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pvartree.cpp
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/***************************************************************************************************
* The PolyMoSim project is distributed under the following license:
*
* Copyright (c) 2006-2022, Christoph Mayer, Forschungsmuseum Alexander Koenig, Bonn, Germany
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* 1. Redistributions of source code (complete or in parts) must retain
* the above copyright notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* 3. All advertising materials mentioning features or any use of this software
* e.g. in publications must display the following acknowledgement:
* This product includes software developed by Christoph Mayer, Forschungsmuseum
* Alexander Koenig, Bonn, Germany.
* 4. Neither the name of the organization nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY CHRISTOPH MAYER ''AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHTHOLDER OR ITS ORGANISATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* IMPORTANT (needs to be included, if code is redistributed):
* Please not that this license is not compatible with the GNU Public License (GPL)
* due to paragraph 3 in the copyright. It is not allowed under any
* circumstances to use the code of this software in projects distributed under the GPL.
* Furthermore, it is not allowed to redistribute the code in projects which are
* distributed under a license which is incompatible with one of the 4 paragraphs above.
*
* This project makes use of code coming from other projects. What follows is a complete
* list of files which make use of external code. Please refer to the copyright within
* these files.
*
* Files in tclap foler: Copyright (c) 2003 Michael E. Smoot
* See copyright in tclap/COPYRIGHT file for details.
* discrete_gamma.c: Copyright 1993-2004 by Ziheng Yang.
* See copyright in this file for details.
* CRandom.h: Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura
* See copyright in this file for details.
***************************************************************************************************/
#include "pvartree.h"
//Makros
#define DD1(x) while((x) < 0.0000001)
#define DD_rates(x) while((x) < 0.01)
#define DD2(x) while((x) <= 0)
using namespace std;
pvartree::pvartree() {
name_i = 0;
startmodel = NULL;
tree.set_read_model_status(false);
}
//pvartree::pvartree(double (ran*)()) {
// ranf = ran;
//}
//void pvartree::set_startmodel(const mymodel* start) {
// startmodel = start;
//}
void pvartree::set_random_generator(double (*ran)(double)) { //Funktion bekommt nur Standardabweichung; neuer Wert wird extern berechnet
ranf = ran; //Mittelwert + neuer Wert
}
//double pvartree::get_mean() const {
// return av;
//}
//void pvartree::set_mean(double a){
// av = a;
//}
void pvartree::read_parameters_from_file(const char *filename) {
vector<faststring> splitted;
faststring dummy;
CFile is;
int number_of_tokens;
stdev_rAC = 0;
stdev_rAG = 0;
stdev_rAT = 0;
stdev_rCG = 0;
stdev_rCT = 0;
stdev_rGT = 0;
stdev_tstv = 0;
stdev_shape = 0;
stdev_inv = 0;
stdev_A = 0;
stdev_G = 0;
stdev_C = 0;
if(!FileExists(filename)) {
cerr << "File: " << filename << " does not exist." << endl;
throw error();
}
is.ffopen(filename);
ignore_spaces(is);
is.getline(dummy);
dummy.replace_char(':' , ' ' );
dummy.replace_char('=' , ' ' );
dummy.ToLower();
while(!is.fail()) {
if(dummy[0] == '#') {
ignore_spaces(is);
is.getline(dummy);
dummy.replace_char(':' , ' ' );
dummy.replace_char('=' , ' ' );
dummy.ToLower();
continue;
}
number_of_tokens = split(splitted, dummy, " ");
if(splitted[0] == "ac") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for rAC");
}
stdev_rAC = splitted[1].ToDouble();
}
else if(splitted[0] == "ag") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for rAG");
}
stdev_rAG = splitted[1].ToDouble();
}
else if(splitted[0] == "at") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for rAT");
}
stdev_rAT = splitted[1].ToDouble();
}
else if(splitted[0] == "cg") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for rCG");
}
stdev_rCG = splitted[1].ToDouble();
}
else if(splitted[0] == "ct") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for rCT");
}
stdev_rCT = splitted[1].ToDouble();
}
else if(splitted[0] == "gt") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for rGT");
}
stdev_rGT = splitted[1].ToDouble();
}
else if(splitted[0] == "tstv" || splitted[0] == "titv") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for tstv");
}
stdev_tstv = splitted[1].ToDouble();
}
else if(splitted[0] == "shape") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for shape");
}
stdev_shape = splitted[1].ToDouble();
}
else if(splitted[0] == "inv") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for inv");
}
stdev_inv = splitted[1].ToDouble();
}
else if(splitted[0] == "pia" || splitted[0] == "a") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for PI_A");
}
stdev_A = splitted[1].ToDouble();
}
else if(splitted[0] == "pic" || splitted[0] == "c") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for PI_C");
}
stdev_C = splitted[1].ToDouble();
}
else if(splitted[0] == "pig" || splitted[0] == "g") {
if(number_of_tokens != 2) {
throw readerror(is.line(), "Bad standard deviation for PI_G");
}
stdev_G = splitted[1].ToDouble();
}
else {
faststring errormsg = "Umkown Parameter: " + splitted[0];
throw readerror(is.line(), errormsg.c_str());
}
ignore_spaces(is);
is.getline(dummy);
dummy.replace_char(':' , ' ' );
dummy.replace_char('=' , ' ' );
dummy.ToLower();
} //while
is.ffclose();
}
/*
void pvartree::set_parameter(istream& is) { //aus Datei einlesen: read_parameter_from_file
string dummy;
cout << "Set up the parameters needed to vary the startmodel: " << endl << endl;
cout << "standard deviation for a: " ;
is >> stdev_a;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_a;
}
cout << "standard deviation for b: " ;
is >> stdev_b;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_b;
}
cout << "standard deviation for c: " ;
is >> stdev_c;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_c;
}
cout << "standard deviation for d: " ;
is >> stdev_d;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_d;
}
cout << "standard deviation for e: " ;
is >> stdev_e;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_e;
}
cout << "standard deviation for f: " ;
is >> stdev_f;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_f;
}
cout << "standard deviation for shape: " ;
is >> stdev_shape;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_shape;
}
cout << "standard deviation for inv: " ;
is >> stdev_inv;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_inv;
}
cout << "standard deviation for A: " ;
is >> stdev_A;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_A;
}
cout << "standard deviation for G: " ;
is >> stdev_G;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_G;
}
cout << "standard deviation for C: " ;
is >> stdev_C;
while(is.fail()) {
cout << "error taking floating point value, please retry: " ;
is.clear();
is.ignore(100, '\n');
is >> stdev_C;
}
is.clear();
is.ignore(100, '\n');
// get the random generator?!
}
*/
void pvartree::vary_tree(int tree_counter, istream& is, const char* modelfilename, double scalefactor, const faststring& defaultmodel) {
BasicNode* node;
faststring temp;
faststring dummy;
/*
set_parameter(); // ausserhalb nur einmal setzen; für alle Bäume gleich xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
*/
inmodel.create(modelfilename);
if(defaultmodel == "") {
temp = "default";
}else if(defaultmodel == "default"){
temp = "default";
}else{
temp = defaultmodel;
}
startmodel = (const nuc_model*)inmodel.get_model(temp);
dummy = "Model_default_";
dummy.append(faststring(tree_counter) );
tree.read_tree(is, scalefactor); //can read from open stringstream //was für scalenfaktor muss in dem fall mitgeschickt werden??????????????
node = tree.get_treeroot();
// outmodel.append(dummy.c_str(), startmodel);
// Need to copy start model using new name:
nuc_model* newmodel;
newmodel = new nuc_model(dummy,
(nuc_model::enummodeltype)startmodel->get_modeltype(),
startmodel->get_ratetype(),
startmodel->get_tstv(),
startmodel->get_rAC(),
startmodel->get_rAG(),
startmodel->get_rAT(),
startmodel->get_rCG(),
startmodel->get_rCT(),
startmodel->get_rGT(),
startmodel->get_shape(),
startmodel->get_inv(),
startmodel->get_ncat(),
startmodel->get_PI_A(),
startmodel->get_PI_C(),
startmodel->get_PI_G(),
startmodel->get_PI_T() );
outmodel.append_a_copy(newmodel, dummy);
vary_models(node, tree_counter);
delete newmodel;
}
void pvartree::vary_models(BasicNode* node, int tree_counter) {
const nuc_model* temp;
faststring dummy;
nuc_model* newmodel = NULL;
unsigned i;
if(node->get_parent() == NULL){
dummy = "Model_default_";
dummy.append(faststring(tree_counter));
node->set_model(startmodel); // richtige Modell mit falschem Namen
node->set_model_name(dummy); // ------ überflüssig, da der Modelname im Knoten schon durch set_model gesetzt wird
} else {
temp = (const nuc_model*)node->get_parent()->get_model();
dummy = "Model_t";
dummy.append( faststring(tree_counter) );
dummy.push_back('_');
dummy.append( faststring(name_i));
cout << endl;
cout << "name: " << dummy << endl;
newmodel = vary_parent_model(temp, dummy);
// xxxxxxxxxx memory leak - is newmodel deleted?
// outmodel.append(dummy.c_str(), newmodel);
outmodel.append_a_copy(newmodel, dummy);
node->set_model(newmodel);
// delete newmodel;
// node->set_model_name(dummy);
}
for(i = 0; i < node->get_num_children(); i++) {
name_i++;
vary_models(node->get_childlist()[i], tree_counter);
}
if (newmodel)
delete newmodel;
}
nuc_model* pvartree::vary_parent_model(const nuc_model* orig_model, faststring& modname) { //die richtigen Fallunterscheidungen implementieren
double nAC, nAG, nAT, nCG, nCT, nGT;
double ntstv;
double nshape;
double ninv;
double npiA, npiG, npiC, npiT;
molecular_model<4>::enumratetype nrate;
nuc_model::enummodeltype ntype;
double orig_AC, orig_AG, orig_AT, orig_CG, orig_CT, orig_GT;
nAC = orig_AC = orig_model->get_rAC();
nAG = orig_AG = orig_model->get_rAG();
nAT = orig_AT = orig_model->get_rAT();
nCG = orig_CG = orig_model->get_rCG();
nCT = orig_CT = orig_model->get_rCT();
nGT = orig_GT = orig_model->get_rGT();
double orig_tstv;
ntstv = orig_tstv= orig_model->get_tstv();
double orig_shape;
double orig_inv;
int orig_ncat;
double orig_piA;
double orig_piC;
double orig_piG;
nshape = orig_shape = orig_model->get_shape();
ninv = orig_inv = orig_model->get_inv();
orig_ncat = orig_model->get_ncat();
npiA = orig_piA = orig_model->get_PI_A();
npiC = orig_piC = orig_model->get_PI_C();
npiG = orig_piG = orig_model->get_PI_G();
npiT = 1 - npiA - npiC - npiG;
nrate = orig_model->get_ratetype();
ntype = (nuc_model::enummodeltype)orig_model->get_modeltype();
DEBUGCODE( cerr << "nrate: " << nrate << endl );
DEBUGCODE( cerr << "ntype: " << ntype << endl );
if(nrate == molecular_model<4>::ratetype_invgamma) {
DD2( nshape = orig_shape + ranf(stdev_shape) ){}
DEBUGCODE( cerr << "nshape: " << nshape << endl );
DD2( ninv = orig_inv + ranf(stdev_inv) ){}
DEBUGCODE( cerr << " ninv: " << ninv << endl );
}
else if(nrate == molecular_model<4>::ratetype_propinv) {
DD2( ninv = orig_inv + ranf(stdev_inv) ){}
nshape = 1000000;
DEBUGCODE( cerr << " ninv: " << ninv << endl );
}
else if(nrate == molecular_model<4>::ratetype_gamma) {
ninv = 0;
DD2( nshape = orig_shape + ranf(stdev_shape) ){}
DEBUGCODE( cerr << "nshape: " << nshape << endl );
}
else
{
nshape = 1000000;
ninv = 0;
}
if(ntype != nuc_model::JC)
{
if(ntype != nuc_model::K2P)
{
do{
DD1( npiA = orig_piA + ranf(stdev_A) ){};
DEBUGCODE( cerr << "npiA: " << npiA << endl );
DD1( npiC = orig_piC + ranf(stdev_C) ){};
DEBUGCODE( cerr << "npiC: " << npiC << endl );
DD1( npiG = orig_piG + ranf(stdev_G) ){};
DEBUGCODE( cerr << "npiG: " << npiG << endl );
npiT = 1 - (npiA + npiC + npiG);
DEBUGCODE( cerr << "npiT: " << npiT << endl );
} while(npiA < 0.1 || npiC < 0.1 ||npiG < 0.1 ||npiT < 0.1); //kleinere Werte ergeben keinen biologischen Sinn
}
if(ntype != nuc_model::F81 && ntype != nuc_model::GTR) {
DD1( ntstv = orig_tstv + ranf(stdev_tstv) ){};
DEBUGCODE( cerr << "ntstv: " << ntstv << endl );
}
if(ntype == nuc_model::GTR) {
DD_rates( nAC = orig_AC + ranf(stdev_rAC) ){}
DEBUGCODE( cerr << "nAC: " << nAC << endl );