/* * MrBayes 3 * * (c) 2002-2010 * * John P. Huelsenbeck * Dept. Integrative Biology * University of California, Berkeley * Berkeley, CA 94720-3140 * johnh@berkeley.edu * * Fredrik Ronquist * Swedish Museum of Natural History * Box 50007 * SE-10405 Stockholm, SWEDEN * fredrik.ronquist@nrm.se * * With important contributions by * * Paul van der Mark (paulvdm@sc.fsu.edu) * Maxim Teslenko (maxim.teslenko@nrm.se) * * and by many users (run 'acknowledgements' to see more info) * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details (www.gnu.org). * */ #include #include #include #include #include #include #include #include "mb.h" #include "globals.h" #include "bayes.h" #include "best.h" #include "model.h" #include "command.h" #include "mcmc.h" #include "mbmath.h" #include "tree.h" #include "utils.h" const char* const svnRevisionModelC="$Rev: 513 $"; /* Revision keyword which is expended/updated by svn on each commit/update*/ #if defined(__MWERKS__) #include "SIOUX.h" #endif #ifndef NDEBUG #include #endif #undef DEBUG_ADDDUMMYCHARS #undef DEBUG_CONSTRAINTS #undef DEBUG_COMPRESSDATA /* local prototypes */ int AddDummyChars (void); void AllocateCppEvents (Param *p); MCMCMove *AllocateMove (MoveType *moveType, Param *param); int AllocateNormalParams (void); int AllocateTreeParams (void); void CheckCharCodingType (Matrix *m, CharInfo *ci); int CheckExpandedModels (void); int CompressData (void); int InitializeChainTrees (Param *p, int from, int to, int isRooted); int FillBrlensSubParams (Param *param, int chn, int state); void FreeCppEvents (Param *p); void FreeMove (MCMCMove *mv); void GetPossibleAAs (int aaCode, int aa[]); void GetPossibleNucs (int nucCode, int nuc[]); void GetPossibleRestrictionSites (int resSiteCode, int *sites); int GetUserTreeFromName (int *index, char *treeName); void InitializeMcmcTrees (Param *p); int IsApplicable (Param *param); int IsApplicableTreeAgeMove (Param *param); int IsModelSame (int whichParam, int part1, int part2, int *isApplic1, int *isApplic2); int NumActiveParts (void); int NumNonExcludedChar (void); int NumStates (int part); int PrintCompMatrix (void); int PrintMatrix (void); int ProcessStdChars (SafeLong *seed); int SetModelParams (void); int SetPopSizeParam (Param *param, int chn, int state, PolyTree *pt); int SetRelaxedClockParam (Param *param, int chn, int state, PolyTree *pt); int SetUpLinkTable (void); int ShowMoves (int used); int ShowParameters (int showStartVals, int showMoves, int showAllAvailable); int UpdateCppEvolLength (int *nEvents, MrBFlt **pos, MrBFlt **rateMult, MrBFlt *evolLength, TreeNode *p, MrBFlt baseRate); /* globals */ int *activeParams[NUM_LINKED]; /* a table holding the parameter link status */ int localOutGroup; /* outgroup for non-excluded taxa */ Calibration **localTaxonCalibration = NULL; /* stores local taxon calibrations (ages) */ char **localTaxonNames = NULL; /* points to names of non-excluded taxa */ Model *modelParams; /* holds model params */ ModelInfo *modelSettings; /* stores important info on model params */ MCMCMove **moves; /* vector of pointers to applicable moves */ int numApplicableMoves; /* number of moves applicable to model parameters */ int numCurrentDivisions; /* number of partitions of data */ int numGlobalChains; /* number of global chains */ int numLocalTaxa; /* number of non-excluded taxa */ int numLocalChar; /* number of non-excluded characters */ int numParams; /* number of parameters in model */ int numTopologies; /* number of topologies for one chain and state */ int numTrees; /* number of trees for one chain and state */ Param *params; /* vector of parameters */ Param *printParams; /* vector of subst model parameters to print */ ShowmovesParams showmovesParams; /* holds parameters for Showmoves command */ Param *treePrintparams; /* vector of tree parameters to print */ int setUpAnalysisSuccess; /* Set to YES if analysis is set without error */ /* globals used to describe and change the current model; allocated in AllocCharacters and SetPartition */ int *numVars; /* number of variables in setting arrays */ int *activeParts; /* partitions changes should apply to */ int *linkTable[NUM_LINKED]; /* how parameters are linked across parts */ int *tempLinkUnlink[NUM_LINKED]; /* for changing parameter linkage */ int *tempLinkUnlinkVec; /* for changing parameter linkage */ MrBFlt *tempNum; /* vector of numbers used for setting arrays */ /* Aamodel parameters */ MrBFlt aaJones[20][20]; /* rates for Jones model */ MrBFlt aaDayhoff[20][20]; /* rates for Dayhoff model */ MrBFlt aaMtrev24[20][20]; /* rates for mtrev24 model */ MrBFlt aaMtmam[20][20]; /* rates for mtmam model */ MrBFlt aartREV[20][20]; /* rates for rtREV model */ MrBFlt aaWAG[20][20]; /* rates for WAG model */ MrBFlt aacpREV[20][20]; /* rates for aacpREV model */ MrBFlt aaVt[20][20]; /* rates for VT model */ MrBFlt aaBlosum[20][20]; /* rates for Blosum62 model */ MrBFlt jonesPi[20]; /* stationary frequencies for Jones model */ MrBFlt dayhoffPi[20]; /* stationary frequencies for Dayhoff model */ MrBFlt mtrev24Pi[20]; /* stationary frequencies for mtrev24 model */ MrBFlt mtmamPi[20]; /* stationary frequencies for mtmam model */ MrBFlt rtrevPi[20]; /* stationary frequencies for rtREV model */ MrBFlt wagPi[20]; /* stationary frequencies for WAG model */ MrBFlt cprevPi[20]; /* stationary frequencies for aacpREV model */ MrBFlt vtPi[20]; /* stationary frequencies for VT model */ MrBFlt blosPi[20]; /* stationary frequencies for Blosum62 model */ /* parser flags and variables */ static int fromI, toJ, isNegative, foundDash, foundExp, foundEqual; int foundComma, foundBeta, foundAaSetting, modelIsFixed, linkNum, foundLeftPar, tempNumStates; MrBFlt tempStateFreqs[200], tempAaModelPrs[10]; char colonPr[100], clockPr[30]; /* other local variables (this file) */ MrBFlt empiricalFreqs[200]; /* emprical base frequencies for partition */ int intValsRowSize = 0; /* row size of intValues matrix */ int *intValues = NULL; /* stores int values of chain parameters */ Tree **mcmcTree; /* pointers to trees for mcmc */ int paramValsRowSize = 0; /* row size of paramValues matrix */ MrBFlt *paramValues = NULL; /* stores actual values and subvalues of chain parameters */ int *relevantParts = NULL; /* partitions that are affected by this move */ Param **subParamPtrs; /* pointer to subparams for topology params */ int *stateSize; /* # states for each compressed char */ /*----------------------------------------------------------------------- | | AddDummyChars: Add dummy characters to relevant partitions | ------------------------------------------------------------------------*/ int AddDummyChars (void) { int i, j, k, d, numIncompatible, numDeleted, numStdChars, oldRowSize, newRowSize, numDummyChars, newColumn, newChar, oldColumn, oldChar, isCompat, *tempChar, numIncompatibleChars; SafeLong *tempMatrix; CLFlt *tempSitesOfPat; ModelInfo *m; ModelParams *mp; CharInfo cinfo; Matrix matrix; extern int NBits(int x); /* set pointers to NULL */ tempMatrix = NULL; tempSitesOfPat = NULL; tempChar = NULL; /* check how many dummy characters needed in total */ numDummyChars = 0; numStdChars = 0; for (d=0; dnumDummyChars = 0; if (mp->dataType == RESTRICTION && !strcmp(mp->parsModel,"No")) { if (!strcmp(mp->coding, "Variable")) m->numDummyChars = 2; else if (!strcmp(mp->coding, "Noabsencesites") || !strcmp(mp->coding, "Nopresencesites")) m->numDummyChars = 1; else if (!strcmp(mp->coding, "Informative")) m->numDummyChars = 2 + 2 * numLocalTaxa; } if (mp->dataType == STANDARD && !strcmp(mp->parsModel,"No")) { if (!strcmp(mp->coding, "Variable")) m->numDummyChars = 2; else if (!strcmp(mp->coding, "Informative")) m->numDummyChars = 2 + 2 * numLocalTaxa; numStdChars += (m->numChars + m->numDummyChars); } numDummyChars += m->numDummyChars; m->numChars += m->numDummyChars; } /* exit if dummy characters not needed */ if (numDummyChars == 0) return NO_ERROR; /* print original compressed matrix */ # if 0 MrBayesPrint ("Compressed matrix before adding dummy characters...\n"); PrintCompMatrix(); # endif /* set row sizes for old and new matrices */ oldRowSize = compMatrixRowSize; compMatrixRowSize += numDummyChars; newRowSize = compMatrixRowSize; numCompressedChars += numDummyChars; /* allocate space for new data */ tempMatrix = (SafeLong *) SafeCalloc (numLocalTaxa * newRowSize, sizeof(SafeLong)); tempSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); tempChar = (int *) SafeCalloc (compMatrixRowSize, sizeof(int)); if (!tempMatrix || !tempSitesOfPat || !tempChar) { MrBayesPrint ("%s Problem allocating temporary variables in AddDummyChars\n", spacer); goto errorExit; } /* initialize indices */ oldChar = newChar = newColumn = numDeleted = 0; /* set up matrix struct */ matrix.origin = compMatrix; matrix.nRows = numLocalTaxa; matrix.rowSize = oldRowSize; /* loop over divisions */ for (d=0; dnumDummyChars > 0) { MrBayesPrint("%s Adding dummy characters (unobserved site patterns) for division %d\n", spacer, d+1); if (!strcmp(mp->coding, "Variable") || !strcmp(mp->coding, "Informative")) { for (k=0; k<2; k++) { for (i=0; icoding, "Informative")) { for (k=0; k<2; k++) { for (i=0; i< numLocalTaxa; i++) { for (j=0; jcoding, "Noabsencesites")) { for (i=0; icoding, "Nopresencesites")) { for (i=0; icompMatrixStart; oldColumncompMatrixStop; oldColumn++) { isCompat = YES; /* first check if the character is supposed to be present */ if (m->numDummyChars > 0) { /* set up matrix struct */ matrix.column = oldColumn; /* set up charinfo struct */ cinfo.dType = mp->dataType; cinfo.cType = charInfo[origChar[oldChar]].ctype; cinfo.nStates = charInfo[origChar[oldChar]].numStates; CheckCharCodingType(&matrix, &cinfo); if (!strcmp(mp->coding, "Variable") && cinfo.variable == NO) isCompat = NO; else if (!strcmp(mp->coding, "Informative") && cinfo.informative == NO) isCompat = NO; else if (!strcmp(mp->coding, "Noabsencesites") && cinfo.constant[0] == YES) isCompat = NO; else if (!strcmp(mp->coding, "Nopresencesites") && cinfo.constant[1] == YES) isCompat = NO; } if (isCompat == NO) { numIncompatible++; numIncompatibleChars += (int) numSitesOfPat[oldChar]; oldChar++; } else { /* add character */ for (i=0; icompMatrixStart+1) % m->nCharsPerSite == 0) { newChar++; oldChar++; } } } /* print a warning if there are incompatible characters */ if (numIncompatible > 0) { m->numChars -= numIncompatible; m->numUncompressedChars -= numIncompatibleChars; numDeleted += numIncompatible; if (numIncompatibleChars > 1) { MrBayesPrint ("%s WARNING: There are %d characters incompatible with the specified\n", spacer, numIncompatibleChars); MrBayesPrint ("%s coding bias. These characters will be excluded.\n", spacer); } else { MrBayesPrint ("%s WARNING: There is one character incompatible with the specified\n", spacer); MrBayesPrint ("%s coding bias. This character will be excluded.\n", spacer); } } /* update division comp matrix and comp char pointers */ m->compCharStop = newChar; m->compMatrixStop = newColumn; m->compCharStart = newChar - m->numChars; m->compMatrixStart = newColumn - m->nCharsPerSite * m->numChars; } /* next division */ /* compress matrix if necessary */ if (numDeleted > 0) { for (i=k=0; inEvents = (int **) SafeCalloc (2*numGlobalChains, sizeof (int *)); p->nEvents[0] = (int *) SafeCalloc (2*numGlobalChains*(2*numLocalTaxa), sizeof (int)); for (i=1; i<2*numGlobalChains; i++) p->nEvents[i] = p->nEvents[i-1] + (2*numLocalTaxa); p->position = (MrBFlt ***) SafeCalloc (2*numGlobalChains, sizeof (MrBFlt **)); p->position[0] = (MrBFlt **) SafeCalloc (2*numGlobalChains*(2*numLocalTaxa), sizeof (MrBFlt *)); for (i=1; i<2*numGlobalChains; i++) p->position[i] = p->position[i-1] + (2*numLocalTaxa); p->rateMult = (MrBFlt ***) SafeCalloc (2*numGlobalChains, sizeof (MrBFlt **)); p->rateMult[0] = (MrBFlt **) SafeCalloc (2*numGlobalChains*(2*numLocalTaxa), sizeof (MrBFlt *)); for (i=1; i<2*numGlobalChains; i++) p->rateMult[i] = p->rateMult[i-1] + (2*numLocalTaxa); } /*---------------------------------------------------------------------------- | | AllocateMove: Allocate space for and initialize one applicable move | -----------------------------------------------------------------------------*/ MCMCMove *AllocateMove (MoveType *moveType, Param *param) { int i, j, nameLength; char *partitionDescriptor = ""; MCMCMove *temp; if ((temp = (MCMCMove *) SafeCalloc (1, sizeof (MCMCMove))) == NULL) return (NULL); /* calculate length */ if (strcmp (moveType->paramName, "") == 0) nameLength = (int) (strlen (moveType->shortName) + strlen (param->name)) + 10; else { partitionDescriptor = param->name; while (*partitionDescriptor != '\0') { if (*partitionDescriptor == '{') break; partitionDescriptor++; } nameLength = (int) (strlen (moveType->shortName) + strlen (moveType->paramName) + strlen (partitionDescriptor)) + 10; } /* add length of names of subparams */ if (moveType->subParams == YES) { for (i=0; inSubParams; i++) nameLength += (int)(strlen(param->subParams[i]->name)) + 1; } if ((temp->name = (char *) SafeCalloc (nameLength, sizeof (char))) == NULL) { free (temp); return NULL; } if ((temp->nAccepted = (int *) SafeCalloc (5*numGlobalChains, sizeof (int))) == NULL) { free (temp->name); free (temp); return NULL; } temp->nTried = temp->nAccepted + numGlobalChains; temp->nBatches = temp->nAccepted + 2*numGlobalChains; temp->nTotAccepted = temp->nAccepted + 3*numGlobalChains; temp->nTotTried = temp->nAccepted + 4*numGlobalChains; if ((temp->relProposalProb = (MrBFlt *) SafeCalloc (4*numGlobalChains, sizeof (MrBFlt))) == NULL) { free (temp->nAccepted); free (temp->name); free (temp); return NULL; } temp->cumProposalProb = temp->relProposalProb + numGlobalChains; temp->targetRate = temp->relProposalProb + 2*numGlobalChains; temp->lastAcceptanceRate = temp->relProposalProb + 3*numGlobalChains; if ((temp->tuningParam = (MrBFlt **) SafeCalloc (numGlobalChains, sizeof (MrBFlt *))) == NULL) { free (temp->relProposalProb); free (temp->nAccepted); free (temp->name); free (temp); return NULL; } if ((temp->tuningParam[0] = (MrBFlt *) SafeCalloc (moveType->numTuningParams*numGlobalChains, sizeof (MrBFlt))) == NULL) { free (temp->tuningParam); free (temp->relProposalProb); free (temp->nAccepted); free (temp->name); free (temp); return NULL; } for (i=1; ituningParam[i] = temp->tuningParam[0] + (i * moveType->numTuningParams); /* set default values */ if (strcmp(moveType->paramName, "") != 0) sprintf (temp->name, "%s(%s%s)", moveType->shortName, moveType->paramName, partitionDescriptor); else { sprintf (temp->name, "%s(%s", moveType->shortName, param->name); if (moveType->subParams == YES) { for (i=0; inSubParams; i++) { strcat(temp->name,","); strcat(temp->name,param->subParams[i]->name); } } strcat (temp->name,")"); } temp->moveType = moveType; temp->moveFxn = moveType->moveFxn; for (i=0; irelProposalProb[i] = moveType->relProposalProb; temp->cumProposalProb[i] = 0.0; temp->nAccepted[i] = 0; temp->nTried[i] = 0; temp->nBatches[i] = 0; temp->nTotAccepted[i] = 0; temp->nTotTried[i] = 0; temp->targetRate[i] = moveType->targetRate; temp->lastAcceptanceRate[i] = 0.0; for (j=0; jnumTuningParams; j++) temp->tuningParam[i][j] = moveType->tuningParam[j]; } return (temp); } /*---------------------------------------------------------------------- | | AllocateNormalParams: Allocate space for normal parameters | -----------------------------------------------------------------------*/ int AllocateNormalParams (void) { int i, k, nOfParams, nOfIntParams; Param *p; /* Count the number of param values and subvalues */ nOfParams = 0; nOfIntParams = 0; for (k=0; k 0) intValues = (int *) SafeRealloc ((void *) intValues, nOfIntParams * sizeof(int)); } else { paramValues = (MrBFlt *) SafeCalloc (nOfParams, sizeof(MrBFlt)); if (nOfIntParams > 0) intValues = (int *) SafeCalloc (nOfIntParams, sizeof(int)); else intValues = NULL; } if (!paramValues || (nOfIntParams > 0 && !intValues)) { MrBayesPrint ("%s Problem allocating paramValues\n", spacer); if (paramValues) free (paramValues); if (intValues) free (intValues); return ERROR; } else memAllocs[ALLOC_PARAMVALUES] = YES; /* set pointers to values for chain 1 state 0 */ /* this scheme keeps the chain and state values together */ nOfParams = 0; nOfIntParams = 0; for (k=0; knValues > 0) p->values = paramValues + nOfParams; else p->values = NULL; nOfParams += p->nValues; if (p->nSubValues > 0) p->subValues = paramValues + nOfParams; else p->subValues = NULL; nOfParams += p->nSubValues; if (p->nIntValues > 0) p->intValues = intValues + nOfIntParams; else p->intValues = NULL; nOfIntParams += p->nIntValues; } /* allocate space for cpp events */ for (k=0; kparamType == P_CPPEVENTS) AllocateCppEvents(p); } return (NO_ERROR); } /*---------------------------------------------------------------------- | | AllocateTreeParams: Allocate space for tree parameters | -----------------------------------------------------------------------*/ int AllocateTreeParams (void) { int i, j, k, n, nOfParams, nOfTrees, isRooted, numSubParamPtrs; Param *p, *q; /* Count the number of trees and dated trees */ /* based on branch length parameters */ /* One tree is needed for each brlen parameter. A topology may apply to several trees; a topology parameter contains pointers to all trees it applies to */ numTrees = 0; numTopologies = 0; for (k=0; krelParts[0]].topology; q->nSubParams++; if (p->printParam == YES) q->nPrintSubParams++; } else if (params[k].paramType == P_TOPOLOGY) { q = modelSettings[p->relParts[0]].speciesTree; if (q != NULL) q->nSubParams++; } } /* Make sure there is also one subparam for a parsimony tree */ for (k=0; knSubParams == 0) p->nSubParams = 1; } /* Count subparams for relaxed clock parameters */ for (k=0; kparamType == P_CPPEVENTS) { q = modelSettings[p->relParts[0]].cppRate; q->nSubParams++; q = modelSettings[p->relParts[0]].cppMultDev; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } else if (p->paramType == P_TK02BRANCHRATES) { q = modelSettings[p->relParts[0]].tk02var; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } else if (p->paramType == P_IGRBRANCHLENS) { q = modelSettings[p->relParts[0]].igrvar; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } } /* set pointers to subparams */ nOfParams = 0; for (k=0; knSubParams > 0) { p->subParams = subParamPtrs + nOfParams; nOfParams += p->nSubParams; } } assert (nOfParams == numSubParamPtrs); /* Set brlens param pointers and tree values */ /* the scheme below keeps trees for the same state and chain together */ nOfTrees = 0; for (k=0; kparamType == P_BRLENS) || (p->paramType == P_TOPOLOGY && p->paramId == TOPOLOGY_PARSIMONY_UNIFORM) || (p->paramType == P_TOPOLOGY && p->paramId == TOPOLOGY_PARSIMONY_CONSTRAINED) || (p->paramType == P_SPECIESTREE)) { /* allocate space for trees and initialize trees */ p->treeIndex = nOfTrees; p->tree = mcmcTree + nOfTrees; nOfTrees++; } } /* Set topology params and associated brlen subparams */ for (k=0; kparamType == P_TOPOLOGY) { if (p->paramId == TOPOLOGY_PARSIMONY_UNIFORM || p->paramId == TOPOLOGY_PARSIMONY_CONSTRAINED) /* pure parsimony topology case */ { /* there is no brlen subparam */ /* so let subparam point to the param itself */ q = p->subParams[0] = p; /* p->tree and p->treeIndex have been set above */ } else { /* first set brlens pointers for any parsimony partitions */ for (i=j=0; inRelParts; i++) { if (modelSettings[p->relParts[i]].parsModelId == YES) { modelSettings[p->relParts[i]].brlens = p; } } /* now proceed with pointer assignment */ q = modelSettings[p->relParts[0]].brlens; n = 0; /* number of stored subParams */ i = 0; /* relevant partition number */ while (i < p->nRelParts) { for (j=0; jsubParams[j]) break; if (j == n && q != p) /* a new tree (brlens) for this topology */ { p->subParams[n++] = q; } q = modelSettings[p->relParts[++i]].brlens; } p->tree = p->subParams[0]->tree; p->treeIndex = p->subParams[0]->treeIndex; } } else if (p->paramType == P_SPECIESTREE) { /* now proceed with pointer assignment */ q = modelSettings[p->relParts[0]].topology; n = 0; /* number of stored subParams */ i = 0; /* relevant partition number */ while (i < p->nRelParts) { for (j=0; jsubParams[j]) break; if (j == n && q != p) /* a new topology for this species tree */ { p->subParams[n++] = q; } q = modelSettings[p->relParts[++i]].topology; } } } /* Check for constraints */ for (k=0; kparamType == P_TOPOLOGY) { if (!strcmp(modelParams[p->relParts[0]].topologyPr, "Constraints")) { for (i=0; inSubParams; i++) { q = p->subParams[i]; q->checkConstraints = YES; } } else { for (i=0; inSubParams; i++) { q = p->subParams[i]; q->checkConstraints = NO; } } } } /* update paramId */ for (k=0; kparamType == P_TOPOLOGY) { if (p->nSubParams > 1) { if (p->paramId == TOPOLOGY_NCL_UNIFORM_HOMO) p->paramId = TOPOLOGY_NCL_UNIFORM_HETERO; else if (p->paramId == TOPOLOGY_NCL_CONSTRAINED_HOMO) p->paramId = TOPOLOGY_NCL_CONSTRAINED_HETERO; else if (p->paramId == TOPOLOGY_NCL_FIXED_HOMO) p->paramId = TOPOLOGY_NCL_FIXED_HETERO; else { MrBayesPrint ("%s A clock tree cannot have more than one set of branch lengths\n", spacer); printf("nparam:%d paramid:%d",p->nSubParams,p->paramId); return (ERROR); } } } } /* finally initialize trees */ for (k=0; kparamType == P_BRLENS) { /* find type of tree */ if (!strcmp(modelParams[p->relParts[0]].brlensPr,"Clock")) isRooted = YES; else isRooted = NO; if (InitializeChainTrees (p, 0, numGlobalChains, isRooted) == ERROR) return (ERROR); } else if (p->paramType == P_SPECIESTREE) { if (InitializeChainTrees (p, 0, numGlobalChains, YES) == ERROR) return (ERROR); } } /* now initialize subparam pointers for relaxed clock models */ /* use nSubParams to point to the next available subParam by first resetting all nSubParams to 0 */ for (k=0; kparamType == P_CPPRATE || p->paramType == P_CPPMULTDEV || p->paramType == P_BRLENS || p->paramType == P_TK02VAR || p->paramType == P_IGRVAR) p->nSubParams = 0; } for (k=0; kparamType == P_CPPEVENTS) { q = modelSettings[p->relParts[0]].cppRate; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].cppMultDev; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } else if (p->paramType == P_TK02BRANCHRATES) { q = modelSettings[p->relParts[0]].tk02var; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } else if (p->paramType == P_IGRBRANCHLENS) { q = modelSettings[p->relParts[0]].igrvar; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } } return (NO_ERROR); } int AreDoublesEqual (MrBFlt x, MrBFlt y, MrBFlt tol) { if ((x - y) < -tol || (x - y) > tol) return (NO); else return (YES); } int ChangeNumChains (int from, int to) { int i, i1, j, k, m, st, nRuns, fromIndex, toIndex, run, chn, *tempIntVals, nCppEventParams, *toEvents, *fromEvents; MCMCMove **tempMoves, *fromMove, *toMove; Tree **tempTrees; MrBFlt *tempVals, **toRateMult, **toPosition, **fromRateMult, **fromPosition, *stdStateFreqsOld; Param *p, *q, *cppEventParams = NULL; Tree **oldMcmcTree, *tree; if(from == to) return (NO_ERROR); /* set new number of chains */ chainParams.numChains = to; nRuns = chainParams.numRuns; numGlobalChains = chainParams.numRuns * chainParams.numChains; /* Do the normal parameters */ /* first save old values */ tempVals = paramValues; paramValues = NULL; tempIntVals = intValues; intValues = NULL; memAllocs[ALLOC_PARAMS] = NO; /* .. and old cpp events parameters */ nCppEventParams = 0; for (i=0; iparamType == P_CPPEVENTS) nCppEventParams++; } cppEventParams = (Param *) SafeCalloc (nCppEventParams, sizeof(Param)); for (i=0; iparamType == P_CPPEVENTS) { cppEventParams[j].nEvents = p->nEvents; p->nEvents = NULL; cppEventParams[j].position = p->position; p->position = NULL; cppEventParams[j].rateMult = p->rateMult; p->rateMult = NULL; j++; } } if (AllocateNormalParams () == ERROR) return (ERROR); /* then fill all params */ FillNormalParams (&globalSeed, 0, numGlobalChains); /* finally overwrite with old values if present */ for (run=0; runparamType == P_CPPEVENTS) { fromIndex = 2*(run*from + chn); toIndex = 2*(run*to + chn); fromEvents = cppEventParams[i1].nEvents[fromIndex]; toEvents = p->nEvents[toIndex]; fromPosition = cppEventParams[i1].position[fromIndex]; toPosition = p->position[toIndex]; fromRateMult = cppEventParams[i1].rateMult[fromIndex]; toRateMult = p->rateMult[toIndex]; for (j=0; j<2*numLocalTaxa; j++) { toEvents[j] = fromEvents[j]; toPosition[j] = (MrBFlt *) SafeRealloc ((void *)toPosition[j], toEvents[j]*sizeof(MrBFlt)); toRateMult[j] = (MrBFlt *) SafeRealloc ((void *)toRateMult[j], toEvents[j]*sizeof(MrBFlt)); for (k=0; k 0) free (tempIntVals); for (i=0; i 0) free (cppEventParams); /* then do the trees (they cannot be done before the parameters because otherwise FillTreeParams will overwrite relaxed clock parameters that need to be saved) */ /* reallocate trees */ tempTrees = (Tree **) SafeCalloc (2*nRuns*from*numTrees, sizeof(Tree *)); for (i=0; i<2*nRuns*from*numTrees; i++) tempTrees[i] = mcmcTree[i]; oldMcmcTree = mcmcTree; mcmcTree = (Tree **) SafeRealloc ((void *)(mcmcTree), (size_t)(2*numGlobalChains*numTrees*sizeof(Tree*))); for (i=0; i<2*nRuns*to*numTrees; i++) mcmcTree[i] = NULL; /* move the old trees over */ for (run=0; run= to) continue; /*Here we move only one tree per chain/state?! Should not we move numTrees??*/ fromIndex = 2*(run*from + chn) * numTrees; toIndex = 2*(run*to + chn) * numTrees; for(k=0;k<2*numTrees;k++) { mcmcTree[toIndex+k] = tempTrees[fromIndex+k]; tempTrees[fromIndex+k] = NULL; } } } /* remove any remaining old trees */ for (i=0; i<2*nRuns*from*numTrees; i++) if (tempTrees[i] != NULL) FreeTree (tempTrees[i]); free (tempTrees); /* now fill in the tree parameters */ for (i=0; iparamType == P_TOPOLOGY) { p->tree += (mcmcTree - oldMcmcTree); /* calculate new address */ for (j=0; jnSubParams; j++) { q = p->subParams[j]; assert( q->paramType==P_BRLENS ); //assert( q->paramType != P_CPPEVENTS && q->paramType != TK02BRANCHRATES); q->tree += (mcmcTree - oldMcmcTree); /* calculate new address */ if (to > from) for (run=0; run 1) sprintf (tree->name, "mcmc.tree%d_%d", p->treeIndex+1, run*to + m +1); else /* if (numTrees == 1) */ sprintf (tree->name, "mcmc.tree_%d", run*to + m +1); } } InitializeChainTrees (q, run*to + from, run*to + to , GetTree (q, 0, 0)->isRooted); } } } else if (p->paramType == P_CPPEVENTS || p->paramType == P_TK02BRANCHRATES || p->paramType == P_IGRBRANCHLENS) p->tree += (mcmcTree - oldMcmcTree); else assert( p->paramType==P_BRLENS || p->tree==NULL ); } /* fill new tree parameters */ if (to > from) { for (run=0; run 0 ) { assert(memAllocs[ALLOC_STDSTATEFREQS] == YES); stdStateFreqsOld=stdStateFreqs; stdStateFreqs = (MrBFlt *) SafeMalloc ((size_t)stdStateFreqsRowSize * 2 * numGlobalChains * sizeof (MrBFlt)); if (!stdStateFreqs) { MrBayesPrint ("%s Problem reallocating stdStateFreqs\n", spacer); return (ERROR); } /* set pointers */ for (k=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; p->stdStateFreqs += stdStateFreqs-stdStateFreqsOld; } for (run=0; run= to) break; fromIndex = 2*(run*from + chn)*stdStateFreqsRowSize; toIndex = 2*(run*to + chn)*stdStateFreqsRowSize; for(k=0;k<2*stdStateFreqsRowSize;k++) { stdStateFreqs[toIndex+k]=stdStateFreqsOld[fromIndex+k]; } } /* set new chains */ FillStdStateFreqs( run*to+from, run*to+to, &globalSeed); } free(stdStateFreqsOld); } /* Do the moves */ /* first allocate space and set up default moves */ tempMoves = moves; moves = NULL; memAllocs[ALLOC_MOVES] = NO; SetMoves (); /* then overwrite with old values if present */ for (i=0; irelProposalProb[toIndex] = fromMove->relProposalProb[fromIndex]; for (j=0; jmoveType->numTuningParams; j++) { toMove->tuningParam[toIndex][j] = fromMove->tuningParam[fromIndex][j]; } } } } } /* and free up space */ for (i=0; iparamType == P_CPPEVENTS) { printf ("Trees before changing number of runs\n"); for (j=0; jnEvents[2*j][k]); for (i1=0; i1nEvents[2*j][k]; i1++) { if (i1 == 0) printf ("( %lf %lf,", p->position[2*j][k], p->rateMult[2*j][k]); else if (i1 == p->nEvents[2*j][k]-1) printf (" %lf %lf)", p->position[2*j][k], p->rateMult[2*j][k]); else printf (" %lf %lf,", p->position[2*j][k], p->rateMult[2*j][k]); } printf("\n"); } for (k=0; k<2*numLocalTaxa; k++) { printf ("%d -- %d:", k, p->nEvents[2*j][k]); for (i1=0; i1nEvents[2*j+1][k]; i1++) { if (i1 == 0) printf ("( %lf %lf,", p->position[2*j+1][k], p->rateMult[2*j+1][k]); else if (i1 == p->nEvents[2*j][k]-1) printf (" %lf %lf)", p->position[2*j+1][k], p->rateMult[2*j+1][k]); else printf (" %lf %lf,", p->position[2*j+1][k], p->rateMult[2*j+1][k]); } printf("\n"); } } } } #endif /* set new number of runs */ chainParams.numRuns = to; nChains = chainParams.numChains; numGlobalChains = chainParams.numRuns * chainParams.numChains; /* do the trees, free tree's memory if we reduce number of trees. */ for (i=to*2*nChains*numTrees; iparamType == P_CPPEVENTS) { p->nEvents = (int **) SafeRealloc ((void *)p->nEvents, 2*numGlobalChains*sizeof (int *)); p->nEvents[0] = (int *) SafeRealloc ((void *)p->nEvents[0], 2*numGlobalChains*(2*numLocalTaxa)*sizeof (int)); for (i=1; i<2*numGlobalChains; i++) p->nEvents[i] = p->nEvents[i-1] + (2*numLocalTaxa); if (from > to) { for (j=numGlobalChains; jposition[2*j+0][k]); p->position[2*j+0][k] = NULL; free (p->rateMult[2*j+0][k]); p->rateMult[2*j+0][k] = NULL; } } } p->position = (MrBFlt ***) SafeRealloc ((void *)p->position, 2*numGlobalChains*sizeof (MrBFlt **)); p->position[0] = (MrBFlt **) SafeRealloc ((void *)p->position[0], 2*numGlobalChains*(2*numLocalTaxa)*sizeof (MrBFlt *)); for (i=1; i<2*numGlobalChains; i++) p->position[i] = p->position[i-1] + (2*numLocalTaxa); p->rateMult = (MrBFlt ***) SafeRealloc ((void *)p->rateMult, 2*numGlobalChains*sizeof (MrBFlt **)); p->rateMult[0] = (MrBFlt **) SafeRealloc ((void *)p->rateMult[0], 2*numGlobalChains*(2*numLocalTaxa)*sizeof (MrBFlt *)); for (i=1; i<2*numGlobalChains; i++) p->rateMult[i] = p->rateMult[i-1] + (2*numLocalTaxa); if (to > from) { for (j=from*nChains; jnEvents[2*j+0][k] = 0; p->position[2*j+0][k] = NULL; p->rateMult[2*j+0][k] = NULL; p->nEvents[2*j+1][k] = 0; p->position[2*j+1][k] = NULL; p->rateMult[2*j+1][k] = NULL; } } } } } /* and finally the normal parameters */ oldParamValues = paramValues; paramValues = (MrBFlt *) SafeRealloc ((void *) paramValues, paramValsRowSize * 2 * numGlobalChains * sizeof (MrBFlt)); oldintValues = intValues; intValues = (int *) SafeRealloc ((void *) intValues, intValsRowSize * 2 * numGlobalChains * sizeof (int)); if (paramValues == NULL) { memAllocs[ALLOC_PARAMVALUES] = NO; MrBayesPrint ("%s Problem reallocating paramValues\n", spacer); return (ERROR); } for (i=0; i from) FillNormalParams (&globalSeed, from*nChains, to*nChains); /* now fill in the tree parameters */ for (i=0; iparamType == P_TOPOLOGY) { p->tree += (mcmcTree - oldMcmcTree); /* calculate new address */ for (j=0; jnSubParams; j++) { q = p->subParams[j]; assert( q->paramType==P_BRLENS ); q->tree += (mcmcTree - oldMcmcTree); /* calculate new address */ InitializeChainTrees (q, from*nChains, to*nChains, GetTree (q, 0, 0)->isRooted); } } else if (p->paramType == P_CPPEVENTS || p->paramType == P_TK02BRANCHRATES || p->paramType == P_IGRBRANCHLENS) p->tree += (mcmcTree - oldMcmcTree); } FillTreeParams (&globalSeed, from*nChains, to*nChains); /* fix stationary frequencies for standard data */ if( stdStateFreqsRowSize > 0 ) { assert(memAllocs[ALLOC_STDSTATEFREQS] == YES); stdStateFreqsOld=stdStateFreqs; stdStateFreqs = (MrBFlt *) SafeRealloc ((void *) stdStateFreqs, stdStateFreqsRowSize * 2 * numGlobalChains * sizeof (MrBFlt)); if (!stdStateFreqs) { MrBayesPrint ("%s Problem reallocating stdStateFreqs\n", spacer); return (ERROR); } /* set pointers */ for (k=n=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; p->stdStateFreqs += stdStateFreqs-stdStateFreqsOld; } FillStdStateFreqs( from*nChains, to*nChains, &globalSeed); } /* do the moves */ for (i=0; imoveType; moves[i]->tuningParam = (MrBFlt **) SafeRealloc ((void *) moves[i]->tuningParam, (size_t) (numGlobalChains * sizeof (MrBFlt *))); moves[i]->tuningParam[0] = (MrBFlt *) SafeRealloc ((void *) moves[i]->tuningParam[0], (size_t) (numGlobalChains * mvt->numTuningParams * sizeof (MrBFlt))); for (j=1; jtuningParam[j] = moves[i]->tuningParam[0] + j * mvt->numTuningParams; moves[i]->relProposalProb = (MrBFlt *) SafeRealloc ((void *) moves[i]->relProposalProb, (size_t) (4 * numGlobalChains * sizeof (MrBFlt))); moves[i]->cumProposalProb = moves[i]->relProposalProb + numGlobalChains; moves[i]->targetRate = moves[i]->relProposalProb + 2*numGlobalChains; moves[i]->lastAcceptanceRate = moves[i]->relProposalProb + 3*numGlobalChains; moves[i]->nAccepted = (int *) SafeRealloc ((void *) moves[i]->nAccepted, (size_t) (5 * numGlobalChains * sizeof (int))); moves[i]->nTried = moves[i]->nAccepted + numGlobalChains; moves[i]->nBatches = moves[i]->nAccepted + 2*numGlobalChains; moves[i]->nTotAccepted = moves[i]->nAccepted + 3*numGlobalChains; moves[i]->nTotTried = moves[i]->nAccepted + 4*numGlobalChains; /* initialize all values to default */ for (j=0; jnAccepted[j] = 0; moves[i]->nTried[j] = 0; moves[i]->nBatches[j] = 0; moves[i]->nTotAccepted[j] = 0; moves[i]->nTotTried[j] = 0; moves[i]->relProposalProb[j] = mvt->relProposalProb; moves[i]->cumProposalProb[j] = 0.0; moves[i]->lastAcceptanceRate[j] = 0.0; for (k=0; knumTuningParams; k++) moves[i]->tuningParam[j][k] = mvt->tuningParam[k]; moves[i]->targetRate[j] = mvt->targetRate; } } #if 0 for (i=0; iparamType == P_CPPEVENTS) { printf ("Trees after changing number of runs\n"); for (j=0; jnEvents[2*j][k]); assert (p->nEvents[2*j] >= 0); for (i1=0; i1nEvents[2*j][k]; i1++) { if (i1 == 0) printf ("( %lf %lf,", p->position[2*j][k], p->rateMult[2*j][k]); else if (i1 == p->nEvents[2*j][k]-1) printf (" %lf %lf)", p->position[2*j][k], p->rateMult[2*j][k]); else printf (" %lf %lf,", p->position[2*j][k], p->rateMult[2*j][k]); } printf("\n"); } for (k=0; k<2*numLocalTaxa; k++) { printf ("%d -- %d:", k, p->nEvents[2*j+1][k]); assert (p->nEvents[2*j+1] >= 0); for (i1=0; i1nEvents[2*j+1][k]; i1++) { if (i1 == 0) printf ("( %lf %lf,", p->position[2*j+1][k], p->rateMult[2*j+1][k]); else if (i1 == p->nEvents[2*j][k]-1) printf (" %lf %lf)", p->position[2*j+1][k], p->rateMult[2*j+1][k]); else printf (" %lf %lf,", p->position[2*j+1][k], p->rateMult[2*j+1][k]); } printf ("\n"); } } } } #endif return (NO_ERROR); } /*----------------------------------------------------------- | | CheckCharCodingType: check if character is parsimony- | informative, variable, or constant | -----------------------------------------------------------*/ void CheckCharCodingType (Matrix *m, CharInfo *ci) { int i, j, k, x, n1[10], n2[10], largest, smallest, numPartAmbig, numConsidered, numInformative, lastInformative=0, uniqueBits, newPoss, oldPoss, combinations[2048], *newComb, *oldComb, *tempComb; extern int NBits (int x); /* set up comb pointers */ oldComb = combinations; newComb = oldComb + 1024; /* set counters to 0 */ numPartAmbig = numConsidered = 0; /* set variable and informative to yes */ ci->variable = ci->informative = YES; /* set constant to no and state counters to 0 for all states */ for (i=0; i<10; i++) { ci->constant[i] = NO; n1[i] = n2[i] = 0; } for (i=0; inRows; i++) { /* retrieve character */ x = (int) m->origin[m->column + i*m->rowSize]; /* add it to counters if not all ambiguous */ if (NBits(x) < ci->nStates) { numConsidered++; if (NBits(x) > 1) numPartAmbig++; for (j=0; j<10; j++) { if (((1<constant[i] = YES; ci->variable = ci->informative = NO; } } /* return if variable is no or if a restriction site char */ if (ci->variable == NO || ci->dType == RESTRICTION) return; /* the character is either (variable and uninformative) or informative */ /* first consider unambiguous characters */ /* find smallest and largest unambiguous state for this character */ smallest = 9; largest = 0; for (i=0; i<10; i++) { if (n2[i] > 0) { if (i < smallest) smallest = i; if (i > largest) largest = i; } } /* count the number of informative states in the unambiguous codings */ for (i=numInformative=0; i<10; i++) { if (ci->cType == ORD && n2[i] > 0 && i != smallest && i != largest) { numInformative++; lastInformative = i; } else if (n2[i] > 1) { numInformative++; lastInformative = i; } } /* set informative */ if (numInformative > 1) ci->informative = YES; else ci->informative = NO; /* we can return now unless informative is no and numPartAmbig is not 0 */ if (!(numPartAmbig > 0 && ci->informative == NO)) return; /* check if partially ambiguous observations make this character informative after all */ /* first set the bits for the taken states */ x = 0; for (i=0; i<10; i++) { if (n2[i] > 0 && i != lastInformative) x |= (1<nRows; i++) { x = (int) m->origin[m->column + i*m->rowSize]; /* if partambig */ if (NBits(x) > 1 && NBits(x) < ci->nStates) { /* remove lastInformative */ x &= !(1<nRows) ci->informative = YES; return; } /*----------------------------------------------------------- | | CheckModel: check model and warn user if strange things | are discovered. | -------------------------------------------------------------*/ int CheckModel (void) { int i, j, k, answer; Tree *t; TreeNode *p; /* there should only be one calibrated tree */ for (i=0; iisCalibrated == YES) break; } if (i < numTrees) { if (!strcmp(modelParams[t->relParts[0]].clockRatePr, "Fixed") && AreDoublesEqual(modelParams[t->relParts[0]].clockRateFix, 1.0, 1E-6) == YES) { MrBayesPrint("%s WARNING: You have calibrated the tree but the clock rate is fixed to 1.0.\n", spacer); MrBayesPrint("%s This means that time is measured in expected changes per time unit. If\n", spacer); MrBayesPrint("%s the calibrations use a different time scale, you need to modify the model\n", spacer); MrBayesPrint("%s by introducing a prior for the clock rate ('prset clockratepr').\n", spacer); if (noWarn == NO) { answer = WantTo("Do you want to continue with the run regardless"); if (answer == YES) { MrBayesPrint("%s Continuing with the run...\n\n", spacer); } else { MrBayesPrint("%s Stopping the run...\n\n", spacer); return (ERROR); } } } } /* check coalescence model */ for (i=0; irelParts[0]].clockPr,"Coalescence") || !strcmp(modelParams[t->relParts[0]].clockPr,"Speciestreecoalescence")) && AreDoublesEqual(modelParams[t->relParts[0]].clockRateFix, 1.0, 1E-6) == YES) { MrBayesPrint("%s WARNING: You are using a coalescent model but the clock rate is fixed to 1.0.\n", spacer); MrBayesPrint("%s This is likely to be incorrect unless you have set the population size prior\n", spacer); MrBayesPrint("%s ('prset popsizepr') to reflect an appropriate prior on theta. \n", spacer); if (noWarn == NO) { answer = WantTo("Do you want to continue with the run regardless"); if (answer == YES) { MrBayesPrint("%s Continuing with the run...\n\n", spacer); } else { MrBayesPrint("%s Stopping the run...\n\n", spacer); return (ERROR); } } } } /* Check consistency of best model. First we guarantee that if one topology has a species tree prior, then all topologies have the same prior. Then we make sure that all clock trees have a coalescence prior. */ j = 0; for (i=0; i 0) { if (j != numCurrentDivisions) { MrBayesPrint("%s ERROR: If one gene tree has a speciestree prior then all\n", spacer); MrBayesPrint("%s gene trees must have the same prior.\n", spacer); return (ERROR); } for (i=0; irelParts[0]].clockPr,"Speciestreecoalescence") != 0) { MrBayesPrint("%s ERROR: All gene trees must have a speciestreecoalescence prior\n", spacer); MrBayesPrint("%s if they fold into a species tree.\n", spacer); return (ERROR); } if (t->isCalibrated == YES) { for (k=0; knNodes-1; k++) { p = t->allDownPass[k]; if (p->calibration != NULL) { MrBayesPrint("%s ERROR: Gene trees cannot be individually calibrated\n", spacer); MrBayesPrint("%s if they fold into a species tree.\n", spacer); return (ERROR); } } } } } else { for (i=0; iisCalibrated == YES && strcmp(modelParams[t->relParts[0]].clockPr, "Uniform") != 0) { for (k=0; knNodes-1; k++) { p = t->allDownPass[k]; if (p->calibration != NULL) { MrBayesPrint("%s ERROR: MrBayes does not yet suppport calibration of tips or\n", spacer); MrBayesPrint("%s interior nodes under the %s clock prior.\n", spacer, modelParams[t->relParts[0]].clockPr); return (ERROR); } } } } } return NO_ERROR; } /*----------------------------------------------------------- | | CheckExpandedModels: check data partitions that have | the codon or doublet model specified | -------------------------------------------------------------*/ int CheckExpandedModels (void) { int c, d, i, t, s, s1, s2, s3, whichNuc, uniqueId, numCharsInPart, firstChar, lastChar, contiguousPart, badBreak, badExclusion, nGone, nuc1, nuc2, nuc3, foundStopCodon, posNucs1[4], posNucs2[4], posNucs3[4], oneGoodCodon, foundUnpaired, nPair, allCheckedOut; char *tempStr; int tempStrSize=100; ModelParams *mp; tempStr = (char *) SafeMalloc((size_t) (tempStrSize * sizeof(char))); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } /* first, set charId to 0 for all characters */ for (i=0; idataType == DNA || mp->dataType == RNA) { if (!strcmp(mp->nucModel,"Codon") || !strcmp(mp->nucModel,"Protein")) { /* start check that the codon model is appropriate for this partition */ /* find first character in this partition */ for (c=0; c=0; c--) { if (partitionId[c][partitionNum] == d+1) break; } lastChar = c; /*printf (" last character = %d\n", lastChar);*/ /* check that the number of characters in partition is divisible by 3 */ numCharsInPart = 0; for (c=firstChar; c<=lastChar; c++) { if (charInfo[c].isExcluded == YES || partitionId[c][partitionNum] != d+1) continue; numCharsInPart++; } /*printf (" numCharsInPart = %d\n", numCharsInPart);*/ if (numCharsInPart % 3 != 0) { if (numCurrentDivisions == 1) { MrBayesPrint ("%s The number of characters is not divisible by three.\n", spacer); MrBayesPrint ("%s You specified a %s model which requires triplets\n", spacer, mp->nucModel); MrBayesPrint ("%s However, you only have %d characters.\n", spacer, numCharsInPart); } else { MrBayesPrint ("%s The number of characters in partition %d is not\n", spacer, d+1); MrBayesPrint ("%s divisible by three. You specified a %s model\n", spacer, mp->nucModel); MrBayesPrint ("%s which requires triplets. \n", spacer); MrBayesPrint ("%s However, you only have %d characters in this \n", spacer, numCharsInPart); MrBayesPrint ("%s partition \n", spacer); } free (tempStr); return (ERROR); } /* check that all of the characters in the partition are contiguous */ contiguousPart = YES; for (c=firstChar; c<=lastChar; c++) { if (partitionId[c][partitionNum] != d+1) contiguousPart = NO; } if (contiguousPart == NO) { MrBayesPrint ("%s Partition %d is not contiguous. You specified that\n", spacer, d+1); MrBayesPrint ("%s a %s model be used for this partition. However, there\n", spacer, mp->nucModel); MrBayesPrint ("%s is another partition that is between some of the characters\n", spacer); MrBayesPrint ("%s in this partition. \n", spacer); free (tempStr); return (ERROR); } /* check that there is not a break inside a triplet of characters */ badBreak = NO; whichNuc = 0; for (c=firstChar; c<=lastChar; c++) { whichNuc++; if (charInfo[c].bigBreakAfter == YES && whichNuc != 3) badBreak = YES; if (whichNuc == 3) whichNuc = 0; } if (badBreak == YES) { MrBayesPrint ("%s You specified a databreak inside of a coding triplet.\n", spacer); MrBayesPrint ("%s This is a problem, as you imply that part of the codon\n", spacer); MrBayesPrint ("%s lies in one gene and the remainder in another gene. \n", spacer); free (tempStr); return (ERROR); } /* make certain excluded characters are in triplets */ badExclusion = NO; whichNuc = nGone = 0; for (c=firstChar; c<=lastChar; c++) { whichNuc++; if (charInfo[c].isExcluded == YES) nGone++; if (whichNuc == 3) { if (nGone == 1 || nGone == 2) badExclusion = YES; whichNuc = nGone = 0; } } if (badExclusion == YES) { MrBayesPrint ("%s In excluding characters, you failed to remove all of the\n", spacer); MrBayesPrint ("%s sites of at least one codon. If you exclude sites, make \n", spacer); MrBayesPrint ("%s certain to exclude all of the sites in the codon(s). \n", spacer); free (tempStr); return (ERROR); } /* check that there are no stop codons */ foundStopCodon = NO; for (c=firstChar; c<=lastChar; c+=3) { if (charInfo[c].isExcluded == NO) { for (t=0; tcodon[s1*16 + s2*4 + s3] != 21) oneGoodCodon = YES; } s++; } } } if (oneGoodCodon == NO) { foundStopCodon = YES; MrBayesPrint ("%s Stop codon: taxon %s, sites %d to %d (%c%c%c, %s code)\n", spacer, taxaNames[t], c+1, c+3, WhichNuc (nuc1), WhichNuc (nuc2), WhichNuc (nuc3), mp->geneticCode); } } } } if (foundStopCodon == YES) { MrBayesPrint ("%s At least one stop codon was found. Stop codons are not\n", spacer); MrBayesPrint ("%s allowed under the codon models. \n", spacer); free (tempStr); return (ERROR); } /* everything checks out. Now we can initialize charId */ whichNuc = 0; for (c=firstChar; c<=lastChar; c++) { whichNuc++; charInfo[c].charId = uniqueId; if (whichNuc == 3) { whichNuc = 0; uniqueId++; } } allCheckedOut++; /* end check that the codon model is appropriate for this partition */ } else if (!strcmp(mp->nucModel,"Doublet")) { /* start check that the doublet model is appropriate for this partition */ /* Check that pairsId does not equal 0 for any of the characters in the partition. If it does, then this means that at least one site was not appropriately paired. Remember, that pairsId is initialized 1, 2, 3, ... for the first pair, second pair, etc. Also, check that every pair is only represented two times. */ foundUnpaired = NO; for (c=0; c= 0 && nuc2 >= 0) { charInfo[nuc1].charId = charInfo[nuc2].charId = uniqueId; uniqueId++; } else { MrBayesPrint ("%s Weird doublet problem in partition %d.\n", spacer, d+1); free (tempStr); return (ERROR); } } } allCheckedOut++; /* end check that the doublet model is appropriate for this partition */ } } } /* if (allCheckedOut > 0) MrBayesPrint ("%s Codon/Doublet models successfully checked\n", spacer); */ # if 0 for (c=0; crelParts[0]]; if (p->paramType == P_SPECIESTREE) nTaxa = numSpecies; else nTaxa = numLocalTaxa; /* figure out whether the trees are clock */ if (!strcmp(mp->brlensPr,"Clock")) isClock = YES; else isClock = NO; /* figure out whether the trees are calibrated */ if (!strcmp(mp->brlensPr,"Clock") && (strcmp(mp->nodeAgePr,"Calibrated") == 0 || strcmp(mp->clockRatePr,"Fixed") != 0 || (strcmp(mp->clockRatePr, "Fixed") == 0 && AreDoublesEqual(mp->clockRateFix, 1.0, 1E-6) == NO))) isCalibrated = YES; else isCalibrated = NO; if (p->checkConstraints == YES) { for (i=0; iactiveConstraints[i] == YES && definedConstraintsType[i] == HARD ) numActiveHardConstraints++; } } /* allocate space for and construct the trees */ /* NOTE: The memory allocation scheme used here must match GetTree and GetTreeFromIndex */ for (i=from; itreeIndex + 2*i*numTrees; if (*treeHandle) free(*treeHandle); if ((*treeHandle = AllocateTree (nTaxa)) == NULL) { MrBayesPrint ("%s Problem allocating mcmc trees\n", spacer); return (ERROR); } treeHandle = mcmcTree + p->treeIndex + (2*i + 1)*numTrees; if (*treeHandle) free(*treeHandle); if ((*treeHandle = AllocateTree (nTaxa)) == NULL) { MrBayesPrint ("%s Problem allocating mcmc trees\n", spacer); return (ERROR); } } /* initialize the trees */ for (i=from; i 1) sprintf (tree->name, "mcmc.tree%d_%d", p->treeIndex+1, i+1); else /* if (numTrees == 1) */ sprintf (tree->name, "mcmc.tree_%d", i+1); tree->nRelParts = p->nRelParts; tree->relParts = p->relParts; tree->isRooted = isRooted; tree->isClock = isClock; tree->isCalibrated = isCalibrated; if (p->paramType == P_SPECIESTREE) { tree->nNodes = 2*numSpecies; tree->nIntNodes = numSpecies - 1; } else if (tree->isRooted == YES) { tree->nNodes = 2*numLocalTaxa; tree->nIntNodes = numLocalTaxa - 1; } else /* if (tree->isRooted == NO) */ { tree->nNodes = 2*numLocalTaxa - 2; tree->nIntNodes = numLocalTaxa - 2; } if (p->checkConstraints == YES) { tree->checkConstraints = YES; tree->nConstraints = mp->numActiveConstraints; tree->nLocks = numActiveHardConstraints; tree->constraints = mp->activeConstraints; } else { tree->checkConstraints = NO; tree->nConstraints = tree->nLocks = 0; tree->constraints = NULL; } } } return (NO_ERROR); } /*----------------------------------------------------------- | | CompressData: compress original data matrix | -------------------------------------------------------------*/ int CompressData (void) { int a, c, d, i, j, k, t, col[3], isSame, newRow, newColumn, *isTaken, *tempSitesOfPat, *tempChar; SafeLong *tempMatrix; ModelInfo *m; ModelParams *mp; # if defined DEBUG_COMPRESSDATA if (PrintMatrix() == ERROR) goto errorExit; getchar(); # endif /* set all pointers that will be allocated locally to NULL */ isTaken = NULL; tempMatrix = NULL; tempSitesOfPat = NULL; tempChar = NULL; /* allocate indices pointing from original to compressed matrix */ if (memAllocs[ALLOC_COMPCOLPOS] == YES) { free (compColPos); compColPos = NULL; memAllocs[ALLOC_COMPCOLPOS] = NO; } compColPos = (int *)SafeMalloc((size_t) (numChar * sizeof(int))); if (!compColPos) { MrBayesPrint ("%s Problem allocating compColPos (%d)\n", spacer, numChar * sizeof(int)); goto errorExit; } for (i=0; icompMatrixStart = newColumn; /* set compressed character offset for this division */ m->compCharStart = numCompressedChars; /* set number of compressed characters to 0 for this division */ m->numChars = 0; /* find the number of original characters per model site */ m->nCharsPerSite = 1; if (mp->dataType == DNA || mp->dataType == RNA) { if (!strcmp(mp->nucModel, "Doublet")) m->nCharsPerSite = 2; if (!strcmp(mp->nucModel, "Codon") || !strcmp(mp->nucModel, "Protein")) m->nCharsPerSite = 3; } /* sort and compress the characters for this division */ for (c=0; cnCharsPerSite > 1) { j = 1; if (charInfo[c].charId == 0) { MrBayesPrint("%s Character %d is not properly defined\n", spacer, c+1); goto errorExit; } for (i=c+1; i= m->nCharsPerSite) { MrBayesPrint("%s Too many matches in charId (division %d char %d)\n", spacer, d, numCompressedChars); goto errorExit; } else { col[j++] = i; isTaken[i] = YES; } } } if (j != m->nCharsPerSite) { MrBayesPrint ("%s Too few matches in charId (division %d char %d)\n", spacer, d, numCompressedChars); goto errorExit; } } /* add character to temporary matrix in column(s) at newColumn */ for (t=newRow=0; tnCharsPerSite; k++) { tempMatrix[pos(newRow,newColumn+k,numLocalChar)] = matrix[pos(t,col[k],numChar)]; } newRow++; } /* is it unique? */ isSame = NO; if (mp->dataType != CONTINUOUS) { for (i=m->compMatrixStart; inCharsPerSite) { isSame = YES; for (j=0; jnCharsPerSite; k++) if (tempMatrix[pos(j,newColumn+k,numLocalChar)] != tempMatrix[pos(j,i+k,numLocalChar)]) { isSame = NO; break; } if (isSame == NO) break; } if (isSame == YES) break; } } /* if subject to data augmentation, it is always unique */ if (!strcmp(mp->augmentData, "Yes")) { for (k=0; knCharsPerSite; k++) { if (charInfo[col[k]].isMissAmbig == YES) isSame = NO; } } if (isSame == NO) { /* if it is unique then it should be added */ tempSitesOfPat[numCompressedChars] = 1; for (k=0; knCharsPerSite; k++) { compColPos[col[k]] = newColumn + k; compCharPos[col[k]] = numCompressedChars; tempChar[newColumn + k] = col[k]; } newColumn+=m->nCharsPerSite; m->numChars++; numCompressedChars++; } else { /* if it is not unique then simply update tempSitesOfPat */ /* calculate compressed character position and put it into a */ /* (i points to compressed column position) */ a = m->compCharStart + ((i - m->compMatrixStart) / m->nCharsPerSite); tempSitesOfPat[a]++; for (k=0; knCharsPerSite; k++) { compColPos[col[k]] = i; compCharPos[col[k]] = a; /* tempChar (pointing from compressed to uncompresed) */ /* can only be set for first pattern */ } } } /* next character */ /* check that the partition has at least a single character */ if (m->numChars <= 0) { MrBayesPrint ("%s You must have at least one site in a partition. Partition %d\n", spacer, d+1); MrBayesPrint ("%s has %d site patterns.\n", spacer, m->numChars); goto errorExit; } m->compCharStop = m->compCharStart + m->numChars; m->compMatrixStop = newColumn; } /* next division */ compMatrixRowSize = newColumn; /* now we know the size, so we can allocate space for the compressed matrix ... */ if (memAllocs[ALLOC_COMPMATRIX] == YES) { free (compMatrix); compMatrix = NULL; memAllocs[ALLOC_COMPMATRIX] = NO; } compMatrix = (SafeLong *) SafeCalloc (compMatrixRowSize * numLocalTaxa, sizeof(SafeLong)); if (!compMatrix) { MrBayesPrint ("%s Problem allocating compMatrix (%d)\n", spacer, compMatrixRowSize * numLocalTaxa * sizeof(SafeLong)); goto errorExit; } memAllocs[ALLOC_COMPMATRIX] = YES; if (memAllocs[ALLOC_NUMSITESOFPAT] == YES) { free (numSitesOfPat); numSitesOfPat = NULL; memAllocs[ALLOC_NUMSITESOFPAT] = NO; } numSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!numSitesOfPat) { MrBayesPrint ("%s Problem allocating numSitesOfPat (%d)\n", spacer, numCompressedChars * sizeof(MrBFlt)); goto errorExit; } memAllocs[ALLOC_NUMSITESOFPAT] = YES; if (memAllocs[ALLOC_ORIGCHAR] == YES) { free (origChar); origChar = NULL; memAllocs[ALLOC_ORIGCHAR] = NO; } origChar = (int *)SafeMalloc((size_t) (compMatrixRowSize * sizeof(int))); if (!origChar) { MrBayesPrint ("%s Problem allocating origChar (%d)\n", spacer, numCompressedChars * sizeof(int)); goto errorExit; } memAllocs[ALLOC_ORIGCHAR] = YES; /* ... and copy the data there */ for (i=0; i numCurrentDivisions) { MrBayesPrint ("%s Partition delimiter is too large\n", spacer); return (ERROR); } if (fromI == -1) fromI = tempInt; else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; for (i=fromI-1; i=0; i--) { if (activeParts[i] == YES) { lastActive = i; break; } } /* MrBayesPrint ("\n"); */ if (numCurrentDivisions == 1) MrBayesPrint ("%s Successfully set likelihood model parameters\n", spacer); else { if (nApplied == numCurrentDivisions || nApplied == 0) { MrBayesPrint ("%s Successfully set likelihood model parameters to all\n", spacer); MrBayesPrint ("%s applicable data partitions \n", spacer); } else { MrBayesPrint ("%s Successfully set likelihood model parameters to\n", spacer); if (nApplied == 1) MrBayesPrint ("%s partition", spacer); else MrBayesPrint ("%s partitions", spacer); for (i=0; i 1) MrBayesPrint (" and %d", i+1); else MrBayesPrint (" %d", i+1); if (nApplied > 2 && i != lastActive) MrBayesPrint (","); } } MrBayesPrint (" (if applicable)\n"); } } if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); return (NO_ERROR); } int DoLsetParm (char *parmName, char *tkn) { int i, j, tempInt, nApplied; char tempStr[100]; if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before the model can be defined\n", spacer); return (ERROR); } if (inValidCommand == YES) { for (i=0; i numCurrentDivisions) { MrBayesPrint ("%s Partition delimiter is too large\n", spacer); return (ERROR); } if (fromI == -1) fromI = tempInt; else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; for (i=fromI-1; i= 2 && tempInt < MAX_GAMMA_CATS) { nApplied = NumActiveParts (); for (i=0; i= 1 && tempInt <= 1000) { nApplied = NumActiveParts (); for (i=0; i= 2 && tempInt < MAX_GAMMA_CATS) { nApplied = NumActiveParts (); for (i=0; i= 2 && tempInt < MAX_GAMMA_CATS) { nApplied = NumActiveParts (); for (i=0; i= 2 && tempInt < MAX_GAMMA_CATS) { nApplied = NumActiveParts (); for (i=0; i)' instead.\n", spacer); return (ERROR); } /* set Parsmodel (useParsModel) *******************************************************/ else if (!strcmp(parmName, "Parsmodel")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i$(,)= -- apply to run and chain $(,)= -- apply to chain for all runs $(,)= -- apply to all chains of run $prob(,)= -- change relative proposal probability $targetrate(,)= -- change target acc rate for autotuning the parsing is complicated by the fact that the move name can look something like: eTBR(Tau{all}) eTBR(Tau{1,4,5}) so we need to assemble the move name from several tokens that are parsed out separately; here we receive only the first part (before the left parenthesis) */ /* copy to local move name */ SafeStrcpy(&tempName, tkn); mv = NULL; foundComma = foundEqual = NO; expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else return (ERROR); } else if (expecting == Expecting(ALPHA)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat(&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else { /* we have a parameter name; now find the parameter name, case insensitive */ SafeStrcpy(&localTkn, tkn); for (i=0; i<(int)strlen(localTkn); i++) localTkn[i] = tolower(localTkn[i]); nMatches = j = 0; for (i=0; imoveType->numTuningParams; i++) { SafeStrcpy(&temp, mv->moveType->shortTuningName[i]); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strncmp(localTkn,temp,strlen(localTkn)) == 0) { j = i; nMatches++; } } if (strncmp(localTkn,"prob",strlen(localTkn)) == 0) { j = -1; nMatches++; } else if (strncmp(localTkn,"targetrate",strlen(localTkn)) == 0) { j = -2; nMatches++; } if (nMatches == 0) { MrBayesPrint ("%s Could not find move parameter to change '%s'\n", spacer, localTkn); return (ERROR); } else if (nMatches > 1) { MrBayesPrint ("%s Several move parameters matched the abbreviated name '%s'\n", spacer, localTkn); return (ERROR); } if (j == -1) { theValue = mv->relProposalProb; theValueMin = 0.0; theValueMax = 1000.0; jump = 1; } else if (j == -2) { theValue = mv->targetRate; theValueMin = 0.10; theValueMax = 0.70; jump = 1; } else { theValue = &mv->tuningParam[0][j]; theValueMin = mv->moveType->minimum[j]; theValueMax = mv->moveType->maximum[j]; jump = mv->moveType->numTuningParams; } chainIndex = -1; runIndex = -1; expecting = Expecting(LEFTPAR) | Expecting(EQUALSIGN); } } else if (expecting == Expecting(LEFTCURL) || expecting == Expecting(RIGHTCURL)) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else if (expecting == Expecting(DOLLAR)) { /* we know that the name is complete now; find the move by its name, case insensitive */ SafeStrcpy(&localTkn, tempName); j=(int)strlen(localTkn); for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strncmp(temp,localTkn,strlen(localTkn)) == 0) { j = i; nMatches++; } } if (nMatches == 0) { MrBayesPrint ("%s Could not find move '%s'\n", spacer, localTkn); return (ERROR); } else if (nMatches > 1) { MrBayesPrint ("%s Several moves matched the abbreviated name '%s'\n", spacer, localTkn); return (ERROR); } else mv = moves[j]; foundComma = foundEqual = NO; expecting = Expecting(ALPHA); } else if (expecting == Expecting(LEFTPAR)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else /* if (mv != NULL) */ { /* we will be reading in run and chain indices */ expecting = Expecting(NUMBER) | Expecting(COMMA); } } else if (expecting == Expecting(NUMBER)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else if (foundEqual == YES) { sscanf (tkn, "%lf", &tempFloat); if (tempFloat < theValueMin || tempFloat > theValueMax) { MrBayesPrint ("%s The value is out of range (min = %lf; max = %lf)\n", spacer, theValueMin, theValueMax); return (ERROR); } if (runIndex == -1 && chainIndex == -1) { for (i=0; i= 0) { theValue += chainIndex*jump; for (i=0; i= 0 && chainIndex == -1) { theValue += runIndex*chainParams.numChains*jump; for (i=0; i= 0 && chainIndex >= 0) */ { theValue[runIndex*chainParams.numChains*jump+chainIndex*jump] = tempFloat; } expecting = Expecting (PARAMETER) | Expecting(SEMICOLON); } else /* if (foundEqual == NO) */ { sscanf (tkn, "%d", &tempInt); if (foundComma == NO) { if (tempInt <= 0 || tempInt > chainParams.numRuns) { MrBayesPrint ("%s Run index is out of range (min=1; max=%d)\n", spacer, chainParams.numRuns); return (ERROR); } runIndex = tempInt - 1; expecting = Expecting(COMMA); } else { if (tempInt <= 0 || tempInt > chainParams.numChains) { MrBayesPrint ("%s Chain index is out of range (min=1; max=%d)\n", spacer, chainParams.numChains); return (ERROR); } chainIndex = tempInt - 1; expecting = Expecting(RIGHTPAR); } } } else if (expecting == Expecting(COMMA)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else { /* we will be reading in chain index, if present */ foundComma = YES; expecting = Expecting(RIGHTPAR) | Expecting(NUMBER); } } else if (expecting == Expecting(RIGHTPAR)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else expecting = Expecting(EQUALSIGN); } else if (expecting == Expecting(EQUALSIGN)) { foundEqual = YES; expecting = Expecting(NUMBER); } else return (ERROR); SafeFree ((void **)&temp); SafeFree ((void **)&localTkn); return (NO_ERROR); } int DoPrset (void) { int i, nApplied, lastActive=0; nApplied = NumActiveParts (); for (i=numCurrentDivisions; i>=0; i--) { if (activeParts[i] == YES) { lastActive = i; break; } } if (numCurrentDivisions == 1) MrBayesPrint ("%s Successfully set prior model parameters\n", spacer); else { if (nApplied == numCurrentDivisions || nApplied == 0) { MrBayesPrint ("%s Successfully set prior model parameters to all\n", spacer); MrBayesPrint ("%s applicable data partitions \n", spacer); } else { MrBayesPrint ("%s Successfully set prior model parameters to\n", spacer); if (nApplied == 1) MrBayesPrint ("%s partition", spacer); else MrBayesPrint ("%s partitions", spacer); for (i=0; i 1) MrBayesPrint (" and %d", i+1); else MrBayesPrint (" %d", i+1); if (nApplied > 2 && i != lastActive) MrBayesPrint (","); } } MrBayesPrint (" (if applicable)\n"); } } if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); return (NO_ERROR); } int DoPrsetParm (char *parmName, char *tkn) { int i, j, k, tempInt, nApplied, index, ns, flag=0; MrBFlt tempD, sum; char tempStr[100]; if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before the model can be defined\n", spacer); return (ERROR); } if (inValidCommand == YES) { for (i=0; i numCurrentDivisions) { MrBayesPrint ("%s Partition delimiter is too large\n", spacer); return (ERROR); } if (fromI == -1) fromI = tempInt; else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; for (i=fromI-1; i ALPHA_MAX) { MrBayesPrint ("%s Beta parameter cannot be greater than %1.2lf\n", spacer, ALPHA_MAX); return (ERROR); } if (tempD < ALPHA_MIN) { MrBayesPrint ("%s Beta parameter cannot be less than %1.2lf\n", spacer, ALPHA_MIN); return (ERROR); } modelParams[i].tRatioDir[numVars[i]++] = tempD; if (numVars[i] < 2) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Tratiopr to Beta(%1.2lf,%1.2lf)\n", spacer, modelParams[i].tRatioDir[0], modelParams[i].tRatioDir[1]); else MrBayesPrint ("%s Setting Tratiopr to Beta(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].tRatioDir[0], modelParams[i].tRatioDir[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].tRatioPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].tRatioFix = tempD; if (modelParams[i].tRatioFix > KAPPA_MAX) { MrBayesPrint ("%s Tratio cannot be greater than %1.2lf\n", spacer, KAPPA_MAX); return (ERROR); } if (modelParams[i].tRatioFix < 0.0) { MrBayesPrint ("%s Tratio cannot be less than %1.2lf\n", spacer, 0.0); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Tratiopr to Fixed(%1.2lf)\n", spacer, modelParams[i].tRatioFix); else MrBayesPrint ("%s Setting Tratiopr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].tRatioFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Revmatpr (revMatPr) ************************************************************/ else if (!strcmp(parmName, "Revmatpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Dirichlet parameter cannot be greater than %1.2lf\n", spacer, ALPHA_MAX); return (ERROR); } if (tempD < ALPHA_MIN) { MrBayesPrint ("%s Dirichlet parameter cannot be less than %1.2lf\n", spacer, ALPHA_MIN); return (ERROR); } } else if (!strcmp(tempStr,"Fixed")) { if (tempD > KAPPA_MAX) { MrBayesPrint ("%s Rate value cannot be greater than %1.2lf\n", spacer, KAPPA_MAX); return (ERROR); } if (tempD < 0.0001) { MrBayesPrint ("%s Rate value cannot be less than %1.2lf\n", spacer, 0.0001); return (ERROR); } } tempNum[tempNumStates++] = tempD; if (tempNumStates == 1 && !strcmp(tempStr,"Dirichlet")) expecting = Expecting(COMMA) | Expecting(RIGHTPAR); else if (tempNumStates < 6) expecting = Expecting(COMMA); else expecting = Expecting(RIGHTPAR); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { nApplied = NumActiveParts (); for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Dirichlet parameter cannot be greater than %1.2lf\n", spacer, ALPHA_MAX); return (ERROR); } if (tempD < ALPHA_MIN) { MrBayesPrint ("%s Dirichlet parameter cannot be less than %1.2lf\n", spacer, ALPHA_MIN); return (ERROR); } } else if (!strcmp(tempStr,"Fixed")) { if (tempD > KAPPA_MAX) { MrBayesPrint ("%s Rate value cannot be greater than %1.2lf\n", spacer, KAPPA_MAX); return (ERROR); } if (tempD < 0.0001) { MrBayesPrint ("%s Rate value cannot be less than %1.2lf\n", spacer, 0.0001); return (ERROR); } } tempStateFreqs[tempNumStates++] = tempD; if (tempNumStates == 1 && !strcmp(tempStr,"Dirichlet")) expecting = Expecting(COMMA) | Expecting(RIGHTPAR); else if (tempNumStates < 190) expecting = Expecting(COMMA); else expecting = Expecting(RIGHTPAR); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { nApplied = NumActiveParts (); for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Dirichlet parameter cannot be greater than %1.2lf\n", spacer, ALPHA_MAX); return (ERROR); } if (tempD < ALPHA_MIN) { MrBayesPrint ("%s Dirichlet parameter cannot be less than %1.2lf\n", spacer, ALPHA_MIN); return (ERROR); } modelParams[i].omegaDir[numVars[i]++] = tempD; if (numVars[i] < 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Omegapr to Dirichlet(%1.2lf,%1.2lf)\n", spacer, modelParams[i].omegaDir[0], modelParams[i].omegaDir[1]); else MrBayesPrint ("%s Setting Omegapr to Dirichlet(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].omegaDir[0], modelParams[i].omegaDir[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].omegaPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].omegaFix = tempD; if (modelParams[i].omegaFix > KAPPA_MAX) { MrBayesPrint ("%s Omega ratio cannot be greater than %1.2lf\n", spacer, KAPPA_MAX); return (ERROR); } if (modelParams[i].omegaFix < 0.0) { MrBayesPrint ("%s Omega ratio cannot be less than %1.2lf\n", spacer, 0.0); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Omegapr to Fixed(%1.2lf)\n", spacer, modelParams[i].omegaFix); else MrBayesPrint ("%s Setting Omegapr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].omegaFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Ny98omega1pr (ny98omega1pr) ********************************************************/ else if (!strcmp(parmName, "Ny98omega1pr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].ny98omega3Uni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ny98omega3pr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].ny98omega3Uni[0], modelParams[i].ny98omega3Uni[1]); else MrBayesPrint ("%s Setting Ny98omega3pr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].ny98omega3Uni[0], modelParams[i].ny98omega3Uni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].ny98omega3pr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].ny98omega3Exp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ny98omega3pr to Exponential(%1.2lf)\n", spacer, modelParams[i].ny98omega3Exp); else MrBayesPrint ("%s Setting Ny98omega3pr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].ny98omega3Exp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].ny98omega3pr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].ny98omega3Fixed = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ny98omega3pr to Fixed(%1.2lf)\n", spacer, modelParams[i].ny98omega3Fixed); else MrBayesPrint ("%s Setting Ny98omega3pr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].ny98omega3Fixed, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set M3omegapr (m3omegapr) ********************************************************/ else if (!strcmp(parmName, "M3omegapr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].m3omegaFixed[1] || modelParams[i].m3omegaFixed[0] >= modelParams[i].m3omegaFixed[2] || modelParams[i].m3omegaFixed[1] >= modelParams[i].m3omegaFixed[2]) { MrBayesPrint ("%s The three omega values must be ordered, such that omega1 < omega2 < omega3\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M3omegapr to Fixed(%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].m3omegaFixed[0], modelParams[i].m3omegaFixed[1], modelParams[i].m3omegaFixed[2]); else MrBayesPrint ("%s Setting M3omegapr to Fixed(%1.2lf,%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m3omegaFixed[0], modelParams[i].m3omegaFixed[1], modelParams[i].m3omegaFixed[2], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Codoncatfreqs (codonCatFreqPr) ********************************************************/ else if (!strcmp(parmName, "Codoncatfreqs")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].shapeUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (modelParams[i].shapeUni[1] > MAX_SHAPE_PARAM) { MrBayesPrint ("%s Upper value for uniform cannot be greater than %1.2lf\n", spacer, MAX_SHAPE_PARAM); return (ERROR); } if (modelParams[i].shapeUni[0] < MIN_SHAPE_PARAM) { MrBayesPrint ("%s Lower value for uniform cannot be less than %1.2lf\n", spacer, MIN_SHAPE_PARAM); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Shapepr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].shapeUni[0], modelParams[i].shapeUni[1]); else MrBayesPrint ("%s Setting Shapepr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].shapeUni[0], modelParams[i].shapeUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].shapePr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].shapeExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Shapepr to Exponential(%1.2lf)\n", spacer, modelParams[i].shapeExp); else MrBayesPrint ("%s Setting Shapepr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].shapeExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].shapePr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].shapeFix = tempD; if (modelParams[i].shapeFix > MAX_SHAPE_PARAM) { MrBayesPrint ("%s Shape parameter cannot be greater than %1.2lf\n", spacer, MAX_SHAPE_PARAM); return (ERROR); } if (modelParams[i].shapeFix < MIN_SHAPE_PARAM) { MrBayesPrint ("%s Shape parameter cannot be less than %1.2lf\n", spacer, MIN_SHAPE_PARAM); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Shapepr to Fixed(%1.2lf)\n", spacer, modelParams[i].shapeFix); else MrBayesPrint ("%s Setting Shapepr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].shapeFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Pinvarpr (pInvarPr) ************************************************************/ else if (!strcmp(parmName, "Pinvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].pInvarUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (modelParams[i].pInvarUni[1] > 1.0) { MrBayesPrint ("%s Upper value for uniform should be less than or equal to 1.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Pinvarpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].pInvarUni[0], modelParams[i].pInvarUni[1]); else MrBayesPrint ("%s Setting Pinvarpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].pInvarUni[0], modelParams[i].pInvarUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].pInvarPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD > 1.0) { MrBayesPrint ("%s Value for Pinvar should be in the interval (0, 1)\n", spacer); return (ERROR); } modelParams[i].pInvarFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Pinvarpr to Fixed(%1.2lf)\n", spacer, modelParams[i].pInvarFix); else MrBayesPrint ("%s Setting Pinvarpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].pInvarFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Ratecorrpr (adGammaCorPr) ******************************************************/ else if (!strcmp(parmName, "Ratecorrpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].corrUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (modelParams[i].corrUni[1] > 1.0) { MrBayesPrint ("%s Upper value for uniform should be less than or equal to 1.0\n", spacer); return (ERROR); } if (modelParams[i].corrUni[0] < -1.0) { MrBayesPrint ("%s Lower value for uniform should be greater than or equal to -1.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ratecorrpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].corrUni[0], modelParams[i].corrUni[1]); else MrBayesPrint ("%s Setting Ratecorrpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].corrUni[0], modelParams[i].corrUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].adGammaCorPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (foundDash == YES) tempD *= -1.0; if (tempD > 1.0 || tempD < -1.0) { MrBayesPrint ("%s Value for Ratecorrpr should be in the interval (-1, +1)\n", spacer); return (ERROR); } modelParams[i].corrFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ratecorrpr to Fixed(%1.2lf)\n", spacer, modelParams[i].corrFix); else MrBayesPrint ("%s Setting Ratecorrpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].corrFix, i+1); expecting = Expecting(RIGHTPAR); } } } foundDash = NO; } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER) | Expecting(DASH); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Browncorrpr (brownCorPr) ******************************************************/ else if (!strcmp(parmName, "Browncorrpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].brownCorrUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (modelParams[i].brownCorrUni[1] > 1.0) { MrBayesPrint ("%s Upper value for uniform should be less than or equal to 1.0\n", spacer); return (ERROR); } if (modelParams[i].brownCorrUni[0] < -1.0) { MrBayesPrint ("%s Lower value for uniform should be greater than or equal to -1.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Browncorrpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].brownCorrUni[0], modelParams[i].brownCorrUni[1]); else MrBayesPrint ("%s Setting Browncorrpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brownCorrUni[0], modelParams[i].brownCorrUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].brownCorPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (foundDash == YES) tempD *= -1.0; if (tempD > 1.0 || tempD < -1.0) { MrBayesPrint ("%s Value for Browncorrpr should be in the interval (-1, +1)\n", spacer); return (ERROR); } modelParams[i].brownCorrFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Browncorrpr to Fixed(%1.2lf)\n", spacer, modelParams[i].brownCorrFix); else MrBayesPrint ("%s Setting Browncorrpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].brownCorrFix, i+1); expecting = Expecting(RIGHTPAR); } } } foundDash = NO; } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER) | Expecting(DASH); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Ratepr (ratePr) *****************************************************************/ else if (!strcmp(parmName, "Ratepr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Ratemultiplier Dirichlet parameter %lf out of range\n", spacer, tempD); return (ERROR); } /* set the parameter */ modelParams[i].ratePrDir = tempD; /* check if all partitions have been filled in */ for (i=0; i 1) MrBayesPrint ("s"); for (i=k=0; i 1) MrBayesPrint (", and %d", i+1); else if (k == 1) MrBayesPrint (" %d", i+1); else MrBayesPrint (", %d", i+1); } } MrBayesPrint ("\n"); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Covswitchpr (covSwitchPr) ******************************************************/ else if (!strcmp(parmName, "Covswitchpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].covswitchUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Covswitchpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].covswitchUni[0], modelParams[i].covswitchUni[1]); else MrBayesPrint ("%s Setting Covswitchpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].covswitchUni[0], modelParams[i].covswitchUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].covSwitchPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].covswitchExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Covswitchpr to Exponential(%1.2lf)\n", spacer, modelParams[i].covswitchExp); else MrBayesPrint ("%s Setting Covswitchpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].covswitchExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].covSwitchPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].covswitchFix[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Covswitchpr to Fixed(%1.4lf,%1.4lf)\n", spacer, modelParams[i].covswitchFix[0], modelParams[i].covswitchFix[1]); else MrBayesPrint ("%s Setting Covswitchpr to Fixed(%1.4lf,%1.4lf) for partition %d\n", spacer, modelParams[i].covswitchFix[0], modelParams[i].covswitchFix[1], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Symdirihyperpr (symPiPr) ******************************************************/ else if (!strcmp(parmName, "Symdirihyperpr")) { if (expecting == Expecting(EQUALSIGN)) { foundBeta = NO; expecting = Expecting(ALPHA); } else if (expecting == Expecting(ALPHA)) { if (foundBeta == NO) { /* expecting to see Uniform, Exponential, or Fixed */ if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].symBetaUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Symdirihyperpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].symBetaUni[0], modelParams[i].symBetaUni[1]); else MrBayesPrint ("%s Setting Symdirihyperpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].symBetaUni[0], modelParams[i].symBetaUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else { MrBayesPrint ("%s Problem setting Symdirihyperpr\n", spacer); return (ERROR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Statefreqpr (stateFreqPr) ******************************************************/ else if (!strcmp(parmName, "Statefreqpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsSame ("Equal", tkn) == DIFFERENT && IsSame ("Empirical", tkn) == DIFFERENT) { /* the user wants to specify a dirichlet or fixed prior */ if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i numDefinedConstraints) { MrBayesPrint ("%s Constraint number is too large\n", spacer); return (ERROR); } if (fromI == -1) { if ( foundDash == YES ) { MrBayesPrint ("%s Unexpected dash\n", spacer); return (ERROR); } fromI = tempInt; tempActiveConstraints[fromI-1] = YES; } else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; //for (i=fromI-1; i numUserTrees) { MrBayesPrint ("%s Tree number is too large\n", spacer); return (ERROR); } if (tempInt < 1) { MrBayesPrint ("%s Tree number is too small\n", spacer); return (ERROR); } fromI = tempInt; expecting = Expecting(RIGHTPAR); /* only one tree number acceptable */ } } else if (expecting == Expecting(COMMA)) { foundComma = YES; expecting = Expecting(NUMBER) | Expecting(ALPHA); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { /* find out whether we need a tree number or constraint number(s) */ nApplied = NumActiveParts (); for (i=0; i 0.000001) { MrBayesPrint ("%s Lower value for uniform must equal 0.0\n", spacer); return (ERROR); } if (modelParams[i].brlensUni[0] >= modelParams[i].brlensUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Unconstrained:Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].brlensUni[0], modelParams[i].brlensUni[1]); else MrBayesPrint ("%s Setting Brlenspr to Unconstrained:Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brlensUni[0], modelParams[i].brlensUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].unconstrainedPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brlensExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Unconstrained:Exponential(%1.2lf)\n", spacer, modelParams[i].brlensExp); else MrBayesPrint ("%s Setting Brlenspr to Unconstrained:Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].brlensExp, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (!strcmp(colonPr,"Fixed") || !strcmp(colonPr,"Clock")) { sscanf (tkn, "%d", &tempInt); if (tempInt < 1 || tempInt > numUserTrees) { MrBayesPrint ("%s Tree needs to be in the range %d to %d\n", spacer, 1, numUserTrees); return (ERROR); } fromI = tempInt; expecting = Expecting(RIGHTPAR); } foundLeftPar = NO; } else if (expecting == Expecting(COLON)) { expecting = Expecting(ALPHA); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { if (!strcmp(colonPr,"Fixed") || (!strcmp(colonPr,"Clock") && !strcmp(clockPr,"Fixed"))) { /* index of a tree which set up branch lengths*/ nApplied = NumActiveParts (); for (i=0; iname); else MrBayesPrint ("%s Setting Brlenspr to Fixed(%s) for partition %d\n", spacer, userTree[fromI-1]->name, i+1); } else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Fixed(%s)\n", spacer, userTree[fromI-1]->name); else MrBayesPrint ("%s Setting Brlenspr to Fixed(%s) for partition %d\n", spacer, userTree[fromI-1]->name, i+1); } } } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Speciationpr (speciationPr) ****************************************************/ else if (!strcmp(parmName, "Speciationpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].speciationUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Speciationpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].speciationUni[0], modelParams[i].speciationUni[1]); else MrBayesPrint ("%s Setting Speciationpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].speciationUni[0], modelParams[i].speciationUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].speciationPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].speciationExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Speciationpr to Exponential(%1.2lf)\n", spacer, modelParams[i].speciationExp); else MrBayesPrint ("%s Setting Speciationpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].speciationExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].speciationPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (AreDoublesEqual(tempD, 0.0, ETA)==YES) { MrBayesPrint ("%s Speciation rate cannot be fixed to 0.0\n", spacer); return (ERROR); } modelParams[i].speciationFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Speciationpr to Fixed(%1.2lf)\n", spacer, modelParams[i].speciationFix); else MrBayesPrint ("%s Setting Speciationpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].speciationFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Extinctionpr (extinctionPr) ****************************************************/ else if (!strcmp(parmName, "Extinctionpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i 1.0) { MrBayesPrint ("%s Relative extinction rate must be in the range [0,1]\n", spacer); return (ERROR); } modelParams[i].extinctionFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Extinctionpr to Fixed(%1.2lf)\n", spacer, modelParams[i].extinctionFix); else MrBayesPrint ("%s Setting Extinctionpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].extinctionFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set SampleStrat (sampleStrat) *****************************************************/ else if (!strcmp(parmName, "Samplestrat")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); } else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i 0.0) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].popSizeUni[1]) { MrBayesPrint ("%s Lower value for Uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].popSizeUni[0], modelParams[i].popSizeUni[1]); else MrBayesPrint ("%s Setting Popsizepr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeUni[0], modelParams[i].popSizeUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].popSizePr,"Lognormal")) { sscanf (tkn, "%lf", &tempD); modelParams[i].popSizeLognormal[numVars[i]++] = tempD; if (numVars[i] == 1) { expecting = Expecting(COMMA); } else { if (modelParams[i].popSizeLognormal[1] <= 0.0) { MrBayesPrint ("%s Standard deviation of Lognormal must be a positive number\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Lognormal(%1.2lf,%1.2lf)\n", spacer, modelParams[i].popSizeLognormal[0], modelParams[i].popSizeLognormal[1]); else MrBayesPrint ("%s Setting Popsizepr to Lognormal(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeLognormal[0], modelParams[i].popSizeLognormal[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].popSizePr,"Normal")) { sscanf (tkn, "%lf", &tempD); modelParams[i].popSizeNormal[numVars[i]++] = tempD; if (numVars[i] == 1) { if (modelParams[i].popSizeNormal[0] <= 0.0) { MrBayesPrint ("%s Mean of Truncated Normal must be a positive number\n", spacer); return (ERROR); } expecting = Expecting(COMMA); } else { if (modelParams[i].popSizeNormal[1] <= 0.0) { MrBayesPrint ("%s Standard deviation of Truncated Normal must be a positive number\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Truncated Normal(%1.2lf,%1.2lf)\n", spacer, modelParams[i].popSizeNormal[0], modelParams[i].popSizeNormal[1]); else MrBayesPrint ("%s Setting Popsizepr to Truncated Normal(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeNormal[0], modelParams[i].popSizeNormal[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].popSizePr,"Gamma")) { sscanf (tkn, "%lf", &tempD); modelParams[i].popSizeLognormal[numVars[i]++] = tempD; if (numVars[i] == 1) { if (modelParams[i].popSizeGamma[0] <= 0.0) { MrBayesPrint ("%s Shape (alpha) of Gamma must be a positive number\n", spacer); return (ERROR); } expecting = Expecting(COMMA); } else { if (modelParams[i].popSizeGamma[1] <= 0.0) { MrBayesPrint ("%s Rate (beta) of Gamma must be a positive number\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Gamma(%1.2lf,%1.2lf)\n", spacer, modelParams[i].popSizeGamma[0], modelParams[i].popSizeGamma[1]); else MrBayesPrint ("%s Setting Popsizepr to Gamma(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeGamma[0], modelParams[i].popSizeGamma[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].popSizePr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (AreDoublesEqual(tempD, 0.0, ETA)==YES) { MrBayesPrint ("%s Popsizepr cannot be fixed to 0.0\n", spacer); return (ERROR); } modelParams[i].popSizeFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Fixed(%1.2lf)\n", spacer, modelParams[i].popSizeFix); else MrBayesPrint ("%s Setting Popsizepr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Popvarpr (popVarPr) **************************************************************/ else if (!strcmp(parmName, "Popvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i CPPLAMBDA_MAX) { MrBayesPrint ("%s CPP rate must be in the range %f - %f\n", spacer, CPPLAMBDA_MIN, CPPLAMBDA_MAX); return (ERROR); } modelParams[i].cppRateFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Cppratepr to Fixed(%1.2lf)\n", spacer, modelParams[i].cppRateFix); else MrBayesPrint ("%s Setting Cppratepr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].cppRateFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Compound Poisson Process rate multiplier standard deviation (log scale) ***********************/ else if (!strcmp(parmName, "Cppmultdevpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i POSREAL_MAX) { MrBayesPrint ("%s The log standard deviation of rate multipliers must be in the range %f - %f\n", spacer, POSREAL_MIN, POSREAL_MAX); return (ERROR); } modelParams[i].cppMultDevFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Cppmultdevpr to Fixed(%1.2lf)\n", spacer, modelParams[i].cppMultDevFix); else MrBayesPrint ("%s Setting Cppmultdevpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].cppMultDevFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set prior for variance of lognormal of autocorrelated rates (tk02varPr) ***********************/ else if (!strcmp(parmName, "TK02varpr") || !strcmp(parmName,"Bmvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].tk02varUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting TK02varpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].tk02varUni[0], modelParams[i].tk02varUni[1]); else MrBayesPrint ("%s Setting TK02varpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].tk02varUni[0], modelParams[i].tk02varUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].tk02varPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].tk02varExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting TK02varpr to Exponential(%1.2lf)\n", spacer, modelParams[i].tk02varExp); else MrBayesPrint ("%s Setting TK02varpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].tk02varExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].tk02varPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD < TK02VAR_MIN || tempD > TK02VAR_MAX) { MrBayesPrint ("%s Ratevar (nu) must be in the range %f - %f\n", spacer, TK02VAR_MIN, TK02VAR_MAX); return (ERROR); } modelParams[i].tk02varFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting TK02varpr to Fixed(%1.2lf)\n", spacer, modelParams[i].tk02varFix); else MrBayesPrint ("%s Setting TK02varpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].tk02varFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set prior for shape of independent branch rate gamma distribution (igrvarPr) ***********************/ else if (!strcmp(parmName, "Igrvarpr") || !strcmp(parmName, "Ibrvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].igrvarUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Igrvarpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].igrvarUni[0], modelParams[i].igrvarUni[1]); else MrBayesPrint ("%s Setting Igrvarpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].igrvarUni[0], modelParams[i].igrvarUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].igrvarPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].igrvarExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Igrvarpr to Exponential(%1.2lf)\n", spacer, modelParams[i].igrvarExp); else MrBayesPrint ("%s Setting Igrvarpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].igrvarExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].igrvarPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD < IGRVAR_MIN || tempD > IGRVAR_MAX) { MrBayesPrint ("%s Igrvar must be in the range %f - %f\n", spacer, IGRVAR_MIN, IGRVAR_MAX); return (ERROR); } modelParams[i].igrvarFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Igrvarpr to Fixed(%1.2lf)\n", spacer, modelParams[i].igrvarFix); else MrBayesPrint ("%s Setting Igrvarpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].igrvarFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Growthpr (growthPr) **************************************************************/ else if (!strcmp(parmName, "Growthpr")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); isNegative = NO; } else if (expecting == Expecting(ALPHA)) { isNegative = NO; if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].growthUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Growthpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].growthUni[0], modelParams[i].growthUni[1]); else MrBayesPrint ("%s Setting Growthpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].growthUni[0], modelParams[i].growthUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].growthPr,"Normal")) { sscanf (tkn, "%lf", &tempD); if (isNegative == YES) tempD *= -1.0; modelParams[i].growthNorm[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (modelParams[i].growthNorm[1] < 0.0) { MrBayesPrint ("%s Variance for normal distribution should be greater than zero\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Growthpr to Normal(%1.2lf,%1.2lf)\n", spacer, modelParams[i].growthNorm[0], modelParams[i].growthNorm[1]); else MrBayesPrint ("%s Setting Growthpr to Normal(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].growthNorm[0], modelParams[i].growthNorm[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].growthPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].growthExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Growthpr to Exponential(%1.2lf)\n", spacer, modelParams[i].growthExp); else MrBayesPrint ("%s Setting Growthpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].growthExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].growthPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (isNegative == YES) tempD *= -1.0; modelParams[i].growthFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Growthpr to Fixed(%1.2lf)\n", spacer, modelParams[i].growthFix); else MrBayesPrint ("%s Setting Growthpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].growthFix, i+1); expecting = Expecting(RIGHTPAR); } } } isNegative = NO; } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Aamodelpr (aaModelPr) **************************************************************/ else if (!strcmp(parmName, "Aamodelpr")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); foundAaSetting = foundExp = modelIsFixed = foundDash = NO; fromI = 0; for (i=0; i<10; i++) tempAaModelPrs[i] = 0.0; } else if (expecting == Expecting(ALPHA)) { if (foundAaSetting == NO) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= 10) { MrBayesPrint ("%s Too many arguments in Aamodelpr\n", spacer); return (ERROR); } if (modelIsFixed == NO) { if (foundExp == YES) { if (foundDash == YES) tempAaModelPrs[fromI++] = -tempD; else tempAaModelPrs[fromI++] = tempD; expecting = Expecting(RIGHTPAR); } else { if (foundDash == YES) { MrBayesPrint ("%s Unexpected \"-\" in Aamodelpr\n", spacer); return (ERROR); } else { if (tempD <= 0.000000000001) tempAaModelPrs[fromI++] = -1000000000; else tempAaModelPrs[fromI++] = (MrBFlt) log(tempD); } expecting = Expecting(COMMA) | Expecting(RIGHTPAR); } foundDash = NO; } else { MrBayesPrint ("%s Not expecting a number\n", spacer); return (ERROR); } } else if (expecting == Expecting(LEFTPAR)) { if (modelIsFixed == YES) expecting = Expecting(ALPHA); else { if (foundExp == YES) expecting = Expecting(NUMBER) | Expecting(DASH); else expecting = Expecting(NUMBER) | Expecting(ALPHA); } } else if (expecting == Expecting(RIGHTPAR)) { if (modelIsFixed == YES) expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); else { if (foundExp == YES) { foundExp = NO; expecting = Expecting(COMMA) | Expecting(RIGHTPAR); } else { if (fromI < 10) { MrBayesPrint ("%s Too few arguments in Aamodelpr\n", spacer); return (ERROR); } nApplied = NumActiveParts (); for (i=0; i= modelParams[i].brownScalesUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brownscalepr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].brownScalesUni[0], modelParams[i].brownScalesUni[1]); else MrBayesPrint ("%s Setting Brownscalepr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brownScalesUni[0], modelParams[i].brownScalesUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].brownScalesPr,"Gamma")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brownScalesGamma[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brownscalepr to Gamma Mean=%1.2lf Var=%1.2lf\n", spacer, modelParams[i].brownScalesGamma[0], modelParams[i].brownScalesGamma[1]); else MrBayesPrint ("%s Setting Brownscalepr to Gamma Mean=%1.2lf Var=%1.2lf for partition %d\n", spacer, modelParams[i].brownScalesGamma[0], modelParams[i].brownScalesGamma[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].brownScalesPr,"Gammamean")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brownScalesGammaMean = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brownscalepr to Gamma Mean= Var=%1.2lf\n", spacer, modelParams[i].brownScalesGammaMean); else MrBayesPrint ("%s Setting Brownscalepr to Gamma Mean= Var=%1.2lf for partition %d\n", spacer, modelParams[i].brownScalesGammaMean, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].brownScalesPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brownScalesFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brownscalepr to Fixed(%1.2lf)\n", spacer, modelParams[i].brownScalesFix); else MrBayesPrint ("%s Setting Brownscalepr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].brownScalesFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set M10betapr (m10betapr) ********************************************************/ else if (!strcmp(parmName, "M10betapr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].m10betaUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10betapr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].m10betaUni[0], modelParams[i].m10betaUni[1]); else MrBayesPrint ("%s Setting M10betapr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m10betaUni[0], modelParams[i].m10betaUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].m10betapr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].m10betaExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10betapr to Exponential(%1.2lf)\n", spacer, modelParams[i].m10betaExp); else MrBayesPrint ("%s Setting M10betapr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].m10betaExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].m10betapr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].m10betaFix[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10betapr to Fixed(%1.2lf,%1.2lf)\n", spacer, modelParams[i].m10betaFix[0], modelParams[i].m10betaFix[1]); else MrBayesPrint ("%s Setting M10betapr to Fixed(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m10betaFix[0], modelParams[i].m10betaFix[1], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set M10gammapr (m10gammapr) ********************************************************/ else if (!strcmp(parmName, "M10gammapr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].m10gammaUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10gammapr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].m10gammaUni[0], modelParams[i].m10gammaUni[1]); else MrBayesPrint ("%s Setting M10gammapr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m10gammaUni[0], modelParams[i].m10gammaUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].m10gammapr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].m10gammaExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10gammapr to Exponential(%1.2lf)\n", spacer, modelParams[i].m10gammaExp); else MrBayesPrint ("%s Setting M10gammapr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].m10gammaExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].m10gammapr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].m10gammaFix[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10gammapr to Fixed(%1.2lf,%1.2lf)\n", spacer, modelParams[i].m10gammaFix[0], modelParams[i].m10gammaFix[1]); else MrBayesPrint ("%s Setting M10gammapr to Fixed(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m10gammaFix[0], modelParams[i].m10gammaFix[1], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoReport (void) { /* TODO: smart update */ if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); return (NO_ERROR); } int DoReportParm (char *parmName, char *tkn) { int i, tempInt, nApplied; char tempStr[100]; if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before the report settings can be altered\n", spacer); return (ERROR); } if (inValidCommand == YES) { for (i=0; i numCurrentDivisions) { MrBayesPrint ("%s Partition delimiter is too large\n", spacer); return (ERROR); } if (fromI == -1) fromI = tempInt; else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; for (i=fromI-1; i 1) { strcpy(modelParams[i].ratemultFormat, tempStr); if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting rate multiplier (ratemult) format to %s\n", spacer, modelParams[i].ratemultFormat); else MrBayesPrint ("%s Setting rate multiplier (ratemult) format to %s for partition %d\n", spacer, modelParams[i].ratemultFormat, i+1); } } } else { MrBayesPrint ("%s Invalid rate multiplier (ratemult) format \n", spacer); return (ERROR); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set report format of tree ***************************************************/ else if (!strcmp(parmName, "Tree")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting (ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); tempInt = NO; for (i=0; i(,)=(,...) -- apply to run and chain (,)=(,...) -- apply to chain for all runs (,)=(,...) -- apply to all chains of run topology and branch length parameters are specified like (,)=| parameter names will often be followed by partition specifiers like: pi{all} pinvar{1,4,5} so we need to assemble the parameter name from several tokens that are parsed out separately; here we receive only the first part (before the left curly, if present) */ /* copy to local parameter name */ SafeStrcpy (&tempName, tkn); param = NULL; runIndex = chainIndex = -1; useSubvalues = NO; useIntValues = NO; useStdStateFreqs = NO; foundComma = foundEqual = foundName = foundDash = NO; expecting = Expecting(LEFTCURL) | Expecting(LEFTPAR) | Expecting(EQUALSIGN); } else return (ERROR); } else if (expecting == Expecting(ALPHA)) { if (param == NULL) { /* we are still assembling the parameter name */ SafeStrcat (&tempName, tkn); expecting = Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(EQUALSIGN); } else { /* we have a tree name; now find the tree based on name, case insensitive */ if (GetUserTreeFromName (&treeIndex, tkn) == ERROR || treeIndex == -1) { MrBayesPrint ("%s Error in finding user tree\n", spacer); return (ERROR); } /* set the tree parameter */ for (i=0; iparamType != P_POPSIZE) { if (param->paramType == P_TOPOLOGY || param->paramType == P_BRLENS || param->paramType == P_SPECIESTREE) { /* topology or brlens or speciestree params */ theTree = GetTree (param, chainId, 0); usrTree = GetTree (param, chainId, 1); /* use as scratch space */ } else { /* relaxed clock params */ theTree = GetTree (modelSettings[param->relParts[0]].brlens, chainId, 0); usrTree = GetTree (modelSettings[param->relParts[0]].brlens, chainId, 1); } CopyToTreeFromTree (usrTree, theTree); if (param->paramType == P_SPECIESTREE) thePolyTree = AllocatePolyTree(numSpecies); else thePolyTree = AllocatePolyTree (numTaxa); CopyToPolyTreeFromPolyTree (thePolyTree, userTree[treeIndex]); if (param->paramType == P_SPECIESTREE) { ResetIntNodeIndices(thePolyTree); } else { PrunePolyTree (thePolyTree); ResetTipIndices(thePolyTree); } RandResolve (NULL, thePolyTree, &globalSeed, theTree->isRooted); if (param->paramType == P_SPECIESTREE) ret=CopyToSpeciesTreeFromPolyTree (usrTree, thePolyTree); else ret=CopyToTreeFromPolyTree (usrTree, thePolyTree); FreePolyTree (thePolyTree); if(ret==ERROR) return ERROR; } else { /* param->paramType == P_POPSIZE */ theTree = GetTree (modelSettings[param->relParts[0]].speciesTree, chainId, 0); usrTree = GetTree (modelSettings[param->relParts[0]].speciesTree, chainId, 1); CopyToTreeFromTree (usrTree, theTree); thePolyTree = AllocatePolyTree(numSpecies); CopyToPolyTreeFromPolyTree (thePolyTree, userTree[treeIndex]); ResetIntNodeIndices(thePolyTree); RandResolve (NULL, thePolyTree, &globalSeed, theTree->isRooted); CopyToSpeciesTreeFromPolyTree (usrTree, thePolyTree); FreePolyTree (thePolyTree); } if (param->paramType == P_TOPOLOGY) { if (theTree->checkConstraints == YES && CheckSetConstraints (usrTree) == ERROR) { MrBayesPrint ("%s Could not set the constraints for topology parameter '%s'\n", spacer, param->name); return (ERROR); } if (ResetTopologyFromTree (theTree, usrTree) == ERROR) { MrBayesPrint ("%s Could not set the topology parameter '%s'\n", spacer, param->name); return (ERROR); } if (theTree->checkConstraints == YES && CheckSetConstraints (theTree)==ERROR) { MrBayesPrint ("%s Could not set the constraints for topology parameter '%s'\n", spacer, param->name); return (ERROR); } FillTopologySubParams (param, chainId, 0, &globalSeed); //MrBayesPrint ("%s Branch lengths and relaxed clock subparamiters of a paramiter '%s' are reset.\n", spacer, param->name); if (param->paramId == TOPOLOGY_SPECIESTREE) FillSpeciesTreeParams(&globalSeed, chainId, chainId+1); assert (IsTreeConsistent(param, chainId, 0) == YES); } else if (param->paramType == P_BRLENS) { if (usrTree->allDownPass[0]->length == 0.0) { MrBayesPrint ("%s User tree '%s' does not have branch lengths so it cannot be used in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } /* if (theTree->isClock == YES && IsClockSatisfied (usrTree,0.001) == NO) { MrBayesPrint ("%s Branch lengths of the user tree '%s' does not satisfy clock in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); ShowNodes(usrTree->root,0,YES); return (ERROR); } */ /* if (theTree->isCalibrated == YES && IsCalibratedClockSatisfied (usrTree,0.001) == NO) //no calibration is set up in usertree so do not do this check { MrBayesPrint ("%s Branch lengths of the user tree '%s' does not satisfy clock calibrations in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } */ if (AreTopologiesSame (theTree, usrTree) == NO) { MrBayesPrint ("%s Topology of user tree '%s' wrong in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } //assert (IsTreeConsistent(param, chainId, 0) == YES); /* reset node depths to ensure that non-dated tips have node depth 0.0 */ /*if (usrTree->isClock == YES) SetNodeDepths(usrTree); */ if (ResetBrlensFromTree (theTree, usrTree) == ERROR) { MrBayesPrint ("%s Could not set parameter '%s' from user tree '%s'\n", spacer, param->name, userTree[treeIndex]->name); return (ERROR); } if (theTree->isClock == YES && !strcmp(modelParams[theTree->relParts[0]].treeAgePr,"Fixed")) { if (!strcmp(modelParams[theTree->relParts[0]].clockPr,"Uniform")) ResetRootHeight (theTree, modelParams[theTree->relParts[0]].treeAgeFix); } /* the test will find suitable clock rate and ages of nodes in theTree */ if (theTree->isClock == YES && IsClockSatisfied (theTree,0.001) == NO) { MrBayesPrint ("%s Non-calibrated tips are not at the same level after setting up starting tree branch lengthes(%s) from user tree '%s'.\n", spacer, param->name, userTree[treeIndex]->name); ShowNodes(theTree->root,0,YES); return (ERROR); } if (theTree->isCalibrated == YES && IsCalibratedClockSatisfied (theTree,&minRate,&maxRate, 0.001) == NO) { MrBayesPrint ("%s Problem setting calibrated tree parameters\n", spacer); return (ERROR); } if (theTree->isCalibrated == YES && !strcmp(modelParams[theTree->relParts[0]].clockRatePr, "Fixed")) { clockRate = modelParams[theTree->relParts[0]].clockRateFix; if(( clockRate < minRate && AreDoublesEqual (clockRate, minRate , 0.0001) == NO ) || ( clockRate > maxRate && AreDoublesEqual (clockRate, maxRate , 0.0001) == NO )) { MrBayesPrint("%s Fixed branch lengths do not satisfy fixed clockrate", spacer); return (ERROR); } } theTree->fromUserTree=YES; FillBrlensSubParams (param, chainId, 0); //MrBayesPrint ("%s Rrelaxed clock subparamiters of a paramiter '%s' are reset.\n", spacer, param->name); //assert (IsTreeConsistent(param, chainId, 0) == YES); if (param->paramId == BRLENS_CLOCK_SPCOAL) FillSpeciesTreeParams(&globalSeed, chainId, chainId+1); //assert (IsTreeConsistent(param, chainId, 0) == YES); } else if (param->paramType == P_CPPEVENTS || param->paramType == P_TK02BRANCHRATES || param->paramType == P_IGRBRANCHLENS) { if( theTree->isCalibrated == YES && theTree->fromUserTree == NO ) {/*if theTree is not set from user tree then we can not garanty that branch lenghts will stay the same by the time we start mcmc run because of clockrate adjustment.*/ MrBayesPrint ("%s Set starting values for branch lenghtes first! Starting value of relaxed paramiters could be set up only for trees where branch lengths are already set up from user tree.\n", spacer, param->name); return (ERROR); } if ( theTree->isCalibrated == NO && IsClockSatisfied (usrTree, 0.0001) == NO ) // user tree is not calibrated so do not check it if calibration is in place { MrBayesPrint ("%s Branch lengths of the user tree '%s' do not satisfy clock in setting parameter '%s'\n", spacer, userTree[treeIndex], param->name); ShowNodes(usrTree->root,0,YES); return (ERROR); } /* if (theTree->isCalibrated == YES && IsCalibratedClockSatisfied (usrTree,0.0001) == NO) { MrBayesPrint ("%s Branch lengths of the user tree '%s' do not satisfy clock calibrations in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } */ if (AreTopologiesSame (theTree, usrTree) == NO) { MrBayesPrint ("%s Topology of user tree '%s' is wrong in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } if (SetRelaxedClockParam (param, chainId, 0, userTree[treeIndex]) == ERROR) { MrBayesPrint ("%s Could not set parameter '%s' from user tree '%s'\n", spacer, param->name, userTree[treeIndex]->name); return (ERROR); } //assert (IsTreeConsistent(param, chainId, 0) == YES); } else if (param->paramType == P_POPSIZE) { if (AreTopologiesSame (theTree, usrTree) == NO) { MrBayesPrint ("%s Topology of user tree '%s' is wrong in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } if (SetPopSizeParam (param, chainId, 0, userTree[treeIndex]) == ERROR) { MrBayesPrint ("%s Could not set parameter '%s' from user tree '%s'\n", spacer, param->name, userTree[treeIndex]->name); return (ERROR); } } else if (param->paramType == P_SPECIESTREE) { if (IsSpeciesTreeConsistent (usrTree, chainId) == NO) { MrBayesPrint ("%s User-specified species tree '%s' is inconsistent with gene trees\n", spacer, userTree[treeIndex]->name); return (ERROR); } if (CopyToTreeFromTree (theTree, usrTree) == ERROR) { MrBayesPrint ("%s Could not set the species tree parameter '%s'\n", spacer, param->name); return (ERROR); } assert (IsTreeConsistent(param, chainId, 0) == YES); } } /* for (j=0; jmin; theValueMax = param->max; /* we are reading in a parameter value */ if (param->paramType==P_OMEGA && nValuesRead==numExpectedValues && useSubvalues == NO) { /* continue with subvalues */ nValuesRead = 0; numExpectedValues = param->nSubValues/2; useSubvalues = YES; theValueMin = ETA; theValueMax = 1.0; } if (param->paramType==P_OMEGA && nValuesRead==numExpectedValues && useSubvalues == NO) { /* continue with subvalues */ nValuesRead = 0; numExpectedValues = param->nSubValues/2; useSubvalues = YES; theValueMin = ETA; theValueMax = 1.0; } if (param->nIntValues > 0 && nValuesRead==numExpectedValues && useIntValues == NO) { /* continue with intValues */ nValuesRead = 0; numExpectedValues = param->nIntValues; useIntValues = YES; } if (param->paramType==P_PI && modelSettings[param->relParts[0]].dataType == STANDARD && param->paramId != SYMPI_EQUAL && nValuesRead==numExpectedValues && useStdStateFreqs == NO) { /* we have read alpha_symdir, continue with multistate char state freqs */ nValuesRead = 0; numExpectedValues = param->nStdStateFreqs; if (param->hasBinaryStd == YES) numExpectedValues -= 2 * modelSettings[param->relParts[0]].numBetaCats; useStdStateFreqs = YES; theValueMin = ETA; theValueMax = 1.0; } nValuesRead++; if (nValuesRead > numExpectedValues) { if (param->paramType == P_OMEGA) MrBayesPrint ("%s Only %d values were expected for parameter '%s'\n", spacer, param->nValues+param->nSubValues/2, param->name); else if (param->nIntValues > 0) MrBayesPrint ("%s Only %d values were expected for parameter '%s'\n", spacer, param->nValues+param->nIntValues, param->name); else MrBayesPrint ("%s Only %d values were expected for parameter '%s'\n", spacer, numExpectedValues, param->name); return (ERROR); } if (useIntValues == YES) sscanf (tkn, "%d", &tempInt); else sscanf (tkn, "%lf", &tempFloat); if (foundDash == YES) { if (useIntValues == NO) tempFloat = -tempFloat; else tempInt = -tempInt; foundDash = NO; } if (useIntValues == NO && (tempFloat < theValueMin || tempFloat > theValueMax)) { MrBayesPrint ("%s The value is out of range (min = %lf; max = %lf)\n", spacer, theValueMin, theValueMax); return (ERROR); } for (i=0; ihasBinaryStd == YES) theValue += 2 * modelSettings[param->relParts[0]].numBetaCats; } else return (ERROR); if( param->paramType == P_CLOCKRATE ) { if( UpdateClockRate(tempFloat, i*chainParams.numChains+j) == ERROR) { return (ERROR); } } theValue[nValuesRead-1] = tempFloat; } } } expecting = Expecting (COMMA) | Expecting(RIGHTPAR); } else /* if (foundEqual == NO) */ { sscanf (tkn, "%d", &tempInt); if (foundComma == NO) { if (tempInt <= 0 || tempInt > chainParams.numRuns) { MrBayesPrint ("%s Run index is out of range (min=1; max=%d)\n", spacer, chainParams.numRuns); return (ERROR); } runIndex = tempInt - 1; expecting = Expecting(COMMA); } else { if (tempInt <= 0 || tempInt > chainParams.numChains) { MrBayesPrint ("%s Chain index is out of range (min=1; max=%d)\n", spacer, chainParams.numChains); return (ERROR); } chainIndex = tempInt - 1; foundComma = NO; expecting = Expecting(RIGHTPAR); } } } else if (expecting == Expecting(COMMA)) { if (foundEqual == YES) { /* we expect another parameter value */ expecting = Expecting(NUMBER); } else /* if (foundEqual == NO) */ { /* we will be reading in chain index, if present */ foundComma = YES; expecting = Expecting(RIGHTPAR) | Expecting(NUMBER); /* if the comma is in a list of partitions (so between { and }) we have to add the comma to the parameter name */ if (param == NULL && strchr(tempName, '}')==NULL && strchr(tempName, '{')!=NULL ) SafeStrcat (&tempName, ","); } } else if (expecting == Expecting(RIGHTPAR)) { if (foundEqual == NO) { /* this is the end of the run and chain specification */ expecting = Expecting(EQUALSIGN); } else /* if (foundEqual == YES) */ { /* this is the end of the parameter values */ if (nValuesRead != numExpectedValues) { MrBayesPrint ("%s Expected %d values but only found %d values for parameter '%s'\n", spacer, numExpectedValues, nValuesRead, param->name); return (ERROR); } /* Post processing needed for some parameters */ if (param->paramType == P_SHAPE || param->paramType == P_CORREL) { for (i=0; iparamType == P_SHAPE) { if (DiscreteGamma (subValue, value[0], value[0], param->nSubValues, 0) == ERROR) return (ERROR); } else if (param->paramType == P_CORREL) AutodGamma (subValue, value[0], (int)(sqrt(param->nSubValues) + 0.5)); } } } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else if (expecting == Expecting(EQUALSIGN)) { foundEqual = YES; foundName = YES; /* we now know that the name is complete; try to find the parameter with this name (case insensitive) */ for (i=0; i<(int)strlen(tempName); i++) tempName[i] = tolower(tempName[i]); /* first check exact matches */ nMatches = j = 0; for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strcmp(tempName,temp) == 0) { j = i; nMatches++; } } /* now check unambiguous abbreviation matches */ if (nMatches == 0) { nMatches = j = 0; for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strncmp(tempName,temp,strlen(tempName)) == 0) { j = i; nMatches++; } } } if (nMatches == 0) { extern char *tokenP; MrBayesPrint ("%s Could not find parameter '%s': ignoring values\n", spacer, tempName); while(*tokenP && *tokenP++!=')') {}; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); return (*tokenP ? NO_ERROR:ERROR); } else if (nMatches > 1) { MrBayesPrint ("%s Several parameters matched the abbreviated name '%s'\n", spacer, tempName); return (ERROR); } param = ¶ms[j]; if (param->printParam == NO && !(param->paramType == P_TOPOLOGY && strcmp(modelParams[param->relParts[0]].topologyPr,"Fixed")!=0) && !param->paramType == P_CPPEVENTS && !param->paramType == P_TK02BRANCHRATES && !param->paramType == P_IGRBRANCHLENS && !(param->paramType == P_POPSIZE && param->nValues > 1)) { MrBayesPrint ("%s The parameter '%s' is fixed so the starting value cannot be set\n", spacer, param->name); return (ERROR); } if (param->paramType == P_BRLENS || param->paramType == P_TOPOLOGY || param->paramType == P_CPPEVENTS || param->paramType == P_TK02BRANCHRATES || param->paramType == P_IGRBRANCHLENS || param->paramType == P_SPECIESTREE || (param->paramType == P_POPSIZE && param->nValues > 1)) { /* all these parameters are set from a tree */ expecting = Expecting (ALPHA); } else /* run of the mill character */ { theValueMin = param->min; theValueMax = param->max; if ((param->paramType == P_PI && modelParams[param->relParts[0]].dataType != STANDARD)) { useSubvalues = YES; useIntValues = NO; numExpectedValues = param->nSubValues; } else if (param->nValues == 0 && param->nIntValues > 0) { useSubvalues = NO; useIntValues = YES; numExpectedValues = param->nIntValues; } else if (param->nValues > 0) { useSubvalues = NO; useIntValues = NO; numExpectedValues = param->nValues; } else { MrBayesPrint ("%s Not expecting any values for parameter '%s'\n", spacer, param->name); return (ERROR); } nValuesRead = 0; expecting = Expecting(LEFTPAR); } } else return (ERROR); SafeFree ((void **)&temp); return (NO_ERROR); } int DoUnlink (void) { int i, j; MrBayesPrint ("%s Unlinking\n", spacer); /* update status of linkTable */ for (j=0; jisRooted == YES) MrBayesPrint ("\n Tree '%s' [rooted]:\n\n", t->name); else MrBayesPrint ("\n Tree '%s' [unrooted]:\n\n", t->name); if (ShowTree (t) == ERROR) return (ERROR); else MrBayesPrint ("\n"); } } } return (NO_ERROR); } int DoShowModel (void) { if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before the model can be defined\n", spacer); return (ERROR); } if (ShowModel() == ERROR) return (ERROR); return (NO_ERROR); } int DoShowMoves (void) { if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before moves can be assigned\n", spacer); return (ERROR); } MrBayesPrint ("%s Moves that will be used by MCMC sampler (rel. proposal prob. > 0.0):\n\n", spacer); if (ShowMoves(YES) == ERROR) return (ERROR); if (showmovesParams.allavailable == YES) { MrBayesPrint ("%s Other available moves (rel. proposal prob. = 0.0):\n\n", spacer); if (ShowMoves(NO) == ERROR) return (ERROR); } else MrBayesPrint ("%s Use 'Showmoves allavailable=yes' to see a list of all available moves\n", spacer); return (NO_ERROR); } int DoShowmovesParm (char *parmName, char *tkn) { char tempStr[100]; if (expecting == Expecting(PARAMETER)) { expecting = Expecting(EQUALSIGN); } else { /* set Allavailable **********************************************************/ if (!strcmp(parmName, "Allavailable")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) showmovesParams.allavailable = YES; else showmovesParams.allavailable = NO; } else { MrBayesPrint ("%s Invalid argument for allavailable\n", spacer); return (ERROR); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoShowParams (void) { if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before model parameters can be shown\n", spacer); return (ERROR); } if (ShowParameters(YES, YES, YES) == ERROR) return (ERROR); return (NO_ERROR); } /*------------------------------------------------------------------------ | | FillNormalParams: Allocate and fill in non-tree parameters | -------------------------------------------------------------------------*/ int FillNormalParams (SafeLong *seed, int fromChain, int toChain) { int i, j, k, chn, tempInt, *intValue; MrBFlt *bs, *value, *subValue, scaler; Tree *tree; Param *p; ModelInfo *m; ModelParams *mp; /* fill in values for nontree params for state 0 of chains */ for (chn=fromChain; chnrelParts[0]]; m = &modelSettings[p->relParts[0]]; /* find model settings and nStates, pInvar, invar cond likes */ value = GetParamVals (p, chn, 0); subValue = GetParamSubVals (p, chn, 0); intValue = GetParamIntVals (p, chn, 0); if (p->paramType == P_TRATIO) { /* Fill in tratios **************************************************************************************/ if (p->paramId == TRATIO_DIR) value[0] = 1.0; else if (p->paramId == TRATIO_FIX) value[0] = mp->tRatioFix; } else if (p->paramType == P_REVMAT) { /* Fill in revMat ***************************************************************************************/ /* rates are stored in order, AC or AR first, using the Dirichlet parameterization */ if (p->paramId == REVMAT_DIR) { for (j=0; jnValues; j++) value[j] = 1.0 / (MrBFlt) (p->nValues); } else if (p->paramId == REVMAT_FIX) { scaler = 0.0; if (m->dataType == PROTEIN) { for (j=0; j<190; j++) scaler += (value[j] = mp->aaRevMatFix[j]); for (j=0; j<190; j++) value[j] /= scaler; } else { for (j=0; j<6; j++) scaler += (value[j] = mp->revMatFix[j]); for (j=0; j<6; j++) value[j] /= scaler; } } else if (p->paramId == REVMAT_MIX) { for (j=0; j<6; j++) { value[j] = 1.0 / 6.0; intValue[j] = 0; } } } else if (p->paramType == P_OMEGA) { /* Fill in omega ****************************************************************************************/ if (p->nValues == 1) { if (p->paramId == OMEGA_DIR) value[0] = 1.0; else if (p->paramId == OMEGA_FIX) value[0] = mp->omegaFix; } else { if (!strcmp(mp->omegaVar, "Ny98")) { if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BUF || p->paramId == OMEGA_BED || p->paramId == OMEGA_BEF || p->paramId == OMEGA_BFD || p->paramId == OMEGA_BFF) value[0] = RandomNumber(seed); else if (p->paramId == OMEGA_FUD || p->paramId == OMEGA_FUF || p->paramId == OMEGA_FED || p->paramId == OMEGA_FEF || p->paramId == OMEGA_FFD || p->paramId == OMEGA_FFF) value[0] = mp->ny98omega1Fixed; value[1] = 1.0; if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BUF || p->paramId == OMEGA_FUD || p->paramId == OMEGA_FUF) value[2] = mp->ny98omega3Uni[0] + RandomNumber(seed) * (mp->ny98omega3Uni[1] - mp->ny98omega3Uni[0]); else if (p->paramId == OMEGA_BED || p->paramId == OMEGA_BEF || p->paramId == OMEGA_FED || p->paramId == OMEGA_FEF) value[2] = (1.0 + -(1.0/mp->ny98omega3Exp) * log(1.0 - RandomNumber(seed))); else value[2] = mp->ny98omega3Fixed; if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BED || p->paramId == OMEGA_BFD || p->paramId == OMEGA_FUD || p->paramId == OMEGA_FED || p->paramId == OMEGA_FFD) { subValue[3] = mp->codonCatDir[0]; subValue[4] = mp->codonCatDir[1]; subValue[5] = mp->codonCatDir[2]; DirichletRandomVariable (&subValue[3], &subValue[0], 3, seed); } else { subValue[0] = mp->codonCatFreqFix[0]; subValue[1] = mp->codonCatFreqFix[1]; subValue[2] = mp->codonCatFreqFix[2]; subValue[3] = 0.0; subValue[4] = 0.0; subValue[5] = 0.0; } } else if (!strcmp(mp->omegaVar, "M3")) { if (p->paramId == OMEGA_FD || p->paramId == OMEGA_FF) { value[0] = mp->m3omegaFixed[0]; value[1] = mp->m3omegaFixed[1]; value[2] = mp->m3omegaFixed[2]; } else { value[0] = 0.1; value[1] = 1.0; value[2] = 3.0; } if (p->paramId == OMEGA_ED || p->paramId == OMEGA_FD) { subValue[3] = mp->codonCatDir[0]; subValue[4] = mp->codonCatDir[1]; subValue[5] = mp->codonCatDir[2]; DirichletRandomVariable (&subValue[3], &subValue[0], 3, seed); } else { subValue[0] = mp->codonCatFreqFix[0]; subValue[1] = mp->codonCatFreqFix[1]; subValue[2] = mp->codonCatFreqFix[2]; subValue[3] = 0.0; subValue[4] = 0.0; subValue[5] = 0.0; } } else if (!strcmp(mp->omegaVar, "M10")) { if (p->paramId == OMEGA_10UUB || p->paramId == OMEGA_10UEB || p->paramId == OMEGA_10UFB || p->paramId == OMEGA_10EUB || p->paramId == OMEGA_10EEB || p->paramId == OMEGA_10EFB || p->paramId == OMEGA_10FUB || p->paramId == OMEGA_10FEB || p->paramId == OMEGA_10FFB) { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 2] = mp->codonCatDir[0]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 3] = mp->codonCatDir[1]; DirichletRandomVariable (&subValue[mp->numM10BetaCats + mp->numM10GammaCats + 2], &subValue[mp->numM10BetaCats + mp->numM10GammaCats + 0], 2, seed); } else { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 0] = mp->codonCatFreqFix[0]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 1] = mp->codonCatFreqFix[1]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 2] = 0.0; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 3] = 0.0; } for (i=0; inumM10BetaCats; i++) subValue[i] = subValue[mp->numM10BetaCats + mp->numM10GammaCats + 0] / mp->numM10BetaCats; for (i=mp->numM10BetaCats; inumM10BetaCats+mp->numM10GammaCats; i++) subValue[i] = subValue[mp->numM10BetaCats + mp->numM10GammaCats + 1] / mp->numM10GammaCats; if (p->paramId == OMEGA_10FUB || p->paramId == OMEGA_10FUF || p->paramId == OMEGA_10FEB || p->paramId == OMEGA_10FEF || p->paramId == OMEGA_10FFB || p->paramId == OMEGA_10FFF) { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 4] = mp->m10betaFix[0]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 5] = mp->m10betaFix[1]; } else { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 4] = 1.0; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 5] = 1.0; } if (p->paramId == OMEGA_10UFB || p->paramId == OMEGA_10UFF || p->paramId == OMEGA_10EFB || p->paramId == OMEGA_10EFF || p->paramId == OMEGA_10FFB || p->paramId == OMEGA_10FFF) { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 6] = mp->m10gammaFix[0]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 7] = mp->m10gammaFix[1]; } else { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 6] = 1.0; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 7] = 1.0; } BetaBreaks (subValue[mp->numM10BetaCats + mp->numM10GammaCats + 4], subValue[mp->numM10BetaCats + mp->numM10GammaCats + 5], &value[0], mp->numM10BetaCats); if (DiscreteGamma (&value[mp->numM10BetaCats], subValue[mp->numM10BetaCats + mp->numM10GammaCats + 6], subValue[mp->numM10BetaCats + mp->numM10GammaCats + 7], mp->numM10GammaCats, 0) == ERROR) return (ERROR); for (i=0; inumM10GammaCats; i++) value[mp->numM10BetaCats + i] += 1.0; } else { } } } else if (p->paramType == P_PI) { /* Fill in state frequencies ****************************************************************************/ /* note that standard chars are mainly dealt with in ProcessStdChars in mcmc.c */ if (p->paramId == SYMPI_UNI || p->paramId == SYMPI_UNI_MS) value[0] = 1.0; else if (p->paramId == SYMPI_EXP || p->paramId == SYMPI_EXP_MS) value[0] = 1.0; else if (p->paramId == SYMPI_FIX || p->paramId == SYMPI_FIX_MS) value[0] = mp->symBetaFix; else if (p->paramId == PI_DIR) { if (mp->numDirParams != mp->nStates && mp->numDirParams != 0) { MrBayesPrint ("%s Mismatch between number of dirichlet parameters (%d) and the number of states (%d)\n", spacer, mp->numDirParams, m->numStates); return ERROR; } /* if user has not set dirichlet parameters, go with default */ /* overall variance equals number of states */ if (mp->numDirParams == 0) for (i=0; inStates; i++) value[i] = mp->stateFreqsDir[i] = 1.0; else for (i=0; inumStates; i++) value[i] = mp->stateFreqsDir[i]; /* now fill in subvalues */ for (i=0; inumStates; i++) subValue[i] = (1.0 / mp->nStates); } else if (p->paramId == PI_USER) { for (i=0; inumStates; i++) subValue[i] = mp->stateFreqsFix[i]; } else if (p->paramId == PI_FIXED) { if (!strcmp(mp->aaModelPr, "Fixed")) { if (!strcmp(mp->aaModel, "Jones")) { for (i=0; inStates; i++) subValue[i] = jonesPi[i]; } else if (!strcmp(mp->aaModel, "Dayhoff")) { for (i=0; inStates; i++) subValue[i] = dayhoffPi[i]; } else if (!strcmp(mp->aaModel, "Mtrev")) { for (i=0; inStates; i++) subValue[i] = mtrev24Pi[i]; } else if (!strcmp(mp->aaModel, "Mtmam")) { for (i=0; inStates; i++) subValue[i] = mtmamPi[i]; } else if (!strcmp(mp->aaModel, "Wag")) { for (i=0; inStates; i++) subValue[i] = wagPi[i]; } else if (!strcmp(mp->aaModel, "Rtrev")) { for (i=0; inStates; i++) subValue[i] = rtrevPi[i]; } else if (!strcmp(mp->aaModel, "Cprev")) { for (i=0; inStates; i++) subValue[i] = cprevPi[i]; } else if (!strcmp(mp->aaModel, "Vt")) { for (i=0; inStates; i++) subValue[i] = vtPi[i]; } else if (!strcmp(mp->aaModel, "Blosum")) { for (i=0; inStates; i++) subValue[i] = blosPi[i]; } } } else if (p->paramId == PI_EMPIRICAL) { if (GetEmpiricalFreqs (p->relParts, p->nRelParts) == ERROR) return (ERROR); for (i=0; inStates; i++) subValue[i] = empiricalFreqs[i]; } else if (p->paramId == PI_EQUAL) { for (i=0; inStates; i++) subValue[i] = (1.0 / mp->nStates); } } else if (p->paramType == P_SHAPE) { /* Fill in gamma values ********************************************************************************/ /* first get hyperprior */ if (p->paramId == SHAPE_UNI) { value[0] = 100.0; if (value[0] < mp->shapeUni[0] || value[0] > mp->shapeUni[1]) value[0] = mp->shapeUni[0] + (mp->shapeUni[1] - mp->shapeUni[0]) * 0.5; } else if (p->paramId == SHAPE_EXP) value[0] = 100.0; else if (p->paramId == SHAPE_FIX) value[0] = mp->shapeFix; /* now fill in rates */ if (DiscreteGamma (subValue, value[0], value[0], mp->numGammaCats, 0) == ERROR) return (ERROR); } else if (p->paramType == P_PINVAR) { /* Fill in pInvar ***************************************************************************************/ if (p->paramId == PINVAR_UNI) value[0] = 0.0; else if (p->paramId == PINVAR_FIX) value[0] = mp->pInvarFix; } else if (p->paramType == P_CORREL) { /* Fill in correlation parameter of adgamma model *******************************************************/ if (p->paramId == CORREL_UNI) value[0] = 0.0; else if (p->paramId == CORREL_FIX) value[0] = mp->corrFix; /* Fill in correlation matrices */ AutodGamma (subValue, value[0], mp->numGammaCats); } else if (p->paramType == P_SWITCH) { /* Fill in switchRates for covarion model ***************************************************************/ for (j=0; j<2; j++) { if (p->paramId == SWITCH_UNI) value[j] = RandomNumber(seed) * (mp->covswitchUni[1] - mp->covswitchUni[0]) + mp->covswitchUni[0]; else if (p->paramId == SWITCH_EXP) value[j] = (-(1.0/mp->covswitchExp) * log(1.0 - RandomNumber(seed))); else if (p->paramId == SWITCH_FIX) value[j] = mp->covswitchFix[j]; } } else if (p->paramType == P_RATEMULT) { /* Fill in division rates *****************************************************************************/ for (j=0; jnValues; j++) { value[j] = 1.0; /* fill in more info about the divisions if this is a true rate multiplier and not a base rate */ if (p->nSubValues > 0) { /* num uncompressed chars */ subValue[j] = (modelSettings[p->relParts[j]].numUncompressedChars); /* Dirichlet parameters */ subValue[p->nValues + j] = modelParams[p->relParts[j]].ratePrDir; } } } else if (p->paramType == P_GENETREERATE) { /* Fill in division rates *****************************************************************************/ for (j=0; jnValues; j++) { value[j] = 1.0; /* Dirichlet parameters fixed to 1.0 for now */ subValue[p->nValues + j] = 1.0; /* Get number of uncompressed chars from tree */ tree = GetTreeFromIndex(j, 0, 0); subValue[j] = 0.0; for (i=0; inRelParts; i++) { /* num uncompressed chars */ subValue[j] += (modelSettings[tree->relParts[i]].numUncompressedChars); } } } else if (p->paramType == P_SPECRATE) { /* Fill in speciation rates *****************************************************************************/ if (p->paramId == SPECRATE_FIX) value[0] = mp->speciationFix; else value[0] = 1.0; } else if (p->paramType == P_EXTRATE) { /* Fill in extinction rates *****************************************************************************/ if (p->paramId == EXTRATE_FIX) value[0] = mp->extinctionFix; else value[0] = 0.2; } else if (p->paramType == P_POPSIZE) { /* Fill in population size ****************************************************************************************/ for (j=0; jnValues; j++) { if (p->paramId == POPSIZE_UNI) value[j] = RandomNumber(seed) * (mp->popSizeUni[1] - mp->popSizeUni[0]) + mp->popSizeUni[0]; else if (p->paramId == POPSIZE_LOGNORMAL) value[j] = exp(mp->popSizeLognormal[0]); else if (p->paramId == POPSIZE_NORMAL) value[j] = mp->popSizeNormal[0]; else if (p->paramId == POPSIZE_GAMMA) value[j] = mp->popSizeGamma[0] / mp->popSizeGamma[1]; else if (p->paramId == POPSIZE_FIX) value[j] = mp->popSizeFix; } } else if (p->paramType == P_AAMODEL) { /* Fill in theta ****************************************************************************************/ if (p->paramId == AAMODEL_MIX) { /* amino acid model ID's AAMODEL_POISSON 0 AAMODEL_JONES 1 AAMODEL_DAY 2 AAMODEL_MTREV 3 AAMODEL_MTMAM 4 AAMODEL_WAG 5 AAMODEL_RTREV 6 AAMODEL_CPREV 7 AAMODEL_VT 8 AAMODEL_BLOSUM 9 */ /* set the amino acid model (the meaning of the numbers is defined) */ tempInt = (int)(RandomNumber(seed) * 10); value[0] = tempInt; /* we need to make certain that the aa frequencies are filled in correctly */ bs = GetParamSubVals (m->stateFreq, chn, 0); if (tempInt == AAMODEL_POISSON) { for (i=0; inStates; i++) bs[i] = (1.0 / 20.0); } else if (tempInt == AAMODEL_JONES) { for (i=0; inStates; i++) bs[i] = jonesPi[i]; } else if (tempInt == AAMODEL_DAY) { for (i=0; inStates; i++) bs[i] = dayhoffPi[i]; } else if (tempInt == AAMODEL_MTREV) { for (i=0; inStates; i++) bs[i] = mtrev24Pi[i]; } else if (tempInt == AAMODEL_MTMAM) { for (i=0; inStates; i++) bs[i] = mtmamPi[i]; } else if (tempInt == AAMODEL_WAG) { for (i=0; inStates; i++) bs[i] = wagPi[i]; } else if (tempInt == AAMODEL_RTREV) { for (i=0; inStates; i++) bs[i] = rtrevPi[i]; } else if (tempInt == AAMODEL_CPREV) { for (i=0; inStates; i++) bs[i] = cprevPi[i]; } else if (tempInt == AAMODEL_VT) { for (i=0; inStates; i++) bs[i] = vtPi[i]; } else if (tempInt == AAMODEL_BLOSUM) { for (i=0; inStates; i++) bs[i] = blosPi[i]; } for (i=0; inSubValues; i++) { subValue[i] = mp->aaModelPrProbs[i]; } } } else if (p->paramType == P_CPPRATE) { /* Fill in lambda (cpp rate) ********************************************************************************************/ if (p->paramId == CPPRATE_EXP) value[0] = (-(1.0/mp->cppRateExp) * log(1.0 - RandomNumber(seed))); else if (p->paramId == CPPRATE_FIX) value[0] = mp->cppRateFix; } else if (p->paramType == P_CPPMULTDEV) { /* Fill in log standard deviation (for relaxed clock rate multiplier) ***********************************************************/ if (p->paramId == CPPMULTDEV_FIX) value[0] = mp->cppMultDevFix; } else if (p->paramType == P_CPPEVENTS) { /* We fill in these when we fill in tree params **************************************************************************/ } else if (p->paramType == P_TK02VAR) { /* Fill in variance of relaxed clock lognormal **************************************************************************/ if (p->paramId == TK02VAR_UNI) value[0] = RandomNumber(seed) * (mp->tk02varUni[1] - mp->tk02varUni[0]) + mp->tk02varUni[0]; else if (p->paramId == TK02VAR_EXP) value[0] = (-(1.0/mp->tk02varExp) * log(1.0 - RandomNumber(seed))); else if (p->paramId == TK02VAR_FIX) value[0] = mp->tk02varFix; } else if (p->paramType == P_TK02BRANCHRATES) { /* We fill in these when we fill in tree params **************************************************************************/ } else if (p->paramType == P_IGRVAR) { /* Fill in variance of relaxed clock lognormal **************************************************************************/ if (p->paramId == IGRVAR_UNI) value[0] = RandomNumber(seed) * (mp->igrvarUni[1] - mp->igrvarUni[0]) + mp->igrvarUni[0]; else if (p->paramId == IGRVAR_EXP) value[0] = (-(1.0/mp->igrvarExp) * log(1.0 - RandomNumber(seed))); else if (p->paramId == IGRVAR_FIX) value[0] = mp->igrvarFix; } else if (p->paramType == P_IGRBRANCHLENS) { /* We fill in these when we fill in tree params **************************************************************************/ } else if (p->paramType == P_CLOCKRATE) { /* Fill in base rate of molecular clock **************************************************************************/ if (p->paramId == CLOCKRATE_FIX) value[0] = mp->clockRateFix; else if (p->paramId == CLOCKRATE_NORMAL) value[0] = mp->clockRateNormal[0]; else if (p->paramId == CLOCKRATE_LOGNORMAL) value[0] = exp(mp->clockRateLognormal[0]); else if (p->paramId == CLOCKRATE_EXP) value[0] = 1.0/(mp->clockRateExp); else if (p->paramId == CLOCKRATE_GAMMA) value[0] = mp->clockRateGamma[0]/mp->clockRateGamma[1]; } } /* next param */ } /* next chain */ return NO_ERROR; } int FillRelPartsString (Param *p, char **relPartString) { int i, n, filledString; char *tempStr; int tempStrSize=50; tempStr = (char *) SafeMalloc((size_t) (tempStrSize * sizeof(char))); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } if (numCurrentDivisions == 1) { filledString = NO; SafeStrcpy (relPartString, ""); } else { filledString = YES; if (p->nRelParts == numCurrentDivisions) { SafeStrcpy (relPartString, "{all}"); } else { SafeStrcpy (relPartString, "{"); for (i=n=0; inRelParts; i++) { n++; SafeSprintf(&tempStr, &tempStrSize, "%d", p->relParts[i] + 1); SafeStrcat (relPartString, tempStr); if (n < p->nRelParts) SafeStrcat (relPartString, ","); } SafeStrcat (relPartString, "}"); } } free (tempStr); return (filledString); } /* FillTopologySubParams: Fill subparams (brlens) for a topology */ int FillTopologySubParams (Param *param, int chn, int state, SafeLong *seed) { int i,returnVal; Tree *tree, *tree1; Param *q; MrBFlt clockRate; PolyTree *sourceTree; MrBFlt minRate,maxRate; tree = GetTree (param, chn, state); for (i=1; inSubParams; i++) { q = param->subParams[i]; tree1 = GetTree (q, chn, state); if (CopyToTreeFromTree(tree1, tree) == ERROR) return (ERROR); } for (i=0; inSubParams; i++) { q = param->subParams[i]; tree = GetTree (q, chn, state); if (q->paramId == BRLENS_FIXED || q->paramId == BRLENS_CLOCK_FIXED) { if (param->paramId == TOPOLOGY_NCL_FIXED || param->paramId == TOPOLOGY_NCL_FIXED_HOMO || param->paramId == TOPOLOGY_NCL_FIXED_HETERO || param->paramId == TOPOLOGY_CL_FIXED || param->paramId == TOPOLOGY_RCL_FIXED || param->paramId == TOPOLOGY_CCL_FIXED || param->paramId == TOPOLOGY_RCCL_FIXED|| param->paramId == TOPOLOGY_FIXED) { sourceTree = AllocatePolyTree(numTaxa); CopyToPolyTreeFromPolyTree (sourceTree, userTree[modelParams[q->relParts[0]].brlensFix]); PrunePolyTree (sourceTree); ResetTipIndices (sourceTree); ResetIntNodeIndices (sourceTree); if (tree->isRooted != sourceTree->isRooted) { MrBayesPrint("%s Cannot set fixed branch lengths because of mismatch in rootedness", spacer); FreePolyTree (sourceTree); return (ERROR); } if (CopyToTreeFromPolyTree(tree,sourceTree) == ERROR) { MrBayesPrint("%s Problem setting fixed branch lengths", spacer); FreePolyTree (sourceTree); return (ERROR); } FreePolyTree (sourceTree); if (tree->isClock == YES && IsClockSatisfied(tree, 1E-6) == NO) { MrBayesPrint("%s Fixed branch lengths do not satisfy clock", spacer); return (ERROR); } if (tree->isCalibrated == YES && IsCalibratedClockSatisfied(tree,&minRate,&maxRate, 1E-6) == NO) { MrBayesPrint("%s Fixed branch lengths do not satisfy calibrations", spacer); return (ERROR); } if (tree->isCalibrated == YES && !strcmp(modelParams[tree->relParts[0]].clockRatePr, "Fixed")) { clockRate = modelParams[tree->relParts[0]].clockRateFix; if(( clockRate < minRate && AreDoublesEqual (clockRate, minRate , 0.0001) == NO ) || ( clockRate > maxRate && AreDoublesEqual (clockRate, maxRate , 0.0001) == NO )) { MrBayesPrint("%s Fixed branch lengths do not satisfy fixed clockrate", spacer); return (ERROR); } } tree->fromUserTree=YES; returnVal = NO_ERROR; } else { MrBayesPrint("%s Fixed branch lengths can only be used for a fixed topology\n", spacer); return (ERROR); } } else if (tree->isCalibrated == YES) { assert (tree->isClock == YES); clockRate = *GetParamVals(modelSettings[tree->relParts[0]].clockRate, chn, state ); returnVal = InitCalibratedBrlens (tree, clockRate, seed); if ( IsClockSatisfied (tree,0.001) == NO) { MrBayesPrint ("%s Branch lengths of the tree does not satisfy clock\n", spacer); return (ERROR); } tree->fromUserTree=NO; } else if (tree->isClock == YES) returnVal = InitClockBrlens (tree); else returnVal = InitBrlens (tree, 0.1); if( returnVal == ERROR ) return (ERROR); if( FillBrlensSubParams (q, chn, state) == ERROR ) return (ERROR); } return (NO_ERROR); } /* FillBrlensSubParams: Fill any relaxed clock subparams of a brlens param */ int FillBrlensSubParams (Param *param, int chn, int state) { int i, j, *nEvents; MrBFlt *brlen, *branchRate, **position, **rateMult; Tree *tree; TreeNode *p; Param *q; tree = GetTree (param, chn, state); for (i=0; inSubParams; i++) { q = param->subParams[i]; if (q->paramType == P_CPPEVENTS) { nEvents = q->nEvents[2*chn+state]; position = q->position[2*chn+state]; rateMult = q->rateMult[2*chn+state]; brlen = GetParamSubVals (q, chn, state); for (j=0; jnNodes-1; j++) { p = tree->allDownPass[j]; if (nEvents[p->index] != 0) { free (position[p->index]); position[p->index] = NULL; free (rateMult[p->index]); rateMult[p->index] = NULL; } nEvents[p->index] = 0; assert( j==tree->nNodes-2 || fabs(p->length - (p->anc->nodeDepth - p->nodeDepth)) < 0.000001); brlen[p->index] = p->length; } } else if (q->paramType == P_TK02BRANCHRATES || q->paramType == P_IGRBRANCHLENS) { branchRate = GetParamVals (q, chn, state); brlen = GetParamSubVals (q, chn, state); for (j=0; jnNodes-1; j++) { p = tree->allDownPass[j]; assert( j==tree->nNodes-2 || fabs(p->length - (p->anc->nodeDepth - p->nodeDepth)) < 0.000001); branchRate[p->index] = 1.0; brlen[p->index] = p->length; } } } return (NO_ERROR); } /*Note: In PruneConstraintPartitions() we can not relay on specific rootnes of a tree since different partitions may theoreticly have different clock models, whil constraints apply to all partitions/trees */ int PruneConstraintPartitions() { int i, j, constraintId, nLongsNeeded; nLongsNeeded = (numLocalTaxa - 1) / nBitsInALong + 1; for (constraintId=0; constraintIdcheckConstraints == NO) return YES; /* get some handy numbers */ numTaxa = t->nNodes - t->nIntNodes - (t->isRooted == YES ? 1 : 0); nLongsNeeded = (numTaxa - 1) / nBitsInALong + 1; if ( t->bitsets == NULL) { AllocateTreePartitions(t); } else { ResetTreePartitions(t); /*Inefficient function, rewrite faster version*/ } #if ! defined (NDEBUG) for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if(p->isLocked == YES) { if ( IsUnionEqThird (definedConstraintPruned[p->lockID], definedConstraintPruned[p->lockID], p->partition, nLongsNeeded) == NO && IsUnionEqThird (definedConstraintTwoPruned[p->lockID], definedConstraintTwoPruned[p->lockID], p->partition, nLongsNeeded) == NO) { printf ("DEBUG ERROR: Locked node does not represent right partition. \n"); return ABORT; } else { locs_count++; } } } if(locs_count != t->nLocks) { printf("DEBUG ERROR: lock_count:%d should be lock_count:%d\n", locs_count, t->nLocks); return ABORT; } #endif for (k=0; kconstraints[k] == YES && definedConstraintsType[k] == HARD ) { if( t->isRooted == YES ) { CheckFirst = YES; CheckSecond = NO; } else { /*exactly one of next two will be YES*/ CheckFirst = IsBitSet(localOutGroup, definedConstraintPruned[k])==YES ? NO : YES; CheckSecond = IsBitSet(localOutGroup, definedConstraintTwoPruned[k])==YES ? NO : YES; assert( (CheckFirst^CheckSecond)==1 ); } for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->anc != NULL) { if(CheckFirst==YES && IsPartNested(definedConstraintPruned[k], p->partition, nLongsNeeded) && IsPartNested(p->partition,definedConstraintPruned[k], nLongsNeeded) ) break; if(CheckSecond==YES && IsPartNested(definedConstraintTwoPruned[k], p->partition, nLongsNeeded) && IsPartNested(p->partition, definedConstraintTwoPruned[k], nLongsNeeded) ) break; } } if( i==t->nIntNodes ) { printf ("DEBUG ERROR: Hard constraint is not satisfied. \n"); return ABORT; //assert(0); } } #endif if( t->constraints[k] == NO || definedConstraintsType[k] == HARD ) continue; if( definedConstraintsType[k] == PARTIAL ) { /* alternative way if (t->isRooted == NO && !IsBitSet(localOutGroup, definedConstraintPruned[k])) { m = FirstTaxonInPartition (constraintPartition, nLongsNeeded); for (i=0; t->nodes[i].index != m; i++) ; p = &t->nodes[i]; p=p->anc; while( !IsPartNested(definedConstraintPruned[k], p->partition, nLongsNeeded) ) p=p->anc; if( IsSectionEmpty(definedConstraintTwoPruned[k], p->partition, nLongsNeeded) ) continue; } */ if( t->isRooted == YES ) { CheckFirst = YES; CheckSecond = NO; /*In rooted case even if we have a node with partition fully containing second set and not containing the first set it would not sutisfy the constraint*/ } else { if ( NumBits(definedConstraintTwoPruned[k], nLongsNeeded) == 1) continue; /*one or two of the next two statments will be YES*/ CheckFirst = IsBitSet(localOutGroup, definedConstraintPruned[k])==YES ? NO : YES; CheckSecond = IsBitSet(localOutGroup, definedConstraintTwoPruned[k])==YES ? NO : YES; assert( (CheckFirst|CheckSecond)==1 ); } for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->anc != NULL) { if( CheckFirst==YES && IsPartNested( definedConstraintPruned[k], p->partition, nLongsNeeded) && IsSectionEmpty(definedConstraintTwoPruned[k], p->partition, nLongsNeeded) ) break; if( CheckSecond==YES && IsPartNested(definedConstraintTwoPruned[k], p->partition, nLongsNeeded) && IsSectionEmpty(definedConstraintPruned[k], p->partition, nLongsNeeded) ) break; } } if( i==t->nIntNodes ) return NO; } else { assert(definedConstraintsType[k] == NEGATIVE); if( t->isRooted == YES ) { CheckFirst = YES; CheckSecond = NO; } else { /*exactly one of next two will be YES*/ CheckFirst = IsBitSet(localOutGroup, definedConstraintPruned[k])==YES ? NO : YES; CheckSecond = IsBitSet(localOutGroup, definedConstraintTwoPruned[k])==YES ? NO : YES; assert( (CheckFirst^CheckSecond)==1 ); } for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->anc != NULL) { if(CheckFirst==YES && !IsPartNested(definedConstraintPruned[k], p->partition, nLongsNeeded) && !IsSectionEmpty(definedConstraintPruned[k], p->partition, nLongsNeeded) ) break; if(CheckSecond==YES && !IsPartNested(definedConstraintTwoPruned[k], p->partition, nLongsNeeded) && !IsSectionEmpty(definedConstraintTwoPruned[k], p->partition, nLongsNeeded) ) break; } } if( i==t->nIntNodes ) return NO; } } return YES; } /*------------------------------------------------------------------ | | FillTreeParams: Fill in trees and branch lengths | Note: should be run after FillNormalParams becouse | clockrate needs to be set if calibrated tree needs | to be filled. | ------------------------------------------------------------------*/ int FillTreeParams (SafeLong *seed, int fromChain, int toChain) { int i, k, chn, nTaxa, tmp; Param *p, *q; Tree *tree; PolyTree *constraintTree; PolyTree *constraintTreeRef; if( PruneConstraintPartitions() == ERROR ) return ERROR; /* Build starting trees for state 0 */ for (chn=fromChain; chnparamType == P_TOPOLOGY) { // assert (p->nSubParams == 1); // this assert seems to be wrong -- FR 2010-12-25 q = p->subParams[0]; tree = GetTree (q, chn, 0); if (tree->isRooted == YES) nTaxa = tree->nNodes - tree->nIntNodes - 1; else nTaxa = tree->nNodes - tree->nIntNodes; /* fixed topology */ if (p->paramId == TOPOLOGY_NCL_FIXED || p->paramId == TOPOLOGY_NCL_FIXED_HOMO || p->paramId == TOPOLOGY_NCL_FIXED_HETERO || p->paramId == TOPOLOGY_CL_FIXED || p->paramId == TOPOLOGY_RCL_FIXED || p->paramId == TOPOLOGY_CCL_FIXED || p->paramId == TOPOLOGY_RCCL_FIXED|| p->paramId == TOPOLOGY_FIXED || p->paramId == TOPOLOGY_PARSIMONY_FIXED ) { constraintTree = AllocatePolyTree (numTaxa); CopyToPolyTreeFromPolyTree (constraintTree, userTree[modelParams[p->relParts[0]].topologyFix]); PrunePolyTree (constraintTree); ResetTipIndices(constraintTree); ResetIntNodeIndices(constraintTree); RandResolve (NULL, constraintTree, seed, constraintTree->isRooted); if (tree->nIntNodes != constraintTree->nIntNodes) { if(tree->isRooted != constraintTree->isRooted ) { MrBayesPrint ("%s Could not fix topology because user tree '%s' differs in rootedness with the model tree.\n", spacer, userTree[modelParams[p->relParts[0]].topologyFix]->name); MrBayesPrint ("%s The user tree %s is%srooted, while expected model tree is%srooted.\n", spacer, userTree[modelParams[p->relParts[0]].topologyFix]->name, (constraintTree->isRooted?" ":" not "), (tree->isRooted?" ":" not ")); } else MrBayesPrint ("%s Could not fix topology because user tree '%s' is not fully resolved.\n", spacer, userTree[modelParams[p->relParts[0]].topologyFix]->name); FreePolyTree (constraintTree); return (ERROR); } if (CopyToTreeFromPolyTree(tree, constraintTree) == ERROR) { MrBayesPrint ("%s Could not fix topology according to user tree '%s'\n", spacer, userTree[modelParams[p->relParts[0]].topologyFix]->name); FreePolyTree (constraintTree); return (ERROR); } FreePolyTree (constraintTree); } /* constrained topology */ else if (tree->nConstraints > 0) { constraintTreeRef = AllocatePolyTree (nTaxa); if (!constraintTreeRef) return (ERROR); if (BuildConstraintTree (tree, constraintTreeRef, localTaxonNames) == ERROR) { FreePolyTree (constraintTreeRef); return (ERROR); } if ( AllocatePolyTreePartitions (constraintTreeRef) == ERROR ) return (ERROR); constraintTree = AllocatePolyTree (nTaxa); if (!constraintTree) return (ERROR); if ( AllocatePolyTreePartitions (constraintTree) == ERROR ) return (ERROR); for(i=0;i<100;i++) { CopyToPolyTreeFromPolyTree(constraintTree,constraintTreeRef); tmp = RandResolve (tree, constraintTree, &globalSeed, tree->isRooted); if ( tmp != NO_ERROR ) { if (tmp == ERROR) { FreePolyTree (constraintTree); return (ERROR); } else { assert (tmp == ABORT); continue; } } CopyToTreeFromPolyTree(tree, constraintTree); if( DoesTreeSatisfyConstraints(tree)==YES ) break; } #if defined (DEBUG_CONSTRAINTS) if (theTree->checkConstraints == YES && CheckConstraints (tree) == ERROR) { printf ("Error in constraints of starting tree\n"); getchar(); } #endif FreePolyTree (constraintTree); FreePolyTree (constraintTreeRef); if(i==100) { MrBayesPrint ("%s Could not build a starting tree satisfying all constraints\n", spacer); return (ERROR); } } /* random topology */ else { if (tree->isRooted == YES) { if (BuildRandomRTopology (tree, &globalSeed) == ERROR) return (ERROR); } else { if (BuildRandomUTopology (tree, &globalSeed) == ERROR) return (ERROR); if (MoveCalculationRoot (tree, localOutGroup) == ERROR) return (ERROR); } } if (LabelTree (tree, localTaxonNames) == ERROR) return (ERROR); if (q == p) continue; /* this is a parsimony tree without branch lengths */ if (InitializeTreeCalibrations (tree) == ERROR) return (ERROR); if (FillTopologySubParams(p, chn, 0, seed)== ERROR) return (ERROR); } } } if (numTopologies > 1 && !strcmp(modelParams[0].topologyPr,"Speciestree")) { if (FillSpeciesTreeParams(seed, fromChain, toChain) == ERROR) return (ERROR); } return (NO_ERROR); } void FreeCppEvents (Param *p) { int i, j; if (p->paramType != P_CPPEVENTS) return; if (p->nEvents != NULL) { free (p->nEvents[0]); free (p->nEvents); for (i=0; iposition[2*i][j]); free (p->rateMult[2*i][j]); free (p->position[2*i+1][j]); free (p->rateMult[2*i+1][j]); } } free (p->position[0]); free (p->position); free (p->rateMult[0]); free (p->rateMult); p->nEvents = NULL; p->position = NULL; p->rateMult = NULL; } } int FreeModel (void) { int i; Param *p; if (memAllocs[ALLOC_MODEL] == YES) { for (i=0; iparamType == P_CPPEVENTS) FreeCppEvents(p); } free (paramValues); paramValues = NULL; free (intValues); intValues = NULL; paramValsRowSize = intValsRowSize = 0; memAllocs[ALLOC_PARAMVALUES] = NO; } if (memAllocs[ALLOC_PARAMS] == YES) { for (i=0; ituningParam[0]); free (mv->tuningParam); free (mv->relProposalProb); free (mv->nAccepted); free (mv->name); free (mv); } /* Compute empirical state freq are return it in global array empiricalFreqs[] */ int GetEmpiricalFreqs (int *relParts, int nRelParts) { int i, j, k, m, n, thePartition, nuc[20], ns, temp, isDNA, isProtein, firstRel; MrBFlt freqN[20], sum, sumN[20]/*, rawCounts[20]*/; isDNA = isProtein = NO; ns = 0; firstRel = 0; for (i=0; idataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_DOUBLET) n *= 2; else if (m->nucModelId == NUCMODEL_CODON) n *= 3; } m->numUncompressedChars = n; } return (NO_ERROR); } int *GetParamIntVals (Param *parm, int chain, int state) { return parm->intValues + (2 * chain + state) * intValsRowSize; } MrBFlt *GetParamStdStateFreqs (Param *parm, int chain, int state) { return parm->stdStateFreqs + (2 * chain + state) * stdStateFreqsRowSize; } MrBFlt *GetParamSubVals (Param *parm, int chain, int state) { return parm->subValues + (2 * chain + state) * paramValsRowSize; } MrBFlt *GetParamVals (Param *parm, int chain, int state) { return parm->values + (2 * chain + state) * paramValsRowSize; } void GetPossibleAAs (int aaCode, int aa[]) { int m; for (m=0; m<20; m++) aa[m] = 0; if (aaCode > 0 && aaCode <= 20) aa[aaCode-1] = 1; else { for (m=0; m<20; m++) aa[m] = 1; } # if 0 printf ("%2d -- ", aaCode); for (m=0; m<20; m++) printf("%d", aa[m]); printf ("\n"); # endif } void GetPossibleNucs (int nucCode, int nuc[]) { if (nucCode == 1) { nuc[0] = 1; nuc[1] = 0; nuc[2] = 0; nuc[3] = 0; } else if (nucCode == 2) { nuc[0] = 0; nuc[1] = 1; nuc[2] = 0; nuc[3] = 0; } else if (nucCode == 3) { nuc[0] = 1; nuc[1] = 1; nuc[2] = 0; nuc[3] = 0; } else if (nucCode == 4) { nuc[0] = 0; nuc[1] = 0; nuc[2] = 1; nuc[3] = 0; } else if (nucCode == 5) { nuc[0] = 1; nuc[1] = 0; nuc[2] = 1; nuc[3] = 0; } else if (nucCode == 6) { nuc[0] = 0; nuc[1] = 1; nuc[2] = 1; nuc[3] = 0; } else if (nucCode == 7) { nuc[0] = 1; nuc[1] = 1; nuc[2] = 1; nuc[3] = 0; } else if (nucCode == 8) { nuc[0] = 0; nuc[1] = 0; nuc[2] = 0; nuc[3] = 1; } else if (nucCode == 9) { nuc[0] = 1; nuc[1] = 0; nuc[2] = 0; nuc[3] = 1; } else if (nucCode == 10) { nuc[0] = 0; nuc[1] = 1; nuc[2] = 0; nuc[3] = 1; } else if (nucCode == 11) { nuc[0] = 1; nuc[1] = 1; nuc[2] = 0; nuc[3] = 1; } else if (nucCode == 12) { nuc[0] = 0; nuc[1] = 0; nuc[2] = 1; nuc[3] = 1; } else if (nucCode == 13) { nuc[0] = 1; nuc[1] = 0; nuc[2] = 1; nuc[3] = 1; } else if (nucCode == 14) { nuc[0] = 0; nuc[1] = 1; nuc[2] = 1; nuc[3] = 1; } else { nuc[0] = 1; nuc[1] = 1; nuc[2] = 1; nuc[3] = 1; } } void GetPossibleRestrictionSites (int resSiteCode, int *sites) { int m; for (m=0; m<2; m++) sites[m] = 0; if (resSiteCode == 1) sites[0] = 1; else if (resSiteCode == 2) sites[1] = 1; else sites[0] = sites[1] = 1; # if 0 printf ("%2d -- ", aaCode); for (m=0; m<20; m++) printf("%d", aa[m]); printf ("\n"); # endif } Tree *GetTree (Param *parm, int chain, int state) { return mcmcTree[parm->treeIndex + ((2 * chain + state) * numTrees)]; } Tree *GetTreeFromIndex (int index, int chain, int state) { return mcmcTree[index + ((2 * chain + state) * numTrees)]; } /*----------------------------------------------------------- | | GetUserTreeFromName: Do case-insensitive search for user | tree, return index if match, -1 and ERROR if error | ------------------------------------------------------------*/ int GetUserTreeFromName (int *index, char *treeName) { int i, j, k, nMatches; char localName[100], temp[100]; (*index) = -1; /* appropriate return if no match */ if ((int)strlen(treeName) > 99) { MrBayesPrint ("%s Too many characters in tree name\n", spacer); return (ERROR); } strcpy (localName, treeName); for (i=0; i<(int)strlen(localName); i++) localName[i] = tolower(localName[i]); nMatches = j = 0; for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strcmp(localName,temp) == 0) { j = i; nMatches++; } } if (nMatches==0) { for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strncmp(localName,temp,strlen(localName)) == 0) { j = i; nMatches++; } } } if (nMatches == 0) { MrBayesPrint ("%s Could not find tree '%s'\n", spacer, localName); return (ERROR); } else if (nMatches > 1) { MrBayesPrint ("%s Several trees matched the abbreviated name '%s'\n", spacer, localName); return (ERROR); } else { (*index) = j; return (NO_ERROR); } } int InitializeLinks (void) { int i, j; linkNum = 0; for (i=0; iisCalibrated == NO) return (NO_ERROR); /* Set tip calibrations */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->right == NULL && localTaxonCalibration[p->index]->prior != unconstrained) { p->isDated = YES; p->calibration = localTaxonCalibration[p->index]; if (p->calibration->prior == fixed) p->age = p->calibration->age; else if (p->calibration->prior == uniform) p->age = p->calibration->min; else { assert(p->calibration->prior == offsetExponential); p->age = p->calibration->offset; } } else if (p->left == NULL && p->right == NULL) { p->isDated = NO; p->calibration = NULL; p->age = -1.0; } } /* Initialize interior calibrations */ if (CheckSetConstraints(t) == ERROR) return (ERROR); return (NO_ERROR); } int IsApplicable (Param *param) { if (param == NULL) return NO; return YES; } int IsApplicableTreeAgeMove (Param *param) { Tree *t; TreeNode *p; if (param == NULL) return NO; if (param->paramType != P_BRLENS) return NO; t = GetTree (param, 0, 0); p = t->root->left; if (p->isDated == NO) return NO; if (p->calibration->prior == fixed) return NO; else return YES; } int IsModelSame (int whichParam, int part1, int part2, int *isApplic1, int *isApplic2) { int i, isSame, isFirstNucleotide, isSecondNucleotide, isFirstProtein, isSecondProtein, nDiff, temp1, temp2; isSame = YES; *isApplic1 = YES; *isApplic2 = YES; isFirstNucleotide = isSecondNucleotide = NO; if (modelSettings[part1].dataType == DNA || modelSettings[part1].dataType == RNA) isFirstNucleotide = YES; if (modelSettings[part2].dataType == DNA || modelSettings[part2].dataType == RNA) isSecondNucleotide = YES; isFirstProtein = isSecondProtein = NO; if (modelSettings[part1].dataType == PROTEIN) isFirstProtein = YES; if (modelSettings[part2].dataType == PROTEIN) isSecondProtein = YES; if (whichParam == P_TRATIO) { /* Check the ti/tv rate ratio for partitions 1 and 2. */ /* Check if the model is parsimony for either partition */ if (!strcmp(modelParams[part1].parsModel, "Yes")) *isApplic1 = NO; /* part1 has a parsimony model and tratio does not apply */ if (!strcmp(modelParams[part2].parsModel, "Yes")) *isApplic2 = NO; /* part2 has a parsimony model and tratio does not apply */ /* Check that the data are nucleotide for both partitions 1 and 2 */ if (isFirstNucleotide == NO) *isApplic1 = NO; /* part1 is not nucleotide data so tratio does not apply */ if (isSecondNucleotide == NO) *isApplic2 = NO; /* part2 is not nucleotide data so tratio does not apply */ /* check that nst=2 for both partitions */ if (strcmp(modelParams[part1].nst, "2")) *isApplic1 = NO; /* part1 does not have nst=2 and tratio does not apply */ if (strcmp(modelParams[part2].nst, "2")) *isApplic2 = NO; /* part2 does not have nst=2 and tratio does not apply */ /* Check if part1 & part2 are restriction */ if (modelParams[part1].dataType == RESTRICTION) *isApplic1 = NO; if (modelParams[part2].dataType == RESTRICTION) *isApplic2 = NO; /* If Nst = 2 for both part1 and part2, we now need to check if the prior is the same for both. */ if (!strcmp(modelParams[part1].tRatioPr,"Beta") && !strcmp(modelParams[part2].tRatioPr,"Beta")) { if (AreDoublesEqual (modelParams[part1].tRatioDir[0], modelParams[part2].tRatioDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].tRatioDir[1], modelParams[part2].tRatioDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].tRatioPr,"Fixed") && !strcmp(modelParams[part2].tRatioPr,"Fixed")) { if (AreDoublesEqual (modelParams[part1].tRatioFix, modelParams[part2].tRatioFix, (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ /* Check to see if tratio is inapplicable for either partition. */ if ((*isApplic1) == NO || (*isApplic2) == NO) isSame = NO; /* if tratio is inapplicable for either partition, then the parameter cannot be the same */ } else if (whichParam == P_REVMAT) { /* Check the GTR rates for partitions 1 and 2. */ /* Check if the model is parsimony for either partition */ if (!strcmp(modelParams[part1].parsModel, "Yes")) *isApplic1 = NO; /* part1 has a parsimony model and GTR rates do not apply */ if (!strcmp(modelParams[part2].parsModel, "Yes")) *isApplic2 = NO; /* part2 has a parsimony model and GTR rates do not apply */ /* Check that the data are nucleotide or protein for both partitions 1 and 2 */ if (isFirstNucleotide == NO && isFirstProtein == NO) *isApplic1 = NO; /* part1 is not nucleotide or protein data so GTR rates do not apply */ if (isSecondNucleotide == NO && isSecondProtein == NO) *isApplic2 = NO; /* part2 is not nucleotide or protein data so GTR rates do not apply */ /* check that nst=6 or mixed for both partitions if nucleotide */ if (isFirstNucleotide == YES && strcmp(modelParams[part1].nst, "6") && strcmp(modelParams[part1].nst, "Mixed")) *isApplic1 = NO; /* part1 does not have nst=6/Mixed and GTR rates do not apply */ if (isSecondNucleotide == YES && strcmp(modelParams[part2].nst, "6") && strcmp(modelParams[part2].nst, "Mixed")) *isApplic2 = NO; /* part2 does not have nst=6/Mixed and GTR rates do not apply */ /* check that model is GTR for both partitions if protein */ if (isFirstProtein == YES && (strcmp(modelParams[part1].aaModel,"Gtr")!=0 || strcmp(modelParams[part1].aaModelPr,"Fixed")!=0)) *isApplic1 = NO; if (isSecondProtein == YES && (strcmp(modelParams[part2].aaModel,"Gtr")!=0 || strcmp(modelParams[part2].aaModelPr,"Fixed")!=0)) *isApplic2 = NO; /* check that data type is the same for both partitions */ if (isFirstNucleotide == YES && isSecondNucleotide == NO) isSame = NO; if (isFirstProtein == YES && isSecondProtein == NO) isSame = NO; /* GTR applies to both part1 and part2. We now need to check if the prior is the same for both. */ if (isFirstNucleotide == YES) { if (strcmp(modelParams[part1].nst, modelParams[part2].nst) != 0) isSame = NO; if (!strcmp(modelParams[part1].nst,"Mixed")) { if (AreDoublesEqual (modelParams[part1].revMatSymDir, modelParams[part2].revMatSymDir, (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].nst,"6") && !strcmp(modelParams[part1].revMatPr,"Dirichlet") && !strcmp(modelParams[part2].revMatPr,"Dirichlet")) { for (i=0; i<6; i++) { if (AreDoublesEqual (modelParams[part1].revMatDir[i], modelParams[part2].revMatDir[i], (MrBFlt) 0.00001) == NO) isSame = NO; } } else if (!strcmp(modelParams[part1].nst,"6") && !strcmp(modelParams[part1].revMatPr,"Fixed") && !strcmp(modelParams[part2].revMatPr,"Fixed")) { for (i=0; i<6; i++) { if (AreDoublesEqual (modelParams[part1].revMatFix[i], modelParams[part2].revMatFix[i], (MrBFlt) 0.00001) == NO) isSame = NO; } } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else /* if (isFirstProtein == YES) */ { if (!strcmp(modelParams[part1].aaRevMatPr,"Dirichlet") && !strcmp(modelParams[part2].aaRevMatPr,"Dirichlet")) { for (i=0; i<190; i++) { if (AreDoublesEqual (modelParams[part1].aaRevMatDir[i], modelParams[part2].aaRevMatDir[i], (MrBFlt) 0.00001) == NO) isSame = NO; } } else if (!strcmp(modelParams[part1].aaRevMatPr,"Fixed") && !strcmp(modelParams[part2].aaRevMatPr,"Fixed")) { for (i=0; i<190; i++) { if (AreDoublesEqual (modelParams[part1].aaRevMatFix[i], modelParams[part2].aaRevMatFix[i], (MrBFlt) 0.00001) == NO) isSame = NO; } } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } /* Check to see if the GTR rates are inapplicable for either partition. */ if ((*isApplic1) == NO || (*isApplic2) == NO) isSame = NO; /* if GTR rates are inapplicable for either partition, then the parameter cannot be the same */ } else if (whichParam == P_OMEGA) { /* Check the nonsynonymous/synonymous rate ratio for partitions 1 and 2. */ /* Check if the model is parsimony for either partition */ if (!strcmp(modelParams[part1].parsModel, "Yes")) *isApplic1 = NO; /* part1 has a parsimony model and nonsynonymous/synonymous rate ratio does not apply */ if (!strcmp(modelParams[part2].parsModel, "Yes")) *isApplic2 = NO; /* part2 has a parsimony model and nonsynonymous/synonymous rate ratio does not apply */ /* Check that the data are nucleotide for both partitions 1 and 2 */ if (isFirstNucleotide == NO) *isApplic1 = NO; /* part1 is not nucleotide data so a nonsynonymous/synonymous rate ratio does not apply */ if (isSecondNucleotide == NO) *isApplic2 = NO; /* part2 is not nucleotide data so a nonsynonymous/synonymous rate ratio does not apply */ /* Check that the model structure is the same for both. The nucmodel should be "codon". */ if (strcmp(modelParams[part1].nucModel, "Codon")) *isApplic1 = NO; /* part1 does not have Nucmodel = Codon and nonsynonymous/synonymous rate ratio does not apply */ if (strcmp(modelParams[part2].nucModel, "Codon")) *isApplic2 = NO; /* part2 does not have Nucmodel = Codon and nonsynonymous/synonymous rate ratio does not apply */ /* Assuming that Nucmodel = Codon for both part1 and part2, we now need to check if the prior is the same for both. */ if (!strcmp(modelParams[part1].omegaVar, "M3") && !strcmp(modelParams[part2].omegaVar, "M3")) { if (!strcmp(modelParams[part1].m3omegapr, "Exponential") && !strcmp(modelParams[part2].m3omegapr, "Exponential")) { } else if (!strcmp(modelParams[part1].m3omegapr, "Fixed") && !strcmp(modelParams[part1].m3omegapr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].m3omegaFixed[0], modelParams[part2].m3omegaFixed[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].m3omegaFixed[1], modelParams[part2].m3omegaFixed[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ if (!strcmp(modelParams[part1].codonCatFreqPr, "Dirichlet") && !strcmp(modelParams[part2].codonCatFreqPr, "Dirichlet")) { if (AreDoublesEqual (modelParams[part1].codonCatDir[0], modelParams[part2].codonCatDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[1], modelParams[part2].codonCatDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[2], modelParams[part2].codonCatDir[2], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].codonCatFreqPr, "Fixed") && !strcmp(modelParams[part1].codonCatFreqPr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[0], modelParams[part2].codonCatFreqFix[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[1], modelParams[part2].codonCatFreqFix[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[2], modelParams[part2].codonCatFreqFix[2], (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else if (!strcmp(modelParams[part1].omegaVar, "M10") && !strcmp(modelParams[part2].omegaVar, "M10")) { if (!strcmp(modelParams[part1].codonCatFreqPr, "Dirichlet") && !strcmp(modelParams[part2].codonCatFreqPr, "Dirichlet")) { if (AreDoublesEqual (modelParams[part1].codonCatDir[0], modelParams[part2].codonCatDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[1], modelParams[part2].codonCatDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].codonCatFreqPr, "Fixed") && !strcmp(modelParams[part1].codonCatFreqPr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[0], modelParams[part2].codonCatFreqFix[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[1], modelParams[part2].codonCatFreqFix[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else if (!strcmp(modelParams[part1].omegaVar, "Ny98") && !strcmp(modelParams[part2].omegaVar, "Ny98")) { if (!strcmp(modelParams[part1].ny98omega1pr, "Beta") && !strcmp(modelParams[part2].ny98omega1pr, "Beta")) { if (AreDoublesEqual (modelParams[part1].ny98omega1Beta[0], modelParams[part2].ny98omega1Beta[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].ny98omega1Beta[1], modelParams[part2].ny98omega1Beta[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].ny98omega1pr, "Fixed") && !strcmp(modelParams[part1].ny98omega1pr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].ny98omega1Fixed, modelParams[part2].ny98omega1Fixed, (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ if (!strcmp(modelParams[part1].ny98omega3pr, "Uniform") && !strcmp(modelParams[part2].ny98omega3pr, "Uniform")) { if (AreDoublesEqual (modelParams[part1].ny98omega3Uni[0], modelParams[part2].ny98omega3Uni[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].ny98omega3Uni[1], modelParams[part2].ny98omega3Uni[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].ny98omega3pr, "Exponential") && !strcmp(modelParams[part1].ny98omega3pr, "Exponential")) { if (AreDoublesEqual (modelParams[part1].ny98omega3Exp, modelParams[part2].ny98omega3Exp, (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].ny98omega3pr, "Fixed") && !strcmp(modelParams[part1].ny98omega3pr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].ny98omega3Fixed, modelParams[part2].ny98omega3Fixed, (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ if (!strcmp(modelParams[part1].codonCatFreqPr, "Dirichlet") && !strcmp(modelParams[part2].codonCatFreqPr, "Dirichlet")) { if (AreDoublesEqual (modelParams[part1].codonCatDir[0], modelParams[part2].codonCatDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[1], modelParams[part2].codonCatDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[2], modelParams[part2].codonCatDir[2], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].codonCatFreqPr, "Fixed") && !strcmp(modelParams[part1].codonCatFreqPr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[0], modelParams[part2].codonCatFreqFix[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[1], modelParams[part2].codonCatFreqFix[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[2], modelParams[part2].codonCatFreqFix[2], (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else if (!strcmp(modelParams[part1].omegaVar, "Equal") && !strcmp(modelParams[part2].omegaVar, "Equal")) { if (!strcmp(modelParams[part1].omegaPr,"Dirichlet") && !strcmp(modelParams[part2].omegaPr,"Dirichlet")) { if (AreDoublesEqual (modelParams[part1].omegaDir[0], modelParams[part2].omegaDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].omegaDir[1], modelParams[part2].omegaDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].omegaPr,"Fixed") && !strcmp(modelParams[part2].omegaPr,"Fixed")) { if (AreDoublesEqual (modelParams[part1].omegaFix, modelParams[part2].omegaFix, (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ /* Check to see if omega is inapplicable for either partition. */ if ((*isApplic1) == NO || (*isApplic2) == NO) isSame = NO; /* if omega is inapplicable for either partition, then the parameter cannot be the same */ } else if (whichParam == P_PI) { /* Check the state frequencies for partitions 1 and 2. */ /* Check if the model is parsimony for either partition */ if (!strcmp(modelParams[part1].parsModel, "Yes")) *isApplic1 = NO; /* part1 has a parsimony model and state frequencies do not apply */ if (!strcmp(modelParams[part2].parsModel, "Yes")) *isApplic2 = NO; /* part2 has a parsimony model and state frequencies do not apply */ /* Check that the data are not CONTINUOUS for partitions 1 and 2 */ if (modelParams[part1].dataType == CONTINUOUS) *isApplic1 = NO; /* state frequencies do not make sense for part1 */ if (modelParams[part2].dataType == CONTINUOUS) *isApplic2 = NO; /* state frequencies do not make sense for part2 */ /* Now, check that the data are the same (i.e., both nucleotide or both amino acid, or whatever). */ if (isFirstNucleotide != isSecondNucleotide) isSame = NO; /* data are not both nucleotide or both note nucleotide */ else if (modelParams[part1].dataType != modelParams[part2].dataType && isFirstNucleotide == NO) isSame = NO; /* data are not the same */ /* Check that the model structure is the same for both partitions */ if (strcmp(modelParams[part1].nucModel, modelParams[part2].nucModel)) isSame = NO; /* the nucleotide models are different */ if (strcmp(modelParams[part1].covarionModel, modelParams[part2].covarionModel) && !(!strcmp(modelParams[part1].nucModel, "Codon") && !strcmp(modelParams[part2].nucModel, "Codon"))) isSame = NO; /* the models have different covarion struture */ /* If both partitions have nucmodel=codon, then we also have to make certain that the same genetic code is used. */ if (!strcmp(modelParams[part1].nucModel, "Codon") && !strcmp(modelParams[part2].nucModel, "Codon")) { if (strcmp(modelParams[part1].geneticCode,modelParams[part2].geneticCode)) isSame = NO; /* the models have different genetic codes */ } /* Let's see if the prior is the same. */ if (modelParams[part1].dataType == STANDARD && modelParams[part2].dataType == STANDARD) { /* The data are morphological (STANDARD). The state frequencies are specified by a symmetric beta distribution, the parameter of which needs to be the same to apply to both partitions. Note that symPiPr = -1 is equivalent to setting the variance to 0.0. */ if (!strcmp(modelParams[part1].symPiPr,"Uniform") && !strcmp(modelParams[part2].symPiPr,"Uniform")) { if (AreDoublesEqual (modelParams[part1].symBetaUni[0], modelParams[part2].symBetaUni[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].symBetaUni[1], modelParams[part2].symBetaUni[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (modelParams[part1].numBetaCats != modelParams[part2].numBetaCats) isSame = NO; /* can't link because the discrete beta approximation is different */ } else if (!strcmp(modelParams[part1].symPiPr,"Exponential") && !strcmp(modelParams[part2].symPiPr,"Exponential")) { if (AreDoublesEqual (modelParams[part1].symBetaExp, modelParams[part2].symBetaExp, (MrBFlt) 0.00001) == NO) isSame = NO; if (modelParams[part1].numBetaCats != modelParams[part2].numBetaCats) isSame = NO; /* can't link because the discrete beta approximation is different */ } else if (!strcmp(modelParams[part1].symPiPr,"Fixed") && !strcmp(modelParams[part2].symPiPr,"Fixed")) { if (AreDoublesEqual (modelParams[part1].symBetaFix, modelParams[part2].symBetaFix, (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].symBetaFix, (MrBFlt) -1.0, (MrBFlt) 0.00001) == NO && modelParams[part1].numBetaCats != modelParams[part2].numBetaCats) isSame = NO; /* can't link because the discrete beta approximation is different */ } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } if (modelSettings[part1].dataType == PROTEIN && modelSettings[part2].dataType == PROTEIN) { /* We are dealing with protein data. */ if (!strcmp(modelParams[part1].aaModelPr, modelParams[part2].aaModelPr)) { if (!strcmp(modelParams[part1].aaModelPr, "Fixed")) { /* only have a single, fixed, amino acid rate matrix */ if (!strcmp(modelParams[part1].aaModel, modelParams[part2].aaModel)) {} else isSame = NO; /* we have different amino acid models, and the state frequencies must be different */ /* if we have an equalin model or Gtr model, then we need to check the prior on the state frequencies */ if (!strcmp(modelParams[part1].aaModel, "Equalin") || !strcmp(modelParams[part1].aaModel, "Gtr")) { if (!strcmp(modelParams[part1].stateFreqPr, modelParams[part2].stateFreqPr)) { /* the prior form is the same */ if (!strcmp(modelParams[part1].stateFreqPr, "Dirichlet")) /* both prior models must be dirichlet */ { for (i=0; idataType == DNA || mp->dataType == RNA) whichChar = &WhichNuc; if (mp->dataType == PROTEIN) whichChar = &WhichAA; if (mp->dataType == RESTRICTION) whichChar = &WhichRes; if (mp->dataType == STANDARD) whichChar = &WhichStand; MrBayesPrint ("\nCompressed matrix for division %d\n\n", d+1); k = 66; if (mp->dataType == CONTINUOUS) k /= 4; for (c=m->compMatrixStart; ccompMatrixStop; c+=k) { for (i=0; i= m->compMatrixStop) break; if (mp->dataType == CONTINUOUS) MrBayesPrint ("%3d ", compMatrix[pos(i,j,compMatrixRowSize)]); else MrBayesPrint ("%c", whichChar((int)compMatrix[pos(i,j,compMatrixRowSize)])); } MrBayesPrint("\n"); } MrBayesPrint("\nNo. sites "); for (j=c; j= m->compMatrixStop) break; i = (int) numSitesOfPat[m->compCharStart + (0*numCompressedChars) + (j - m->compMatrixStart)/m->nCharsPerSite]; /* NOTE: We are printing the unadulterated site pat nums */ if (i>9) i = 'A' + i - 10; else i = '0' + i; if (mp->dataType == CONTINUOUS) MrBayesPrint(" %c ", i); else { if ((j-m->compMatrixStart) % m->nCharsPerSite == 0) MrBayesPrint ("%c", i); else MrBayesPrint(" "); } } MrBayesPrint ("\nOrig. char "); for (j=c; j= m->compMatrixStop) break; i = origChar[j]; if (i>9) i = '0' + (i % 10); else i = '0' +i; if (mp->dataType == CONTINUOUS) MrBayesPrint(" %c ", i); else MrBayesPrint ("%c", i); } if (mp->dataType == STANDARD && m->nStates != NULL) { MrBayesPrint ("\nNo. states "); for (j=c; j= m->compMatrixStop) break; i = m->nStates[j-m->compCharStart]; MrBayesPrint ("%d", i); } MrBayesPrint ("\nCharType "); for (j=c; j= m->compMatrixStop) break; i = m->cType[j-m->compMatrixStart]; if (i == ORD) MrBayesPrint ("%c", 'O'); else if (i == UNORD) MrBayesPrint ("%c", 'U'); else MrBayesPrint ("%c", 'I'); } MrBayesPrint ("\ntiIndex "); for (j=c; j= m->compMatrixStop) break; i = m->tiIndex[j-m->compCharStart]; MrBayesPrint ("%d", i % 10); } MrBayesPrint ("\nbsIndex "); for (j=c; j= m->compMatrixStop) break; i = m->bsIndex[j-m->compCharStart]; MrBayesPrint ("%d", i % 10); } } MrBayesPrint ("\n\n"); } MrBayesPrint ("Press return to continue\n"); getchar(); } /* next division */ return NO_ERROR; } /*---------------------------------------------------------------------- | | PrintMatrix: Print data matrix | | ------------------------------------------------------------------------*/ int PrintMatrix (void) { int i, j=0, c, printWidth, nextColumn; extern char WhichAA (int x); extern char WhichNuc (int x); extern char WhichRes (int x); extern char WhichStand (int x); if (!matrix) return ERROR; MrBayesPrint ("\nData matrix\n\n"); printWidth = 79; for (c=0; c= numChar) break; if (charInfo[j].charType == CONTINUOUS && nextColumn < printWidth - 3) break; if (charInfo[j].charType == CONTINUOUS) { MrBayesPrint ("%3d ", matrix[pos(i,j,numChar)]); nextColumn += 3; } else if (charInfo[j].charType == DNA || charInfo[j].charType == RNA) MrBayesPrint ("%c", WhichNuc(matrix[pos(i,j,numChar)])); else if (charInfo[j].charType == PROTEIN) MrBayesPrint ("%c", WhichAA(matrix[pos(i,j,numChar)])); else if (charInfo[j].charType == RESTRICTION) MrBayesPrint ("%c", WhichRes(matrix[pos(i,j,numChar)])); else if (charInfo[j].charType == STANDARD) MrBayesPrint ("%c", WhichStand(matrix[pos(i,j,numChar)])); j++; } MrBayesPrint("\n"); } MrBayesPrint ("\n"); } return NO_ERROR; } /*-------------------------------------------------------------- | | ProcessStdChars: process standard characters | ---------------------------------------------------------------*/ int ProcessStdChars (SafeLong *seed) { int c, d, i, j, k, n, ts, index, numStandardChars, origCharPos, *bsIndex; char piHeader[30]; ModelInfo *m; ModelParams *mp=NULL; Param *p; /* set character type, no. states, ti index and bs index for standard characters */ /* first calculate how many standard characters we have */ numStandardChars = 0; for (d=0; ddataType != STANDARD) continue; numStandardChars += m->numChars; } /* return if there are no standard characters */ if (numStandardChars == 0) return (NO_ERROR); /* we are still here so we have standard characters and need to deal with them */ /* first allocate space for stdType, stateSize, tiIndex, bsIndex */ if (memAllocs[ALLOC_STDTYPE] == YES) { free (stdType); stdType = NULL; memAllocs[ALLOC_STDTYPE] = NO; } stdType = (int *)SafeCalloc((size_t) (4 * numStandardChars), sizeof(int)); if (!stdType) { MrBayesPrint ("%s Problem allocating stdType (%d ints)\n", 4 * numStandardChars); return ERROR; } memAllocs[ALLOC_STDTYPE] = YES; stateSize = stdType + numStandardChars; tiIndex = stateSize + numStandardChars; bsIndex = tiIndex + numStandardChars; /* then fill in stdType and stateSize, set pointers */ /* also fill in isTiNeeded for each division and tiIndex for each character */ for (d=j=0; ddataType != STANDARD) continue; m->cType = stdType + j; m->nStates = stateSize + j; m->tiIndex = tiIndex + j; m->bsIndex = bsIndex + j; m->cijkLength = 0; m->nCijkParts = 0; for (c=0; cnumChars; c++) { if (origChar[c+m->compMatrixStart] < 0) { /* this is a dummy character */ m->cType[c] = UNORD; m->nStates[c] = 2; } else { /* this is an ordinary character */ m->cType[c] = charInfo[origChar[c + m->compMatrixStart]].ctype; m->nStates[c] = charInfo[origChar[c + m->compMatrixStart]].numStates; } /* check ctype settings */ if (m->nStates[c] < 2) { MrBayesPrint ("%s WARNING: Compressed character %d (original character %d) of division %d has less \n", spacer, c+m->compCharStart,origChar[c+m->compCharStart]+1, d+1); MrBayesPrint ("%s than two observed states; it will be assumed to have two states.\n", spacer); m->nStates[c] = 2; } if (m->nStates[c] > 6 && m->cType[c] != UNORD) { MrBayesPrint ("%s Only unordered model supported for characters with more than 6 states\n", spacer); return ERROR; } if (m->nStates[c] == 2 && m->cType[c] == ORD) m->cType[c] = UNORD; if (m->cType[c] == IRREV) { MrBayesPrint ("%s Irreversible model not yet supported\n", spacer); return ERROR; } /* find max number of states */ if (m->nStates[c] > m->numModelStates) m->numModelStates = m->nStates[c]; /* update Cijk info */ if (strcmp(mp->symPiPr,"Fixed") != 0 || AreDoublesEqual(mp->symBetaFix, -1.0, 0.00001) == NO) { /* Asymmetry between stationary state frequencies -- we need one cijk and eigenvalue set for each multistate character */ if (m->nStates[c] > 2 && (m->cType[c] == UNORD || m->cType[c] == ORD)) { ts = m->nStates[c]; m->cijkLength += (ts * ts * ts) + (2 * ts); m->nCijkParts++; } } /* set the ti probs needed */ if (m->stateFreq->nValues == 0 || m->nStates[c] == 2) { if (m->cType[c] == UNORD) m->isTiNeeded[m->nStates[c]-2] = YES; if (m->cType[c] == ORD) m->isTiNeeded[m->nStates[c]+6] = YES; if (m->cType[c] == IRREV) m->isTiNeeded[m->nStates[c]+11] = YES; } } /* set ti index for each compressed character first */ /* set bs index later (below) */ /* set base index, valid for binary chars */ m->tiIndex[c] = 0; /* first adjust for unordered characters */ for (k=0; k<9; k++) { if (m->isTiNeeded [k] == NO) continue; for (c=0; cnumChars; c++) { if (m->cType[c] != UNORD || m->nStates[c] > k + 2) { m->tiIndex[c] += (k + 2) * (k + 2) * m->numGammaCats; } } } /* second for ordered characters */ for (k=9; k<13; k++) { if (m->isTiNeeded [k] == NO) continue; for (c=0; cnumChars; c++) { if (m->cType[c] == IRREV || (m->cType[c] == ORD && m->nStates[c] > k - 6)) { m->tiIndex[c] += (k - 6) * (k - 6) * m->numGammaCats; } } } /* third for irrev characters */ for (k=13; k<18; k++) { if (m->isTiNeeded [k] == NO) continue; for (c=0; cnumChars; c++) { if (m->cType[c] == IRREV && m->nStates[c] > k - 11) { m->tiIndex[c] += (k - 11) * (k - 11) * m->numGammaCats; } } } /* finally take beta cats into account in tiIndex */ /* the beta cats will only be used for binary characters */ /* multistate characters get their ti indices reset here */ if (m->numBetaCats > 1 && m->isTiNeeded[0] == YES) { k = 4 * m->numBetaCats * m->numGammaCats; /* offset for binary character ti probs */ for (c=0; cnumChars; c++) { if (m->nStates[c] > 2) { m->tiIndex[c] = k; k += m->nStates[c] * m->nStates[c] * m->numGammaCats; } } } j += m->numChars; } /* deal with bsIndex */ for (k=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; p->nSympi = 0; p->hasBinaryStd = NO; for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[0] == YES) break; if (i < p->nRelParts) p->hasBinaryStd = YES; if (p->paramId == SYMPI_EQUAL) { /* calculate the number of state frequencies needed */ /* also set bsIndex appropriately */ for (n=index=0; n<9; n++) { for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[n] == YES) break; if (i < p->nRelParts) { for (i=0; inRelParts; i++) { m = &modelSettings[p->relParts[i]]; for (c=0; cnumChars; c++) { if (m->cType[c] != UNORD || m->nStates[c] > n + 2) { m->bsIndex[c] += (n + 2); } } } index += (n + 2); } } for (n=9; n<13; n++) { for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[n] == YES) break; if (i < p->nRelParts) { for (i=0; inRelParts; i++) { m = &modelSettings[p->relParts[i]]; for (c=0; cnumChars; c++) { if (m->cType[c] == ORD && m->nStates[c] > n - 6) { m->bsIndex[c] += (n - 6); } } } index += (n - 6); } } p->nStdStateFreqs = index; } else { /* if not equal we need space for beta category frequencies */ index = 0; if (p->hasBinaryStd == YES) index += (2 * modelSettings[p->relParts[0]].numBetaCats); /* as well as one set of frequencies for each multistate character */ for (i=0; inRelParts; i++) { m = &modelSettings[p->relParts[i]]; for (c=0; cnumChars; c++) { if (m->nStates[c] > 2 && (m->cType[c] == UNORD || m->cType[c] == ORD)) { m->bsIndex[c] = index; index += m->nStates[c]; p->nSympi++; } } } } p->nStdStateFreqs = index; } /* allocate space for bsIndex, sympiIndex, stdStateFreqs; then fill */ /* first count number of sympis needed */ for (k=n=i=0; knSympi; /* nsympi calculated above */ } /* then allocate and fill in */ if (n > 0) { if (memAllocs[ALLOC_SYMPIINDEX] == YES) { sympiIndex = (int *) SafeRealloc ((void *) sympiIndex, 3*n * sizeof (int)); for (i=0; i<3*n; i++) sympiIndex[i] = 0; } else sympiIndex = (int *) SafeCalloc (3*n, sizeof (int)); if (!sympiIndex) { MrBayesPrint ("%s Problem allocating sympiIndex\n", spacer); return (ERROR); } else memAllocs[ALLOC_SYMPIINDEX] = YES; /* set up sympi pointers and fill sympiIndex */ for (k=i=0; knSympi > 0) { p->printParam = YES; /* print even if fixed alpha_symdir */ index = 0; p->sympiBsIndex = sympiIndex + i; p->sympinStates = sympiIndex + i + n; p->sympiCType = sympiIndex + i + (2 * n); for (j=0; jnRelParts; j++) { m = &modelSettings[p->relParts[j]]; for (c=0; cnumChars; c++) { if (m->nStates[c] > 2 && (m->cType[c] == UNORD || m->cType[c] == ORD)) { p->sympinStates[index] = m->nStates[c]; p->sympiBsIndex[index] = m->bsIndex[c]; p->sympiCType[index] = m->cType[c]; origCharPos = origChar[m->compCharStart + c]; for (ts=0; tsnStates[c]; ts++) { sprintf(piHeader, "\tpi_%d(%d)", origCharPos+1, ts); SafeStrcat(&p->paramHeader, piHeader); } index++; } } } assert (index == p->nSympi); i += p->nSympi; } } assert (i == n); } /* count space needed for state frequencies */ for (k=n=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; n += p->nStdStateFreqs; } stdStateFreqsRowSize = n; /* allocate space */ if (memAllocs[ALLOC_STDSTATEFREQS] == YES) { free (stdStateFreqs); stdStateFreqs = NULL; memAllocs[ALLOC_STDSTATEFREQS] = NO; } stdStateFreqs = (MrBFlt *) SafeCalloc (n * 2 * numGlobalChains, sizeof (MrBFlt)); if (!stdStateFreqs) { MrBayesPrint ("%s Problem allocating stdStateFreqs in ProcessStdChars\n", spacer); return (ERROR); } else memAllocs[ALLOC_STDSTATEFREQS] = YES; /* set pointers */ for (k=n=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; p->stdStateFreqs = stdStateFreqs + n; n += p->nStdStateFreqs; } FillStdStateFreqs( 0 , numGlobalChains, seed); return (NO_ERROR); } /*-------------------------------------------------------------- | | FillStdStateFreqs: fills stationary frequencies for standard data divisions of chains in range [chfrom, chto) | ---------------------------------------------------------------*/ void FillStdStateFreqs(int chfrom, int chto, SafeLong *seed) { int chn, n, i, j, k, b, c, nb, index; MrBFlt *subValue, sum, symDir[10]; Param *p; for (chn=chfrom; chnparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; subValue = GetParamStdStateFreqs (p, chn, 0); if (p->paramId == SYMPI_EQUAL) { for (n=index=0; n<9; n++) { for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[n] == YES) break; if (i < p->nRelParts) { for (j=0; j<(n+2); j++) { subValue[index++] = (1.0 / (n + 2)); } } } for (n=9; n<13; n++) { for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[n] == YES) break; if (i < p->nRelParts) { for (j=0; j<(n-6); j++) { subValue[index++] = (1.0 / (n - 6)); } } } } /* Deal with transition asymmetry for standard characters */ /* First, fill in stationary frequencies for beta categories if needed; */ /* discard category frequencies (assume equal) */ if (p->paramId == SYMPI_FIX || p->paramId == SYMPI_UNI || p->paramId == SYMPI_EXP || p->paramId == SYMPI_FIX_MS || p->paramId == SYMPI_UNI_MS || p->paramId == SYMPI_EXP_MS) { if (p->hasBinaryStd == YES) { nb=modelParams[p->relParts[0]].numBetaCats; BetaBreaks (p->values[0], p->values[0], subValue, nb); b = 2*nb; for (i=b-2; i>0; i-=2) { subValue[i] = subValue[i/2]; } for (i=1; ivalues[0]; for (c=0; cnSympi; c++) { /* now fill in subvalues */ DirichletRandomVariable (symDir, subValue, p->sympinStates[c], seed); sum = 0.0; for (i=0; isympinStates[c]; i++) { if (subValue[i] < 0.0001) subValue[i] = 0.0001; sum += subValue[i]; } for (i=0; irelParts[0]].nStates; i++) subValue[i] /= sum; subValue += p->sympinStates[c]; } } } /* next parameter */ } /* next chain */ } int SetAARates (void) { int i, j; MrBFlt diff, sum, scaler; /* A R N D C Q E G H I L K M F P S T W Y V */ /* jones */ aaJones[ 0][ 0] = 0; aaJones[ 0][ 1] = 58; aaJones[ 0][ 2] = 54; aaJones[ 0][ 3] = 81; aaJones[ 0][ 4] = 56; aaJones[ 0][ 5] = 57; aaJones[ 0][ 6] = 105; aaJones[ 0][ 7] = 179; aaJones[ 0][ 8] = 27; aaJones[ 0][ 9] = 36; aaJones[ 0][10] = 30; aaJones[ 0][11] = 35; aaJones[ 0][12] = 54; aaJones[ 0][13] = 15; aaJones[ 0][14] = 194; aaJones[ 0][15] = 378; aaJones[ 0][16] = 475; aaJones[ 0][17] = 9; aaJones[ 0][18] = 11; aaJones[ 0][19] = 298; aaJones[ 1][ 0] = 58; aaJones[ 1][ 1] = 0; aaJones[ 1][ 2] = 45; aaJones[ 1][ 3] = 16; aaJones[ 1][ 4] = 113; aaJones[ 1][ 5] = 310; aaJones[ 1][ 6] = 29; aaJones[ 1][ 7] = 137; aaJones[ 1][ 8] = 328; aaJones[ 1][ 9] = 22; aaJones[ 1][10] = 38; aaJones[ 1][11] = 646; aaJones[ 1][12] = 44; aaJones[ 1][13] = 5; aaJones[ 1][14] = 74; aaJones[ 1][15] = 101; aaJones[ 1][16] = 64; aaJones[ 1][17] = 126; aaJones[ 1][18] = 20; aaJones[ 1][19] = 17; aaJones[ 2][ 0] = 54; aaJones[ 2][ 1] = 45; aaJones[ 2][ 2] = 0; aaJones[ 2][ 3] = 528; aaJones[ 2][ 4] = 34; aaJones[ 2][ 5] = 86; aaJones[ 2][ 6] = 58; aaJones[ 2][ 7] = 81; aaJones[ 2][ 8] = 391; aaJones[ 2][ 9] = 47; aaJones[ 2][10] = 12; aaJones[ 2][11] = 263; aaJones[ 2][12] = 30; aaJones[ 2][13] = 10; aaJones[ 2][14] = 15; aaJones[ 2][15] = 503; aaJones[ 2][16] = 232; aaJones[ 2][17] = 8; aaJones[ 2][18] = 70; aaJones[ 2][19] = 16; aaJones[ 3][ 0] = 81; aaJones[ 3][ 1] = 16; aaJones[ 3][ 2] = 528; aaJones[ 3][ 3] = 0; aaJones[ 3][ 4] = 10; aaJones[ 3][ 5] = 49; aaJones[ 3][ 6] = 767; aaJones[ 3][ 7] = 130; aaJones[ 3][ 8] = 112; aaJones[ 3][ 9] = 11; aaJones[ 3][10] = 7; aaJones[ 3][11] = 26; aaJones[ 3][12] = 15; aaJones[ 3][13] = 4; aaJones[ 3][14] = 15; aaJones[ 3][15] = 59; aaJones[ 3][16] = 38; aaJones[ 3][17] = 4; aaJones[ 3][18] = 46; aaJones[ 3][19] = 31; aaJones[ 4][ 0] = 56; aaJones[ 4][ 1] = 113; aaJones[ 4][ 2] = 34; aaJones[ 4][ 3] = 10; aaJones[ 4][ 4] = 0; aaJones[ 4][ 5] = 9; aaJones[ 4][ 6] = 5; aaJones[ 4][ 7] = 59; aaJones[ 4][ 8] = 69; aaJones[ 4][ 9] = 17; aaJones[ 4][10] = 23; aaJones[ 4][11] = 7; aaJones[ 4][12] = 31; aaJones[ 4][13] = 78; aaJones[ 4][14] = 14; aaJones[ 4][15] = 223; aaJones[ 4][16] = 42; aaJones[ 4][17] = 115; aaJones[ 4][18] = 209; aaJones[ 4][19] = 62; aaJones[ 5][ 0] = 57; aaJones[ 5][ 1] = 310; aaJones[ 5][ 2] = 86; aaJones[ 5][ 3] = 49; aaJones[ 5][ 4] = 9; aaJones[ 5][ 5] = 0; aaJones[ 5][ 6] = 323; aaJones[ 5][ 7] = 26; aaJones[ 5][ 8] = 597; aaJones[ 5][ 9] = 9; aaJones[ 5][10] = 72; aaJones[ 5][11] = 292; aaJones[ 5][12] = 43; aaJones[ 5][13] = 4; aaJones[ 5][14] = 164; aaJones[ 5][15] = 53; aaJones[ 5][16] = 51; aaJones[ 5][17] = 18; aaJones[ 5][18] = 24; aaJones[ 5][19] = 20; aaJones[ 6][ 0] = 105; aaJones[ 6][ 1] = 29; aaJones[ 6][ 2] = 58; aaJones[ 6][ 3] = 767; aaJones[ 6][ 4] = 5; aaJones[ 6][ 5] = 323; aaJones[ 6][ 6] = 0; aaJones[ 6][ 7] = 119; aaJones[ 6][ 8] = 26; aaJones[ 6][ 9] = 12; aaJones[ 6][10] = 9; aaJones[ 6][11] = 181; aaJones[ 6][12] = 18; aaJones[ 6][13] = 5; aaJones[ 6][14] = 18; aaJones[ 6][15] = 30; aaJones[ 6][16] = 32; aaJones[ 6][17] = 10; aaJones[ 6][18] = 7; aaJones[ 6][19] = 45; aaJones[ 7][ 0] = 179; aaJones[ 7][ 1] = 137; aaJones[ 7][ 2] = 81; aaJones[ 7][ 3] = 130; aaJones[ 7][ 4] = 59; aaJones[ 7][ 5] = 26; aaJones[ 7][ 6] = 119; aaJones[ 7][ 7] = 0; aaJones[ 7][ 8] = 23; aaJones[ 7][ 9] = 6; aaJones[ 7][10] = 6; aaJones[ 7][11] = 27; aaJones[ 7][12] = 14; aaJones[ 7][13] = 5; aaJones[ 7][14] = 24; aaJones[ 7][15] = 201; aaJones[ 7][16] = 33; aaJones[ 7][17] = 55; aaJones[ 7][18] = 8; aaJones[ 7][19] = 47; aaJones[ 8][ 0] = 27; aaJones[ 8][ 1] = 328; aaJones[ 8][ 2] = 391; aaJones[ 8][ 3] = 112; aaJones[ 8][ 4] = 69; aaJones[ 8][ 5] = 597; aaJones[ 8][ 6] = 26; aaJones[ 8][ 7] = 23; aaJones[ 8][ 8] = 0; aaJones[ 8][ 9] = 16; aaJones[ 8][10] = 56; aaJones[ 8][11] = 45; aaJones[ 8][12] = 33; aaJones[ 8][13] = 40; aaJones[ 8][14] = 115; aaJones[ 8][15] = 73; aaJones[ 8][16] = 46; aaJones[ 8][17] = 8; aaJones[ 8][18] = 573; aaJones[ 8][19] = 11; aaJones[ 9][ 0] = 36; aaJones[ 9][ 1] = 22; aaJones[ 9][ 2] = 47; aaJones[ 9][ 3] = 11; aaJones[ 9][ 4] = 17; aaJones[ 9][ 5] = 9; aaJones[ 9][ 6] = 12; aaJones[ 9][ 7] = 6; aaJones[ 9][ 8] = 16; aaJones[ 9][ 9] = 0; aaJones[ 9][10] = 229; aaJones[ 9][11] = 21; aaJones[ 9][12] = 479; aaJones[ 9][13] = 89; aaJones[ 9][14] = 10; aaJones[ 9][15] = 40; aaJones[ 9][16] = 245; aaJones[ 9][17] = 9; aaJones[ 9][18] = 32; aaJones[ 9][19] = 961; aaJones[10][ 0] = 30; aaJones[10][ 1] = 38; aaJones[10][ 2] = 12; aaJones[10][ 3] = 7; aaJones[10][ 4] = 23; aaJones[10][ 5] = 72; aaJones[10][ 6] = 9; aaJones[10][ 7] = 6; aaJones[10][ 8] = 56; aaJones[10][ 9] = 229; aaJones[10][10] = 0; aaJones[10][11] = 14; aaJones[10][12] = 388; aaJones[10][13] = 248; aaJones[10][14] = 102; aaJones[10][15] = 59; aaJones[10][16] = 25; aaJones[10][17] = 52; aaJones[10][18] = 24; aaJones[10][19] = 180; aaJones[11][ 0] = 35; aaJones[11][ 1] = 646; aaJones[11][ 2] = 263; aaJones[11][ 3] = 26; aaJones[11][ 4] = 7; aaJones[11][ 5] = 292; aaJones[11][ 6] = 181; aaJones[11][ 7] = 27; aaJones[11][ 8] = 45; aaJones[11][ 9] = 21; aaJones[11][10] = 14; aaJones[11][11] = 0; aaJones[11][12] = 65; aaJones[11][13] = 4; aaJones[11][14] = 21; aaJones[11][15] = 47; aaJones[11][16] = 103; aaJones[11][17] = 10; aaJones[11][18] = 8; aaJones[11][19] = 14; aaJones[12][ 0] = 54; aaJones[12][ 1] = 44; aaJones[12][ 2] = 30; aaJones[12][ 3] = 15; aaJones[12][ 4] = 31; aaJones[12][ 5] = 43; aaJones[12][ 6] = 18; aaJones[12][ 7] = 14; aaJones[12][ 8] = 33; aaJones[12][ 9] = 479; aaJones[12][10] = 388; aaJones[12][11] = 65; aaJones[12][12] = 0; aaJones[12][13] = 43; aaJones[12][14] = 16; aaJones[12][15] = 29; aaJones[12][16] = 226; aaJones[12][17] = 24; aaJones[12][18] = 18; aaJones[12][19] = 323; aaJones[13][ 0] = 15; aaJones[13][ 1] = 5; aaJones[13][ 2] = 10; aaJones[13][ 3] = 4; aaJones[13][ 4] = 78; aaJones[13][ 5] = 4; aaJones[13][ 6] = 5; aaJones[13][ 7] = 5; aaJones[13][ 8] = 40; aaJones[13][ 9] = 89; aaJones[13][10] = 248; aaJones[13][11] = 4; aaJones[13][12] = 43; aaJones[13][13] = 0; aaJones[13][14] = 17; aaJones[13][15] = 92; aaJones[13][16] = 12; aaJones[13][17] = 53; aaJones[13][18] = 536; aaJones[13][19] = 62; aaJones[14][ 0] = 194; aaJones[14][ 1] = 74; aaJones[14][ 2] = 15; aaJones[14][ 3] = 15; aaJones[14][ 4] = 14; aaJones[14][ 5] = 164; aaJones[14][ 6] = 18; aaJones[14][ 7] = 24; aaJones[14][ 8] = 115; aaJones[14][ 9] = 10; aaJones[14][10] = 102; aaJones[14][11] = 21; aaJones[14][12] = 16; aaJones[14][13] = 17; aaJones[14][14] = 0; aaJones[14][15] = 285; aaJones[14][16] = 118; aaJones[14][17] = 6; aaJones[14][18] = 10; aaJones[14][19] = 23; aaJones[15][ 0] = 378; aaJones[15][ 1] = 101; aaJones[15][ 2] = 503; aaJones[15][ 3] = 59; aaJones[15][ 4] = 223; aaJones[15][ 5] = 53; aaJones[15][ 6] = 30; aaJones[15][ 7] = 201; aaJones[15][ 8] = 73; aaJones[15][ 9] = 40; aaJones[15][10] = 59; aaJones[15][11] = 47; aaJones[15][12] = 29; aaJones[15][13] = 92; aaJones[15][14] = 285; aaJones[15][15] = 0; aaJones[15][16] = 477; aaJones[15][17] = 35; aaJones[15][18] = 63; aaJones[15][19] = 38; aaJones[16][ 0] = 475; aaJones[16][ 1] = 64; aaJones[16][ 2] = 232; aaJones[16][ 3] = 38; aaJones[16][ 4] = 42; aaJones[16][ 5] = 51; aaJones[16][ 6] = 32; aaJones[16][ 7] = 33; aaJones[16][ 8] = 46; aaJones[16][ 9] = 245; aaJones[16][10] = 25; aaJones[16][11] = 103; aaJones[16][12] = 226; aaJones[16][13] = 12; aaJones[16][14] = 118; aaJones[16][15] = 477; aaJones[16][16] = 0; aaJones[16][17] = 12; aaJones[16][18] = 21; aaJones[16][19] = 112; aaJones[17][ 0] = 9; aaJones[17][ 1] = 126; aaJones[17][ 2] = 8; aaJones[17][ 3] = 4; aaJones[17][ 4] = 115; aaJones[17][ 5] = 18; aaJones[17][ 6] = 10; aaJones[17][ 7] = 55; aaJones[17][ 8] = 8; aaJones[17][ 9] = 9; aaJones[17][10] = 52; aaJones[17][11] = 10; aaJones[17][12] = 24; aaJones[17][13] = 53; aaJones[17][14] = 6; aaJones[17][15] = 35; aaJones[17][16] = 12; aaJones[17][17] = 0; aaJones[17][18] = 71; aaJones[17][19] = 25; aaJones[18][ 0] = 11; aaJones[18][ 1] = 20; aaJones[18][ 2] = 70; aaJones[18][ 3] = 46; aaJones[18][ 4] = 209; aaJones[18][ 5] = 24; aaJones[18][ 6] = 7; aaJones[18][ 7] = 8; aaJones[18][ 8] = 573; aaJones[18][ 9] = 32; aaJones[18][10] = 24; aaJones[18][11] = 8; aaJones[18][12] = 18; aaJones[18][13] = 536; aaJones[18][14] = 10; aaJones[18][15] = 63; aaJones[18][16] = 21; aaJones[18][17] = 71; aaJones[18][18] = 0; aaJones[18][19] = 16; aaJones[19][ 0] = 298; aaJones[19][ 1] = 17; aaJones[19][ 2] = 16; aaJones[19][ 3] = 31; aaJones[19][ 4] = 62; aaJones[19][ 5] = 20; aaJones[19][ 6] = 45; aaJones[19][ 7] = 47; aaJones[19][ 8] = 11; aaJones[19][ 9] = 961; aaJones[19][10] = 180; aaJones[19][11] = 14; aaJones[19][12] = 323; aaJones[19][13] = 62; aaJones[19][14] = 23; aaJones[19][15] = 38; aaJones[19][16] = 112; aaJones[19][17] = 25; aaJones[19][18] = 16; aaJones[19][19] = 0; jonesPi[ 0] = 0.076748; jonesPi[ 1] = 0.051691; jonesPi[ 2] = 0.042645; jonesPi[ 3] = 0.051544; jonesPi[ 4] = 0.019803; jonesPi[ 5] = 0.040752; jonesPi[ 6] = 0.061830; jonesPi[ 7] = 0.073152; jonesPi[ 8] = 0.022944; jonesPi[ 9] = 0.053761; jonesPi[10] = 0.091904; jonesPi[11] = 0.058676; jonesPi[12] = 0.023826; jonesPi[13] = 0.040126; jonesPi[14] = 0.050901; jonesPi[15] = 0.068765; jonesPi[16] = 0.058565; jonesPi[17] = 0.014261; jonesPi[18] = 0.032102; jonesPi[19] = 0.066005; /* dayhoff */ aaDayhoff[ 0][ 0] = 0; aaDayhoff[ 0][ 1] = 27; aaDayhoff[ 0][ 2] = 98; aaDayhoff[ 0][ 3] = 120; aaDayhoff[ 0][ 4] = 36; aaDayhoff[ 0][ 5] = 89; aaDayhoff[ 0][ 6] = 198; aaDayhoff[ 0][ 7] = 240; aaDayhoff[ 0][ 8] = 23; aaDayhoff[ 0][ 9] = 65; aaDayhoff[ 0][10] = 41; aaDayhoff[ 0][11] = 26; aaDayhoff[ 0][12] = 72; aaDayhoff[ 0][13] = 18; aaDayhoff[ 0][14] = 250; aaDayhoff[ 0][15] = 409; aaDayhoff[ 0][16] = 371; aaDayhoff[ 0][17] = 0; aaDayhoff[ 0][18] = 24; aaDayhoff[ 0][19] = 208; aaDayhoff[ 1][ 0] = 27; aaDayhoff[ 1][ 1] = 0; aaDayhoff[ 1][ 2] = 32; aaDayhoff[ 1][ 3] = 0; aaDayhoff[ 1][ 4] = 23; aaDayhoff[ 1][ 5] = 246; aaDayhoff[ 1][ 6] = 1; aaDayhoff[ 1][ 7] = 9; aaDayhoff[ 1][ 8] = 240; aaDayhoff[ 1][ 9] = 64; aaDayhoff[ 1][10] = 15; aaDayhoff[ 1][11] = 464; aaDayhoff[ 1][12] = 90; aaDayhoff[ 1][13] = 14; aaDayhoff[ 1][14] = 103; aaDayhoff[ 1][15] = 154; aaDayhoff[ 1][16] = 26; aaDayhoff[ 1][17] = 201; aaDayhoff[ 1][18] = 8; aaDayhoff[ 1][19] = 24; aaDayhoff[ 2][ 0] = 98; aaDayhoff[ 2][ 1] = 32; aaDayhoff[ 2][ 2] = 0; aaDayhoff[ 2][ 3] = 905; aaDayhoff[ 2][ 4] = 0; aaDayhoff[ 2][ 5] = 103; aaDayhoff[ 2][ 6] = 148; aaDayhoff[ 2][ 7] = 139; aaDayhoff[ 2][ 8] = 535; aaDayhoff[ 2][ 9] = 77; aaDayhoff[ 2][10] = 34; aaDayhoff[ 2][11] = 318; aaDayhoff[ 2][12] = 1; aaDayhoff[ 2][13] = 14; aaDayhoff[ 2][14] = 42; aaDayhoff[ 2][15] = 495; aaDayhoff[ 2][16] = 229; aaDayhoff[ 2][17] = 23; aaDayhoff[ 2][18] = 95; aaDayhoff[ 2][19] = 15; aaDayhoff[ 3][ 0] = 120; aaDayhoff[ 3][ 1] = 0; aaDayhoff[ 3][ 2] = 905; aaDayhoff[ 3][ 3] = 0; aaDayhoff[ 3][ 4] = 0; aaDayhoff[ 3][ 5] = 134; aaDayhoff[ 3][ 6] = 1153; aaDayhoff[ 3][ 7] = 125; aaDayhoff[ 3][ 8] = 86; aaDayhoff[ 3][ 9] = 24; aaDayhoff[ 3][10] = 0; aaDayhoff[ 3][11] = 71; aaDayhoff[ 3][12] = 0; aaDayhoff[ 3][13] = 0; aaDayhoff[ 3][14] = 13; aaDayhoff[ 3][15] = 95; aaDayhoff[ 3][16] = 66; aaDayhoff[ 3][17] = 0; aaDayhoff[ 3][18] = 0; aaDayhoff[ 3][19] = 18; aaDayhoff[ 4][ 0] = 36; aaDayhoff[ 4][ 1] = 23; aaDayhoff[ 4][ 2] = 0; aaDayhoff[ 4][ 3] = 0; aaDayhoff[ 4][ 4] = 0; aaDayhoff[ 4][ 5] = 0; aaDayhoff[ 4][ 6] = 0; aaDayhoff[ 4][ 7] = 11; aaDayhoff[ 4][ 8] = 28; aaDayhoff[ 4][ 9] = 44; aaDayhoff[ 4][10] = 0; aaDayhoff[ 4][11] = 0; aaDayhoff[ 4][12] = 0; aaDayhoff[ 4][13] = 0; aaDayhoff[ 4][14] = 19; aaDayhoff[ 4][15] = 161; aaDayhoff[ 4][16] = 16; aaDayhoff[ 4][17] = 0; aaDayhoff[ 4][18] = 96; aaDayhoff[ 4][19] = 49; aaDayhoff[ 5][ 0] = 89; aaDayhoff[ 5][ 1] = 246; aaDayhoff[ 5][ 2] = 103; aaDayhoff[ 5][ 3] = 134; aaDayhoff[ 5][ 4] = 0; aaDayhoff[ 5][ 5] = 0; aaDayhoff[ 5][ 6] = 716; aaDayhoff[ 5][ 7] = 28; aaDayhoff[ 5][ 8] = 606; aaDayhoff[ 5][ 9] = 18; aaDayhoff[ 5][10] = 73; aaDayhoff[ 5][11] = 153; aaDayhoff[ 5][12] = 114; aaDayhoff[ 5][13] = 0; aaDayhoff[ 5][14] = 153; aaDayhoff[ 5][15] = 56; aaDayhoff[ 5][16] = 53; aaDayhoff[ 5][17] = 0; aaDayhoff[ 5][18] = 0; aaDayhoff[ 5][19] = 35; aaDayhoff[ 6][ 0] = 198; aaDayhoff[ 6][ 1] = 1; aaDayhoff[ 6][ 2] = 148; aaDayhoff[ 6][ 3] = 1153; aaDayhoff[ 6][ 4] = 0; aaDayhoff[ 6][ 5] = 716; aaDayhoff[ 6][ 6] = 0; aaDayhoff[ 6][ 7] = 81; aaDayhoff[ 6][ 8] = 43; aaDayhoff[ 6][ 9] = 61; aaDayhoff[ 6][10] = 11; aaDayhoff[ 6][11] = 83; aaDayhoff[ 6][12] = 30; aaDayhoff[ 6][13] = 0; aaDayhoff[ 6][14] = 51; aaDayhoff[ 6][15] = 79; aaDayhoff[ 6][16] = 34; aaDayhoff[ 6][17] = 0; aaDayhoff[ 6][18] = 22; aaDayhoff[ 6][19] = 37; aaDayhoff[ 7][ 0] = 240; aaDayhoff[ 7][ 1] = 9; aaDayhoff[ 7][ 2] = 139; aaDayhoff[ 7][ 3] = 125; aaDayhoff[ 7][ 4] = 11; aaDayhoff[ 7][ 5] = 28; aaDayhoff[ 7][ 6] = 81; aaDayhoff[ 7][ 7] = 0; aaDayhoff[ 7][ 8] = 10; aaDayhoff[ 7][ 9] = 0; aaDayhoff[ 7][10] = 7; aaDayhoff[ 7][11] = 27; aaDayhoff[ 7][12] = 17; aaDayhoff[ 7][13] = 15; aaDayhoff[ 7][14] = 34; aaDayhoff[ 7][15] = 234; aaDayhoff[ 7][16] = 30; aaDayhoff[ 7][17] = 0; aaDayhoff[ 7][18] = 0; aaDayhoff[ 7][19] = 54; aaDayhoff[ 8][ 0] = 23; aaDayhoff[ 8][ 1] = 240; aaDayhoff[ 8][ 2] = 535; aaDayhoff[ 8][ 3] = 86; aaDayhoff[ 8][ 4] = 28; aaDayhoff[ 8][ 5] = 606; aaDayhoff[ 8][ 6] = 43; aaDayhoff[ 8][ 7] = 10; aaDayhoff[ 8][ 8] = 0; aaDayhoff[ 8][ 9] = 7; aaDayhoff[ 8][10] = 44; aaDayhoff[ 8][11] = 26; aaDayhoff[ 8][12] = 0; aaDayhoff[ 8][13] = 48; aaDayhoff[ 8][14] = 94; aaDayhoff[ 8][15] = 35; aaDayhoff[ 8][16] = 22; aaDayhoff[ 8][17] = 27; aaDayhoff[ 8][18] = 127; aaDayhoff[ 8][19] = 44; aaDayhoff[ 9][ 0] = 65; aaDayhoff[ 9][ 1] = 64; aaDayhoff[ 9][ 2] = 77; aaDayhoff[ 9][ 3] = 24; aaDayhoff[ 9][ 4] = 44; aaDayhoff[ 9][ 5] = 18; aaDayhoff[ 9][ 6] = 61; aaDayhoff[ 9][ 7] = 0; aaDayhoff[ 9][ 8] = 7; aaDayhoff[ 9][ 9] = 0; aaDayhoff[ 9][10] = 257; aaDayhoff[ 9][11] = 46; aaDayhoff[ 9][12] = 336; aaDayhoff[ 9][13] = 196; aaDayhoff[ 9][14] = 12; aaDayhoff[ 9][15] = 24; aaDayhoff[ 9][16] = 192; aaDayhoff[ 9][17] = 0; aaDayhoff[ 9][18] = 37; aaDayhoff[ 9][19] = 889; aaDayhoff[10][ 0] = 41; aaDayhoff[10][ 1] = 15; aaDayhoff[10][ 2] = 34; aaDayhoff[10][ 3] = 0; aaDayhoff[10][ 4] = 0; aaDayhoff[10][ 5] = 73; aaDayhoff[10][ 6] = 11; aaDayhoff[10][ 7] = 7; aaDayhoff[10][ 8] = 44; aaDayhoff[10][ 9] = 257; aaDayhoff[10][10] = 0; aaDayhoff[10][11] = 18; aaDayhoff[10][12] = 527; aaDayhoff[10][13] = 157; aaDayhoff[10][14] = 32; aaDayhoff[10][15] = 17; aaDayhoff[10][16] = 33; aaDayhoff[10][17] = 46; aaDayhoff[10][18] = 28; aaDayhoff[10][19] = 175; aaDayhoff[11][ 0] = 26; aaDayhoff[11][ 1] = 464; aaDayhoff[11][ 2] = 318; aaDayhoff[11][ 3] = 71; aaDayhoff[11][ 4] = 0; aaDayhoff[11][ 5] = 153; aaDayhoff[11][ 6] = 83; aaDayhoff[11][ 7] = 27; aaDayhoff[11][ 8] = 26; aaDayhoff[11][ 9] = 46; aaDayhoff[11][10] = 18; aaDayhoff[11][11] = 0; aaDayhoff[11][12] = 243; aaDayhoff[11][13] = 0; aaDayhoff[11][14] = 33; aaDayhoff[11][15] = 96; aaDayhoff[11][16] = 136; aaDayhoff[11][17] = 0; aaDayhoff[11][18] = 13; aaDayhoff[11][19] = 10; aaDayhoff[12][ 0] = 72; aaDayhoff[12][ 1] = 90; aaDayhoff[12][ 2] = 1; aaDayhoff[12][ 3] = 0; aaDayhoff[12][ 4] = 0; aaDayhoff[12][ 5] = 114; aaDayhoff[12][ 6] = 30; aaDayhoff[12][ 7] = 17; aaDayhoff[12][ 8] = 0; aaDayhoff[12][ 9] = 336; aaDayhoff[12][10] = 527; aaDayhoff[12][11] = 243; aaDayhoff[12][12] = 0; aaDayhoff[12][13] = 92; aaDayhoff[12][14] = 17; aaDayhoff[12][15] = 62; aaDayhoff[12][16] = 104; aaDayhoff[12][17] = 0; aaDayhoff[12][18] = 0; aaDayhoff[12][19] = 258; aaDayhoff[13][ 0] = 18; aaDayhoff[13][ 1] = 14; aaDayhoff[13][ 2] = 14; aaDayhoff[13][ 3] = 0; aaDayhoff[13][ 4] = 0; aaDayhoff[13][ 5] = 0; aaDayhoff[13][ 6] = 0; aaDayhoff[13][ 7] = 15; aaDayhoff[13][ 8] = 48; aaDayhoff[13][ 9] = 196; aaDayhoff[13][10] = 157; aaDayhoff[13][11] = 0; aaDayhoff[13][12] = 92; aaDayhoff[13][13] = 0; aaDayhoff[13][14] = 11; aaDayhoff[13][15] = 46; aaDayhoff[13][16] = 13; aaDayhoff[13][17] = 76; aaDayhoff[13][18] = 698; aaDayhoff[13][19] = 12; aaDayhoff[14][ 0] = 250; aaDayhoff[14][ 1] = 103; aaDayhoff[14][ 2] = 42; aaDayhoff[14][ 3] = 13; aaDayhoff[14][ 4] = 19; aaDayhoff[14][ 5] = 153; aaDayhoff[14][ 6] = 51; aaDayhoff[14][ 7] = 34; aaDayhoff[14][ 8] = 94; aaDayhoff[14][ 9] = 12; aaDayhoff[14][10] = 32; aaDayhoff[14][11] = 33; aaDayhoff[14][12] = 17; aaDayhoff[14][13] = 11; aaDayhoff[14][14] = 0; aaDayhoff[14][15] = 245; aaDayhoff[14][16] = 78; aaDayhoff[14][17] = 0; aaDayhoff[14][18] = 0; aaDayhoff[14][19] = 48; aaDayhoff[15][ 0] = 409; aaDayhoff[15][ 1] = 154; aaDayhoff[15][ 2] = 495; aaDayhoff[15][ 3] = 95; aaDayhoff[15][ 4] = 161; aaDayhoff[15][ 5] = 56; aaDayhoff[15][ 6] = 79; aaDayhoff[15][ 7] = 234; aaDayhoff[15][ 8] = 35; aaDayhoff[15][ 9] = 24; aaDayhoff[15][10] = 17; aaDayhoff[15][11] = 96; aaDayhoff[15][12] = 62; aaDayhoff[15][13] = 46; aaDayhoff[15][14] = 245; aaDayhoff[15][15] = 0; aaDayhoff[15][16] = 550; aaDayhoff[15][17] = 75; aaDayhoff[15][18] = 34; aaDayhoff[15][19] = 30; aaDayhoff[16][ 0] = 371; aaDayhoff[16][ 1] = 26; aaDayhoff[16][ 2] = 229; aaDayhoff[16][ 3] = 66; aaDayhoff[16][ 4] = 16; aaDayhoff[16][ 5] = 53; aaDayhoff[16][ 6] = 34; aaDayhoff[16][ 7] = 30; aaDayhoff[16][ 8] = 22; aaDayhoff[16][ 9] = 192; aaDayhoff[16][10] = 33; aaDayhoff[16][11] = 136; aaDayhoff[16][12] = 104; aaDayhoff[16][13] = 13; aaDayhoff[16][14] = 78; aaDayhoff[16][15] = 550; aaDayhoff[16][16] = 0; aaDayhoff[16][17] = 0; aaDayhoff[16][18] = 42; aaDayhoff[16][19] = 157; aaDayhoff[17][ 0] = 0; aaDayhoff[17][ 1] = 201; aaDayhoff[17][ 2] = 23; aaDayhoff[17][ 3] = 0; aaDayhoff[17][ 4] = 0; aaDayhoff[17][ 5] = 0; aaDayhoff[17][ 6] = 0; aaDayhoff[17][ 7] = 0; aaDayhoff[17][ 8] = 27; aaDayhoff[17][ 9] = 0; aaDayhoff[17][10] = 46; aaDayhoff[17][11] = 0; aaDayhoff[17][12] = 0; aaDayhoff[17][13] = 76; aaDayhoff[17][14] = 0; aaDayhoff[17][15] = 75; aaDayhoff[17][16] = 0; aaDayhoff[17][17] = 0; aaDayhoff[17][18] = 61; aaDayhoff[17][19] = 0; aaDayhoff[18][ 0] = 24; aaDayhoff[18][ 1] = 8; aaDayhoff[18][ 2] = 95; aaDayhoff[18][ 3] = 0; aaDayhoff[18][ 4] = 96; aaDayhoff[18][ 5] = 0; aaDayhoff[18][ 6] = 22; aaDayhoff[18][ 7] = 0; aaDayhoff[18][ 8] = 127; aaDayhoff[18][ 9] = 37; aaDayhoff[18][10] = 28; aaDayhoff[18][11] = 13; aaDayhoff[18][12] = 0; aaDayhoff[18][13] = 698; aaDayhoff[18][14] = 0; aaDayhoff[18][15] = 34; aaDayhoff[18][16] = 42; aaDayhoff[18][17] = 61; aaDayhoff[18][18] = 0; aaDayhoff[18][19] = 28; aaDayhoff[19][ 0] = 208; aaDayhoff[19][ 1] = 24; aaDayhoff[19][ 2] = 15; aaDayhoff[19][ 3] = 18; aaDayhoff[19][ 4] = 49; aaDayhoff[19][ 5] = 35; aaDayhoff[19][ 6] = 37; aaDayhoff[19][ 7] = 54; aaDayhoff[19][ 8] = 44; aaDayhoff[19][ 9] = 889; aaDayhoff[19][10] = 175; aaDayhoff[19][11] = 10; aaDayhoff[19][12] = 258; aaDayhoff[19][13] = 12; aaDayhoff[19][14] = 48; aaDayhoff[19][15] = 30; aaDayhoff[19][16] = 157; aaDayhoff[19][17] = 0; aaDayhoff[19][18] = 28; aaDayhoff[19][19] = 0; dayhoffPi[ 0] = 0.087127; dayhoffPi[ 1] = 0.040904; dayhoffPi[ 2] = 0.040432; dayhoffPi[ 3] = 0.046872; dayhoffPi[ 4] = 0.033474; dayhoffPi[ 5] = 0.038255; dayhoffPi[ 6] = 0.049530; dayhoffPi[ 7] = 0.088612; dayhoffPi[ 8] = 0.033618; dayhoffPi[ 9] = 0.036886; dayhoffPi[10] = 0.085357; dayhoffPi[11] = 0.080482; dayhoffPi[12] = 0.014753; dayhoffPi[13] = 0.039772; dayhoffPi[14] = 0.050680; dayhoffPi[15] = 0.069577; dayhoffPi[16] = 0.058542; dayhoffPi[17] = 0.010494; dayhoffPi[18] = 0.029916; dayhoffPi[19] = 0.064718; /* mtrev24 */ aaMtrev24[ 0][ 0] = 0.00; aaMtrev24[ 0][ 1] = 23.18; aaMtrev24[ 0][ 2] = 26.95; aaMtrev24[ 0][ 3] = 17.67; aaMtrev24[ 0][ 4] = 59.93; aaMtrev24[ 0][ 5] = 1.90; aaMtrev24[ 0][ 6] = 9.77; aaMtrev24[ 0][ 7] = 120.71; aaMtrev24[ 0][ 8] = 13.90; aaMtrev24[ 0][ 9] = 96.49; aaMtrev24[ 0][10] = 25.46; aaMtrev24[ 0][11] = 8.36; aaMtrev24[ 0][12] = 141.88; aaMtrev24[ 0][13] = 6.37; aaMtrev24[ 0][14] = 54.31; aaMtrev24[ 0][15] = 387.86; aaMtrev24[ 0][16] = 480.72; aaMtrev24[ 0][17] = 1.90; aaMtrev24[ 0][18] = 6.48; aaMtrev24[ 0][19] = 195.06; aaMtrev24[ 1][ 0] = 23.18; aaMtrev24[ 1][ 1] = 0.00; aaMtrev24[ 1][ 2] = 13.24; aaMtrev24[ 1][ 3] = 1.90; aaMtrev24[ 1][ 4] = 103.33; aaMtrev24[ 1][ 5] = 220.99; aaMtrev24[ 1][ 6] = 1.90; aaMtrev24[ 1][ 7] = 23.03; aaMtrev24[ 1][ 8] = 165.23; aaMtrev24[ 1][ 9] = 1.90; aaMtrev24[ 1][10] = 15.58; aaMtrev24[ 1][11] = 141.40; aaMtrev24[ 1][12] = 1.90; aaMtrev24[ 1][13] = 4.69; aaMtrev24[ 1][14] = 23.64; aaMtrev24[ 1][15] = 6.04; aaMtrev24[ 1][16] = 2.08; aaMtrev24[ 1][17] = 21.95; aaMtrev24[ 1][18] = 1.90; aaMtrev24[ 1][19] = 7.64; aaMtrev24[ 2][ 0] = 26.95; aaMtrev24[ 2][ 1] = 13.24; aaMtrev24[ 2][ 2] = 0.00; aaMtrev24[ 2][ 3] = 794.38; aaMtrev24[ 2][ 4] = 58.94; aaMtrev24[ 2][ 5] = 173.56; aaMtrev24[ 2][ 6] = 63.05; aaMtrev24[ 2][ 7] = 53.30; aaMtrev24[ 2][ 8] = 496.13; aaMtrev24[ 2][ 9] = 27.10; aaMtrev24[ 2][10] = 15.16; aaMtrev24[ 2][11] = 608.70; aaMtrev24[ 2][12] = 65.41; aaMtrev24[ 2][13] = 15.20; aaMtrev24[ 2][14] = 73.31; aaMtrev24[ 2][15] = 494.39; aaMtrev24[ 2][16] = 238.46; aaMtrev24[ 2][17] = 10.68; aaMtrev24[ 2][18] = 191.36; aaMtrev24[ 2][19] = 1.90; aaMtrev24[ 3][ 0] = 17.67; aaMtrev24[ 3][ 1] = 1.90; aaMtrev24[ 3][ 2] = 794.38; aaMtrev24[ 3][ 3] = 0.00; aaMtrev24[ 3][ 4] = 1.90; aaMtrev24[ 3][ 5] = 55.28; aaMtrev24[ 3][ 6] = 583.55; aaMtrev24[ 3][ 7] = 56.77; aaMtrev24[ 3][ 8] = 113.99; aaMtrev24[ 3][ 9] = 4.34; aaMtrev24[ 3][10] = 1.90; aaMtrev24[ 3][11] = 2.31; aaMtrev24[ 3][12] = 1.90; aaMtrev24[ 3][13] = 4.98; aaMtrev24[ 3][14] = 13.43; aaMtrev24[ 3][15] = 69.02; aaMtrev24[ 3][16] = 28.01; aaMtrev24[ 3][17] = 19.86; aaMtrev24[ 3][18] = 21.21; aaMtrev24[ 3][19] = 1.90; aaMtrev24[ 4][ 0] = 59.93; aaMtrev24[ 4][ 1] = 103.33; aaMtrev24[ 4][ 2] = 58.94; aaMtrev24[ 4][ 3] = 1.90; aaMtrev24[ 4][ 4] = 0.00; aaMtrev24[ 4][ 5] = 75.24; aaMtrev24[ 4][ 6] = 1.90; aaMtrev24[ 4][ 7] = 30.71; aaMtrev24[ 4][ 8] = 141.49; aaMtrev24[ 4][ 9] = 62.73; aaMtrev24[ 4][10] = 25.65; aaMtrev24[ 4][11] = 1.90; aaMtrev24[ 4][12] = 6.18; aaMtrev24[ 4][13] = 70.80; aaMtrev24[ 4][14] = 31.26; aaMtrev24[ 4][15] = 277.05; aaMtrev24[ 4][16] = 179.97; aaMtrev24[ 4][17] = 33.60; aaMtrev24[ 4][18] = 254.77; aaMtrev24[ 4][19] = 1.90; aaMtrev24[ 5][ 0] = 1.90; aaMtrev24[ 5][ 1] = 220.99; aaMtrev24[ 5][ 2] = 173.56; aaMtrev24[ 5][ 3] = 55.28; aaMtrev24[ 5][ 4] = 75.24; aaMtrev24[ 5][ 5] = 0.00; aaMtrev24[ 5][ 6] = 313.56; aaMtrev24[ 5][ 7] = 6.75; aaMtrev24[ 5][ 8] = 582.40; aaMtrev24[ 5][ 9] = 8.34; aaMtrev24[ 5][10] = 39.70; aaMtrev24[ 5][11] = 465.58; aaMtrev24[ 5][12] = 47.37; aaMtrev24[ 5][13] = 19.11; aaMtrev24[ 5][14] = 137.29; aaMtrev24[ 5][15] = 54.11; aaMtrev24[ 5][16] = 94.93; aaMtrev24[ 5][17] = 1.90; aaMtrev24[ 5][18] = 38.82; aaMtrev24[ 5][19] = 19.00; aaMtrev24[ 6][ 0] = 9.77; aaMtrev24[ 6][ 1] = 1.90; aaMtrev24[ 6][ 2] = 63.05; aaMtrev24[ 6][ 3] = 583.55; aaMtrev24[ 6][ 4] = 1.90; aaMtrev24[ 6][ 5] = 313.56; aaMtrev24[ 6][ 6] = 0.00; aaMtrev24[ 6][ 7] = 28.28; aaMtrev24[ 6][ 8] = 49.12; aaMtrev24[ 6][ 9] = 3.31; aaMtrev24[ 6][10] = 1.90; aaMtrev24[ 6][11] = 313.86; aaMtrev24[ 6][12] = 1.90; aaMtrev24[ 6][13] = 2.67; aaMtrev24[ 6][14] = 12.83; aaMtrev24[ 6][15] = 54.71; aaMtrev24[ 6][16] = 14.82; aaMtrev24[ 6][17] = 1.90; aaMtrev24[ 6][18] = 13.12; aaMtrev24[ 6][19] = 21.14; aaMtrev24[ 7][ 0] = 120.71; aaMtrev24[ 7][ 1] = 23.03; aaMtrev24[ 7][ 2] = 53.30; aaMtrev24[ 7][ 3] = 56.77; aaMtrev24[ 7][ 4] = 30.71; aaMtrev24[ 7][ 5] = 6.75; aaMtrev24[ 7][ 6] = 28.28; aaMtrev24[ 7][ 7] = 0.00; aaMtrev24[ 7][ 8] = 1.90; aaMtrev24[ 7][ 9] = 5.98; aaMtrev24[ 7][10] = 2.41; aaMtrev24[ 7][11] = 22.73; aaMtrev24[ 7][12] = 1.90; aaMtrev24[ 7][13] = 1.90; aaMtrev24[ 7][14] = 1.90; aaMtrev24[ 7][15] = 125.93; aaMtrev24[ 7][16] = 11.17; aaMtrev24[ 7][17] = 10.92; aaMtrev24[ 7][18] = 3.21; aaMtrev24[ 7][19] = 2.53; aaMtrev24[ 8][ 0] = 13.90; aaMtrev24[ 8][ 1] = 165.23; aaMtrev24[ 8][ 2] = 496.13; aaMtrev24[ 8][ 3] = 113.99; aaMtrev24[ 8][ 4] = 141.49; aaMtrev24[ 8][ 5] = 582.40; aaMtrev24[ 8][ 6] = 49.12; aaMtrev24[ 8][ 7] = 1.90; aaMtrev24[ 8][ 8] = 0.00; aaMtrev24[ 8][ 9] = 12.26; aaMtrev24[ 8][10] = 11.49; aaMtrev24[ 8][11] = 127.67; aaMtrev24[ 8][12] = 11.97; aaMtrev24[ 8][13] = 48.16; aaMtrev24[ 8][14] = 60.97; aaMtrev24[ 8][15] = 77.46; aaMtrev24[ 8][16] = 44.78; aaMtrev24[ 8][17] = 7.08; aaMtrev24[ 8][18] = 670.14; aaMtrev24[ 8][19] = 1.90; aaMtrev24[ 9][ 0] = 96.49; aaMtrev24[ 9][ 1] = 1.90; aaMtrev24[ 9][ 2] = 27.10; aaMtrev24[ 9][ 3] = 4.34; aaMtrev24[ 9][ 4] = 62.73; aaMtrev24[ 9][ 5] = 8.34; aaMtrev24[ 9][ 6] = 3.31; aaMtrev24[ 9][ 7] = 5.98; aaMtrev24[ 9][ 8] = 12.26; aaMtrev24[ 9][ 9] = 0.00; aaMtrev24[ 9][10] = 329.09; aaMtrev24[ 9][11] = 19.57; aaMtrev24[ 9][12] = 517.98; aaMtrev24[ 9][13] = 84.67; aaMtrev24[ 9][14] = 20.63; aaMtrev24[ 9][15] = 47.70; aaMtrev24[ 9][16] = 368.43; aaMtrev24[ 9][17] = 1.90; aaMtrev24[ 9][18] = 25.01; aaMtrev24[ 9][19] =1222.94; aaMtrev24[10][ 0] = 25.46; aaMtrev24[10][ 1] = 15.58; aaMtrev24[10][ 2] = 15.16; aaMtrev24[10][ 3] = 1.90; aaMtrev24[10][ 4] = 25.65; aaMtrev24[10][ 5] = 39.70; aaMtrev24[10][ 6] = 1.90; aaMtrev24[10][ 7] = 2.41; aaMtrev24[10][ 8] = 11.49; aaMtrev24[10][ 9] = 329.09; aaMtrev24[10][10] = 0.00; aaMtrev24[10][11] = 14.88; aaMtrev24[10][12] = 537.53; aaMtrev24[10][13] = 216.06; aaMtrev24[10][14] = 40.10; aaMtrev24[10][15] = 73.61; aaMtrev24[10][16] = 126.40; aaMtrev24[10][17] = 32.44; aaMtrev24[10][18] = 44.15; aaMtrev24[10][19] = 91.67; aaMtrev24[11][ 0] = 8.36; aaMtrev24[11][ 1] = 141.40; aaMtrev24[11][ 2] = 608.70; aaMtrev24[11][ 3] = 2.31; aaMtrev24[11][ 4] = 1.90; aaMtrev24[11][ 5] = 465.58; aaMtrev24[11][ 6] = 313.86; aaMtrev24[11][ 7] = 22.73; aaMtrev24[11][ 8] = 127.67; aaMtrev24[11][ 9] = 19.57; aaMtrev24[11][10] = 14.88; aaMtrev24[11][11] = 0.00; aaMtrev24[11][12] = 91.37; aaMtrev24[11][13] = 6.44; aaMtrev24[11][14] = 50.10; aaMtrev24[11][15] = 105.79; aaMtrev24[11][16] = 136.33; aaMtrev24[11][17] = 24.00; aaMtrev24[11][18] = 51.17; aaMtrev24[11][19] = 1.90; aaMtrev24[12][ 0] = 141.88; aaMtrev24[12][ 1] = 1.90; aaMtrev24[12][ 2] = 65.41; aaMtrev24[12][ 3] = 1.90; aaMtrev24[12][ 4] = 6.18; aaMtrev24[12][ 5] = 47.37; aaMtrev24[12][ 6] = 1.90; aaMtrev24[12][ 7] = 1.90; aaMtrev24[12][ 8] = 11.97; aaMtrev24[12][ 9] = 517.98; aaMtrev24[12][10] = 537.53; aaMtrev24[12][11] = 91.37; aaMtrev24[12][12] = 0.00; aaMtrev24[12][13] = 90.82; aaMtrev24[12][14] = 18.84; aaMtrev24[12][15] = 111.16; aaMtrev24[12][16] = 528.17; aaMtrev24[12][17] = 21.71; aaMtrev24[12][18] = 39.96; aaMtrev24[12][19] = 387.54; aaMtrev24[13][ 0] = 6.37; aaMtrev24[13][ 1] = 4.69; aaMtrev24[13][ 2] = 15.20; aaMtrev24[13][ 3] = 4.98; aaMtrev24[13][ 4] = 70.80; aaMtrev24[13][ 5] = 19.11; aaMtrev24[13][ 6] = 2.67; aaMtrev24[13][ 7] = 1.90; aaMtrev24[13][ 8] = 48.16; aaMtrev24[13][ 9] = 84.67; aaMtrev24[13][10] = 216.06; aaMtrev24[13][11] = 6.44; aaMtrev24[13][12] = 90.82; aaMtrev24[13][13] = 0.00; aaMtrev24[13][14] = 17.31; aaMtrev24[13][15] = 64.29; aaMtrev24[13][16] = 33.85; aaMtrev24[13][17] = 7.84; aaMtrev24[13][18] = 465.58; aaMtrev24[13][19] = 6.35; aaMtrev24[14][ 0] = 54.31; aaMtrev24[14][ 1] = 23.64; aaMtrev24[14][ 2] = 73.31; aaMtrev24[14][ 3] = 13.43; aaMtrev24[14][ 4] = 31.26; aaMtrev24[14][ 5] = 137.29; aaMtrev24[14][ 6] = 12.83; aaMtrev24[14][ 7] = 1.90; aaMtrev24[14][ 8] = 60.97; aaMtrev24[14][ 9] = 20.63; aaMtrev24[14][10] = 40.10; aaMtrev24[14][11] = 50.10; aaMtrev24[14][12] = 18.84; aaMtrev24[14][13] = 17.31; aaMtrev24[14][14] = 0.00; aaMtrev24[14][15] = 169.90; aaMtrev24[14][16] = 128.22; aaMtrev24[14][17] = 4.21; aaMtrev24[14][18] = 16.21; aaMtrev24[14][19] = 8.23; aaMtrev24[15][ 0] = 387.86; aaMtrev24[15][ 1] = 6.04; aaMtrev24[15][ 2] = 494.39; aaMtrev24[15][ 3] = 69.02; aaMtrev24[15][ 4] = 277.05; aaMtrev24[15][ 5] = 54.11; aaMtrev24[15][ 6] = 54.71; aaMtrev24[15][ 7] = 125.93; aaMtrev24[15][ 8] = 77.46; aaMtrev24[15][ 9] = 47.70; aaMtrev24[15][10] = 73.61; aaMtrev24[15][11] = 105.79; aaMtrev24[15][12] = 111.16; aaMtrev24[15][13] = 64.29; aaMtrev24[15][14] = 169.90; aaMtrev24[15][15] = 0.00; aaMtrev24[15][16] = 597.21; aaMtrev24[15][17] = 38.58; aaMtrev24[15][18] = 64.92; aaMtrev24[15][19] = 1.90; aaMtrev24[16][ 0] = 480.72; aaMtrev24[16][ 1] = 2.08; aaMtrev24[16][ 2] = 238.46; aaMtrev24[16][ 3] = 28.01; aaMtrev24[16][ 4] = 179.97; aaMtrev24[16][ 5] = 94.93; aaMtrev24[16][ 6] = 14.82; aaMtrev24[16][ 7] = 11.17; aaMtrev24[16][ 8] = 44.78; aaMtrev24[16][ 9] = 368.43; aaMtrev24[16][10] = 126.40; aaMtrev24[16][11] = 136.33; aaMtrev24[16][12] = 528.17; aaMtrev24[16][13] = 33.85; aaMtrev24[16][14] = 128.22; aaMtrev24[16][15] = 597.21; aaMtrev24[16][16] = 0.00; aaMtrev24[16][17] = 9.99; aaMtrev24[16][18] = 38.73; aaMtrev24[16][19] = 204.54; aaMtrev24[17][ 0] = 1.90; aaMtrev24[17][ 1] = 21.95; aaMtrev24[17][ 2] = 10.68; aaMtrev24[17][ 3] = 19.86; aaMtrev24[17][ 4] = 33.60; aaMtrev24[17][ 5] = 1.90; aaMtrev24[17][ 6] = 1.90; aaMtrev24[17][ 7] = 10.92; aaMtrev24[17][ 8] = 7.08; aaMtrev24[17][ 9] = 1.90; aaMtrev24[17][10] = 32.44; aaMtrev24[17][11] = 24.00; aaMtrev24[17][12] = 21.71; aaMtrev24[17][13] = 7.84; aaMtrev24[17][14] = 4.21; aaMtrev24[17][15] = 38.58; aaMtrev24[17][16] = 9.99; aaMtrev24[17][17] = 0.00; aaMtrev24[17][18] = 26.25; aaMtrev24[17][19] = 5.37; aaMtrev24[18][ 0] = 6.48; aaMtrev24[18][ 1] = 1.90; aaMtrev24[18][ 2] = 191.36; aaMtrev24[18][ 3] = 21.21; aaMtrev24[18][ 4] = 254.77; aaMtrev24[18][ 5] = 38.82; aaMtrev24[18][ 6] = 13.12; aaMtrev24[18][ 7] = 3.21; aaMtrev24[18][ 8] = 670.14; aaMtrev24[18][ 9] = 25.01; aaMtrev24[18][10] = 44.15; aaMtrev24[18][11] = 51.17; aaMtrev24[18][12] = 39.96; aaMtrev24[18][13] = 465.58; aaMtrev24[18][14] = 16.21; aaMtrev24[18][15] = 64.92; aaMtrev24[18][16] = 38.73; aaMtrev24[18][17] = 26.25; aaMtrev24[18][18] = 0.00; aaMtrev24[18][19] = 1.90; aaMtrev24[19][ 0] = 195.06; aaMtrev24[19][ 1] = 7.64; aaMtrev24[19][ 2] = 1.90; aaMtrev24[19][ 3] = 1.90; aaMtrev24[19][ 4] = 1.90; aaMtrev24[19][ 5] = 19.00; aaMtrev24[19][ 6] = 21.14; aaMtrev24[19][ 7] = 2.53; aaMtrev24[19][ 8] = 1.90; aaMtrev24[19][ 9] =1222.94; aaMtrev24[19][10] = 91.67; aaMtrev24[19][11] = 1.90; aaMtrev24[19][12] = 387.54; aaMtrev24[19][13] = 6.35; aaMtrev24[19][14] = 8.23; aaMtrev24[19][15] = 1.90; aaMtrev24[19][16] = 204.54; aaMtrev24[19][17] = 5.37; aaMtrev24[19][18] = 1.90; aaMtrev24[19][19] = 0.00; mtrev24Pi[ 0] = 0.072; mtrev24Pi[ 1] = 0.019; mtrev24Pi[ 2] = 0.039; mtrev24Pi[ 3] = 0.019; mtrev24Pi[ 4] = 0.006; mtrev24Pi[ 5] = 0.025; mtrev24Pi[ 6] = 0.024; mtrev24Pi[ 7] = 0.056; mtrev24Pi[ 8] = 0.028; mtrev24Pi[ 9] = 0.088; mtrev24Pi[10] = 0.168; mtrev24Pi[11] = 0.023; mtrev24Pi[12] = 0.054; mtrev24Pi[13] = 0.061; mtrev24Pi[14] = 0.054; mtrev24Pi[15] = 0.072; mtrev24Pi[16] = 0.086; mtrev24Pi[17] = 0.029; mtrev24Pi[18] = 0.033; mtrev24Pi[19] = 0.043; /* mtmam */ aaMtmam[ 0][ 0] = 0; aaMtmam[ 0][ 1] = 32; aaMtmam[ 0][ 2] = 2; aaMtmam[ 0][ 3] = 11; aaMtmam[ 0][ 4] = 0; aaMtmam[ 0][ 5] = 0; aaMtmam[ 0][ 6] = 0; aaMtmam[ 0][ 7] = 78; aaMtmam[ 0][ 8] = 8; aaMtmam[ 0][ 9] = 75; aaMtmam[ 0][10] = 21; aaMtmam[ 0][11] = 0; aaMtmam[ 0][12] = 76; aaMtmam[ 0][13] = 0; aaMtmam[ 0][14] = 53; aaMtmam[ 0][15] = 342; aaMtmam[ 0][16] = 681; aaMtmam[ 0][17] = 5; aaMtmam[ 0][18] = 0; aaMtmam[ 0][19] = 398; aaMtmam[ 1][ 0] = 32; aaMtmam[ 1][ 1] = 0; aaMtmam[ 1][ 2] = 4; aaMtmam[ 1][ 3] = 0; aaMtmam[ 1][ 4] = 186; aaMtmam[ 1][ 5] = 246; aaMtmam[ 1][ 6] = 0; aaMtmam[ 1][ 7] = 18; aaMtmam[ 1][ 8] = 232; aaMtmam[ 1][ 9] = 0; aaMtmam[ 1][10] = 6; aaMtmam[ 1][11] = 50; aaMtmam[ 1][12] = 0; aaMtmam[ 1][13] = 0; aaMtmam[ 1][14] = 9; aaMtmam[ 1][15] = 3; aaMtmam[ 1][16] = 0; aaMtmam[ 1][17] = 16; aaMtmam[ 1][18] = 0; aaMtmam[ 1][19] = 0; aaMtmam[ 2][ 0] = 2; aaMtmam[ 2][ 1] = 4; aaMtmam[ 2][ 2] = 0; aaMtmam[ 2][ 3] = 864; aaMtmam[ 2][ 4] = 0; aaMtmam[ 2][ 5] = 8; aaMtmam[ 2][ 6] = 0; aaMtmam[ 2][ 7] = 47; aaMtmam[ 2][ 8] = 458; aaMtmam[ 2][ 9] = 19; aaMtmam[ 2][10] = 0; aaMtmam[ 2][11] = 408; aaMtmam[ 2][12] = 21; aaMtmam[ 2][13] = 6; aaMtmam[ 2][14] = 33; aaMtmam[ 2][15] = 446; aaMtmam[ 2][16] = 110; aaMtmam[ 2][17] = 6; aaMtmam[ 2][18] = 156; aaMtmam[ 2][19] = 0; aaMtmam[ 3][ 0] = 11; aaMtmam[ 3][ 1] = 0; aaMtmam[ 3][ 2] = 864; aaMtmam[ 3][ 3] = 0; aaMtmam[ 3][ 4] = 0; aaMtmam[ 3][ 5] = 49; aaMtmam[ 3][ 6] = 569; aaMtmam[ 3][ 7] = 79; aaMtmam[ 3][ 8] = 11; aaMtmam[ 3][ 9] = 0; aaMtmam[ 3][10] = 0; aaMtmam[ 3][11] = 0; aaMtmam[ 3][12] = 0; aaMtmam[ 3][13] = 5; aaMtmam[ 3][14] = 2; aaMtmam[ 3][15] = 16; aaMtmam[ 3][16] = 0; aaMtmam[ 3][17] = 0; aaMtmam[ 3][18] = 0; aaMtmam[ 3][19] = 10; aaMtmam[ 4][ 0] = 0; aaMtmam[ 4][ 1] = 186; aaMtmam[ 4][ 2] = 0; aaMtmam[ 4][ 3] = 0; aaMtmam[ 4][ 4] = 0; aaMtmam[ 4][ 5] = 0; aaMtmam[ 4][ 6] = 0; aaMtmam[ 4][ 7] = 0; aaMtmam[ 4][ 8] = 305; aaMtmam[ 4][ 9] = 41; aaMtmam[ 4][10] = 27; aaMtmam[ 4][11] = 0; aaMtmam[ 4][12] = 0; aaMtmam[ 4][13] = 7; aaMtmam[ 4][14] = 0; aaMtmam[ 4][15] = 347; aaMtmam[ 4][16] = 114; aaMtmam[ 4][17] = 65; aaMtmam[ 4][18] = 530; aaMtmam[ 4][19] = 0; aaMtmam[ 5][ 0] = 0; aaMtmam[ 5][ 1] = 246; aaMtmam[ 5][ 2] = 8; aaMtmam[ 5][ 3] = 49; aaMtmam[ 5][ 4] = 0; aaMtmam[ 5][ 5] = 0; aaMtmam[ 5][ 6] = 274; aaMtmam[ 5][ 7] = 0; aaMtmam[ 5][ 8] = 550; aaMtmam[ 5][ 9] = 0; aaMtmam[ 5][10] = 20; aaMtmam[ 5][11] = 242; aaMtmam[ 5][12] = 22; aaMtmam[ 5][13] = 0; aaMtmam[ 5][14] = 51; aaMtmam[ 5][15] = 30; aaMtmam[ 5][16] = 0; aaMtmam[ 5][17] = 0; aaMtmam[ 5][18] = 54; aaMtmam[ 5][19] = 33; aaMtmam[ 6][ 0] = 0; aaMtmam[ 6][ 1] = 0; aaMtmam[ 6][ 2] = 0; aaMtmam[ 6][ 3] = 569; aaMtmam[ 6][ 4] = 0; aaMtmam[ 6][ 5] = 274; aaMtmam[ 6][ 6] = 0; aaMtmam[ 6][ 7] = 22; aaMtmam[ 6][ 8] = 22; aaMtmam[ 6][ 9] = 0; aaMtmam[ 6][10] = 0; aaMtmam[ 6][11] = 215; aaMtmam[ 6][12] = 0; aaMtmam[ 6][13] = 0; aaMtmam[ 6][14] = 0; aaMtmam[ 6][15] = 21; aaMtmam[ 6][16] = 4; aaMtmam[ 6][17] = 0; aaMtmam[ 6][18] = 0; aaMtmam[ 6][19] = 20; aaMtmam[ 7][ 0] = 78; aaMtmam[ 7][ 1] = 18; aaMtmam[ 7][ 2] = 47; aaMtmam[ 7][ 3] = 79; aaMtmam[ 7][ 4] = 0; aaMtmam[ 7][ 5] = 0; aaMtmam[ 7][ 6] = 22; aaMtmam[ 7][ 7] = 0; aaMtmam[ 7][ 8] = 0; aaMtmam[ 7][ 9] = 0; aaMtmam[ 7][10] = 0; aaMtmam[ 7][11] = 0; aaMtmam[ 7][12] = 0; aaMtmam[ 7][13] = 0; aaMtmam[ 7][14] = 0; aaMtmam[ 7][15] = 112; aaMtmam[ 7][16] = 0; aaMtmam[ 7][17] = 0; aaMtmam[ 7][18] = 1; aaMtmam[ 7][19] = 5; aaMtmam[ 8][ 0] = 8; aaMtmam[ 8][ 1] = 232; aaMtmam[ 8][ 2] = 458; aaMtmam[ 8][ 3] = 11; aaMtmam[ 8][ 4] = 305; aaMtmam[ 8][ 5] = 550; aaMtmam[ 8][ 6] = 22; aaMtmam[ 8][ 7] = 0; aaMtmam[ 8][ 8] = 0; aaMtmam[ 8][ 9] = 0; aaMtmam[ 8][10] = 26; aaMtmam[ 8][11] = 0; aaMtmam[ 8][12] = 0; aaMtmam[ 8][13] = 0; aaMtmam[ 8][14] = 53; aaMtmam[ 8][15] = 20; aaMtmam[ 8][16] = 1; aaMtmam[ 8][17] = 0; aaMtmam[ 8][18] =1525; aaMtmam[ 8][19] = 0; aaMtmam[ 9][ 0] = 75; aaMtmam[ 9][ 1] = 0; aaMtmam[ 9][ 2] = 19; aaMtmam[ 9][ 3] = 0; aaMtmam[ 9][ 4] = 41; aaMtmam[ 9][ 5] = 0; aaMtmam[ 9][ 6] = 0; aaMtmam[ 9][ 7] = 0; aaMtmam[ 9][ 8] = 0; aaMtmam[ 9][ 9] = 0; aaMtmam[ 9][10] = 232; aaMtmam[ 9][11] = 6; aaMtmam[ 9][12] = 378; aaMtmam[ 9][13] = 57; aaMtmam[ 9][14] = 5; aaMtmam[ 9][15] = 0; aaMtmam[ 9][16] = 360; aaMtmam[ 9][17] = 0; aaMtmam[ 9][18] = 16; aaMtmam[ 9][19] =2220; aaMtmam[10][ 0] = 21; aaMtmam[10][ 1] = 6; aaMtmam[10][ 2] = 0; aaMtmam[10][ 3] = 0; aaMtmam[10][ 4] = 27; aaMtmam[10][ 5] = 20; aaMtmam[10][ 6] = 0; aaMtmam[10][ 7] = 0; aaMtmam[10][ 8] = 26; aaMtmam[10][ 9] = 232; aaMtmam[10][10] = 0; aaMtmam[10][11] = 4; aaMtmam[10][12] = 609; aaMtmam[10][13] = 246; aaMtmam[10][14] = 43; aaMtmam[10][15] = 74; aaMtmam[10][16] = 34; aaMtmam[10][17] = 12; aaMtmam[10][18] = 25; aaMtmam[10][19] = 100; aaMtmam[11][ 0] = 0; aaMtmam[11][ 1] = 50; aaMtmam[11][ 2] = 408; aaMtmam[11][ 3] = 0; aaMtmam[11][ 4] = 0; aaMtmam[11][ 5] = 242; aaMtmam[11][ 6] = 215; aaMtmam[11][ 7] = 0; aaMtmam[11][ 8] = 0; aaMtmam[11][ 9] = 6; aaMtmam[11][10] = 4; aaMtmam[11][11] = 0; aaMtmam[11][12] = 59; aaMtmam[11][13] = 0; aaMtmam[11][14] = 18; aaMtmam[11][15] = 65; aaMtmam[11][16] = 50; aaMtmam[11][17] = 0; aaMtmam[11][18] = 67; aaMtmam[11][19] = 0; aaMtmam[12][ 0] = 76; aaMtmam[12][ 1] = 0; aaMtmam[12][ 2] = 21; aaMtmam[12][ 3] = 0; aaMtmam[12][ 4] = 0; aaMtmam[12][ 5] = 22; aaMtmam[12][ 6] = 0; aaMtmam[12][ 7] = 0; aaMtmam[12][ 8] = 0; aaMtmam[12][ 9] = 378; aaMtmam[12][10] = 609; aaMtmam[12][11] = 59; aaMtmam[12][12] = 0; aaMtmam[12][13] = 11; aaMtmam[12][14] = 0; aaMtmam[12][15] = 47; aaMtmam[12][16] = 691; aaMtmam[12][17] = 13; aaMtmam[12][18] = 0; aaMtmam[12][19] = 832; aaMtmam[13][ 0] = 0; aaMtmam[13][ 1] = 0; aaMtmam[13][ 2] = 6; aaMtmam[13][ 3] = 5; aaMtmam[13][ 4] = 7; aaMtmam[13][ 5] = 0; aaMtmam[13][ 6] = 0; aaMtmam[13][ 7] = 0; aaMtmam[13][ 8] = 0; aaMtmam[13][ 9] = 57; aaMtmam[13][10] = 246; aaMtmam[13][11] = 0; aaMtmam[13][12] = 11; aaMtmam[13][13] = 0; aaMtmam[13][14] = 17; aaMtmam[13][15] = 90; aaMtmam[13][16] = 8; aaMtmam[13][17] = 0; aaMtmam[13][18] = 682; aaMtmam[13][19] = 6; aaMtmam[14][ 0] = 53; aaMtmam[14][ 1] = 9; aaMtmam[14][ 2] = 33; aaMtmam[14][ 3] = 2; aaMtmam[14][ 4] = 0; aaMtmam[14][ 5] = 51; aaMtmam[14][ 6] = 0; aaMtmam[14][ 7] = 0; aaMtmam[14][ 8] = 53; aaMtmam[14][ 9] = 5; aaMtmam[14][10] = 43; aaMtmam[14][11] = 18; aaMtmam[14][12] = 0; aaMtmam[14][13] = 17; aaMtmam[14][14] = 0; aaMtmam[14][15] = 202; aaMtmam[14][16] = 78; aaMtmam[14][17] = 7; aaMtmam[14][18] = 8; aaMtmam[14][19] = 0; aaMtmam[15][ 0] = 342; aaMtmam[15][ 1] = 3; aaMtmam[15][ 2] = 446; aaMtmam[15][ 3] = 16; aaMtmam[15][ 4] = 347; aaMtmam[15][ 5] = 30; aaMtmam[15][ 6] = 21; aaMtmam[15][ 7] = 112; aaMtmam[15][ 8] = 20; aaMtmam[15][ 9] = 0; aaMtmam[15][10] = 74; aaMtmam[15][11] = 65; aaMtmam[15][12] = 47; aaMtmam[15][13] = 90; aaMtmam[15][14] = 202; aaMtmam[15][15] = 0; aaMtmam[15][16] = 614; aaMtmam[15][17] = 17; aaMtmam[15][18] = 107; aaMtmam[15][19] = 0; aaMtmam[16][ 0] = 681; aaMtmam[16][ 1] = 0; aaMtmam[16][ 2] = 110; aaMtmam[16][ 3] = 0; aaMtmam[16][ 4] = 114; aaMtmam[16][ 5] = 0; aaMtmam[16][ 6] = 4; aaMtmam[16][ 7] = 0; aaMtmam[16][ 8] = 1; aaMtmam[16][ 9] = 360; aaMtmam[16][10] = 34; aaMtmam[16][11] = 50; aaMtmam[16][12] = 691; aaMtmam[16][13] = 8; aaMtmam[16][14] = 78; aaMtmam[16][15] = 614; aaMtmam[16][16] = 0; aaMtmam[16][17] = 0; aaMtmam[16][18] = 0; aaMtmam[16][19] = 237; aaMtmam[17][ 0] = 5; aaMtmam[17][ 1] = 16; aaMtmam[17][ 2] = 6; aaMtmam[17][ 3] = 0; aaMtmam[17][ 4] = 65; aaMtmam[17][ 5] = 0; aaMtmam[17][ 6] = 0; aaMtmam[17][ 7] = 0; aaMtmam[17][ 8] = 0; aaMtmam[17][ 9] = 0; aaMtmam[17][10] = 12; aaMtmam[17][11] = 0; aaMtmam[17][12] = 13; aaMtmam[17][13] = 0; aaMtmam[17][14] = 7; aaMtmam[17][15] = 17; aaMtmam[17][16] = 0; aaMtmam[17][17] = 0; aaMtmam[17][18] = 14; aaMtmam[17][19] = 0; aaMtmam[18][ 0] = 0; aaMtmam[18][ 1] = 0; aaMtmam[18][ 2] = 156; aaMtmam[18][ 3] = 0; aaMtmam[18][ 4] = 530; aaMtmam[18][ 5] = 54; aaMtmam[18][ 6] = 0; aaMtmam[18][ 7] = 1; aaMtmam[18][ 8] =1525; aaMtmam[18][ 9] = 16; aaMtmam[18][10] = 25; aaMtmam[18][11] = 67; aaMtmam[18][12] = 0; aaMtmam[18][13] = 682; aaMtmam[18][14] = 8; aaMtmam[18][15] = 107; aaMtmam[18][16] = 0; aaMtmam[18][17] = 14; aaMtmam[18][18] = 0; aaMtmam[18][19] = 0; aaMtmam[19][ 0] = 398; aaMtmam[19][ 1] = 0; aaMtmam[19][ 2] = 0; aaMtmam[19][ 3] = 10; aaMtmam[19][ 4] = 0; aaMtmam[19][ 5] = 33; aaMtmam[19][ 6] = 20; aaMtmam[19][ 7] = 5; aaMtmam[19][ 8] = 0; aaMtmam[19][ 9] =2220; aaMtmam[19][10] = 100; aaMtmam[19][11] = 0; aaMtmam[19][12] = 832; aaMtmam[19][13] = 6; aaMtmam[19][14] = 0; aaMtmam[19][15] = 0; aaMtmam[19][16] = 237; aaMtmam[19][17] = 0; aaMtmam[19][18] = 0; aaMtmam[19][19] = 0; mtmamPi[ 0] = 0.0692; mtmamPi[ 1] = 0.0184; mtmamPi[ 2] = 0.0400; mtmamPi[ 3] = 0.0186; mtmamPi[ 4] = 0.0065; mtmamPi[ 5] = 0.0238; mtmamPi[ 6] = 0.0236; mtmamPi[ 7] = 0.0557; mtmamPi[ 8] = 0.0277; mtmamPi[ 9] = 0.0905; mtmamPi[10] = 0.1675; mtmamPi[11] = 0.0221; mtmamPi[12] = 0.0561; mtmamPi[13] = 0.0611; mtmamPi[14] = 0.0536; mtmamPi[15] = 0.0725; mtmamPi[16] = 0.0870; mtmamPi[17] = 0.0293; mtmamPi[18] = 0.0340; mtmamPi[19] = 0.0428; /* rtRev */ aartREV[ 0][ 0] = 0; aartREV[ 1][ 0] = 34; aartREV[ 2][ 0] = 51; aartREV[ 3][ 0] = 10; aartREV[ 4][ 0] = 439; aartREV[ 5][ 0] = 32; aartREV[ 6][ 0] = 81; aartREV[ 7][ 0] = 135; aartREV[ 8][ 0] = 30; aartREV[ 9][ 0] = 1; aartREV[10][ 0] = 45; aartREV[11][ 0] = 38; aartREV[12][ 0] = 235; aartREV[13][ 0] = 1; aartREV[14][ 0] = 97; aartREV[15][ 0] = 460; aartREV[16][ 0] = 258; aartREV[17][ 0] = 5; aartREV[18][ 0] = 55; aartREV[19][ 0] = 197; aartREV[ 0][ 1] = 34; aartREV[ 1][ 1] = 0; aartREV[ 2][ 1] = 35; aartREV[ 3][ 1] = 30; aartREV[ 4][ 1] = 92; aartREV[ 5][ 1] = 221; aartREV[ 6][ 1] = 10; aartREV[ 7][ 1] = 41; aartREV[ 8][ 1] = 90; aartREV[ 9][ 1] = 24; aartREV[10][ 1] = 18; aartREV[11][ 1] = 593; aartREV[12][ 1] = 57; aartREV[13][ 1] = 7; aartREV[14][ 1] = 24; aartREV[15][ 1] = 102; aartREV[16][ 1] = 64; aartREV[17][ 1] = 13; aartREV[18][ 1] = 47; aartREV[19][ 1] = 29; aartREV[ 0][ 2] = 51; aartREV[ 1][ 2] = 35; aartREV[ 2][ 2] = 0; aartREV[ 3][ 2] = 384; aartREV[ 4][ 2] = 128; aartREV[ 5][ 2] = 236; aartREV[ 6][ 2] = 79; aartREV[ 7][ 2] = 94; aartREV[ 8][ 2] = 320; aartREV[ 9][ 2] = 35; aartREV[10][ 2] = 15; aartREV[11][ 2] = 123; aartREV[12][ 2] = 1; aartREV[13][ 2] = 49; aartREV[14][ 2] = 33; aartREV[15][ 2] = 294; aartREV[16][ 2] = 148; aartREV[17][ 2] = 16; aartREV[18][ 2] = 28; aartREV[19][ 2] = 21; aartREV[ 0][ 3] = 10; aartREV[ 1][ 3] = 30; aartREV[ 2][ 3] = 384; aartREV[ 3][ 3] = 0; aartREV[ 4][ 3] = 1; aartREV[ 5][ 3] = 78; aartREV[ 6][ 3] = 542; aartREV[ 7][ 3] = 61; aartREV[ 8][ 3] = 91; aartREV[ 9][ 3] = 1; aartREV[10][ 3] = 5; aartREV[11][ 3] = 20; aartREV[12][ 3] = 1; aartREV[13][ 3] = 1; aartREV[14][ 3] = 55; aartREV[15][ 3] = 136; aartREV[16][ 3] = 55; aartREV[17][ 3] = 1; aartREV[18][ 3] = 1; aartREV[19][ 3] = 6; aartREV[ 0][ 4] = 439; aartREV[ 1][ 4] = 92; aartREV[ 2][ 4] = 128; aartREV[ 3][ 4] = 1; aartREV[ 4][ 4] = 0; aartREV[ 5][ 4] = 70; aartREV[ 6][ 4] = 1; aartREV[ 7][ 4] = 48; aartREV[ 8][ 4] = 124; aartREV[ 9][ 4] = 104; aartREV[10][ 4] = 110; aartREV[11][ 4] = 16; aartREV[12][ 4] = 156; aartREV[13][ 4] = 70; aartREV[14][ 4] = 1; aartREV[15][ 4] = 75; aartREV[16][ 4] = 117; aartREV[17][ 4] = 55; aartREV[18][ 4] = 131; aartREV[19][ 4] = 295; aartREV[ 0][ 5] = 32; aartREV[ 1][ 5] = 221; aartREV[ 2][ 5] = 236; aartREV[ 3][ 5] = 78; aartREV[ 4][ 5] = 70; aartREV[ 5][ 5] = 0; aartREV[ 6][ 5] = 372; aartREV[ 7][ 5] = 18; aartREV[ 8][ 5] = 387; aartREV[ 9][ 5] = 33; aartREV[10][ 5] = 54; aartREV[11][ 5] = 309; aartREV[12][ 5] = 158; aartREV[13][ 5] = 1; aartREV[14][ 5] = 68; aartREV[15][ 5] = 225; aartREV[16][ 5] = 146; aartREV[17][ 5] = 10; aartREV[18][ 5] = 45; aartREV[19][ 5] = 36; aartREV[ 0][ 6] = 81; aartREV[ 1][ 6] = 10; aartREV[ 2][ 6] = 79; aartREV[ 3][ 6] = 542; aartREV[ 4][ 6] = 1; aartREV[ 5][ 6] = 372; aartREV[ 6][ 6] = 0; aartREV[ 7][ 6] = 70; aartREV[ 8][ 6] = 34; aartREV[ 9][ 6] = 1; aartREV[10][ 6] = 21; aartREV[11][ 6] = 141; aartREV[12][ 6] = 1; aartREV[13][ 6] = 1; aartREV[14][ 6] = 52; aartREV[15][ 6] = 95; aartREV[16][ 6] = 82; aartREV[17][ 6] = 17; aartREV[18][ 6] = 1; aartREV[19][ 6] = 35; aartREV[ 0][ 7] = 135; aartREV[ 1][ 7] = 41; aartREV[ 2][ 7] = 94; aartREV[ 3][ 7] = 61; aartREV[ 4][ 7] = 48; aartREV[ 5][ 7] = 18; aartREV[ 6][ 7] = 70; aartREV[ 7][ 7] = 0; aartREV[ 8][ 7] = 68; aartREV[ 9][ 7] = 1; aartREV[10][ 7] = 3; aartREV[11][ 7] = 30; aartREV[12][ 7] = 37; aartREV[13][ 7] = 7; aartREV[14][ 7] = 17; aartREV[15][ 7] = 152; aartREV[16][ 7] = 7; aartREV[17][ 7] = 23; aartREV[18][ 7] = 21; aartREV[19][ 7] = 3; aartREV[ 0][ 8] = 30; aartREV[ 1][ 8] = 90; aartREV[ 2][ 8] = 320; aartREV[ 3][ 8] = 91; aartREV[ 4][ 8] = 124; aartREV[ 5][ 8] = 387; aartREV[ 6][ 8] = 34; aartREV[ 7][ 8] = 68; aartREV[ 8][ 8] = 0; aartREV[ 9][ 8] = 34; aartREV[10][ 8] = 51; aartREV[11][ 8] = 76; aartREV[12][ 8] = 116; aartREV[13][ 8] = 141; aartREV[14][ 8] = 44; aartREV[15][ 8] = 183; aartREV[16][ 8] = 49; aartREV[17][ 8] = 48; aartREV[18][ 8] = 307; aartREV[19][ 8] = 1; aartREV[ 0][ 9] = 1; aartREV[ 1][ 9] = 24; aartREV[ 2][ 9] = 35; aartREV[ 3][ 9] = 1; aartREV[ 4][ 9] = 104; aartREV[ 5][ 9] = 33; aartREV[ 6][ 9] = 1; aartREV[ 7][ 9] = 1; aartREV[ 8][ 9] = 34; aartREV[ 9][ 9] = 0; aartREV[10][ 9] = 385; aartREV[11][ 9] = 34; aartREV[12][ 9] = 375; aartREV[13][ 9] = 64; aartREV[14][ 9] = 10; aartREV[15][ 9] = 4; aartREV[16][ 9] = 72; aartREV[17][ 9] = 39; aartREV[18][ 9] = 26; aartREV[19][ 9] =1048; aartREV[ 0][10] = 45; aartREV[ 1][10] = 18; aartREV[ 2][10] = 15; aartREV[ 3][10] = 5; aartREV[ 4][10] = 110; aartREV[ 5][10] = 54; aartREV[ 6][10] = 21; aartREV[ 7][10] = 3; aartREV[ 8][10] = 51; aartREV[ 9][10] = 385; aartREV[10][10] = 0; aartREV[11][10] = 23; aartREV[12][10] = 581; aartREV[13][10] = 179; aartREV[14][10] = 22; aartREV[15][10] = 24; aartREV[16][10] = 25; aartREV[17][10] = 47; aartREV[18][10] = 64; aartREV[19][10] = 112; aartREV[ 0][11] = 38; aartREV[ 1][11] = 593; aartREV[ 2][11] = 123; aartREV[ 3][11] = 20; aartREV[ 4][11] = 16; aartREV[ 5][11] = 309; aartREV[ 6][11] = 141; aartREV[ 7][11] = 30; aartREV[ 8][11] = 76; aartREV[ 9][11] = 34; aartREV[10][11] = 23; aartREV[11][11] = 0; aartREV[12][11] = 134; aartREV[13][11] = 14; aartREV[14][11] = 43; aartREV[15][11] = 77; aartREV[16][11] = 110; aartREV[17][11] = 6; aartREV[18][11] = 1; aartREV[19][11] = 19; aartREV[ 0][12] = 235; aartREV[ 1][12] = 57; aartREV[ 2][12] = 1; aartREV[ 3][12] = 1; aartREV[ 4][12] = 156; aartREV[ 5][12] = 158; aartREV[ 6][12] = 1; aartREV[ 7][12] = 37; aartREV[ 8][12] = 116; aartREV[ 9][12] = 375; aartREV[10][12] = 581; aartREV[11][12] = 134; aartREV[12][12] = 0; aartREV[13][12] = 247; aartREV[14][12] = 1; aartREV[15][12] = 1; aartREV[16][12] = 131; aartREV[17][12] = 111; aartREV[18][12] = 74; aartREV[19][12] = 236; aartREV[ 0][13] = 1; aartREV[ 1][13] = 7; aartREV[ 2][13] = 49; aartREV[ 3][13] = 1; aartREV[ 4][13] = 70; aartREV[ 5][13] = 1; aartREV[ 6][13] = 1; aartREV[ 7][13] = 7; aartREV[ 8][13] = 141; aartREV[ 9][13] = 64; aartREV[10][13] = 179; aartREV[11][13] = 14; aartREV[12][13] = 247; aartREV[13][13] = 0; aartREV[14][13] = 11; aartREV[15][13] = 20; aartREV[16][13] = 69; aartREV[17][13] = 182; aartREV[18][13] =1017; aartREV[19][13] = 92; aartREV[ 0][14] = 97; aartREV[ 1][14] = 24; aartREV[ 2][14] = 33; aartREV[ 3][14] = 55; aartREV[ 4][14] = 1; aartREV[ 5][14] = 68; aartREV[ 6][14] = 52; aartREV[ 7][14] = 17; aartREV[ 8][14] = 44; aartREV[ 9][14] = 10; aartREV[10][14] = 22; aartREV[11][14] = 43; aartREV[12][14] = 1; aartREV[13][14] = 11; aartREV[14][14] = 0; aartREV[15][14] = 134; aartREV[16][14] = 62; aartREV[17][14] = 9; aartREV[18][14] = 14; aartREV[19][14] = 25; aartREV[ 0][15] = 460; aartREV[ 1][15] = 102; aartREV[ 2][15] = 294; aartREV[ 3][15] = 136; aartREV[ 4][15] = 75; aartREV[ 5][15] = 225; aartREV[ 6][15] = 95; aartREV[ 7][15] = 152; aartREV[ 8][15] = 183; aartREV[ 9][15] = 4; aartREV[10][15] = 24; aartREV[11][15] = 77; aartREV[12][15] = 1; aartREV[13][15] = 20; aartREV[14][15] = 134; aartREV[15][15] = 0; aartREV[16][15] = 671; aartREV[17][15] = 14; aartREV[18][15] = 31; aartREV[19][15] = 39; aartREV[ 0][16] = 258; aartREV[ 1][16] = 64; aartREV[ 2][16] = 148; aartREV[ 3][16] = 55; aartREV[ 4][16] = 117; aartREV[ 5][16] = 146; aartREV[ 6][16] = 82; aartREV[ 7][16] = 7; aartREV[ 8][16] = 49; aartREV[ 9][16] = 72; aartREV[10][16] = 25; aartREV[11][16] = 110; aartREV[12][16] = 131; aartREV[13][16] = 69; aartREV[14][16] = 62; aartREV[15][16] = 671; aartREV[16][16] = 0; aartREV[17][16] = 1; aartREV[18][16] = 34; aartREV[19][16] = 196; aartREV[ 0][17] = 5; aartREV[ 1][17] = 13; aartREV[ 2][17] = 16; aartREV[ 3][17] = 1; aartREV[ 4][17] = 55; aartREV[ 5][17] = 10; aartREV[ 6][17] = 17; aartREV[ 7][17] = 23; aartREV[ 8][17] = 48; aartREV[ 9][17] = 39; aartREV[10][17] = 47; aartREV[11][17] = 6; aartREV[12][17] = 111; aartREV[13][17] = 182; aartREV[14][17] = 9; aartREV[15][17] = 14; aartREV[16][17] = 1; aartREV[17][17] = 0; aartREV[18][17] = 176; aartREV[19][17] = 26; aartREV[ 0][18] = 55; aartREV[ 1][18] = 47; aartREV[ 2][18] = 28; aartREV[ 3][18] = 1; aartREV[ 4][18] = 131; aartREV[ 5][18] = 45; aartREV[ 6][18] = 1; aartREV[ 7][18] = 21; aartREV[ 8][18] = 307; aartREV[ 9][18] = 26; aartREV[10][18] = 64; aartREV[11][18] = 1; aartREV[12][18] = 74; aartREV[13][18] =1017; aartREV[14][18] = 14; aartREV[15][18] = 31; aartREV[16][18] = 34; aartREV[17][18] = 176; aartREV[18][18] = 0; aartREV[19][18] = 59; aartREV[ 0][19] = 197; aartREV[ 1][19] = 29; aartREV[ 2][19] = 21; aartREV[ 3][19] = 6; aartREV[ 4][19] = 295; aartREV[ 5][19] = 36; aartREV[ 6][19] = 35; aartREV[ 7][19] = 3; aartREV[ 8][19] = 1; aartREV[ 9][19] =1048; aartREV[10][19] = 112; aartREV[11][19] = 19; aartREV[12][19] = 236; aartREV[13][19] = 92; aartREV[14][19] = 25; aartREV[15][19] = 39; aartREV[16][19] = 196; aartREV[17][19] = 26; aartREV[18][19] = 59; aartREV[19][19] = 0; rtrevPi[ 0] = 0.0646; rtrevPi[ 1] = 0.0453; rtrevPi[ 2] = 0.0376; rtrevPi[ 3] = 0.0422; rtrevPi[ 4] = 0.0114; rtrevPi[ 5] = 0.0606; rtrevPi[ 6] = 0.0607; rtrevPi[ 7] = 0.0639; rtrevPi[ 8] = 0.0273; rtrevPi[ 9] = 0.0679; rtrevPi[10] = 0.1018; rtrevPi[11] = 0.0751; rtrevPi[12] = 0.0150; rtrevPi[13] = 0.0287; rtrevPi[14] = 0.0681; rtrevPi[15] = 0.0488; rtrevPi[16] = 0.0622; rtrevPi[17] = 0.0251; rtrevPi[18] = 0.0318; rtrevPi[19] = 0.0619; /* wag */ aaWAG[ 0][ 0] = 0.0000000; aaWAG[ 1][ 0] = 0.5515710; aaWAG[ 2][ 0] = 0.5098480; aaWAG[ 3][ 0] = 0.7389980; aaWAG[ 4][ 0] = 1.0270400; aaWAG[ 5][ 0] = 0.9085980; aaWAG[ 6][ 0] = 1.5828500; aaWAG[ 7][ 0] = 1.4167200; aaWAG[ 8][ 0] = 0.3169540; aaWAG[ 9][ 0] = 0.1933350; aaWAG[10][ 0] = 0.3979150; aaWAG[11][ 0] = 0.9062650; aaWAG[12][ 0] = 0.8934960; aaWAG[13][ 0] = 0.2104940; aaWAG[14][ 0] = 1.4385500; aaWAG[15][ 0] = 3.3707900; aaWAG[16][ 0] = 2.1211100; aaWAG[17][ 0] = 0.1131330; aaWAG[18][ 0] = 0.2407350; aaWAG[19][ 0] = 2.0060100; aaWAG[ 0][ 1] = 0.5515710; aaWAG[ 1][ 1] = 0.0000000; aaWAG[ 2][ 1] = 0.6353460; aaWAG[ 3][ 1] = 0.1473040; aaWAG[ 4][ 1] = 0.5281910; aaWAG[ 5][ 1] = 3.0355000; aaWAG[ 6][ 1] = 0.4391570; aaWAG[ 7][ 1] = 0.5846650; aaWAG[ 8][ 1] = 2.1371500; aaWAG[ 9][ 1] = 0.1869790; aaWAG[10][ 1] = 0.4976710; aaWAG[11][ 1] = 5.3514200; aaWAG[12][ 1] = 0.6831620; aaWAG[13][ 1] = 0.1027110; aaWAG[14][ 1] = 0.6794890; aaWAG[15][ 1] = 1.2241900; aaWAG[16][ 1] = 0.5544130; aaWAG[17][ 1] = 1.1639200; aaWAG[18][ 1] = 0.3815330; aaWAG[19][ 1] = 0.2518490; aaWAG[ 0][ 2] = 0.5098480; aaWAG[ 1][ 2] = 0.6353460; aaWAG[ 2][ 2] = 0.0000000; aaWAG[ 3][ 2] = 5.4294200; aaWAG[ 4][ 2] = 0.2652560; aaWAG[ 5][ 2] = 1.5436400; aaWAG[ 6][ 2] = 0.9471980; aaWAG[ 7][ 2] = 1.1255600; aaWAG[ 8][ 2] = 3.9562900; aaWAG[ 9][ 2] = 0.5542360; aaWAG[10][ 2] = 0.1315280; aaWAG[11][ 2] = 3.0120100; aaWAG[12][ 2] = 0.1982210; aaWAG[13][ 2] = 0.0961621; aaWAG[14][ 2] = 0.1950810; aaWAG[15][ 2] = 3.9742300; aaWAG[16][ 2] = 2.0300600; aaWAG[17][ 2] = 0.0719167; aaWAG[18][ 2] = 1.0860000; aaWAG[19][ 2] = 0.1962460; aaWAG[ 0][ 3] = 0.7389980; aaWAG[ 1][ 3] = 0.1473040; aaWAG[ 2][ 3] = 5.4294200; aaWAG[ 3][ 3] = 0.0000000; aaWAG[ 4][ 3] = 0.0302949; aaWAG[ 5][ 3] = 0.6167830; aaWAG[ 6][ 3] = 6.1741600; aaWAG[ 7][ 3] = 0.8655840; aaWAG[ 8][ 3] = 0.9306760; aaWAG[ 9][ 3] = 0.0394370; aaWAG[10][ 3] = 0.0848047; aaWAG[11][ 3] = 0.4798550; aaWAG[12][ 3] = 0.1037540; aaWAG[13][ 3] = 0.0467304; aaWAG[14][ 3] = 0.4239840; aaWAG[15][ 3] = 1.0717600; aaWAG[16][ 3] = 0.3748660; aaWAG[17][ 3] = 0.1297670; aaWAG[18][ 3] = 0.3257110; aaWAG[19][ 3] = 0.1523350; aaWAG[ 0][ 4] = 1.0270400; aaWAG[ 1][ 4] = 0.5281910; aaWAG[ 2][ 4] = 0.2652560; aaWAG[ 3][ 4] = 0.0302949; aaWAG[ 4][ 4] = 0.0000000; aaWAG[ 5][ 4] = 0.0988179; aaWAG[ 6][ 4] = 0.0213520; aaWAG[ 7][ 4] = 0.3066740; aaWAG[ 8][ 4] = 0.2489720; aaWAG[ 9][ 4] = 0.1701350; aaWAG[10][ 4] = 0.3842870; aaWAG[11][ 4] = 0.0740339; aaWAG[12][ 4] = 0.3904820; aaWAG[13][ 4] = 0.3980200; aaWAG[14][ 4] = 0.1094040; aaWAG[15][ 4] = 1.4076600; aaWAG[16][ 4] = 0.5129840; aaWAG[17][ 4] = 0.7170700; aaWAG[18][ 4] = 0.5438330; aaWAG[19][ 4] = 1.0021400; aaWAG[ 0][ 5] = 0.9085980; aaWAG[ 1][ 5] = 3.0355000; aaWAG[ 2][ 5] = 1.5436400; aaWAG[ 3][ 5] = 0.6167830; aaWAG[ 4][ 5] = 0.0988179; aaWAG[ 5][ 5] = 0.0000000; aaWAG[ 6][ 5] = 5.4694700; aaWAG[ 7][ 5] = 0.3300520; aaWAG[ 8][ 5] = 4.2941100; aaWAG[ 9][ 5] = 0.1139170; aaWAG[10][ 5] = 0.8694890; aaWAG[11][ 5] = 3.8949000; aaWAG[12][ 5] = 1.5452600; aaWAG[13][ 5] = 0.0999208; aaWAG[14][ 5] = 0.9333720; aaWAG[15][ 5] = 1.0288700; aaWAG[16][ 5] = 0.8579280; aaWAG[17][ 5] = 0.2157370; aaWAG[18][ 5] = 0.2277100; aaWAG[19][ 5] = 0.3012810; aaWAG[ 0][ 6] = 1.5828500; aaWAG[ 1][ 6] = 0.4391570; aaWAG[ 2][ 6] = 0.9471980; aaWAG[ 3][ 6] = 6.1741600; aaWAG[ 4][ 6] = 0.0213520; aaWAG[ 5][ 6] = 5.4694700; aaWAG[ 6][ 6] = 0.0000000; aaWAG[ 7][ 6] = 0.5677170; aaWAG[ 8][ 6] = 0.5700250; aaWAG[ 9][ 6] = 0.1273950; aaWAG[10][ 6] = 0.1542630; aaWAG[11][ 6] = 2.5844300; aaWAG[12][ 6] = 0.3151240; aaWAG[13][ 6] = 0.0811339; aaWAG[14][ 6] = 0.6823550; aaWAG[15][ 6] = 0.7049390; aaWAG[16][ 6] = 0.8227650; aaWAG[17][ 6] = 0.1565570; aaWAG[18][ 6] = 0.1963030; aaWAG[19][ 6] = 0.5887310; aaWAG[ 0][ 7] = 1.4167200; aaWAG[ 1][ 7] = 0.5846650; aaWAG[ 2][ 7] = 1.1255600; aaWAG[ 3][ 7] = 0.8655840; aaWAG[ 4][ 7] = 0.3066740; aaWAG[ 5][ 7] = 0.3300520; aaWAG[ 6][ 7] = 0.5677170; aaWAG[ 7][ 7] = 0.0000000; aaWAG[ 8][ 7] = 0.2494100; aaWAG[ 9][ 7] = 0.0304501; aaWAG[10][ 7] = 0.0613037; aaWAG[11][ 7] = 0.3735580; aaWAG[12][ 7] = 0.1741000; aaWAG[13][ 7] = 0.0499310; aaWAG[14][ 7] = 0.2435700; aaWAG[15][ 7] = 1.3418200; aaWAG[16][ 7] = 0.2258330; aaWAG[17][ 7] = 0.3369830; aaWAG[18][ 7] = 0.1036040; aaWAG[19][ 7] = 0.1872470; aaWAG[ 0][ 8] = 0.3169540; aaWAG[ 1][ 8] = 2.1371500; aaWAG[ 2][ 8] = 3.9562900; aaWAG[ 3][ 8] = 0.9306760; aaWAG[ 4][ 8] = 0.2489720; aaWAG[ 5][ 8] = 4.2941100; aaWAG[ 6][ 8] = 0.5700250; aaWAG[ 7][ 8] = 0.2494100; aaWAG[ 8][ 8] = 0.0000000; aaWAG[ 9][ 8] = 0.1381900; aaWAG[10][ 8] = 0.4994620; aaWAG[11][ 8] = 0.8904320; aaWAG[12][ 8] = 0.4041410; aaWAG[13][ 8] = 0.6793710; aaWAG[14][ 8] = 0.6961980; aaWAG[15][ 8] = 0.7401690; aaWAG[16][ 8] = 0.4733070; aaWAG[17][ 8] = 0.2625690; aaWAG[18][ 8] = 3.8734400; aaWAG[19][ 8] = 0.1183580; aaWAG[ 0][ 9] = 0.1933350; aaWAG[ 1][ 9] = 0.1869790; aaWAG[ 2][ 9] = 0.5542360; aaWAG[ 3][ 9] = 0.0394370; aaWAG[ 4][ 9] = 0.1701350; aaWAG[ 5][ 9] = 0.1139170; aaWAG[ 6][ 9] = 0.1273950; aaWAG[ 7][ 9] = 0.0304501; aaWAG[ 8][ 9] = 0.1381900; aaWAG[ 9][ 9] = 0.0000000; aaWAG[10][ 9] = 3.1709700; aaWAG[11][ 9] = 0.3238320; aaWAG[12][ 9] = 4.2574600; aaWAG[13][ 9] = 1.0594700; aaWAG[14][ 9] = 0.0999288; aaWAG[15][ 9] = 0.3194400; aaWAG[16][ 9] = 1.4581600; aaWAG[17][ 9] = 0.2124830; aaWAG[18][ 9] = 0.4201700; aaWAG[19][ 9] = 7.8213000; aaWAG[ 0][10] = 0.3979150; aaWAG[ 1][10] = 0.4976710; aaWAG[ 2][10] = 0.1315280; aaWAG[ 3][10] = 0.0848047; aaWAG[ 4][10] = 0.3842870; aaWAG[ 5][10] = 0.8694890; aaWAG[ 6][10] = 0.1542630; aaWAG[ 7][10] = 0.0613037; aaWAG[ 8][10] = 0.4994620; aaWAG[ 9][10] = 3.1709700; aaWAG[10][10] = 0.0000000; aaWAG[11][10] = 0.2575550; aaWAG[12][10] = 4.8540200; aaWAG[13][10] = 2.1151700; aaWAG[14][10] = 0.4158440; aaWAG[15][10] = 0.3447390; aaWAG[16][10] = 0.3266220; aaWAG[17][10] = 0.6653090; aaWAG[18][10] = 0.3986180; aaWAG[19][10] = 1.8003400; aaWAG[ 0][11] = 0.9062650; aaWAG[ 1][11] = 5.3514200; aaWAG[ 2][11] = 3.0120100; aaWAG[ 3][11] = 0.4798550; aaWAG[ 4][11] = 0.0740339; aaWAG[ 5][11] = 3.8949000; aaWAG[ 6][11] = 2.5844300; aaWAG[ 7][11] = 0.3735580; aaWAG[ 8][11] = 0.8904320; aaWAG[ 9][11] = 0.3238320; aaWAG[10][11] = 0.2575550; aaWAG[11][11] = 0.0000000; aaWAG[12][11] = 0.9342760; aaWAG[13][11] = 0.0888360; aaWAG[14][11] = 0.5568960; aaWAG[15][11] = 0.9671300; aaWAG[16][11] = 1.3869800; aaWAG[17][11] = 0.1375050; aaWAG[18][11] = 0.1332640; aaWAG[19][11] = 0.3054340; aaWAG[ 0][12] = 0.8934960; aaWAG[ 1][12] = 0.6831620; aaWAG[ 2][12] = 0.1982210; aaWAG[ 3][12] = 0.1037540; aaWAG[ 4][12] = 0.3904820; aaWAG[ 5][12] = 1.5452600; aaWAG[ 6][12] = 0.3151240; aaWAG[ 7][12] = 0.1741000; aaWAG[ 8][12] = 0.4041410; aaWAG[ 9][12] = 4.2574600; aaWAG[10][12] = 4.8540200; aaWAG[11][12] = 0.9342760; aaWAG[12][12] = 0.0000000; aaWAG[13][12] = 1.1906300; aaWAG[14][12] = 0.1713290; aaWAG[15][12] = 0.4939050; aaWAG[16][12] = 1.5161200; aaWAG[17][12] = 0.5157060; aaWAG[18][12] = 0.4284370; aaWAG[19][12] = 2.0584500; aaWAG[ 0][13] = 0.2104940; aaWAG[ 1][13] = 0.1027110; aaWAG[ 2][13] = 0.0961621; aaWAG[ 3][13] = 0.0467304; aaWAG[ 4][13] = 0.3980200; aaWAG[ 5][13] = 0.0999208; aaWAG[ 6][13] = 0.0811339; aaWAG[ 7][13] = 0.0499310; aaWAG[ 8][13] = 0.6793710; aaWAG[ 9][13] = 1.0594700; aaWAG[10][13] = 2.1151700; aaWAG[11][13] = 0.0888360; aaWAG[12][13] = 1.1906300; aaWAG[13][13] = 0.0000000; aaWAG[14][13] = 0.1614440; aaWAG[15][13] = 0.5459310; aaWAG[16][13] = 0.1719030; aaWAG[17][13] = 1.5296400; aaWAG[18][13] = 6.4542800; aaWAG[19][13] = 0.6498920; aaWAG[ 0][14] = 1.4385500; aaWAG[ 1][14] = 0.6794890; aaWAG[ 2][14] = 0.1950810; aaWAG[ 3][14] = 0.4239840; aaWAG[ 4][14] = 0.1094040; aaWAG[ 5][14] = 0.9333720; aaWAG[ 6][14] = 0.6823550; aaWAG[ 7][14] = 0.2435700; aaWAG[ 8][14] = 0.6961980; aaWAG[ 9][14] = 0.0999288; aaWAG[10][14] = 0.4158440; aaWAG[11][14] = 0.5568960; aaWAG[12][14] = 0.1713290; aaWAG[13][14] = 0.1614440; aaWAG[14][14] = 0.0000000; aaWAG[15][14] = 1.6132800; aaWAG[16][14] = 0.7953840; aaWAG[17][14] = 0.1394050; aaWAG[18][14] = 0.2160460; aaWAG[19][14] = 0.3148870; aaWAG[ 0][15] = 3.3707900; aaWAG[ 1][15] = 1.2241900; aaWAG[ 2][15] = 3.9742300; aaWAG[ 3][15] = 1.0717600; aaWAG[ 4][15] = 1.4076600; aaWAG[ 5][15] = 1.0288700; aaWAG[ 6][15] = 0.7049390; aaWAG[ 7][15] = 1.3418200; aaWAG[ 8][15] = 0.7401690; aaWAG[ 9][15] = 0.3194400; aaWAG[10][15] = 0.3447390; aaWAG[11][15] = 0.9671300; aaWAG[12][15] = 0.4939050; aaWAG[13][15] = 0.5459310; aaWAG[14][15] = 1.6132800; aaWAG[15][15] = 0.0000000; aaWAG[16][15] = 4.3780200; aaWAG[17][15] = 0.5237420; aaWAG[18][15] = 0.7869930; aaWAG[19][15] = 0.2327390; aaWAG[ 0][16] = 2.1211100; aaWAG[ 1][16] = 0.5544130; aaWAG[ 2][16] = 2.0300600; aaWAG[ 3][16] = 0.3748660; aaWAG[ 4][16] = 0.5129840; aaWAG[ 5][16] = 0.8579280; aaWAG[ 6][16] = 0.8227650; aaWAG[ 7][16] = 0.2258330; aaWAG[ 8][16] = 0.4733070; aaWAG[ 9][16] = 1.4581600; aaWAG[10][16] = 0.3266220; aaWAG[11][16] = 1.3869800; aaWAG[12][16] = 1.5161200; aaWAG[13][16] = 0.1719030; aaWAG[14][16] = 0.7953840; aaWAG[15][16] = 4.3780200; aaWAG[16][16] = 0.0000000; aaWAG[17][16] = 0.1108640; aaWAG[18][16] = 0.2911480; aaWAG[19][16] = 1.3882300; aaWAG[ 0][17] = 0.1131330; aaWAG[ 1][17] = 1.1639200; aaWAG[ 2][17] = 0.0719167; aaWAG[ 3][17] = 0.1297670; aaWAG[ 4][17] = 0.7170700; aaWAG[ 5][17] = 0.2157370; aaWAG[ 6][17] = 0.1565570; aaWAG[ 7][17] = 0.3369830; aaWAG[ 8][17] = 0.2625690; aaWAG[ 9][17] = 0.2124830; aaWAG[10][17] = 0.6653090; aaWAG[11][17] = 0.1375050; aaWAG[12][17] = 0.5157060; aaWAG[13][17] = 1.5296400; aaWAG[14][17] = 0.1394050; aaWAG[15][17] = 0.5237420; aaWAG[16][17] = 0.1108640; aaWAG[17][17] = 0.0000000; aaWAG[18][17] = 2.4853900; aaWAG[19][17] = 0.3653690; aaWAG[ 0][18] = 0.2407350; aaWAG[ 1][18] = 0.3815330; aaWAG[ 2][18] = 1.0860000; aaWAG[ 3][18] = 0.3257110; aaWAG[ 4][18] = 0.5438330; aaWAG[ 5][18] = 0.2277100; aaWAG[ 6][18] = 0.1963030; aaWAG[ 7][18] = 0.1036040; aaWAG[ 8][18] = 3.8734400; aaWAG[ 9][18] = 0.4201700; aaWAG[10][18] = 0.3986180; aaWAG[11][18] = 0.1332640; aaWAG[12][18] = 0.4284370; aaWAG[13][18] = 6.4542800; aaWAG[14][18] = 0.2160460; aaWAG[15][18] = 0.7869930; aaWAG[16][18] = 0.2911480; aaWAG[17][18] = 2.4853900; aaWAG[18][18] = 0.0000000; aaWAG[19][18] = 0.3147300; aaWAG[ 0][19] = 2.0060100; aaWAG[ 1][19] = 0.2518490; aaWAG[ 2][19] = 0.1962460; aaWAG[ 3][19] = 0.1523350; aaWAG[ 4][19] = 1.0021400; aaWAG[ 5][19] = 0.3012810; aaWAG[ 6][19] = 0.5887310; aaWAG[ 7][19] = 0.1872470; aaWAG[ 8][19] = 0.1183580; aaWAG[ 9][19] = 7.8213000; aaWAG[10][19] = 1.8003400; aaWAG[11][19] = 0.3054340; aaWAG[12][19] = 2.0584500; aaWAG[13][19] = 0.6498920; aaWAG[14][19] = 0.3148870; aaWAG[15][19] = 0.2327390; aaWAG[16][19] = 1.3882300; aaWAG[17][19] = 0.3653690; aaWAG[18][19] = 0.3147300; aaWAG[19][19] = 0.0000000; wagPi[ 0] = 0.08662790; wagPi[ 1] = 0.04397200; wagPi[ 2] = 0.03908940; wagPi[ 3] = 0.05704510; wagPi[ 4] = 0.01930780; wagPi[ 5] = 0.03672810; wagPi[ 6] = 0.05805890; wagPi[ 7] = 0.08325180; wagPi[ 8] = 0.02443130; wagPi[ 9] = 0.04846600; wagPi[10] = 0.08620970; wagPi[11] = 0.06202860; wagPi[12] = 0.01950273; wagPi[13] = 0.03843190; wagPi[14] = 0.04576310; wagPi[15] = 0.06951790; wagPi[16] = 0.06101270; wagPi[17] = 0.01438590; wagPi[18] = 0.03527420; wagPi[19] = 0.07089560; /* cpRev */ aacpREV[ 0][ 0] = 0; aacpREV[ 0][ 1] = 105; aacpREV[ 0][ 2] = 227; aacpREV[ 0][ 3] = 175; aacpREV[ 0][ 4] = 669; aacpREV[ 0][ 5] = 157; aacpREV[ 0][ 6] = 499; aacpREV[ 0][ 7] = 665; aacpREV[ 0][ 8] = 66; aacpREV[ 0][ 9] = 145; aacpREV[ 0][10] = 197; aacpREV[ 0][11] = 236; aacpREV[ 0][12] = 185; aacpREV[ 0][13] = 68; aacpREV[ 0][14] = 490; aacpREV[ 0][15] = 2440; aacpREV[ 0][16] = 1340; aacpREV[ 0][17] = 14; aacpREV[ 0][18] = 56; aacpREV[ 0][19] = 968; aacpREV[ 1][ 0] = 105; aacpREV[ 1][ 1] = 0; aacpREV[ 1][ 2] = 357; aacpREV[ 1][ 3] = 43; aacpREV[ 1][ 4] = 823; aacpREV[ 1][ 5] = 1745; aacpREV[ 1][ 6] = 152; aacpREV[ 1][ 7] = 243; aacpREV[ 1][ 8] = 715; aacpREV[ 1][ 9] = 136; aacpREV[ 1][10] = 203; aacpREV[ 1][11] = 4482; aacpREV[ 1][12] = 125; aacpREV[ 1][13] = 53; aacpREV[ 1][14] = 87; aacpREV[ 1][15] = 385; aacpREV[ 1][16] = 314; aacpREV[ 1][17] = 230; aacpREV[ 1][18] = 323; aacpREV[ 1][19] = 92; aacpREV[ 2][ 0] = 227; aacpREV[ 2][ 1] = 357; aacpREV[ 2][ 2] = 0; aacpREV[ 2][ 3] = 4435; aacpREV[ 2][ 4] = 538; aacpREV[ 2][ 5] = 768; aacpREV[ 2][ 6] = 1055; aacpREV[ 2][ 7] = 653; aacpREV[ 2][ 8] = 1405; aacpREV[ 2][ 9] = 168; aacpREV[ 2][10] = 113; aacpREV[ 2][11] = 2430; aacpREV[ 2][12] = 61; aacpREV[ 2][13] = 97; aacpREV[ 2][14] = 173; aacpREV[ 2][15] = 2085; aacpREV[ 2][16] = 1393; aacpREV[ 2][17] = 40; aacpREV[ 2][18] = 754; aacpREV[ 2][19] = 83; aacpREV[ 3][ 0] = 175; aacpREV[ 3][ 1] = 43; aacpREV[ 3][ 2] = 4435; aacpREV[ 3][ 3] = 0; aacpREV[ 3][ 4] = 10; aacpREV[ 3][ 5] = 400; aacpREV[ 3][ 6] = 3691; aacpREV[ 3][ 7] = 431; aacpREV[ 3][ 8] = 331; aacpREV[ 3][ 9] = 10; aacpREV[ 3][10] = 10; aacpREV[ 3][11] = 412; aacpREV[ 3][12] = 47; aacpREV[ 3][13] = 22; aacpREV[ 3][14] = 170; aacpREV[ 3][15] = 590; aacpREV[ 3][16] = 266; aacpREV[ 3][17] = 18; aacpREV[ 3][18] = 281; aacpREV[ 3][19] = 75; aacpREV[ 4][ 0] = 669; aacpREV[ 4][ 1] = 823; aacpREV[ 4][ 2] = 538; aacpREV[ 4][ 3] = 10; aacpREV[ 4][ 4] = 0; aacpREV[ 4][ 5] = 10; aacpREV[ 4][ 6] = 10; aacpREV[ 4][ 7] = 303; aacpREV[ 4][ 8] = 441; aacpREV[ 4][ 9] = 280; aacpREV[ 4][10] = 396; aacpREV[ 4][11] = 48; aacpREV[ 4][12] = 159; aacpREV[ 4][13] = 726; aacpREV[ 4][14] = 285; aacpREV[ 4][15] = 2331; aacpREV[ 4][16] = 576; aacpREV[ 4][17] = 435; aacpREV[ 4][18] = 1466; aacpREV[ 4][19] = 592; aacpREV[ 5][ 0] = 157; aacpREV[ 5][ 1] = 1745; aacpREV[ 5][ 2] = 768; aacpREV[ 5][ 3] = 400; aacpREV[ 5][ 4] = 10; aacpREV[ 5][ 5] = 0; aacpREV[ 5][ 6] = 3122; aacpREV[ 5][ 7] = 133; aacpREV[ 5][ 8] = 1269; aacpREV[ 5][ 9] = 92; aacpREV[ 5][10] = 286; aacpREV[ 5][11] = 3313; aacpREV[ 5][12] = 202; aacpREV[ 5][13] = 10; aacpREV[ 5][14] = 323; aacpREV[ 5][15] = 396; aacpREV[ 5][16] = 241; aacpREV[ 5][17] = 53; aacpREV[ 5][18] = 391; aacpREV[ 5][19] = 54; aacpREV[ 6][ 0] = 499; aacpREV[ 6][ 1] = 152; aacpREV[ 6][ 2] = 1055; aacpREV[ 6][ 3] = 3691; aacpREV[ 6][ 4] = 10; aacpREV[ 6][ 5] = 3122; aacpREV[ 6][ 6] = 0; aacpREV[ 6][ 7] = 379; aacpREV[ 6][ 8] = 162; aacpREV[ 6][ 9] = 148; aacpREV[ 6][10] = 82; aacpREV[ 6][11] = 2629; aacpREV[ 6][12] = 113; aacpREV[ 6][13] = 145; aacpREV[ 6][14] = 185; aacpREV[ 6][15] = 568; aacpREV[ 6][16] = 369; aacpREV[ 6][17] = 63; aacpREV[ 6][18] = 142; aacpREV[ 6][19] = 200; aacpREV[ 7][ 0] = 665; aacpREV[ 7][ 1] = 243; aacpREV[ 7][ 2] = 653; aacpREV[ 7][ 3] = 431; aacpREV[ 7][ 4] = 303; aacpREV[ 7][ 5] = 133; aacpREV[ 7][ 6] = 379; aacpREV[ 7][ 7] = 0; aacpREV[ 7][ 8] = 19; aacpREV[ 7][ 9] = 40; aacpREV[ 7][10] = 20; aacpREV[ 7][11] = 263; aacpREV[ 7][12] = 21; aacpREV[ 7][13] = 25; aacpREV[ 7][14] = 28; aacpREV[ 7][15] = 691; aacpREV[ 7][16] = 92; aacpREV[ 7][17] = 82; aacpREV[ 7][18] = 10; aacpREV[ 7][19] = 91; aacpREV[ 8][ 0] = 66; aacpREV[ 8][ 1] = 715; aacpREV[ 8][ 2] = 1405; aacpREV[ 8][ 3] = 331; aacpREV[ 8][ 4] = 441; aacpREV[ 8][ 5] = 1269; aacpREV[ 8][ 6] = 162; aacpREV[ 8][ 7] = 19; aacpREV[ 8][ 8] = 0; aacpREV[ 8][ 9] = 29; aacpREV[ 8][10] = 66; aacpREV[ 8][11] = 305; aacpREV[ 8][12] = 10; aacpREV[ 8][13] = 127; aacpREV[ 8][14] = 152; aacpREV[ 8][15] = 303; aacpREV[ 8][16] = 32; aacpREV[ 8][17] = 69; aacpREV[ 8][18] = 1971; aacpREV[ 8][19] = 25; aacpREV[ 9][ 0] = 145; aacpREV[ 9][ 1] = 136; aacpREV[ 9][ 2] = 168; aacpREV[ 9][ 3] = 10; aacpREV[ 9][ 4] = 280; aacpREV[ 9][ 5] = 92; aacpREV[ 9][ 6] = 148; aacpREV[ 9][ 7] = 40; aacpREV[ 9][ 8] = 29; aacpREV[ 9][ 9] = 0; aacpREV[ 9][10] = 1745; aacpREV[ 9][11] = 345; aacpREV[ 9][12] = 1772; aacpREV[ 9][13] = 454; aacpREV[ 9][14] = 117; aacpREV[ 9][15] = 216; aacpREV[ 9][16] = 1040; aacpREV[ 9][17] = 42; aacpREV[ 9][18] = 89; aacpREV[ 9][19] = 4797; aacpREV[10][ 0] = 197; aacpREV[10][ 1] = 203; aacpREV[10][ 2] = 113; aacpREV[10][ 3] = 10; aacpREV[10][ 4] = 396; aacpREV[10][ 5] = 286; aacpREV[10][ 6] = 82; aacpREV[10][ 7] = 20; aacpREV[10][ 8] = 66; aacpREV[10][ 9] = 1745; aacpREV[10][10] = 0; aacpREV[10][11] = 218; aacpREV[10][12] = 1351; aacpREV[10][13] = 1268; aacpREV[10][14] = 219; aacpREV[10][15] = 516; aacpREV[10][16] = 156; aacpREV[10][17] = 159; aacpREV[10][18] = 189; aacpREV[10][19] = 865; aacpREV[11][ 0] = 236; aacpREV[11][ 1] = 4482; aacpREV[11][ 2] = 2430; aacpREV[11][ 3] = 412; aacpREV[11][ 4] = 48; aacpREV[11][ 5] = 3313; aacpREV[11][ 6] = 2629; aacpREV[11][ 7] = 263; aacpREV[11][ 8] = 305; aacpREV[11][ 9] = 345; aacpREV[11][10] = 218; aacpREV[11][11] = 0; aacpREV[11][12] = 193; aacpREV[11][13] = 72; aacpREV[11][14] = 302; aacpREV[11][15] = 868; aacpREV[11][16] = 918; aacpREV[11][17] = 10; aacpREV[11][18] = 247; aacpREV[11][19] = 249; aacpREV[12][ 0] = 185; aacpREV[12][ 1] = 125; aacpREV[12][ 2] = 61; aacpREV[12][ 3] = 47; aacpREV[12][ 4] = 159; aacpREV[12][ 5] = 202; aacpREV[12][ 6] = 113; aacpREV[12][ 7] = 21; aacpREV[12][ 8] = 10; aacpREV[12][ 9] = 1772; aacpREV[12][10] = 1351; aacpREV[12][11] = 193; aacpREV[12][12] = 0; aacpREV[12][13] = 327; aacpREV[12][14] = 100; aacpREV[12][15] = 93; aacpREV[12][16] = 645; aacpREV[12][17] = 86; aacpREV[12][18] = 215; aacpREV[12][19] = 475; aacpREV[13][ 0] = 68; aacpREV[13][ 1] = 53; aacpREV[13][ 2] = 97; aacpREV[13][ 3] = 22; aacpREV[13][ 4] = 726; aacpREV[13][ 5] = 10; aacpREV[13][ 6] = 145; aacpREV[13][ 7] = 25; aacpREV[13][ 8] = 127; aacpREV[13][ 9] = 454; aacpREV[13][10] = 1268; aacpREV[13][11] = 72; aacpREV[13][12] = 327; aacpREV[13][13] = 0; aacpREV[13][14] = 43; aacpREV[13][15] = 487; aacpREV[13][16] = 148; aacpREV[13][17] = 468; aacpREV[13][18] = 2370; aacpREV[13][19] = 317; aacpREV[14][ 0] = 490; aacpREV[14][ 1] = 87; aacpREV[14][ 2] = 173; aacpREV[14][ 3] = 170; aacpREV[14][ 4] = 285; aacpREV[14][ 5] = 323; aacpREV[14][ 6] = 185; aacpREV[14][ 7] = 28; aacpREV[14][ 8] = 152; aacpREV[14][ 9] = 117; aacpREV[14][10] = 219; aacpREV[14][11] = 302; aacpREV[14][12] = 100; aacpREV[14][13] = 43; aacpREV[14][14] = 0; aacpREV[14][15] = 1202; aacpREV[14][16] = 260; aacpREV[14][17] = 49; aacpREV[14][18] = 97; aacpREV[14][19] = 122; aacpREV[15][ 0] = 2440; aacpREV[15][ 1] = 385; aacpREV[15][ 2] = 2085; aacpREV[15][ 3] = 590; aacpREV[15][ 4] = 2331; aacpREV[15][ 5] = 396; aacpREV[15][ 6] = 568; aacpREV[15][ 7] = 691; aacpREV[15][ 8] = 303; aacpREV[15][ 9] = 216; aacpREV[15][10] = 516; aacpREV[15][11] = 868; aacpREV[15][12] = 93; aacpREV[15][13] = 487; aacpREV[15][14] = 1202; aacpREV[15][15] = 0; aacpREV[15][16] = 2151; aacpREV[15][17] = 73; aacpREV[15][18] = 522; aacpREV[15][19] = 167; aacpREV[16][ 0] = 1340; aacpREV[16][ 1] = 314; aacpREV[16][ 2] = 1393; aacpREV[16][ 3] = 266; aacpREV[16][ 4] = 576; aacpREV[16][ 5] = 241; aacpREV[16][ 6] = 369; aacpREV[16][ 7] = 92; aacpREV[16][ 8] = 32; aacpREV[16][ 9] = 1040; aacpREV[16][10] = 156; aacpREV[16][11] = 918; aacpREV[16][12] = 645; aacpREV[16][13] = 148; aacpREV[16][14] = 260; aacpREV[16][15] = 2151; aacpREV[16][16] = 0; aacpREV[16][17] = 29; aacpREV[16][18] = 71; aacpREV[16][19] = 760; aacpREV[17][ 0] = 14; aacpREV[17][ 1] = 230; aacpREV[17][ 2] = 40; aacpREV[17][ 3] = 18; aacpREV[17][ 4] = 435; aacpREV[17][ 5] = 53; aacpREV[17][ 6] = 63; aacpREV[17][ 7] = 82; aacpREV[17][ 8] = 69; aacpREV[17][ 9] = 42; aacpREV[17][10] = 159; aacpREV[17][11] = 10; aacpREV[17][12] = 86; aacpREV[17][13] = 468; aacpREV[17][14] = 49; aacpREV[17][15] = 73; aacpREV[17][16] = 29; aacpREV[17][17] = 0; aacpREV[17][18] = 346; aacpREV[17][19] = 10; aacpREV[18][ 0] = 56; aacpREV[18][ 1] = 323; aacpREV[18][ 2] = 754; aacpREV[18][ 3] = 281; aacpREV[18][ 4] = 1466; aacpREV[18][ 5] = 391; aacpREV[18][ 6] = 142; aacpREV[18][ 7] = 10; aacpREV[18][ 8] = 1971; aacpREV[18][ 9] = 89; aacpREV[18][10] = 189; aacpREV[18][11] = 247; aacpREV[18][12] = 215; aacpREV[18][13] = 2370; aacpREV[18][14] = 97; aacpREV[18][15] = 522; aacpREV[18][16] = 71; aacpREV[18][17] = 346; aacpREV[18][18] = 0; aacpREV[18][19] = 119; aacpREV[19][ 0] = 968; aacpREV[19][ 1] = 92; aacpREV[19][ 2] = 83; aacpREV[19][ 3] = 75; aacpREV[19][ 4] = 592; aacpREV[19][ 5] = 54; aacpREV[19][ 6] = 200; aacpREV[19][ 7] = 91; aacpREV[19][ 8] = 25; aacpREV[19][ 9] = 4797; aacpREV[19][10] = 865; aacpREV[19][11] = 249; aacpREV[19][12] = 475; aacpREV[19][13] = 317; aacpREV[19][14] = 122; aacpREV[19][15] = 167; aacpREV[19][16] = 760; aacpREV[19][17] = 10; aacpREV[19][18] = 119; aacpREV[19][19] = 0; cprevPi[0] = 0.076; cprevPi[1] = 0.062; cprevPi[2] = 0.041; cprevPi[3] = 0.037; cprevPi[4] = 0.009; cprevPi[5] = 0.038; cprevPi[6] = 0.049; cprevPi[7] = 0.084; cprevPi[8] = 0.025; cprevPi[9] = 0.081; cprevPi[10] = 0.101; cprevPi[11] = 0.050; cprevPi[12] = 0.022; cprevPi[13] = 0.051; cprevPi[14] = 0.043; cprevPi[15] = 0.062; cprevPi[16] = 0.054; cprevPi[17] = 0.018; cprevPi[18] = 0.031; cprevPi[19] = 0.066; /* VT model */ aaVt[ 0][ 0] = 0.000000; aaVt[ 0][ 1] = 0.233108; aaVt[ 0][ 2] = 0.199097; aaVt[ 0][ 3] = 0.265145; aaVt[ 0][ 4] = 0.227333; aaVt[ 0][ 5] = 0.310084; aaVt[ 0][ 6] = 0.567957; aaVt[ 0][ 7] = 0.876213; aaVt[ 0][ 8] = 0.078692; aaVt[ 0][ 9] = 0.222972; aaVt[ 0][10] = 0.424630; aaVt[ 0][11] = 0.393245; aaVt[ 0][12] = 0.211550; aaVt[ 0][13] = 0.116646; aaVt[ 0][14] = 0.399143; aaVt[ 0][15] = 1.817198; aaVt[ 0][16] = 0.877877; aaVt[ 0][17] = 0.030309; aaVt[ 0][18] = 0.087061; aaVt[ 0][19] = 1.230985; aaVt[ 1][ 0] = 0.233108; aaVt[ 1][ 1] = 0.000000; aaVt[ 1][ 2] = 0.210797; aaVt[ 1][ 3] = 0.105191; aaVt[ 1][ 4] = 0.031726; aaVt[ 1][ 5] = 0.493763; aaVt[ 1][ 6] = 0.255240; aaVt[ 1][ 7] = 0.156945; aaVt[ 1][ 8] = 0.213164; aaVt[ 1][ 9] = 0.081510; aaVt[ 1][10] = 0.192364; aaVt[ 1][11] = 1.755838; aaVt[ 1][12] = 0.087930; aaVt[ 1][13] = 0.042569; aaVt[ 1][14] = 0.128480; aaVt[ 1][15] = 0.292327; aaVt[ 1][16] = 0.204109; aaVt[ 1][17] = 0.046417; aaVt[ 1][18] = 0.097010; aaVt[ 1][19] = 0.113146; aaVt[ 2][ 0] = 0.199097; aaVt[ 2][ 1] = 0.210797; aaVt[ 2][ 2] = 0.000000; aaVt[ 2][ 3] = 0.883422; aaVt[ 2][ 4] = 0.027495; aaVt[ 2][ 5] = 0.275700; aaVt[ 2][ 6] = 0.270417; aaVt[ 2][ 7] = 0.362028; aaVt[ 2][ 8] = 0.290006; aaVt[ 2][ 9] = 0.087225; aaVt[ 2][10] = 0.069245; aaVt[ 2][11] = 0.503060; aaVt[ 2][12] = 0.057420; aaVt[ 2][13] = 0.039769; aaVt[ 2][14] = 0.083956; aaVt[ 2][15] = 0.847049; aaVt[ 2][16] = 0.471268; aaVt[ 2][17] = 0.010459; aaVt[ 2][18] = 0.093268; aaVt[ 2][19] = 0.049824; aaVt[ 3][ 0] = 0.265145; aaVt[ 3][ 1] = 0.105191; aaVt[ 3][ 2] = 0.883422; aaVt[ 3][ 3] = 0.000000; aaVt[ 3][ 4] = 0.010313; aaVt[ 3][ 5] = 0.205842; aaVt[ 3][ 6] = 1.599461; aaVt[ 3][ 7] = 0.311718; aaVt[ 3][ 8] = 0.134252; aaVt[ 3][ 9] = 0.011720; aaVt[ 3][10] = 0.060863; aaVt[ 3][11] = 0.261101; aaVt[ 3][12] = 0.012182; aaVt[ 3][13] = 0.016577; aaVt[ 3][14] = 0.160063; aaVt[ 3][15] = 0.461519; aaVt[ 3][16] = 0.178197; aaVt[ 3][17] = 0.011393; aaVt[ 3][18] = 0.051664; aaVt[ 3][19] = 0.048769; aaVt[ 4][ 0] = 0.227333; aaVt[ 4][ 1] = 0.031726; aaVt[ 4][ 2] = 0.027495; aaVt[ 4][ 3] = 0.010313; aaVt[ 4][ 4] = 0.000000; aaVt[ 4][ 5] = 0.004315; aaVt[ 4][ 6] = 0.005321; aaVt[ 4][ 7] = 0.050876; aaVt[ 4][ 8] = 0.016695; aaVt[ 4][ 9] = 0.046398; aaVt[ 4][10] = 0.091709; aaVt[ 4][11] = 0.004067; aaVt[ 4][12] = 0.023690; aaVt[ 4][13] = 0.051127; aaVt[ 4][14] = 0.011137; aaVt[ 4][15] = 0.175270; aaVt[ 4][16] = 0.079511; aaVt[ 4][17] = 0.007732; aaVt[ 4][18] = 0.042823; aaVt[ 4][19] = 0.163831; aaVt[ 5][ 0] = 0.310084; aaVt[ 5][ 1] = 0.493763; aaVt[ 5][ 2] = 0.275700; aaVt[ 5][ 3] = 0.205842; aaVt[ 5][ 4] = 0.004315; aaVt[ 5][ 5] = 0.000000; aaVt[ 5][ 6] = 0.960976; aaVt[ 5][ 7] = 0.128660; aaVt[ 5][ 8] = 0.315521; aaVt[ 5][ 9] = 0.054602; aaVt[ 5][10] = 0.243530; aaVt[ 5][11] = 0.738208; aaVt[ 5][12] = 0.120801; aaVt[ 5][13] = 0.026235; aaVt[ 5][14] = 0.156570; aaVt[ 5][15] = 0.358017; aaVt[ 5][16] = 0.248992; aaVt[ 5][17] = 0.021248; aaVt[ 5][18] = 0.062544; aaVt[ 5][19] = 0.112027; aaVt[ 6][ 0] = 0.567957; aaVt[ 6][ 1] = 0.255240; aaVt[ 6][ 2] = 0.270417; aaVt[ 6][ 3] = 1.599461; aaVt[ 6][ 4] = 0.005321; aaVt[ 6][ 5] = 0.960976; aaVt[ 6][ 6] = 0.000000; aaVt[ 6][ 7] = 0.250447; aaVt[ 6][ 8] = 0.104458; aaVt[ 6][ 9] = 0.046589; aaVt[ 6][10] = 0.151924; aaVt[ 6][11] = 0.888630; aaVt[ 6][12] = 0.058643; aaVt[ 6][13] = 0.028168; aaVt[ 6][14] = 0.205134; aaVt[ 6][15] = 0.406035; aaVt[ 6][16] = 0.321028; aaVt[ 6][17] = 0.018844; aaVt[ 6][18] = 0.055200; aaVt[ 6][19] = 0.205868; aaVt[ 7][ 0] = 0.876213; aaVt[ 7][ 1] = 0.156945; aaVt[ 7][ 2] = 0.362028; aaVt[ 7][ 3] = 0.311718; aaVt[ 7][ 4] = 0.050876; aaVt[ 7][ 5] = 0.128660; aaVt[ 7][ 6] = 0.250447; aaVt[ 7][ 7] = 0.000000; aaVt[ 7][ 8] = 0.058131; aaVt[ 7][ 9] = 0.051089; aaVt[ 7][10] = 0.087056; aaVt[ 7][11] = 0.193243; aaVt[ 7][12] = 0.046560; aaVt[ 7][13] = 0.050143; aaVt[ 7][14] = 0.124492; aaVt[ 7][15] = 0.612843; aaVt[ 7][16] = 0.136266; aaVt[ 7][17] = 0.023990; aaVt[ 7][18] = 0.037568; aaVt[ 7][19] = 0.082579; aaVt[ 8][ 0] = 0.078692; aaVt[ 8][ 1] = 0.213164; aaVt[ 8][ 2] = 0.290006; aaVt[ 8][ 3] = 0.134252; aaVt[ 8][ 4] = 0.016695; aaVt[ 8][ 5] = 0.315521; aaVt[ 8][ 6] = 0.104458; aaVt[ 8][ 7] = 0.058131; aaVt[ 8][ 8] = 0.000000; aaVt[ 8][ 9] = 0.020039; aaVt[ 8][10] = 0.103552; aaVt[ 8][11] = 0.153323; aaVt[ 8][12] = 0.021157; aaVt[ 8][13] = 0.079807; aaVt[ 8][14] = 0.078892; aaVt[ 8][15] = 0.167406; aaVt[ 8][16] = 0.101117; aaVt[ 8][17] = 0.020009; aaVt[ 8][18] = 0.286027; aaVt[ 8][19] = 0.068575; aaVt[ 9][ 0] = 0.222972; aaVt[ 9][ 1] = 0.081510; aaVt[ 9][ 2] = 0.087225; aaVt[ 9][ 3] = 0.011720; aaVt[ 9][ 4] = 0.046398; aaVt[ 9][ 5] = 0.054602; aaVt[ 9][ 6] = 0.046589; aaVt[ 9][ 7] = 0.051089; aaVt[ 9][ 8] = 0.020039; aaVt[ 9][ 9] = 0.000000; aaVt[ 9][10] = 2.089890; aaVt[ 9][11] = 0.093181; aaVt[ 9][12] = 0.493845; aaVt[ 9][13] = 0.321020; aaVt[ 9][14] = 0.054797; aaVt[ 9][15] = 0.081567; aaVt[ 9][16] = 0.376588; aaVt[ 9][17] = 0.034954; aaVt[ 9][18] = 0.086237; aaVt[ 9][19] = 3.654430; aaVt[10][ 0] = 0.424630; aaVt[10][ 1] = 0.192364; aaVt[10][ 2] = 0.069245; aaVt[10][ 3] = 0.060863; aaVt[10][ 4] = 0.091709; aaVt[10][ 5] = 0.243530; aaVt[10][ 6] = 0.151924; aaVt[10][ 7] = 0.087056; aaVt[10][ 8] = 0.103552; aaVt[10][ 9] = 2.089890; aaVt[10][10] = 0.000000; aaVt[10][11] = 0.201204; aaVt[10][12] = 1.105667; aaVt[10][13] = 0.946499; aaVt[10][14] = 0.169784; aaVt[10][15] = 0.214977; aaVt[10][16] = 0.243227; aaVt[10][17] = 0.083439; aaVt[10][18] = 0.189842; aaVt[10][19] = 1.337571; aaVt[11][ 0] = 0.393245; aaVt[11][ 1] = 1.755838; aaVt[11][ 2] = 0.503060; aaVt[11][ 3] = 0.261101; aaVt[11][ 4] = 0.004067; aaVt[11][ 5] = 0.738208; aaVt[11][ 6] = 0.888630; aaVt[11][ 7] = 0.193243; aaVt[11][ 8] = 0.153323; aaVt[11][ 9] = 0.093181; aaVt[11][10] = 0.201204; aaVt[11][11] = 0.000000; aaVt[11][12] = 0.096474; aaVt[11][13] = 0.038261; aaVt[11][14] = 0.212302; aaVt[11][15] = 0.400072; aaVt[11][16] = 0.446646; aaVt[11][17] = 0.023321; aaVt[11][18] = 0.068689; aaVt[11][19] = 0.144587; aaVt[12][ 0] = 0.211550; aaVt[12][ 1] = 0.087930; aaVt[12][ 2] = 0.057420; aaVt[12][ 3] = 0.012182; aaVt[12][ 4] = 0.023690; aaVt[12][ 5] = 0.120801; aaVt[12][ 6] = 0.058643; aaVt[12][ 7] = 0.046560; aaVt[12][ 8] = 0.021157; aaVt[12][ 9] = 0.493845; aaVt[12][10] = 1.105667; aaVt[12][11] = 0.096474; aaVt[12][12] = 0.000000; aaVt[12][13] = 0.173052; aaVt[12][14] = 0.010363; aaVt[12][15] = 0.090515; aaVt[12][16] = 0.184609; aaVt[12][17] = 0.022019; aaVt[12][18] = 0.073223; aaVt[12][19] = 0.307309; aaVt[13][ 0] = 0.116646; aaVt[13][ 1] = 0.042569; aaVt[13][ 2] = 0.039769; aaVt[13][ 3] = 0.016577; aaVt[13][ 4] = 0.051127; aaVt[13][ 5] = 0.026235; aaVt[13][ 6] = 0.028168; aaVt[13][ 7] = 0.050143; aaVt[13][ 8] = 0.079807; aaVt[13][ 9] = 0.321020; aaVt[13][10] = 0.946499; aaVt[13][11] = 0.038261; aaVt[13][12] = 0.173052; aaVt[13][13] = 0.000000; aaVt[13][14] = 0.042564; aaVt[13][15] = 0.138119; aaVt[13][16] = 0.085870; aaVt[13][17] = 0.128050; aaVt[13][18] = 0.898663; aaVt[13][19] = 0.247329; aaVt[14][ 0] = 0.399143; aaVt[14][ 1] = 0.128480; aaVt[14][ 2] = 0.083956; aaVt[14][ 3] = 0.160063; aaVt[14][ 4] = 0.011137; aaVt[14][ 5] = 0.156570; aaVt[14][ 6] = 0.205134; aaVt[14][ 7] = 0.124492; aaVt[14][ 8] = 0.078892; aaVt[14][ 9] = 0.054797; aaVt[14][10] = 0.169784; aaVt[14][11] = 0.212302; aaVt[14][12] = 0.010363; aaVt[14][13] = 0.042564; aaVt[14][14] = 0.000000; aaVt[14][15] = 0.430431; aaVt[14][16] = 0.207143; aaVt[14][17] = 0.014584; aaVt[14][18] = 0.032043; aaVt[14][19] = 0.129315; aaVt[15][ 0] = 1.817198; aaVt[15][ 1] = 0.292327; aaVt[15][ 2] = 0.847049; aaVt[15][ 3] = 0.461519; aaVt[15][ 4] = 0.175270; aaVt[15][ 5] = 0.358017; aaVt[15][ 6] = 0.406035; aaVt[15][ 7] = 0.612843; aaVt[15][ 8] = 0.167406; aaVt[15][ 9] = 0.081567; aaVt[15][10] = 0.214977; aaVt[15][11] = 0.400072; aaVt[15][12] = 0.090515; aaVt[15][13] = 0.138119; aaVt[15][14] = 0.430431; aaVt[15][15] = 0.000000; aaVt[15][16] = 1.767766; aaVt[15][17] = 0.035933; aaVt[15][18] = 0.121979; aaVt[15][19] = 0.127700; aaVt[16][ 0] = 0.877877; aaVt[16][ 1] = 0.204109; aaVt[16][ 2] = 0.471268; aaVt[16][ 3] = 0.178197; aaVt[16][ 4] = 0.079511; aaVt[16][ 5] = 0.248992; aaVt[16][ 6] = 0.321028; aaVt[16][ 7] = 0.136266; aaVt[16][ 8] = 0.101117; aaVt[16][ 9] = 0.376588; aaVt[16][10] = 0.243227; aaVt[16][11] = 0.446646; aaVt[16][12] = 0.184609; aaVt[16][13] = 0.085870; aaVt[16][14] = 0.207143; aaVt[16][15] = 1.767766; aaVt[16][16] = 0.000000; aaVt[16][17] = 0.020437; aaVt[16][18] = 0.094617; aaVt[16][19] = 0.740372; aaVt[17][ 0] = 0.030309; aaVt[17][ 1] = 0.046417; aaVt[17][ 2] = 0.010459; aaVt[17][ 3] = 0.011393; aaVt[17][ 4] = 0.007732; aaVt[17][ 5] = 0.021248; aaVt[17][ 6] = 0.018844; aaVt[17][ 7] = 0.023990; aaVt[17][ 8] = 0.020009; aaVt[17][ 9] = 0.034954; aaVt[17][10] = 0.083439; aaVt[17][11] = 0.023321; aaVt[17][12] = 0.022019; aaVt[17][13] = 0.128050; aaVt[17][14] = 0.014584; aaVt[17][15] = 0.035933; aaVt[17][16] = 0.020437; aaVt[17][17] = 0.000000; aaVt[17][18] = 0.124746; aaVt[17][19] = 0.022134; aaVt[18][ 0] = 0.087061; aaVt[18][ 1] = 0.097010; aaVt[18][ 2] = 0.093268; aaVt[18][ 3] = 0.051664; aaVt[18][ 4] = 0.042823; aaVt[18][ 5] = 0.062544; aaVt[18][ 6] = 0.055200; aaVt[18][ 7] = 0.037568; aaVt[18][ 8] = 0.286027; aaVt[18][ 9] = 0.086237; aaVt[18][10] = 0.189842; aaVt[18][11] = 0.068689; aaVt[18][12] = 0.073223; aaVt[18][13] = 0.898663; aaVt[18][14] = 0.032043; aaVt[18][15] = 0.121979; aaVt[18][16] = 0.094617; aaVt[18][17] = 0.124746; aaVt[18][18] = 0.000000; aaVt[18][19] = 0.125733; aaVt[19][ 0] = 1.230985; aaVt[19][ 1] = 0.113146; aaVt[19][ 2] = 0.049824; aaVt[19][ 3] = 0.048769; aaVt[19][ 4] = 0.163831; aaVt[19][ 5] = 0.112027; aaVt[19][ 6] = 0.205868; aaVt[19][ 7] = 0.082579; aaVt[19][ 8] = 0.068575; aaVt[19][ 9] = 3.654430; aaVt[19][10] = 1.337571; aaVt[19][11] = 0.144587; aaVt[19][12] = 0.307309; aaVt[19][13] = 0.247329; aaVt[19][14] = 0.129315; aaVt[19][15] = 0.127700; aaVt[19][16] = 0.740372; aaVt[19][17] = 0.022134; aaVt[19][18] = 0.125733; aaVt[19][19] = 0.000000; vtPi[ 0] = 0.078837; vtPi[ 1] = 0.051238; vtPi[ 2] = 0.042313; vtPi[ 3] = 0.053066; vtPi[ 4] = 0.015175; vtPi[ 5] = 0.036713; vtPi[ 6] = 0.061924; vtPi[ 7] = 0.070852; vtPi[ 8] = 0.023082; vtPi[ 9] = 0.062056; vtPi[10] = 0.096371; vtPi[11] = 0.057324; vtPi[12] = 0.023771; vtPi[13] = 0.043296; vtPi[14] = 0.043911; vtPi[15] = 0.063403; vtPi[16] = 0.055897; vtPi[17] = 0.013272; vtPi[18] = 0.034399; vtPi[19] = 0.073101; /* Blosum62 */ aaBlosum[ 0][ 0] = 0.000000000000; aaBlosum[ 0][ 1] = 0.735790389698; aaBlosum[ 0][ 2] = 0.485391055466; aaBlosum[ 0][ 3] = 0.543161820899; aaBlosum[ 0][ 4] = 1.459995310470; aaBlosum[ 0][ 5] = 1.199705704602; aaBlosum[ 0][ 6] = 1.170949042800; aaBlosum[ 0][ 7] = 1.955883574960; aaBlosum[ 0][ 8] = 0.716241444998; aaBlosum[ 0][ 9] = 0.605899003687; aaBlosum[ 0][10] = 0.800016530518; aaBlosum[ 0][11] = 1.295201266783; aaBlosum[ 0][12] = 1.253758266664; aaBlosum[ 0][13] = 0.492964679748; aaBlosum[ 0][14] = 1.173275900924; aaBlosum[ 0][15] = 4.325092687057; aaBlosum[ 0][16] = 1.729178019485; aaBlosum[ 0][17] = 0.465839367725; aaBlosum[ 0][18] = 0.718206697586; aaBlosum[ 0][19] = 2.187774522005; aaBlosum[ 1][ 0] = 0.735790389698; aaBlosum[ 1][ 1] = 0.000000000000; aaBlosum[ 1][ 2] = 1.297446705134; aaBlosum[ 1][ 3] = 0.500964408555; aaBlosum[ 1][ 4] = 0.227826574209; aaBlosum[ 1][ 5] = 3.020833610064; aaBlosum[ 1][ 6] = 1.360574190420; aaBlosum[ 1][ 7] = 0.418763308518; aaBlosum[ 1][ 8] = 1.456141166336; aaBlosum[ 1][ 9] = 0.232036445142; aaBlosum[ 1][10] = 0.622711669692; aaBlosum[ 1][11] = 5.411115141489; aaBlosum[ 1][12] = 0.983692987457; aaBlosum[ 1][13] = 0.371644693209; aaBlosum[ 1][14] = 0.448133661718; aaBlosum[ 1][15] = 1.122783104210; aaBlosum[ 1][16] = 0.914665954563; aaBlosum[ 1][17] = 0.426382310122; aaBlosum[ 1][18] = 0.720517441216; aaBlosum[ 1][19] = 0.438388343772; aaBlosum[ 2][ 0] = 0.485391055466; aaBlosum[ 2][ 1] = 1.297446705134; aaBlosum[ 2][ 2] = 0.000000000000; aaBlosum[ 2][ 3] = 3.180100048216; aaBlosum[ 2][ 4] = 0.397358949897; aaBlosum[ 2][ 5] = 1.839216146992; aaBlosum[ 2][ 6] = 1.240488508640; aaBlosum[ 2][ 7] = 1.355872344485; aaBlosum[ 2][ 8] = 2.414501434208; aaBlosum[ 2][ 9] = 0.283017326278; aaBlosum[ 2][10] = 0.211888159615; aaBlosum[ 2][11] = 1.593137043457; aaBlosum[ 2][12] = 0.648441278787; aaBlosum[ 2][13] = 0.354861249223; aaBlosum[ 2][14] = 0.494887043702; aaBlosum[ 2][15] = 2.904101656456; aaBlosum[ 2][16] = 1.898173634533; aaBlosum[ 2][17] = 0.191482046247; aaBlosum[ 2][18] = 0.538222519037; aaBlosum[ 2][19] = 0.312858797993; aaBlosum[ 3][ 0] = 0.543161820899; aaBlosum[ 3][ 1] = 0.500964408555; aaBlosum[ 3][ 2] = 3.180100048216; aaBlosum[ 3][ 3] = 0.000000000000; aaBlosum[ 3][ 4] = 0.240836614802; aaBlosum[ 3][ 5] = 1.190945703396; aaBlosum[ 3][ 6] = 3.761625208368; aaBlosum[ 3][ 7] = 0.798473248968; aaBlosum[ 3][ 8] = 0.778142664022; aaBlosum[ 3][ 9] = 0.418555732462; aaBlosum[ 3][10] = 0.218131577594; aaBlosum[ 3][11] = 1.032447924952; aaBlosum[ 3][12] = 0.222621897958; aaBlosum[ 3][13] = 0.281730694207; aaBlosum[ 3][14] = 0.730628272998; aaBlosum[ 3][15] = 1.582754142065; aaBlosum[ 3][16] = 0.934187509431; aaBlosum[ 3][17] = 0.145345046279; aaBlosum[ 3][18] = 0.261422208965; aaBlosum[ 3][19] = 0.258129289418; aaBlosum[ 4][ 0] = 1.459995310470; aaBlosum[ 4][ 1] = 0.227826574209; aaBlosum[ 4][ 2] = 0.397358949897; aaBlosum[ 4][ 3] = 0.240836614802; aaBlosum[ 4][ 4] = 0.000000000000; aaBlosum[ 4][ 5] = 0.329801504630; aaBlosum[ 4][ 6] = 0.140748891814; aaBlosum[ 4][ 7] = 0.418203192284; aaBlosum[ 4][ 8] = 0.354058109831; aaBlosum[ 4][ 9] = 0.774894022794; aaBlosum[ 4][10] = 0.831842640142; aaBlosum[ 4][11] = 0.285078800906; aaBlosum[ 4][12] = 0.767688823480; aaBlosum[ 4][13] = 0.441337471187; aaBlosum[ 4][14] = 0.356008498769; aaBlosum[ 4][15] = 1.197188415094; aaBlosum[ 4][16] = 1.119831358516; aaBlosum[ 4][17] = 0.527664418872; aaBlosum[ 4][18] = 0.470237733696; aaBlosum[ 4][19] = 1.116352478606; aaBlosum[ 5][ 0] = 1.199705704602; aaBlosum[ 5][ 1] = 3.020833610064; aaBlosum[ 5][ 2] = 1.839216146992; aaBlosum[ 5][ 3] = 1.190945703396; aaBlosum[ 5][ 4] = 0.329801504630; aaBlosum[ 5][ 5] = 0.000000000000; aaBlosum[ 5][ 6] = 5.528919177928; aaBlosum[ 5][ 7] = 0.609846305383; aaBlosum[ 5][ 8] = 2.435341131140; aaBlosum[ 5][ 9] = 0.236202451204; aaBlosum[ 5][10] = 0.580737093181; aaBlosum[ 5][11] = 3.945277674515; aaBlosum[ 5][12] = 2.494896077113; aaBlosum[ 5][13] = 0.144356959750; aaBlosum[ 5][14] = 0.858570575674; aaBlosum[ 5][15] = 1.934870924596; aaBlosum[ 5][16] = 1.277480294596; aaBlosum[ 5][17] = 0.758653808642; aaBlosum[ 5][18] = 0.958989742850; aaBlosum[ 5][19] = 0.530785790125; aaBlosum[ 6][ 0] = 1.170949042800; aaBlosum[ 6][ 1] = 1.360574190420; aaBlosum[ 6][ 2] = 1.240488508640; aaBlosum[ 6][ 3] = 3.761625208368; aaBlosum[ 6][ 4] = 0.140748891814; aaBlosum[ 6][ 5] = 5.528919177928; aaBlosum[ 6][ 6] = 0.000000000000; aaBlosum[ 6][ 7] = 0.423579992176; aaBlosum[ 6][ 8] = 1.626891056982; aaBlosum[ 6][ 9] = 0.186848046932; aaBlosum[ 6][10] = 0.372625175087; aaBlosum[ 6][11] = 2.802427151679; aaBlosum[ 6][12] = 0.555415397470; aaBlosum[ 6][13] = 0.291409084165; aaBlosum[ 6][14] = 0.926563934846; aaBlosum[ 6][15] = 1.769893238937; aaBlosum[ 6][16] = 1.071097236007; aaBlosum[ 6][17] = 0.407635648938; aaBlosum[ 6][18] = 0.596719300346; aaBlosum[ 6][19] = 0.524253846338; aaBlosum[ 7][ 0] = 1.955883574960; aaBlosum[ 7][ 1] = 0.418763308518; aaBlosum[ 7][ 2] = 1.355872344485; aaBlosum[ 7][ 3] = 0.798473248968; aaBlosum[ 7][ 4] = 0.418203192284; aaBlosum[ 7][ 5] = 0.609846305383; aaBlosum[ 7][ 6] = 0.423579992176; aaBlosum[ 7][ 7] = 0.000000000000; aaBlosum[ 7][ 8] = 0.539859124954; aaBlosum[ 7][ 9] = 0.189296292376; aaBlosum[ 7][10] = 0.217721159236; aaBlosum[ 7][11] = 0.752042440303; aaBlosum[ 7][12] = 0.459436173579; aaBlosum[ 7][13] = 0.368166464453; aaBlosum[ 7][14] = 0.504086599527; aaBlosum[ 7][15] = 1.509326253224; aaBlosum[ 7][16] = 0.641436011405; aaBlosum[ 7][17] = 0.508358924638; aaBlosum[ 7][18] = 0.308055737035; aaBlosum[ 7][19] = 0.253340790190; aaBlosum[ 8][ 0] = 0.716241444998; aaBlosum[ 8][ 1] = 1.456141166336; aaBlosum[ 8][ 2] = 2.414501434208; aaBlosum[ 8][ 3] = 0.778142664022; aaBlosum[ 8][ 4] = 0.354058109831; aaBlosum[ 8][ 5] = 2.435341131140; aaBlosum[ 8][ 6] = 1.626891056982; aaBlosum[ 8][ 7] = 0.539859124954; aaBlosum[ 8][ 8] = 0.000000000000; aaBlosum[ 8][ 9] = 0.252718447885; aaBlosum[ 8][10] = 0.348072209797; aaBlosum[ 8][11] = 1.022507035889; aaBlosum[ 8][12] = 0.984311525359; aaBlosum[ 8][13] = 0.714533703928; aaBlosum[ 8][14] = 0.527007339151; aaBlosum[ 8][15] = 1.117029762910; aaBlosum[ 8][16] = 0.585407090225; aaBlosum[ 8][17] = 0.301248600780; aaBlosum[ 8][18] = 4.218953969389; aaBlosum[ 8][19] = 0.201555971750; aaBlosum[ 9][ 0] = 0.605899003687; aaBlosum[ 9][ 1] = 0.232036445142; aaBlosum[ 9][ 2] = 0.283017326278; aaBlosum[ 9][ 3] = 0.418555732462; aaBlosum[ 9][ 4] = 0.774894022794; aaBlosum[ 9][ 5] = 0.236202451204; aaBlosum[ 9][ 6] = 0.186848046932; aaBlosum[ 9][ 7] = 0.189296292376; aaBlosum[ 9][ 8] = 0.252718447885; aaBlosum[ 9][ 9] = 0.000000000000; aaBlosum[ 9][10] = 3.890963773304; aaBlosum[ 9][11] = 0.406193586642; aaBlosum[ 9][12] = 3.364797763104; aaBlosum[ 9][13] = 1.517359325954; aaBlosum[ 9][14] = 0.388355409206; aaBlosum[ 9][15] = 0.357544412460; aaBlosum[ 9][16] = 1.179091197260; aaBlosum[ 9][17] = 0.341985787540; aaBlosum[ 9][18] = 0.674617093228; aaBlosum[ 9][19] = 8.311839405458; aaBlosum[10][ 0] = 0.800016530518; aaBlosum[10][ 1] = 0.622711669692; aaBlosum[10][ 2] = 0.211888159615; aaBlosum[10][ 3] = 0.218131577594; aaBlosum[10][ 4] = 0.831842640142; aaBlosum[10][ 5] = 0.580737093181; aaBlosum[10][ 6] = 0.372625175087; aaBlosum[10][ 7] = 0.217721159236; aaBlosum[10][ 8] = 0.348072209797; aaBlosum[10][ 9] = 3.890963773304; aaBlosum[10][10] = 0.000000000000; aaBlosum[10][11] = 0.445570274261; aaBlosum[10][12] = 6.030559379572; aaBlosum[10][13] = 2.064839703237; aaBlosum[10][14] = 0.374555687471; aaBlosum[10][15] = 0.352969184527; aaBlosum[10][16] = 0.915259857694; aaBlosum[10][17] = 0.691474634600; aaBlosum[10][18] = 0.811245856323; aaBlosum[10][19] = 2.231405688913; aaBlosum[11][ 0] = 1.295201266783; aaBlosum[11][ 1] = 5.411115141489; aaBlosum[11][ 2] = 1.593137043457; aaBlosum[11][ 3] = 1.032447924952; aaBlosum[11][ 4] = 0.285078800906; aaBlosum[11][ 5] = 3.945277674515; aaBlosum[11][ 6] = 2.802427151679; aaBlosum[11][ 7] = 0.752042440303; aaBlosum[11][ 8] = 1.022507035889; aaBlosum[11][ 9] = 0.406193586642; aaBlosum[11][10] = 0.445570274261; aaBlosum[11][11] = 0.000000000000; aaBlosum[11][12] = 1.073061184332; aaBlosum[11][13] = 0.266924750511; aaBlosum[11][14] = 1.047383450722; aaBlosum[11][15] = 1.752165917819; aaBlosum[11][16] = 1.303875200799; aaBlosum[11][17] = 0.332243040634; aaBlosum[11][18] = 0.717993486900; aaBlosum[11][19] = 0.498138475304; aaBlosum[12][ 0] = 1.253758266664; aaBlosum[12][ 1] = 0.983692987457; aaBlosum[12][ 2] = 0.648441278787; aaBlosum[12][ 3] = 0.222621897958; aaBlosum[12][ 4] = 0.767688823480; aaBlosum[12][ 5] = 2.494896077113; aaBlosum[12][ 6] = 0.555415397470; aaBlosum[12][ 7] = 0.459436173579; aaBlosum[12][ 8] = 0.984311525359; aaBlosum[12][ 9] = 3.364797763104; aaBlosum[12][10] = 6.030559379572; aaBlosum[12][11] = 1.073061184332; aaBlosum[12][12] = 0.000000000000; aaBlosum[12][13] = 1.773855168830; aaBlosum[12][14] = 0.454123625103; aaBlosum[12][15] = 0.918723415746; aaBlosum[12][16] = 1.488548053722; aaBlosum[12][17] = 0.888101098152; aaBlosum[12][18] = 0.951682162246; aaBlosum[12][19] = 2.575850755315; aaBlosum[13][ 0] = 0.492964679748; aaBlosum[13][ 1] = 0.371644693209; aaBlosum[13][ 2] = 0.354861249223; aaBlosum[13][ 3] = 0.281730694207; aaBlosum[13][ 4] = 0.441337471187; aaBlosum[13][ 5] = 0.144356959750; aaBlosum[13][ 6] = 0.291409084165; aaBlosum[13][ 7] = 0.368166464453; aaBlosum[13][ 8] = 0.714533703928; aaBlosum[13][ 9] = 1.517359325954; aaBlosum[13][10] = 2.064839703237; aaBlosum[13][11] = 0.266924750511; aaBlosum[13][12] = 1.773855168830; aaBlosum[13][13] = 0.000000000000; aaBlosum[13][14] = 0.233597909629; aaBlosum[13][15] = 0.540027644824; aaBlosum[13][16] = 0.488206118793; aaBlosum[13][17] = 2.074324893497; aaBlosum[13][18] = 6.747260430801; aaBlosum[13][19] = 0.838119610178; aaBlosum[14][ 0] = 1.173275900924; aaBlosum[14][ 1] = 0.448133661718; aaBlosum[14][ 2] = 0.494887043702; aaBlosum[14][ 3] = 0.730628272998; aaBlosum[14][ 4] = 0.356008498769; aaBlosum[14][ 5] = 0.858570575674; aaBlosum[14][ 6] = 0.926563934846; aaBlosum[14][ 7] = 0.504086599527; aaBlosum[14][ 8] = 0.527007339151; aaBlosum[14][ 9] = 0.388355409206; aaBlosum[14][10] = 0.374555687471; aaBlosum[14][11] = 1.047383450722; aaBlosum[14][12] = 0.454123625103; aaBlosum[14][13] = 0.233597909629; aaBlosum[14][14] = 0.000000000000; aaBlosum[14][15] = 1.169129577716; aaBlosum[14][16] = 1.005451683149; aaBlosum[14][17] = 0.252214830027; aaBlosum[14][18] = 0.369405319355; aaBlosum[14][19] = 0.496908410676; aaBlosum[15][ 0] = 4.325092687057; aaBlosum[15][ 1] = 1.122783104210; aaBlosum[15][ 2] = 2.904101656456; aaBlosum[15][ 3] = 1.582754142065; aaBlosum[15][ 4] = 1.197188415094; aaBlosum[15][ 5] = 1.934870924596; aaBlosum[15][ 6] = 1.769893238937; aaBlosum[15][ 7] = 1.509326253224; aaBlosum[15][ 8] = 1.117029762910; aaBlosum[15][ 9] = 0.357544412460; aaBlosum[15][10] = 0.352969184527; aaBlosum[15][11] = 1.752165917819; aaBlosum[15][12] = 0.918723415746; aaBlosum[15][13] = 0.540027644824; aaBlosum[15][14] = 1.169129577716; aaBlosum[15][15] = 0.000000000000; aaBlosum[15][16] = 5.151556292270; aaBlosum[15][17] = 0.387925622098; aaBlosum[15][18] = 0.796751520761; aaBlosum[15][19] = 0.561925457442; aaBlosum[16][ 0] = 1.729178019485; aaBlosum[16][ 1] = 0.914665954563; aaBlosum[16][ 2] = 1.898173634533; aaBlosum[16][ 3] = 0.934187509431; aaBlosum[16][ 4] = 1.119831358516; aaBlosum[16][ 5] = 1.277480294596; aaBlosum[16][ 6] = 1.071097236007; aaBlosum[16][ 7] = 0.641436011405; aaBlosum[16][ 8] = 0.585407090225; aaBlosum[16][ 9] = 1.179091197260; aaBlosum[16][10] = 0.915259857694; aaBlosum[16][11] = 1.303875200799; aaBlosum[16][12] = 1.488548053722; aaBlosum[16][13] = 0.488206118793; aaBlosum[16][14] = 1.005451683149; aaBlosum[16][15] = 5.151556292270; aaBlosum[16][16] = 0.000000000000; aaBlosum[16][17] = 0.513128126891; aaBlosum[16][18] = 0.801010243199; aaBlosum[16][19] = 2.253074051176; aaBlosum[17][ 0] = 0.465839367725; aaBlosum[17][ 1] = 0.426382310122; aaBlosum[17][ 2] = 0.191482046247; aaBlosum[17][ 3] = 0.145345046279; aaBlosum[17][ 4] = 0.527664418872; aaBlosum[17][ 5] = 0.758653808642; aaBlosum[17][ 6] = 0.407635648938; aaBlosum[17][ 7] = 0.508358924638; aaBlosum[17][ 8] = 0.301248600780; aaBlosum[17][ 9] = 0.341985787540; aaBlosum[17][10] = 0.691474634600; aaBlosum[17][11] = 0.332243040634; aaBlosum[17][12] = 0.888101098152; aaBlosum[17][13] = 2.074324893497; aaBlosum[17][14] = 0.252214830027; aaBlosum[17][15] = 0.387925622098; aaBlosum[17][16] = 0.513128126891; aaBlosum[17][17] = 0.000000000000; aaBlosum[17][18] = 4.054419006558; aaBlosum[17][19] = 0.266508731426; aaBlosum[18][ 0] = 0.718206697586; aaBlosum[18][ 1] = 0.720517441216; aaBlosum[18][ 2] = 0.538222519037; aaBlosum[18][ 3] = 0.261422208965; aaBlosum[18][ 4] = 0.470237733696; aaBlosum[18][ 5] = 0.958989742850; aaBlosum[18][ 6] = 0.596719300346; aaBlosum[18][ 7] = 0.308055737035; aaBlosum[18][ 8] = 4.218953969389; aaBlosum[18][ 9] = 0.674617093228; aaBlosum[18][10] = 0.811245856323; aaBlosum[18][11] = 0.717993486900; aaBlosum[18][12] = 0.951682162246; aaBlosum[18][13] = 6.747260430801; aaBlosum[18][14] = 0.369405319355; aaBlosum[18][15] = 0.796751520761; aaBlosum[18][16] = 0.801010243199; aaBlosum[18][17] = 4.054419006558; aaBlosum[18][18] = 0.000000000000; aaBlosum[18][19] = 1.000000000000; aaBlosum[19][ 0] = 2.187774522005; aaBlosum[19][ 1] = 0.438388343772; aaBlosum[19][ 2] = 0.312858797993; aaBlosum[19][ 3] = 0.258129289418; aaBlosum[19][ 4] = 1.116352478606; aaBlosum[19][ 5] = 0.530785790125; aaBlosum[19][ 6] = 0.524253846338; aaBlosum[19][ 7] = 0.253340790190; aaBlosum[19][ 8] = 0.201555971750; aaBlosum[19][ 9] = 8.311839405458; aaBlosum[19][10] = 2.231405688913; aaBlosum[19][11] = 0.498138475304; aaBlosum[19][12] = 2.575850755315; aaBlosum[19][13] = 0.838119610178; aaBlosum[19][14] = 0.496908410676; aaBlosum[19][15] = 0.561925457442; aaBlosum[19][16] = 2.253074051176; aaBlosum[19][17] = 0.266508731426; aaBlosum[19][18] = 1.000000000000; aaBlosum[19][19] = 0.000000000000; blosPi[ 0] = 0.074; blosPi[ 1] = 0.052; blosPi[ 2] = 0.045; blosPi[ 3] = 0.054; blosPi[ 4] = 0.025; blosPi[ 5] = 0.034; blosPi[ 6] = 0.054; blosPi[ 7] = 0.074; blosPi[ 8] = 0.026; blosPi[ 9] = 0.068; blosPi[10] = 0.099; blosPi[11] = 0.058; blosPi[12] = 0.025; blosPi[13] = 0.047; blosPi[14] = 0.039; blosPi[15] = 0.057; blosPi[16] = 0.051; blosPi[17] = 0.013; blosPi[18] = 0.032; blosPi[19] = 0.073; /* now, check that the matrices are symmetrical */ for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaJones[i][j] - aaJones[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: Jones model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaDayhoff[i][j] - aaDayhoff[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: Dayhoff model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaMtrev24[i][j] - aaMtrev24[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: mtrev24 model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaMtmam[i][j] - aaMtmam[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: mtmam model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aartREV[i][j] - aartREV[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: aartREV model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaWAG[i][j] - aaWAG[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: aaWAG model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aacpREV[i][j] - aacpREV[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: cpREV model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaVt[i][j] - aaVt[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: Vt model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaBlosum[i][j] - aaBlosum[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: Blosum model is not symmetrical.\n"); return (ERROR); } } } /* rescale stationary frequencies, to make certain they sum to 1.0 */ sum = 0.0; for (i=0; i<20; i++) sum += jonesPi[i]; for (i=0; i<20; i++) jonesPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += dayhoffPi[i]; for (i=0; i<20; i++) dayhoffPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += mtrev24Pi[i]; for (i=0; i<20; i++) mtrev24Pi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += mtmamPi[i]; for (i=0; i<20; i++) mtmamPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += rtrevPi[i]; for (i=0; i<20; i++) rtrevPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += wagPi[i]; for (i=0; i<20; i++) wagPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += cprevPi[i]; for (i=0; i<20; i++) cprevPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += vtPi[i]; for (i=0; i<20; i++) vtPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += blosPi[i]; for (i=0; i<20; i++) blosPi[i] /= sum; /* multiply entries by amino acid frequencies */ for (i=0; i<20; i++) { for (j=0; j<20; j++) { aaJones[i][j] *= jonesPi[j]; aaDayhoff[i][j] *= dayhoffPi[j]; aaMtrev24[i][j] *= mtrev24Pi[j]; aaMtmam[i][j] *= mtmamPi[j]; aartREV[i][j] *= rtrevPi[j]; aaWAG[i][j] *= wagPi[j]; aacpREV[i][j] *= cprevPi[j]; aaVt[i][j] *= vtPi[j]; aaBlosum[i][j] *= blosPi[j]; } } /* rescale, so branch lengths are in terms of expected number of amino acid substitutions per site */ scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += jonesPi[i] * aaJones[i][j]; scaler += jonesPi[j] * aaJones[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaJones[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += dayhoffPi[i] * aaDayhoff[i][j]; scaler += dayhoffPi[j] * aaDayhoff[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaDayhoff[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += mtrev24Pi[i] * aaMtrev24[i][j]; scaler += mtrev24Pi[j] * aaMtrev24[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaMtrev24[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += mtmamPi[i] * aaMtmam[i][j]; scaler += mtmamPi[j] * aaMtmam[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaMtmam[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += rtrevPi[i] * aartREV[i][j]; scaler += rtrevPi[j] * aartREV[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aartREV[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += wagPi[i] * aaWAG[i][j]; scaler += wagPi[j] * aaWAG[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaWAG[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += cprevPi[i] * aacpREV[i][j]; scaler += cprevPi[j] * aacpREV[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aacpREV[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += vtPi[i] * aaVt[i][j]; scaler += vtPi[j] * aaVt[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaVt[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += blosPi[i] * aaBlosum[i][j]; scaler += blosPi[j] * aaBlosum[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaBlosum[i][j] *= scaler; /* set diagonal of matrix */ for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaJones[i][j]; } aaJones[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaDayhoff[i][j]; } aaDayhoff[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaMtrev24[i][j]; } aaMtrev24[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaMtmam[i][j]; } aaMtmam[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aartREV[i][j]; } aartREV[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaWAG[i][j]; } aaWAG[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aacpREV[i][j]; } aacpREV[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaVt[i][j]; } aaVt[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaBlosum[i][j]; } aaBlosum[i][i] = -sum; } # if 0 for (i=0; i<20; i++) { MrBayesPrint ("%s ", spacer); for (j=0; j<20; j++) { if (aaJones[i][j] < 0.0) MrBayesPrint ("%1.3lf ", aaJones[i][j]); else MrBayesPrint (" %1.3lf ", aaJones[i][j]); } MrBayesPrint ("\n"); } # endif return (NO_ERROR); } int SetLocalTaxa (void) { int i, j; /* free memory if allocated */ if (memAllocs[ALLOC_LOCTAXANAMES] == YES) { free (localTaxonNames); localTaxonNames = NULL; memAllocs[ALLOC_LOCTAXANAMES] = NO; } if (memAllocs[ALLOC_LOCALTAXONCALIBRATION] == YES) { free (localTaxonCalibration); localTaxonCalibration = NULL; memAllocs[ALLOC_LOCALTAXONCALIBRATION] = NO; } /* count number of non-excluded taxa */ numLocalTaxa = 0; for (i=0; iupDateCijk = YES; m->upDateCl = YES; m->upDateAll = YES; /* make certain that we start with a parsimony branch length of zero */ for (j=0; jparsTreeLength[j*2] = m->parsTreeLength[j*2+1] = 0.0; m->tRatio = NULL; m->revMat = NULL; m->omega = NULL; m->stateFreq = NULL; m->shape = NULL; m->pInvar = NULL; m->correlation = NULL; m->switchRates = NULL; m->rateMult = NULL; m->topology = NULL; m->brlens = NULL; m->speciationRates = NULL; m->extinctionRates = NULL; m->popSize = NULL; m->aaModel = NULL; m->cppRate = NULL; m->cppEvents = NULL; m->cppMultDev = NULL; m->tk02var = NULL; m->tk02BranchRates = NULL; m->igrvar = NULL; m->igrBranchRates = NULL; m->clockRate = NULL; m->CondLikeDown = NULL; m->CondLikeRoot = NULL; m->CondLikeScaler = NULL; m->Likelihood = NULL; m->TiProbs = NULL; m->CondLikeUp = NULL; m->StateCode = NULL; m->PrintAncStates = NULL; m->PrintSiteRates = NULL; m->printPosSel = NO; m->printAncStates = NO; m->printSiteRates = NO; m->nStates = NULL; m->bsIndex = NULL; m->cType = NULL; m->tiIndex = NULL; m->gibbsGamma = NO; m->gibbsFreq = 0; m->parsimonyBasedMove = NO; #if defined (BEAGLE_ENABLED) m->beagleInstance = -1; /* beagle instance */ m->logLikelihoods = NULL; /* array of log likelihoods from Beagle */ m->inRates = NULL; /* array of category rates for Beagle */ m->branchLengths = NULL; /* array of branch lengths for Beagle */ m->tiProbIndices = NULL; /* array of trans prob indices for Beagle */ m->inWeights = NULL; /* array of weights for Beagle root likelihood */ m->bufferIndices = NULL; /* array of partial indices for root likelihood */ m->childBufferIndices = NULL; /* array of child partial indices (unrooted) */ m->childTiProbIndices = NULL; /* array of child ti prob indices (unrooted) */ m->cumulativeScaleIndices = NULL; /* array of cumulative scale indices */ #endif /* likelihood calculator flags */ m->useSSE = NO; /* use SSE code for this partition? */ m->useBeagle = NO; /* use Beagle for this partition? */ /* set all memory pointers to NULL */ m->parsSets = NULL; m->numParsSets = 0; m->parsNodeLens = NULL; m->numParsNodeLens = 0; m->condLikes = NULL; m->tiProbs = NULL; m->scalers = NULL; m->numCondLikes = 0; m->numTiProbs = 0; m->numScalers = 0; m->condLikeIndex = NULL; m->condLikeScratchIndex = NULL; m->tiProbsIndex = NULL; m->tiProbsScratchIndex = NULL; m->nodeScalerIndex = NULL; m->nodeScalerScratchIndex = NULL; m->siteScalerIndex = NULL; m->siteScalerScratchIndex = -1; m->cijks = NULL; m->nCijkParts = 0; m->cijkIndex = NULL; m->cijkScratchIndex = -1; } /* set state of all chains to zero */ for (chn=0; chnnucModel,"Protein") && (mp->dataType == DNA || mp->dataType == RNA)) m->dataType = PROTEIN; else m->dataType = mp->dataType; /* nuc model structure */ if (!strcmp(mp->nucModel, "4by4")) m->nucModelId = NUCMODEL_4BY4; else if (!strcmp(mp->nucModel, "Doublet")) m->nucModelId = NUCMODEL_DOUBLET; else if (!strcmp(mp->nucModel, "Protein")) m->nucModelId = NUCMODEL_AA; else /* if (!strcmp(mp->nucModelId, "Codon")) */ m->nucModelId = NUCMODEL_CODON; /* model nst */ if (!strcmp(mp->nst, "1")) m->nst = 1; else if (!strcmp(mp->nst, "2")) m->nst = 2; else if (!strcmp(mp->nst, "6")) m->nst = 6; else m->nst = NST_MIXED; /* We set the aa model here. We have two options. First, the model could be fixed, in which case mp->aaModel has been set. We then go ahead and also set the model settings. Second, the model could be mixed. In this case, the amino acid matrix is considered a parameter, and we will deal with it below. It doesn't hurt to set it here anyway (it will be overwritten later). */ if (!strcmp(mp->aaModelPr, "Fixed")) { if (!strcmp(mp->aaModel, "Poisson")) m->aaModelId = AAMODEL_POISSON; else if (!strcmp(mp->aaModel, "Equalin")) m->aaModelId = AAMODEL_EQ; else if (!strcmp(mp->aaModel, "Jones")) m->aaModelId = AAMODEL_JONES; else if (!strcmp(mp->aaModel, "Dayhoff")) m->aaModelId = AAMODEL_DAY; else if (!strcmp(mp->aaModel, "Mtrev")) m->aaModelId = AAMODEL_MTREV; else if (!strcmp(mp->aaModel, "Mtmam")) m->aaModelId = AAMODEL_MTMAM; else if (!strcmp(mp->aaModel, "Wag")) m->aaModelId = AAMODEL_WAG; else if (!strcmp(mp->aaModel, "Rtrev")) m->aaModelId = AAMODEL_RTREV; else if (!strcmp(mp->aaModel, "Cprev")) m->aaModelId = AAMODEL_CPREV; else if (!strcmp(mp->aaModel, "Vt")) m->aaModelId = AAMODEL_VT; else if (!strcmp(mp->aaModel, "Blosum")) m->aaModelId = AAMODEL_BLOSUM; else if (!strcmp(mp->aaModel, "Gtr")) m->aaModelId = AAMODEL_GTR; else { MrBayesPrint ("%s Uncertain amino acid model\n", spacer); return (ERROR); } } else m->aaModelId = -1; /* parsimony model? */ if (!strcmp(mp->parsModel, "Yes")) m->parsModelId = YES; else m->parsModelId = NO; /* number of gamma categories */ if (activeParams[P_SHAPE][i] > 0) m->numGammaCats = mp->numGammaCats; else m->numGammaCats = 1; /* number of beta categories */ if (mp->dataType == STANDARD && !(AreDoublesEqual(mp->symBetaFix, -1.0, 0.00001) == YES && !strcmp(mp->symPiPr,"Fixed"))) m->numBetaCats = mp->numBetaCats; else m->numBetaCats = 1; /* number of omega categories */ if ((mp->dataType == DNA || mp->dataType == RNA) && (!strcmp(mp->omegaVar, "Ny98") || !strcmp(mp->omegaVar, "M3")) && !strcmp(mp->nucModel, "Codon")) { m->numOmegaCats = 3; m->numGammaCats = 1; /* if we are here, then we cannot have gamma or beta variation */ m->numBetaCats = 1; } else if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp(mp->omegaVar, "M10") && !strcmp(mp->nucModel, "Codon")) { m->numOmegaCats = mp->numM10BetaCats + mp->numM10GammaCats; m->numGammaCats = 1; /* if we are here, then we cannot have gamma or beta variation */ m->numBetaCats = 1; } else m->numOmegaCats = 1; /* number of transition matrices depends on numGammaCats, numBetaCats, and numOmegaCats */ m->numTiCats = m->numGammaCats * m->numBetaCats * m->numOmegaCats; /* TODO: check that numStates and numModelStates are set appropriately for codon and doublet models */ /* number of observable states */ if (m->dataType == STANDARD) m->numStates = 0; /* zero, meaining variable */ else if (!strcmp(mp->nucModel,"Protein") && (mp->dataType == DNA || mp->dataType == RNA)) m->numStates = 20; else m->numStates = mp->nStates; /* number of model states including hidden ones */ if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp (mp->covarionModel, "Yes") && !strcmp(mp->nucModel, "4by4")) m->numModelStates = mp->nStates * 2; else if (mp->dataType == PROTEIN && !strcmp (mp->covarionModel, "Yes")) m->numModelStates = mp->nStates * 2; else if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp(mp->nucModel,"Protein") && !strcmp (mp->covarionModel, "Yes")) m->numModelStates = 20 * 2; else if (mp->dataType == CONTINUOUS) m->numModelStates = 0; else if (mp->dataType == STANDARD) { /* use max possible for now; we don't know what chars will be included */ m->numModelStates = 10; } else m->numModelStates = m->numStates; /* Fill in some information for calculating cijk. We will use m->cijkLength to figure out if we need to diagonalize Q to calculate transition probabilities. If cijkLength = 0, then we won't bother. We use cijkLength later in this function. */ m->cijkLength = 0; m->nCijkParts = 1; if (m->dataType == PROTEIN) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); if (!strcmp (mp->covarionModel, "Yes")) { m->cijkLength *= m->numGammaCats; m->nCijkParts = m->numGammaCats; } } else if (m->dataType == STANDARD) { /* set to 0 for now, update in ProcessStdChars */ m->nCijkParts = 0; } else if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_4BY4) { if (!strcmp (mp->covarionModel, "No") && m->nst != 6 && m->nst != 203) m->cijkLength = 0; else { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); } if (!strcmp (mp->covarionModel, "Yes")) { m->cijkLength *= m->numGammaCats; m->nCijkParts = m->numGammaCats; } } else if (m->nucModelId == NUCMODEL_DOUBLET) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); } else if (m->nucModelId == NUCMODEL_CODON) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); m->cijkLength *= m->numOmegaCats; m->nCijkParts = m->numOmegaCats; } else { MrBayesPrint ("%s ERROR: Something is wrong if you are here.\n"); return ERROR; } } /* check if we should calculate ancestral states */ if (!strcmp(mp->inferAncStates,"Yes")) { if (m->dataType == PROTEIN && !strcmp(mp->covarionModel, "No")) m->printAncStates = YES; else if (m->dataType == DNA || m->dataType == RNA) { if (!strcmp(mp->nucModel,"4by4") && !strcmp(mp->covarionModel, "No")) m->printAncStates = YES; if (!strcmp(mp->nucModel,"Doublet")) m->printAncStates = YES; if (!strcmp(mp->nucModel,"Codon") && !strcmp(mp->omegaVar,"Equal")) m->printAncStates = YES; } else if (m->dataType == STANDARD || m->dataType == RESTRICTION) m->printAncStates = YES; if (m->printAncStates == YES) inferAncStates = YES; else MrBayesPrint ("%s Print out of ancestral states is not applicable for devision %d.\n",spacer,i); } /* check if we should calculate site rates */ if (!strcmp(mp->inferSiteRates,"Yes")) { if (m->numGammaCats > 1) { m->printSiteRates = YES; inferSiteRates = YES; } } /* check if we should calculate positive selection */ if (!strcmp(mp->inferPosSel, "Yes")) { if (m->numOmegaCats > 1) { m->printPosSel = YES; inferPosSel = YES; } } /* check if we should calculate site omegas */ if (!strcmp(mp->inferSiteOmegas, "Yes")) { if (m->numOmegaCats > 1) { m->printSiteOmegas = YES; inferSiteOmegas = YES; } } /* check if we should use gibbs sampling of gamma (don't use it with pinvar or invgamma) */ if (!strcmp(mp->useGibbs,"Yes") && m->numGammaCats > 1) { if (m->dataType == DNA || m->dataType == RNA || m->dataType == PROTEIN) { if (activeParams[P_CORREL][i] <= 0 && m->printSiteRates == NO && activeParams[P_PINVAR][i] <= 0) { m->gibbsGamma = YES; m->gibbsFreq = mp->gibbsFreq; } } } } return (NO_ERROR); } /*----------------------------------------------------------------- | | SetModelParams: Set up parameter structs for all model | parameters, including trees | |----------------------------------------------------------------*/ int SetModelParams (void) { int c, i, j, k, n, n1, n2, *isPartTouched, numRelParts, nRelParts, areAllPartsParsimony, nClockBrlens, nRelaxedBrlens, nCalibratedBrlens; char tempCodon[15], tempMult[15], *tempStr,temp[30]; char static *partString=NULL;/*mad static to avoid posible memory leak on return ERROR if it would be introduced later */ Param *p; ModelParams *mp; ModelInfo *m; int tempStrSize = 300; tempStr = (char *) SafeMalloc((size_t) (tempStrSize * sizeof(char))); isPartTouched = (int *) SafeMalloc ((size_t) (numCurrentDivisions * sizeof (int))); if (!tempStr || !isPartTouched) { MrBayesPrint ("%s Problem allocating tempString (%d) or isPartTouched (%d)\n", spacer, tempStrSize * sizeof(char), numCurrentDivisions * sizeof(int)); return (ERROR); } # if defined DEBUG_SETCHAINPARAMS /* only for debugging */ MrBFlt lnPriorRatio = 0.0, lnProposalRatio = 0.0; # endif /* allocate space for parameters */ if (memAllocs[ALLOC_PARAMS] == YES) { for (i=0; i numParams) numParams = activeParams[j][i]; if (activeParams[j][i] > 0) nRelParts++; } } params = (Param *) SafeMalloc (numParams * sizeof(Param)); relevantParts = (int *) SafeMalloc (nRelParts * sizeof(int)); if (!params || !relevantParts) { MrBayesPrint ("%s Problem allocating params and relevantParts\n", spacer); if (params) free (params); if (relevantParts) free (relevantParts); free (tempStr); return ERROR; } else memAllocs[ALLOC_PARAMS] = YES; /* fill in info on each parameter */ nRelParts = 0; /* now cumulative number of relevant partitions */ for (k=0; k 0) break; } /* find pointer to modelParams and modelSettings of first relevant partition */ /* this will be handy later on */ for (i=0; imin = p->max = NEG_INFINITY; /* Parameter nValues and nSubValues, which are needed for memory allocation are calculated for each case in the code below. nSympi, however, is only used for one special type of parameter and it therefore makes sense to initialize it to 0 here. The same applies to hasBinaryStd and nIntValues. To be safe, we set all to 0 here. */ p->nValues = 0; p->nSubValues = 0; p->nIntValues = 0; p->nSympi = 0; p->hasBinaryStd = NO; /* should this parameter be printed to a file? */ p->printParam = NO; /* set print subparams to 0 */ p->nPrintSubParams = 0; /* check constraints for tree parameter ? */ p->checkConstraints = NO; /* set index number of parameter */ p->index = k; /* set prior function to NULL */ p->LnPriorRatio = NULL; /* set prior paramters to NULL */ p->priorParams = NULL; /* set affectsLikelihood to NO */ p->affectsLikelihood = NO; /* set cpp event pointers to NULL */ p->nEvents = NULL; p->position = NULL; p->rateMult = NULL; /* set header and name to NULL */ p->paramHeader = NULL; p->name = NULL; /* set up relevant partitions */ p->nRelParts = numRelParts; p->relParts = relevantParts + nRelParts; nRelParts += numRelParts; for (i=n=0; irelParts[n++] = i; /* get partition descriptor */ SafeStrcat(&partString,""); FillRelPartsString (p, &partString); /* set up information for parameter */ if (j == P_TRATIO) { /* Set up tratio ****************************************************************************************/ p->paramType = P_TRATIO; p->nValues = 1; /* we store only the ratio */ p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Transition and transversion rates"; SafeStrcat(&p->name, "Tratio"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->tRatioPr,"Beta")) { p->paramId = TRATIO_DIR; } else p->paramId = TRATIO_FIX; if (p->paramId != TRATIO_FIX) p->printParam = YES; if (!strcmp(mp->tratioFormat,"Ratio")) { /* report ti/tv ratio */ SafeStrcat (&p->paramHeader,"kappa"); SafeStrcat (&p->paramHeader, partString); } else { /* report prop. of ratesum (Dirichlet) */ SafeStrcat (&p->paramHeader,"ti"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\ttv"); SafeStrcat (&p->paramHeader, partString); } } else if (j == P_REVMAT) { /* Set up revMat ****************************************************************************************/ p->paramType = P_REVMAT; if (m->dataType == PROTEIN) p->nValues = 190; else p->nValues = 6; p->nSubValues = 0; if (!strcmp(mp->nst, "Mixed")) p->nIntValues = 6; else p->nIntValues = 0; p->min = 0.0; p->max = 1.0; /* adjust later for REVMAT_MIX, see a few lines below */ for (i=0; iparamTypeName = "Rates of reversible rate matrix"; SafeStrcat(&p->name, "Revmat"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->nst, "Mixed")) { p->paramId = REVMAT_MIX; p->max = 1E6; /* some large value */ } else if (!strcmp(mp->revMatPr,"Dirichlet")) p->paramId = REVMAT_DIR; else p->paramId = REVMAT_FIX; if (p->paramId != REVMAT_FIX) p->printParam = YES; if (m->dataType == PROTEIN) { for (n1=0; n1<20; n1++) { for (n2=n1+1; n2<20; n2++) { if (n1==0 && n2==1) { sprintf (temp, "r(%c<->%c)", StateCode_AA(n1), StateCode_AA(n2)); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } else { sprintf (temp, "\tr(%c<->%c)", StateCode_AA(n1), StateCode_AA(n2)); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } } } } else { for (n1=0; n1<4; n1++) { for (n2=n1+1; n2<4; n2++) { if (n1==0 && n2==1) { sprintf (temp, "r(%c<->%c)", StateCode_NUC4(n1), StateCode_NUC4(n2)); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } else { sprintf (temp, "\tr(%c<->%c)", StateCode_NUC4(n1), StateCode_NUC4(n2)); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } } } if (p->paramId == REVMAT_MIX) { sprintf (temp, "\tgtrsubmodel"); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); sprintf (temp, "\tk_revmat"); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } } } else if (j == P_OMEGA) { /* Set up omega *****************************************************************************************/ p->paramType = P_OMEGA; p->min = 0.0; p->max = POS_INFINITY; if (!strcmp(mp->omegaVar, "M3")) { p->nValues = 3; /* omega values */ p->nSubValues = 6; /* category frequencies plus cache */ for (i=0; im3omegapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_EF; else if (!strcmp(mp->m3omegapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_ED; else if (!strcmp(mp->m3omegapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_FF; else if (!strcmp(mp->m3omegapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_FD; if (p->paramId != OMEGA_FF) p->printParam = YES; SafeStrcat (&p->paramHeader, "omega(1)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tomega(2)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tomega(3)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(1)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(2)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(3)"); SafeStrcat (&p->paramHeader, partString); } else if (!strcmp(mp->omegaVar, "M10")) { p->nValues = mp->numM10BetaCats + mp->numM10GammaCats; p->nSubValues = mp->numM10BetaCats + mp->numM10GammaCats + 8; for (i=0; im10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10UUB; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10UUF; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10UEB; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10UEF; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10UFB; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10UFF; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10EUB; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10EUF; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10EEB; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10EEF; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10EFB; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10EFF; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10FUB; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10FUF; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10FEB; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10FEF; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10FFB; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed") ) p->paramId = OMEGA_10FFF; if (p->paramId != OMEGA_10FFF) p->printParam = YES; for (i=0; inValues; i++) { if (i==0) sprintf (temp, "omega(%d)", i+1); else sprintf (temp, "\tomega(%d)", i+1); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } SafeStrcat (&p->paramHeader, "\tbeta(alpha)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tbeta(beta)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tgamma(alpha)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tgamma(beta)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(1)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(2)"); SafeStrcat (&p->paramHeader, partString); } else if (!strcmp(mp->omegaVar, "Ny98")) { p->nValues = 3; /* omega values */ p->nSubValues = 6; /* omega category frequencies plus cache */ for (i=0; iny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_BUD; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_BUF; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_BED; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_BEF; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_BFD; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_BFF; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_FUD; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_FUF; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_FED; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_FEF; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_FFD; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_FFF; if (p->paramId != OMEGA_FFF) p->printParam = YES; SafeStrcat (&p->paramHeader, "omega(-)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tomega(N)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tomega(+)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(-)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(N)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(+)"); SafeStrcat (&p->paramHeader, partString); } else { p->nValues = 1; p->nSubValues = 0; for (i=0; iomegaPr,"Dirichlet")) p->paramId = OMEGA_DIR; else p->paramId = OMEGA_FIX; if (p->paramId != OMEGA_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "omega"); SafeStrcat (&p->paramHeader, partString); } p->paramTypeName = "Positive selection (omega) model"; SafeStrcat(&p->name, "Omega"); SafeStrcat(&p->name, partString); } else if (j == P_PI) { /* Set up state frequencies *****************************************************************************/ p->paramType = P_PI; p->min = 0.0; p->max = 1.0; for (i=0; idataType == STANDARD) { p->paramTypeName = "Symmetric diricihlet/beta distribution alpha_i parameter"; SafeStrcat(&p->name, "Alpha_symdir"); /* boundaries for alpha_i */ p->min = ETA; p->max = POS_INFINITY; } else { p->paramTypeName = "Stationary state frequencies"; SafeStrcat(&p->name, "Pi"); } SafeStrcat(&p->name, partString); /* find the parameter x prior type */ /* and the number of values and subvalues needed */ if (mp->dataType == STANDARD) { /* find number of model states */ m->numModelStates = 2; for (c=0; cnRelParts; i++) { if (partitionId[c][partitionNum] == p->relParts[i] + 1 && charInfo[c].numStates > m->numModelStates) m->numModelStates = charInfo[c].numStates; } } for (i=0; inRelParts; i++) modelSettings[p->relParts[i]].numModelStates = m->numModelStates; /* symmetric hyperprior with only one variable (0 if equal) */ p->nValues = 1; /* set to 0 below for the SYMPI_EQUAL model */ if (!strcmp(mp->symPiPr,"Uniform")) { if (m->numModelStates > 2) p->paramId = SYMPI_UNI_MS; else p->paramId = SYMPI_UNI; } else if (!strcmp(mp->symPiPr,"Exponential")) { if (m->numModelStates > 2) p->paramId = SYMPI_EXP_MS; else p->paramId = SYMPI_EXP; } else if (!strcmp(mp->symPiPr,"Fixed")) { if (AreDoublesEqual(mp->symBetaFix, -1.0, 0.00001) == YES) { p->paramId = SYMPI_EQUAL; p->nValues = 0; } else { if (m->numModelStates > 2) p->paramId = SYMPI_FIX_MS; else p->paramId = SYMPI_FIX; } } p->nSubValues = 0; /* store state frequencies in p->stdStateFreqs */ if (p->paramId == SYMPI_EXP || p->paramId == SYMPI_EXP_MS || p->paramId == SYMPI_UNI || p->paramId == SYMPI_UNI_MS) p->printParam = YES; SafeStrcat (&p->paramHeader, "alpha_symdir"); SafeStrcat (&p->paramHeader, partString); /* further processing done in ProcessStdChars */ } else { /* deal with all models except standard */ /* no hyperprior or fixed to one value, set default to 0 */ p->nValues = 0; /* one subvalue for each state */ p->nSubValues = mp->nStates; /* mp->nStates is set to 20 if DNA || RNA && nucmodel==PROTEIN */ if (!strcmp(mp->stateFreqPr, "Dirichlet")) { p->paramId = PI_DIR; p->nValues = mp->nStates; } else if (!strcmp(mp->stateFreqPr, "Fixed") && !strcmp(mp->stateFreqsFixType,"User")) p->paramId = PI_USER; else if (!strcmp(mp->stateFreqPr, "Fixed") && !strcmp(mp->stateFreqsFixType,"Empirical")) p->paramId = PI_EMPIRICAL; else if (!strcmp(mp->stateFreqPr, "Fixed") && !strcmp(mp->stateFreqsFixType,"Equal")) { p->paramId = PI_EQUAL; } if (m->dataType == PROTEIN) { if (!strcmp(mp->aaModelPr, "Fixed")) { if (!strcmp(mp->aaModel, "Poisson")) p->paramId = PI_EQUAL; else if (!strcmp(mp->aaModel, "Equalin") || !strcmp(mp->aaModel, "Gtr")) { /* p->paramId stays to what it was set to above */ } else p->paramId = PI_FIXED; } else p->paramId = PI_FIXED; } if (p->paramId == PI_DIR) p->printParam = YES; if (m->dataType == DNA || m->dataType == RNA) { if (!strcmp(mp->nucModel, "4by4")) { sprintf(temp, "pi(%c)", StateCode_NUC4(0)); SafeStrcat (&p->paramHeader,temp); SafeStrcat (&p->paramHeader,partString); for (n1=1; n1<4; n1++) { sprintf(temp, "\tpi(%c)", StateCode_NUC4(n1)); SafeStrcat (&p->paramHeader,temp); SafeStrcat (&p->paramHeader,partString); } } else if (!strcmp(mp->nucModel, "Doublet")) { State_DOUBLET(tempCodon,0); sprintf(temp, "pi(%s)", tempCodon); SafeStrcat (&p->paramHeader,temp); SafeStrcat (&p->paramHeader,partString); for (n1=1; n1<16; n1++) { State_DOUBLET(tempCodon,n1); sprintf(temp, "\tpi(%s)", tempCodon); SafeStrcat (&p->paramHeader,temp); SafeStrcat (&p->paramHeader,partString); } } else if (!strcmp(mp->nucModel, "Codon")) { for (c=0; cnSubValues; c++) { if (mp->codonNucs[c][0] == 0) strcpy (tempCodon, "pi(A"); else if (mp->codonNucs[c][0] == 1) strcpy (tempCodon, "pi(C"); else if (mp->codonNucs[c][0] == 2) strcpy (tempCodon, "pi(G"); else strcpy (tempCodon, "pi(T"); if (mp->codonNucs[c][1] == 0) strcat (tempCodon, "A"); else if (mp->codonNucs[c][1] == 1) strcat (tempCodon, "C"); else if (mp->codonNucs[c][1] == 2) strcat (tempCodon, "G"); else strcat (tempCodon, "T"); if (mp->codonNucs[c][2] == 0) strcat (tempCodon, "A)"); else if (mp->codonNucs[c][2] == 1) strcat (tempCodon, "C)"); else if (mp->codonNucs[c][2] == 2) strcat (tempCodon, "G)"); else strcat (tempCodon, "T)"); if (c == 0) { SafeStrcat (&p->paramHeader, tempCodon); SafeStrcat (&p->paramHeader, partString); } else { SafeStrcat (&p->paramHeader, "\t"); SafeStrcat (&p->paramHeader, tempCodon); SafeStrcat (&p->paramHeader, partString); } } } } else if (m->dataType == PROTEIN) { if (FillRelPartsString (p, &partString) == YES) { SafeSprintf(&tempStr, &tempStrSize, "pi(Ala)%s\tpi(Arg)%s\tpi(Asn)%s\tpi(Asp)%s\tpi(Cys)%s\tpi(Gln)%s\tpi(Glu)%s\tpi(Gly)%s\tpi(His)%s\tpi(Ile)%s\tpi(Leu)%s\tpi(Lys)%s\tpi(Met)%s\tpi(Phe)%s\tpi(Pro)%s\tpi(Ser)%s\tpi(Thr)%s\tpi(Trp)%s\tpi(Tyr)%s\tpi(Val)%s", partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString); SafeStrcat (&p->paramHeader, tempStr); } else SafeStrcat (&p->paramHeader, "pi(Ala)\tpi(Arg)\tpi(Asn)\tpi(Asp)\tpi(Cys)\tpi(Gln)\tpi(Glu)\tpi(Gly)\tpi(His)\tpi(Ile)\tpi(Leu)\tpi(Lys)\tpi(Met)\tpi(Phe)\tpi(Pro)\tpi(Ser)\tpi(Thr)\tpi(Trp)\tpi(Tyr)\tpi(Val)"); } else if (mp->dataType == RESTRICTION) { if (FillRelPartsString (p, &partString) == YES) { SafeSprintf(&tempStr, &tempStrSize, "pi(0)%s\tpi(1)%s", partString, partString); SafeStrcat (&p->paramHeader, tempStr); } else SafeStrcat (&p->paramHeader, "pi(0)\tpi(1)"); } else { MrBayesPrint ("%s Unknown data type in SetModelParams\n", spacer); } } } else if (j == P_SHAPE) { /* Set up shape parameter of gamma **********************************************************************/ p->paramType = P_SHAPE; p->nValues = 1; p->nSubValues = mp->numGammaCats; p->min = 1E-6; p->max = 200; for (i=0; iparamTypeName = "Shape of scaled gamma distribution of site rates"; SafeStrcat(&p->name, "Alpha"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ mp = &modelParams[p->relParts[0]]; if (!strcmp(mp->shapePr,"Uniform")) p->paramId = SHAPE_UNI; else if (!strcmp(mp->shapePr,"Exponential")) p->paramId = SHAPE_EXP; else p->paramId = SHAPE_FIX; if (p->paramId != SHAPE_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "alpha"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_PINVAR) { /* Set up proportion of invariable sites ****************************************************************/ p->paramType = P_PINVAR; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = 1.0; for (i=0; iparamTypeName = "Proportion of invariable sites"; SafeStrcat(&p->name, "Pinvar"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->pInvarPr,"Uniform")) p->paramId = PINVAR_UNI; else p->paramId = PINVAR_FIX; if (p->paramId != PINVAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "pinvar"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CORREL) { /* Set up correlation parameter of adgamma model ********************************************************/ p->paramType = P_CORREL; p->nValues = 1; p->nSubValues = mp->numGammaCats * mp->numGammaCats; p->min = -1.0; p->max = 1.0; for (i=0; iparamTypeName = "Autocorrelation of gamma distribution of site rates"; SafeStrcat(&p->name, "Rho"); SafeStrcat(&p->name, partString); chainHasAdgamma = YES; /* find the parameter x prior type */ if (!strcmp(mp->adGammaCorPr,"Uniform")) p->paramId = CORREL_UNI; else p->paramId = CORREL_FIX; if (p->paramId != CORREL_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "rho"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_SWITCH) { /* Set up switchRates for covarion model ****************************************************************/ p->paramType = P_SWITCH; p->nValues = 2; p->nSubValues = mp->numGammaCats * mp->numGammaCats; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Switch rates of covarion model"; SafeStrcat(&p->name, "s_cov"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->covSwitchPr,"Uniform")) p->paramId = SWITCH_UNI; else if (!strcmp(mp->covSwitchPr,"Exponential")) p->paramId = SWITCH_EXP; else p->paramId = SWITCH_FIX; if (p->paramId != SWITCH_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "s(off->on)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\ts(on->off)"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_RATEMULT) { /* Set up rateMult for partition specific rates ***********************************************************/ p->paramType = P_RATEMULT; if (!strcmp(mp->ratePr,"Fixed")) { p->nValues = 1; p->nSubValues = 0; } else { p->nValues = p->nRelParts = numRelParts; /* keep scaled division rates in value */ p->nSubValues = p->nValues * 2; /* keep number of uncompressed chars for scaling in subValue */ /* also keep Dirichlet prior parameters here */ } p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Partition-specific rate multiplier"; SafeStrcat(&p->name, "Ratemultiplier"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (p->nSubValues == 0) p->paramId = RATEMULT_FIX; else p->paramId = RATEMULT_DIR; if (p->paramId != RATEMULT_FIX) p->printParam = YES; for (i=0; iparamHeader, tempMult); else { SafeStrcat (&p->paramHeader, "\t"); SafeStrcat (&p->paramHeader, tempMult); } } } } else if (j == P_GENETREERATE) { /* Set up rateMult for partition specific rates ***********************************************************/ p->paramType = P_GENETREERATE; p->nValues = p->nRelParts = numRelParts; /* keep scaled division rates in value */ p->nSubValues = p->nValues * 2; /* keep number of uncompressed chars for scaling in subValue */ /* also keep Dirichlet prior parameters here */ p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Gene-specific rate multiplier"; SafeStrcat(&p->name, "Generatemult"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = GENETREERATEMULT_DIR; p->printParam = YES; for (i=0; iparamHeader, tempMult); else { SafeStrcat (&p->paramHeader, "\t"); SafeStrcat (&p->paramHeader, tempMult); } } } } else if (j == P_TOPOLOGY) { /* Set up topology **************************************************************************************/ p->paramType = P_TOPOLOGY; p->nValues = 0; p->nSubValues = 0; p->min = NEG_INFINITY; /* NA */ p->max = NEG_INFINITY; /* NA */ for (i=0; iparamTypeName = "Topology"; SafeStrcat(&p->name, "Tau"); SafeStrcat(&p->name, partString); /* check that the model is not parsimony for all of the relevant partitions */ areAllPartsParsimony = YES; for (i=0; inRelParts; i++) { if (modelSettings[p->relParts[i]].parsModelId == NO) areAllPartsParsimony = NO; } if (areAllPartsParsimony == YES) p->paramTypeName = "Topology"; /*Why again we set it to "Topology" we did it unconditionally 1 lines before*/ /* check if topology is clock or nonclock */ nClockBrlens = 0; nCalibratedBrlens = 0; nRelaxedBrlens = 0; if (areAllPartsParsimony == NO) { for (i=0; inRelParts; i++) { if (!strcmp(modelParams[p->relParts[i]].brlensPr, "Clock")) { nClockBrlens++; if (strcmp(modelParams[p->relParts[i]].clockVarPr,"Strict") != 0) /* not strict */ nRelaxedBrlens++; if (!strcmp(modelParams[p->relParts[i]].nodeAgePr,"Calibrated")) nCalibratedBrlens++; } } } /* now find the parameter x prior type */ if (areAllPartsParsimony == YES) { if (!strcmp(mp->topologyPr, "Uniform")) p->paramId = TOPOLOGY_PARSIMONY_UNIFORM; else if (!strcmp(mp->topologyPr,"Constraints")) p->paramId = TOPOLOGY_PARSIMONY_CONSTRAINED; else p->paramId = TOPOLOGY_PARSIMONY_FIXED; /* For this case, we also need to set the brlens ptr of the relevant partitions so that it points to the topology parameter, since the rest of the program will try to access the tree through this pointer. In FillTreeParams, we will make sure that a pure parsimony topology parameter contains a pointer to the relevant tree (like a brlens parameter would normally) */ for (i=0; inRelParts; i++) modelSettings[p->relParts[i]].brlens = p; } else { /* we assume for now that there is only one branch length; we will correct this later in AllocateTreeParams if there are more than one set of branch lengths, which is only possible for non-clock trees */ if (!strcmp(mp->topologyPr, "Speciestree")) p->paramId = TOPOLOGY_SPECIESTREE; else if (!strcmp(mp->topologyPr, "Uniform") && nClockBrlens == 0) p->paramId = TOPOLOGY_NCL_UNIFORM_HOMO; else if (!strcmp(mp->topologyPr,"Constraints") && nClockBrlens == 0) p->paramId = TOPOLOGY_NCL_CONSTRAINED_HOMO; else if (!strcmp(mp->topologyPr,"Fixed") && nClockBrlens == 0) p->paramId = TOPOLOGY_NCL_FIXED_HOMO; /* all below have clock branch lengths */ else if (!strcmp(mp->topologyPr, "Uniform") && nRelaxedBrlens == 0) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_CCL_UNIFORM; else p->paramId = TOPOLOGY_CL_UNIFORM; } else if (!strcmp(mp->topologyPr,"Constraints") && nRelaxedBrlens == 0) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_CCL_CONSTRAINED; else p->paramId = TOPOLOGY_CL_CONSTRAINED; } else if (!strcmp(mp->topologyPr,"Fixed") && nRelaxedBrlens == 0) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_CCL_FIXED; else p->paramId = TOPOLOGY_CL_FIXED; } /* all below have relaxed clock branch lengths */ else if (!strcmp(mp->topologyPr, "Uniform")) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_RCCL_UNIFORM; else p->paramId = TOPOLOGY_RCL_UNIFORM; } else if (!strcmp(mp->topologyPr,"Constraints")) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_RCCL_CONSTRAINED; else p->paramId = TOPOLOGY_RCL_CONSTRAINED; } else if (!strcmp(mp->topologyPr,"Fixed")) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_RCCL_FIXED; else p->paramId = TOPOLOGY_RCL_FIXED; } } /* should we print the topology? */ for (i=0; inRelParts; i++) if (strcmp(modelParams[p->relParts[i]].treeFormat,"Topology") != 0) break; if (i == p->nRelParts) p->printParam = YES; else p->printParam = NO; /* but always print parsimony topology */ if (areAllPartsParsimony == YES) p->printParam = YES; /* and never print fixed topologies */ if (p->paramId == TOPOLOGY_RCL_FIXED || p->paramId == TOPOLOGY_RCCL_FIXED || p->paramId == TOPOLOGY_CL_FIXED || p->paramId == TOPOLOGY_CCL_FIXED || p->paramId == TOPOLOGY_NCL_FIXED || p->paramId == TOPOLOGY_PARSIMONY_FIXED) p->printParam = NO; } else if (j == P_BRLENS) { /* Set up branch lengths ********************************************************************************/ p->paramType = P_BRLENS; p->nValues = 0; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Branch lengths"; SafeStrcat(&p->name, "V"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (modelSettings[p->relParts[0]].parsModelId == YES) p->paramId = BRLENS_PARSIMONY; else { if (!strcmp(mp->brlensPr, "Clock")) { if (!strcmp(mp->clockPr,"Uniform")) p->paramId = BRLENS_CLOCK_UNI; else if (!strcmp(mp->clockPr,"Coalescence")) p->paramId = BRLENS_CLOCK_COAL; else if (!strcmp(mp->clockPr,"Birthdeath")) p->paramId = BRLENS_CLOCK_BD; else if (!strcmp(mp->clockPr,"Speciestreecoalescence")) p->paramId = BRLENS_CLOCK_SPCOAL; else if (!strcmp(mp->clockPr,"Fixed")) p->paramId = BRLENS_CLOCK_FIXED; } else if (!strcmp(mp->brlensPr, "Unconstrained")) { if (!strcmp(mp->unconstrainedPr,"Uniform")) p->paramId = BRLENS_UNI; else if (!strcmp(mp->unconstrainedPr,"Exponential")) p->paramId = BRLENS_EXP; } else if (!strcmp(mp->brlensPr,"Fixed")) { p->paramId = BRLENS_FIXED; } } /* should we print the branch lengths? */ for (i=0; inRelParts; i++) if (strcmp(modelParams[p->relParts[i]].treeFormat,"Topology") != 0) break; if (i < p->nRelParts) p->printParam = YES; else p->printParam = NO; } else if (j == P_SPECIESTREE) { /* Set up species tree **************************************************************************************/ p->paramType = P_SPECIESTREE; p->nValues = 0; p->nSubValues = 0; p->min = NEG_INFINITY; /* NA */ p->max = NEG_INFINITY; /* NA */ for (i=0; iparamTypeName = "Species tree"; SafeStrcat(&p->name, "Spt"); SafeStrcat(&p->name, partString); /* check that the model is not parsimony for all of the relevant partitions */ areAllPartsParsimony = YES; for (i=0; inRelParts; i++) { if (modelSettings[p->relParts[i]].parsModelId == NO) areAllPartsParsimony = NO; } /* find the parameter x prior type */ p->paramId = SPECIESTREE_UNIFORM; /* should we print the tree? */ p->printParam = YES; } else if (j == P_SPECRATE) { /* Set up speciation rate ******************************************************************************/ p->paramType = P_SPECRATE; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Speciation rate"; SafeStrcat(&p->name, "Lambda-mu"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->speciationPr,"Uniform")) p->paramId = SPECRATE_UNI; else if (!strcmp(mp->speciationPr,"Exponential")) p->paramId = SPECRATE_EXP; else p->paramId = SPECRATE_FIX; if (p->paramId != SPECRATE_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "lambda-mu"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_EXTRATE) { /* Set up extinction rates ******************************************************************************/ p->paramType = P_EXTRATE; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Extinction rate"; SafeStrcat(&p->name, "Mu/lambda"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->extinctionPr,"Beta")) p->paramId = EXTRATE_BETA; else p->paramId = EXTRATE_FIX; if (p->paramId != EXTRATE_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "mu/lambda"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_POPSIZE) { /* Set up population size *****************************************************************************************/ p->paramType = P_POPSIZE; if (!strcmp(mp->topologyPr,"Speciestree") && !strcmp(mp->popVarPr, "Variable")) p->nValues = 2 * numSpecies - 1; else p->nValues = 1; p->nSubValues = 0; p->min = POS_MIN; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Coalescent process population size parameter"; SafeStrcat(&p->name, "Popsize"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->popSizePr,"Uniform")) { p->paramId = POPSIZE_UNI; p->LnPriorRatio = &LnProbRatioUniform; p->priorParams = mp->popSizeUni; p->LnPriorProb = &LnPriorProbUniform; } else if (!strcmp(mp->popSizePr,"Normal")) { p->paramId = POPSIZE_NORMAL; p->LnPriorRatio = &LnProbRatioTruncatedNormal; p->priorParams = mp->popSizeNormal; p->LnPriorProb = &LnPriorProbTruncatedNormal; } else if (!strcmp(mp->popSizePr,"Lognormal")) { p->paramId = POPSIZE_LOGNORMAL; p->LnPriorRatio = &LnProbRatioLognormal; p->priorParams = mp->popSizeLognormal; p->LnPriorProb = &LnPriorProbLognormal; } else if (!strcmp(mp->popSizePr,"Gamma")) { p->paramId = POPSIZE_GAMMA; p->LnPriorRatio = &LnProbRatioGamma; p->priorParams = mp->popSizeGamma; p->LnPriorProb = &LnPriorProbGamma; } else { p->paramId = POPSIZE_FIX; p->LnPriorRatio = NULL; p->priorParams = &mp->popSizeFix; p->LnPriorProb = &LnPriorProbFix; } if (p->paramId != POPSIZE_FIX && p->nValues == 1) p->printParam = YES; SafeStrcat (&p->paramHeader, "Popsize"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_GROWTH) { /* Set up growth rate ************************************************************************************/ p->paramType = P_GROWTH; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Population growth rate"; SafeStrcat(&p->name, "R_pop"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->growthPr,"Uniform")) p->paramId = GROWTH_UNI; else if (!strcmp(mp->growthPr,"Exponential")) p->paramId = GROWTH_EXP; else if (!strcmp(mp->growthPr,"Normal")) p->paramId = GROWTH_NORMAL; else p->paramId = GROWTH_FIX; if (p->paramId != GROWTH_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "growthRate"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_AAMODEL) { /* Set up aamodel *****************************************************************************************/ p->paramType = P_AAMODEL; p->nValues = 1; p->nSubValues = 10; p->min = 0; p->max = 9; for (i=0; iparamTypeName = "Aminoacid model"; SafeStrcat(&p->name, "Aamodel"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->aaModelPr,"Mixed")) p->paramId = AAMODEL_MIX; else p->paramId = AAMODEL_FIX; if (p->paramId != AAMODEL_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "aamodel"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CPPRATE) { /* Set up cpprate *****************************************************************************************/ p->paramType = P_CPPRATE; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Rate of rate-multiplying compound Poisson process"; SafeStrcat(&p->name, "Lambda_cpp"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->cppRatePr,"Exponential")) p->paramId = CPPRATE_EXP; else p->paramId = CPPRATE_FIX; if (p->paramId != CPPRATE_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "cppRate"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CPPMULTDEV) { /* Set up sigma of cpp rate multipliers *****************************************************************************************/ p->paramType = P_CPPMULTDEV; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Standard deviation (log) of CPP rate multipliers"; SafeStrcat(&p->name, "Sigma_cpp"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->cppMultDevPr,"Fixed")) p->paramId = CPPMULTDEV_FIX; if (p->paramId != CPPMULTDEV_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "sigma_cpp"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CPPEVENTS) { /* Set up cpp events parameter *****************************************************************************************/ p->paramType = P_CPPEVENTS; p->nValues = 0; p->nSubValues = 2*numLocalTaxa; /* keep effective branch lengths here (for all nodes to be on the safe side) */ p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Events of rate-multiplying compound Poisson process"; SafeStrcat(&p->name, "Cpp"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = CPPEVENTS; /* should we print values to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "cppEvents"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_TK02VAR) { /* Set up tk02 relaxed clock variance parameter *****************************************************************************************/ p->paramType = P_TK02VAR; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Variance of lognormal distribution of branch rates"; SafeStrcat(&p->name, "TK02var"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->tk02varPr,"Uniform")) p->paramId = TK02VAR_UNI; else if (!strcmp(mp->tk02varPr,"Exponential")) p->paramId = TK02VAR_EXP; else p->paramId = TK02VAR_FIX; if (p->paramId != TK02VAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "tk02_var"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_TK02BRANCHRATES) { /* Set up tk02 relaxed clock rates parameter *****************************************************************************************/ p->paramType = P_TK02BRANCHRATES; p->nValues = 2*numLocalTaxa; /* use to hold the branch rates; we need one rate for the root */ p->nSubValues = 2*numLocalTaxa; /* use to hold the effective branch lengths */ p->min = RATE_MIN; p->max = RATE_MAX; for (i=0; iparamTypeName = "Branch rates of tk02 relaxed clock"; SafeStrcat(&p->name, "TK02branchrates"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = TK02BRANCHRATES; /* should we print values to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "tk02_branchrates"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_IGRVAR) { /* Set up igr relaxed clock scaled gamma shape parameter *****************************************************************************************/ p->paramType = P_IGRVAR; p->nValues = 1; p->nSubValues = 0; p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Variance increase of igr model branch lenths"; SafeStrcat(&p->name, "Igrvar"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->igrvarPr,"Uniform")) p->paramId = IGRVAR_UNI; else if (!strcmp(mp->igrvarPr,"Exponential")) p->paramId = IGRVAR_EXP; else p->paramId = IGRVAR_FIX; if (p->paramId != IGRVAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "igrvar"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_IGRBRANCHLENS) { /* Set up igr relaxed clock rates parameter *****************************************************************************************/ p->paramType = P_IGRBRANCHLENS; p->nValues = 2*numLocalTaxa; /* use to hold the branch rates; we need one rate for the root */ p->nSubValues = 2*numLocalTaxa; /* use to hold the effective branch lengths */ p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Branch lengths of relaxed clock"; SafeStrcat(&p->name, "Igrbranchlens"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = IGRBRANCHLENS; /* should we print values to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "ibr_branchlens"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CLOCKRATE) { /* Set up clockRate ****************************************************************************************/ p->paramType = P_CLOCKRATE; p->nValues = 1; p->nSubValues = 0; p->min = POS_MIN; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Base rate of clock"; SafeStrcat(&p->name, "Clockrate"); SafeStrcat(&p->name, partString); /* parameter does affect likelihoods */ p->affectsLikelihood = YES; /* find the parameter x prior type */ if (!strcmp(mp->clockRatePr,"Normal")) { p->paramId = CLOCKRATE_NORMAL; p->LnPriorRatio = &LnProbRatioNormal; p->priorParams = mp->clockRateNormal; p->LnPriorProb = &LnPriorProbNormal; } else if (!strcmp(mp->clockRatePr,"Lognormal")) { p->paramId = CLOCKRATE_LOGNORMAL; p->LnPriorRatio = &LnProbRatioLognormal; p->priorParams = mp->clockRateLognormal; p->LnPriorProb = &LnPriorProbLognormal; } else if (!strcmp(mp->clockRatePr,"Exponential")) { p->paramId = CLOCKRATE_EXP; p->LnPriorRatio = &LnProbRatioExponential; p->priorParams = &mp->clockRateExp; p->LnPriorProb = &LnPriorProbExponential; } else if (!strcmp(mp->clockRatePr,"Gamma")) { p->paramId = CLOCKRATE_GAMMA; p->LnPriorRatio = &LnProbRatioGamma; p->priorParams = mp->clockRateGamma; p->LnPriorProb = &LnPriorProbGamma; } else { p->paramId = CLOCKRATE_FIX; p->LnPriorRatio = NULL; p->priorParams = &mp->clockRateFix; p->LnPriorProb = &LnPriorProbFix; } SafeStrcat (&p->paramHeader, "Clockrate"); SafeStrcat (&p->paramHeader, partString); if (p->paramId != CLOCKRATE_FIX) p->printParam = YES; } } free (tempStr); free (isPartTouched); SafeFree ((void **)&partString); return (NO_ERROR); } /*---------------------------------------------------------------------------- | | SetMoves: This function will set up the applicable moves that could | potentially be used in updating the model parameters | -----------------------------------------------------------------------------*/ int SetMoves (void) { int i, j, k, moveIndex; Param *param; /* free up previous moves if any */ if (memAllocs[ALLOC_MOVES] == YES) { for (i=0; i userLevel) continue; if (moveTypes[i].isApplicable(param) == NO) continue; for (j=0; jparamId) { numApplicableMoves++; break; } } } /* then allocate space for move pointers */ moves = (MCMCMove **) SafeMalloc (numApplicableMoves * sizeof (MCMCMove *)); if (!moves) { MrBayesPrint ("%s Problem allocating moves\n", spacer); return (ERROR); } memAllocs[ALLOC_MOVES] = YES; /* finally allocate space for and set move defaults */ moveIndex = 0; for (k=0; k userLevel) continue; if (moveTypes[i].isApplicable(param) == NO) continue; for (j=0; jparamId) { if ((moves[moveIndex] = AllocateMove (&moveTypes[i], param)) == NULL) break; else { moves[moveIndex]->parm = param; moveIndex++; break; } } } } } if (moveIndex < numApplicableMoves) { for (i=0; inNodes - pt->nIntNodes - 1) / nBitsInALong; /* Get pointer to values to be set */ values = GetParamVals (param, chn, state); /* Get species tree */ speciesTree = GetTree (modelSettings[param->relParts[0]].speciesTree, chn, state); /* Set them based on index of matching partitions */ AllocatePolyTreePartitions(pt); AllocateTreePartitions(speciesTree); for (i=0; inNodes; i++) { pp = pt->allDownPass[i]; for (j=0; jnNodes-1; j++) { p = speciesTree->allDownPass[j]; for (k=0; kpartition[k] != p->partition[k]) break; } if (k == nLongsNeeded) break; } if (j == speciesTree->nNodes - 1) { MrBayesPrint ("%s Non-matching partitions when setting population size parameter", spacer); FreePolyTreePartitions(pt); FreeTreePartitions(speciesTree); return (ERROR); } values[p->index] = pt->popSize[pp->index]; } FreePolyTreePartitions(pt); FreeTreePartitions(speciesTree); return (NO_ERROR); } /* SetRelaxedClockParam: set values for a relaxed clock param from an input tree */ int SetRelaxedClockParam (Param *param, int chn, int state, PolyTree *pt) { int i, j, k, *nEvents=NULL, *nEventsP=NULL, nLongsNeeded, isEventSet; MrBFlt *effectiveBranchLengthP=NULL, *tk02BranchRate=NULL, *igrBranchLen =NULL, **position=NULL, **rateMult=NULL, **positionP=NULL, **rateMultP=NULL, baseRate; Tree *t; PolyNode *pp; TreeNode *p=NULL, *q; nLongsNeeded = 1 + (numLocalTaxa - 1) / nBitsInALong; /* set pointers to the right set of values */ isEventSet = NO; if (param->paramType == P_CPPEVENTS) { /* find the right event set */ for (i=0; inESets; i++) { if (!strcmp(param->name,pt->eSetName[i])) break; } if (i == pt->nESets) { for (i=0; inBSets; i++) if (!strcmp(param->name,pt->bSetName[i])) break; if (i == pt->nBSets) return (NO_ERROR); else isEventSet = NO; } else isEventSet = YES; if (isEventSet == YES) { nEventsP = pt->nEvents[i]; positionP = pt->position[i]; rateMultP = pt->rateMult[i]; } else effectiveBranchLengthP = pt->effectiveBrLen[i]; nEvents = param->nEvents[2*chn+state]; position = param->position[2*chn+state]; rateMult = param->rateMult[2*chn+state]; } else if (param->paramType == P_TK02BRANCHRATES) { /* find the right effective branch length set */ for (i=0; inBSets; i++) if (!strcmp(param->name,pt->bSetName[i])) break; if (i == pt->nBSets) return (NO_ERROR); effectiveBranchLengthP = pt->effectiveBrLen[i]; tk02BranchRate = GetParamVals (param, chn, state); } else if (param->paramType == P_IGRBRANCHLENS) { /* find the right effective branch length set */ for (i=0; inBSets; i++) if (!strcmp(param->name,pt->bSetName[i])) break; if (i == pt->nBSets) return (NO_ERROR); effectiveBranchLengthP = pt->effectiveBrLen[i]; igrBranchLen = GetParamSubVals (param, chn, state); } t = GetTree (param, chn, state); AllocatePolyTreePartitions (pt); AllocateTreePartitions (t); for (i=pt->nNodes-1; i>=0; i--) { pp = pt->allDownPass[i]; for (j=0; jnNodes; j++) { p = t->allDownPass[j]; for (k=0; kpartition[k] != pp->partition[k]) break; if (k == nLongsNeeded) break; /* match */ } if (param->paramType == P_CPPEVENTS) { if (isEventSet == NO) { if (nEvents[p->index] != 1) { position[p->index] = (MrBFlt *) SafeRealloc ((void *) position[p->index], 1*sizeof(MrBFlt)); rateMult[p->index] = (MrBFlt *) SafeRealloc ((void *) rateMult[p->index], 1*sizeof(MrBFlt)); nEvents [p->index] = 1; } position[p->index][0] = 0.5; if (p->anc->anc == NULL) rateMult[p->index][0] = 1.0; else { baseRate = 1.0; q = p->anc; while (q->anc != NULL) { baseRate *= rateMult[q->index][0]; q = q->anc; } rateMult[p->index][0] = 2.0 * effectiveBranchLengthP[pp->index] / (p->length * baseRate) - 1.0; } } else { if (nEvents[p->index] != nEventsP[pp->index]) { if (nEventsP[pp->index] == 0) { free (position[p->index]); free (rateMult[p->index]); } else { position[p->index] = (MrBFlt *) SafeRealloc ((void *) position[p->index], nEventsP[pp->index]*sizeof(MrBFlt)); rateMult[p->index] = (MrBFlt *) SafeRealloc ((void *) rateMult[p->index], nEventsP[pp->index]*sizeof(MrBFlt)); } nEvents[p->index] = nEventsP[pp->index]; } for (j=0; jindex]; j++) { position[p->index][j] = positionP[pp->index][j]; rateMult[p->index][j] = rateMultP[pp->index][j]; } } } else if (param->paramType == P_TK02BRANCHRATES) { if (p->anc->anc == NULL) tk02BranchRate[p->index] = 1.0; else tk02BranchRate[p->index] = 2.0 * effectiveBranchLengthP[pp->index] / p->length - tk02BranchRate[p->anc->index]; } else if (param->paramType == P_IGRBRANCHLENS) { igrBranchLen[p->index] = effectiveBranchLengthP[pp->index]; } } if (param->paramType == P_CPPEVENTS) { if (UpdateCppEvolLengths (param, t->root->left, chn) == ERROR) return (ERROR); } else if (param->paramType == P_TK02BRANCHRATES) { if (UpdateTK02EvolLengths (param, t, chn) == ERROR) return (ERROR); } else if (param->paramType == P_IGRBRANCHLENS) { if (UpdateIgrBranchRates (param, t, chn) == ERROR) return (ERROR); } FreePolyTreePartitions (pt); FreeTreePartitions (t); return (NO_ERROR); } /*------------------------------------------------------------------------ | | SetUpAnalysis: Set parameters and moves | ------------------------------------------------------------------------*/ int SetUpAnalysis (SafeLong *seed) { setUpAnalysisSuccess=NO; /* calculate number of characters and taxa */ numLocalChar = NumNonExcludedChar (); /* we are checking later to make sure no partition is without characters */ SetLocalTaxa (); if (numLocalTaxa <= 2) { MrBayesPrint ("%s There must be at least two included taxa, now there is %s\n", spacer, numLocalTaxa == 0 ? "none" : "only one"); return (ERROR); } /* calculate number of global chains */ numGlobalChains = chainParams.numRuns * chainParams.numChains; /* Set up link table */ if (SetUpLinkTable () == ERROR) return (ERROR); /* Check that the settings for doublet or codon models are correct. */ if (CheckExpandedModels() == ERROR) return (ERROR); /* Set up model info */ if (SetModelInfo() == ERROR) return (ERROR); /* Calculate number of (uncompressed) characters for each division */ if (GetNumDivisionChars() == ERROR) return (ERROR); /* Compress data and calculate some things needed for setting up params. */ if (CompressData() == ERROR) return (ERROR); /* Add dummy characters, if needed. */ if (AddDummyChars() == ERROR) return (ERROR); /* Set up parameters for the chain. */ if (SetModelParams () == ERROR) return (ERROR); /* Allocate normal params */ if (AllocateNormalParams () == ERROR) return (ERROR); /* Allocate tree params */ if (AllocateTreeParams () == ERROR) return (ERROR); /* Set default number of trees for sumt to appropriate number */ sumtParams.numTrees = numTrees; /* Fill in normal parameters */ if (FillNormalParams (seed, 0, numGlobalChains) == ERROR) return (ERROR); /* Process standard characters (calculates bsIndex, tiIndex, and more). */ if (ProcessStdChars(seed) == ERROR) return (ERROR); /* Fill in trees */ if (FillTreeParams (seed, 0, numGlobalChains) == ERROR) return (ERROR); /* Set the applicable moves that could be used by the chain. */ if (SetMoves () == ERROR) return (ERROR); setUpAnalysisSuccess=YES; return (NO_ERROR); } int SetUpLinkTable (void) { int i, j, k, m, paramCount, isApplicable1, isApplicable2, isFirst, isSame; int *check, *modelId; check = (int *) SafeMalloc ((size_t) (2*numCurrentDivisions * sizeof (int))); if (!check) { MrBayesPrint ("%s Problem allocating check (%d)\n", spacer, 2*numCurrentDivisions * sizeof(int)); return (ERROR); } modelId = check + numCurrentDivisions; for (j=0; j 1) { paramCount = 0; for (j=0; j 0) { m = activeParams[j][i]; paramCount++; for (k=i; klevel = DEVELOPER; mt->numTuningParams = 0; mt->minimum[0] = mt->minimum[1] = -1000000000.0; mt->maximum[0] = mt->maximum[1] = 1000000000.0; mt->tuningParam[0] = mt->tuningParam[1] = 0.0; mt->nApplicable = 0; mt->name = mt->tuningName[0] = mt->tuningName[1] = ""; mt->paramName = ""; mt->subParams = NO; mt->relProposalProb = 0.0; mt->parsimonyBased = NO; mt->isApplicable = &IsApplicable; mt->Autotune = NULL; mt->targetRate = -1.0; } /* Moves are in alphabetic order after parameter name, which matches the name of a move function if there is a separate move function for the parameter. See mcmc.h for declaration of move functions. Since 2010-10-04, some parameters use generalized move functions and do not have their own. */ i = 0; /* Move_Aamodel */ mt = &moveTypes[i++]; mt->name = "Uniform random pick"; mt->shortName = "Uniform"; mt->applicableTo[0] = AAMODEL_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Aamodel; mt->relProposalProb = 1.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_Adgamma */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = CORREL_UNI; mt->nApplicable = 1; mt->moveFxn = &Move_Adgamma; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* window size */ mt->minimum[0] = 0.001; mt->maximum[0] = 1.999; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Beta */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = SYMPI_UNI; mt->applicableTo[1] = SYMPI_EXP; mt->applicableTo[2] = SYMPI_UNI_MS; mt->applicableTo[3] = SYMPI_EXP_MS; mt->nApplicable = 4; mt->moveFxn = &Move_Beta; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* so-called lambda */ mt->minimum[0] = 0.001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_BrLen */ mt = &moveTypes[i++]; mt->name = "Random brlen hit with multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BRLENS_UNI; mt->applicableTo[1] = BRLENS_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_BrLen; mt->relProposalProb = 20.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (2.0); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_ClockRateM */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = CLOCKRATE_NORMAL; mt->applicableTo[1] = CLOCKRATE_LOGNORMAL; mt->applicableTo[2] = CLOCKRATE_GAMMA; mt->applicableTo[3] = CLOCKRATE_EXP; mt->nApplicable = 4; mt->moveFxn = &Move_ClockRateM; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_Extinction */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = EXTRATE_BETA; mt->nApplicable = 1; mt->moveFxn = &Move_Extinction; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.0; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_ExtSPR */ mt = &moveTypes[i++]; mt->name = "Extending SPR"; mt->shortName = "ExtSPR"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtSPR; mt->relProposalProb = 5.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.05); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_ExtSPRClock */ mt = &moveTypes[i++]; mt->name = "Extending SPR for clock trees"; mt->shortName = "ExtSprClock"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_ExtSPRClock; mt->relProposalProb = 5.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* extension probability */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_ExtSS */ mt = &moveTypes[i++]; mt->name = "Extending subtree swapper"; mt->shortName = "ExtSS"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtSS; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.05); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_ExtSSClock */ mt = &moveTypes[i++]; mt->name = "Extending subtree swapper"; mt->shortName = "ExtSsClock"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_ExtSSClock; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* extension probability */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_ExtTBR */ mt = &moveTypes[i++]; mt->name = "Extending TBR"; mt->shortName = "ExtTBR"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtTBR; mt->relProposalProb = 5.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.05); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99; mt->minimum[1] = 0.00001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_ExtTBR1 */ mt = &moveTypes[i++]; mt->name = "Extending TBR variant 1"; mt->shortName = "eTBR1"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtTBR1; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.8; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.6); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_ExtTBR2 */ mt = &moveTypes[i++]; mt->name = "Extending TBR variant 2"; mt->shortName = "eTBR2"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtTBR2; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.8; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.6); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_ExtTBR3 */ mt = &moveTypes[i++]; mt->name = "Extending TBR variant 3"; mt->shortName = "eTBR3"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtTBR3; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.8; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.6); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_ExtTBR4 */ mt = &moveTypes[i++]; mt->name = "Extending TBR variant 4"; mt->shortName = "eTBR4"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtTBR4; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.8; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.6); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_GammaShape_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = SHAPE_UNI; mt->applicableTo[1] = SHAPE_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_GammaShape_M; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_GeneRate_Dir */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = GENETREERATEMULT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_GeneRate_Dir; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1000.0; /* alphaPi */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_GeneTree1 */ mt = &moveTypes[i++]; mt->name = "Extending SPR move for gene trees in species trees"; mt->shortName = "ExtSPRClockGS"; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "prob"; mt->applicableTo[0] = TOPOLOGY_SPECIESTREE; mt->nApplicable = 1; mt->moveFxn = &Move_GeneTree1; mt->relProposalProb = 10.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* Tuning parameter value */ mt->minimum[0] = 0.00001; /* Minimum value of tuning param */ mt->maximum[0] = 10000000.0; /* Maximum value of tuning param */ mt->parsimonyBased = NO; /* It does not use parsimony scores */ mt->level = STANDARD_USER; /* Move_GeneTree2 */ mt = &moveTypes[i++]; mt->name = "NNI move for gene trees in species trees"; mt->shortName = "NNIClockGS"; mt->applicableTo[0] = TOPOLOGY_SPECIESTREE; mt->nApplicable = 1; mt->moveFxn = &Move_GeneTree2; mt->relProposalProb = 10.0; mt->numTuningParams = 0; /* No tuning parameters */ mt->parsimonyBased = NO; /* It does not use parsimony scores */ mt->level = STANDARD_USER; /* Move_GeneTree3 */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased SPR for gene trees in species trees"; mt->shortName = "ParsSPRClockGS"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "reweighting probability"; mt->shortTuningName[1] = "r"; mt->applicableTo[0] = TOPOLOGY_SPECIESTREE; mt->nApplicable = 1; mt->moveFxn = &Move_GeneTree3; mt->relProposalProb = 5.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.1; /* warp */ mt->tuningParam[1] = 0.05; /* upweight and downweight probability */ mt->minimum[0] = 0.01; mt->maximum[0] = 10.0; mt->minimum[1] = 0.0; mt->maximum[1] = 0.30; mt->parsimonyBased = YES; mt->level = STANDARD_USER; /* Move_Growth */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = GROWTH_UNI; mt->applicableTo[1] = GROWTH_EXP; mt->applicableTo[2] = GROWTH_NORMAL; mt->nApplicable = 3; mt->moveFxn = &Move_Growth; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 5.0; /* window size */ mt->minimum[0] = -10000.0; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Local */ mt = &moveTypes[i++]; mt->name = "BAMBE's LOCAL"; mt->shortName = "Local"; mt->subParams = YES; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_Local; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_LocalClock */ mt = &moveTypes[i++]; mt->name = "Modified LOCAL for clock trees"; mt->shortName = "LocalClock"; mt->subParams = YES; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->nApplicable = 4; mt->moveFxn = &Move_LocalClock; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (2.0); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_NNI */ mt = &moveTypes[i++]; mt->name = "NNI move for parsimony trees"; mt->shortName = "ParsNNI"; mt->applicableTo[0] = TOPOLOGY_PARSIMONY_UNIFORM; mt->applicableTo[1] = TOPOLOGY_PARSIMONY_CONSTRAINED; mt->nApplicable = 2; mt->moveFxn = &Move_NNI; mt->relProposalProb = 10.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; /* no extra parsimony scores are needed */ mt->level = STANDARD_USER; /* Move_NNIClock */ mt = &moveTypes[i++]; mt->name = "NNI move for clock trees"; mt->shortName = "NNIClock"; mt->subParams = YES; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_NNIClock; mt->relProposalProb = 10.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_NNI */ mt = &moveTypes[i++]; mt->name = "NNI move"; mt->shortName = "NNI"; mt->subParams = YES; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_NNI; mt->relProposalProb = 5.0; mt->numTuningParams = 0; mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_NNI_Hetero */ mt = &moveTypes[i++]; mt->name = "NNI move for trees with independent brlens"; mt->shortName = "MultNNI"; mt->subParams = YES; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HETERO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HETERO; mt->nApplicable = 2; /* 3; */ mt->moveFxn = &Move_NNI_Hetero; mt->relProposalProb = 15.0; mt->numTuningParams = 0; mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_NodeSlider */ mt = &moveTypes[i++]; mt->name = "Node slider (uniform on possible positions)"; mt->shortName = "Nodeslider"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BRLENS_UNI; mt->applicableTo[1] = BRLENS_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_NodeSlider; mt->relProposalProb = 5.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_NodeSliderClock */ mt = &moveTypes[i++]; mt->name = "Node depth window slider (clock-constrained)"; mt->shortName = "NodesliderClock"; mt->tuningName[0] = "Window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = BRLENS_CLOCK_UNI; mt->applicableTo[1] = BRLENS_CLOCK_COAL; mt->applicableTo[2] = BRLENS_CLOCK_BD; mt->nApplicable = 3; mt->moveFxn = &Move_NodeSliderClock; mt->relProposalProb = 20.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.05; /* window size */ mt->minimum[0] = 0.000001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_NodeSliderGeneTree */ mt = &moveTypes[i++]; mt->name = "Node depth slider for gene trees"; mt->shortName = "Nodeslider"; mt->tuningName[0] = "Window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = BRLENS_CLOCK_SPCOAL; mt->nApplicable = 1; mt->moveFxn = &Move_NodeSliderGeneTree; mt->relProposalProb = 20.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.05; /* window size */ mt->minimum[0] = 0.00000001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Omega */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = OMEGA_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Omega; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.0; /* sliding window size */ mt->minimum[0] = 0.0; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Omega_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = OMEGA_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Omega_M; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_OmegaBeta_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->paramName = "Omega_beta_M10"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = OMEGA_10UUB; mt->applicableTo[1] = OMEGA_10UUF; mt->applicableTo[2] = OMEGA_10UEB; mt->applicableTo[3] = OMEGA_10UEF; mt->applicableTo[4] = OMEGA_10UFB; mt->applicableTo[5] = OMEGA_10UFF; mt->applicableTo[6] = OMEGA_10EUB; mt->applicableTo[7] = OMEGA_10EUF; mt->applicableTo[8] = OMEGA_10EEB; mt->applicableTo[9] = OMEGA_10EEF; mt->applicableTo[10] = OMEGA_10EFB; mt->applicableTo[11] = OMEGA_10EFF; mt->nApplicable = 12; mt->moveFxn = &Move_OmegaBeta_M; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.00000001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_OmegaCat */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->paramName = "Omega_pi"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = OMEGA_BUD; mt->applicableTo[1] = OMEGA_BED; mt->applicableTo[2] = OMEGA_BFD; mt->applicableTo[3] = OMEGA_FUD; mt->applicableTo[4] = OMEGA_FED; mt->applicableTo[5] = OMEGA_FFD; mt->applicableTo[6] = OMEGA_ED; mt->applicableTo[7] = OMEGA_FD; mt->applicableTo[8] = OMEGA_10UUB; mt->applicableTo[9] = OMEGA_10UEB; mt->applicableTo[10] = OMEGA_10UFB; mt->applicableTo[11] = OMEGA_10EUB; mt->applicableTo[12] = OMEGA_10EEB; mt->applicableTo[13] = OMEGA_10EFB; mt->applicableTo[14] = OMEGA_10FUB; mt->applicableTo[15] = OMEGA_10FEB; mt->applicableTo[16] = OMEGA_10FFB; mt->nApplicable = 17; mt->moveFxn = &Move_OmegaCat; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 300.0; /* alpha-pi */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_OmegaGamma_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->paramName = "Omega_shape_M10"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = OMEGA_10UUB; mt->applicableTo[1] = OMEGA_10UUF; mt->applicableTo[2] = OMEGA_10UEB; mt->applicableTo[3] = OMEGA_10UEF; mt->applicableTo[4] = OMEGA_10EUB; mt->applicableTo[5] = OMEGA_10EUF; mt->applicableTo[6] = OMEGA_10EEB; mt->applicableTo[7] = OMEGA_10EEF; mt->applicableTo[8] = OMEGA_10FUB; mt->applicableTo[9] = OMEGA_10FUF; mt->applicableTo[10] = OMEGA_10FEB; mt->applicableTo[11] = OMEGA_10FEF; mt->nApplicable = 12; mt->moveFxn = &Move_OmegaGamma_M; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.00000001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_OmegaM3 */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = OMEGA_ED; mt->applicableTo[1] = OMEGA_EF; mt->nApplicable = 2; mt->moveFxn = &Move_OmegaM3; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_OmegaPur : Let it be here so that omega moves are listed in logical order!*/ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->paramName = "Omega_pur"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = OMEGA_BUD; mt->applicableTo[1] = OMEGA_BUF; mt->applicableTo[2] = OMEGA_BED; mt->applicableTo[3] = OMEGA_BEF; mt->applicableTo[4] = OMEGA_BFD; mt->applicableTo[5] = OMEGA_BFF; mt->nApplicable = 6; mt->moveFxn = &Move_OmegaPur; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_OmegaPos */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->paramName = "Omega_pos"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = OMEGA_BUD; mt->applicableTo[1] = OMEGA_BUF; mt->applicableTo[2] = OMEGA_BED; mt->applicableTo[3] = OMEGA_BEF; mt->applicableTo[4] = OMEGA_FUD; mt->applicableTo[5] = OMEGA_FUF; mt->applicableTo[6] = OMEGA_FED; mt->applicableTo[7] = OMEGA_FEF; mt->nApplicable = 8; mt->moveFxn = &Move_OmegaPos; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.0; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_ParsEraser1 */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased eraser version 1"; mt->shortName = "pEraser1"; mt->subParams = YES; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->tuningName[1] = "parsimony warp factor"; mt->shortTuningName[1] = "warp"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->nApplicable = 1; mt->moveFxn = &Move_ParsEraser1; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* alphaPi */ mt->tuningParam[1] = 0.1; /* warp */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000.0; mt->minimum[1] = 0.00001; mt->maximum[1] = 0.99999; mt->parsimonyBased = YES; mt->level = DEVELOPER; /* Move_ParsSPR */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased SPR"; mt->shortName = "ParsSPR"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->tuningName[2] = "reweighting probability"; mt->shortTuningName[2] = "r"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ParsSPR; mt->relProposalProb = 5.0; mt->numTuningParams = 3; mt->tuningParam[0] = 0.1; /* warp */ mt->tuningParam[1] = 2.0 * log (1.05); /* multiplier tuning parameter lambda */ mt->tuningParam[2] = 0.05; /* upweight and downweight probability */ mt->minimum[0] = 0.0; mt->maximum[0] = 1.0; mt->minimum[1] = 2.0 * log (0.001); mt->maximum[1] = 2.0 * log (1000.0); mt->minimum[2] = 0.0; mt->maximum[2] = 0.30; mt->parsimonyBased = YES; mt->level = STANDARD_USER; /* Move_ParsSPRClock */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased SPR for clock trees"; mt->shortName = "ParsSPRClock"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "reweighting probability"; mt->shortTuningName[1] = "r"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_ParsSPRClock; mt->relProposalProb = 5.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.1; /* warp */ mt->tuningParam[1] = 0.05; /* upweight and downweight probability */ mt->minimum[0] = 0.01; mt->maximum[0] = 10.0; mt->minimum[1] = 0.0; mt->maximum[1] = 0.30; mt->parsimonyBased = YES; mt->level = STANDARD_USER; /* Move_Pinvar */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = PINVAR_UNI; mt->nApplicable = 1; mt->moveFxn = &Move_Pinvar; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.001; mt->maximum[0] = 0.999; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_PopsizeM */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = POPSIZE_UNI; mt->applicableTo[1] = POPSIZE_LOGNORMAL; mt->applicableTo[2] = POPSIZE_NORMAL; mt->applicableTo[3] = POPSIZE_GAMMA; mt->nApplicable = 4; mt->moveFxn = &Move_PopSizeM; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log(1.5); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_RanSPR1 */ mt = &moveTypes[i++]; mt->name = "Random SPR version 1"; mt->shortName = "rSPR1"; mt->subParams = YES; mt->tuningName[0] = "Move probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_RanSPR1; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.8; /* move probability */ mt->tuningParam[1] = 2.0 * log (1.6); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_RanSPR2 */ mt = &moveTypes[i++]; mt->name = "Random SPR version 2"; mt->shortName = "rSPR2"; mt->subParams = YES; mt->tuningName[0] = "Move probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_RanSPR2; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.8; /* move probability */ mt->tuningParam[1] = 2.0 * log (1.6); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_RanSPR3 */ mt = &moveTypes[i++]; mt->name = "Random SPR version 3"; mt->shortName = "rSPR3"; mt->subParams = YES; mt->tuningName[0] = "Move probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_RanSPR3; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.8; /* move probability */ mt->tuningParam[1] = 2.0 * log (1.6); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_RanSPR4 */ mt = &moveTypes[i++]; mt->name = "Random SPR version 4"; mt->shortName = "rSPR4"; mt->subParams = YES; mt->tuningName[0] = "Move probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_RanSPR4; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.8; /* move probability */ mt->tuningParam[1] = 2.0 * log (1.6); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00000001; mt->maximum[1] = 10000000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_RateMult_Dir */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = RATEMULT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_RateMult_Dir; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 50.0; /* alphaPi per site */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_RateMult_Slider */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = RATEMULT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_RateMult_Slider; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 0.05; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Revmat_Dir */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = REVMAT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_Dir; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per rate */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Revmat_DirMix */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = REVMAT_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_DirMix; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per rate */ mt->minimum[0] = 0.01; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Revmat_Slider */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = REVMAT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_Slider; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 0.15; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Revmat_SplitMerge1 */ mt = &moveTypes[i++]; mt->name = "Split-merge move 1"; mt->shortName = "Splitmerge1"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = REVMAT_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_SplitMerge1; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 10.0; /* alphaPi per rate */ mt->minimum[0] = 0.5; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Revmat_SplitMerge2 */ mt = &moveTypes[i++]; mt->name = "Split-merge move 2"; mt->shortName = "Splitmerge2"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = REVMAT_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_SplitMerge2; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 10.0; /* alphaPi per rate */ mt->minimum[0] = 0.5; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Speciation */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = SPECRATE_UNI; mt->applicableTo[1] = SPECRATE_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_Speciation; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.0; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Speciation_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = SPECRATE_UNI; mt->applicableTo[1] = SPECRATE_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_Speciation_M; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_SpeciesTree */ mt = &moveTypes[i++]; mt->name = "Species tree move"; mt->shortName = "Distmatrixmove"; mt->tuningName[0] = "Divider of rate of truncated exponential"; mt->shortTuningName[0] = "lambdadiv"; mt->applicableTo[0] = SPECIESTREE_UNIFORM; mt->nApplicable = 1; mt->moveFxn = &Move_SpeciesTree; mt->relProposalProb = 10.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.2; /* Default tuning parameter value */ mt->minimum[0] = 0.00001; /* Minimum value of tuning param */ mt->maximum[0] = 10000000.0; /* Maximum value of tuning param */ mt->parsimonyBased = NO; /* It does not use parsimony scores */ mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; /* Autotune this move as a mutliplier move (larger is more bold) */ mt->targetRate = 0.25; /* Target acceptance rate */ /* Move_SPRClock */ /* not correctly balanced yet !! */ mt = &moveTypes[i++]; mt->name = "Clock-constrained SPR"; mt->shortName = "cSPR"; mt->subParams = YES; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->tuningName[1] = "Exponential parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->nApplicable = 2; mt->moveFxn = &Move_SPRClock; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 10.0; /* alphaPi */ mt->tuningParam[1] = 10.0; /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000.0; mt->minimum[1] = 0.00001; mt->maximum[1] = 10000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_Statefreqs */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = PI_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Statefreqs; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per state */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Statefreqs_Slider */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = PI_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Statefreqs_Slider; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 0.20; /* window size (change in proportions) */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_StatefreqsSymDirMultistate */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->paramName = "Pi_symdir"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = SYMPI_FIX_MS; mt->applicableTo[1] = SYMPI_UNI_MS; mt->applicableTo[2] = SYMPI_EXP_MS; mt->nApplicable = 3; mt->moveFxn = &Move_StatefreqsSymDirMultistate; mt->relProposalProb = 5.0; mt->numTuningParams = 1; mt->tuningParam[0] = 50.0; /* alphaPi */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_SwitchRate */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = SWITCH_UNI; mt->applicableTo[1] = SWITCH_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_SwitchRate; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.0; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_SwitchRate_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = SWITCH_UNI; mt->applicableTo[1] = SWITCH_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_SwitchRate_M; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_Tratio_Dir */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = TRATIO_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Tratio_Dir; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 50.0; /* alphaPi */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_TreeStretch */ mt = &moveTypes[i++]; mt->name = "Tree stretch"; mt->shortName = "TreeStretch"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BRLENS_CLOCK_UNI; mt->applicableTo[1] = BRLENS_CLOCK_BD; mt->applicableTo[2] = BRLENS_CLOCK_COAL; mt->nApplicable = 3; mt->moveFxn = &Move_TreeStretch; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log(1.01); /* lambda */ mt->minimum[0] = 0.00000001; mt->maximum[0] = 2.0 * log(2.0); mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /*************************** Begin Code added by Jeremy Brown *************************/ /* Move_TreeLen */ mt = &moveTypes[i++]; mt->name = "Whole treelength hit with multiplier"; mt->shortName = "TLMultiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BRLENS_UNI; mt->applicableTo[1] = BRLENS_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_TreeLen; mt->relProposalProb = 2.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (2.0); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /*************************** End Code added by Jeremy Brown *************************/ /* Move_UnrootedSlider */ /* Non-clock version of the SPRclock; this move is not correctly balanced yet */ mt = &moveTypes[i++]; mt->name = "Branch slider"; mt->shortName = "Bslider"; mt->subParams = YES; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda_m"; mt->tuningName[1] = "Exponential parameter"; mt->shortTuningName[1] = "lambda_e"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->nApplicable = 1; mt->moveFxn = &Move_UnrootedSlider; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda multiplier*/ mt->tuningParam[1] = 10.0; /* lambda exponential */ mt->minimum[0] = 0.00000001; mt->maximum[0] = 10000000.0; mt->minimum[1] = 0.00001; mt->maximum[1] = 10000.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; /* Move_AddDeleteCPPEvent */ mt = &moveTypes[i++]; mt->name = "Random addition/deletion of CPP event"; mt->shortName = "Add_delete"; mt->applicableTo[0] = CPPEVENTS; mt->nApplicable = 1; mt->moveFxn = &Move_AddDeleteCPPEvent; mt->relProposalProb = 1.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_CPPEventPosition */ mt = &moveTypes[i++]; mt->name = "Random draw of CPP event position from prior"; mt->shortName = "Prior_draw_pos"; mt->applicableTo[0] = CPPEVENTS; mt->nApplicable = 1; mt->moveFxn = &Move_CPPEventPosition; mt->relProposalProb = 2.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_CPPRate */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = CPPRATE_EXP; mt->nApplicable = 1; mt->moveFxn = &Move_CPPRate; mt->relProposalProb = 2.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_CPPRateMultiplierMult */ mt = &moveTypes[i++]; mt->name = "Random CPP rate multiplier hit with multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = CPPEVENTS; mt->nApplicable = 1; mt->moveFxn = &Move_CPPRateMultiplierMult; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_CPPRateMultiplierRnd */ mt = &moveTypes[i++]; mt->name = "Random draw of CPP rate multiplier from prior"; mt->shortName = "Prior_draw_mult"; mt->applicableTo[0] = CPPEVENTS; mt->nApplicable = 1; mt->moveFxn = &Move_CPPRateMultiplierRnd; mt->relProposalProb = 2.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_Nu */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = TK02VAR_EXP; mt->applicableTo[1] = TK02VAR_UNI; mt->nApplicable = 2; mt->moveFxn = &Move_Nu; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_TK02BranchRate */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = TK02BRANCHRATES; mt->nApplicable = 1; mt->moveFxn = &Move_TK02BranchRate; mt->relProposalProb = 5.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.001; mt->maximum[0] = 10.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_IgrVar */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = IGRVAR_EXP; mt->applicableTo[1] = IGRVAR_UNI; mt->nApplicable = 2; mt->moveFxn = &Move_IgrVar; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_IgrBranchLen */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = IGRBRANCHLENS; mt->nApplicable = 1; mt->moveFxn = &Move_IgrBranchLen; mt->relProposalProb = 5.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.2); /* lambda */ mt->minimum[0] = 0.001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; numMoveTypes = i; } /* ShowModel: Display model on screen */ int ShowModel (void) { int i, j, ns; MrBayesPrint ("%s Model settings:\n\n", spacer); for (i=0; i 1) MrBayesPrint ("%s Settings for partition %d --\n", spacer, i+1); else MrBayesPrint ("%s Data not partitioned --\n", spacer); if (modelParams[i].dataType == DNA) { MrBayesPrint ("%s Datatype = DNA\n", spacer); ns = 4; } else if (modelParams[i].dataType == RNA) { MrBayesPrint ("%s Datatype = RNA\n", spacer); ns = 4; } else if (modelParams[i].dataType == PROTEIN) { MrBayesPrint ("%s Datatype = Protein\n", spacer); ns = 20; } else if (modelParams[i].dataType == RESTRICTION) { MrBayesPrint ("%s Datatype = Restriction\n", spacer); ns = 2; } else if (modelParams[i].dataType == STANDARD) { MrBayesPrint ("%s Datatype = Standard\n", spacer); ns = 10; } else if (modelParams[i].dataType == CONTINUOUS) { MrBayesPrint ("%s Datatype = Continuous\n", spacer); } if (modelSettings[i].dataType == CONTINUOUS) { /* begin description of continuous models */ if (!strcmp(modelParams[i].brownCorPr, "Fixed") && AreDoublesEqual(modelParams[i].brownCorrFix, 0.0, ETA)==YES) MrBayesPrint ("%s Model = Independent Brownian motion\n", spacer); else MrBayesPrint ("%s Model = Correlated Brownian motion\n", spacer); /* end description of continuous models */ } else { /* begin description of discrete models */ if (!strcmp(modelParams[i].parsModel, "Yes")) { MrBayesPrint ("%s Parsmodel = %s\n", spacer, modelParams[i].parsModel); } else { /* dna characters in this partition */ if (modelSettings[i].dataType == DNA || modelSettings[i].dataType == RNA) { /* general form of the rate matrix */ MrBayesPrint ("%s Nucmodel = %s\n", spacer, modelParams[i].nucModel); /* constraints on rates of substitution */ MrBayesPrint ("%s Nst = %s\n", spacer, modelParams[i].nst); if (!strcmp(modelParams[i].nst, "2")) { if (!strcmp(modelParams[i].tRatioPr,"Beta")) { MrBayesPrint ("%s Transition and transversion rates, expressed\n", spacer); MrBayesPrint ("%s as proportions of the rate sum, have a\n", spacer); MrBayesPrint ("%s Beta(%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].tRatioDir[0], modelParams[i].tRatioDir[1]); } else { MrBayesPrint ("%s Transition/transversion rate ratio is fixed to %1.2lf.\n", spacer, modelParams[i].tRatioFix); } } else if (!strcmp(modelParams[i].nst, "6")) { if (!strcmp(modelParams[i].revMatPr,"Dirichlet")) { MrBayesPrint ("%s Substitution rates, expressed as proportions\n", spacer); MrBayesPrint ("%s of the rate sum, have a Dirichlet prior\n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].revMatDir[0], modelParams[i].revMatDir[1], modelParams[i].revMatDir[2], modelParams[i].revMatDir[3], modelParams[i].revMatDir[4], modelParams[i].revMatDir[5]); } else { MrBayesPrint ("%s Substitution rates are fixed to be \n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf).\n", spacer, modelParams[i].revMatFix[0], modelParams[i].revMatFix[1], modelParams[i].revMatFix[2], modelParams[i].revMatFix[3], modelParams[i].revMatFix[4], modelParams[i].revMatFix[5]); } } else if (!strcmp(modelParams[i].nst, "Mixed")) { if (!strcmp(modelParams[i].revMatPr,"Dirichlet")) { MrBayesPrint ("%s Substitution rates, expressed as proportions\n", spacer); MrBayesPrint ("%s of the rate sum, have a Dirichlet prior\n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].revMatDir[0], modelParams[i].revMatDir[1], modelParams[i].revMatDir[2], modelParams[i].revMatDir[3], modelParams[i].revMatDir[4], modelParams[i].revMatDir[5]); } else { MrBayesPrint ("%s Substitution rates are fixed to be \n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf).\n", spacer, modelParams[i].revMatFix[0], modelParams[i].revMatFix[1], modelParams[i].revMatFix[2], modelParams[i].revMatFix[3], modelParams[i].revMatFix[4], modelParams[i].revMatFix[5]); } } if (!strcmp(modelParams[i].nucModel,"Codon")) { /* what is the distribution on the nonsyn./syn. rate ratio */ if (!strcmp(modelParams[i].omegaVar, "Equal")) { if (!strcmp(modelParams[i].omegaPr,"Dirichlet")) { MrBayesPrint ("%s Nonsynonymous and synonymous rates, expressed\n", spacer); MrBayesPrint ("%s as proportions of the rate sum, have a\n", spacer); MrBayesPrint ("%s Dirichlet(%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].omegaDir[0], modelParams[i].omegaDir[1]); } else { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio is fixed to %1.2lf.\n", spacer, modelParams[i].omegaFix); } } else if (!strcmp(modelParams[i].omegaVar, "Ny98")) { if (!strcmp(modelParams[i].ny98omega1pr, "Beta")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for purifying selection\n", spacer); MrBayesPrint ("%s (class 1) has a Beta(%1.2lf,%1.2lf) on the interval (0,1).\n", spacer, modelParams[i].ny98omega1Beta[0], modelParams[i].ny98omega1Beta[1]); } else { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for purifying selection\n", spacer); MrBayesPrint ("%s (class 1) is fixed to %1.2lf.\n", spacer, modelParams[i].ny98omega1Fixed); } MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for neutral selection\n", spacer); MrBayesPrint ("%s (class 2) is fixed to 1.0.\n", spacer); if (!strcmp(modelParams[i].ny98omega3pr, "Uniform")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for positive selection\n", spacer); MrBayesPrint ("%s is uniformly distributed on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].ny98omega3Uni[0], modelParams[i].ny98omega3Uni[1]); } else if (!strcmp(modelParams[i].ny98omega3pr, "Exponential")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for positive selection\n", spacer); MrBayesPrint ("%s is exponentially distributed with parameter (%1.2lf).\n", spacer, modelParams[i].ny98omega3Exp); } else { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for positive \n", spacer); MrBayesPrint ("%s selection is fixed to %1.2lf.\n", spacer, modelParams[i].ny98omega3Fixed); } } else if (!strcmp(modelParams[i].omegaVar, "M3")) { if (!strcmp(modelParams[i].m3omegapr, "Exponential")) { MrBayesPrint ("%s Nonsynonymous and synonymous rates for the tree classes of\n", spacer); MrBayesPrint ("%s omega are exponentially distributed random variables.\n", spacer); } else if (!strcmp(modelParams[i].m3omegapr, "Fixed")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for the three omega\n", spacer); MrBayesPrint ("%s are fixed to %1.2lf, %1.2lf, and %1.2lf.\n", spacer, modelParams[i].m3omegaFixed[0], modelParams[i].m3omegaFixed[1], modelParams[i].m3omegaFixed[2]); } } else if (!strcmp(modelParams[i].omegaVar, "M10")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for purifying \n", spacer); MrBayesPrint ("%s selection (class 1) has a Beta(alpha1,beta1) on the \n", spacer); MrBayesPrint ("%s interval (0,1). Nonsynonymous/synonymous rate ratio \n", spacer); MrBayesPrint ("%s for positive selection (class 2) has an offset \n", spacer); MrBayesPrint ("%s Gamma(alpha2,beta2) on the interval (1,Infinity).\n", spacer); } /* genetic code that is used (if nucmodel=codon) */ MrBayesPrint ("%s Code = %s\n", spacer, modelParams[i].geneticCode); } } /* amino acid characters in this partition */ else if (modelSettings[i].dataType == PROTEIN) { if (modelParams[i].dataType == DNA || modelParams[i].dataType == RNA) MrBayesPrint ("%s Nucmodel = %s\n", spacer, modelParams[i].nucModel); /* constraints on rates of substitution in 20 X 20 matrix */ if (!strcmp(modelParams[i].aaModelPr, "Mixed")) MrBayesPrint ("%s Aamodel = Mixture of models with fixed rate matrices\n", spacer); else MrBayesPrint ("%s Aamodel = %s\n", spacer, modelParams[i].aaModel); /* revmat rates */ if (!strcmp(modelParams[i].aaModelPr, "Mixed")) MrBayesPrint ("%s Substitution rates come from the mixture of models\n", spacer); else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && (!strcmp(modelParams[i].aaModel, "Poisson") || !strcmp(modelParams[i].aaModel, "Equalin"))) MrBayesPrint ("%s Substitution rates are fixed to be equal\n", spacer); else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && strcmp(modelParams[i].aaModel, "Gtr")!=0) MrBayesPrint ("%s Substitution rates are fixed to the %s rates\n", spacer, modelParams[i].aaModel); else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && !strcmp(modelParams[i].aaModel, "Gtr")) { if (!strcmp(modelParams[i].aaRevMatPr,"Dirichlet")) { for (j=0; j<190; j++) if (AreDoublesEqual(modelParams[i].aaRevMatDir[0], modelParams[i].aaRevMatDir[j], 0.00001) == NO) break; if (j == 190) { MrBayesPrint ("%s Substitution rates have a Dirichlet(%1.2lf,%1.2lf,...) prior\n", spacer, modelParams[i].aaRevMatDir[0], modelParams[i].aaRevMatDir[0]); } else { MrBayesPrint ("%s Substitution rates have a Dirichlet(\n", spacer); for (j=0; j<190; j++) { if (j % 10 == 0) MrBayesPrint ("%s ", spacer); MrBayesPrint ("%1.2lf", modelParams[i].aaRevMatDir[j]); if (j == 189) MrBayesPrint (") prior\n"); else if ((j+1) % 10 == 0) MrBayesPrint (",\n"); else MrBayesPrint (","); } } } else /* if (!strcmp(modelParams[i].aaRevMatPr,"Fixed")) */ { for (j=0; j<190; j++) if (AreDoublesEqual(modelParams[i].aaRevMatFix[0], modelParams[i].aaRevMatFix[j], 0.00001) == NO) break; if (j == 190) { MrBayesPrint ("%s Substitution rates are fixed to (%1.1lf,%1.1lf,...)\n", spacer, modelParams[i].aaRevMatFix[0], modelParams[i].aaRevMatFix[0]); } else { MrBayesPrint ("%s Substitution rates are fixed to (\n", spacer); for (j=0; j<190; j++) { if (j % 10 == 0) MrBayesPrint ("%s ", spacer); MrBayesPrint ("%1.1lf", modelParams[i].aaRevMatFix[j]); if (j == 189) MrBayesPrint (") prior\n"); else if ((j+1) % 10 == 0) MrBayesPrint (",\n"); else MrBayesPrint (","); } } } } } /* restriction site or morphological characters in this partition */ else if (modelSettings[i].dataType == RESTRICTION || modelSettings[i].dataType == STANDARD) { /* what type of characters are sampled? */ MrBayesPrint ("%s Coding = %s\n", spacer, modelParams[i].coding); } /* is there rate variation in a single site across the tree? */ if (((modelSettings[i].dataType == DNA || modelSettings[i].dataType == RNA) && !strcmp(modelParams[i].nucModel, "4by4")) || modelSettings[i].dataType == PROTEIN) { /* do rates change on tree accoding to covarion model? */ MrBayesPrint ("%s Covarion = %s\n", spacer, modelParams[i].covarionModel); if (!strcmp(modelParams[i].covarionModel, "Yes")) { /* distribution on switching parameters, if appropriate */ if (!strcmp(modelParams[i].covSwitchPr,"Uniform")) { MrBayesPrint ("%s Switching rates have independent uniform dist-\n", spacer); MrBayesPrint ("%s ributions on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].covswitchUni[0], modelParams[i].covswitchUni[1]); } else if (!strcmp(modelParams[i].covSwitchPr,"Exponential")) { MrBayesPrint ("%s Switching rates have independent exponential\n", spacer); MrBayesPrint ("%s distributions with parameters (%1.2lf).\n", spacer, modelParams[i].covswitchExp); } else { MrBayesPrint ("%s Switching rates are fixed to %1.2lf and %1.2lf.\n", spacer, modelParams[i].covswitchFix[0], modelParams[i].covswitchFix[0]); } ns *= 2; } } /* now, let's deal with variation in omega */ if ((modelParams[i].dataType == DNA || modelParams[i].dataType == RNA) && !strcmp(modelParams[i].nucModel,"Codon")) { MrBayesPrint ("%s Omegavar = %s\n", spacer, modelParams[i].omegaVar); if (!strcmp(modelParams[i].geneticCode, "Universal")) ns = 61; else if (!strcmp(modelParams[i].geneticCode, "Vertmt")) ns = 60; else if (!strcmp(modelParams[i].geneticCode, "Mycoplasma")) ns = 62; else if (!strcmp(modelParams[i].geneticCode, "Yeast")) ns = 62; else if (!strcmp(modelParams[i].geneticCode, "Ciliates")) ns = 63; else if (!strcmp(modelParams[i].geneticCode, "Metmt")) ns = 62; } /* what assumptions are made about the state frequencies? */ if (modelParams[i].dataType != CONTINUOUS) { if (modelParams[i].dataType == STANDARD) MrBayesPrint ("%s # States = Variable, up to 10\n", spacer); else if (modelSettings[i].numStates != modelSettings[i].numModelStates) MrBayesPrint ("%s # States = %d (in the model)\n", spacer, modelSettings[i].numModelStates); else MrBayesPrint ("%s # States = %d\n", spacer, ns); if (modelSettings[i].dataType == STANDARD) { if (!strcmp(modelParams[i].symPiPr,"Fixed")) { if (AreDoublesEqual(modelParams[i].symBetaFix, -1.0,ETA)==YES) MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); else MrBayesPrint ("%s Symmetric Dirichlet is fixed to %1.2lf\n", spacer, modelParams[i].symBetaFix); } else if (!strcmp(modelParams[i].symPiPr,"Uniform")) { MrBayesPrint ("%s Symmetric Dirichlet has a Uniform(%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].symBetaUni[0], modelParams[i].symBetaUni[1]); } else { MrBayesPrint ("%s Symmetric Dirichlet has a Exponential(%1.2lf) prior\n", spacer, modelParams[i].symBetaExp); } } else if (modelSettings[i].dataType == RESTRICTION) { /* distribution on state frequencies for restriction site model */ if (!strcmp(modelParams[i].stateFreqPr,"Dirichlet")) { MrBayesPrint ("%s State frequencies have a Dirichlet (%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].stateFreqsDir[0], modelParams[i].stateFreqsDir[1]); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Equal")) { MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"User")) { MrBayesPrint ("%s State frequencies have been fixed by the user\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Empirical")) { MrBayesPrint ("%s State frequencies have been fixed to the empirical frequencies in the data\n", spacer); } } else if (modelSettings[i].dataType == PROTEIN) { /* distribution on state frequencies for aminoacid model */ if (!strcmp(modelParams[i].aaModelPr, "Fixed") && (strcmp(modelParams[i].aaModel, "Equalin")==0 || strcmp(modelParams[i].aaModel, "Gtr")==0)) { if (!strcmp(modelParams[i].stateFreqPr,"Dirichlet")) { MrBayesPrint ("%s State frequencies have a Dirichlet prior\n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,", spacer, modelParams[i].stateFreqsDir[0], modelParams[i].stateFreqsDir[1], modelParams[i].stateFreqsDir[2], modelParams[i].stateFreqsDir[3], modelParams[i].stateFreqsDir[4]); MrBayesPrint ("%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,\n", modelParams[i].stateFreqsDir[5], modelParams[i].stateFreqsDir[6], modelParams[i].stateFreqsDir[7], modelParams[i].stateFreqsDir[8], modelParams[i].stateFreqsDir[9]); MrBayesPrint ("%s %1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,", spacer, modelParams[i].stateFreqsDir[10], modelParams[i].stateFreqsDir[11], modelParams[i].stateFreqsDir[12], modelParams[i].stateFreqsDir[13], modelParams[i].stateFreqsDir[14]); MrBayesPrint ("%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", modelParams[i].stateFreqsDir[15], modelParams[i].stateFreqsDir[16], modelParams[i].stateFreqsDir[17], modelParams[i].stateFreqsDir[18], modelParams[i].stateFreqsDir[19]); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Equal")) { MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"User")) { MrBayesPrint ("%s State frequencies have been fixed by the user\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Empirical")) { MrBayesPrint ("%s State frequencies have been fixed to the empirical frequencies in the data\n", spacer); } } else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && !strcmp(modelParams[i].aaModel, "Poisson")) { MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); } else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && strcmp(modelParams[i].aaModel, "Equalin") && strcmp(modelParams[i].aaModel, "Poisson")) { MrBayesPrint ("%s State frequencies are fixed to the %s frequencies\n", spacer, modelParams[i].aaModel); } else { MrBayesPrint ("%s State frequencies come from the mixture of models\n", spacer); } } else { /* distribution on state frequencies for all other models */ if (!strcmp(modelParams[i].stateFreqPr,"Dirichlet")) { MrBayesPrint ("%s State frequencies have a Dirichlet prior\n", spacer); if (!strcmp(modelParams[i].nucModel, "Doublet")) { MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,\n", spacer, modelParams[i].stateFreqsDir[0], modelParams[i].stateFreqsDir[1], modelParams[i].stateFreqsDir[2], modelParams[i].stateFreqsDir[3]); MrBayesPrint ("%s %1.2lf,%1.2lf,%1.2lf,%1.2lf,\n", spacer, modelParams[i].stateFreqsDir[4], modelParams[i].stateFreqsDir[5], modelParams[i].stateFreqsDir[6], modelParams[i].stateFreqsDir[7]); MrBayesPrint ("%s %1.2lf,%1.2lf,%1.2lf,%1.2lf,\n", spacer, modelParams[i].stateFreqsDir[8], modelParams[i].stateFreqsDir[9], modelParams[i].stateFreqsDir[10], modelParams[i].stateFreqsDir[11]); MrBayesPrint ("%s %1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].stateFreqsDir[12], modelParams[i].stateFreqsDir[13], modelParams[i].stateFreqsDir[14], modelParams[i].stateFreqsDir[15]); } else if (!strcmp(modelParams[i].nucModel, "4by4")) { MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].stateFreqsDir[0], modelParams[i].stateFreqsDir[1], modelParams[i].stateFreqsDir[2], modelParams[i].stateFreqsDir[3]); } } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Equal")) { MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"User")) { MrBayesPrint ("%s State frequencies have been fixed by the user\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Empirical")) { MrBayesPrint ("%s State frequencies have been fixed to the empirical frequencies in the data\n", spacer); } } } else MrBayesPrint ("%s # States = Infinity\n", spacer); /* now, let's deal with rate variation across sites */ if (modelSettings[i].dataType != CONTINUOUS) { if (((modelSettings[i].dataType == DNA || modelSettings[i].dataType == RNA) && strcmp(modelParams[i].nucModel,"Codon")) || modelSettings[i].dataType == PROTEIN || modelSettings[i].dataType == RESTRICTION || modelSettings[i].dataType == STANDARD) { if (!strcmp(modelParams[i].covarionModel, "No")) MrBayesPrint ("%s Rates = %s\n", spacer, modelParams[i].ratesModel); else { if (!strcmp(modelParams[i].ratesModel, "Propinv")) MrBayesPrint ("%s Rates = Equal ", spacer); else if (!strcmp(modelParams[i].ratesModel, "Invgamma")) MrBayesPrint ("%s Rates = Gamma ", spacer); else MrBayesPrint ("%s Rates = %s ", spacer, modelParams[i].ratesModel); MrBayesPrint ("(+ Propinv induced by covarion model)\n"); } if ((modelParams[i].dataType == RESTRICTION || modelParams[i].dataType == STANDARD) && !strcmp(modelParams[i].ratesModel, "Adgamma")) { } else { if ((!strcmp(modelParams[i].ratesModel, "Invgamma") || !strcmp(modelParams[i].ratesModel, "Gamma") || !strcmp(modelParams[i].ratesModel, "Adgamma"))) { /* distribution on shape parameter, if appropriate */ if (!strcmp(modelParams[i].shapePr,"Uniform")) { MrBayesPrint ("%s Gamma shape parameter is uniformly dist-\n", spacer); MrBayesPrint ("%s ributed on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].shapeUni[0], modelParams[i].shapeUni[1]); } else if (!strcmp(modelParams[i].shapePr,"Exponential")) { MrBayesPrint ("%s Gamma shape parameter is exponentially\n", spacer); MrBayesPrint ("%s distributed with parameter (%1.2lf).\n", spacer, modelParams[i].shapeExp); } else { MrBayesPrint ("%s Gamma shape parameter is fixed to %1.2lf.\n", spacer, modelParams[i].shapeFix); } } if ((!strcmp(modelParams[i].ratesModel, "Propinv") || !strcmp(modelParams[i].ratesModel, "Invgamma")) && !strcmp(modelParams[i].covarionModel, "No")) { /* distribution on pInvar parameter, if appropriate */ if (!strcmp(modelParams[i].pInvarPr,"Uniform")) { MrBayesPrint ("%s Proportion of invariable sites is uniformly dist-\n", spacer); MrBayesPrint ("%s ributed on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].pInvarUni[0], modelParams[i].pInvarUni[1]); } else { MrBayesPrint ("%s Proportion of invariable sites is fixed to %1.2lf.\n", spacer, modelParams[i].pInvarFix); } } if (!strcmp(modelParams[i].ratesModel, "Adgamma")) { /* distribution on correlation parameter, if appropriate */ if (!strcmp(modelParams[i].adGammaCorPr,"Uniform")) { MrBayesPrint ("%s Rate correlation parameter is uniformly dist-\n", spacer); MrBayesPrint ("%s ributed on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].corrUni[0], modelParams[i].corrUni[1]); } else { MrBayesPrint ("%s Rate correlation parameter is fixed to %1.2lf.\n", spacer, modelParams[i].corrFix); } } if (!strcmp(modelParams[i].ratesModel, "Gamma") || !strcmp(modelParams[i].ratesModel, "Invgamma") || !strcmp(modelParams[i].ratesModel, "Adgamma")) { /* how many categories is the continuous gamma approximated by? */ MrBayesPrint ("%s Gamma distribution is approximated using %d categories.\n", spacer, modelParams[i].numGammaCats); if (!strcmp(modelParams[i].useGibbs,"Yes")) MrBayesPrint ("%s Rate categories sampled using Gibbs sampling.\n", spacer, modelParams[i].numGammaCats); else MrBayesPrint ("%s Likelihood summarized over all rate categories in each generation.\n", spacer, modelParams[i].numGammaCats); } } } } } /* end description of discrete models */ } if (i != numCurrentDivisions - 1) MrBayesPrint ("\n"); } MrBayesPrint ("\n"); ShowParameters (NO, NO, NO); return (NO_ERROR); } /*------------------------------------------------------------------------------ | | ShowMoves: Show applicable moves | ------------------------------------------------------------------------------*/ int ShowMoves (int used) { int i, k, run, chain, chainIndex, areRunsSame, areChainsSame, numPrintedMoves; MCMCMove *mv; chainIndex = 0; numPrintedMoves = 0; for (i=0; irelProposalProb[k] > 0.000001) break; } if (k == numGlobalChains && used == YES) continue; if (k < numGlobalChains && used == NO) continue; numPrintedMoves++; /* print move number and name */ MrBayesPrint ("%s %4d -- Move = %s\n", spacer, numPrintedMoves, mv->name); /* print move type */ MrBayesPrint ("%s Type = %s\n", spacer, mv->moveType->name); /* print parameter */ if (mv->parm->nSubParams > 0) MrBayesPrint ("%s Parameters = %s [param. %d] (%s)\n", spacer, mv->parm->name, mv->parm->index+1, mv->parm->paramTypeName); else MrBayesPrint ("%s Parameter = %s [param. %d] (%s)\n", spacer, mv->parm->name, mv->parm->index+1, mv->parm->paramTypeName); for (k=0; kparm->nSubParams; k++) MrBayesPrint ("%s %s [param. %d] (%s)\n", spacer, mv->parm->subParams[k]->name, mv->parm->subParams[k]->index+1, mv->parm->subParams[k]->paramTypeName); /* print tuning parameters */ for (k=0; kmoveType->numTuningParams; k++) { if (k==0) MrBayesPrint ("%s Tuningparam = %s (%s)\n", spacer, mv->moveType->shortTuningName[k], mv->moveType->tuningName[k]); else MrBayesPrint ("%s %s (%s)\n", spacer, mv->moveType->shortTuningName[k], mv->moveType->tuningName[k]); } /* loop over tuning parameters */ for (k=0; kmoveType->numTuningParams; k++) { /* find if tuning parameters are different for different runs */ areRunsSame = YES; for (run=1; runtuningParam[chainIndex][k], mv->tuningParam[chain][k], 0.000001) == NO) { areRunsSame = NO; break; } } if (areRunsSame == NO) break; } /* now print values */ for (run=0; run= 1) break; /* find out if chains are different within this run */ areChainsSame = YES; for (chain=1; chaintuningParam[chainIndex][k], mv->tuningParam[chainIndex-chain][k],0.000001) == NO) { areChainsSame = NO; break; } } /* now we can print the values */ for (chain=0; chain= 1) break; if (run == 0 && chain == 0) MrBayesPrint ("%s%22s = %1.3lf", spacer, mv->moveType->shortTuningName[k], mv->tuningParam[chainIndex][k]); else MrBayesPrint ("%s %1.3lf", spacer, mv->tuningParam[chainIndex][k]); if (areChainsSame == NO && areRunsSame == YES) MrBayesPrint (" [chain %d]\n", chain+1); else if (areChainsSame == YES && areRunsSame == NO) MrBayesPrint (" [run %d]\n", run+1); else if (areChainsSame == NO && areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); else MrBayesPrint ("\n"); } } } /* next tuning parameter */ /* print target acceptance rate for autotuning */ if (mv->moveType->targetRate > 0.0 && mv->moveType->targetRate < 1.0) { /* first find out if the targets are different in different runs */ areRunsSame = YES; for (run=1; runtargetRate[chainIndex], mv->targetRate[chain], 0.000001) == NO) { areRunsSame = NO; break; } } if (areRunsSame == NO) break; } /* now print values */ for (run=0; run= 1) break; /* find out if chains are different within this run */ areChainsSame = YES; for (chain=1; chaintargetRate[chainIndex], mv->targetRate[chainIndex-chain], 0.000001) == NO) { areChainsSame = NO; break; } } /* now we can print the values */ for (chain=0; chain= 1) break; if (run == 0 && chain == 0) MrBayesPrint ("%s Targetrate = %1.3lf", spacer, mv->targetRate[chainIndex]); else MrBayesPrint ("%s %1.3lf", spacer, mv->targetRate[chainIndex]); if (areChainsSame == NO && areRunsSame == YES) MrBayesPrint (" [chain %d]\n", chain+1); else if (areChainsSame == YES && areRunsSame == NO) MrBayesPrint (" [run %d]\n", run+1); else if (areChainsSame == NO && areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); else MrBayesPrint ("\n"); } } } /* finally print the relative proposal probability */ /* first find out if the probabilities are different in different runs */ areRunsSame = YES; for (run=1; runrelProposalProb[chainIndex], mv->relProposalProb[chain], 0.000001) == NO) { areRunsSame = NO; break; } } if (areRunsSame == NO) break; } /* now print values */ for (run=0; run= 1) break; /* find out if chains are different within this run */ areChainsSame = YES; for (chain=1; chainrelProposalProb[chainIndex], mv->relProposalProb[chainIndex-chain], 0.000001) == NO) { areChainsSame = NO; break; } } /* now we can print the values */ for (chain=0; chain= 1) break; if (run == 0 && chain == 0) MrBayesPrint ("%s Rel. prob. = %1.1lf", spacer, mv->relProposalProb[chainIndex]); else MrBayesPrint ("%s %1.1lf", spacer, mv->relProposalProb[chainIndex]); if (areChainsSame == NO && areRunsSame == YES) MrBayesPrint (" [chain %d]\n", chain+1); else if (areChainsSame == YES && areRunsSame == NO) MrBayesPrint (" [run %d]\n", run+1); else if (areChainsSame == NO && areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); else MrBayesPrint ("\n"); } } MrBayesPrint ("\n"); } /* next move */ if (numPrintedMoves == 0) { if (used == YES) { MrBayesPrint ("%s No moves currently used for this analysis. All parameters\n", spacer); MrBayesPrint ("%s will be fixed to their starting values.\n\n", spacer); } else { MrBayesPrint ("%s No additional moves available for this model.\n\n", spacer); } } return (NO_ERROR); } /*------------------------------------------------------------------------------ | | ShowParameters: Show parameter table and parameter info | ------------------------------------------------------------------------------*/ int ShowParameters (int showStartVals, int showMoves, int showAllAvailable) { int a, b, d, i, j, k, m, n, run, chain, shouldPrint, isSame, areRunsSame, areChainsSame, nValues, chainIndex, refIndex, numPrinted, numMovedChains, printedCol, screenWidth=100; Param *p; Model *mp; ModelInfo *ms; MrBFlt *value, *refValue, *subValue; MCMCMove *mv; MrBayesPrint ("%s Active parameters: \n\n", spacer); if (numCurrentDivisions > 1) { MrBayesPrint ("%s Partition(s)\n", spacer); MrBayesPrint ("%s Parameters ", spacer); for (i=0; i 1) { MrBayesPrint ("%s ---------------", spacer); for (i=0; i 1) MrBayesPrint ("%s Parameters can be linked or unlinked across partitions using 'link' and 'unlink'\n\n", spacer); for (i=0; iparamType; mp = &modelParams[p->relParts[0]]; ms = &modelSettings[p->relParts[0]]; /* print parameter number and name */ MrBayesPrint ("%s %4d -- Parameter = %s\n", spacer, i+1, p->name); MrBayesPrint ("%s Type = %s\n", spacer, p->paramTypeName); /* print prior */ if (j == P_TRATIO) { if (!strcmp(mp->tRatioPr,"Beta")) MrBayesPrint ("%s Prior = Beta(%1.2lf,%1.2lf)\n", spacer, mp->tRatioDir[0], modelParams[i].tRatioDir[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->tRatioFix); } else if (j == P_REVMAT) { if (ms->numModelStates != 20) { if (!strcmp(mp->nst,"Mixed")) { MrBayesPrint ("%s Prior = All GTR submodels have equal probability\n", spacer); MrBayesPrint ("%s All revmat rates have a Symmetric Dirichlet(%1.2lf) prior\n", spacer, mp->revMatSymDir); } else if (!strcmp(mp->revMatPr,"Dirichlet")) { MrBayesPrint ("%s Prior = Dirichlet(%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, mp->revMatDir[0], mp->revMatDir[1], mp->revMatDir[2], mp->revMatDir[3], mp->revMatDir[4], mp->revMatDir[5]); } else MrBayesPrint ("%s Prior = Fixed(%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, mp->revMatFix[0], mp->revMatFix[1], mp->revMatFix[2], mp->revMatFix[3], mp->revMatFix[4], mp->revMatFix[5]); } else if (ms->numModelStates == 20) { if (!strcmp(mp->revMatPr,"Dirichlet")) MrBayesPrint ("%s Prior = Dirichlet\n", spacer); else MrBayesPrint ("%s Prior = Fixed(user-specified)\n", spacer); } } else if (j == P_OMEGA) { if (!strcmp(mp->omegaVar,"Equal")) { if (!strcmp(mp->omegaPr,"Dirichlet")) MrBayesPrint ("%s Prior = Dirichlet(%1.2lf,%1.2lf)\n", spacer, mp->omegaDir[0], mp->omegaDir[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->omegaFix); } else if (!strcmp(mp->omegaVar,"Ny98")) { if (!strcmp(mp->ny98omega1pr,"Beta")) MrBayesPrint ("%s Prior = Beta(%1.2lf,%1.2lf) on omega(1)\n", spacer, mp->ny98omega1Beta[0], mp->ny98omega1Beta[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf) on omega(1)\n", spacer, mp->ny98omega1Fixed); MrBayesPrint ("%s Fixed(1.00) on omega(2)\n", spacer); if (!strcmp(mp->ny98omega3pr,"Uniform")) MrBayesPrint ("%s Uniform(%1.2lf,%1.2lf) on omega(3)\n", spacer, mp->ny98omega3Uni[0], mp->ny98omega3Uni[1]); else if (!strcmp(mp->ny98omega3pr,"Exponential")) MrBayesPrint ("%s Exponential(%1.2lf) on omega(3)\n", spacer, mp->ny98omega3Exp); else MrBayesPrint ("%s Fixed(%1.2lf) on omega(3)\n", spacer, mp->ny98omega3Fixed); if (!strcmp(mp->codonCatFreqPr,"Dirichlet")) MrBayesPrint ("%s Dirichlet(%1.2lf,%1.2lf,%1.2lf) on pi(-), pi(N), and pi(+)\n", spacer, mp->codonCatDir[0], mp->codonCatDir[1], mp->codonCatDir[2]); else MrBayesPrint ("%s Fixed(%1.2lf,%1.2lf,%1.2lf) on pi(-), pi(N), and pi(+)\n", spacer, mp->codonCatFreqFix[0], mp->codonCatFreqFix[1], mp->codonCatFreqFix[2]); } else if (!strcmp(mp->omegaVar,"M3")) { if (!strcmp(mp->m3omegapr,"Exponential")) MrBayesPrint ("%s dN1, dN2, dN3, and dS are all exponential random variables\n", spacer); else MrBayesPrint ("%s Fixed(%1.2lf,%1.2lf,%1.2lf) for the three selection categories\n", spacer, mp->m3omegaFixed[0], mp->m3omegaFixed[1], mp->m3omegaFixed[2]); if (!strcmp(mp->codonCatFreqPr,"Dirichlet")) MrBayesPrint ("%s Dirichlet(%1.2lf,%1.2lf,%1.2lf) on pi(1), pi(2), and pi(3)\n", spacer, mp->codonCatDir[0], mp->codonCatDir[1], mp->codonCatDir[2]); else MrBayesPrint ("%s Fixed(%1.2lf,%1.2lf,%1.2lf) on pi(1), pi(2), and pi(3)\n", spacer, mp->codonCatFreqFix[0], mp->codonCatFreqFix[1], mp->codonCatFreqFix[2]); } else if (!strcmp(mp->omegaVar,"M10")) { MrBayesPrint ("%s With probability pi(1), omega is drawn from a Beta(alpha1,beta1) \n", spacer); MrBayesPrint ("%s distribution and with probability pi(2), omega is drawn from\n", spacer); MrBayesPrint ("%s a Gamma(alpha2,beta2) distribution.\n", spacer); if (!strcmp(mp->m10betapr,"Uniform")) { MrBayesPrint ("%s The parameters of the beta distribution each follow \n", spacer); MrBayesPrint ("%s independent Uniform(%1.2lf,%1.2lf) priors\n", spacer, mp->m10betaUni[0], mp->m10betaUni[1]); } else if (!strcmp(mp->m10betapr,"Exponential")) { MrBayesPrint ("%s The parameters of the beta distribution each follow \n", spacer); MrBayesPrint ("%s independent Exponential(%1.2lf) priors\n", spacer, mp->m10betaExp); } else { MrBayesPrint ("%s The parameters of the beta distribution are fixed to \n", spacer); MrBayesPrint ("%s %1.2lf and %1.2lf\n", spacer, mp->m10betaFix[0], mp->m10betaFix[0]); } if (!strcmp(mp->m10gammapr,"Uniform")) { MrBayesPrint ("%s The parameters of the gamma distribution each follow \n", spacer); MrBayesPrint ("%s independent Uniform(%1.2lf,%1.2lf) priors\n", spacer, mp->m10gammaUni[0], mp->m10gammaUni[1]); } else if (!strcmp(mp->m10gammapr,"Exponential")) { MrBayesPrint ("%s The parameters of the gamma distribution each follow \n", spacer); MrBayesPrint ("%s independent Exponential(%1.2lf) priors\n", spacer, mp->m10gammaExp); } else { MrBayesPrint ("%s The parameters of the gamma distribution are fixed to \n", spacer); MrBayesPrint ("%s %1.2lf and %1.2lf\n", spacer, mp->m10gammaFix[0], mp->m10gammaFix[0]); } if (!strcmp(mp->codonCatFreqPr,"Dirichlet")) MrBayesPrint ("%s Dirichlet(%1.2lf,%1.2lf) on pi(1) and pi(2)\n", spacer, mp->codonCatDir[0], mp->codonCatDir[1]); else MrBayesPrint ("%s Fixed(%1.2lf,%1.2lf) on pi(1) and pi(2)\n", spacer, mp->codonCatFreqFix[0], mp->codonCatFreqFix[1]); } } else if (j == P_PI) { if (ms->dataType == STANDARD) { if (!strcmp(mp->symPiPr, "Uniform")) MrBayesPrint ("%s Prior = Symmetric dirichlet with uniform(%1.2lf,%1.2lf) variance parameter\n", spacer, mp->symBetaUni[0], mp->symBetaUni[1]); else if (!strcmp(mp->symPiPr, "Exponential")) MrBayesPrint ("%s Prior = Symmetric dirichlet with exponential(%1.2lf) variance parameter\n", spacer, mp->symBetaExp); else { /* mp->symBetaFix == -1 */ if (AreDoublesEqual(mp->symBetaFix, 1.0, ETA)==YES) MrBayesPrint ("%s Prior = State frequencies are equal\n", spacer); else MrBayesPrint ("%s Prior = Symmetric dirichlet with fixed(%1.2lf) variance parameter\n", spacer, mp->symBetaFix); } } else if (ms->dataType == PROTEIN) { if (!strcmp(mp->aaModelPr, "Fixed") && (!strcmp(mp->aaModel, "Equalin") || !strcmp(mp->aaModel, "Gtr"))) { if (!strcmp(mp->stateFreqPr,"Dirichlet")) { MrBayesPrint ("%s Prior = Dirichlet\n", spacer); } else if (!strcmp(mp->stateFreqPr,"Fixed") && !strcmp(mp->stateFreqsFixType,"Equal")) { MrBayesPrint ("%s Prior = Fixed (equal frequencies)\n", spacer); } else if (!strcmp(mp->stateFreqPr,"Fixed") && !strcmp(mp->stateFreqsFixType,"User")) { MrBayesPrint ("%s Prior = Fixed (user-specified)\n", spacer); } else if (!strcmp(mp->stateFreqPr,"Fixed") && !strcmp(mp->stateFreqsFixType,"Empirical")) { MrBayesPrint ("%s Prior = Fixed (empirical frequencies)\n", spacer); } } else if (!strcmp(mp->aaModelPr, "Fixed") && !strcmp(mp->aaModel, "Poisson")) { MrBayesPrint ("%s Prior = Fixed (equal frequencies)\n", spacer); } else if (!strcmp(mp->aaModelPr, "Fixed") && strcmp(mp->aaModel, "Equalin") && strcmp(mp->aaModel, "Poisson")) { MrBayesPrint ("%s Prior = Fixed (%s frequencies)\n", spacer, mp->aaModel); } else { MrBayesPrint ("%s Prior = Fixed (from mixture of models)\n", spacer); } } else { if (!strcmp(mp->stateFreqPr,"Dirichlet")) MrBayesPrint ("%s Prior = Dirichlet\n", spacer); else MrBayesPrint ("%s Prior = Fixed\n", spacer); } } else if (j == P_SHAPE) { if (!strcmp(mp->shapePr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->shapeUni[0], mp->shapeUni[1]); else if (!strcmp(mp->shapePr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->shapeExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->shapeFix); } else if (j == P_PINVAR) { if (!strcmp(mp->pInvarPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->pInvarUni[0], mp->pInvarUni[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->pInvarFix); } else if (j == P_CORREL) { if (!strcmp(mp->adGammaCorPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->corrUni[0], mp->corrUni[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->corrFix); } else if (j == P_SWITCH) { if (!strcmp(mp->covSwitchPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->covswitchUni[0], mp->covswitchUni[1]); else if (!strcmp(mp->covSwitchPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->covswitchExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf,%1.2lf)\n", spacer, mp->covswitchFix[0], mp->covswitchFix[1]); } else if (j == P_RATEMULT) { if (p->nValues == 1) MrBayesPrint ("%s Prior = Fixed(1.0)\n", spacer); else { MrBayesPrint ("%s Prior = Dirichlet(", spacer); for (d=n=0; d screenWidth) { MrBayesPrint("\n%s ", spacer); printedCol = strlen(spacer) + 25 + 10; } if (n == numTrees-2) MrBayesPrint ("1.00)\n"); else MrBayesPrint ("1.00,"); printedCol += 5; } } else if (j == P_TOPOLOGY) { if (!strcmp(mp->topologyPr,"Uniform")) MrBayesPrint ("%s Prior = All topologies equally probable a priori\n", spacer); else if (!strcmp(mp->topologyPr,"Speciestree")) MrBayesPrint ("%s Prior = Topology constrained to fold within species tree\n", spacer); else if (!strcmp(mp->topologyPr,"Constraints")) MrBayesPrint ("%s Prior = Prior on topologies obeys constraints\n", spacer); else MrBayesPrint ("%s Prior = Prior is fixed to the topology of user tree '%s'\n", spacer, userTree[mp->topologyFix]->name); } else if (j == P_BRLENS) { if (!strcmp(mp->parsModel, "Yes")) MrBayesPrint ("%s Prior = Reconstructed using parsimony\n", spacer); else { if (!strcmp(mp->brlensPr,"Unconstrained")) { MrBayesPrint ("%s Prior = Unconstrained:%s", spacer, mp->unconstrainedPr); if (!strcmp(mp->unconstrainedPr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->brlensUni[0], mp->brlensUni[1]); else MrBayesPrint ("(%1.1lf)\n", mp->brlensExp); } else if (!strcmp(mp->brlensPr, "Clock")) { MrBayesPrint ("%s Prior = Clock:%s\n", spacer, mp->clockPr); if (!strcmp(mp->clockPr,"Uniform") && !strcmp(mp->nodeAgePr,"Unconstrained")) { if (!strcmp(mp->treeAgePr, "Fixed")) MrBayesPrint ("%s Tree age is fixed to %1.3lf\n", spacer, mp->treeAgeFix); else if (!strcmp(mp->treeAgePr, "Gamma")) MrBayesPrint ("%s Tree age has a Gamma(%1.3lf,%1.3lf) distribution\n", spacer, mp->treeAgeGamma[0], mp->treeAgeGamma[1]); else MrBayesPrint ("%s Tree age has an Exponential(%1.3lf) distribution\n", spacer, mp->treeAgeExp); } else if (!strcmp(mp->clockPr, "Birthdeath") && !strcmp(mp->nodeAgePr,"Unconstrained")) { MrBayesPrint ("%s Tree age has a Uniform(0,infinity) distribution\n", spacer); } if (!strcmp(mp->nodeAgePr,"Calibrated")) { b = 0; for (a=0; aactiveConstraints[a] == YES && nodeCalibration[a].prior != unconstrained) b++; } if (b > 1) MrBayesPrint ("%s Node depths are constrained by the following age constraints:\n", spacer); else MrBayesPrint ("%s Node depths are calibrated by the following age constraint:\n", spacer); for (a=0; aactiveConstraints[a] == YES && nodeCalibration[a].prior != unconstrained) { MrBayesPrint ("%s -- The age of node '%s' is %s\n", spacer, constraintNames[a], nodeCalibration[a].name); for (k=0; kclockPr,"Uniform")) { if (!strcmp(mp->treeAgePr, "Fixed")) MrBayesPrint ("%s -- Tree age is fixed to %1.3lf\n", spacer, mp->treeAgeFix); else if (!strcmp(mp->treeAgePr, "Gamma")) MrBayesPrint ("%s -- Tree age has a Gamma(%1.3lf,%1.3lf) distribution\n", spacer, mp->treeAgeGamma[0], mp->treeAgeGamma[1]); else MrBayesPrint ("%s -- Tree age has an Exponential(%1.3lf) distribution\n", spacer, mp->treeAgeExp); } else if (!strcmp(mp->clockPr,"Birthdeath")) { MrBayesPrint ("%s -- Tree age has a Uniform(0,infinity) distribution\n", spacer); } } } else MrBayesPrint ("%s Node ages are not constrained\n", spacer); } else { assert(!strcmp(mp->brlensPr, "Fixed")); MrBayesPrint ("%s Prior = Fixed, branch lengths are fixed to the ones of the user tree '%s'\n", spacer, userTree[mp->topologyFix]->name); } } } else if (j == P_SPECRATE) { if (!strcmp(mp->speciationPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->speciationUni[0], mp->speciationUni[1]); else if (!strcmp(mp->speciationPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->speciationExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->speciationFix); } else if (j == P_EXTRATE) { if (!strcmp(mp->extinctionPr,"Beta")) MrBayesPrint ("%s Prior = Beta(%1.2lf,%1.2lf)\n", spacer, mp->extinctionBeta[0], mp->extinctionBeta[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->extinctionFix); } else if (j == P_POPSIZE) { if (!strcmp(mp->popSizePr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->popSizeUni[0], mp->popSizeUni[1]); else if (!strcmp(mp->popSizePr,"Lognormal")) MrBayesPrint ("%s Prior = Lognormal(%1.2lf,%1.2lf)\n", spacer, mp->popSizeLognormal[0], mp->popSizeLognormal[1]); else if (!strcmp(mp->popSizePr,"Normal")) MrBayesPrint ("%s Prior = Truncated Normal(%1.2lf,%1.2lf)\n", spacer, mp->popSizeNormal[0], mp->popSizeNormal[1]); else if (!strcmp(mp->popSizePr,"Gamma")) MrBayesPrint ("%s Prior = Gamma(%1.2lf,%1.2lf)\n", spacer, mp->popSizeGamma[0], mp->popSizeGamma[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->popSizeFix); if (!strcmp(mp->topologyPr,"Speciestree")) { if (!strcmp(mp->popVarPr,"Equal") || !strcmp(mp->popSizePr,"Fixed")) MrBayesPrint ("%s Population size the same across species tree\n", spacer); else MrBayesPrint ("%s Population size varies across branches in species tree\n", spacer); } } else if (j == P_GROWTH) { if (!strcmp(mp->growthPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->growthUni[0], mp->growthUni[1]); else if (!strcmp(mp->growthPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->growthExp); else if (!strcmp(mp->growthPr,"Normal")) MrBayesPrint ("%s Prior = Normal(%1.2lf,%1.2lf)\n", spacer, mp->growthNorm[0], mp->growthNorm[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->growthFix); } else if (j == P_AAMODEL) { if (!strcmp(mp->aaModelPr,"Mixed")) { /* check to see if you have a uniform prior */ isSame = YES; for (a=0; a<9; a++) for (b=a+1; b<10; b++) /* mp->aaModelPrProbs[a] != mp->aaModelPrProbs[b] */ if (AreDoublesEqual(mp->aaModelPrProbs[a], mp->aaModelPrProbs[b], ETA)==FALSE) isSame = NO; MrBayesPrint ("%s Prior = Poisson, Jones, Dayhoff, Mtrev, Mtmam, Wag, Rtrev,\n", spacer); if (isSame == YES) MrBayesPrint ("%s Cprev, Vt, and Blosum models have equal prior probability\n", spacer); else MrBayesPrint ("%s Cprev, Vt, and Blosum models have unequal prior probability\n", spacer); } else MrBayesPrint ("%s Prior = Fixed(%s)\n", spacer, mp->aaModel); } else if (j == P_BRCORR) { if (!strcmp(mp->brownCorPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->brownCorrUni[0], mp->brownCorrUni[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->brownCorrFix); } else if (j == P_BRSIGMA) { if (!strcmp(mp->brownScalesPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->brownScalesUni[0], mp->brownScalesUni[1]); else if (!strcmp(mp->brownScalesPr,"Gammamean")) MrBayesPrint ("%s Prior = Gamma Mean= Var=%1.2lf\n", spacer, mp->brownScalesGammaMean); else if (!strcmp(mp->brownScalesPr,"Gamma")) MrBayesPrint ("%s Prior = Gamma Mean=%lf Var=%1.2lf\n", spacer, mp->brownScalesGamma[0], mp->brownScalesGamma[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->brownScalesFix); } else if (j == P_CPPRATE) { if (!strcmp(mp->cppRatePr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->cppRateExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->cppRateFix); } else if (j == P_CPPMULTDEV) { if (!strcmp(mp->cppMultDevPr,"Fixed")) MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->cppMultDevFix); } else if (j == P_CPPEVENTS) { MrBayesPrint ("%s Prior = Poisson (%s) [Events]\n", spacer, modelSettings[p->relParts[0]].cppRate->name); MrBayesPrint ("%s Lognormal (0.00,%1.2lf) [Rate multipliers]\n", spacer, mp->cppMultDevFix); } else if (j == P_TK02VAR) { if (!strcmp(mp->tk02varPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->tk02varUni[0], mp->tk02varUni[1]); else if (!strcmp(mp->tk02varPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->tk02varExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->tk02varFix); } else if (j == P_TK02BRANCHRATES) { MrBayesPrint ("%s Prior = LogNormal (expectation = r_0, variance = %s * v) \n", spacer, modelSettings[p->relParts[0]].tk02var->name); MrBayesPrint ("%s [r_0 is beginning rate of branch, v is branch length]\n", spacer); } else if (j == P_IGRVAR) { if (!strcmp(mp->igrvarPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->igrvarUni[0], mp->igrvarUni[1]); else if (!strcmp(mp->igrvarPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->igrvarExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->igrvarFix); } else if (j == P_IGRBRANCHLENS) { MrBayesPrint ("%s Prior = Scaledgamma (variance = %s * v) \n", spacer, modelSettings[p->relParts[0]].igrvar->name); MrBayesPrint ("%s [where v is branch length]\n", spacer); } else if (j == P_CLOCKRATE) { if (!strcmp(mp->clockRatePr,"Normal")) MrBayesPrint ("%s Prior = Normal(%1.6lf,%1.6lf)\n", spacer, mp->clockRateNormal[0], mp->clockRateNormal[1]); else if (!strcmp(mp->clockRatePr,"Lognormal")) MrBayesPrint ("%s Prior = Lognormal(%1.6lf,%1.6lf)\n", spacer, mp->clockRateLognormal[0], mp->clockRateLognormal[1]); else if (!strcmp(mp->clockRatePr,"Gamma")) MrBayesPrint ("%s Prior = Gamma(%1.6lf,%1.6lf)\n", spacer, mp->clockRateGamma[0], mp->clockRateGamma[1]); else if (!strcmp(mp->clockRatePr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.6lf)\n", spacer, mp->clockRateExp); else MrBayesPrint ("%s Prior = Fixed(%1.6lf)\n", spacer, mp->clockRateFix); if (!strcmp(mp->clockVarPr,"Strict")) MrBayesPrint ("%s The clock rate is constant (strict clock)\n", spacer); else if (!strcmp(mp->clockVarPr,"Cpp")) MrBayesPrint ("%s The clock rate varies according to a CPP model\n", spacer); else if (!strcmp(mp->clockVarPr,"TK02")) MrBayesPrint ("%s The clock rate varies according to a Brownian motion model\n", spacer); else /* if (!strcmp(mp->clockVarPr,"Igr")) */ MrBayesPrint ("%s The clock rate varies according to an independent scaled gamma (white noise) model\n", spacer); } else if (j == P_SPECIESTREE) { MrBayesPrint ("%s Prior = Uniform on topologies and branch lengths\n", spacer); } /* print partitions */ if (numCurrentDivisions > 1) { if (p->nRelParts == 1) MrBayesPrint ("%s Partition = %d\n", spacer, p->relParts[0]+1); else if (p->nRelParts == 2) { MrBayesPrint ("%s Partitions = %d and %d\n", spacer, p->relParts[0]+1, p->relParts[1]+1); } else if (p->nRelParts == numCurrentDivisions) { MrBayesPrint ("%s Partitions = All\n", spacer); } else /* if (p->nRelParts > 2) */ { MrBayesPrint ("%s Partitions = ", spacer); for (j=0; jnRelParts; j++) { if (j == p->nRelParts - 2) MrBayesPrint ("%d, and ", p->relParts[j]+1); else if (j == p->nRelParts - 1) MrBayesPrint ("%d\n", p->relParts[j]+1); else MrBayesPrint ("%d, ", p->relParts[j]+1); } } } /* show subparams */ if (p->nSubParams > 0) { if (p->nSubParams == 1) MrBayesPrint ("%s Subparam. = %s\n", spacer, p->subParams[0]->name); else { printedCol = 0; for (k=0; knSubParams; k++) { if (k == 0) { MrBayesPrint ("%s Subparams = %s", spacer, p->subParams[k]->name); printedCol = strlen(spacer) + 25 + strlen(p->subParams[k]->name); } else if (k == p->nSubParams - 1) { if (printedCol + 5 > screenWidth) MrBayesPrint ("\n%s and ", spacer); else if (printedCol + (int)(strlen(p->subParams[k]->name)) + 5 > screenWidth) MrBayesPrint (" and \n%s ", spacer); else MrBayesPrint (" and "); MrBayesPrint ("%s\n", p->subParams[k]->name); } else { if (printedCol + (int)(strlen(p->subParams[k]->name)) + 2 > screenWidth) { MrBayesPrint (", \n%s ", spacer); printedCol = strlen(spacer) + 25; } else { MrBayesPrint(", "); printedCol += 2; } MrBayesPrint ("%s", p->subParams[k]->name); printedCol += strlen(p->subParams[k]->name); } } } } /* show used moves */ if (showMoves == YES && (p->printParam == YES || p->paramType == P_TOPOLOGY || p->paramType == P_BRLENS)) { /* check if runs are same */ areRunsSame = YES; for (run=1; runparm != p) continue; chainIndex = run*chainParams.numChains + chain; refIndex = chain; if (AreDoublesEqual (mv->relProposalProb[chainIndex], mv->relProposalProb[refIndex], 0.000001) == NO) areRunsSame = NO; } } } for (run=0; run 0 && areRunsSame == YES) continue; /* check if chains are same */ areChainsSame = YES; for (chain=1; chainparm != p) continue; chainIndex = run*chainParams.numChains + chain; refIndex = run*chainParams.numChains; if (AreDoublesEqual (mv->relProposalProb[chainIndex], mv->relProposalProb[refIndex], 0.000001) == NO) areChainsSame = NO; } } /* now print moves */ for (chain=0; chain 0 && areChainsSame == YES) continue; if (run==0 && chain==0) MrBayesPrint ("%s Moves = ", spacer); else MrBayesPrint ("%s ", spacer); numPrinted = 0; printedCol = strlen(spacer) + 25; for (k=0; kparm != p) continue; chainIndex = run*chainParams.numChains + chain; if (mv->relProposalProb[chainIndex] <= 0.000001) continue; if (numPrinted == 0) { MrBayesPrint ("%s ", mv->name, mv->relProposalProb[chainIndex]); printedCol += 9 + strlen(mv->name) + (int)(log10(mv->relProposalProb[chainIndex])) + 3; } else { if (printedCol + 11 + (int)(strlen(mv->name)) + (int)(log10(mv->relProposalProb[chainIndex])) + 3 > screenWidth) { MrBayesPrint(", \n%s ", spacer); printedCol = 25 + strlen(spacer); } else { MrBayesPrint(", "); printedCol += 2; } MrBayesPrint ("%s ", mv->name, mv->relProposalProb[chainIndex]); printedCol += (9 + (int)(strlen(mv->name)) + (int)(log10(mv->relProposalProb[chainIndex])) + 3); } numPrinted++; } if (numPrinted == 0 && p->paramType == P_BRLENS) { for (k=0; ktree) break; MrBayesPrint ("For moves, see param. '%s'", params[k].name); } else if (numPrinted == 0) MrBayesPrint ("WARNING! No moves -- param. fixed to startvals"); if (areRunsSame == YES && areChainsSame == YES) MrBayesPrint ("\n"); else if (areRunsSame == YES && areChainsSame == NO) MrBayesPrint (" [chain %d]\n", chain+1); else if (areRunsSame == NO && areChainsSame == YES) MrBayesPrint (" [run %d]\n", run+1); else /* if (areRunsSame == NO && areChainsSame == NO) */ MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); } } } /* show available moves */ if (showAllAvailable == YES && (p->printParam == YES || p->paramType == P_TOPOLOGY || p->paramType == P_BRLENS)) { /* loop over moves */ numPrinted = 0; printedCol = 0; for (k=0; kparm != p) continue; numMovedChains = 0; for (run=0; runrelProposalProb[chainIndex] > 0.000001) numMovedChains++; } } if (numMovedChains == 0) { if (numPrinted == 0) { MrBayesPrint ("%s Not used = ", spacer); printedCol = strlen(spacer) + 25; } else if (printedCol + 2 + (int)(strlen(mv->moveType->shortName)) > screenWidth) { MrBayesPrint (", \n%s ", spacer); printedCol = strlen(spacer) + 25; } else { MrBayesPrint (", "); printedCol += 2; } MrBayesPrint("%s", mv->moveType->shortName); printedCol += strlen(mv->moveType->shortName); numPrinted++; } } if (numPrinted > 0) MrBayesPrint ("\n"); } /* show startvals */ if (showStartVals == YES && (p->printParam == YES || p->paramType == P_TOPOLOGY || p->paramType == P_BRLENS || p->paramType == P_SPECIESTREE || p->paramType == P_POPSIZE)) { if (p->paramType == P_TOPOLOGY || p->paramType == P_BRLENS || p->paramType == P_SPECIESTREE) { /* check if they are the same */ areRunsSame = YES; if (p->paramType == P_TOPOLOGY) { for (run=1; runparamType == P_BRLENS) { for (run=1; runparamType == P_SPECIESTREE) { for (run=1; run 0 && areRunsSame == YES) break; for (chain=0; chainname); else MrBayesPrint ("%s tree '%s'", spacer, GetTree (p, run*chainParams.numChains+chain, 0)->name); if (chainParams.numChains > 1 && areRunsSame == YES) MrBayesPrint (" [chain %d]", chain+1); else if (chainParams.numChains == 1 && areRunsSame == NO) MrBayesPrint (" [run %d]", run+1); else if (areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]", run+1, chain+1); MrBayesPrint ("\n"); } } } /* end topology and brlens parameters */ else if (p->paramType == P_OMEGA && p->paramId != OMEGA_DIR && p->paramId != OMEGA_FIX && p->paramId != OMEGA_FFF && p->paramId != OMEGA_FF && p->paramId != OMEGA_10FFF) { /* we need to print values and subvalues for the category frequencies in a NY98-like model. */ areRunsSame = YES; for (run=1; runnValues; for (k=0; krelParts[0]].omegaVar, "M10")) { value += (mp->numM10BetaCats + mp->numM10GammaCats); refValue += (mp->numM10BetaCats + mp->numM10GammaCats); for (k=0; k<4; k++) { if (AreDoublesEqual (value[k + 4], refValue[k + 4], 0.000001) == NO) { areRunsSame = NO; break; } } for (k=0; k<2; k++) { if (AreDoublesEqual (value[k], refValue[k], 0.000001) == NO) { areRunsSame = NO; break; } } } else { for (k=0; k<3; k++) { if (AreDoublesEqual (value[k], refValue[k], 0.000001) == NO) { areRunsSame = NO; break; } } } if (areRunsSame == NO) break; } if (areRunsSame == NO) break; } /* now print values */ for (run=0; runnValues; if (run == 0 && chain == 0) MrBayesPrint ("%s Startvals = (%1.3lf", spacer, value[0]); else MrBayesPrint ("%s (%1.3lf", spacer, value[0]); for (k=1; krelParts[0]].omegaVar, "M10")) { for (k=0; k<4; k++) { MrBayesPrint (",%1.3lf", subValue[k + mp->numM10BetaCats+mp->numM10GammaCats+4]); } for (k=0; k<2; k++) { MrBayesPrint (",%1.3lf", subValue[k + mp->numM10BetaCats+mp->numM10GammaCats]); } } else { for (k=0; k<3; k++) { MrBayesPrint (",%1.3lf", subValue[k]); } } MrBayesPrint (")"); if (chainParams.numChains > 1 && areRunsSame == YES) MrBayesPrint (" [chain %d]\n", chain+1); else if (chainParams.numChains == 1 && areRunsSame == NO) MrBayesPrint (" [run %d]\n", run+1); else if (areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); } } } else { /* run of the mill parameter */ if( p->paramType == P_CLOCKRATE ) { for (j=0; jparamType == P_PI && modelParams[p->relParts[0]].dataType != STANDARD)) { nValues = p->nSubValues; value = GetParamSubVals (p, run*chainParams.numChains+chain, 0); refValue = GetParamSubVals (p, chain, 0); } else { nValues = p->nValues; value = GetParamVals (p, run*chainParams.numChains+chain, 0); refValue = GetParamVals (p, chain, 0); } for (k=0; k 0 && areRunsSame == YES) break; areChainsSame = YES; for (chain=1; chainparamType == P_PI && modelParams[p->relParts[0]].dataType != STANDARD)) { nValues = p->nSubValues; value = GetParamSubVals (p, run*chainParams.numChains+chain, 0); refValue = GetParamSubVals (p, run*chainParams.numChains, 0); } else { nValues = p->nValues; value = GetParamVals (p, run*chainParams.numChains+chain, 0); refValue = GetParamVals (p, run*chainParams.numChains, 0); } for (k=0; k 0) continue; if ((p->paramType == P_PI && modelParams[p->relParts[0]].dataType != STANDARD)) { nValues = p->nSubValues; value = GetParamSubVals (p, run*chainParams.numChains+chain, 0); } else { nValues = p->nValues; value = GetParamVals (p, run*chainParams.numChains+chain, 0); } if (run == 0 && chain == 0) MrBayesPrint ("%s Startvals = (%1.3lf", spacer, value[0]); else MrBayesPrint ("%s (%1.3lf", spacer, value[0]); for (k=1; knNodes-2; i++) { p = t->allDownPass[i]; brlens[p->index] = p->length * (tk02Rate[p->index] + tk02Rate[p->anc->index]) / 2.0; } return (NO_ERROR); } /*------------------------------------------------- | | UpdateCppEvolLengths: Recalculate effective | evolutionary lengths and set update flags | for Cpp relaxed clock model | --------------------------------------------------*/ int UpdateCppEvolLengths (Param *param, TreeNode *p, int chain) { int i, *nEvents; TreeNode *q; MrBFlt baseRate = 1.0, **pos, **rateMult, *evolLength; i = 2*chain + state[chain]; nEvents = param->nEvents[i]; pos = param->position[i]; rateMult = param->rateMult[i]; evolLength = GetParamSubVals (param, chain, state[chain]); q = p->anc; while (q->anc != NULL) { for (i=0; iindex]; i++) baseRate *= rateMult[q->index][i]; q = q->anc; } if (UpdateCppEvolLength (nEvents, pos, rateMult, evolLength, p, baseRate)==ERROR) return (ERROR); return(NO_ERROR); } /*------------------------------------------------- | | UpdateClockRate: Update clockRate of the given chain. Above all it will enforce fixed clockrate prior if it is set. Eroor will be returned if fixed clockrate prior may not be respected. | @param clockRate is the new clockRate to setup. Clock rate value could be set as positive, 0.0 or negative value. | The function does the fallowing depending on one of this three values: | positive - check that this 'positive' value is suitable rate. At the end re-enforce(update) the 'positive' value as clock rate on all trees. | 0.0 - check if current rate is suitable, if not update it with minimal suitable value. At the end re-enforce(update) the resulting clock rate on all trees. | negative - check if current rate is suitable, if not update it with minimal suitable value. At the end re-enforce(update) the resulting clock rate ONLY if clock rate was changed | @return ERROR if clockRate can not be set up, NO_ERROR otherwise. | --------------------------------------------------*/ int UpdateClockRate(MrBFlt clockRate, int chain) { int i, updateTrees; MrBFlt *clockRatep; Tree *t, *t_calibrated=NULL; MrBFlt mintmp,maxtmp,minClockRate,maxClockRate; clockRatep=NULL; minClockRate = 0.0; maxClockRate = MRBFLT_MAX; for (i=0; iisCalibrated == NO) continue; if( clockRatep == NULL ) { clockRatep = GetParamVals(modelSettings[t->relParts[0]].clockRate, chain, 0); t_calibrated = t; assert(clockRatep); } findAllowedClockrate (t, &mintmp, &maxtmp ); if( minClockRate < mintmp ) minClockRate = mintmp; if( maxClockRate > maxtmp ) maxClockRate = maxtmp; } /* clock rate is the same for all trees of a given chain*/ if( clockRatep != NULL) { if( minClockRate > maxClockRate) { MrBayesPrint ("%s ERROR: Calibrated trees require uncomatable clockrates for run:%d chain:%d.\n", spacer, chain/chainParams.numChains, chain%chainParams.numChains); *clockRatep=0; return (ERROR); } if (!strcmp(modelParams[t_calibrated->relParts[0]].clockRatePr, "Fixed")) { if( clockRate < 0.0 && AreDoublesEqual (*clockRatep, modelParams[t_calibrated->relParts[0]].clockRateFix, 0.0001) == YES ) { updateTrees = NO; } else { updateTrees = YES; } *clockRatep = modelParams[t_calibrated->relParts[0]].clockRateFix; if((*clockRatep < minClockRate && AreDoublesEqual (*clockRatep, minClockRate, 0.0001) == NO) || (*clockRatep > maxClockRate && AreDoublesEqual (*clockRatep, maxClockRate, 0.0001) == NO) ) { MrBayesPrint ("%s ERROR: Calibrated trees require clockrate in range from %f to %f, while clockrate prior is fixed to:%f for run:%d chain:%d.\n", spacer, minClockRate, minClockRate, *clockRatep, chain/chainParams.numChains, chain%chainParams.numChains); *clockRatep=0; return (ERROR); } if( clockRate > 0.0 ) { if ( AreDoublesEqual (*clockRatep, clockRate, 0.0001) == NO ) { MrBayesPrint ("%s ERROR: Requested clockrate:%f does not match fixed clockrate prior :%f.\n", spacer, clockRate, *clockRatep); *clockRatep=0; return (ERROR); } } } else {/*clock prior is not fixed*/ updateTrees = YES; if( clockRate > 0.0 ) { *clockRatep = clockRate; if((*clockRatep < minClockRate && AreDoublesEqual (*clockRatep, minClockRate, 0.0001) == NO) || (*clockRatep > maxClockRate && AreDoublesEqual (*clockRatep, maxClockRate, 0.0001) == NO) ) { MrBayesPrint ("%s ERROR: Calibrated trees require clockrate in range from %f to %f, while requested clockrate is:%f for run:%d chain:%d.\n", spacer, minClockRate, minClockRate, clockRate, chain/chainParams.numChains, chain%chainParams.numChains); *clockRatep=0; return (ERROR); } } else if ( clockRate == 0.0 ) { if((*clockRatep < minClockRate && AreDoublesEqual (*clockRatep, minClockRate, 0.0001) == NO) || (*clockRatep > maxClockRate && AreDoublesEqual (*clockRatep, maxClockRate, 0.0001) == NO) ) { *clockRatep = minClockRate; } } else// if ( clockRate < 0.0 ) { if((*clockRatep < minClockRate && AreDoublesEqual (*clockRatep, minClockRate, 0.0001) == NO) || (*clockRatep > maxClockRate && AreDoublesEqual (*clockRatep, maxClockRate, 0.0001) == NO) ) { *clockRatep = minClockRate; } else { updateTrees = NO; } } } if(updateTrees == YES) { for (i=0; iisCalibrated == NO) continue; UpdateTreeWithClockrate (t,*clockRatep); } } } return (NO_ERROR); } /*---------------------------------------------- | | UpdateCppEvolLength: Recursive function to | update evolLength of one node for Cpp | relaxed clock model | -----------------------------------------------*/ int UpdateCppEvolLength (int *nEvents, MrBFlt **pos, MrBFlt **rateMult, MrBFlt *evolLength, TreeNode *p, MrBFlt baseRate) { int i; MrBFlt endRate; if (p != NULL) { #ifndef NDEBUG if (baseRate < POS_MIN || baseRate > POS_INFINITY) { printf("baseRate out of bounds (%.15e for node %d\n", baseRate, p->index); return (ERROR); } #endif p->upDateTi = YES; p->upDateCl = YES; if (nEvents[p->index] == 0) { evolLength[p->index] = p->length * baseRate; } else { /* note that event positions are from the top of the branch (more recent) to the bottom of the branch (older) */ /* The algorithm below successively multiplies in the more basal rate multipliers, starting from the top of the branch. The length of the branch is first assumed to be 1.0; at the end we multiply the result with the true length of the branch. */ evolLength[p->index] = pos[p->index][0] * rateMult[p->index][0]; for (i=1; iindex]; i++) { evolLength[p->index] += (pos[p->index][i] - pos[p->index][i-1]); evolLength[p->index] *= rateMult[p->index][i]; } evolLength[p->index] += (1.0 - pos[p->index][nEvents[p->index]-1]); evolLength[p->index] *= baseRate; evolLength[p->index] *= p->length; } /* calculate end rate; we can do this in any order */ endRate = baseRate; for (i=0; iindex]; i++) endRate *= rateMult[p->index][i]; #ifndef NDEBUG if (endRate < POS_MIN || endRate > POS_INFINITY) { printf ("endRate out of bounds (%.15e for node %d)\n", endRate, p->index); return (ERROR); } if (p->anc != NULL && p->anc->anc != NULL && (evolLength[p->index] < POS_MIN || evolLength[p->index] > POS_INFINITY)) { printf ("Effective branch length out of bounds (%.15e for node %d)\n", evolLength[p->index], p->index); return (ERROR); } #endif /* call left and right descendants */ if (UpdateCppEvolLength (nEvents, pos, rateMult, evolLength, p->left, endRate)==ERROR) return (ERROR); if (UpdateCppEvolLength (nEvents, pos, rateMult, evolLength, p->right, endRate)==ERROR) return (ERROR); } return (NO_ERROR); } /* UpdateIgrBranchRates: update branch rates for igr model */ int UpdateIgrBranchRates(Param *param, Tree *t, int chain) { int i; MrBFlt *igrRate, *brlens; TreeNode *p; igrRate = GetParamVals (param, chain, state[chain]); brlens = GetParamSubVals (param, chain, state[chain]); for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; igrRate[p->index] = brlens[p->index] / p->length; } return (NO_ERROR); }