS5mY7afa+acrgJFUF8/MlFBCvu8xSYyoQwm9BPgKsEI9ECFC\n34el9vd6xRCYvnyfMBjFYpQN/WUGva4bwG4FDhqJVFaWn7rMPlUm0jxEu8pNABx+ULZxr+im7E2Y\nN21xitfa9nDssYxacwQo7feLe8ukupwMKMuAWDcBEHzfms8gDClJ+yxJV/zOOsAK0DEJ4Bhe07XE\nBWt9XEs6iYENMr4v37TaXevK0swju773PbFtG5IEy8u4+27s3o3hcMJ3mySAn/u1Z4/wvOrKyaO/\nhFOBNWc/SAO/BnSEmA+CGdLF2whIyki7LpCmrSzz8lwA79D6C6ooBH0CyJj558gNyg7x2PAzNSPQ\npKzhGzIZn+tY8J2Pfxx796LVKnrTvg9jB+iihLgh1CuAN5jKPfA8ELWfXmarVXqlxpul5hJCOR4p\nfIq4DMyYsrTLJvO+o3RU4+0qURfvyK7y6h07hlFE/r0VLEethzv9qEUzTyaSa05b2XSKIskW0foI\nW9TvY3V1RIWiNJDnMFQofuZwE4CtoOmfu/gJT8D6OrXOjLFcSYNvE8a9wHZz+umM8ZoelwBix7fS\npp8U6AMXzcz007SAbbA5UzbG9/inua69VmzbNuG7TRLAv+p1/fWi14OUOHy4YMzSyknrq/UYCQCv\nADqetxAEPW5mQtuhJONJU0RR8RXHDSE0IKUkAQzVX4ocacgNyvPCPPcYWUGZ6WhmRJ8pW+CMmHdS\nVm7jjtyg7EySleTWEEo7NCFuCBXS/ypEgTyzsd6im9O0kK7GsfWt5BvLus4dgeeDJeA+oGNeTttB\nNuER2VWSgft3TjghiaIh40bwmfmxbMuAu4AdNm7awp+EuWRWTAvb1PhaW8PKSpEDCLycZQltbDhS\nXTcBUGR/MdAHPnrzzSnwjoUFMN2qdr7ZAx4L3ArMjvGaruw5ynICEHWwI2loURfOzfXTNGK+x5VF\n6+Rn97m78UaxdWsxbNuzB3v2IEnwwAP41rcm0X+SAH5e11VXiaWlmpWTQ18vWuEdJzC9HmgLMRME\nPfKzJTvDmZliOxQAedATjYva+gCZeR2U0rMJgIw+lGoIUXQ/xKicVmb4mVsYfZlXStE/dvr+NgiO\nutKMJlQRw+gxViqodK4pDtLr6vfRaoFG4uTbTttqFP5MGyQz9vTu/NASm08A7mDy05YDY4DjxJA+\nBLvKZSCO44FZG46tCn6MbRmPtl0hpHVf0TqWUmWZF0XFugZtaXS7CAIohSgqkKU2AcSxIlwdtZ7K\nBw6vLgEIYId5NzeAD62sHAX+oNHQbHrMUzUd1B4L3ABMA32zvWG9pl2OUMZIEuNIJ0X0zzKCbfBV\nu6S8aw1n4PQIrptvFtPT1WHb3Xfjyisn0X+SAH5e13XXiaWl+pUTAlptOBZafwC0gbbvzzabhZ3L\n4iIWFzE/j6mpYiJq40IQFNFTSkgZKvUXWv/XPOejyFTrJikg6dNrAEG28pVlN6jKCQBj3KAu/drX\nsH+/ZejbZHAMiBC3UlH2N7Q9ELua0G5DKbTbAArjmpUVvrBG/6LdWM7KzRDefPeAU4AfGfmpi2zi\npT23q6wtY3PDwrxmy5ZNxo2Iy85lSd2Gtr2BHSEocAdaJ0pFUvbTtEesDmvVSb7NRCsi+T/h6gYD\nJMmA3IPN1gInA45bm5o15NSmWZf7RJoeAt5k2mi67N9L79HpwHeBHm3qOWvMFW2PV9511yy5kur/\nH2dnN9M0MsC4cVAT5UAj8kc0bGs0sLKC++/H7bcXw7YvfGES+icJ4Od43XSTmJvD5ib278cdd1RX\nTvZpHQixnTm5Uw6YAhqeNxWGHikjFxexcyeWlort0DBElhWtYd8vQicz8/KlfADYNn7+KYxaRjlH\neDcB1M4/UxpjWpSQ6UtAypwV5pX2tG1SU5jILdtZqTzPA9sDabWKuG/pzZbab8lFaVrgB+rWle2/\na6vgxwHfB9ZZrg3rWhmZ6VBVlqo4C/MI8L35+fUkGXEjmHt75U8NvIQpgujufUupyNjWx1oPpdxM\n0zCK2r4PoFjSbjZHby5hAQkGF0V9QsMau7TKgcNNAIVbchlEar8+ofUq8Lpyzua61ScAlwGzJgfY\noyrHao7z+dGs9v/H6en1NE3MuxbXaQ2GdcdN+fBHAjRsO3wY992HW27BysqE7zZJAD/3i6+c3HEH\n7r+/ZuWERKJNITomB3yBCOm+P9Vswm7GLi1h1y5s2zbCA6ytwfeLhV6KDlGEJPGzzPO8j/n+H0jZ\nM8G6U8cBru3hVgQwqBv3kYj78o9/HIcPY3V1NJy0KCFmcOgq/GxlPRIOKjXMsh6V/6urRQ/Eav8t\nzWJtDWtr5I6g0pToeHaQyKevtvkuWBvnccB36+wqRZ1dpagzLEuAi+bmUinXk8RyI8bFsucBOXOz\n4Ui1GLgbmKNOi9YNpcIs8+MYQrSVKl4v4btpDyBNaWMjT5JBkhTHjjrBrts9ty24rud5jPhdibAf\nAf7jGNPmBltv9MxTatlzQdnoTThmGClwwdRUrtR6lhXQwDFaA2s+rMpygxy4RD/U2P3DH4rZWUg5\n4btNEsC/6vWwVk4IaLVmTui+EK0gEJQAZmaKFtD27UUC8DxEUeHqvrk5AsebMSyJcM4V4h1aj8Pv\nPOj8Mxk//4yB/9Xee0fHld13nt/73quAQiRAEmBEFQACINlMndgMHSXLam1LlpVGY1mSpW5Jlm3Z\no11p5LVmnGa9R2OtvevjsWcVRnLbaq2tHEZSS93N2IHN3MwBBJgzQYSqevnd/eO+++q+elUg2WQn\n6fc9dXj6SDwsVKHq97v3Fz7fX3zhCzh7NoYSMk1hpWIpVirJeRhPKUzbQBqwOTeDoOi6WctKF4th\n1DNN5HJhdUscgUUZRAxEWlZRbC3IUJKkzzvxDqf4cx9jec4vAg1AToawtBxM0usTOwRh9CeNjVdt\n24vmpiI+R5wbsQpIRbyHaGVPWEPL6tmbgX0iwnKeCgLd9+E4AeD4fs5xUmJdQ4A/gwCe5yncTXNK\nt+Tk8V+E0QZNE/4BKqZJ/Y3/D+BddeZWIV+CSANRO92tBZKLEo8LfCeb5cCY6/pyGEEcDuz4rIEZ\nnzUI4hbB13/837yZzZpV3Wy7cgXf+x5Ff0oAr+LBX0B8b2jlRHy7fi6WVBlLi+3QhobKaLx4CDyA\nyAERMDmai9e0aCieMzbMeRdQkrXvbK1R7poJwFW8k5Ljj+MATp+GaYb1elmWCSKUUC0rFVdaqUR5\nxRZ1GM7LQZDxvJRtTysWDQCeF/oViwQgjsCCA1ou+2Jp2XVL0RE40Xe15TwMj9cisowNA2XOb5PT\njRklByRpaBHr7buZDIAx1w2i5VWZ4dRYthrwxLvNWJaxlHT0FQvbngLMSQNFeTMwONd8X/yEdhCU\nPS9j24aua9G0ru878mFH6xpBUEXcNGslgKi0lTUM3/P8IPBlzz8rb4fNQDtQBH4MrEqMEmm1PqiQ\n5DV1eCGia9jA9wxDuP1UVhpln8apNWtQrj9rUL7us/+sWSiVcO4cjh7FwYNwHJw/j//5Pyn6UwJ4\ntSRWTkSb92WsnGQAMAaxHRqNxotHNHCpfBcrNl5xj2/xeBx4v/xuNytDkHqdJl40fz2FG9Qk8OJ7\n34uzZ0OPgVJJuJQEpjkZkSSig3l8O7RquqMsPjScZ4Ig5Xma4wBo5jzrujDNMLEBoROA48BxLNsu\nO07ZdU3JLKqaJLHqe4/YQEYaN+4EisAk8L9IhmjSV9YFHtc0AVuusi2L8Btq12QlYADTNa1J0xp0\nPa1uMAggXRA4vm9zngoCA5jgXDRCdM4BBL7viXUNz0tpmiFBb1wiS10J3RSXDxuIXriKq6uSLy2X\nDcMQQ2KG9CxLK/ZbjUAbUErMrbIpjyy/z1hznHSdEV5vwITvs/isQZR43DqwjbqzBtdR/9mzh7W1\nVfPdTp7E009T9KcE8Grp5ldOxJGQiTJOtOkazcXbdrgCJmf/I/pC5OxYNRf/ZeB9QLvsM6uzRlV9\nYFcxAtTqOOcVgR1r1+L8+crikmXBslx5Ki8nTuXqkJ8TnzMpAgYQAGmxtOy6AeduEOQ8L2tZKcPQ\nNU0EGs/3XemOYEt3BEupgJvKWdhSnkgtJmxsbCy5bkqA6mQQ/A4wBowCx+Q8brMyLTrOuSHpN+qF\nqYobYQMPAjnGpmlai2FkUqnKxrLiOqB7XoPnGZ6nMcaC4DznhpybFKNTbhDYnKc1LaXg6ric1lW5\nm9HsfFUJJdkDED/nM3PmoFxOBYFwUhPwVEPu/QpUXyOQA7YCixIN5HqKZg28mrvWSr9h6l3r5KxB\nNG5bvL47d0NDNd/t0CG8+CJFf0oAr5bUlZOI73ajKydcmZhE1YKoQOFbFoIAxWL1BI4YxJRbXRFj\nywe+AkwCn47PGlWhSblyi9dqDXE7wChwWLhBQXpMep7vOCFKyPNUlBDj3JBDI81KiUl8+XcBHBiX\niAKDcxYEYc+Qc9P3M7puaJquaUw9AsfdEWw5A6NuLNdcWg7nCA0j5fu6cFCJM3DSwFlgZh3bMj3R\n2Kxqm78VaGKsTdenpdOasPMVy8yihBUlb9uGbacch7kugL8AvhgEUaoWBaIMY2nOBa4uRiuS3E1X\nIdZZCnWnXKdUIno2SKfhOLqApwZBEv8gKEA5IAAOAgvih/F6+j7n72Gsuf6sQU2LdjfBjKs3a2AC\n6691/D98mOk6Ll/GiRM4eBAXLqBYxDe+QaGfEsCrqJorJyMj2LDhxj6IleAijo1iK0p4N0ZYNIFH\nFmORY2PqFGbgeXbczCt6/Gfg04ChzMBES/8iDnqJBBCd/S3gyba2rGEUJyYMJSiHKCHfF6dyOwjg\neZY8VLYodgXR9EsZKAJLgUl5vgtndTjnQeABThBYvi9qIHrIKeAxYnPcHcGK76yp0GY/3rqAYeie\npwdBkh5qAHcCO4AWGZtyyqyLltgYUBPAO4AmxloMoz2TYY2NoZ1vc3M4yM85REVL1Mp0HYwZwtGX\n84uyNhXdsTLC7F4mAPWWFsTN3x2l3lWWcKEa8wXAk6tWYXQ0hKcyxhjTajGaxE/lKFvfkWXFFPoO\n529nTMwaNEVVoHgCwJTTxslZA1++up9PGf337WOZDDjHpUs4fhz79mFsjPhulABeC4mVE5Xv9jJW\nTr7O2Bzp5miLs79otF69Gi5/FYtIpcIS0MREDBFj2xDboXIK00nQtT4PTAB/D6Rqmeclx/jE9/Cr\nhtGgaVdtW9Smo65mBSXk+04QpFy3DLQAc4C05C1DFkyi77wJNAKNwDgwClxQ1nyEAY7DWFpUyQV6\nPjp3S/SpADu7yvx7sgaidhHFq3iytxeTkzIAVjvQiiDYI2HaVsK/tx784F1iwkrXp2UyTDi6RI6+\nuVy41SX2GMbHQ4YH5wiCTBA4nH8R+JzvN8kkbUdQEMVXuepJvVolFDMBdYhkik+nyhkVpXnGmGLE\nqFKAMsB98jPzU+Dpa53BR4BOOWvQWMf4ntdKAF6tXetAFn8mpnzSZ59lXV0VvtuePTBN4rtRAniN\ndEtWTsQZMAXYnFu+X3acnAj0gv0g3BzFoVKMRQozL4kHsKPJyGhBNO7kJY5yvyf/+1/ibBw14ojR\nl78GmhibFFYqQZDy/RRjGmORl1YUlDXPM4C5jDUwlmFM/DUWoSZE2VpOv6h+rXtku5XLe4/DeZox\nQw5QxiKgSq2II9uiKXIkxkhCtozSkuUJ+8YoE3QCw0ARaK01N1XVEveARiCtac2plJHLhYaOs2ah\nsxPt7WhsrHgviwtc1ND2PLGzbQTBCWAWYAJuHLeg13rSeglALaFU6akPfADnzlW5x1Qt/apvgugK\n/BPwR0AZeAT4O8Ys4HP108A+zvsY6wBKyqyBIZfF1EuMeoPhtWYNAmXWYEv9Z9yyhXV1wTRx7hyG\nhrBvHxwHly7hhz+k6E8J4LXQ0aO3YOVElHQNzq0gKPt+0XUzpqlPTobot3IZ2WzF2ysy8yoWUS47\n0XZoEFiJEcyoB6vq3cC3an3HPsHYTCALTIiygzSEqkIJiUvAfN8fF8Bqxhp1vUHXNdH5FH9L9KWD\nwPZ94c+uKz1VAG8GNsr9AF/+qGnOU8LlPP5cU6Dnk41EX3HsGgcqgU8B1QXxJqfYAT4CdMm5qVzi\nEqAmgP8ApBnL6HqzMHRsb0dXV+joO3166L1sWaGTpfitiba5bcNxBDX7nzXtw0HQCljyGaeY1k0m\nADs+qFODmaMsfyFhwRgkrCtF4skAMwATaAKagXHg/2LsEvBf6wTlIc5nMNYe7zOlapUTPckCqUIG\nqcC4IrC9fvTftYtNn45iEWfOhM02x8GZM3jySYr+lABeI73tbayvj//t397UysmnOP8mYzqQ4rzs\n+2nX1S1rmljgdF2Uy6Gbo2jAilBimrAsy7JKjlNyXTMIqrZD1dH4mu7bSX2J8z9grEkdxoijhJi0\nBe/lvAS0MdZmGC2GoYlxVTGrqnjO6J6XE9MvAONcUDyjMH1amSSx47OquizUJO1kHeUGIBIAS9SR\nXbm0/NzKlRgdRRxPVMWqi4wb3wpYwBYgV2duKjqMiz2vBuFk0NwcWtnMnh1auudyAMKd3ojdFHf0\nFeWof2Tso5w3AuU6pafkcKRaQqk6RCe6wG6EaeLKaEDSSUYkj6gt3KosSURjo59n7CLwlVrR+RLn\nAKYz1qhsCxtKFSj697VEAlDP/mPAwSluG/tYUxPGxsJm2/HjME2MjBDfjRLAa6S/+ZtTwElg88yZ\nG9au3WPb6ZERd9s2fPCDL+cTWRbVWFEE9zzNtjnQHAQZ0Q2O9gCkL65n22IIpyy5NGYtulY9P696\nuiCrQLVHYjhnwJ2AAzRpWodhNGcyaGgIXQzFFmucYQDbTgtStO8Lv3sRyLLAR4B/AlpkNL9m91Wd\nJopm/5HwHol4pU+99a24dEl1UInooUlQXdQPWAY8neBGqIyzvxbRTdMyhoFsFqL9K3oAwtFX8OwE\nuEld2K5y9AUA/D3wO4khVKPOII0bX9eoynyJJoAZofocldEk94Gr4KlRM+BjwNfikTqq13+IsX+u\nE6Mvc84YS8n3LaV0mI34znAy+tvAS1P2Gw4dYuk0Rkdx8iQOHsS5c8R3owTwGsn3AeCxxxYAfwSs\nBtYCHwBKmcyuwcF1g4MvAM3nz5cOHeIPPHADH9CSPDGF26GM+YATBA2umzEMYecCIAgCV4zGK3Px\ndnT2V/AAptIkrPLzmoKw+G3O38tYkzK9k40fhJnolGpaeyrVLM6/LS1oaUFTU1inikaYRJ1K18FY\nWi7Eeoy5nKclmfL9wOOS2p+kViARAdUbgJOgtkWlBgu4KnonVREwbtyoxkG1J1zinDEm3BrUnnlK\nFsoZY5qmpcS+nvBsiBx9xSSo8GFW7XwTLmbRFeTvgQ+iggfPxt1X1JfPEzEUU4xslsvhOyBffhU5\n1VWosUzpCWeAnKaFN7bEHOdvMfbNOsE6IkakEu9bFWwDCi3u4LVazUeOMMbCgZ8DB4jvRgngNdUn\nP8k//nE2MnK0UBjq7v7JwEBbU1MfcBewFvgM0ACMdHVt7uraAMwyzcvDw97WrfjIR67xeZ2UnGRd\njMZ7ni+IwZ6X1vVwO1Qaobi+7waB4/sxM69a26F2LcbW1ON93+b8HYyVJUooGw/KbwEaGGsSZjWt\nrWhvx/TpaG9HaysaGkKamxhgFdA62Q9IB4HDudhCSikrqe8BHpfE5sY63deqyr4jD61VQTA6Tk4C\nWx9+GFeuiBEpS42ACUKRCwTxCKjGsipXrJRc2dMY04QZTuToq6z+Vtv5ioc0TkjWo/4b8D5Zds/J\nN1ydp9Li1J2aJZRqFYsiAVhyVNeJe5Z5cRdG9R3Ialp0XUuyg97J2A/qR+3ordOV2dCqWQMxAnvu\nWqF/2zY2bRo8D5cuYXgY+/ahWCS+GyWA11pf/jIHMG0au/9++7bbLvT2Xuju3tLb+7Xu7tnAEmA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EMP3dj3rSgH/sqcG0HAXJcDHuc5z2uwbUMMv0srGN/zbNe1XNfyPEvaFYS0ajn9ov6ZXMWq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\n6P8HQDginrFaGAAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.image(zoom=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Queries about LAMMPS simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "System(zlo=0.0, atom_style='atomic', boundaries='p,p p,p p,p', kspace_style='none', ntypes=1, dimensions=3, units='lj', orthogonal_box=[6.71838, 6.71838, 6.71838], yhi=6.71838, style='lj/cut', ylo=0.0, natoms=256, zhi=6.71838, atom_map='array', xhi=6.71838, xlo=0.0)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "256"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.system.natoms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Communication(comm_style='brick', nthreads=1, proc_grid=[1, 1, 1], mpi_version='MPI v3.0', nprocs=1, ghost_velocity=False, comm_layout='uniform')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.communication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'group': 'all', 'name': '1', 'style': 'nve'}]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.fixes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'group': 'all', 'name': 'thermo_temp', 'style': 'temp'},\n",
+       " {'group': 'all', 'name': 'thermo_press', 'style': 'pressure'},\n",
+       " {'group': 'all', 'name': 'thermo_pe', 'style': 'pe'}]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.computes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.dumps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'all', 'type': 'static'}]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.groups"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with LAMMPS Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "L.variable(\"a index 2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a': ,\n",
+       " 'fx': }"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "L.variable(\"t equal temp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a': ,\n",
+       " 'fx': ,\n",
+       " 't': }"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "if sys.version_info < (3, 0):\n",
+    "    # In Python 2 'print' is a restricted keyword, which is why you have to use the lmp_print function instead.\n",
+    "    x = float(L.lmp_print('\"${a}\"'))\n",
+    "else:\n",
+    "    # In Python 3 the print function can be redefined.\n",
+    "    # x = float(L.print('\"${a}\"')\")\n",
+    "    \n",
+    "    # To avoid a syntax error in Python 2 executions of this notebook, this line is packed into an eval statement\n",
+    "    x = float(eval(\"L.print('\\\"${a}\\\"')\"))\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.12985322128803"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['t'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.564926610644015"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.eval(\"v_t/2.0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "L.variable(\"b index a b c\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['b'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.eval(\"v_b\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c']"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['b'].definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "L.variable(\"i loop 10\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['i'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.next(\"i\")\n",
+    "L.variables['i'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.6881595982135622"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.eval(\"ke\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing Atom data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['charge',\n",
+       " 'force',\n",
+       " 'id',\n",
+       " 'index',\n",
+       " 'lmp',\n",
+       " 'mass',\n",
+       " 'mol',\n",
+       " 'position',\n",
+       " 'type',\n",
+       " 'velocity']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[x for x in dir(L.atoms[0]) if not x.startswith('__')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.01214313575723994, -0.030471417344883317, -0.09285770282156164)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.2400638079872712, -0.5469382338431744, -1.6999922351145154)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].velocity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.255291613596607, 4.609419196795491, 11.488556813439391)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.255291613596607,\n",
+       " 16.37266096862701,\n",
+       " 4.159322561393292,\n",
+       " -10.358734928539349,\n",
+       " -0.9411205246407262,\n",
+       " 2.938717474528063,\n",
+       " -4.466552742048257,\n",
+       " 3.300931493929359,\n",
+       " 7.504103848449664,\n",
+       " 0.32879193591527645,\n",
+       " -0.9406476961963371,\n",
+       " -4.245722284654334,\n",
+       " 6.018469438850848,\n",
+       " 6.224548675112736,\n",
+       " -1.3057190929343863,\n",
+       " -4.54408845092879,\n",
+       " 4.302630565338362,\n",
+       " -1.7752937195364038,\n",
+       " -0.3537035449320157,\n",
+       " -1.3938874511858526,\n",
+       " -3.2776639119871187,\n",
+       " 3.160948290623288,\n",
+       " -2.790188852792064,\n",
+       " -0.9421802510034946,\n",
+       " -13.724814879107763,\n",
+       " -6.790155378420829,\n",
+       " 17.396062702331758,\n",
+       " -5.771329649495489,\n",
+       " -3.5023908236078265,\n",
+       " 7.496961111326418,\n",
+       " -14.794500895776752,\n",
+       " -14.720706631690351,\n",
+       " -11.222160684284093,\n",
+       " -17.894954250497932,\n",
+       " 8.404340282980602,\n",
+       " -5.645941702083396,\n",
+       " 24.30811488965594,\n",
+       " 4.642599675775289,\n",
+       " 4.729539604038768,\n",
+       " -0.4323819470652316,\n",
+       " 4.429779896132127,\n",
+       " 6.291952145215263,\n",
+       " 0.4284419246044248,\n",
+       " -1.3300758523850174,\n",
+       " 2.714350256153217,\n",
+       " -6.096739470326565,\n",
+       " -0.5583077705790587,\n",
+       " 1.4115895642030756,\n",
+       " 17.316303747789764,\n",
+       " 12.123094224551103,\n",
+       " 6.520722209593786,\n",
+       " -17.68459440341121,\n",
+       " -23.744743068321586,\n",
+       " -6.046503331470429,\n",
+       " -3.892946532289541,\n",
+       " 1.9824465478702231,\n",
+       " 2.1510901134880336,\n",
+       " -21.457945483950954,\n",
+       " -16.011060818843365,\n",
+       " 10.889879999299424,\n",
+       " 10.953581565497077,\n",
+       " -10.500586583044381,\n",
+       " 7.261088472660403,\n",
+       " 11.771997854536748,\n",
+       " -0.6923053428387291,\n",
+       " -11.71992686359301,\n",
+       " 10.161122642269508,\n",
+       " -10.998509349838962,\n",
+       " 7.762360982853242,\n",
+       " -0.39669551447512674,\n",
+       " 4.704847032648839,\n",
+       " 1.9525374247998633,\n",
+       " -1.9996359541569817,\n",
+       " 5.205417057028795,\n",
+       " 0.6514801200578717,\n",
+       " 2.6509735204951146,\n",
+       " -17.194397304879793,\n",
+       " 9.615345441194968,\n",
+       " 15.97002988281231,\n",
+       " -3.3083188642938164,\n",
+       " 1.079294935322761,\n",
+       " -2.9159102982605685,\n",
+       " 8.339297765197136,\n",
+       " -3.959256903323581,\n",
+       " 2.874519411544632,\n",
+       " -8.035228252740698,\n",
+       " -14.03231374489075,\n",
+       " 2.4699091593746414,\n",
+       " 12.451219709891634,\n",
+       " 5.257386554469884,\n",
+       " 4.871163799433557,\n",
+       " 6.481746305583672,\n",
+       " -1.236500768727892,\n",
+       " 16.497903799989224,\n",
+       " -3.2737818963838174,\n",
+       " -7.729520697941898,\n",
+       " 6.983322280758721,\n",
+       " -2.1783280707695516,\n",
+       " -40.295683406889985,\n",
+       " 14.060933848332962,\n",
+       " 2.3173930526619286,\n",
+       " -2.046819216934295,\n",
+       " -5.575438681868664,\n",
+       " 0.051326704791966424,\n",
+       " 8.719879836383923,\n",
+       " -10.449448588532217,\n",
+       " -0.12770355595051575,\n",
+       " -11.59085661910744,\n",
+       " -1.2611793341907793,\n",
+       " -1.6398395331461924,\n",
+       " -16.543263269385008,\n",
+       " 6.946375245763642,\n",
+       " 2.6458297416802523,\n",
+       " -10.025322437003299,\n",
+       " 4.163259032034149,\n",
+       " -10.984017942787375,\n",
+       " 0.0189726292183942,\n",
+       " 11.047577918499082,\n",
+       " 6.636325674666171,\n",
+       " -3.559549874028051,\n",
+       " -3.7556182807178997,\n",
+       " 0.027033528848196417,\n",
+       " 10.127352215962617,\n",
+       " 1.7697881024264828,\n",
+       " 20.985782565194718,\n",
+       " 2.1010189398183607,\n",
+       " 2.5666116097092524,\n",
+       " -16.99457029010233,\n",
+       " 9.481467066173956,\n",
+       " -5.0148231799442655,\n",
+       " -9.95818988004336,\n",
+       " 9.866111938538626,\n",
+       " -13.377742489960198,\n",
+       " -3.2000295963348737,\n",
+       " 4.221680371419796,\n",
+       " 6.6302163287475135,\n",
+       " 0.009615374538821303,\n",
+       " 5.935463079394487,\n",
+       " 6.744551723461018,\n",
+       " -9.64051876692669,\n",
+       " -7.248950200244623,\n",
+       " 21.67197587791844,\n",
+       " -23.8217504755792,\n",
+       " 4.597217761709829,\n",
+       " -7.3169739101387385,\n",
+       " -8.714043619104718,\n",
+       " 4.276550340709232,\n",
+       " -1.8682033065758508,\n",
+       " 2.3693051462352748,\n",
+       " 1.8634844311192382,\n",
+       " 2.700781615273778,\n",
+       " -3.138893562685977,\n",
+       " -7.838876642135683,\n",
+       " 7.105203286115525,\n",
+       " -1.083149867355154,\n",
+       " -8.230419208731144,\n",
+       " -12.233603659735682,\n",
+       " 10.833640166542056,\n",
+       " 9.721449220701018,\n",
+       " -1.877609728717964,\n",
+       " 6.949261765399482,\n",
+       " 4.333500197216083,\n",
+       " -1.8425880411098108,\n",
+       " 15.518947781103941,\n",
+       " 43.730190585283616,\n",
+       " -2.1799812023845067,\n",
+       " 19.718357414093497,\n",
+       " 3.3198524110110186,\n",
+       " 2.136021263737773,\n",
+       " -22.822582785055513,\n",
+       " 10.584653305299993,\n",
+       " -5.9440297813706735,\n",
+       " -7.6460245439162735,\n",
+       " -4.180198871509111,\n",
+       " -15.206132091014798,\n",
+       " -3.039031521362778,\n",
+       " -4.08036797778845,\n",
+       " 15.77348833812475,\n",
+       " -7.999055949825264,\n",
+       " -8.352682108827025,\n",
+       " -10.629465258967437,\n",
+       " 6.9086941795359555,\n",
+       " 7.811955849517036,\n",
+       " 9.648408899213731,\n",
+       " -1.3137506877264686,\n",
+       " 4.136163787610965,\n",
+       " 16.07136107010353,\n",
+       " -22.199319830558398,\n",
+       " 19.00507586305845,\n",
+       " 10.27178610721686,\n",
+       " 13.228301605295682,\n",
+       " -14.665041029548497,\n",
+       " 7.120484977464804,\n",
+       " -13.014879391562197,\n",
+       " 7.016429546425751,\n",
+       " 0.8514431074593181,\n",
+       " 7.076572599898325,\n",
+       " 5.862896580033373,\n",
+       " 10.198650097607718,\n",
+       " -5.217161885689705,\n",
+       " 1.841119452808099,\n",
+       " 23.5916329013764,\n",
+       " -8.679317739549196,\n",
+       " -9.082715536688122,\n",
+       " 4.622153070057964,\n",
+       " -5.874112058912108,\n",
+       " -4.317367532903269,\n",
+       " -7.524652255692546,\n",
+       " 7.045622469998213,\n",
+       " -6.215660840266272,\n",
+       " 0.5747623660814143,\n",
+       " 4.153063384795775,\n",
+       " 14.564757922330578,\n",
+       " 0.07090910607276352,\n",
+       " 1.4106570324329886,\n",
+       " -1.2051371465387875,\n",
+       " -19.453922015562327,\n",
+       " 2.158901789908676,\n",
+       " -1.7127770369716167,\n",
+       " 13.941756348756183,\n",
+       " -11.407906832323556,\n",
+       " -12.643375679433458,\n",
+       " 3.8913549718334743,\n",
+       " -0.12892051724766565,\n",
+       " -3.8354255637031587,\n",
+       " 3.593509155189163,\n",
+       " 2.3291099495398613,\n",
+       " 0.4476595712151561,\n",
+       " -16.190835039283897,\n",
+       " -16.273156965726642,\n",
+       " -44.64517813872991,\n",
+       " -0.7695530880869927,\n",
+       " 33.99022078707833,\n",
+       " -0.4197585361810044,\n",
+       " 0.30699564709585314,\n",
+       " -10.994998378422109,\n",
+       " 1.7245510624229585,\n",
+       " -2.8257173859741753,\n",
+       " -1.1216135118661077,\n",
+       " -0.3196709164205188,\n",
+       " 0.9628747760085039,\n",
+       " -13.512075985764719,\n",
+       " -0.13854498486337197,\n",
+       " 6.335139515373413,\n",
+       " -0.3014898864186137,\n",
+       " -2.969691585007467,\n",
+       " 26.20545138119855,\n",
+       " -12.312340606934061,\n",
+       " 11.195269213941275,\n",
+       " -0.2729563418493044,\n",
+       " 2.1363235059731647,\n",
+       " 3.5188084767077807,\n",
+       " -5.536023143253481,\n",
+       " 9.718400670863252,\n",
+       " -12.352077461650115,\n",
+       " -1.4659962575175773]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['fx'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python/examples/ipython/interface_usage.ipynb.orig b/python/examples/ipython/interface_usage.ipynb.orig
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/examples/ipython/interface_usage_bonds.ipynb b/python/examples/ipython/interface_usage_bonds.ipynb
new file mode 100644
index 0000000000..509385e78c
--- /dev/null
+++ b/python/examples/ipython/interface_usage_bonds.ipynb
@@ -0,0 +1,916 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using LAMMPS with iPython and Jupyter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "LAMMPS can be run interactively using iPython easily. This tutorial shows how to set this up."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Download the latest version of LAMMPS into a folder (we will calls this `$LAMMPS_DIR` from now on)\n",
+    "2. Compile LAMMPS as a shared library and enable PNG support\n",
+    "   ```bash\n",
+    "   cd $LAMMPS_DIR/src\n",
+    "   make yes-molecule\n",
+    "   python2 Make.py -m mpi -png -a file\n",
+    "   make mode=shlib auto\n",
+    "   ```\n",
+    "\n",
+    "3. Create a python virtualenv\n",
+    "   ```bash\n",
+    "   virtualenv testing\n",
+    "   source testing/bin/activate\n",
+    "   ```\n",
+    "\n",
+    "4. Inside the virtualenv install the lammps package\n",
+    "   ```\n",
+    "   (testing) cd $LAMMPS_DIR/python\n",
+    "   (testing) python install.py\n",
+    "   (testing) cd   # move to your working directory\n",
+    "   ```\n",
+    "\n",
+    "5. Install jupyter and ipython in the virtualenv\n",
+    "   ```bash\n",
+    "   (testing) pip install ipython jupyter\n",
+    "   ```\n",
+    "\n",
+    "6. Run jupyter notebook\n",
+    "   ```bash\n",
+    "   (testing) jupyter notebook\n",
+    "   ```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from lammps import IPyLammps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LAMMPS output is captured by PyLammps wrapper\n"
+     ]
+    }
+   ],
+   "source": [
+    "L = IPyLammps()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 2d circle of particles inside a box with LJ walls\n",
+    "import math\n",
+    "\n",
+    "b = 0\n",
+    "x = 50\n",
+    "y = 20\n",
+    "d = 20\n",
+    "\n",
+    "# careful not to slam into wall too hard\n",
+    "\n",
+    "v = 0.3\n",
+    "w = 0.08\n",
+    "                \n",
+    "L.units(\"lj\")\n",
+    "L.dimension(2)\n",
+    "L.atom_style(\"bond\")\n",
+    "L.boundary(\"f f p\")\n",
+    "\n",
+    "L.lattice(\"hex\", 0.85)\n",
+    "L.region(\"box\", \"block\", 0, x, 0, y, -0.5, 0.5)\n",
+    "L.create_box(1, \"box\", \"bond/types\", 1, \"extra/bond/per/atom\", 6)\n",
+    "L.region(\"circle\", \"sphere\", d/2.0+1.0, d/2.0/math.sqrt(3.0)+1, 0.0, d/2.0)\n",
+    "L.create_atoms(1, \"region\", \"circle\")\n",
+    "L.mass(1, 1.0)\n",
+    "\n",
+    "L.velocity(\"all create 0.5 87287 loop geom\")\n",
+    "L.velocity(\"all set\", v, w, 0, \"sum yes\")\n",
+    "\n",
+    "L.pair_style(\"lj/cut\", 2.5)\n",
+    "L.pair_coeff(1, 1, 10.0, 1.0, 2.5)\n",
+    "\n",
+    "L.bond_style(\"harmonic\")\n",
+    "L.bond_coeff(1, 10.0, 1.2)\n",
+    "\n",
+    "L.create_bonds(\"all\", \"all\", 1, 1.0, 1.5)\n",
+    "\n",
+    "L.neighbor(0.3, \"bin\")\n",
+    "L.neigh_modify(\"delay\", 0, \"every\", 1, \"check yes\")\n",
+    "\n",
+    "L.fix(1, \"all\", \"nve\")\n",
+    "\n",
+    "L.fix(2, \"all wall/lj93 xlo 0.0 1 1 2.5 xhi\", x, \"1 1 2.5\")\n",
+    "L.fix(3, \"all wall/lj93 ylo 0.0 1 1 2.5 yhi\", y, \"1 1 2.5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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APKx1oqWF44/nhhvSBwzoZxhb43Gz9JbWRiRCnz4sWOAZN67I4fiypWUvLNca8HV1YRgs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\nTkSuQmCC\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.image(zoom=1.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9u3YtHhgAOu32rEOHePbZ/V984YJNUH7++aUbN/7ZMN7UGvjCYhmzerXTYunw+2sNoxxC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g4WIgpetdvTi4okdCpKNXSAE170eAxwDQw7dMgoRFcgUN+fIbOuutoCZoiBtG3bIo8dc3d2lrrd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\nBIAQQggJACGEEBIAQgghJACEEEKc8n8BV6onOVwQeAcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.thermo_style(\"custom step temp epair press\")\n",
+    "L.thermo(100)\n",
+    "output = L.run(40000)\n",
+    "L.image(zoom=1.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Queries about LAMMPS simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "System(ntypes=1, nimpropertypes=0, nbonds=1014, nangles=0, orthogonal_box=[58.2767, 40.3753, 1.16553], ndihedrals=0, yhi=40.3753, atom_style='bond', bond_style='harmonic', zlo=-0.582767, improper_style='none', xlo=0.0, style='lj/cut', xhi=58.2767, kspace_style='none', zhi=0.582767, nbondtypes=1, angle_style='none', dihedral_style='none', dimensions=2, natoms=361, boundaries='f,f f,f p,p', ylo=0.0, nimpropers=0, nangletypes=0, molecule_type='standard', atom_map='array', units='lj')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "361"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.system.natoms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1014"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.system.nbonds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.system.nbondtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Communication(comm_style='brick', comm_layout='uniform', proc_grid=[1, 1, 1], nthreads=1, mpi_version='MPI v3.0', nprocs=1, ghost_velocity=False)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.communication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'group': 'all', 'name': '1', 'style': 'nve'},\n",
+       " {'group': 'all', 'name': '2', 'style': 'wall/lj93'},\n",
+       " {'group': 'all', 'name': '3', 'style': 'wall/lj93'}]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.fixes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'group': 'all', 'name': 'thermo_temp', 'style': 'temp'},\n",
+       " {'group': 'all', 'name': 'thermo_press', 'style': 'pressure'},\n",
+       " {'group': 'all', 'name': 'thermo_pe', 'style': 'pe'}]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.computes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.dumps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'all', 'type': 'static'}]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.groups"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with LAMMPS Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "L.variable(\"a index 2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a': }"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "L.variable(\"t equal temp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a': ,\n",
+       " 't': }"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "if sys.version_info < (3, 0):\n",
+    "    # In Python 2 'print' is a restricted keyword, which is why you have to use the lmp_print function instead.\n",
+    "    x = float(L.lmp_print('\"${a}\"'))\n",
+    "else:\n",
+    "    # In Python 3 the print function can be redefined.\n",
+    "    # x = float(L.print('\"${a}\"')\")\n",
+    "    \n",
+    "    # To avoid a syntax error in Python 2 executions of this notebook, this line is packed into an eval statement\n",
+    "    x = float(eval(\"L.print('\\\"${a}\\\"')\"))\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.363092663783386"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['t'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.181546331891693"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.eval(\"v_t/2.0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "L.variable(\"b index a b c\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['b'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.eval(\"v_b\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['b'].definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "L.variable(\"i loop 10\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.variables['i'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.next(\"i\")\n",
+    "L.variables['i'].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3620868669308006"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.eval(\"ke\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing Atom data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['charge',\n",
+       " 'force',\n",
+       " 'id',\n",
+       " 'index',\n",
+       " 'lmp',\n",
+       " 'mass',\n",
+       " 'mol',\n",
+       " 'position',\n",
+       " 'type',\n",
+       " 'velocity']"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[x for x in dir(L.atoms[0]) if not x.startswith('__')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(14.081609521719168, 0.920541427075243, 0.0)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.08810676648167072, -0.8755151505832499, 0.0)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].velocity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.809523377179948, -1.4360462290451692, 0.0)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.atoms[0].type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python/examples/ipython/interface_usage_bonds.ipynb.orig b/python/examples/ipython/interface_usage_bonds.ipynb.orig
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/examples/ipython/simple.ipynb b/python/examples/ipython/simple.ipynb
new file mode 100644
index 0000000000..752bf93af8
--- /dev/null
+++ b/python/examples/ipython/simple.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using LAMMPS with iPython and Jupyter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "LAMMPS can be run interactively using iPython easily. This tutorial shows how to set this up."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Download the latest version of LAMMPS into a folder (we will calls this `$LAMMPS_DIR` from now on)\n",
+    "2. Compile LAMMPS as a shared library and enable PNG support\n",
+    "   ```bash\n",
+    "   cd $LAMMPS_DIR/src\n",
+    "   python2 Make.py -m mpi -png -a file\n",
+    "   make mode=shlib auto\n",
+    "   ```\n",
+    "\n",
+    "3. Create a python virtualenv\n",
+    "   ```bash\n",
+    "   virtualenv testing\n",
+    "   source testing/bin/activate\n",
+    "   ```\n",
+    "\n",
+    "4. Inside the virtualenv install the lammps package\n",
+    "   ```\n",
+    "   (testing) cd $LAMMPS_DIR/python\n",
+    "   (testing) python install.py\n",
+    "   (testing) cd   # move to your working directory\n",
+    "   ```\n",
+    "\n",
+    "5. Install jupyter and ipython in the virtualenv\n",
+    "   ```bash\n",
+    "   (testing) pip install ipython jupyter\n",
+    "   ```\n",
+    "\n",
+    "6. Run jupyter notebook\n",
+    "   ```bash\n",
+    "   (testing) jupyter notebook\n",
+    "   ```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from lammps import IPyLammps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LAMMPS output is captured by PyLammps wrapper\n"
+     ]
+    }
+   ],
+   "source": [
+    "L = IPyLammps()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Neighbor list info ...',\n",
+       " '  1 neighbor list requests',\n",
+       " '  update every 20 steps, delay 0 steps, check no',\n",
+       " '  max neighbors/atom: 2000, page size: 100000',\n",
+       " '  master list distance cutoff = 2.8',\n",
+       " '  ghost atom cutoff = 2.8',\n",
+       " '  binsize = 1.4, bins = 5 5 5',\n",
+       " 'Setting up Verlet run ...',\n",
+       " '  Unit style    : lj',\n",
+       " '  Current step  : 0',\n",
+       " '  Time step     : 0.005',\n",
+       " 'Memory usage per processor = 2.04486 Mbytes',\n",
+       " 'Step Temp E_pair E_mol TotEng Press ',\n",
+       " '       0         1.44   -6.7733681            0   -4.6218056   -5.0244179 ',\n",
+       " '      10    1.1298532   -6.3095502            0   -4.6213906   -2.6058175 ',\n",
+       " 'Loop time of 0.001143 on 1 procs for 10 steps with 256 atoms',\n",
+       " '',\n",
+       " 'Performance: 3779527.555 tau/day, 8748.906 timesteps/s',\n",
+       " '291.6% CPU use with 1 MPI tasks x no OpenMP threads',\n",
+       " '',\n",
+       " 'MPI task timing breakdown:',\n",
+       " 'Section |  min time  |  avg time  |  max time  |%varavg| %total',\n",
+       " '---------------------------------------------------------------',\n",
+       " 'Pair    | 0.001048   | 0.001048   | 0.001048   |   0.0 | 91.69',\n",
+       " 'Neigh   | 0          | 0          | 0          |   0.0 |  0.00',\n",
+       " 'Comm    | 5.3e-05    | 5.3e-05    | 5.3e-05    |   0.0 |  4.64',\n",
+       " 'Output  | 6e-06      | 6e-06      | 6e-06      |   0.0 |  0.52',\n",
+       " 'Modify  | 2.1e-05    | 2.1e-05    | 2.1e-05    |   0.0 |  1.84',\n",
+       " 'Other   |            | 1.5e-05    |            |       |  1.31',\n",
+       " '',\n",
+       " 'Nlocal:    256 ave 256 max 256 min',\n",
+       " 'Histogram: 1 0 0 0 0 0 0 0 0 0',\n",
+       " 'Nghost:    1431 ave 1431 max 1431 min',\n",
+       " 'Histogram: 1 0 0 0 0 0 0 0 0 0',\n",
+       " 'Neighs:    9984 ave 9984 max 9984 min',\n",
+       " 'Histogram: 1 0 0 0 0 0 0 0 0 0',\n",
+       " '',\n",
+       " 'Total # of neighbors = 9984',\n",
+       " 'Ave neighs/atom = 39',\n",
+       " 'Neighbor list builds = 0',\n",
+       " 'Dangerous builds not checked']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "# 3d Lennard-Jones melt\n",
+    "\n",
+    "L.units(\"lj\")\n",
+    "L.atom_style(\"atomic\")\n",
+    "L.atom_modify(\"map array\")\n",
+    "\n",
+    "L.lattice(\"fcc\", 0.8442)\n",
+    "L.region(\"box\", \"block\", 0, 4, 0, 4, 0, 4)\n",
+    "L.create_box(1, \"box\")\n",
+    "L.create_atoms(1, \"box\")\n",
+    "L.mass(1, 1.0)\n",
+    "\n",
+    "L.velocity(\"all\", \"create\", 1.44, 87287, \"loop geom\")\n",
+    "\n",
+    "L.pair_style(\"lj/cut\", 2.5)\n",
+    "L.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n",
+    "\n",
+    "L.neighbor(0.3, \"bin\")\n",
+    "L.neigh_modify(\"delay\", 0, \"every\", 20, \"check no\")\n",
+    "\n",
+    "L.fix(\"1 all nve\")\n",
+    "\n",
+    "L.variable(\"fx atom fx\")\n",
+    "\n",
+    "L.info(\"all\")\n",
+    "\n",
+    "L.run(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SKqQUQAD8CnAPkAVcIA9klGDGio3KwJuAFFEhXhzf2YklS9DVhbY25HJRZhwn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EMP3dj3rSgH/sqcG0HAXJcDHuc5z2uwbUMMv0srGN/zbNe1XNfyPEvaFYS0ajn9ov6ZXMWq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\n6P8HQDginrFaGAAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.image(zoom=1.0)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/python/examples/ipython/simple.ipynb.orig b/python/examples/ipython/simple.ipynb.orig
new file mode 100644
index 0000000000..e69de29bb2

From c04c7756318ebfa5240f8cd642a27a0d27cf34db Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 23:04:25 +0000
Subject: [PATCH 16/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15361
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 src/version.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/version.h b/src/version.h
index 1f8d1c7dd6..25bd514c09 100644
--- a/src/version.h
+++ b/src/version.h
@@ -1 +1 @@
-#define LAMMPS_VERSION "15 Jul 2016"
+#define LAMMPS_VERSION "22 Jul 2016"

From caf9483a0a7ca06928388437f01883c04552e233 Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Fri, 22 Jul 2016 23:05:05 +0000
Subject: [PATCH 17/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15362
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 doc/html/Manual.html         | 2 +-
 doc/html/_sources/Manual.txt | 2 +-
 doc/src/Manual.txt           | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/html/Manual.html b/doc/html/Manual.html
index f8779937ec..3803ccee5e 100644
--- a/doc/html/Manual.html
+++ b/doc/html/Manual.html
@@ -135,7 +135,7 @@
   
    
 
    LAMMPS Documentation
    
 
    
-
    15 Jul 2016 version
    
+
    22 Jul 2016 version
    
 
    
 
    
 
    Version info:
    
diff --git a/doc/html/_sources/Manual.txt b/doc/html/_sources/Manual.txt
index 369dcccbbc..2665bfcc56 100644
--- a/doc/html/_sources/Manual.txt
+++ b/doc/html/_sources/Manual.txt
@@ -5,7 +5,7 @@
 LAMMPS Documentation
 ====================
 
-15 Jul 2016 version
+22 Jul 2016 version
 -------------------
 
 Version info:
diff --git a/doc/src/Manual.txt b/doc/src/Manual.txt
index 4a883fe52a..f011eb1b40 100644
--- a/doc/src/Manual.txt
+++ b/doc/src/Manual.txt
@@ -1,7 +1,7 @@
 
 
 LAMMPS Users Manual
-
+
 
 
 
@@ -21,7 +21,7 @@
 
    
 
 LAMMPS Documentation :c,h3
-15 Jul 2016 version :c,h4
+22 Jul 2016 version :c,h4
 
 Version info: :h4
 

From 08ac3d54dd9480dd01016df12e5a5d1b2e1e2d31 Mon Sep 17 00:00:00 2001
From: sjplimp 
Date: Sat, 23 Jul 2016 00:24:54 +0000
Subject: [PATCH 18/18] git-svn-id:
 svn://svn.icms.temple.edu/lammps-ro/trunk@15364
 f3b2605a-c512-4ea7-a41b-209d697bcdaa

---
 src/neighbor.cpp | 11 +++++++++++
 src/neighbor.h   |  1 +
 src/update.cpp   |  5 ++---
 3 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/src/neighbor.cpp b/src/neighbor.cpp
index e09963ecba..eb882d2e96 100644
--- a/src/neighbor.cpp
+++ b/src/neighbor.cpp
@@ -1931,6 +1931,17 @@ void Neighbor::set(int narg, char **arg)
   if (style == MULTI && lmp->citeme) lmp->citeme->add(cite_neigh_multi);
 }
 
+/* ----------------------------------------------------------------------
+   reset timestamps in all NeighList classes
+   so that neighbor lists will rebuild properly with timestep change
+------------------------------------------------------------------------- */
+
+void Neighbor::reset_timestep(bigint ntimestep)
+{
+  for (int i = 0; i < nlist; i++)
+    lists[i]->last_build = -1;
+}
+
 /* ----------------------------------------------------------------------
    modify parameters of the pair-wise neighbor build
 ------------------------------------------------------------------------- */
diff --git a/src/neighbor.h b/src/neighbor.h
index 7a098e9be2..23fd663e2b 100644
--- a/src/neighbor.h
+++ b/src/neighbor.h
@@ -86,6 +86,7 @@ class Neighbor : protected Pointers {
   void build_one(class NeighList *list,
                  int preflag=0);    // create a single one-time neigh list
   void set(int, char **);           // set neighbor style and skin distance
+  void reset_timestep(bigint);      // reset of timestep counter
   void modify_params(int, char**);  // modify parameters that control builds
   bigint memory_usage();
   int exclude_setting();
diff --git a/src/update.cpp b/src/update.cpp
index 8bb5bca487..faf3a8c148 100644
--- a/src/update.cpp
+++ b/src/update.cpp
@@ -448,10 +448,9 @@ void Update::reset_timestep(bigint newstep)
   for (int i = 0; i < modify->ncompute; i++)
     if (modify->compute[i]->timeflag) modify->compute[i]->clearstep();
 
-  // set last_build of all neigh lists to -1 to force rebuild
+  // Neighbor Bin/Stencil/Pair classes store timestamps that need to be cleared
 
-  for (int i = 0; i < neighbor->nlist; i++)
-    neighbor->lists[i]->last_build = -1;
+  neighbor->reset_timestep(ntimestep);
 
   // NOTE: 7Jun12, adding rerun command, don't think this is required