From a260432ef7846ddf5e6f9054da2c273923c024cb Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 12 Oct 2021 14:38:26 +0200 Subject: [PATCH 01/71] stuff --- .gitignore | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.gitignore b/.gitignore index 193da6a..c073c1e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,11 @@ +# Potential folders within +peregrine/ +analysis/ +ms/ +data/ +approx/ +extra/ + ##### Windows # Windows thumbnail cache files Thumbs.db From 36f0ee1174753338a787e1059ca1f398a74c976c Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 14 Mar 2022 10:52:11 +0100 Subject: [PATCH 02/71] new documentation --- src/Collector.cpp | 2 ++ src/Collector.h | 12 ++++++------ src/Freezer.cpp | 2 ++ src/Freezer.h | 18 ++++++++---------- src/GenArch.cpp | 2 ++ src/GenArch.h | 14 +++++++------- src/Individual.cpp | 2 ++ src/Individual.h | 8 ++++++-- src/MetaPop.cpp | 1 + src/MetaPop.h | 10 ++++++++-- src/Network.cpp | 2 ++ src/Network.h | 12 ++++++------ src/Param.cpp | 2 ++ src/Param.h | 11 +++++------ src/Printer.cpp | 2 ++ src/Printer.h | 4 ++-- src/Random.cpp | 4 +++- src/Random.h | 18 ++++++++++++++++-- src/Simul.cpp | 2 ++ src/Simul.h | 8 ++++++-- src/Utilities.cpp | 3 +++ src/Utilities.h | 4 ++-- tests/OutputTests.cpp | 2 -- tests/TestUtilities.cpp | 2 ++ tests/TestUtilities.h | 6 ++++-- 25 files changed, 101 insertions(+), 52 deletions(-) diff --git a/src/Collector.cpp b/src/Collector.cpp index d1e8600..dda3358 100755 --- a/src/Collector.cpp +++ b/src/Collector.cpp @@ -1,5 +1,7 @@ #include "Collector.h" +// This is the source code for the member functions of the Collector class. + // Analytical functions of the Collector module //--------------------------------------------- diff --git a/src/Collector.h b/src/Collector.h index 3efd707..e0d61a7 100755 --- a/src/Collector.h +++ b/src/Collector.h @@ -1,11 +1,11 @@ -#ifndef EXPLICITGENOMESPECIATION_COLLECTOR_H -#define EXPLICITGENOMESPECIATION_COLLECTOR_H +#ifndef SPECIOME_COLLECTOR_H +#define SPECIOME_COLLECTOR_H -// This header contains the declarations of the Collector class and allies -// This class is an analytical module for the simulation +// This header contains the declarations of the Collector class and allies. +// This class is an analytical module for the simulation. // Using its main function "analyze", it computes many statistics from the -// population (genome-wide, locus-specific or edge-specific statistics) -// The content of the Collector is reset every time "analyze" is called +// population (genome-wide, locus-specific or edge-specific statistics). +// The content of the Collector is reset every time "analyze" is called. #include "Utilities.h" #include "MetaPop.h" diff --git a/src/Freezer.cpp b/src/Freezer.cpp index c6f92fa..0522729 100644 --- a/src/Freezer.cpp +++ b/src/Freezer.cpp @@ -1,5 +1,7 @@ #include "Freezer.h" +// This is the source code for the member functions of the Freezer class. + // Constructor Freezer::Freezer() : freezer(new std::ofstream), diff --git a/src/Freezer.h b/src/Freezer.h index b109ae2..b509dc5 100644 --- a/src/Freezer.h +++ b/src/Freezer.h @@ -1,21 +1,19 @@ -#ifndef EXPLICITGENOMESPECIATION_FREEZER_H -#define EXPLICITGENOMESPECIATION_FREEZER_H +#ifndef SPECIOME_FREEZER_H +#define SPECIOME_FREEZER_H // This module is a special printer used for saving individual full genomes, -// directly from the MetaPop +// directly from the MetaPop. +// Note: Using 64bit integers is a way to save space when saving full genomes. +// The stf::write function saves in binary format, meaning that it will +// convert back the integers into their underlying bitsets. +// The resulting binary output file should therefore be interpreted as +// a bit-wise array; each value (allele) is encoded by a single bit. #include "Utilities.h" #include "MetaPop.h" #include "Printer.h" #include -// Note: Using 64bit integers is a way to save space when saving full genomes -// The stf::write function saves in binary format, meaning that it will -// convert back the integers into their underlying bitsets -// The resulting binary output file should therefore be interpreted as -// a bit-wise array; each value (allele) is encoded by a single bit - - class Freezer { diff --git a/src/GenArch.cpp b/src/GenArch.cpp index 32273b7..debb541 100755 --- a/src/GenArch.cpp +++ b/src/GenArch.cpp @@ -1,5 +1,7 @@ #include "GenArch.h" +// This is the source code for the member functions of the GenArch class. + std::vector GenArch::makeChromosomes(const Param &p) const { diff --git a/src/GenArch.h b/src/GenArch.h index aa4bc76..5a4c579 100755 --- a/src/GenArch.h +++ b/src/GenArch.h @@ -1,5 +1,10 @@ -#ifndef EXPLICITGENOMESPECIATION_GENARCH_H -#define EXPLICITGENOMESPECIATION_GENARCH_H +#ifndef SPCIOME_GENARCH_H +#define SPCIOME_GENARCH_H + +// The genetic architecture contains locus-specific details about the +// genotype-phenotype map. It is created using the parameters, but contains +// large arrays of values across the whole genome, and is therefore +// larger than the Param class. #include "Param.h" #include "Random.h" @@ -14,11 +19,6 @@ typedef std::pair Edge; typedef std::vector MultiNet; typedef std::vector > vecStreams; -// The genetic architecture contains locus-specific details about the -// genotype-phenotype map. It is created using the parameters, but contains -// large arrays of values across the whole genome, and is therefore -// larger than the Param class. - class GenArch { friend class Collector; diff --git a/src/Individual.cpp b/src/Individual.cpp index 9436578..305b274 100755 --- a/src/Individual.cpp +++ b/src/Individual.cpp @@ -1,5 +1,7 @@ #include "Individual.h" +// This is the source code for the member functions of the Individual class. + // Constructors //-------------- diff --git a/src/Individual.h b/src/Individual.h index 07cb79a..79fd1fa 100755 --- a/src/Individual.h +++ b/src/Individual.h @@ -1,5 +1,9 @@ -#ifndef EXPLICITGENOMESPECIATION_INDIVIDUAL_H -#define EXPLICITGENOMESPECIATION_INDIVIDUAL_H +#ifndef SPECIOME_INDIVIDUAL_H +#define SPECIOME_INDIVIDUAL_H + +// This header contains the class Individual. Each Individual consists of +// a genome from which traits can be deduced based on the genotype-phenotype map +// (via the develop() function). Individuals can do a few other things too. #include "GenArch.h" #include "Utilities.h" diff --git a/src/MetaPop.cpp b/src/MetaPop.cpp index 404c62b..5beb480 100755 --- a/src/MetaPop.cpp +++ b/src/MetaPop.cpp @@ -1,5 +1,6 @@ #include "MetaPop.h" +// This is the source code for the member functions of the MetaPop class. // Initialization diff --git a/src/MetaPop.h b/src/MetaPop.h index e6c2c91..aa0b34c 100755 --- a/src/MetaPop.h +++ b/src/MetaPop.h @@ -1,5 +1,11 @@ -#ifndef EXPLICITGENOMESPECIATION_METAPOP_H -#define EXPLICITGENOMESPECIATION_METAPOP_H +#ifndef SPECIOME_METAPOP_H +#define SPECIOME_METAPOP_H + +// This header contains the class MetaPop. A MetaPop is a wrapper around a +// vector of Individual objects with a bunch of member functions applying +// a life cycle to those individuals. Note: it is called MetaPop because +// there are two patches linked by dispersal, so the model is technically +// a metapopulation. #include "Param.h" #include "GenArch.h" diff --git a/src/Network.cpp b/src/Network.cpp index c4145c1..71ac7c6 100755 --- a/src/Network.cpp +++ b/src/Network.cpp @@ -1,5 +1,7 @@ #include "Network.h" +// This is the source code for the member functions of the Network class. + bool checkedges(const size_t &e, const size_t &n) { return e <= n * (n - 1u) / 2u; // max number of edges given number of nodes diff --git a/src/Network.h b/src/Network.h index abc2240..b616803 100755 --- a/src/Network.h +++ b/src/Network.h @@ -1,5 +1,9 @@ -#ifndef EXPLICITGENOMESPECIATION_NETWORK_H -#define EXPLICITGENOMESPECIATION_NETWORK_H +#ifndef SPECIOME_NETWORK_H +#define SPECIOME_NETWORK_H + +// A class for a gene regulatory network. One network underlies one of the +// three traits of the simulation. Networks are generated using a +// preferential attachment algorithm, and are part of the bigger class GenArch. #include "Param.h" @@ -10,10 +14,6 @@ typedef std::pair Edge; -// A class for a gene regulatory network. One network underlies one of the -// three traits of the simulation. Networks are generated using a -// preferential attachment algorithm, and are part of the bigger class GenArch. - class Network { diff --git a/src/Param.cpp b/src/Param.cpp index cbe059a..ae5dea2 100755 --- a/src/Param.cpp +++ b/src/Param.cpp @@ -1,5 +1,7 @@ #include "Param.h" +// This is the source code for the member functions of the Param class. + // Constructor //------------ diff --git a/src/Param.h b/src/Param.h index 0a39cd3..e0e7f82 100755 --- a/src/Param.h +++ b/src/Param.h @@ -1,6 +1,9 @@ -#ifndef EXPLICITGENOMESPECIATION_PARAM_H -#define EXPLICITGENOMESPECIATION_PARAM_H +#ifndef SPECIOME_PARAM_H +#define SPECIOME_PARAM_H +// Parameter set. Contains values of the parameters of the simulation. +// All parameters have default values that can be modified by calling +// the program with a parameter file name as unique argument. #include "Utilities.h" #include "Random.h" @@ -10,10 +13,6 @@ #include #include -// Parameter set. Contains values of the parameters of the simulation. -// All parameters have default values that can be modified by calling -// the program with a parameter file name as unique argument. - struct Param { Param(); diff --git a/src/Printer.cpp b/src/Printer.cpp index 41051db..3577688 100644 --- a/src/Printer.cpp +++ b/src/Printer.cpp @@ -1,5 +1,7 @@ #include "Printer.h" +// This is the source code for the member functions of the Printer class. + // Constructor Printer::Printer(const std::string &orderfile) : filenames(whattosave(orderfile)), diff --git a/src/Printer.h b/src/Printer.h index 569646e..dcd8236 100644 --- a/src/Printer.h +++ b/src/Printer.h @@ -1,5 +1,5 @@ -#ifndef EXPLICITGENOMESPECIATION_PRINTER_H -#define EXPLICITGENOMESPECIATION_PRINTER_H +#ifndef SPECIOME_PRINTER_H +#define SPECIOME_PRINTER_H // This module is used to save summary statistics from the Collector to output // files diff --git a/src/Random.cpp b/src/Random.cpp index 3cd6b76..bf29ba0 100755 --- a/src/Random.cpp +++ b/src/Random.cpp @@ -1,6 +1,8 @@ #include "Random.h" -/// Random number generators +// This sript defines random number generators used in the rnd namespace + +// Random number generators std::mt19937_64 rnd::rng; std::mt19937_64 rnd::rng2; diff --git a/src/Random.h b/src/Random.h index de2365f..93440ce 100755 --- a/src/Random.h +++ b/src/Random.h @@ -1,6 +1,20 @@ -#ifndef EXPLICITGENOMESPECIATION_RANDOM_H -#define EXPLICITGENOMESPECIATION_RANDOM_H +#ifndef SPECIOME_RANDOM_H +#define SPECIOME_RANDOM_H +// This header contains aliases for various kinds of probability distributions +// as well as a random number generator. Include this header in other scripts +// to be able to use it. Note that the header rndutils.hpp (cc Hanno Hildebrandt) +// is needed for some distributions. + +// Example usage: +// double x = rnd::normal(0.0, 1.0)(rnd::rng); +// to sample a number from a normal distribution with mean zero and standard +// deviation one. Alternatively, use: +// auto mynormal = rnd::normal(0.0, 1.0); +// to set up the normal distribution and only sample from it when needed: +// double x = mynormal(rnd::rng); +// (This can be useful when sampling many times, as setting up a distribution +// takes time...) #include "rndutils.hpp" #include diff --git a/src/Simul.cpp b/src/Simul.cpp index a3e6af9..0da50db 100755 --- a/src/Simul.cpp +++ b/src/Simul.cpp @@ -1,5 +1,7 @@ #include "Simul.h" +// This is the source code for the main simulation function + int simulate(const std::vector &args) { diff --git a/src/Simul.h b/src/Simul.h index 9e9ae5b..ace6535 100755 --- a/src/Simul.h +++ b/src/Simul.h @@ -1,5 +1,9 @@ -#ifndef EXPLICITGENOMESPECIATION_SIMUL_H -#define EXPLICITGENOMESPECIATION_SIMUL_H +#ifndef SPECIOME_SIMUL_H +#define SPECIOME_SIMUL_H + +// This is the header containing the main simulation function and some +// accompanying functions. The core of the program was encapsulated into a +// simulate() function in order for it to be tested separately. #include "Param.h" #include "GenArch.h" diff --git a/src/Utilities.cpp b/src/Utilities.cpp index a4692c4..0c2ef85 100755 --- a/src/Utilities.cpp +++ b/src/Utilities.cpp @@ -1,5 +1,8 @@ #include "Utilities.h" +// This is the source code with utility functions from the utl and stf +// namespaces + // Square function double utl::sqr(const double &number) { diff --git a/src/Utilities.h b/src/Utilities.h index a18763d..f21a339 100755 --- a/src/Utilities.h +++ b/src/Utilities.h @@ -1,5 +1,5 @@ -#ifndef EXPLICITGENOMESPECIATION_UTILITIES_H -#define EXPLICITGENOMESPECIATION_UTILITIES_H +#ifndef SPECIOME_UTILITIES_H +#define SPECIOME_UTILITIES_H #include #include diff --git a/tests/OutputTests.cpp b/tests/OutputTests.cpp index 076635f..7bac1ec 100644 --- a/tests/OutputTests.cpp +++ b/tests/OutputTests.cpp @@ -11,8 +11,6 @@ // Test the output files -// One big test case in which we make sure everything is saved properly - // Testing correct output files BOOST_AUTO_TEST_CASE(OutputFilesAreCorrectlyWritten) { diff --git a/tests/TestUtilities.cpp b/tests/TestUtilities.cpp index d1feef3..6cd8421 100644 --- a/tests/TestUtilities.cpp +++ b/tests/TestUtilities.cpp @@ -1,5 +1,7 @@ #include "tests/TestUtilities.h" +// Source code for the functions of the tst namespace + std::vector tst::readfile(const std::string &filename) { // Open the input file diff --git a/tests/TestUtilities.h b/tests/TestUtilities.h index 3733105..5add79a 100755 --- a/tests/TestUtilities.h +++ b/tests/TestUtilities.h @@ -1,5 +1,7 @@ -#ifndef EXPLICITGENOMESPECIATION_TESTUTILITIES_H -#define EXPLICITGENOMESPECIATION_TESTUTILITIES_H +#ifndef SPECIOME_TESTUTILITIES_H +#define SPECIOME_TESTUTILITIES_H + +// This is a header for some utility functions used in testing #include #include From 24780a5e0aeead02c90857b1e224f3512d85c1a4 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Sun, 12 Feb 2023 14:10:59 +0100 Subject: [PATCH 03/71] stuff --- .gitignore | 1 + mating | 1 + 2 files changed, 2 insertions(+) create mode 160000 mating diff --git a/.gitignore b/.gitignore index c073c1e..fe7ccc9 100644 --- a/.gitignore +++ b/.gitignore @@ -1046,3 +1046,4 @@ modules.order Module.symvers Mkfile.old dkms.conf +.Rproj.user diff --git a/mating b/mating new file mode 160000 index 0000000..1da164b --- /dev/null +++ b/mating @@ -0,0 +1 @@ +Subproject commit 1da164be33d7fbe93d028219b5e98ba288250ad9 From d12899a840c81ff8a3831d9280da7a17f2ed2c7f Mon Sep 17 00:00:00 2001 From: rscherrer Date: Sun, 12 Feb 2023 14:22:57 +0100 Subject: [PATCH 04/71] stuff --- .gitmodules | 3 +++ CMakeLists.txt | 29 +++++++++++++++++++++++++++++ src/CMakeLists.txt | 15 +++++++++++++++ tests/CMakeLists.txt | 17 +++++++++++++++++ vcpkg.json | 7 +++++++ 5 files changed, 71 insertions(+) create mode 100644 .gitmodules create mode 100644 CMakeLists.txt create mode 100644 src/CMakeLists.txt create mode 100644 tests/CMakeLists.txt create mode 100644 vcpkg.json diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..fe07c99 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "vcpkg"] + path = vcpkg + url = https://github.com/microsoft/vcpkg diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..0255d16 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,29 @@ +# ./CMakeLists.txt + +cmake_minimum_required(VERSION 3.16) + +set(CMAKE_TOOLCHAIN_FILE "${CMAKE_SOURCE_DIR}/vcpkg/scripts/buildsystems/vcpkg.cmake") +if (WIN32) + set(VCPKG_TARGET_TRIPLET x64-windows) +endif() + +project(hemigene) + +find_package(Git REQUIRED) +execute_process(COMMAND ${GIT_EXECUTABLE} submodule update --init --remote + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} + RESULT_VARIABLE GIT_SUBMOD_RESULT) +if (NOT GIT_SUBMOD_RESULT EQUAL "0") + message(FATAL_ERROR "git submodule update --init failed with ${GIT_SUBMOD_RESULT}, please checkout submodules") +endif() + +# we want to use the newest and boldest c++ standard +set(CMAKE_CXX_STANDARD 20) +set(CMAKE_CXX_STANDARD_REQUIRED) + +# boilerplate +set(EXECUTABLE_OUTPUT_PATH ${CMAKE_BINARY_DIR}) +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}) + +add_subdirectory(src) +add_subdirectory(tests) diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt new file mode 100644 index 0000000..8a2341b --- /dev/null +++ b/src/CMakeLists.txt @@ -0,0 +1,15 @@ +# src/CMakeLists.txt + +# collect source and header files inside the current directory +# a bit lazy to do this via 'globbing expressions' but hey... +file(GLOB_RECURSE src + ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp +) + +# instruct CMake to build the binary +# oops, we need to add the sidelined main.cpp source file. +add_executable(hemigene "${CMAKE_SOURCE_DIR}/main.cpp" ${src}) + +# place the binary into ./bin +set_target_properties(speciome PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/bin/$<0:>) diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt new file mode 100644 index 0000000..4fa1448 --- /dev/null +++ b/tests/CMakeLists.txt @@ -0,0 +1,17 @@ +# tests/CMakeLists.txt + +find_package(Boost COMPONENTS unit_test_framework REQUIRED) + +# model 'unit' files +file(GLOB_RECURSE unit + ${CMAKE_SOURCE_DIR}/src/*.cpp +) + +set(TESTS tests) + +foreach(TEST IN LISTS TESTS) + add_executable(${TEST} ${CMAKE_SOURCE_DIR}/tests/${TEST}.cpp ${unit}) + target_include_directories(${TEST} PRIVATE ${CMAKE_SOURE_DIR}) + target_link_libraries(${TEST} PUBLIC Boost::unit_test_framework) + set_target_properties(${TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/bin/tests/$<0:>) +endforeach() diff --git a/vcpkg.json b/vcpkg.json new file mode 100644 index 0000000..b729b75 --- /dev/null +++ b/vcpkg.json @@ -0,0 +1,7 @@ +{ + "name": "speciome", + "version-string": "0.1.0", + "dependencies": [ + "boost-test" + ] +} From 9014618416a3a0452d8dba7b664c735b6bd083ab Mon Sep 17 00:00:00 2001 From: rscherrer Date: Sun, 12 Feb 2023 17:37:58 +0100 Subject: [PATCH 05/71] stuff --- CMakeLists.txt | 2 +- mating | 1 - src/CMakeLists.txt | 2 +- tests/CMakeLists.txt | 26 ++++++++++++++------------ vcpkg | 1 + 5 files changed, 17 insertions(+), 15 deletions(-) delete mode 160000 mating create mode 160000 vcpkg diff --git a/CMakeLists.txt b/CMakeLists.txt index 0255d16..61f3bd7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -7,7 +7,7 @@ if (WIN32) set(VCPKG_TARGET_TRIPLET x64-windows) endif() -project(hemigene) +project(speciome) find_package(Git REQUIRED) execute_process(COMMAND ${GIT_EXECUTABLE} submodule update --init --remote diff --git a/mating b/mating deleted file mode 160000 index 1da164b..0000000 --- a/mating +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 1da164be33d7fbe93d028219b5e98ba288250ad9 diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 8a2341b..822341a 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -9,7 +9,7 @@ file(GLOB_RECURSE src # instruct CMake to build the binary # oops, we need to add the sidelined main.cpp source file. -add_executable(hemigene "${CMAKE_SOURCE_DIR}/main.cpp" ${src}) +add_executable(speciome "${CMAKE_SOURCE_DIR}/main.cpp" ${src}) # place the binary into ./bin set_target_properties(speciome PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/bin/$<0:>) diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 4fa1448..989928a 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -1,17 +1,19 @@ # tests/CMakeLists.txt -find_package(Boost COMPONENTS unit_test_framework REQUIRED) +find_package(Boost COMPONENTS unit_test_framework) -# model 'unit' files -file(GLOB_RECURSE unit - ${CMAKE_SOURCE_DIR}/src/*.cpp -) +if (Boost_FOUND) + + # model 'unit' files + file(GLOB_RECURSE unit ${CMAKE_SOURCE_DIR}/src/*.cpp) -set(TESTS tests) + set(TESTS tests) -foreach(TEST IN LISTS TESTS) - add_executable(${TEST} ${CMAKE_SOURCE_DIR}/tests/${TEST}.cpp ${unit}) - target_include_directories(${TEST} PRIVATE ${CMAKE_SOURE_DIR}) - target_link_libraries(${TEST} PUBLIC Boost::unit_test_framework) - set_target_properties(${TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/bin/tests/$<0:>) -endforeach() + foreach(TEST IN LISTS TESTS) + add_executable(${TEST} ${CMAKE_SOURCE_DIR}/tests/${TEST}.cpp ${unit}) + target_include_directories(${TEST} PRIVATE ${CMAKE_SOURE_DIR}) + target_link_libraries(${TEST} PUBLIC Boost::unit_test_framework) + set_target_properties(${TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/bin/tests/$<0:>) + endforeach() + +endif() diff --git a/vcpkg b/vcpkg new file mode 160000 index 0000000..36fb233 --- /dev/null +++ b/vcpkg @@ -0,0 +1 @@ +Subproject commit 36fb23307e10cc6ffcec566c46c4bb3f567c82c6 From e06e6b39738236a56d57bc5bc1dcbb84bf062e43 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Sun, 12 Feb 2023 17:38:52 +0100 Subject: [PATCH 06/71] stuff --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index fe7ccc9..53be53f 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ ms/ data/ approx/ extra/ +mating/ ##### Windows # Windows thumbnail cache files From f30d1a8dee483ede8c7517e12f54b1d66cd21793 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 13 Feb 2023 11:28:40 +0100 Subject: [PATCH 07/71] stuff --- .vscode/settings.json | 5 +++ main.cpp | 8 ++--- speciome.pri | 25 ------------- speciome.pro | 27 -------------- speciome_test.pri | 8 ----- speciome_test.pro | 36 ------------------- src/Collector.cpp | 2 +- src/{Collector.h => Collector.hpp} | 10 +++--- src/Freezer.cpp | 2 +- src/{Freezer.h => Freezer.hpp} | 10 +++--- src/GenArch.cpp | 2 +- src/{GenArch.h => GenArch.hpp} | 13 ++++--- src/Individual.cpp | 2 +- src/{Individual.h => Individual.hpp} | 11 +++--- src/MetaPop.cpp | 2 +- src/{MetaPop.h => MetaPop.hpp} | 12 +++---- src/Network.cpp | 2 +- src/{Network.h => Network.hpp} | 11 +++--- src/Param.cpp | 2 +- src/{Param.h => Param.hpp} | 8 ++--- src/Printer.cpp | 2 +- src/{Printer.h => Printer.hpp} | 10 +++--- src/Random.cpp | 2 +- src/{Random.h => Random.hpp} | 4 +-- src/Simul.cpp | 2 +- src/{Simul.h => Simul.hpp} | 18 +++++----- {tests => src}/TestUtilities.cpp | 2 +- .../TestUtilities.h => src/TestUtilities.hpp | 4 +-- src/Utilities.cpp | 2 +- src/{Utilities.h => Utilities.hpp} | 4 +-- tests/CMakeLists.txt | 2 +- tests/CollectorTests.cpp | 9 +++-- tests/GenArchTests.cpp | 8 +++-- tests/IndividualTests.cpp | 6 ++-- tests/MetaPopTests.cpp | 8 +++-- tests/OutputTests.cpp | 16 +++++---- tests/SimulTests.cpp | 7 ++-- 37 files changed, 110 insertions(+), 194 deletions(-) create mode 100644 .vscode/settings.json delete mode 100755 speciome.pri delete mode 100755 speciome.pro delete mode 100755 speciome_test.pri delete mode 100755 speciome_test.pro rename src/{Collector.h => Collector.hpp} (98%) rename src/{Freezer.h => Freezer.hpp} (86%) rename src/{GenArch.h => GenArch.hpp} (96%) rename src/{Individual.h => Individual.hpp} (94%) rename src/{MetaPop.h => MetaPop.hpp} (94%) rename src/{Network.h => Network.hpp} (94%) rename src/{Param.h => Param.hpp} (94%) rename src/{Printer.h => Printer.hpp} (77%) rename src/{Random.h => Random.hpp} (96%) rename src/{Simul.h => Simul.hpp} (66%) rename {tests => src}/TestUtilities.cpp (99%) rename tests/TestUtilities.h => src/TestUtilities.hpp (90%) rename src/{Utilities.h => Utilities.hpp} (95%) diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..3ec79d0 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "files.associations": { + "fstream": "cpp" + } +} \ No newline at end of file diff --git a/main.cpp b/main.cpp index 53347f0..28eeca2 100755 --- a/main.cpp +++ b/main.cpp @@ -1,7 +1,7 @@ -#include "src/Param.h" -#include "src/GenArch.h" -#include "src/Random.h" -#include "src/Simul.h" +#include "src/Param.hpp" +#include "src/GenArch.hpp" +#include "src/Random.hpp" +#include "src/Simul.hpp" #include #include #include diff --git a/speciome.pri b/speciome.pri deleted file mode 100755 index 8fe91f4..0000000 --- a/speciome.pri +++ /dev/null @@ -1,25 +0,0 @@ -SOURCES += \ - $$PWD/src/GenArch.cpp \ - $$PWD/src/Param.cpp \ - $$PWD/src/Utilities.cpp \ - $$PWD/src/Random.cpp \ - $$PWD/src/Network.cpp \ - $$PWD/src/Individual.cpp \ - $$PWD/src/MetaPop.cpp \ - $$PWD/src/Simul.cpp \ - $$PWD/src/Collector.cpp \ - $$PWD/src/Printer.cpp \ - $$PWD/src/Freezer.cpp -HEADERS += \ - $$PWD/src/GenArch.h \ - $$PWD/src/Param.h \ - $$PWD/src/Utilities.h \ - $$PWD/src/Random.h \ - $$PWD/src/Network.h \ - $$PWD/src/Individual.h \ - $$PWD/src/MetaPop.h \ - $$PWD/src/Simul.h \ - $$PWD/src/Collector.h \ - $$PWD/src/Printer.h \ - $$PWD/src/Freezer.h \ - $$PWD/src/rndutils.hpp diff --git a/speciome.pro b/speciome.pro deleted file mode 100755 index 78c03c8..0000000 --- a/speciome.pro +++ /dev/null @@ -1,27 +0,0 @@ -# Files -include(speciome.pri) -SOURCES += main.cpp - -# C++14 -CONFIG += c++14 -QMAKE_CXXFLAGS += -std=c++14 -QMAKE_CXXFLAGS_RELEASE += -O3 - -# High warnings levels -QMAKE_CXXFLAGS += -Wall -Wextra -Wshadow -Wnon-virtual-dtor -pedantic -Weffc++ -Werror - -# Allow debug and release mode -CONFIG += debug_and_release - -# In release mode, turn on profiling -# The -pg option means that upon execution the program will save a gmon.out file -# which can then be analyzed by the gprof profiler (the profiler will not run the -# program, just check the file produced during normal execution) -CONFIG(release, debug|release) { - - DEFINES += NDEBUG - - # gprof - QMAKE_CXXFLAGS += -pg - QMAKE_LFLAGS += -pg -} diff --git a/speciome_test.pri b/speciome_test.pri deleted file mode 100755 index 937f1e5..0000000 --- a/speciome_test.pri +++ /dev/null @@ -1,8 +0,0 @@ -SOURCES += $$PWD/tests/SimulTests.cpp -SOURCES += $$PWD/tests/GenArchTests.cpp -SOURCES += $$PWD/tests/MetaPopTests.cpp -SOURCES += $$PWD/tests/IndividualTests.cpp -SOURCES += $$PWD/tests/CollectorTests.cpp -SOURCES += $$PWD/tests/OutputTests.cpp -SOURCES += $$PWD/tests/TestUtilities.cpp -HEADERS += $$PWD/tests/TestUtilities.h diff --git a/speciome_test.pro b/speciome_test.pro deleted file mode 100755 index 48e269c..0000000 --- a/speciome_test.pro +++ /dev/null @@ -1,36 +0,0 @@ -# Files -include(speciome.pri) -include(speciome_test.pri) - -# C++14 -CONFIG += c++14 -QMAKE_CXXFLAGS += -std=c++14 - -QMAKE_CXXFLAGS_DEBUG += -D_GLIBCXX_DEBUG - -# High warnings levels -QMAKE_CXXFLAGS += -Wall -Wextra -Wshadow -Wnon-virtual-dtor -pedantic -Weffc++ -Werror - -# Allow debug and release mode -CONFIG += debug_and_release - -INCLUDEPATH += /usr/lib/x86_64-linux-gnu - -# In debug mode, turn on gcov, memcache and UBSAN -CONFIG(debug, debug|release) { - - # gcov - QMAKE_CXXFLAGS += -fprofile-arcs -ftest-coverage - LIBS += -lgcov - - # helgrind, for helgrind and memcheck - QMAKE_LFLAGS += -pthread -Wl,--no-as-needed - - # UBSAN - QMAKE_CXXFLAGS += -fsanitize=undefined - QMAKE_LFLAGS += -fsanitize=undefined - LIBS += -lubsan -} - -# Boost.Test -LIBS += -lboost_unit_test_framework diff --git a/src/Collector.cpp b/src/Collector.cpp index dda3358..4def7bb 100755 --- a/src/Collector.cpp +++ b/src/Collector.cpp @@ -1,4 +1,4 @@ -#include "Collector.h" +#include "Collector.hpp" // This is the source code for the member functions of the Collector class. diff --git a/src/Collector.h b/src/Collector.hpp similarity index 98% rename from src/Collector.h rename to src/Collector.hpp index e0d61a7..a96a171 100755 --- a/src/Collector.h +++ b/src/Collector.hpp @@ -1,5 +1,5 @@ -#ifndef SPECIOME_COLLECTOR_H -#define SPECIOME_COLLECTOR_H +#ifndef SPECIOME_COLLECTOR_HPP +#define SPECIOME_COLLECTOR_HPP // This header contains the declarations of the Collector class and allies. // This class is an analytical module for the simulation. @@ -7,9 +7,9 @@ // population (genome-wide, locus-specific or edge-specific statistics). // The content of the Collector is reset every time "analyze" is called. -#include "Utilities.h" -#include "MetaPop.h" -#include "GenArch.h" +#include "Utilities.hpp" +#include "MetaPop.hpp" +#include "GenArch.hpp" #include // A class for locus-specific statistics diff --git a/src/Freezer.cpp b/src/Freezer.cpp index 0522729..635c5fd 100644 --- a/src/Freezer.cpp +++ b/src/Freezer.cpp @@ -1,4 +1,4 @@ -#include "Freezer.h" +#include "Freezer.hpp" // This is the source code for the member functions of the Freezer class. diff --git a/src/Freezer.h b/src/Freezer.hpp similarity index 86% rename from src/Freezer.h rename to src/Freezer.hpp index b509dc5..8859db7 100644 --- a/src/Freezer.h +++ b/src/Freezer.hpp @@ -1,5 +1,5 @@ -#ifndef SPECIOME_FREEZER_H -#define SPECIOME_FREEZER_H +#ifndef SPECIOME_FREEZER_HPP +#define SPECIOME_FREEZER_HPP // This module is a special printer used for saving individual full genomes, // directly from the MetaPop. @@ -9,9 +9,9 @@ // The resulting binary output file should therefore be interpreted as // a bit-wise array; each value (allele) is encoded by a single bit. -#include "Utilities.h" -#include "MetaPop.h" -#include "Printer.h" +#include "Utilities.hpp" +#include "MetaPop.hpp" +#include "Printer.hpp" #include class Freezer diff --git a/src/GenArch.cpp b/src/GenArch.cpp index debb541..3a521cd 100755 --- a/src/GenArch.cpp +++ b/src/GenArch.cpp @@ -1,4 +1,4 @@ -#include "GenArch.h" +#include "GenArch.hpp" // This is the source code for the member functions of the GenArch class. diff --git a/src/GenArch.h b/src/GenArch.hpp similarity index 96% rename from src/GenArch.h rename to src/GenArch.hpp index 5a4c579..1487f6f 100755 --- a/src/GenArch.h +++ b/src/GenArch.hpp @@ -1,16 +1,15 @@ -#ifndef SPCIOME_GENARCH_H -#define SPCIOME_GENARCH_H +#ifndef SPCIOME_GENARCH_HPP +#define SPCIOME_GENARCH_HPP // The genetic architecture contains locus-specific details about the // genotype-phenotype map. It is created using the parameters, but contains // large arrays of values across the whole genome, and is therefore // larger than the Param class. -#include "Param.h" -#include "Random.h" -#include "Network.h" - -#include "Utilities.h" +#include "Param.hpp" +#include "Random.hpp" +#include "Network.hpp" +#include "Utilities.hpp" #include #include #include diff --git a/src/Individual.cpp b/src/Individual.cpp index 305b274..c8cc34d 100755 --- a/src/Individual.cpp +++ b/src/Individual.cpp @@ -1,4 +1,4 @@ -#include "Individual.h" +#include "Individual.hpp" // This is the source code for the member functions of the Individual class. diff --git a/src/Individual.h b/src/Individual.hpp similarity index 94% rename from src/Individual.h rename to src/Individual.hpp index 79fd1fa..720cd8f 100755 --- a/src/Individual.h +++ b/src/Individual.hpp @@ -1,14 +1,13 @@ -#ifndef SPECIOME_INDIVIDUAL_H -#define SPECIOME_INDIVIDUAL_H +#ifndef SPECIOME_INDIVIDUAL_HPP +#define SPECIOME_INDIVIDUAL_HPP // This header contains the class Individual. Each Individual consists of // a genome from which traits can be deduced based on the genotype-phenotype map // (via the develop() function). Individuals can do a few other things too. -#include "GenArch.h" -#include "Utilities.h" - -#include "Random.h" +#include "GenArch.hpp" +#include "Utilities.hpp" +#include "Random.hpp" #include #include #include diff --git a/src/MetaPop.cpp b/src/MetaPop.cpp index 5beb480..6a44be7 100755 --- a/src/MetaPop.cpp +++ b/src/MetaPop.cpp @@ -1,4 +1,4 @@ -#include "MetaPop.h" +#include "MetaPop.hpp" // This is the source code for the member functions of the MetaPop class. diff --git a/src/MetaPop.h b/src/MetaPop.hpp similarity index 94% rename from src/MetaPop.h rename to src/MetaPop.hpp index aa0b34c..e41f765 100755 --- a/src/MetaPop.h +++ b/src/MetaPop.hpp @@ -1,5 +1,5 @@ -#ifndef SPECIOME_METAPOP_H -#define SPECIOME_METAPOP_H +#ifndef SPECIOME_METAPOP_HPP +#define SPECIOME_METAPOP_HPP // This header contains the class MetaPop. A MetaPop is a wrapper around a // vector of Individual objects with a bunch of member functions applying @@ -7,10 +7,10 @@ // there are two patches linked by dispersal, so the model is technically // a metapopulation. -#include "Param.h" -#include "GenArch.h" -#include "Individual.h" -#include "Utilities.h" +#include "Param.hpp" +#include "GenArch.hpp" +#include "Individual.hpp" +#include "Utilities.hpp" #include diff --git a/src/Network.cpp b/src/Network.cpp index 71ac7c6..aa171f2 100755 --- a/src/Network.cpp +++ b/src/Network.cpp @@ -1,4 +1,4 @@ -#include "Network.h" +#include "Network.hpp" // This is the source code for the member functions of the Network class. diff --git a/src/Network.h b/src/Network.hpp similarity index 94% rename from src/Network.h rename to src/Network.hpp index b616803..11217e9 100755 --- a/src/Network.h +++ b/src/Network.hpp @@ -1,14 +1,13 @@ -#ifndef SPECIOME_NETWORK_H -#define SPECIOME_NETWORK_H +#ifndef SPECIOME_NETWORK_HPP +#define SPECIOME_NETWORK_HPP // A class for a gene regulatory network. One network underlies one of the // three traits of the simulation. Networks are generated using a // preferential attachment algorithm, and are part of the bigger class GenArch. -#include "Param.h" - -#include "Utilities.h" -#include "Random.h" +#include "Param.hpp" +#include "Utilities.hpp" +#include "Random.hpp" #include #include diff --git a/src/Param.cpp b/src/Param.cpp index ae5dea2..e18a669 100755 --- a/src/Param.cpp +++ b/src/Param.cpp @@ -1,4 +1,4 @@ -#include "Param.h" +#include "Param.hpp" // This is the source code for the member functions of the Param class. diff --git a/src/Param.h b/src/Param.hpp similarity index 94% rename from src/Param.h rename to src/Param.hpp index e0e7f82..817fb15 100755 --- a/src/Param.h +++ b/src/Param.hpp @@ -1,12 +1,12 @@ -#ifndef SPECIOME_PARAM_H -#define SPECIOME_PARAM_H +#ifndef SPECIOME_PARAM_HPP +#define SPECIOME_PARAM_HPP // Parameter set. Contains values of the parameters of the simulation. // All parameters have default values that can be modified by calling // the program with a parameter file name as unique argument. -#include "Utilities.h" -#include "Random.h" +#include "Utilities.hpp" +#include "Random.hpp" #include #include #include diff --git a/src/Printer.cpp b/src/Printer.cpp index 3577688..56b1bb4 100644 --- a/src/Printer.cpp +++ b/src/Printer.cpp @@ -1,4 +1,4 @@ -#include "Printer.h" +#include "Printer.hpp" // This is the source code for the member functions of the Printer class. diff --git a/src/Printer.h b/src/Printer.hpp similarity index 77% rename from src/Printer.h rename to src/Printer.hpp index dcd8236..7a67c5b 100644 --- a/src/Printer.h +++ b/src/Printer.hpp @@ -1,12 +1,12 @@ -#ifndef SPECIOME_PRINTER_H -#define SPECIOME_PRINTER_H +#ifndef SPECIOME_PRINTER_HPP +#define SPECIOME_PRINTER_HPP // This module is used to save summary statistics from the Collector to output // files -#include "Utilities.h" -#include "Collector.h" -#include "MetaPop.h" +#include "Utilities.hpp" +#include "Collector.hpp" +#include "MetaPop.hpp" #include class Printer diff --git a/src/Random.cpp b/src/Random.cpp index bf29ba0..eaac1ec 100755 --- a/src/Random.cpp +++ b/src/Random.cpp @@ -1,4 +1,4 @@ -#include "Random.h" +#include "Random.hpp" // This sript defines random number generators used in the rnd namespace diff --git a/src/Random.h b/src/Random.hpp similarity index 96% rename from src/Random.h rename to src/Random.hpp index 93440ce..6cacb67 100755 --- a/src/Random.h +++ b/src/Random.hpp @@ -1,5 +1,5 @@ -#ifndef SPECIOME_RANDOM_H -#define SPECIOME_RANDOM_H +#ifndef SPECIOME_RANDOM_HPP +#define SPECIOME_RANDOM_HPP // This header contains aliases for various kinds of probability distributions // as well as a random number generator. Include this header in other scripts diff --git a/src/Simul.cpp b/src/Simul.cpp index 0da50db..a46211b 100755 --- a/src/Simul.cpp +++ b/src/Simul.cpp @@ -1,4 +1,4 @@ -#include "Simul.h" +#include "Simul.hpp" // This is the source code for the main simulation function diff --git a/src/Simul.h b/src/Simul.hpp similarity index 66% rename from src/Simul.h rename to src/Simul.hpp index ace6535..95234da 100755 --- a/src/Simul.h +++ b/src/Simul.hpp @@ -1,17 +1,17 @@ -#ifndef SPECIOME_SIMUL_H -#define SPECIOME_SIMUL_H +#ifndef SPECIOME_SIMUL_HPP +#define SPECIOME_SIMUL_HPP // This is the header containing the main simulation function and some // accompanying functions. The core of the program was encapsulated into a // simulate() function in order for it to be tested separately. -#include "Param.h" -#include "GenArch.h" -#include "Random.h" -#include "MetaPop.h" -#include "Collector.h" -#include "Printer.h" -#include "Freezer.h" +#include "Param.hpp" +#include "GenArch.hpp" +#include "Random.hpp" +#include "MetaPop.hpp" +#include "Collector.hpp" +#include "Printer.hpp" +#include "Freezer.hpp" #include #include #include diff --git a/tests/TestUtilities.cpp b/src/TestUtilities.cpp similarity index 99% rename from tests/TestUtilities.cpp rename to src/TestUtilities.cpp index 6cd8421..1a0fe39 100644 --- a/tests/TestUtilities.cpp +++ b/src/TestUtilities.cpp @@ -1,4 +1,4 @@ -#include "tests/TestUtilities.h" +#include "TestUtilities.hpp" // Source code for the functions of the tst namespace diff --git a/tests/TestUtilities.h b/src/TestUtilities.hpp similarity index 90% rename from tests/TestUtilities.h rename to src/TestUtilities.hpp index 5add79a..61579c1 100755 --- a/tests/TestUtilities.h +++ b/src/TestUtilities.hpp @@ -1,5 +1,5 @@ -#ifndef SPECIOME_TESTUTILITIES_H -#define SPECIOME_TESTUTILITIES_H +#ifndef SPECIOME_TESTUTILITIES_HPP +#define SPECIOME_TESTUTILITIES_HPP // This is a header for some utility functions used in testing diff --git a/src/Utilities.cpp b/src/Utilities.cpp index 0c2ef85..481473c 100755 --- a/src/Utilities.cpp +++ b/src/Utilities.cpp @@ -1,4 +1,4 @@ -#include "Utilities.h" +#include "Utilities.hpp" // This is the source code with utility functions from the utl and stf // namespaces diff --git a/src/Utilities.h b/src/Utilities.hpp similarity index 95% rename from src/Utilities.h rename to src/Utilities.hpp index f21a339..3fd7619 100755 --- a/src/Utilities.h +++ b/src/Utilities.hpp @@ -1,5 +1,5 @@ -#ifndef SPECIOME_UTILITIES_H -#define SPECIOME_UTILITIES_H +#ifndef SPECIOME_UTILITIES_HPP +#define SPECIOME_UTILITIES_HPP #include #include diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 989928a..81ba952 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -7,7 +7,7 @@ if (Boost_FOUND) # model 'unit' files file(GLOB_RECURSE unit ${CMAKE_SOURCE_DIR}/src/*.cpp) - set(TESTS tests) + set(TESTS CollectorTests GenArchTests IndividualTests MetaPopTests OutputTests SimulTests) foreach(TEST IN LISTS TESTS) add_executable(${TEST} ${CMAKE_SOURCE_DIR}/tests/${TEST}.cpp ${unit}) diff --git a/tests/CollectorTests.cpp b/tests/CollectorTests.cpp index c82f575..f4f0a34 100755 --- a/tests/CollectorTests.cpp +++ b/tests/CollectorTests.cpp @@ -1,6 +1,9 @@ -#include "src/Collector.h" -#include "src/Random.h" -#include "src/Utilities.h" +#define BOOST_TEST_DYNAMIC_LINK +#define BOOST_TEST_MODULE CollectorTests + +#include "../src/Collector.hpp" +#include "../src/Random.hpp" +#include "../src/Utilities.hpp" #include #include #include diff --git a/tests/GenArchTests.cpp b/tests/GenArchTests.cpp index c6f31ce..381c8a2 100755 --- a/tests/GenArchTests.cpp +++ b/tests/GenArchTests.cpp @@ -1,6 +1,8 @@ -#include "src/GenArch.h" -#include "src/Random.h" -#include "src/Utilities.h" +#define BOOST_TEST_MODULE GenArchTests + +#include "../src/GenArch.hpp" +#include "../src/Random.hpp" +#include "../src/Utilities.hpp" #include #include #include diff --git a/tests/IndividualTests.cpp b/tests/IndividualTests.cpp index d00bca3..6dd06a7 100755 --- a/tests/IndividualTests.cpp +++ b/tests/IndividualTests.cpp @@ -1,5 +1,7 @@ -#include "src/Individual.h" -#include "src/Utilities.h" +#define BOOST_TEST_MODULE IndividualTests + +#include "../src/Individual.hpp" +#include "../src/Utilities.hpp" #include #include diff --git a/tests/MetaPopTests.cpp b/tests/MetaPopTests.cpp index a1db622..de2f36a 100755 --- a/tests/MetaPopTests.cpp +++ b/tests/MetaPopTests.cpp @@ -1,6 +1,8 @@ -#include "src/MetaPop.h" -#include "src/Collector.h" -#include "src/Utilities.h" +#define BOOST_TEST_MODULE MetapopTests + +#include "../src/MetaPop.hpp" +#include "../src/Collector.hpp" +#include "../src/Utilities.hpp" #include // Tests of the good behavior of a metapopulation object diff --git a/tests/OutputTests.cpp b/tests/OutputTests.cpp index 7bac1ec..9cfadfa 100644 --- a/tests/OutputTests.cpp +++ b/tests/OutputTests.cpp @@ -1,9 +1,11 @@ -#include "src/Collector.h" -#include "src/Printer.h" -#include "src/Freezer.h" -#include "src/Random.h" -#include "src/Utilities.h" -#include "tests/TestUtilities.h" +#define BOOST_TEST_MODULE OutputTests + +#include "../src/Collector.hpp" +#include "../src/Printer.hpp" +#include "../src/Freezer.hpp" +#include "../src/Random.hpp" +#include "../src/Utilities.hpp" +#include "../src/TestUtilities.hpp" #include #include #include @@ -58,7 +60,7 @@ BOOST_AUTO_TEST_CASE(OutputFilesAreCorrectlyWritten) std::vector ri = tst::readfile("RI.dat"); // Check output files - BOOST_CHECK_EQUAL(time.size(), 10u);\ + BOOST_CHECK_EQUAL(time.size(), 10u); BOOST_CHECK_EQUAL(ei.size(), 10u); BOOST_CHECK_EQUAL(si.size(), 10u); BOOST_CHECK_EQUAL(ri.size(), 10u); diff --git a/tests/SimulTests.cpp b/tests/SimulTests.cpp index 0952a92..97c931e 100755 --- a/tests/SimulTests.cpp +++ b/tests/SimulTests.cpp @@ -1,8 +1,7 @@ -#define BOOST_TEST_DYN_LINK -#define BOOST_TEST_MAIN +#define BOOST_TEST_MODULE SimulTests -#include "src/Simul.h" -#include "tests/TestUtilities.h" +#include "../src/Simul.hpp" +#include "../src/TestUtilities.hpp" #include #include From a2a3207be25fc6a0196416de007b35296f14f48a Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 13 Feb 2023 11:29:50 +0100 Subject: [PATCH 08/71] stuff --- ci/do_oclint | 42 ------------------------------------- ci/get_code_cov | 5 ----- ci/install_oclint | 3 --- ci/pics/Codecov.png | Bin 1272 -> 0 bytes ci/pics/TravisCI.png | Bin 5416 -> 0 bytes pics/example_fst.png | Bin 50090 -> 0 bytes pics/example_network.gif | Bin 1865605 -> 0 bytes pics/example_summaries.png | Bin 62832 -> 0 bytes pics/example_traits.png | Bin 42532 -> 0 bytes 9 files changed, 50 deletions(-) delete mode 100755 ci/do_oclint delete mode 100755 ci/get_code_cov delete mode 100755 ci/install_oclint delete mode 100755 ci/pics/Codecov.png delete mode 100755 ci/pics/TravisCI.png delete mode 100644 pics/example_fst.png delete mode 100644 pics/example_network.gif delete mode 100644 pics/example_summaries.png delete mode 100644 pics/example_traits.png diff --git a/ci/do_oclint b/ci/do_oclint deleted file mode 100755 index a2c3985..0000000 --- a/ci/do_oclint +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash - -# This script assumes that main.cpp is in the root folder -# otherwise Travis will crash - -cpp_files=`ls *.cpp | egrep -v "^.*_test\.cpp$"` -h_files=`ls *.h` - -./oclint-0.10.3/bin/oclint -o oclint.log \ - -disable-rule ShortVariableName \ - $cpp_files \ - $h_files \ - -- \ - -c -std=c++14 \ - -I/usr/include/c++/5 \ - -I/usr/include/x86_64-linux-gnu/c++/5 - -cat oclint.log - -# Will be 0 if success -# Will be 1 if fail -fail=`egrep "Compiler Errors" oclint.log | wc -l` - -if [ $fail -eq 1 ]; -then - echo "OCLint: Compiler error, but I tolerate this" - # exit 1 # OCLint be mild -else - echo "OCLint: OK" -fi - -# Will be 1 if success -# Will be 0 if fail -success=`egrep "FilesWithViolations=0 P1=0 P2=0 P3=0" oclint.log | wc -l` - -if [ $success -eq 1 ]; -then - echo "OCLint: OK" -else - echo "OCLint: Fail" - exit 1 -fi \ No newline at end of file diff --git a/ci/get_code_cov b/ci/get_code_cov deleted file mode 100755 index 2cbda40..0000000 --- a/ci/get_code_cov +++ /dev/null @@ -1,5 +0,0 @@ -#!/bin/bash -for filename in $(find . | egrep '\.cpp$'); -do - gcov -n -o . $filename > /dev/null; -done diff --git a/ci/install_oclint b/ci/install_oclint deleted file mode 100755 index f6e36d7..0000000 --- a/ci/install_oclint +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -wget https://github.com/oclint/oclint/releases/download/v0.10.3/oclint-0.10.3-x86_64-linux-3.13.0-74-generic.tar.gz -tar -zxf oclint-0.10.3-x86_64-linux-3.13.0-74-generic.tar.gz \ No newline at end of file diff --git a/ci/pics/Codecov.png 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zDQ@5IY7Kt5u9Fq#37$HD<}CEG`ubvGHP~25t;w|F?wEUo(Ml#@_+UgH8ip8oti{t% zUyuL!8L9|ug7QfO!EDeg{W?e#qJ&@thYg202%r_ZXgUwRt>NyJOw#B=rGw)Ws5|Ig zxyf{S45;@J6p>nol`p_mO1vie3B_i0P0d!gVF6wQ;R&fOW8l$s;Tzz#nFR~vo;-_x zP(A|7jpv#wyo7GUF{sbMD2U7)gGoJLL%C-rI)srB*wIrd#uLL76ddT&njnq0!v!Hd z0JQ-P&`aX)*Mo$010fLzg2@PJ2wf{_Yyq4XFm`%aI1@l2r;Z*vG9nz_u&N)#+>V{- zBmKAt&cI22V4ApmG}F*w0+k$yXSfDh$zE$*X9|jEry2>z+W!|>#{awQ Date: Mon, 13 Feb 2023 11:35:32 +0100 Subject: [PATCH 09/71] Update README.md --- README.md | 74 ++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 63 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index a3aa429..f1d0115 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,77 @@ # speciome -Individual-based simulation of adaptive speciation with explicit genomes and genotype-phenotype map. +Simulation program for evolutionary biologists to study speciation with a complex genotype-phenotype map. -## Snapshots +## Prerequisites -These are some possible outcomes of the model (produced in R): +## Build -drawing drawing -drawing drawing +We use [vcpkg](https://github.com/microsoft/vcpkg) from Microsoft to build on multiple platforms. -## Model description +### Linux, MacOS + +To install CMake: + +```bash +sudo apt install cmake # replace 'apt' with your distro package manager +``` + +To build the program: + +```bash +git clone git@github.com:rscherrer/speciome.git +cd speciome +git submodule add https://github.com/microsoft/vcpkg +git submodule update --init --remote +mkdir build && cd build +cmake .. +cmake --build . +``` + +The executable `speciome` is built in `../bin/`. -This is an individual-based simulation of a population of sexual organisms evolving in a system with two habitats linked by dispersal. Individuals can utilize two resources in their environment, and the abundances of these resources between the two habitats can be tuned. Through genetic evolution, the organisms can adapt to utilize different resources, and under some circumstances this can lead to adaptive speciation, where two reproductively isolated species emerge from a single one. +### Windows -There are three quantitative traits in the model: an ecological one, a mating one and a neutral one. The ecological trait determines utilization efficiency of the two resources, which can be subject to a trade-off such that no individual can be efficient at utilizing both resources. The mating trait controls the sexual preference of females for more ecologically similar (when positive) or dissimilar (when negative) males. The target of mate choice is the ecological trait, which makes it a magic trait. The neutral trait is here for control. +[Download](https://github.com/Kitware/CMake/releases/download/v3.23.0/cmake-3.23.0-windows-x86_64.msi) and install CMake. +Make sure you select the option "Add CMake to the system PATH for the current user" when asked by the installer. -Each individual has a diploid genome with a discrete number of loci, which come in either of two alleles: 0 and 1. There are loci coding for each of the three traits, and so the evolution of these traits depends on evolution at the genomic level under mutation, selection, drift, migration and recombination. +To build the program: -This model implements different kinds of genotype-phenotype mapping scenarios (i.e. how the loci code control the traits of the individuals), encompassing the main sources of genetic variation from quantitative genetics: additive, dominance, epistatic and environmental effects. To accomodate epistatic effects in particular, each trait has its own gene regulatory network, which can be built by the simulation using a preferential attachment algorithm, or specified externally, together with the additive effects, dominance coefficients, encoded traits and locations of the loci. +```bash +git clone git@github.com:rscherrer/speciome.git +cd speciome +git submodule add https://github.com/microsoft/vcpkg +git submodule update --init --remote +mkdir build +cd build +cmake .. +cmake --build . --config Release +``` + +The executable `speciome.exe` is built in `../bin/`. + +### IDEs + +Many IDEs support CMake out of the box. 'Open folder' should do the trick... +You can use CMake to generate the input files for your favorite IDE too: + +```bash +git clone git@github.com:rscherrer/speciome.git +cd speciome +git submodule update --init --recursive +mkdir build +cd build +# Generate VisualStudio project files +cmake -G "Visual Studio 17 2022" -A x64 .. +# Generate Xcode project files (Xcode must be installed) +cmake -G Xcode +``` + +This will place the project files in `../build`. + +## Model description -The simulation can record a whole lot of variables, from the degree of reproductive isolation between the species to the genetic differentiation at each gene in the genome, and more. It can be used to test whether different genetic architectures equally lead to speciation, or to explore the traces that are left in the genome as speciation happen, in order to maybe help empiricists make better sense of the patterns found in nature. +See the paper for that. ## About From 8569f09dc8fc690e815da3d12cf63b49ae4613bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 11:37:27 +0100 Subject: [PATCH 10/71] Update README.md --- README.md | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index f1d0115..ffc5732 100644 --- a/README.md +++ b/README.md @@ -69,16 +69,6 @@ cmake -G Xcode This will place the project files in `../build`. -## Model description - -See the paper for that. - -## About - -This program was written in C++14 in QtCreator 4.9.2 (Qt 5.12.4) on Ubuntu 18.04 LTS, but should run on other platforms too. - -This is a copy of the ExplicitGenomeSpeciation program, version 1.0. See https://github.com/rscherrer/ExplicitGenomeSpeciation for more details. - ## Status Branch|[![Travis CI logo](ci/pics/TravisCI.png)](https://travis-ci.org)|[![Codecov logo](ci/pics/Codecov.png)](https://www.codecov.io) @@ -230,6 +220,12 @@ In general we advise the following: * save `time`, as it is useful information for any of the other variables * save `population_sizes` whenever `individual_*` variables or whole individual genomes are saved +## Misc + +This program was written in C++14 in QtCreator 4.9.2 (Qt 5.12.4) on Ubuntu 18.04 LTS, but should run on other platforms too. + +This is a copy of the ExplicitGenomeSpeciation program, version 1.0. See https://github.com/rscherrer/ExplicitGenomeSpeciation for more details. + ## Disclaimer This simulation program was used to get insights into the effect of the genetic architecture on the process of speciation. It was not designed as a statistical inference package or a data processing tool, although its simulations could in theory be used for training machine learning algorithms to recognize various evolutionary scenarios. From 295c588548dec55c21b039e592e93e9f44ed49d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 11:38:01 +0100 Subject: [PATCH 11/71] Update README.md --- README.md | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/README.md b/README.md index ffc5732..8916075 100644 --- a/README.md +++ b/README.md @@ -69,18 +69,7 @@ cmake -G Xcode This will place the project files in `../build`. -## Status - -Branch|[![Travis CI logo](ci/pics/TravisCI.png)](https://travis-ci.org)|[![Codecov logo](ci/pics/Codecov.png)](https://www.codecov.io) ----|---|--- -master|[![Build Status](https://travis-ci.org/rscherrer/speciome.svg?branch=master)](https://travis-ci.org/rscherrer/speciome)|[![codecov.io](https://codecov.io/github/rscherrer/speciome/coverage.svg?branch=master)](https://codecov.io/github/rscherrer/speciome/branch/master) -develop|[![Build Status](https://travis-ci.org/rscherrer/speciome.svg?branch=develop)](https://travis-ci.org/rscherrer/speciome)|[![codecov.io](https://codecov.io/github/rscherrer/speciome/coverage.svg?branch=develop)](https://codecov.io/github/rscherrer/speciome/branch/develop) - -## Build - -All the source and header files needed are in the `src` folder. The main function is in the `main.cpp` file. - -## Usage +## Use The program has a command-line interface (CLI). Running it on its own will run a simulation with default parameters (see below). Non-default parameters can be specified by providing a parameter file, i.e. a text file containing on each row the name of a parameter and the value it should take (e.g. `mutation 0.0001`). It should look something like: From a8b2c6a942bbf49a1a4a7483e2ec8520a609b3f8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 11:38:15 +0100 Subject: [PATCH 12/71] Update README.md --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 8916075..8ce5413 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,6 @@ Simulation program for evolutionary biologists to study speciation with a complex genotype-phenotype map. -## Prerequisites - ## Build We use [vcpkg](https://github.com/microsoft/vcpkg) from Microsoft to build on multiple platforms. From 4238700b0e8042d72bd9424c1ff260dfee7824bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 11:45:17 +0100 Subject: [PATCH 13/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8ce5413..ef28a70 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Build -We use [vcpkg](https://github.com/microsoft/vcpkg) from Microsoft to build on multiple platforms. +We use [vcpkg](https://github.com/microsoft/vcpkg) to install the necessary libraries and CMake as a build system. ### Linux, MacOS From a6dcab4f96bc68b315846db6b53d1ef125ca7081 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 11:46:10 +0100 Subject: [PATCH 14/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ef28a70..f9ac009 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Build -We use [vcpkg](https://github.com/microsoft/vcpkg) to install the necessary libraries and CMake as a build system. +We use [vcpkg](https://github.com/microsoft/vcpkg) to install the necessary libraries and [CMake](https://cmake.org) as build system. ### Linux, MacOS From e85cee62a42e15abbfa1f5994f12346a882150f0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 11:46:44 +0100 Subject: [PATCH 15/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f9ac009..dc7a50c 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Build -We use [vcpkg](https://github.com/microsoft/vcpkg) to install the necessary libraries and [CMake](https://cmake.org) as build system. +We use [CMake](https://cmake.org) to build the program and [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencies. ### Linux, MacOS From 2fe6f9780c6ecb89d19aa5e86055d5caae2e3559 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 12:12:15 +0100 Subject: [PATCH 16/71] Update README.md --- README.md | 37 +++++++++++++++++++++++++------------ 1 file changed, 25 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index dc7a50c..e4a9f2a 100644 --- a/README.md +++ b/README.md @@ -2,19 +2,20 @@ Simulation program for evolutionary biologists to study speciation with a complex genotype-phenotype map. -## Build - -We use [CMake](https://cmake.org) to build the program and [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencies. +## Prerequisites -### Linux, MacOS - -To install CMake: +* CMake ```bash sudo apt install cmake # replace 'apt' with your distro package manager ``` -To build the program: +[Download](https://github.com/Kitware/CMake/releases/download/v3.23.0/cmake-3.23.0-windows-x86_64.msi) and install CMake. +Make sure you select the option "Add CMake to the system PATH for the current user" when asked by the installer. + +## Build + +### Linux, MacOS ```bash git clone git@github.com:rscherrer/speciome.git @@ -26,13 +27,8 @@ cmake .. cmake --build . ``` -The executable `speciome` is built in `../bin/`. - ### Windows -[Download](https://github.com/Kitware/CMake/releases/download/v3.23.0/cmake-3.23.0-windows-x86_64.msi) and install CMake. -Make sure you select the option "Add CMake to the system PATH for the current user" when asked by the installer. - To build the program: ```bash @@ -46,6 +42,23 @@ cmake .. cmake --build . --config Release ``` + +to build the program and [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencies. + + + +To install CMake: + + + +To build the program: + + + +The executable `speciome` is built in `../bin/`. + + + The executable `speciome.exe` is built in `../bin/`. ### IDEs From accaa737a4e6657ad2c290349a230b65afdab4cf Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 13 Feb 2023 12:14:57 +0100 Subject: [PATCH 17/71] stuff --- docs/CMAKE.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 docs/CMAKE.md diff --git a/docs/CMAKE.md b/docs/CMAKE.md new file mode 100644 index 0000000..7d29b20 --- /dev/null +++ b/docs/CMAKE.md @@ -0,0 +1,11 @@ +### Install CMake + +On Linux/MacOs, use: + +```bash +sudo apt install cmake # replace 'apt' with your distro package manager +``` + +[Here](https://github.com/Kitware/CMake/releases/download/v3.23.0/cmake-3.23.0-windows-x86_64.msi) us the download for Windowns. +Make sure to select the option "Add CMake to the system PATH for the current user" when asked by the installer. + From ea71eb9a5079e3cbfcc2c7f6cebb3bd102ddc246 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 12:16:11 +0100 Subject: [PATCH 18/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e4a9f2a..b8be2b2 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Prerequisites -* CMake +* [CMake](https://github.com/rscherrer/speciome/blob/docs/CMAKE.md) ```bash sudo apt install cmake # replace 'apt' with your distro package manager From c2c3b67af4adf53223a3feedfb500ea605f01d62 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 12:16:52 +0100 Subject: [PATCH 19/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b8be2b2..674fb7b 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Prerequisites -* [CMake](https://github.com/rscherrer/speciome/blob/docs/CMAKE.md) +* [CMake](docs/CMAKE.md) ```bash sudo apt install cmake # replace 'apt' with your distro package manager From 6db1fbe6dec5195f67016779ec93bb8f0f54ffdc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 12:18:19 +0100 Subject: [PATCH 20/71] Update CMAKE.md --- docs/CMAKE.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/CMAKE.md b/docs/CMAKE.md index 7d29b20..a7d8c1b 100644 --- a/docs/CMAKE.md +++ b/docs/CMAKE.md @@ -1,4 +1,6 @@ -### Install CMake +## Install CMake + +### Linux, MacOS On Linux/MacOs, use: @@ -6,6 +8,7 @@ On Linux/MacOs, use: sudo apt install cmake # replace 'apt' with your distro package manager ``` -[Here](https://github.com/Kitware/CMake/releases/download/v3.23.0/cmake-3.23.0-windows-x86_64.msi) us the download for Windowns. -Make sure to select the option "Add CMake to the system PATH for the current user" when asked by the installer. +### Windows + +[Download](https://github.com/Kitware/CMake/releases/download/v3.23.0/cmake-3.23.0-windows-x86_64.msi) and make sure to select the option "Add CMake to the system PATH for the current user" when asked by the installer. From bd6c7fed0590e582a6c30730976021cb484ec7b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 12:21:32 +0100 Subject: [PATCH 21/71] Update README.md --- README.md | 33 +++------------------------------ 1 file changed, 3 insertions(+), 30 deletions(-) diff --git a/README.md b/README.md index 674fb7b..c8362c5 100644 --- a/README.md +++ b/README.md @@ -5,13 +5,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Prerequisites * [CMake](docs/CMAKE.md) - -```bash -sudo apt install cmake # replace 'apt' with your distro package manager -``` - -[Download](https://github.com/Kitware/CMake/releases/download/v3.23.0/cmake-3.23.0-windows-x86_64.msi) and install CMake. -Make sure you select the option "Add CMake to the system PATH for the current user" when asked by the installer. +* C++20 ## Build @@ -20,45 +14,24 @@ Make sure you select the option "Add CMake to the system PATH for the current us ```bash git clone git@github.com:rscherrer/speciome.git cd speciome -git submodule add https://github.com/microsoft/vcpkg -git submodule update --init --remote mkdir build && cd build cmake .. cmake --build . ``` -### Windows +The executable `speciome` is built in `../bin/`. -To build the program: +### Windows ```bash git clone git@github.com:rscherrer/speciome.git cd speciome -git submodule add https://github.com/microsoft/vcpkg -git submodule update --init --remote mkdir build cd build cmake .. cmake --build . --config Release ``` - -to build the program and [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencies. - - - -To install CMake: - - - -To build the program: - - - -The executable `speciome` is built in `../bin/`. - - - The executable `speciome.exe` is built in `../bin/`. ### IDEs From 72bd44f2f3af0e595e4641753bfd2cd4dc7f2e23 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 12:23:12 +0100 Subject: [PATCH 22/71] Update README.md --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index c8362c5..9c4cbba 100644 --- a/README.md +++ b/README.md @@ -36,13 +36,12 @@ The executable `speciome.exe` is built in `../bin/`. ### IDEs -Many IDEs support CMake out of the box. 'Open folder' should do the trick... +Many IDEs support CMake out of the box. "Open folder" should do the trick... You can use CMake to generate the input files for your favorite IDE too: ```bash git clone git@github.com:rscherrer/speciome.git cd speciome -git submodule update --init --recursive mkdir build cd build # Generate VisualStudio project files From 3dc9857522c92b6b5a0e3056e3ddc1e15372c4fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 14:24:28 +0100 Subject: [PATCH 23/71] Update README.md --- README.md | 24 +++++++++++++++++++++--- 1 file changed, 21 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 9c4cbba..25dc68c 100644 --- a/README.md +++ b/README.md @@ -54,15 +54,33 @@ This will place the project files in `../build`. ## Use -The program has a command-line interface (CLI). Running it on its own will run a simulation with default parameters (see below). Non-default parameters can be specified by providing a parameter file, i.e. a text file containing on each row the name of a parameter and the value it should take (e.g. `mutation 0.0001`). It should look something like: +Run a simulation with default parameters with: + +``` +./speciome +``` + +Or provide a parameter file with non-default parameter values: ``` ./speciome parameters.txt ``` -## Parameters +### Parameter file + +The parameter file must contain parameter names followed by their values, for example: + +``` +hsymmetry 1 +ecosel 0.6 +allfreq 0.2 +``` + +Parameters that are not provided in the parameter file will take default values. + + -Non-default parameters are supplied in a parameter file. The parameter file must contain on each line, the name of one parameter followed by a blank (e.g. space or tab) and the value(s) that this parameter must take. Parameter names must match those in the table. Parameters that are not in the parameter file will take default values. Parameters that take multiple values must be supplied as such, with values separated by blanks (e.g. `nvertices 30 30 30`). +Parameters that take multiple values must be supplied as such, with values separated by blanks (e.g. `nvertices 30 30 30`). We describe the different parameters (and their default value). From 18b4e645699533ce2a2dd8b64116fcd45b2ae70e Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 13 Feb 2023 14:27:24 +0100 Subject: [PATCH 24/71] stuff --- docs/PARAMETERS.md | 52 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 docs/PARAMETERS.md diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md new file mode 100644 index 0000000..a826f91 --- /dev/null +++ b/docs/PARAMETERS.md @@ -0,0 +1,52 @@ +## List of parameters + +|-----------|------------------| +| rdynamics | Type of resource dynamics | 1 | +| + +Ecological parameters: + +* `rdynamics` (default 1) is the type of resource dynamics (0 for *logistic dynamics*, which assume a biotic resource with an intrinsic growth and a carrying capacity, or 1 for *chemostat dynamics* with and inflow and outflow of some abiotic resource) +* `replenish` (2375) and `capacity` (1) are the growth rate and carrying capacity, respectively, of the resources assuming logistic dynamics (`rdynamics 0`) +* `inflow` (400) and `outflow` (100) are the absolute inflow and relative outflow rates of the resources, respectively, assuming chemostat dynamics (`rdynamics 1`) +* `hsymmetry` (0) is the degree of homogeneity in resource distribution between the two habitats. It basically is a scaling factor to make the realized `replenish` (if logistic resource dynamics) or `inflow` (if chemostat resource dynamics) differ between the habitats, ranging between 0 and 1 +* `ecosel` (1.8) is the ecological trade-off in utilization between the two resources. It must be zero or positive, and the higher it is, the less efficient is utilizing both resources instead of specializing on a single one +* `ecoscale` (1) is a scaling parameter for the phenotypic distance between the two ecological niches along the ecological trait axis. A value of 1 means that the peaks of the two niches in utilization efficiency are at -1 and +1, respectively. This parameter also scales the widths of the resource utilization curves accordingly +* `demesizes` (100, 0) are the number of individuals initialized in both habitats, respectively +* `dispersal` (0.01) is the proportion of individuals sampled to switch habitats every generation +* `birth` (1) is the base birth rate per female, independent of fitness +* `survival` (0.8) is the probability of survival of adult individuals from one generation to the next (it takes one generation for newborns to become adults) +* `sexsel` (10) is the sexual selection coefficient, a positive number mediating the general degree of choosiness of females towards males during the mating season +* `matingcost` (0.01) is the fitness cost of choosy females (that run the risk of remaining unmated) +* `ntrials` (100) is the number of mating trials to perform to measure the degree of reproductive isolation between the species + +Genetic parameters: + +* `nvertices` (30, 30, 30) and `nedges` (30, 0, 0) are the numbers of loci and edges, respectively, in each of the three gene networks (one for each trait in the following order: ecological, mating and neutral). `nvertices` must be at least 2 for each trait, and `nedges` cannot be higher than n (n - 1) / 2 (complete graph), if n is the number of vertice for a given trait. In practice, the preferential attachment algorithm may fail to attach all the requested edges of the network if nedges is close to the maximum possible number of edges. In this case the program will return an error. The number of edges should be at least n-1, which ensures that the network is connected (all the nodes are attached). +* `nchrom` (3) is the number of equally sized chromosomes +* `mutation` (0.001) is the mutation rate per locus +* `recombination` (3) is the recombination rate and corresponds more or less to the average number of crossovers during one recombination event. It is actually the rate of an exponential distribution describing the distance between successive crossover points +* `allfreq` (0.2) is the initial frequency of the 1-allele in the population +* `scaleA` (1, 1, 1), `scaleD` (0, 0, 0), `scaleI` (0, 0, 0) and `scaleE` (0, 0, 0) are scaling factors for each source of phenotypic variation in the quantitative genetics framework, i.e. additive, dominance, epistatic and environmental effects, respectively, for each of the three traits +* `skews` (1, 1, 1) are the skewnesses of each of the three gene networks +* `effectshape` (2) and `effectscale` (1) are the parameters of the distribution of additive effect sizes across loci. If the resulting distribution has variance 0, all the loci are assigned effect size 1 +* `interactionshape` (5) and `interactionscale` (1) are the parameters of the distribution of epistatic interaction weights across edges. If the resulting distribution has variance 0, all the edges are assigned interaction weight 1 +* `dominancevar` (1) is the standard deviation of the distribution of dominance coefficients across loci. If 0, all the loci are assigned dominance coefficient 1 + +General simulation parameters: + +* `tburnin` (0) is the duration of the burn-in period, in generations +* `tend` (10) is the number of generations to simulate (use a negative value to end the simulation during the burn-in) +* `tsave` (10) is the frequency at which to record the data +* `tcomplete` (1000000) is the time at which to force complete reproductive isolation between the two ecotypes (can mimic e.g. genomic incompatibilities between the two species, or the evolution of very good species recognition abilities) +* `talkative` (1) is either 0 or 1 and sets whether the simulation should print status information to the prompt +* `choosewhattosave` (0) is either 0 or 1 and sets whether the variables to save are specified in a separate file, the order file `whattosave.txt` (see below). If 0 all of the output variables are saved every `tsave` generations except for whole genomes +* `datsave` (1) sets whether to save the recorded variables to files +* `burninsave` (0) sets whether to save data during the burn-in phase too (time points belonging to the burn-in are negative) +* `gensave` (0) is either 0 or 1 and sets whether whole genomes should be saved every `tfreeze` generations (see below) +* `archsave` (0) is either 0 or 1 and sets whether the genetic architecture should be saved into file `architecture.txt` (see below) +* `archload` (0) sets whether the genetic architecture of the simulation should be loaded from file `architecture.txt` instead of generated anew +* `parsave` (1) sets whether to save the parameters of the simulation run to file, including the random seed, into a parameter-log file `paramlog.txt` +* `logsave` (0) sets whether the output to prompt should be redirected to a log file `log.txt` +* `seed` is the seed of the random number generator, and it is by default randomly generated based on the clock + From 917d960e959a21c2cd464c6d84907f719b87e3a9 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 13 Feb 2023 14:28:59 +0100 Subject: [PATCH 25/71] stuff --- docs/PARAMETERS.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index a826f91..2c12667 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -1,8 +1,11 @@ ## List of parameters -|-----------|------------------| + +|--|--|--| +| name | meaning | default value | +|--|--|--| | rdynamics | Type of resource dynamics | 1 | -| +|--|--|--| Ecological parameters: From f03565784a5923a7f6936fdca163229ae5db23a1 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 13 Feb 2023 14:30:54 +0100 Subject: [PATCH 26/71] stuff --- docs/PARAMETERS.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index 2c12667..821ec48 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -1,10 +1,11 @@ ## List of parameters - -|--|--|--| | name | meaning | default value | |--|--|--| | rdynamics | Type of resource dynamics | 1 | +| replenish | Resource growth (logistic) | 2375 | +| capacity | Resource capacity (logistic) | 1 | +| inflow | Resource inflow (chemostat) | 400 | |--|--|--| Ecological parameters: From f726dc62c799f3c1140679e37531a80e77952626 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 13 Feb 2023 14:31:55 +0100 Subject: [PATCH 27/71] stuff --- docs/PARAMETERS.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index 821ec48..1c8e19f 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -1,12 +1,12 @@ ## List of parameters -| name | meaning | default value | -|--|--|--| +| name | meaning | default value | note | +|--|--|--|--| | rdynamics | Type of resource dynamics | 1 | | replenish | Resource growth (logistic) | 2375 | | capacity | Resource capacity (logistic) | 1 | | inflow | Resource inflow (chemostat) | 400 | -|--|--|--| +|--|--|--|--| Ecological parameters: From dc2d3393b2e0fc9a118198d041fd639cf6d4bbda Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 14:52:00 +0100 Subject: [PATCH 28/71] Update PARAMETERS.md --- docs/PARAMETERS.md | 66 +++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 62 insertions(+), 4 deletions(-) diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index 1c8e19f..987c663 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -2,12 +2,70 @@ | name | meaning | default value | note | |--|--|--|--| -| rdynamics | Type of resource dynamics | 1 | -| replenish | Resource growth (logistic) | 2375 | -| capacity | Resource capacity (logistic) | 1 | -| inflow | Resource inflow (chemostat) | 400 | +| rdynamics | Type of resource dynamics | 1 | | +| replenish | Resource growth (logistic) | 2375 | | +| capacity | Resource capacity (logistic) | 1 | | +| inflow | Resource inflow (chemostat) | 400 | | +| outflow | Resource outflow (chemostat) | 100 | | +| hsymmetry | Habitat symmetry | 0 | | +| ecosel | Trade-off strength | 1.8 | | +| ecoscale | Ecological scale | 1 | | +| demesizes | Initial population sizes | 100 0 | | +| dispersal | Dispersal rate | 0.01 | | +| birth | Baseline birth rate | 1 | | +| survival | Survival rate | 0.8 | | +| sexsel | Sexual selection coefficient | 10 | | +| matingcost | Cost of choosiness | 0.01 | | +| ntrials | Number of mating trials | 100 | | +| nvertices | Numbers of loci | 30 30 30 | | +| nedges | Numbers of edges | 30 30 30 | | +| nchrom | Number of chromosomes | 3 | | +| mutation | Mutation rate | 0.001 | | +| recombination | Recombination rate | 3 | | +| allfreq | Initial allele frequency | 0.2 | | +| scaleA | Scaling for additive effects | 1 1 1 | | +| scaleD | Scaling for dominance | 0 0 0 | | +| scaleI | Scaling for epistasis | 0 0 0 | | +| scaleE | Scaling for environmental effects | 0 0 0 | | +| skews | Network skewness | 1 1 1 | | +| effectshape | Shape of effet size-distribution | 2 | | +| effectscale | Scale of effect size-distribution | 1 | | +| interactionshape | Shape of interaction weight-distribution | 5 | | +| interactionscale | Scale of interaction weight-distribution | 1 | | +| dominancevar | Standard deviation of dominance coefficient-distribution | 1 | | +| tburnin | Burn-in duration | 0 | | +| tend | Simulation time | 10 | | +| tsave | Saving frequency | 10 | | +| tcomplete | Speciation completion time | 1000000 | | +| talkative | Verbose | 1 | | +| choosewhattosave | Whether to save specific variables | 0 | | +| datsave | Whether to save to file | 1 | | +| burninsave | Whether to save the burn-in | 0 | | +| gensave | Whether genomes should be saved | 0 | | +| archsave | Whether to save architecture | 0 | | +| archload | Whether to load architecture | 0 | | +| parsave | Whether to save parameters | 1 | | +| logsave | Screen output to file | 0 | | +| seed | Random number generator seed | Clock-generated | | + |--|--|--|--| +* `tburnin` (0) is the duration of the burn-in period, in generations +* `tend` (10) is the number of generations to simulate (use a negative value to end the simulation during the burn-in) +* `tsave` (10) is the frequency at which to record the data +* `tcomplete` (1000000) is the time at which to force complete reproductive isolation between the two ecotypes (can mimic e.g. genomic incompatibilities between the two species, or the evolution of very good species recognition abilities) +* `talkative` (1) is either 0 or 1 and sets whether the simulation should print status information to the prompt +* `choosewhattosave` (0) is either 0 or 1 and sets whether the variables to save are specified in a separate file, the order file `whattosave.txt` (see below). If 0 all of the output variables are saved every `tsave` generations except for whole genomes +* `datsave` (1) sets whether to save the recorded variables to files +* `burninsave` (0) sets whether to save data during the burn-in phase too (time points belonging to the burn-in are negative) +* `gensave` (0) is either 0 or 1 and sets whether whole genomes should be saved every `tfreeze` generations (see below) +* `archsave` (0) is either 0 or 1 and sets whether the genetic architecture should be saved into file `architecture.txt` (see below) +* `archload` (0) sets whether the genetic architecture of the simulation should be loaded from file `architecture.txt` instead of generated anew +* `parsave` (1) sets whether to save the parameters of the simulation run to file, including the random seed, into a parameter-log file `paramlog.txt` +* `logsave` (0) sets whether the output to prompt should be redirected to a log file `log.txt` +* `seed` is the seed of the random number generator, and it is by default randomly generated based on the clock + + Ecological parameters: * `rdynamics` (default 1) is the type of resource dynamics (0 for *logistic dynamics*, which assume a biotic resource with an intrinsic growth and a carrying capacity, or 1 for *chemostat dynamics* with and inflow and outflow of some abiotic resource) From cc41d464f415373ab007a7acb6e62d1ad95b69ad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 15:34:41 +0100 Subject: [PATCH 29/71] Update PARAMETERS.md --- docs/PARAMETERS.md | 99 +++++++++++++++++++--------------------------- 1 file changed, 40 insertions(+), 59 deletions(-) diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index 987c663..8ecc079 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -2,75 +2,56 @@ | name | meaning | default value | note | |--|--|--|--| -| rdynamics | Type of resource dynamics | 1 | | -| replenish | Resource growth (logistic) | 2375 | | -| capacity | Resource capacity (logistic) | 1 | | -| inflow | Resource inflow (chemostat) | 400 | | -| outflow | Resource outflow (chemostat) | 100 | | -| hsymmetry | Habitat symmetry | 0 | | -| ecosel | Trade-off strength | 1.8 | | -| ecoscale | Ecological scale | 1 | | -| demesizes | Initial population sizes | 100 0 | | -| dispersal | Dispersal rate | 0.01 | | -| birth | Baseline birth rate | 1 | | -| survival | Survival rate | 0.8 | | -| sexsel | Sexual selection coefficient | 10 | | -| matingcost | Cost of choosiness | 0.01 | | -| ntrials | Number of mating trials | 100 | | -| nvertices | Numbers of loci | 30 30 30 | | -| nedges | Numbers of edges | 30 30 30 | | -| nchrom | Number of chromosomes | 3 | | -| mutation | Mutation rate | 0.001 | | -| recombination | Recombination rate | 3 | | -| allfreq | Initial allele frequency | 0.2 | | +| rdynamics | Type of resource dynamics | 1 | The resource dynamics can be logistic (0) or chemostat-like (1). | +| replenish | Resource growth (logistic) | 2375 | Growth rate of the resource. | +| capacity | Resource capacity (logistic) | 1 | Carrying capacity of the resource. | +| inflow | Resource inflow (chemostat) | 400 | Absolute inflow of the resource. | +| outflow | Resource outflow (chemostat) | 100 | Relative outflow of the resource. | +| hsymmetry | Habitat symmetry | 0 | Degree of homogeneity in resource input between habitats (between 0 and 1). | +| ecosel | Trade-off strength | 1.8 | Utilization trade-off between the two resources. | +| ecoscale | Ecological scale | 1 | Location of the specialist phenotype in ecological trait space. | +| demesizes | Initial population sizes | 100 0 | Initial number of individuals in each habitat. | +| dispersal | Dispersal rate | 0.01 | Probability of dispersal to the other habitat per individual per generation. | +| birth | Baseline birth rate | 1 | Base number of offspring per female per generation. | +| survival | Survival rate | 0.8 | Probability of surviving to the next generation. | +| sexsel | Sexual selection coefficient | 10 | Parameter for the intensity of mate choice. | +| matingcost | Cost of choosiness | 0.01 | Probability that the mating season ends after examining a male. | +| ntrials | Number of mating trials | 100 | Number of trials to perform to measure reproductive isolation. | +| nvertices | Numbers of loci | 30 30 30 | Number of loci coding for each trait. | +| nedges | Numbers of edges | 30 30 30 | Number of edges for each trait. | +| nchrom | Number of chromosomes | 3 | Chromosomes all have equal lengths. | +| mutation | Mutation rate | 0.001 | Probability of mutation per locus. | +| recombination | Recombination rate | 3 | Rate of the exponential distribution describing the distance between two crossovers. | +| allfreq | Initial allele frequency | 0.2 | Starting frequency of the 1-allele. | | scaleA | Scaling for additive effects | 1 1 1 | | | scaleD | Scaling for dominance | 0 0 0 | | | scaleI | Scaling for epistasis | 0 0 0 | | | scaleE | Scaling for environmental effects | 0 0 0 | | -| skews | Network skewness | 1 1 1 | | -| effectshape | Shape of effet size-distribution | 2 | | -| effectscale | Scale of effect size-distribution | 1 | | -| interactionshape | Shape of interaction weight-distribution | 5 | | -| interactionscale | Scale of interaction weight-distribution | 1 | | -| dominancevar | Standard deviation of dominance coefficient-distribution | 1 | | -| tburnin | Burn-in duration | 0 | | -| tend | Simulation time | 10 | | -| tsave | Saving frequency | 10 | | -| tcomplete | Speciation completion time | 1000000 | | -| talkative | Verbose | 1 | | -| choosewhattosave | Whether to save specific variables | 0 | | -| datsave | Whether to save to file | 1 | | -| burninsave | Whether to save the burn-in | 0 | | -| gensave | Whether genomes should be saved | 0 | | -| archsave | Whether to save architecture | 0 | | -| archload | Whether to load architecture | 0 | | -| parsave | Whether to save parameters | 1 | | -| logsave | Screen output to file | 0 | | +| skews | Network skewness | 1 1 1 | Skewness of the preferential attachment algorithm for each trait. | +| effectshape | Shape of effet size-distribution | 2 | Gamma distribution parameter. | +| effectscale | Scale of effect size-distribution | 1 | Gamma distribution parameter. | +| interactionshape | Shape of interaction weight-distribution | 5 | Gamma distribution parameter. | +| interactionscale | Scale of interaction weight-distribution | 1 | Gamma distribution parameter. | +| dominancevar | Standard deviation of dominance coefficient-distribution | 1 | Half-normal distribution parameter. | +| tburnin | Burn-in duration | 0 | In number of generations. | +| tend | Simulation time | 10 | In number of generations. | +| tsave | Saving frequency | 10 | In number of generations. | +| tcomplete | Speciation completion time | 1000000 | In number of generations. | +| talkative | Verbose | 1 | Yes (1) or no (0). | +| choosewhattosave | Whether to save specific variables | 0 | Yes (1) or no (0). | +| datsave | Whether to save to file | 1 | Yes (1) or no (0). | +| burninsave | Whether to save the burn-in | 0 | Yes (1) or no (0). | +| gensave | Whether genomes should be saved | 0 | Yes (1) or no (0). | +| archsave | Whether to save architecture | 0 | Yes (1) or no (0). | +| archload | Whether to load architecture | 0 | Yes (1) or no (0). | +| parsave | Whether to save parameters | 1 | Yes (1) or no (0). | +| logsave | Screen output to file | 0 | Yes (1) or no (0). | | seed | Random number generator seed | Clock-generated | | |--|--|--|--| -* `tburnin` (0) is the duration of the burn-in period, in generations -* `tend` (10) is the number of generations to simulate (use a negative value to end the simulation during the burn-in) -* `tsave` (10) is the frequency at which to record the data -* `tcomplete` (1000000) is the time at which to force complete reproductive isolation between the two ecotypes (can mimic e.g. genomic incompatibilities between the two species, or the evolution of very good species recognition abilities) -* `talkative` (1) is either 0 or 1 and sets whether the simulation should print status information to the prompt -* `choosewhattosave` (0) is either 0 or 1 and sets whether the variables to save are specified in a separate file, the order file `whattosave.txt` (see below). If 0 all of the output variables are saved every `tsave` generations except for whole genomes -* `datsave` (1) sets whether to save the recorded variables to files -* `burninsave` (0) sets whether to save data during the burn-in phase too (time points belonging to the burn-in are negative) -* `gensave` (0) is either 0 or 1 and sets whether whole genomes should be saved every `tfreeze` generations (see below) -* `archsave` (0) is either 0 or 1 and sets whether the genetic architecture should be saved into file `architecture.txt` (see below) -* `archload` (0) sets whether the genetic architecture of the simulation should be loaded from file `architecture.txt` instead of generated anew -* `parsave` (1) sets whether to save the parameters of the simulation run to file, including the random seed, into a parameter-log file `paramlog.txt` -* `logsave` (0) sets whether the output to prompt should be redirected to a log file `log.txt` -* `seed` is the seed of the random number generator, and it is by default randomly generated based on the clock - - Ecological parameters: -* `rdynamics` (default 1) is the type of resource dynamics (0 for *logistic dynamics*, which assume a biotic resource with an intrinsic growth and a carrying capacity, or 1 for *chemostat dynamics* with and inflow and outflow of some abiotic resource) -* `replenish` (2375) and `capacity` (1) are the growth rate and carrying capacity, respectively, of the resources assuming logistic dynamics (`rdynamics 0`) -* `inflow` (400) and `outflow` (100) are the absolute inflow and relative outflow rates of the resources, respectively, assuming chemostat dynamics (`rdynamics 1`) * `hsymmetry` (0) is the degree of homogeneity in resource distribution between the two habitats. It basically is a scaling factor to make the realized `replenish` (if logistic resource dynamics) or `inflow` (if chemostat resource dynamics) differ between the habitats, ranging between 0 and 1 * `ecosel` (1.8) is the ecological trade-off in utilization between the two resources. It must be zero or positive, and the higher it is, the less efficient is utilizing both resources instead of specializing on a single one * `ecoscale` (1) is a scaling parameter for the phenotypic distance between the two ecological niches along the ecological trait axis. A value of 1 means that the peaks of the two niches in utilization efficiency are at -1 and +1, respectively. This parameter also scales the widths of the resource utilization curves accordingly From 77a578b33bb3127ea824d6410bd3fc273eb7b099 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 15:45:05 +0100 Subject: [PATCH 30/71] Update PARAMETERS.md --- docs/PARAMETERS.md | 55 +++++----------------------------------------- 1 file changed, 5 insertions(+), 50 deletions(-) diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index 8ecc079..f50fe07 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -34,12 +34,12 @@ | interactionscale | Scale of interaction weight-distribution | 1 | Gamma distribution parameter. | | dominancevar | Standard deviation of dominance coefficient-distribution | 1 | Half-normal distribution parameter. | | tburnin | Burn-in duration | 0 | In number of generations. | -| tend | Simulation time | 10 | In number of generations. | +| tend | Simulation time | 10 | In number of generations (in addition to the burn-in). | | tsave | Saving frequency | 10 | In number of generations. | -| tcomplete | Speciation completion time | 1000000 | In number of generations. | -| talkative | Verbose | 1 | Yes (1) or no (0). | -| choosewhattosave | Whether to save specific variables | 0 | Yes (1) or no (0). | -| datsave | Whether to save to file | 1 | Yes (1) or no (0). | +| tcomplete | Speciation completion time | 1000000 | Number of generations after which reproductive isolation is imposed by force. | +| talkative | Verbose to screen | 1 | Yes (1) or no (0). | +| choosewhattosave | Whether to choose which variables to save | 0 | Yes (1) or no (0). | +| datsave | Whether to allow data recording to files | 1 | Yes (1) or no (0). | | burninsave | Whether to save the burn-in | 0 | Yes (1) or no (0). | | gensave | Whether genomes should be saved | 0 | Yes (1) or no (0). | | archsave | Whether to save architecture | 0 | Yes (1) or no (0). | @@ -47,49 +47,4 @@ | parsave | Whether to save parameters | 1 | Yes (1) or no (0). | | logsave | Screen output to file | 0 | Yes (1) or no (0). | | seed | Random number generator seed | Clock-generated | | - |--|--|--|--| - -Ecological parameters: - -* `hsymmetry` (0) is the degree of homogeneity in resource distribution between the two habitats. It basically is a scaling factor to make the realized `replenish` (if logistic resource dynamics) or `inflow` (if chemostat resource dynamics) differ between the habitats, ranging between 0 and 1 -* `ecosel` (1.8) is the ecological trade-off in utilization between the two resources. It must be zero or positive, and the higher it is, the less efficient is utilizing both resources instead of specializing on a single one -* `ecoscale` (1) is a scaling parameter for the phenotypic distance between the two ecological niches along the ecological trait axis. A value of 1 means that the peaks of the two niches in utilization efficiency are at -1 and +1, respectively. This parameter also scales the widths of the resource utilization curves accordingly -* `demesizes` (100, 0) are the number of individuals initialized in both habitats, respectively -* `dispersal` (0.01) is the proportion of individuals sampled to switch habitats every generation -* `birth` (1) is the base birth rate per female, independent of fitness -* `survival` (0.8) is the probability of survival of adult individuals from one generation to the next (it takes one generation for newborns to become adults) -* `sexsel` (10) is the sexual selection coefficient, a positive number mediating the general degree of choosiness of females towards males during the mating season -* `matingcost` (0.01) is the fitness cost of choosy females (that run the risk of remaining unmated) -* `ntrials` (100) is the number of mating trials to perform to measure the degree of reproductive isolation between the species - -Genetic parameters: - -* `nvertices` (30, 30, 30) and `nedges` (30, 0, 0) are the numbers of loci and edges, respectively, in each of the three gene networks (one for each trait in the following order: ecological, mating and neutral). `nvertices` must be at least 2 for each trait, and `nedges` cannot be higher than n (n - 1) / 2 (complete graph), if n is the number of vertice for a given trait. In practice, the preferential attachment algorithm may fail to attach all the requested edges of the network if nedges is close to the maximum possible number of edges. In this case the program will return an error. The number of edges should be at least n-1, which ensures that the network is connected (all the nodes are attached). -* `nchrom` (3) is the number of equally sized chromosomes -* `mutation` (0.001) is the mutation rate per locus -* `recombination` (3) is the recombination rate and corresponds more or less to the average number of crossovers during one recombination event. It is actually the rate of an exponential distribution describing the distance between successive crossover points -* `allfreq` (0.2) is the initial frequency of the 1-allele in the population -* `scaleA` (1, 1, 1), `scaleD` (0, 0, 0), `scaleI` (0, 0, 0) and `scaleE` (0, 0, 0) are scaling factors for each source of phenotypic variation in the quantitative genetics framework, i.e. additive, dominance, epistatic and environmental effects, respectively, for each of the three traits -* `skews` (1, 1, 1) are the skewnesses of each of the three gene networks -* `effectshape` (2) and `effectscale` (1) are the parameters of the distribution of additive effect sizes across loci. If the resulting distribution has variance 0, all the loci are assigned effect size 1 -* `interactionshape` (5) and `interactionscale` (1) are the parameters of the distribution of epistatic interaction weights across edges. If the resulting distribution has variance 0, all the edges are assigned interaction weight 1 -* `dominancevar` (1) is the standard deviation of the distribution of dominance coefficients across loci. If 0, all the loci are assigned dominance coefficient 1 - -General simulation parameters: - -* `tburnin` (0) is the duration of the burn-in period, in generations -* `tend` (10) is the number of generations to simulate (use a negative value to end the simulation during the burn-in) -* `tsave` (10) is the frequency at which to record the data -* `tcomplete` (1000000) is the time at which to force complete reproductive isolation between the two ecotypes (can mimic e.g. genomic incompatibilities between the two species, or the evolution of very good species recognition abilities) -* `talkative` (1) is either 0 or 1 and sets whether the simulation should print status information to the prompt -* `choosewhattosave` (0) is either 0 or 1 and sets whether the variables to save are specified in a separate file, the order file `whattosave.txt` (see below). If 0 all of the output variables are saved every `tsave` generations except for whole genomes -* `datsave` (1) sets whether to save the recorded variables to files -* `burninsave` (0) sets whether to save data during the burn-in phase too (time points belonging to the burn-in are negative) -* `gensave` (0) is either 0 or 1 and sets whether whole genomes should be saved every `tfreeze` generations (see below) -* `archsave` (0) is either 0 or 1 and sets whether the genetic architecture should be saved into file `architecture.txt` (see below) -* `archload` (0) sets whether the genetic architecture of the simulation should be loaded from file `architecture.txt` instead of generated anew -* `parsave` (1) sets whether to save the parameters of the simulation run to file, including the random seed, into a parameter-log file `paramlog.txt` -* `logsave` (0) sets whether the output to prompt should be redirected to a log file `log.txt` -* `seed` is the seed of the random number generator, and it is by default randomly generated based on the clock - From 168ff3a5dac9867da16af3f0a8c0c01547e599c4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 15:45:28 +0100 Subject: [PATCH 31/71] Update PARAMETERS.md --- docs/PARAMETERS.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index f50fe07..b2bbcd5 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -1,6 +1,6 @@ ## List of parameters -| name | meaning | default value | note | +| Name | Meaning | Default | Note | |--|--|--|--| | rdynamics | Type of resource dynamics | 1 | The resource dynamics can be logistic (0) or chemostat-like (1). | | replenish | Resource growth (logistic) | 2375 | Growth rate of the resource. | From 56be66533f70102028340321b49c759fdb2cb4b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 15:45:42 +0100 Subject: [PATCH 32/71] Update PARAMETERS.md --- docs/PARAMETERS.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index b2bbcd5..8a3c420 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -47,4 +47,3 @@ | parsave | Whether to save parameters | 1 | Yes (1) or no (0). | | logsave | Screen output to file | 0 | Yes (1) or no (0). | | seed | Random number generator seed | Clock-generated | | -|--|--|--|--| From 0a21d646b0b2bbf5517cfcbf6ae13f00866ba16d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 15:47:16 +0100 Subject: [PATCH 33/71] Update README.md --- README.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 25dc68c..8f191ea 100644 --- a/README.md +++ b/README.md @@ -74,13 +74,10 @@ The parameter file must contain parameter names followed by their values, for ex hsymmetry 1 ecosel 0.6 allfreq 0.2 +nvertices 30 30 30 ``` -Parameters that are not provided in the parameter file will take default values. - - - -Parameters that take multiple values must be supplied as such, with values separated by blanks (e.g. `nvertices 30 30 30`). +Parameters that are not provided in the parameter file will take default values. Note that some parameters take multiple values. Click [here](docs/PARAMETERS.md) for a list of all parameters. We describe the different parameters (and their default value). From e3fdd56f510fcc9b69a487d15913e0740313f283 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 16:01:26 +0100 Subject: [PATCH 34/71] Update README.md --- README.md | 52 ++++++++++++++++++++++++++++------------------------ 1 file changed, 28 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 8f191ea..bc68ceb 100644 --- a/README.md +++ b/README.md @@ -79,6 +79,34 @@ nvertices 30 30 30 Parameters that are not provided in the parameter file will take default values. Note that some parameters take multiple values. Click [here](docs/PARAMETERS.md) for a list of all parameters. +### Model description + +This program runs an individual-based simulation where agents live, reproduce and die, through thousands of generations. Individuals have traits that are genetically encoded and can evolve. The model is built in such a way that under the right conditions, the population may split into two phenotypically distinct and reproductively isolated clusters, or species (i.e. speciaiton has happened). Importantly, the genotype codes for the phenotype in a nonlinear way allowing not only additive but also dominance, epistatic and environmental effects. See the accompanying manuscript for more information. + +### Genetic architecture + +The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click here to see what a genetic architecture file should look like. + +The architecture file is a text file organized in two sections, each delimited by the header "--parameters--" or "--architecture--". + +1. Under the "--parameters--" header are expected the names and values of parameters (or rather hyperparameters) that are important for the set-up of the genetic architecture. Those are `nchrom`, `nvertices` and `nedges`, and should be provided just like in a parameter file (see above). They will be used to read the right numer of entries from the following section, and to override the parameters in the model once the architecture is loaded. + +2. Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. + +* Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. + +* Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. + +* Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. + +Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. + +A randomly generated genetic architecture can be saved as a ready-to-use architecture file if `archsave` is set to 1 (in this case the architecture will be saved in the file `architecture.txt`). For any other architecture file supplied, you need to make sure that the aforementioned expected structure is respected and that all the necessary fields are present. + +Note that in general it is a good idea to save the genetic architecture used if you are going to save and analyze genetic data from the simulation, as the output variables do not contain details about the architecture, which might make them difficult to interpret otherwise. + + + We describe the different parameters (and their default value). Ecological parameters: @@ -129,30 +157,6 @@ General simulation parameters: Note that it is a good idea to set `parsave 1` and `logsave 1`, to make sure the full range of parameter values can be retrieved or the simulation progress can be monitored. Some analysis functions from the R package [speciomer](https://github.com/rscherrer/speciomer) actually expect the parameter-log file "paramlog.txt" to be present. -## Genetic architecture - -The genetic architecture refers to the constant features of the genetic part of the program, that is, features that do not change through time and cannot evolve. These include the number of chromosomes, the numbers of loci and edges for each trait, the trait each locus codes for as well as its location in the genome, its additive effect size and its dominance coefficient, and finally the topology and distribution of interaction weights across edges within each of the three gene networks (one for each trait, the networks are independent). - -A genetic architecture can either be generated anew at the beginning of a simulation by randomly sampling the aforementioned features and building networks with a random preferential attachment algorithm, or it can be supplied to the program in an architecture file `architecture.txt`. To load the genetic architecture from an architecture file, make sure to set parameter `archload` to 1. - -The architecture file is a text file organized in two sections, each delimited by the header "--parameters--" or "--architecture--". - -1. Under the "--parameters--" header are expected the names and values of parameters (or rather hyperparameters) that are important for the set-up of the genetic architecture. Those are `nchrom`, `nvertices` and `nedges`, and should be provided just like in a parameter file (see above). They will be used to read the right numer of entries from the following section, and to override the parameters in the model once the architecture is loaded. - -2. Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. - -* Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. - -* Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. - -* Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. - -Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. - -A randomly generated genetic architecture can be saved as a ready-to-use architecture file if `archsave` is set to 1 (in this case the architecture will be saved in the file `architecture.txt`). For any other architecture file supplied, you need to make sure that the aforementioned expected structure is respected and that all the necessary fields are present. - -Note that in general it is a good idea to save the genetic architecture used if you are going to save and analyze genetic data from the simulation, as the output variables do not contain details about the architecture, which might make them difficult to interpret otherwise. - ## Saving data Many outputs can be saved through time in the simulation. To save the recorded data, set `datsave 1`. To save time and space, the data are saved as `.dat` binary files, with one file per variable. This means that each variable is saved as a vector of values (64 bit double precision floating point numbers). From 8832bac620647894ca832a7e8f7b59411c5005c7 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Mon, 13 Feb 2023 16:16:59 +0100 Subject: [PATCH 35/71] stuff --- README.md | 17 ---------------- docs/ARCHITECTURE.md | 47 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 47 insertions(+), 17 deletions(-) create mode 100644 docs/ARCHITECTURE.md diff --git a/README.md b/README.md index bc68ceb..392899b 100644 --- a/README.md +++ b/README.md @@ -87,23 +87,6 @@ This program runs an individual-based simulation where agents live, reproduce an The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click here to see what a genetic architecture file should look like. -The architecture file is a text file organized in two sections, each delimited by the header "--parameters--" or "--architecture--". - -1. Under the "--parameters--" header are expected the names and values of parameters (or rather hyperparameters) that are important for the set-up of the genetic architecture. Those are `nchrom`, `nvertices` and `nedges`, and should be provided just like in a parameter file (see above). They will be used to read the right numer of entries from the following section, and to override the parameters in the model once the architecture is loaded. - -2. Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. - -* Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. - -* Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. - -* Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. - -Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. - -A randomly generated genetic architecture can be saved as a ready-to-use architecture file if `archsave` is set to 1 (in this case the architecture will be saved in the file `architecture.txt`). For any other architecture file supplied, you need to make sure that the aforementioned expected structure is respected and that all the necessary fields are present. - -Note that in general it is a good idea to save the genetic architecture used if you are going to save and analyze genetic data from the simulation, as the output variables do not contain details about the architecture, which might make them difficult to interpret otherwise. diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md new file mode 100644 index 0000000..8011d17 --- /dev/null +++ b/docs/ARCHITECTURE.md @@ -0,0 +1,47 @@ +## Genetic architecture file + +### Rules + +The architecture file is a text file organized in two sections, each delimited by the header "--parameters--" or "--architecture--". Its name must be "architecture.txt" and it must be present in the working directory in order to be read. + +1. Under the "--parameters--" header are expected the names and values of parameters (or rather hyperparameters) that are important for the set-up of the genetic architecture. Those are `nchrom`, `nvertices` and `nedges`, and should be provided just like in a parameter file (see above). They will be used to read the right numer of entries from the following section, and to override the parameters in the model once the architecture is loaded. + +2. Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. + +* Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. + +* Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. + +* Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. + +Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. + +A randomly-generated genetic architecture can be saved as a ready-to-use architecture file if `archsave` is set to 1 (in this case the architecture will be saved in the file `architecture.txt`). Note that architecture files saved by the program when `archsave` is 1 contain all the parameters of the model that generated the architecture. For any other architecture file supplied, you need to make sure that the aforementioned expected structure is respected and that all the necessary fields are present. Also note that in general it is a good idea to save the genetic architecture used if you are going to save and analyze genetic data from the simulation, as the output variables do not contain details about the architecture, which might make them difficult to interpret otherwise. + +### Example + +This is a file for an architecture with 3 loci coding for each trait, and where each network is a full graph (all three loci are connected in a triangle-shaped network). There are three chromosomes of equal size. All effect sizes, dominance coefficients and interaction weights are 0.1. + + +``` +--parameters-- +nvertices 3 3 3 +nedges 3 3 3 +nchrom 3 + +--architecture-- +chromosomes 0.333333 0.666667 1 +traits 0 0 0 1 1 1 2 2 2 +locations 0.01 0.03 0.4 0.5 0.65 0.7 0.88 0.99 0.999 +effects 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 +dominances 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 +from 0 0 0 1 +to 0 1 2 2 +weights 0 0.1 0.1 0.1 +from 1 3 3 4 +to 1 4 5 5 +weights 1 0.1 0.1 0.1 +from 2 6 6 7 +to 2 7 8 8 +weights 2 0.1 0.1 0.1 +``` \ No newline at end of file From 3064e1dc2db1f16df8fb3f123b0a670c661b93e0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 16:17:46 +0100 Subject: [PATCH 36/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 392899b..55567d9 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ This program runs an individual-based simulation where agents live, reproduce an ### Genetic architecture -The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click here to see what a genetic architecture file should look like. +The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click [here](docs/ARCHITECTURE) to see what a genetic architecture file should look like. From 20fd76874ac36d2ef8de7517177e63d4ebbea843 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 16:18:29 +0100 Subject: [PATCH 37/71] Update ARCHITECTURE.md --- docs/ARCHITECTURE.md | 39 +++++++++++++++++++-------------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index 8011d17..acaf39c 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -1,28 +1,9 @@ ## Genetic architecture file -### Rules - -The architecture file is a text file organized in two sections, each delimited by the header "--parameters--" or "--architecture--". Its name must be "architecture.txt" and it must be present in the working directory in order to be read. - -1. Under the "--parameters--" header are expected the names and values of parameters (or rather hyperparameters) that are important for the set-up of the genetic architecture. Those are `nchrom`, `nvertices` and `nedges`, and should be provided just like in a parameter file (see above). They will be used to read the right numer of entries from the following section, and to override the parameters in the model once the architecture is loaded. - -2. Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. - -* Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. - -* Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. - -* Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. - -Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. - -A randomly-generated genetic architecture can be saved as a ready-to-use architecture file if `archsave` is set to 1 (in this case the architecture will be saved in the file `architecture.txt`). Note that architecture files saved by the program when `archsave` is 1 contain all the parameters of the model that generated the architecture. For any other architecture file supplied, you need to make sure that the aforementioned expected structure is respected and that all the necessary fields are present. Also note that in general it is a good idea to save the genetic architecture used if you are going to save and analyze genetic data from the simulation, as the output variables do not contain details about the architecture, which might make them difficult to interpret otherwise. - ### Example This is a file for an architecture with 3 loci coding for each trait, and where each network is a full graph (all three loci are connected in a triangle-shaped network). There are three chromosomes of equal size. All effect sizes, dominance coefficients and interaction weights are 0.1. - ``` --parameters-- nvertices 3 3 3 @@ -44,4 +25,22 @@ weights 1 0.1 0.1 0.1 from 2 6 6 7 to 2 7 8 8 weights 2 0.1 0.1 0.1 -``` \ No newline at end of file +``` + +### Rules + +The architecture file is a text file organized in two sections, each delimited by the header "--parameters--" or "--architecture--". Its name must be "architecture.txt" and it must be present in the working directory in order to be read. + +1. Under the "--parameters--" header are expected the names and values of parameters (or rather hyperparameters) that are important for the set-up of the genetic architecture. Those are `nchrom`, `nvertices` and `nedges`, and should be provided just like in a parameter file (see above). They will be used to read the right numer of entries from the following section, and to override the parameters in the model once the architecture is loaded. + +2. Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. + +* Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. + +* Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. + +* Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. + +Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. + +A randomly-generated genetic architecture can be saved as a ready-to-use architecture file if `archsave` is set to 1 (in this case the architecture will be saved in the file `architecture.txt`). Note that architecture files saved by the program when `archsave` is 1 contain all the parameters of the model that generated the architecture. For any other architecture file supplied, you need to make sure that the aforementioned expected structure is respected and that all the necessary fields are present. Also note that in general it is a good idea to save the genetic architecture used if you are going to save and analyze genetic data from the simulation, as the output variables do not contain details about the architecture, which might make them difficult to interpret otherwise. From 3d95687805f2f6001f8b545b25fa5022a320c88d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 16:19:01 +0100 Subject: [PATCH 38/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 55567d9..c9b64d6 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ This program runs an individual-based simulation where agents live, reproduce an ### Genetic architecture -The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click [here](docs/ARCHITECTURE) to see what a genetic architecture file should look like. +The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click [here](docs/ARCHITECTURE.md) to see what a genetic architecture file should look like. From ce095f14eec51d25cb3c72ece56ad0c59409815e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 16:19:57 +0100 Subject: [PATCH 39/71] Update ARCHITECTURE.md --- docs/ARCHITECTURE.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index acaf39c..d54ad7f 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -29,18 +29,18 @@ weights 2 0.1 0.1 0.1 ### Rules -The architecture file is a text file organized in two sections, each delimited by the header "--parameters--" or "--architecture--". Its name must be "architecture.txt" and it must be present in the working directory in order to be read. +* The architecture file is a text file organized in two sections, each delimited by the header "--parameters--" or "--architecture--". Its name must be "architecture.txt" and it must be present in the working directory in order to be read. -1. Under the "--parameters--" header are expected the names and values of parameters (or rather hyperparameters) that are important for the set-up of the genetic architecture. Those are `nchrom`, `nvertices` and `nedges`, and should be provided just like in a parameter file (see above). They will be used to read the right numer of entries from the following section, and to override the parameters in the model once the architecture is loaded. +* Under the "--parameters--" header are expected the names and values of parameters (or rather hyperparameters) that are important for the set-up of the genetic architecture. Those are `nchrom`, `nvertices` and `nedges`, and should be provided just like in a parameter file (see above). They will be used to read the right numer of entries from the following section, and to override the parameters in the model once the architecture is loaded. -2. Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. +* Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. -* Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. +- Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. -* Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. +- Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. -* Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. +- Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. -Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. +* Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. A randomly-generated genetic architecture can be saved as a ready-to-use architecture file if `archsave` is set to 1 (in this case the architecture will be saved in the file `architecture.txt`). Note that architecture files saved by the program when `archsave` is 1 contain all the parameters of the model that generated the architecture. For any other architecture file supplied, you need to make sure that the aforementioned expected structure is respected and that all the necessary fields are present. Also note that in general it is a good idea to save the genetic architecture used if you are going to save and analyze genetic data from the simulation, as the output variables do not contain details about the architecture, which might make them difficult to interpret otherwise. From 206656273fae3ef76888dc38a38e8577599ad91c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 16:21:07 +0100 Subject: [PATCH 40/71] Update ARCHITECTURE.md --- docs/ARCHITECTURE.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index d54ad7f..7096fe7 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -35,11 +35,11 @@ weights 2 0.1 0.1 0.1 * Under the "--architecture--" header are expected the names and values of the actual architecture fields, which are essentially lists of parameters. There are three kinds of fields, that differ in how many values they take: chromosome-wise fields, locus-wise fields and edge-wise fields. -- Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. + * Chromosome-wise field: `chromosomes`, consisting of the end location of each chromosome (between 0 and 1, each representing the two ends of the genomes). One value per chromosome. -- Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. + * Locus-wise fields: `traits`, `locations`, `effects` and `dominances` are the encoded traits (0, 1, or 2), genomic locations (between 0 and 1), additive effect sizes and dominance coefficients of each locus in the genome, respectively. One value per locus. -- Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. + * Edge-wise fields: `from`, `to` and `weights` are respectively the indices of the first and second partner, and the interaction weight, of each edge. One value per edge. * Each field should be followed by the values it takes (e.g. `chromosomes 0.333333 0.666667 1`, `locations 0.01 0.02 0.45 0.6 0.8 0.9`), but if the field is an edge-wise field the values should be preceded by the index of the trait of the network that field belongs to (e.g. `weights 0 0.56 0.37 -0.45 0.67 0.1 -0.89`, where 0 in second position refers to the ecological trait). Fields and values are all separated by spaces. From 134ff53a69a5755d848faa65086d2d7240617f33 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 16:31:54 +0100 Subject: [PATCH 41/71] Update README.md --- README.md | 111 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 58 insertions(+), 53 deletions(-) diff --git a/README.md b/README.md index c9b64d6..bfbc5ad 100644 --- a/README.md +++ b/README.md @@ -87,6 +87,64 @@ This program runs an individual-based simulation where agents live, reproduce an The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click [here](docs/ARCHITECTURE.md) to see what a genetic architecture file should look like. +### Saving data + +Set `datsave` to 1 to allow data recording. The data are saved into binary `*.dat` files: + +* `time.dat`: every saved time point +* `population_sizes.dat`: total population size (so across both habitats) +* `ecotype_population_sizes.dat`: population size of each ecotype +* `habitat_resources.dat`: equilibrium resource concentration of each resource in each habitat +* `trait_means.dat`: mean value of each trait across the whole population +* `trait_ecotype_means.dat`: mean value of each trait in each ecotype +* `trait_varP.dat`, `trait_varG.dat`, `trait_varA.dat`, `trait_varD.dat`, `trait_varI.dat`, `trait_varN.dat`: respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each trait +* `trait_varT.dat`: variance in allele frequencies across loci coding for each trait +* `trait_Pst.dat`, `trait_Gst.dat`, `trait_Qst.dat`, `trait_Cst.dat`: respectively the differentiation statistics between ecotypes for the phenotypic, genetic, additive and non-additive variance for each trait +* `trait_Fst.dat`: fixation index, or genetic differentiation between the two ecotypes, for each trait +* `EI.dat`, `SI.dat`, `RI.dat`: ecological, spatial and reproductive isolation between ecotypes, respectively. +* `locus_varP.dat`, `locus_varG.dat`, `locus_varA.dat`, `locus_varD.dat`, `locus_varI.dat`, `locus_varN.dat`: respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each locus in the genome +* `locus_Pst.dat`, `locus_Gst.dat`, `locus_Qst.dat`, `locus_Cst.dat`, `locus_Fst.dat`: respectively the Pst, Gst, Qst, Cst and Fst for each locus +* `locus_alpha.dat`: the average mutational effect (i.e. slope of the regression of genetic values against genotypes across the whole population) of each locus +* `locus_meang.dat`: the mean genetic value of each locus in the whole population +* `locus_freq.dat`: the allele frequency (of the 1-allele) for each locus in the whole population +* `locus_ecotype_freq.dat`: the allele frequencies for each locus within each ecotype +* `locus_ecotype_hobs.dat`: the observed heterozygosity for each locus within each ecotype +* `edge_corgen.dat`, `edge_corbreed.dat`, `edge_corfreq.dat`: respectively the pairwise correlations in genetic value, breeding value and allele frequency between the two interacting loci for each edge in all three networks (ordered by trait) +* `edge_avgi.dat`, `edge_avgj.dat`: the expected epistatic variance in average effect of the first and second interacting loci, respectively, for each edge. `edge_avgi` corresponds to the expected effect of genetic variation at locus i on the variation in the additive effect of allele substitutions at locus j, and vice versa for `edge_avgj`. This is mostly for plotting purposes, to detect genes that are expected to modify the additive effects of their interacting partners. +* `individual_ecotypes.dat`, `individual_habitats.dat`: the ecotype and habitat of each individual +* `individual_traits.dat`: the value of each trait for each individual +* `individual_midparents.dat`: the midparent phenotype (i.e. the mean between maternal and paternal values) for each trait for each individual + + +This means that each variable is saved as a vector of values (64 bit double precision floating point numbers). + +The following variables are saved every `tsave` timepoint: + + + +By default the program will save all these variables. To save only some of them, you have to set `choosewhattosave` to 1. The order file `whattosave.txt` should contain a list of names of variable to save, separated by any type of blanks (e.g. `time EI SI RI locus_Fst`). + +**Note:** the computation of reproductive isolation (RI) requires sampling males and females at random in the population and pair them. This sampling can affect the generation of random numbers down the line. One consequence may be e.g. that different simulations run with the same seed but saving data at different time points may end up giving different results, just because the computation of RI adds to the sampling differently in the two replicates. To avoid this and make sure that the recording of RI does not affect the simulation, the sampling for RI is done using a separate random number generator from the rest of the simulation. + +## Saving whole individual genomes + +Saving the whole genomes of all individuals through time takes a lot of space, for this reason this output is controlled separately from the other output variables. If you set `gensave 1` in addition to `datsave 1` two things will be saved every `tsave` generations: (1) the whole genomes of all individuals in `individual_whole_genomes.dat`, and (2) the genetic values at every locus for every individual in `individual_locus_genvalues.dat`. Both files are binary data files. + +1. Whole genomes are encoded in the freezer file in a different way from other variables. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci, because the organisms are diploid. Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, if N is the total number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. + +2. Genetic values across the genome for each individual are stored in `individual_locus_genvalues.dat` as floating point numbers encoded into a binary file, just as the other output variables, but with one value per locus per individual, per time point. + +**Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. Unfortunately it is not possible to save some variables at a certain frequency and individual genomes less frequently in one simulation, as there is only one `tsave`. To collect data on different timelines, however, it is possible to run multiple simulations with the same `seed`. This can be done either by choosing a seed beforehand, or by saving the random seed of the first simulation (it will be saved in the parameter-log file `paramlog.txt` if `parsave 1`) and use it as `seed` to parametrize the next simulations. + +## Which variables to save + +Some variables need other variables to be saved in order to be interpreted down the line. For example, `time` must be saved in order for the recorded time points to be appended to the resulting data tables. Or, the `population_sizes` in each recorded time point must be known for each individual to be assigned a time point in `individual_*` variables and in individual whole genomes. + +In general we advise the following: + +* have the genetic architecture at hand (e.g. `archsave 1`) to interpret the genetic data you might save (`locus_*`, `edge_*` and whole individual genomes) +* save `time`, as it is useful information for any of the other variables +* save `population_sizes` whenever `individual_*` variables or whole individual genomes are saved @@ -140,59 +198,6 @@ General simulation parameters: Note that it is a good idea to set `parsave 1` and `logsave 1`, to make sure the full range of parameter values can be retrieved or the simulation progress can be monitored. Some analysis functions from the R package [speciomer](https://github.com/rscherrer/speciomer) actually expect the parameter-log file "paramlog.txt" to be present. -## Saving data - -Many outputs can be saved through time in the simulation. To save the recorded data, set `datsave 1`. To save time and space, the data are saved as `.dat` binary files, with one file per variable. This means that each variable is saved as a vector of values (64 bit double precision floating point numbers). - -The following variables are saved every `tsave` timepoint: - -* `time`: every saved time point -* `population_sizes`: total population size (so across both habitats) -* `ecotype_population_sizes`: population size of each ecotype -* `habitat_resources`: equilibrium resource concentration of each resource in each habitat -* `trait_means`: mean value of each trait across the whole population -* `trait_ecotype_means`: mean value of each trait in each ecotype -* `trait_varP`, `trait_varG`, `trait_varA`, `trait_varD`, `trait_varI`, `trait_varN`: respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each trait -* `trait_varT`: variance in allele frequencies across loci coding for each trait -* `trait_Pst`, `trait_Gst`, `trait_Qst`, `trait_Cst`: respectively the differentiation statistics between ecotypes for the phenotypic, genetic, additive and non-additive variance for each trait -* `trait_Fst`: fixation index, or genetic differentiation between the two ecotypes, for each trait -* `EI`, `SI`, `RI`: ecological, spatial and reproductive isolation between ecotypes, respectively. -* `locus_varP`, `locus_varG`, `locus_varA`, `locus_varD`, `locus_varI`, `locus_varN`: respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each locus in the genome -* `locus_Pst`, `locus_Gst`, `locus_Qst`, `locus_Cst`, `locus_Fst`: respectively the Pst, Gst, Qst, Cst and Fst for each locus -* `locus_alpha`: the average mutational effect (i.e. slope of the regression of genetic values against genotypes across the whole population) of each locus -* `locus_meang`: the mean genetic value of each locus in the whole population -* `locus_freq`: the allele frequency (of the 1-allele) for each locus in the whole population -* `locus_ecotype_freq`: the allele frequencies for each locus within each ecotype -* `locus_ecotype_hobs`: the observed heterozygosity for each locus within each ecotype -* `edge_corgen`, `edge_corbreed`, `edge_corfreq`: respectively the pairwise correlations in genetic value, breeding value and allele frequency between the two interacting loci for each edge in all three networks (ordered by trait) -* `edge_avgi`, `edge_avgj`: the expected epistatic variance in average effect of the first and second interacting loci, respectively, for each edge. `edge_avgi` corresponds to the expected effect of genetic variation at locus i on the variation in the additive effect of allele substitutions at locus j, and vice versa for `edge_avgj`. This is mostly for plotting purposes, to detect genes that are expected to modify the additive effects of their interacting partners. -* `individual_ecotypes`, `individual_habitats`: the ecotype and habitat of each individual -* `individual_traits`: the value of each trait for each individual -* `individual_midparents`: the midparent phenotype (i.e. the mean between maternal and paternal values) for each trait for each individual - -By default the program will save all these variables. To save only some of them, you have to set `choosewhattosave` to 1. The order file `whattosave.txt` should contain a list of names of variable to save, separated by any type of blanks (e.g. `time EI SI RI locus_Fst`). - -**Note:** the computation of reproductive isolation (RI) requires sampling males and females at random in the population and pair them. This sampling can affect the generation of random numbers down the line. One consequence may be e.g. that different simulations run with the same seed but saving data at different time points may end up giving different results, just because the computation of RI adds to the sampling differently in the two replicates. To avoid this and make sure that the recording of RI does not affect the simulation, the sampling for RI is done using a separate random number generator from the rest of the simulation. - -## Saving whole individual genomes - -Saving the whole genomes of all individuals through time takes a lot of space, for this reason this output is controlled separately from the other output variables. If you set `gensave 1` in addition to `datsave 1` two things will be saved every `tsave` generations: (1) the whole genomes of all individuals in `individual_whole_genomes.dat`, and (2) the genetic values at every locus for every individual in `individual_locus_genvalues.dat`. Both files are binary data files. - -1. Whole genomes are encoded in the freezer file in a different way from other variables. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci, because the organisms are diploid. Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, if N is the total number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. - -2. Genetic values across the genome for each individual are stored in `individual_locus_genvalues.dat` as floating point numbers encoded into a binary file, just as the other output variables, but with one value per locus per individual, per time point. - -**Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. Unfortunately it is not possible to save some variables at a certain frequency and individual genomes less frequently in one simulation, as there is only one `tsave`. To collect data on different timelines, however, it is possible to run multiple simulations with the same `seed`. This can be done either by choosing a seed beforehand, or by saving the random seed of the first simulation (it will be saved in the parameter-log file `paramlog.txt` if `parsave 1`) and use it as `seed` to parametrize the next simulations. - -## Which variables to save - -Some variables need other variables to be saved in order to be interpreted down the line. For example, `time` must be saved in order for the recorded time points to be appended to the resulting data tables. Or, the `population_sizes` in each recorded time point must be known for each individual to be assigned a time point in `individual_*` variables and in individual whole genomes. - -In general we advise the following: - -* have the genetic architecture at hand (e.g. `archsave 1`) to interpret the genetic data you might save (`locus_*`, `edge_*` and whole individual genomes) -* save `time`, as it is useful information for any of the other variables -* save `population_sizes` whenever `individual_*` variables or whole individual genomes are saved ## Misc From 40a91a73712be51b26246472b19b27df13fee14a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Mon, 13 Feb 2023 16:38:18 +0100 Subject: [PATCH 42/71] Update README.md --- README.md | 48 +++++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index bfbc5ad..bc7ba29 100644 --- a/README.md +++ b/README.md @@ -91,29 +91,31 @@ The genetic architecture refers to the constant features of the genotype-phenoty Set `datsave` to 1 to allow data recording. The data are saved into binary `*.dat` files: -* `time.dat`: every saved time point -* `population_sizes.dat`: total population size (so across both habitats) -* `ecotype_population_sizes.dat`: population size of each ecotype -* `habitat_resources.dat`: equilibrium resource concentration of each resource in each habitat -* `trait_means.dat`: mean value of each trait across the whole population -* `trait_ecotype_means.dat`: mean value of each trait in each ecotype -* `trait_varP.dat`, `trait_varG.dat`, `trait_varA.dat`, `trait_varD.dat`, `trait_varI.dat`, `trait_varN.dat`: respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each trait -* `trait_varT.dat`: variance in allele frequencies across loci coding for each trait -* `trait_Pst.dat`, `trait_Gst.dat`, `trait_Qst.dat`, `trait_Cst.dat`: respectively the differentiation statistics between ecotypes for the phenotypic, genetic, additive and non-additive variance for each trait -* `trait_Fst.dat`: fixation index, or genetic differentiation between the two ecotypes, for each trait -* `EI.dat`, `SI.dat`, `RI.dat`: ecological, spatial and reproductive isolation between ecotypes, respectively. -* `locus_varP.dat`, `locus_varG.dat`, `locus_varA.dat`, `locus_varD.dat`, `locus_varI.dat`, `locus_varN.dat`: respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each locus in the genome -* `locus_Pst.dat`, `locus_Gst.dat`, `locus_Qst.dat`, `locus_Cst.dat`, `locus_Fst.dat`: respectively the Pst, Gst, Qst, Cst and Fst for each locus -* `locus_alpha.dat`: the average mutational effect (i.e. slope of the regression of genetic values against genotypes across the whole population) of each locus -* `locus_meang.dat`: the mean genetic value of each locus in the whole population -* `locus_freq.dat`: the allele frequency (of the 1-allele) for each locus in the whole population -* `locus_ecotype_freq.dat`: the allele frequencies for each locus within each ecotype -* `locus_ecotype_hobs.dat`: the observed heterozygosity for each locus within each ecotype -* `edge_corgen.dat`, `edge_corbreed.dat`, `edge_corfreq.dat`: respectively the pairwise correlations in genetic value, breeding value and allele frequency between the two interacting loci for each edge in all three networks (ordered by trait) -* `edge_avgi.dat`, `edge_avgj.dat`: the expected epistatic variance in average effect of the first and second interacting loci, respectively, for each edge. `edge_avgi` corresponds to the expected effect of genetic variation at locus i on the variation in the additive effect of allele substitutions at locus j, and vice versa for `edge_avgj`. This is mostly for plotting purposes, to detect genes that are expected to modify the additive effects of their interacting partners. -* `individual_ecotypes.dat`, `individual_habitats.dat`: the ecotype and habitat of each individual -* `individual_traits.dat`: the value of each trait for each individual -* `individual_midparents.dat`: the midparent phenotype (i.e. the mean between maternal and paternal values) for each trait for each individual +Name | Variables | +|--|--| +| `time.dat` | Every saved time point. | +| `population_sizes.dat` | Total population size (across both habitats). | +| `ecotype_population_sizes.dat` | Population size of each ecotype. | +| `habitat_resources.dat` | Equilibrium resource concentration of each resource in each habitat. | +| `trait_means.dat` | Mean value of each trait across the whole population. | +| `trait_ecotype_means.dat` | Mean value of each trait in each ecotype. | +| `trait_varP.dat`, `trait_varG.dat`, `trait_varA.dat`, `trait_varD.dat`, `trait_varI.dat`, `trait_varN.dat` | Respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each trait. | +| `trait_varT.dat` | Variance in allele frequencies across loci coding for each trait. | +| `trait_Pst.dat`, `trait_Gst.dat`, `trait_Qst.dat`, `trait_Cst.dat` | Respectively the differentiation statistics between ecotypes for the phenotypic, genetic, additive and non-additive variance for each trait. | +| `trait_Fst.dat` | Fixation index, or genetic differentiation between the two ecotypes, for each trait. | +| `EI.dat`, `SI.dat`, `RI.dat` | Ecological, spatial and reproductive isolation between ecotypes, respectively. | +| `locus_varP.dat`, `locus_varG.dat`, `locus_varA.dat`, `locus_varD.dat`, `locus_varI.dat`, `locus_varN.dat` | Respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each locus in the genome. | +| `locus_Pst.dat`, `locus_Gst.dat`, `locus_Qst.dat`, `locus_Cst.dat`, `locus_Fst.dat` | Respectively the Pst, Gst, Qst, Cst and Fst for each locus. | +| `locus_alpha.dat` | The average mutational effect (i.e. slope of the regression of genetic values against genotypes across the whole population) of each locus. | +| `locus_meang.dat` | The mean genetic value of each locus in the whole population. | +| `locus_freq.dat` | The allele frequency (of the 1-allele) for each locus in the whole population. | +| `locus_ecotype_freq.dat` | The allele frequencies for each locus within each ecotype. | +| `locus_ecotype_hobs.dat` | The observed heterozygosity for each locus within each ecotype. | +| `edge_corgen.dat`, `edge_corbreed.dat`, `edge_corfreq.dat` | Respectively the pairwise correlations in genetic value, breeding value and allele frequency between the two interacting loci for each edge in all three networks (ordered by trait). | +| `edge_avgi.dat`, `edge_avgj.dat` | The expected epistatic variance in average effect of the first and second interacting loci, respectively, for each edge. `edge_avgi` corresponds to the expected effect of genetic variation at locus i on the variation in the additive effect of allele substitutions at locus j, and vice versa for `edge_avgj`. This is mostly for plotting purposes, to detect genes that are expected to modify the additive effects of their interacting partners. | +| `individual_ecotypes.dat`, `individual_habitats.dat` | The ecotype and habitat of each individual. | +| `individual_traits.dat` | The value of each trait for each individual. | +| `individual_midparents.dat` | The midparent phenotype (i.e. the mean between maternal and paternal values) for each trait for each individual. | This means that each variable is saved as a vector of values (64 bit double precision floating point numbers). From 595c00ef4f77da0ee56e6d855244095be177505e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Tue, 14 Feb 2023 09:48:29 +0100 Subject: [PATCH 43/71] Update README.md --- README.md | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index bc7ba29..984026e 100644 --- a/README.md +++ b/README.md @@ -89,7 +89,7 @@ The genetic architecture refers to the constant features of the genotype-phenoty ### Saving data -Set `datsave` to 1 to allow data recording. The data are saved into binary `*.dat` files: +Set `datsave` to 1 to allow data recording. The data are saved every `tsave`generations into binary `*.dat` files: Name | Variables | |--|--| @@ -117,18 +117,23 @@ Name | Variables | | `individual_traits.dat` | The value of each trait for each individual. | | `individual_midparents.dat` | The midparent phenotype (i.e. the mean between maternal and paternal values) for each trait for each individual. | +Each variable is saved as a vector of values (64bit double precision floating point numbers). By default the program will save all variables. Set `choosewhattosave` to 1 to decide which variables to save instead. The program will then expect a file `whattosave.txt` in the working directory. This file should be a list of names of variable to save. For example: -This means that each variable is saved as a vector of values (64 bit double precision floating point numbers). - -The following variables are saved every `tsave` timepoint: - +``` +time +EI +SI +RI +locus_Fst +``` +will save time, speciation metrics EI, SI and RI at each time point, and summary statistic Fst for each locus at each time point. -By default the program will save all these variables. To save only some of them, you have to set `choosewhattosave` to 1. The order file `whattosave.txt` should contain a list of names of variable to save, separated by any type of blanks (e.g. `time EI SI RI locus_Fst`). +### Reading data -**Note:** the computation of reproductive isolation (RI) requires sampling males and females at random in the population and pair them. This sampling can affect the generation of random numbers down the line. One consequence may be e.g. that different simulations run with the same seed but saving data at different time points may end up giving different results, just because the computation of RI adds to the sampling differently in the two replicates. To avoid this and make sure that the recording of RI does not affect the simulation, the sampling for RI is done using a separate random number generator from the rest of the simulation. +The data are saved in binary to speed up the writing (and the reading) process. Plus, different users will need to combine the data in many different ways depending on the question they are asking. To read and assemble the data into analyzable datasets, use our R package [speciomer](https://github.com/rscherrer/speciomer). -## Saving whole individual genomes +### Saving genomes Saving the whole genomes of all individuals through time takes a lot of space, for this reason this output is controlled separately from the other output variables. If you set `gensave 1` in addition to `datsave 1` two things will be saved every `tsave` generations: (1) the whole genomes of all individuals in `individual_whole_genomes.dat`, and (2) the genetic values at every locus for every individual in `individual_locus_genvalues.dat`. Both files are binary data files. From e9dc516d574c67c5c388b9f8f7dd562788406283 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Tue, 14 Feb 2023 09:54:46 +0100 Subject: [PATCH 44/71] Update README.md --- README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 984026e..81e2b33 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,7 @@ The genetic architecture refers to the constant features of the genotype-phenoty Set `datsave` to 1 to allow data recording. The data are saved every `tsave`generations into binary `*.dat` files: -Name | Variables | +| File | Variable | |--|--| | `time.dat` | Every saved time point. | | `population_sizes.dat` | Total population size (across both habitats). | @@ -135,7 +135,14 @@ The data are saved in binary to speed up the writing (and the reading) process. ### Saving genomes -Saving the whole genomes of all individuals through time takes a lot of space, for this reason this output is controlled separately from the other output variables. If you set `gensave 1` in addition to `datsave 1` two things will be saved every `tsave` generations: (1) the whole genomes of all individuals in `individual_whole_genomes.dat`, and (2) the genetic values at every locus for every individual in `individual_locus_genvalues.dat`. Both files are binary data files. +Saving the whole genomes of all individuals through time takes a lot of space, for this reason this output is controlled separately from the other output variables. If you set `gensave 1` in addition to `datsave 1` two things will be saved every `tsave` generations: + +| File | Variable | +|--|--| +| `individual_whole_genomes.dat` | The whole genomes of all individuals. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. You do | +| `individual_locus_genvalues.dat` | | + +(1) the whole genomes of all individuals in `individual_whole_genomes.dat`, and (2) the genetic values at every locus for every individual in `individual_locus_genvalues.dat`. Both files are binary data files. 1. Whole genomes are encoded in the freezer file in a different way from other variables. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci, because the organisms are diploid. Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, if N is the total number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. From 3502c6ac0f55ef95484e65e3567957b97512b1f4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Tue, 14 Feb 2023 09:55:06 +0100 Subject: [PATCH 45/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 81e2b33..533eb1b 100644 --- a/README.md +++ b/README.md @@ -139,7 +139,7 @@ Saving the whole genomes of all individuals through time takes a lot of space, f | File | Variable | |--|--| -| `individual_whole_genomes.dat` | The whole genomes of all individuals. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. You do | +| `individual_whole_genomes.dat` | The whole genomes of all individuals. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. You do not ha| | `individual_locus_genvalues.dat` | | (1) the whole genomes of all individuals in `individual_whole_genomes.dat`, and (2) the genetic values at every locus for every individual in `individual_locus_genvalues.dat`. Both files are binary data files. From 1453e404c4321b67d5bbe50b2881eb9a1ad040d9 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:00:31 +0100 Subject: [PATCH 46/71] stuff --- docs/VARIABLES.md | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 docs/VARIABLES.md diff --git a/docs/VARIABLES.md b/docs/VARIABLES.md new file mode 100644 index 0000000..51e364c --- /dev/null +++ b/docs/VARIABLES.md @@ -0,0 +1,28 @@ +## List of variables + +| File | Variable | +|--|--| +| `time.dat` | Every saved time point. | +| `population_sizes.dat` | Total population size (across both habitats). | +| `ecotype_population_sizes.dat` | Population size of each ecotype. | +| `habitat_resources.dat` | Equilibrium resource concentration of each resource in each habitat. | +| `trait_means.dat` | Mean value of each trait across the whole population. | +| `trait_ecotype_means.dat` | Mean value of each trait in each ecotype. | +| `trait_varP.dat`, `trait_varG.dat`, `trait_varA.dat`, `trait_varD.dat`, `trait_varI.dat`, `trait_varN.dat` | Respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each trait. | +| `trait_varT.dat` | Variance in allele frequencies across loci coding for each trait. | +| `trait_Pst.dat`, `trait_Gst.dat`, `trait_Qst.dat`, `trait_Cst.dat` | Respectively the differentiation statistics between ecotypes for the phenotypic, genetic, additive and non-additive variance for each trait. | +| `trait_Fst.dat` | Fixation index, or genetic differentiation between the two ecotypes, for each trait. | +| `EI.dat`, `SI.dat`, `RI.dat` | Ecological, spatial and reproductive isolation between ecotypes, respectively. | +| `locus_varP.dat`, `locus_varG.dat`, `locus_varA.dat`, `locus_varD.dat`, `locus_varI.dat`, `locus_varN.dat` | Respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each locus in the genome. | +| `locus_Pst.dat`, `locus_Gst.dat`, `locus_Qst.dat`, `locus_Cst.dat`, `locus_Fst.dat` | Respectively the Pst, Gst, Qst, Cst and Fst for each locus. | +| `locus_alpha.dat` | The average mutational effect (i.e. slope of the regression of genetic values against genotypes across the whole population) of each locus. | +| `locus_meang.dat` | The mean genetic value of each locus in the whole population. | +| `locus_freq.dat` | The allele frequency (of the 1-allele) for each locus in the whole population. | +| `locus_ecotype_freq.dat` | The allele frequencies for each locus within each ecotype. | +| `locus_ecotype_hobs.dat` | The observed heterozygosity for each locus within each ecotype. | +| `edge_corgen.dat`, `edge_corbreed.dat`, `edge_corfreq.dat` | Respectively the pairwise correlations in genetic value, breeding value and allele frequency between the two interacting loci for each edge in all three networks (ordered by trait). | +| `edge_avgi.dat`, `edge_avgj.dat` | The expected epistatic variance in average effect of the first and second interacting loci, respectively, for each edge. `edge_avgi` corresponds to the expected effect of genetic variation at locus i on the variation in the additive effect of allele substitutions at locus j, and vice versa for `edge_avgj`. This is mostly for plotting purposes, to detect genes that are expected to modify the additive effects of their interacting partners. | +| `individual_ecotypes.dat`, `individual_habitats.dat` | The ecotype and habitat of each individual. | +| `individual_traits.dat` | The value of each trait for each individual. | +| `individual_midparents.dat` | The midparent phenotype (i.e. the mean between maternal and paternal values) for each trait for each individual. | + From d4b69d34017190bccd271e780dc362340c964176 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:01:37 +0100 Subject: [PATCH 47/71] stuff --- README.md | 36 ++---------------------------------- 1 file changed, 2 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index 533eb1b..ecd83a9 100644 --- a/README.md +++ b/README.md @@ -89,33 +89,7 @@ The genetic architecture refers to the constant features of the genotype-phenoty ### Saving data -Set `datsave` to 1 to allow data recording. The data are saved every `tsave`generations into binary `*.dat` files: - -| File | Variable | -|--|--| -| `time.dat` | Every saved time point. | -| `population_sizes.dat` | Total population size (across both habitats). | -| `ecotype_population_sizes.dat` | Population size of each ecotype. | -| `habitat_resources.dat` | Equilibrium resource concentration of each resource in each habitat. | -| `trait_means.dat` | Mean value of each trait across the whole population. | -| `trait_ecotype_means.dat` | Mean value of each trait in each ecotype. | -| `trait_varP.dat`, `trait_varG.dat`, `trait_varA.dat`, `trait_varD.dat`, `trait_varI.dat`, `trait_varN.dat` | Respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each trait. | -| `trait_varT.dat` | Variance in allele frequencies across loci coding for each trait. | -| `trait_Pst.dat`, `trait_Gst.dat`, `trait_Qst.dat`, `trait_Cst.dat` | Respectively the differentiation statistics between ecotypes for the phenotypic, genetic, additive and non-additive variance for each trait. | -| `trait_Fst.dat` | Fixation index, or genetic differentiation between the two ecotypes, for each trait. | -| `EI.dat`, `SI.dat`, `RI.dat` | Ecological, spatial and reproductive isolation between ecotypes, respectively. | -| `locus_varP.dat`, `locus_varG.dat`, `locus_varA.dat`, `locus_varD.dat`, `locus_varI.dat`, `locus_varN.dat` | Respectively the phenotypic, genetic, additive, dominance, interaction and non-additive variance for each locus in the genome. | -| `locus_Pst.dat`, `locus_Gst.dat`, `locus_Qst.dat`, `locus_Cst.dat`, `locus_Fst.dat` | Respectively the Pst, Gst, Qst, Cst and Fst for each locus. | -| `locus_alpha.dat` | The average mutational effect (i.e. slope of the regression of genetic values against genotypes across the whole population) of each locus. | -| `locus_meang.dat` | The mean genetic value of each locus in the whole population. | -| `locus_freq.dat` | The allele frequency (of the 1-allele) for each locus in the whole population. | -| `locus_ecotype_freq.dat` | The allele frequencies for each locus within each ecotype. | -| `locus_ecotype_hobs.dat` | The observed heterozygosity for each locus within each ecotype. | -| `edge_corgen.dat`, `edge_corbreed.dat`, `edge_corfreq.dat` | Respectively the pairwise correlations in genetic value, breeding value and allele frequency between the two interacting loci for each edge in all three networks (ordered by trait). | -| `edge_avgi.dat`, `edge_avgj.dat` | The expected epistatic variance in average effect of the first and second interacting loci, respectively, for each edge. `edge_avgi` corresponds to the expected effect of genetic variation at locus i on the variation in the additive effect of allele substitutions at locus j, and vice versa for `edge_avgj`. This is mostly for plotting purposes, to detect genes that are expected to modify the additive effects of their interacting partners. | -| `individual_ecotypes.dat`, `individual_habitats.dat` | The ecotype and habitat of each individual. | -| `individual_traits.dat` | The value of each trait for each individual. | -| `individual_midparents.dat` | The midparent phenotype (i.e. the mean between maternal and paternal values) for each trait for each individual. | +Set `datsave` to 1 to allow data recording. The data are saved every `tsave` generations into binary `*.dat` files. Click [here](docs/VARIABLES.md) for a list of the variables that can be saved. Each variable is saved as a vector of values (64bit double precision floating point numbers). By default the program will save all variables. Set `choosewhattosave` to 1 to decide which variables to save instead. The program will then expect a file `whattosave.txt` in the working directory. This file should be a list of names of variable to save. For example: @@ -139,15 +113,9 @@ Saving the whole genomes of all individuals through time takes a lot of space, f | File | Variable | |--|--| -| `individual_whole_genomes.dat` | The whole genomes of all individuals. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. You do not ha| +| `individual_whole_genomes.dat` | The whole genomes of all individuals. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.)| | `individual_locus_genvalues.dat` | | -(1) the whole genomes of all individuals in `individual_whole_genomes.dat`, and (2) the genetic values at every locus for every individual in `individual_locus_genvalues.dat`. Both files are binary data files. - -1. Whole genomes are encoded in the freezer file in a different way from other variables. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci, because the organisms are diploid. Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, if N is the total number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. - -2. Genetic values across the genome for each individual are stored in `individual_locus_genvalues.dat` as floating point numbers encoded into a binary file, just as the other output variables, but with one value per locus per individual, per time point. - **Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. Unfortunately it is not possible to save some variables at a certain frequency and individual genomes less frequently in one simulation, as there is only one `tsave`. To collect data on different timelines, however, it is possible to run multiple simulations with the same `seed`. This can be done either by choosing a seed beforehand, or by saving the random seed of the first simulation (it will be saved in the parameter-log file `paramlog.txt` if `parsave 1`) and use it as `seed` to parametrize the next simulations. ## Which variables to save From 916e937cb5d043a98bae707c9258ed34b506771a Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:02:31 +0100 Subject: [PATCH 48/71] readme --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ecd83a9..af3a711 100644 --- a/README.md +++ b/README.md @@ -113,9 +113,11 @@ Saving the whole genomes of all individuals through time takes a lot of space, f | File | Variable | |--|--| -| `individual_whole_genomes.dat` | The whole genomes of all individuals. To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.)| +| `individual_whole_genomes.dat` | The whole genomes of all individuals.| | `individual_locus_genvalues.dat` | | +To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.) + **Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. Unfortunately it is not possible to save some variables at a certain frequency and individual genomes less frequently in one simulation, as there is only one `tsave`. To collect data on different timelines, however, it is possible to run multiple simulations with the same `seed`. This can be done either by choosing a seed beforehand, or by saving the random seed of the first simulation (it will be saved in the parameter-log file `paramlog.txt` if `parsave 1`) and use it as `seed` to parametrize the next simulations. ## Which variables to save From 72d5128159273a3b04b2a8bd782cb941b81910a0 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:03:32 +0100 Subject: [PATCH 49/71] doc --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index af3a711..bb673ef 100644 --- a/README.md +++ b/README.md @@ -113,8 +113,8 @@ Saving the whole genomes of all individuals through time takes a lot of space, f | File | Variable | |--|--| -| `individual_whole_genomes.dat` | The whole genomes of all individuals.| -| `individual_locus_genvalues.dat` | | +| `individual_whole_genomes.dat` | The whole genome of each individual.| +| `individual_locus_genvalues.dat` | The genetic value of each locus in each individual. | To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.) From 9002a9190834215eccfabd040e95a80636b84d53 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:04:30 +0100 Subject: [PATCH 50/71] documentation --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bb673ef..4fc806a 100644 --- a/README.md +++ b/README.md @@ -116,7 +116,7 @@ Saving the whole genomes of all individuals through time takes a lot of space, f | `individual_whole_genomes.dat` | The whole genome of each individual.| | `individual_locus_genvalues.dat` | The genetic value of each locus in each individual. | -To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.) +**Note:** To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.) **Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. Unfortunately it is not possible to save some variables at a certain frequency and individual genomes less frequently in one simulation, as there is only one `tsave`. To collect data on different timelines, however, it is possible to run multiple simulations with the same `seed`. This can be done either by choosing a seed beforehand, or by saving the random seed of the first simulation (it will be saved in the parameter-log file `paramlog.txt` if `parsave 1`) and use it as `seed` to parametrize the next simulations. From ad8f625e1d25c8e9f5b19226ca9888c43728a3fd Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:08:54 +0100 Subject: [PATCH 51/71] documentation --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4fc806a..b7b2ea3 100644 --- a/README.md +++ b/README.md @@ -118,7 +118,7 @@ Saving the whole genomes of all individuals through time takes a lot of space, f **Note:** To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.) -**Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. Unfortunately it is not possible to save some variables at a certain frequency and individual genomes less frequently in one simulation, as there is only one `tsave`. To collect data on different timelines, however, it is possible to run multiple simulations with the same `seed`. This can be done either by choosing a seed beforehand, or by saving the random seed of the first simulation (it will be saved in the parameter-log file `paramlog.txt` if `parsave 1`) and use it as `seed` to parametrize the next simulations. +**Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. To save regular variables frequently and whole genomes less frequently, we recommend running the same simulation twice with different values of `tsave` and making sure that the `seed` is the same. (The seed used by a simulation where no seed was provided can be retrieved in the `paramlog.txt` file, provided that `parsave` is set to 1, or in the `architecture.txt` file provided that `gensave` is set to 1.) ## Which variables to save From 11a47cd7339d32a880b47c4f18c7efb2eda50059 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:14:34 +0100 Subject: [PATCH 52/71] doc --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b7b2ea3..979e9d7 100644 --- a/README.md +++ b/README.md @@ -77,7 +77,7 @@ allfreq 0.2 nvertices 30 30 30 ``` -Parameters that are not provided in the parameter file will take default values. Note that some parameters take multiple values. Click [here](docs/PARAMETERS.md) for a list of all parameters. +Parameters that are not provided in the parameter file will take default values. Beware that some parameters take multiple values. Click [here](docs/PARAMETERS.md) for a list of all parameters. To allow for replication, if `parsave` is set to 1, the parameters used in the simulation (including any automatically-generated `seed`) will be saved into a file named `paramlog.txt` within the working directory. ### Model description @@ -85,7 +85,7 @@ This program runs an individual-based simulation where agents live, reproduce an ### Genetic architecture -The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click [here](docs/ARCHITECTURE.md) to see what a genetic architecture file should look like. +The genetic architecture refers to the constant features of the genotype-phenotype map. Those are features that do not change through time and cannot evolve, including e.g. the number of chromosomes, numbers and positions of loci and topologies and interaction weights of the gene networks. If `archload` is set to 0, a new architecture is generated at the beginning of the simulation. Otherwise, the program will read an architecture from a file `architecture.txt` that must be present in the working directory. Click [here](docs/ARCHITECTURE.md) to see what a genetic architecture file should look like. If `archsave` is set to 1, the architecture that was used in the simulation (whether generated or provided) will be saved into `architecture.txt`. ### Saving data From 5de5d690b4f97c182db27ed3a8e1174cdce5aec8 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:15:56 +0100 Subject: [PATCH 53/71] doc --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 979e9d7..1401adb 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,7 @@ Simulation program for evolutionary biologists to study speciation with a comple * [CMake](docs/CMAKE.md) * C++20 +* R and speciomer to read and analyze the data ## Build From b8f381c9573b0dd8379d8d8766513861a1e3b89d Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 10:30:45 +0100 Subject: [PATCH 54/71] doc --- README.md | 86 ++++++---------------------------------------- docs/PARAMETERS.md | 16 ++++----- 2 files changed, 19 insertions(+), 83 deletions(-) diff --git a/README.md b/README.md index 1401adb..d90d694 100644 --- a/README.md +++ b/README.md @@ -104,9 +104,16 @@ locus_Fst will save time, speciation metrics EI, SI and RI at each time point, and summary statistic Fst for each locus at each time point. +Some variables need other variables to be saved in order to be interpreted down the line. For example, `time` must be saved in order for the recorded time points to be appended to the resulting data tables. Or, the `population_sizes` in each recorded time point must be known for each individual to be assigned a time point in `individual_*` variables and in individual whole genomes. + ### Reading data -The data are saved in binary to speed up the writing (and the reading) process. Plus, different users will need to combine the data in many different ways depending on the question they are asking. To read and assemble the data into analyzable datasets, use our R package [speciomer](https://github.com/rscherrer/speciomer). +The data are saved in binary to speed up the writing (and the reading) process. Plus, different users will need to combine the data in many different ways depending on the question they are asking. To read and assemble the data into analyzable datasets, use our R package [speciomer](https://github.com/rscherrer/speciomer). Note that some functions in this package will expect certain files (e.g. `paramlog.txt`, `architecture.txt` or `time.dat`) to be present. In general we advise the following: + +* save the generative parameter values (`parsave 1`) +* have the genetic architecture at hand (e.g. `archsave 1`) to interpret the genetic data you might save (`locus_*`, `edge_*` and whole individual genomes) +* save `time`, as it is useful information for any of the other variables +* save `population_sizes` whenever `individual_*` variables or whole individual genomes are saved ### Saving genomes @@ -121,81 +128,10 @@ Saving the whole genomes of all individuals through time takes a lot of space, f **Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. To save regular variables frequently and whole genomes less frequently, we recommend running the same simulation twice with different values of `tsave` and making sure that the `seed` is the same. (The seed used by a simulation where no seed was provided can be retrieved in the `paramlog.txt` file, provided that `parsave` is set to 1, or in the `architecture.txt` file provided that `gensave` is set to 1.) -## Which variables to save - -Some variables need other variables to be saved in order to be interpreted down the line. For example, `time` must be saved in order for the recorded time points to be appended to the resulting data tables. Or, the `population_sizes` in each recorded time point must be known for each individual to be assigned a time point in `individual_*` variables and in individual whole genomes. - -In general we advise the following: - -* have the genetic architecture at hand (e.g. `archsave 1`) to interpret the genetic data you might save (`locus_*`, `edge_*` and whole individual genomes) -* save `time`, as it is useful information for any of the other variables -* save `population_sizes` whenever `individual_*` variables or whole individual genomes are saved - - - -We describe the different parameters (and their default value). - -Ecological parameters: - -* `rdynamics` (default 1) is the type of resource dynamics (0 for *logistic dynamics*, which assume a biotic resource with an intrinsic growth and a carrying capacity, or 1 for *chemostat dynamics* with and inflow and outflow of some abiotic resource) -* `replenish` (2375) and `capacity` (1) are the growth rate and carrying capacity, respectively, of the resources assuming logistic dynamics (`rdynamics 0`) -* `inflow` (400) and `outflow` (100) are the absolute inflow and relative outflow rates of the resources, respectively, assuming chemostat dynamics (`rdynamics 1`) -* `hsymmetry` (0) is the degree of homogeneity in resource distribution between the two habitats. It basically is a scaling factor to make the realized `replenish` (if logistic resource dynamics) or `inflow` (if chemostat resource dynamics) differ between the habitats, ranging between 0 and 1 -* `ecosel` (1.8) is the ecological trade-off in utilization between the two resources. It must be zero or positive, and the higher it is, the less efficient is utilizing both resources instead of specializing on a single one -* `ecoscale` (1) is a scaling parameter for the phenotypic distance between the two ecological niches along the ecological trait axis. A value of 1 means that the peaks of the two niches in utilization efficiency are at -1 and +1, respectively. This parameter also scales the widths of the resource utilization curves accordingly -* `demesizes` (100, 0) are the number of individuals initialized in both habitats, respectively -* `dispersal` (0.01) is the proportion of individuals sampled to switch habitats every generation -* `birth` (1) is the base birth rate per female, independent of fitness -* `survival` (0.8) is the probability of survival of adult individuals from one generation to the next (it takes one generation for newborns to become adults) -* `sexsel` (10) is the sexual selection coefficient, a positive number mediating the general degree of choosiness of females towards males during the mating season -* `matingcost` (0.01) is the fitness cost of choosy females (that run the risk of remaining unmated) -* `ntrials` (100) is the number of mating trials to perform to measure the degree of reproductive isolation between the species - -Genetic parameters: - -* `nvertices` (30, 30, 30) and `nedges` (30, 0, 0) are the numbers of loci and edges, respectively, in each of the three gene networks (one for each trait in the following order: ecological, mating and neutral). `nvertices` must be at least 2 for each trait, and `nedges` cannot be higher than n (n - 1) / 2 (complete graph), if n is the number of vertice for a given trait. In practice, the preferential attachment algorithm may fail to attach all the requested edges of the network if nedges is close to the maximum possible number of edges. In this case the program will return an error. The number of edges should be at least n-1, which ensures that the network is connected (all the nodes are attached). -* `nchrom` (3) is the number of equally sized chromosomes -* `mutation` (0.001) is the mutation rate per locus -* `recombination` (3) is the recombination rate and corresponds more or less to the average number of crossovers during one recombination event. It is actually the rate of an exponential distribution describing the distance between successive crossover points -* `allfreq` (0.2) is the initial frequency of the 1-allele in the population -* `scaleA` (1, 1, 1), `scaleD` (0, 0, 0), `scaleI` (0, 0, 0) and `scaleE` (0, 0, 0) are scaling factors for each source of phenotypic variation in the quantitative genetics framework, i.e. additive, dominance, epistatic and environmental effects, respectively, for each of the three traits -* `skews` (1, 1, 1) are the skewnesses of each of the three gene networks -* `effectshape` (2) and `effectscale` (1) are the parameters of the distribution of additive effect sizes across loci. If the resulting distribution has variance 0, all the loci are assigned effect size 1 -* `interactionshape` (5) and `interactionscale` (1) are the parameters of the distribution of epistatic interaction weights across edges. If the resulting distribution has variance 0, all the edges are assigned interaction weight 1 -* `dominancevar` (1) is the standard deviation of the distribution of dominance coefficients across loci. If 0, all the loci are assigned dominance coefficient 1 - -General simulation parameters: - -* `tburnin` (0) is the duration of the burn-in period, in generations -* `tend` (10) is the number of generations to simulate (use a negative value to end the simulation during the burn-in) -* `tsave` (10) is the frequency at which to record the data -* `tcomplete` (1000000) is the time at which to force complete reproductive isolation between the two ecotypes (can mimic e.g. genomic incompatibilities between the two species, or the evolution of very good species recognition abilities) -* `talkative` (1) is either 0 or 1 and sets whether the simulation should print status information to the prompt -* `choosewhattosave` (0) is either 0 or 1 and sets whether the variables to save are specified in a separate file, the order file `whattosave.txt` (see below). If 0 all of the output variables are saved every `tsave` generations except for whole genomes -* `datsave` (1) sets whether to save the recorded variables to files -* `burninsave` (0) sets whether to save data during the burn-in phase too (time points belonging to the burn-in are negative) -* `gensave` (0) is either 0 or 1 and sets whether whole genomes should be saved every `tfreeze` generations (see below) -* `archsave` (0) is either 0 or 1 and sets whether the genetic architecture should be saved into file `architecture.txt` (see below) -* `archload` (0) sets whether the genetic architecture of the simulation should be loaded from file `architecture.txt` instead of generated anew -* `parsave` (1) sets whether to save the parameters of the simulation run to file, including the random seed, into a parameter-log file `paramlog.txt` -* `logsave` (0) sets whether the output to prompt should be redirected to a log file `log.txt` -* `seed` is the seed of the random number generator, and it is by default randomly generated based on the clock - -Note that it is a good idea to set `parsave 1` and `logsave 1`, to make sure the full range of parameter values can be retrieved or the simulation progress can be monitored. Some analysis functions from the R package [speciomer](https://github.com/rscherrer/speciomer) actually expect the parameter-log file "paramlog.txt" to be present. - - -## Misc - -This program was written in C++14 in QtCreator 4.9.2 (Qt 5.12.4) on Ubuntu 18.04 LTS, but should run on other platforms too. - -This is a copy of the ExplicitGenomeSpeciation program, version 1.0. See https://github.com/rscherrer/ExplicitGenomeSpeciation for more details. - -## Disclaimer - -This simulation program was used to get insights into the effect of the genetic architecture on the process of speciation. It was not designed as a statistical inference package or a data processing tool, although its simulations could in theory be used for training machine learning algorithms to recognize various evolutionary scenarios. +## About -This code comes with no guarantee whatsoever. +This program is a descendent of [ExplicitGenomeSpeciation](https://github.com/rscherrer/ExplicitGenomeSpeciation). Disclaimer: this simulation program was used to get insights into the effect of the genetic architecture on the process of speciation. It was not designed as a statistical inference package or a data processing tool, although its simulations could in theory be used for training machine learning algorithms to recognize various evolutionary scenarios. This code comes with no guarantee whatsoever. ## Permissions -Copyright (c) Raphael Scherrer and G. Sander van Doorn, 2019 +Copyright (c) Raphael Scherrer and G. Sander van Doorn, 2023 diff --git a/docs/PARAMETERS.md b/docs/PARAMETERS.md index 8a3c420..0e4fb0c 100644 --- a/docs/PARAMETERS.md +++ b/docs/PARAMETERS.md @@ -17,8 +17,8 @@ | sexsel | Sexual selection coefficient | 10 | Parameter for the intensity of mate choice. | | matingcost | Cost of choosiness | 0.01 | Probability that the mating season ends after examining a male. | | ntrials | Number of mating trials | 100 | Number of trials to perform to measure reproductive isolation. | -| nvertices | Numbers of loci | 30 30 30 | Number of loci coding for each trait. | -| nedges | Numbers of edges | 30 30 30 | Number of edges for each trait. | +| nvertices | Numbers of loci | 30 30 30 | Number of loci coding for each trait. At least 2 for each trait. | +| nedges | Numbers of edges | 30 30 30 | Number of edges for each trait. Cannot be higher than n * (n - 1) / 2 (complete graph) if n is the number of vertices for a trait. In practice, the preferential attachment algorithm may fail to attach all the requested edges of the network if nedges is close to the maximum possible number of edges. In this case the program will return an error. The number of edges should be at least n-1, which ensures that the network is connected (all the nodes are attached). | | nchrom | Number of chromosomes | 3 | Chromosomes all have equal lengths. | | mutation | Mutation rate | 0.001 | Probability of mutation per locus. | | recombination | Recombination rate | 3 | Rate of the exponential distribution describing the distance between two crossovers. | @@ -28,11 +28,11 @@ | scaleI | Scaling for epistasis | 0 0 0 | | | scaleE | Scaling for environmental effects | 0 0 0 | | | skews | Network skewness | 1 1 1 | Skewness of the preferential attachment algorithm for each trait. | -| effectshape | Shape of effet size-distribution | 2 | Gamma distribution parameter. | -| effectscale | Scale of effect size-distribution | 1 | Gamma distribution parameter. | -| interactionshape | Shape of interaction weight-distribution | 5 | Gamma distribution parameter. | -| interactionscale | Scale of interaction weight-distribution | 1 | Gamma distribution parameter. | -| dominancevar | Standard deviation of dominance coefficient-distribution | 1 | Half-normal distribution parameter. | +| effectshape | Shape of effet size-distribution | 2 | Gamma distribution parameter. If the resulting distribution has variance 0, all the loci are assigned effect size 1. | +| effectscale | Scale of effect size-distribution | 1 | Gamma distribution parameter. If the resulting distribution has variance 0, all the loci are assigned effect size 1. | +| interactionshape | Shape of interaction weight-distribution | 5 | Gamma distribution parameter. If the resulting distribution has variance 0, all the edges are assigned interaction weight 1. | +| interactionscale | Scale of interaction weight-distribution | 1 | Gamma distribution parameter. If the resulting distribution has variance 0, all the edges are assigned interaction weight 1. | +| dominancevar | Standard deviation of dominance coefficient-distribution | 1 | Half-normal distribution parameter. If 0, all the loci are assigned dominance coefficient 1.| | tburnin | Burn-in duration | 0 | In number of generations. | | tend | Simulation time | 10 | In number of generations (in addition to the burn-in). | | tsave | Saving frequency | 10 | In number of generations. | @@ -46,4 +46,4 @@ | archload | Whether to load architecture | 0 | Yes (1) or no (0). | | parsave | Whether to save parameters | 1 | Yes (1) or no (0). | | logsave | Screen output to file | 0 | Yes (1) or no (0). | -| seed | Random number generator seed | Clock-generated | | +| seed | Random number generator seed | Clock-generated | | \ No newline at end of file From a32c5739d7568a998f85ef2fcb985159b100805c Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 11:08:20 +0100 Subject: [PATCH 55/71] doc --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d90d694..1f8d6c5 100644 --- a/README.md +++ b/README.md @@ -134,4 +134,4 @@ This program is a descendent of [ExplicitGenomeSpeciation](https://github.com/rs ## Permissions -Copyright (c) Raphael Scherrer and G. Sander van Doorn, 2023 +Copyright (c) Raphael Scherrer and G. Sander van Doorn, 2023 (open source license will be added upon publication). From 05654a04224b6b0dced3857b8d8cb9519b609545 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 11:28:57 +0100 Subject: [PATCH 56/71] doc --- README.md | 4 +++- docs/BUILD.md | 44 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 47 insertions(+), 1 deletion(-) create mode 100644 docs/BUILD.md diff --git a/README.md b/README.md index 1f8d6c5..456d071 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,8 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Build +(Click [here](docs/BUILD.md) to build as developer.) + ### Linux, MacOS ```bash @@ -24,7 +26,7 @@ The executable `speciome` is built in `../bin/`. ### Windows -```bash +```cmd git clone git@github.com:rscherrer/speciome.git cd speciome mkdir build diff --git a/docs/BUILD.md b/docs/BUILD.md new file mode 100644 index 0000000..c420409 --- /dev/null +++ b/docs/BUILD.md @@ -0,0 +1,44 @@ +## Build (developer) + +We use [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencies needed to build as developer: + +* [Boost.Test](https://github.com/boostorg/test) (latest version) + +### Linux, MacOS + +```bash +git clone git@github.com:rscherrer/speciome.git +cd speciome +git submodule add https://github.com/microsoft/vcpkg +git submodule update --init --remote +mkdir build && cd build +cmake .. +cmake --build . +``` + +### Windows + +```cmd +git clone git@github.com:rscherrer/speciome.git +cd speciome +git submodule add https://github.com/microsoft/vcpkg +git submodule update --init --remote +mkdir build +cd build +cmake .. +cmake --build . --config Release +``` + +### IDEs + +```bash +git clone git@github.com:rscherrer/speciome.git +cd speciome +git submodule update --init --recursive +mkdir build +cd build +# Generate VisualStudio project files +cmake -G "Visual Studio 17 2022" -A x64 .. +# Generate Xcode project files (Xcode must be installed) +cmake -G Xcode +``` \ No newline at end of file From 56cff8f60e48fca1ed13b6fe1981dc07c6309ff3 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 11:34:58 +0100 Subject: [PATCH 57/71] doc --- docs/BUILD.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/docs/BUILD.md b/docs/BUILD.md index c420409..71cc717 100644 --- a/docs/BUILD.md +++ b/docs/BUILD.md @@ -1,8 +1,10 @@ ## Build (developer) -We use [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencies needed to build as developer: +This uses [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencies needed to build as developer: -* [Boost.Test](https://github.com/boostorg/test) (latest version) +* [Boost.Test](https://github.com/boostorg/test) (latest version) for unit testing + +Executables for tests are built in `bin/tests/`. ### Linux, MacOS @@ -29,6 +31,7 @@ cmake .. cmake --build . --config Release ``` + ### IDEs ```bash From cbc501473286a3ddd7645c3037bd9047d010c652 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 11:35:50 +0100 Subject: [PATCH 58/71] doc --- docs/BUILD.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/BUILD.md b/docs/BUILD.md index 71cc717..5e79e0d 100644 --- a/docs/BUILD.md +++ b/docs/BUILD.md @@ -4,8 +4,6 @@ This uses [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencie * [Boost.Test](https://github.com/boostorg/test) (latest version) for unit testing -Executables for tests are built in `bin/tests/`. - ### Linux, MacOS ```bash @@ -18,6 +16,8 @@ cmake .. cmake --build . ``` +Executables for tests are built in `bin/tests/`. + ### Windows ```cmd @@ -31,6 +31,7 @@ cmake .. cmake --build . --config Release ``` +Executables for tests are built in `bin/tests/`. ### IDEs From 58489446e615e16c73bd5935c3977b0958cc1ccb Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 11:36:57 +0100 Subject: [PATCH 59/71] doc --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 456d071..8ece587 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Prerequisites -* [CMake](docs/CMAKE.md) +* [CMake](docs/CMAKE.md) (3.16 or higher) * C++20 * R and speciomer to read and analyze the data From ff805a1817d1fcf932d2a826648d1fdf7f2cfb5d Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 11:42:11 +0100 Subject: [PATCH 60/71] doc --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 8ece587..6c1bb3a 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Prerequisites -* [CMake](docs/CMAKE.md) (3.16 or higher) -* C++20 -* R and speciomer to read and analyze the data +* A C++ compiler +* (optional) [CMake](docs/CMAKE.md) (3.16 or higher) +* (optional) R and speciomer to read and analyze the data ## Build From 221ac39a1efe39378183f5e24d517689da201b5f Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 11:43:32 +0100 Subject: [PATCH 61/71] doc --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 6c1bb3a..e7c1dc6 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,8 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Build +Here are instructions to build with CMake, but you can compile the source code with the tools of your choice. + (Click [here](docs/BUILD.md) to build as developer.) ### Linux, MacOS From 4facd90e182282602e2053b101c2f18b6399a111 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 11:56:56 +0100 Subject: [PATCH 62/71] doc --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e7c1dc6..177828e 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Prerequisites * A C++ compiler -* (optional) [CMake](docs/CMAKE.md) (3.16 or higher) +* (optional) [CMake](docs/CMAKE.md) version 3.16 or higher * (optional) R and speciomer to read and analyze the data ## Build From 5f11ba7ae4aad4a36956bd71fba3c13baa409148 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 12:11:07 +0100 Subject: [PATCH 63/71] allowing two configurations --- CMakeLists_devel.txt | 29 +++++++++++++++++++++++++++++ CMakeLists_user.txt | 15 +++++++++++++++ README.md | 5 ++++- docs/BUILD.md | 3 +++ tests/CMakeLists.txt | 25 +++++++++++-------------- 5 files changed, 62 insertions(+), 15 deletions(-) create mode 100644 CMakeLists_devel.txt create mode 100644 CMakeLists_user.txt diff --git a/CMakeLists_devel.txt b/CMakeLists_devel.txt new file mode 100644 index 0000000..61f3bd7 --- /dev/null +++ b/CMakeLists_devel.txt @@ -0,0 +1,29 @@ +# ./CMakeLists.txt + +cmake_minimum_required(VERSION 3.16) + +set(CMAKE_TOOLCHAIN_FILE "${CMAKE_SOURCE_DIR}/vcpkg/scripts/buildsystems/vcpkg.cmake") +if (WIN32) + set(VCPKG_TARGET_TRIPLET x64-windows) +endif() + +project(speciome) + +find_package(Git REQUIRED) +execute_process(COMMAND ${GIT_EXECUTABLE} submodule update --init --remote + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} + RESULT_VARIABLE GIT_SUBMOD_RESULT) +if (NOT GIT_SUBMOD_RESULT EQUAL "0") + message(FATAL_ERROR "git submodule update --init failed with ${GIT_SUBMOD_RESULT}, please checkout submodules") +endif() + +# we want to use the newest and boldest c++ standard +set(CMAKE_CXX_STANDARD 20) +set(CMAKE_CXX_STANDARD_REQUIRED) + +# boilerplate +set(EXECUTABLE_OUTPUT_PATH ${CMAKE_BINARY_DIR}) +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}) + +add_subdirectory(src) +add_subdirectory(tests) diff --git a/CMakeLists_user.txt b/CMakeLists_user.txt new file mode 100644 index 0000000..bbedfff --- /dev/null +++ b/CMakeLists_user.txt @@ -0,0 +1,15 @@ +# ./CMakeLists.txt + +cmake_minimum_required(VERSION 3.16) + +project(speciome) + +# we want to use the newest and boldest c++ standard +set(CMAKE_CXX_STANDARD 20) +set(CMAKE_CXX_STANDARD_REQUIRED) + +# boilerplate +set(EXECUTABLE_OUTPUT_PATH ${CMAKE_BINARY_DIR}) +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}) + +add_subdirectory(src) \ No newline at end of file diff --git a/README.md b/README.md index 177828e..e049aa2 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,7 @@ Here are instructions to build with CMake, but you can compile the source code w ```bash git clone git@github.com:rscherrer/speciome.git cd speciome +cp CMakeLists_user.txt CMakeLists.txt # user configuration mkdir build && cd build cmake .. cmake --build . @@ -31,6 +32,7 @@ The executable `speciome` is built in `../bin/`. ```cmd git clone git@github.com:rscherrer/speciome.git cd speciome +copy CMakeLists_user.txt CMakeLists.txt :: user configuration mkdir build cd build cmake .. @@ -46,7 +48,8 @@ You can use CMake to generate the input files for your favorite IDE too: ```bash git clone git@github.com:rscherrer/speciome.git -cd speciome +cd speciom +cp CMakeLists_user.txt CMakeLists.txt # user configuration mkdir build cd build # Generate VisualStudio project files diff --git a/docs/BUILD.md b/docs/BUILD.md index 5e79e0d..44622c9 100644 --- a/docs/BUILD.md +++ b/docs/BUILD.md @@ -9,6 +9,7 @@ This uses [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencie ```bash git clone git@github.com:rscherrer/speciome.git cd speciome +cp CMakeLists_devel.txt CMakeLists.txt # developer configuration git submodule add https://github.com/microsoft/vcpkg git submodule update --init --remote mkdir build && cd build @@ -23,6 +24,7 @@ Executables for tests are built in `bin/tests/`. ```cmd git clone git@github.com:rscherrer/speciome.git cd speciome +copy CMakeLists_devel.txt CMakeLists.txt :: developer configuration git submodule add https://github.com/microsoft/vcpkg git submodule update --init --remote mkdir build @@ -38,6 +40,7 @@ Executables for tests are built in `bin/tests/`. ```bash git clone git@github.com:rscherrer/speciome.git cd speciome +cp CMakeLists_devel.txt CMakeLists.txt # developer configuration git submodule update --init --recursive mkdir build cd build diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 81ba952..1a993a3 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -1,19 +1,16 @@ # tests/CMakeLists.txt -find_package(Boost COMPONENTS unit_test_framework) +find_package(Boost COMPONENTS unit_test_framework REQUIRED) -if (Boost_FOUND) - - # model 'unit' files - file(GLOB_RECURSE unit ${CMAKE_SOURCE_DIR}/src/*.cpp) +# model 'unit' files +file(GLOB_RECURSE unit ${CMAKE_SOURCE_DIR}/src/*.cpp) - set(TESTS CollectorTests GenArchTests IndividualTests MetaPopTests OutputTests SimulTests) +set(TESTS CollectorTests GenArchTests IndividualTests MetaPopTests OutputTests SimulTests) - foreach(TEST IN LISTS TESTS) - add_executable(${TEST} ${CMAKE_SOURCE_DIR}/tests/${TEST}.cpp ${unit}) - target_include_directories(${TEST} PRIVATE ${CMAKE_SOURE_DIR}) - target_link_libraries(${TEST} PUBLIC Boost::unit_test_framework) - set_target_properties(${TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/bin/tests/$<0:>) - endforeach() - -endif() +foreach(TEST IN LISTS TESTS) + add_executable(${TEST} ${CMAKE_SOURCE_DIR}/tests/${TEST}.cpp ${unit}) + target_include_directories(${TEST} PRIVATE ${CMAKE_SOURE_DIR}) + target_link_libraries(${TEST} PUBLIC Boost::unit_test_framework) + set_target_properties(${TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/bin/tests/$<0:>) +endforeach() + \ No newline at end of file From 2ff1a4053003133341283802cbd791f7c2ed4e82 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 12:21:24 +0100 Subject: [PATCH 64/71] doc --- README.md | 4 ++-- docs/BUILD.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index e049aa2..7ae8c2e 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ Here are instructions to build with CMake, but you can compile the source code w ### Linux, MacOS -```bash +```shell git clone git@github.com:rscherrer/speciome.git cd speciome cp CMakeLists_user.txt CMakeLists.txt # user configuration @@ -46,7 +46,7 @@ The executable `speciome.exe` is built in `../bin/`. Many IDEs support CMake out of the box. "Open folder" should do the trick... You can use CMake to generate the input files for your favorite IDE too: -```bash +```shell git clone git@github.com:rscherrer/speciome.git cd speciom cp CMakeLists_user.txt CMakeLists.txt # user configuration diff --git a/docs/BUILD.md b/docs/BUILD.md index 44622c9..facfe7f 100644 --- a/docs/BUILD.md +++ b/docs/BUILD.md @@ -6,7 +6,7 @@ This uses [vcpkg](https://github.com/microsoft/vcpkg) to install the dependencie ### Linux, MacOS -```bash +```shell git clone git@github.com:rscherrer/speciome.git cd speciome cp CMakeLists_devel.txt CMakeLists.txt # developer configuration @@ -37,7 +37,7 @@ Executables for tests are built in `bin/tests/`. ### IDEs -```bash +```shell git clone git@github.com:rscherrer/speciome.git cd speciome cp CMakeLists_devel.txt CMakeLists.txt # developer configuration From c4bc092d671c2c2eec358d2afb4219eed2781304 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 12:24:48 +0100 Subject: [PATCH 65/71] doc --- README.md | 13 ++----------- docs/GENOMES.md | 13 +++++++++++++ 2 files changed, 15 insertions(+), 11 deletions(-) create mode 100644 docs/GENOMES.md diff --git a/README.md b/README.md index 7ae8c2e..8453f1f 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Prerequisites -* A C++ compiler +* A C++20 compiler * (optional) [CMake](docs/CMAKE.md) version 3.16 or higher * (optional) R and speciomer to read and analyze the data @@ -124,16 +124,7 @@ The data are saved in binary to speed up the writing (and the reading) process. ### Saving genomes -Saving the whole genomes of all individuals through time takes a lot of space, for this reason this output is controlled separately from the other output variables. If you set `gensave 1` in addition to `datsave 1` two things will be saved every `tsave` generations: - -| File | Variable | -|--|--| -| `individual_whole_genomes.dat` | The whole genome of each individual.| -| `individual_locus_genvalues.dat` | The genetic value of each locus in each individual. | - -**Note:** To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.) - -**Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. To save regular variables frequently and whole genomes less frequently, we recommend running the same simulation twice with different values of `tsave` and making sure that the `seed` is the same. (The seed used by a simulation where no seed was provided can be retrieved in the `paramlog.txt` file, provided that `parsave` is set to 1, or in the `architecture.txt` file provided that `gensave` is set to 1.) +Saving the whole genomes of all individuals through time takes a lot of space, for this reason this output is controlled separately from the other output variables. Click [here](docs/GENOMES.md) for details. ## About diff --git a/docs/GENOMES.md b/docs/GENOMES.md new file mode 100644 index 0000000..4a04977 --- /dev/null +++ b/docs/GENOMES.md @@ -0,0 +1,13 @@ +## Saving genomes + +If you set `gensave 1` in addition to `datsave 1` two things will be saved every `tsave` generations: + +| File | Variable | +|--|--| +| `individual_whole_genomes.dat` | The whole genome of each individual.| +| `individual_locus_genvalues.dat` | The genetic value of each locus in each individual. | + +**Note:** To save space, we use the fact that alleles are binary (0 or 1). Each value in a full genome is an allele at a specific position along one of the two haplotypes of an individual. Therefore, a genome contains twice as many values as there are loci (the organism is diploid). Each value is either 0 or 1 (the two possible alleles). Haplotypes are saved in turns, such that the first N values are all alleles of the first haplotype and the next N values are all alleles of the second haplotype, where N is the number of loci. This does not mean that each saved individual genome is exactly 2N values long, though. In order to save space for this large amount of data, individual genomes are first split into blocks of 64 bits, and each block is converted into a 64bit integer, which is then saved as binary. Therefore, the output file `individual_whole_genomes.dat` must be interpreted on a bit-wise basis in order to retrieve the actual alleles of the individual (i.e. reading it as 64bit integers will show integer-equivalents of chunks of 64 alleles). This also means that for each individual, a multiple of 64 bits will be written to the file, even if 2N alleles is not necessarily a multiple of 64. In other words, for each individual 2N bits will be written to file, and the remaining part of the last 64bit-chunk will be filled with zeros. (You do not have to worry about all that, the R package [speciomer](https://github.com/rscherrer/speciomer) takes care of it.) + +**Important:** whole individual genomes take a lot of space. For this reason we advise against saving them too often. To save regular variables frequently and whole genomes less frequently, we recommend running the same simulation twice with different values of `tsave` and making sure that the `seed` is the same. (The seed used by a simulation where no seed was provided can be retrieved in the `paramlog.txt` file, provided that `parsave` is set to 1, or in the `architecture.txt` file provided that `gensave` is set to 1.) + From c8b2cef6d8cb658ab562aaf158eb2e345553476a Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 14:12:25 +0100 Subject: [PATCH 66/71] doc --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 8453f1f..dad7d0b 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Simulation program for evolutionary biologists to study speciation with a comple ## Prerequisites -* A C++20 compiler +* A C++20 compiler (e.g. [GCC](https://gcc.gnu.org) or [Clang](https://clang.llvm.org)) * (optional) [CMake](docs/CMAKE.md) version 3.16 or higher * (optional) R and speciomer to read and analyze the data @@ -14,6 +14,8 @@ Here are instructions to build with CMake, but you can compile the source code w (Click [here](docs/BUILD.md) to build as developer.) +(Click [here](docs/PEREGRINE.md) to build on the [Peregrine](https://www.rug.nl/society-business/centre-for-information-technology/research/services/hpc/facilities/peregrine-hpc-cluster?lang=en) cluster) + ### Linux, MacOS ```shell From 2d406c5eaef4c774710d0632fdd397a2544597a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Tue, 14 Feb 2023 14:16:38 +0100 Subject: [PATCH 67/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index dad7d0b..68388fb 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ Here are instructions to build with CMake, but you can compile the source code w (Click [here](docs/BUILD.md) to build as developer.) -(Click [here](docs/PEREGRINE.md) to build on the [Peregrine](https://www.rug.nl/society-business/centre-for-information-technology/research/services/hpc/facilities/peregrine-hpc-cluster?lang=en) cluster) +(Click [here](docs/PEREGRINE.md) to build on the [Peregrine](https://www.rug.nl/society-business/centre-for-information-technology/research/services/hpc/facilities/peregrine-hpc-cluster?lang=en) cluster.) ### Linux, MacOS From 535892305df3fc2be69ad2d0fa6539b08f6f8048 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 14:37:28 +0100 Subject: [PATCH 68/71] gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 53be53f..0412210 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ data/ approx/ extra/ mating/ +speciomer/ ##### Windows # Windows thumbnail cache files From d2c471c3505a2bf107535a79138e66b14b5d9032 Mon Sep 17 00:00:00 2001 From: rscherrer Date: Tue, 14 Feb 2023 14:38:10 +0100 Subject: [PATCH 69/71] forget travis --- .travis.yml | 88 ----------------------------------------------------- 1 file changed, 88 deletions(-) delete mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 5c61b26..0000000 --- a/.travis.yml +++ /dev/null @@ -1,88 +0,0 @@ -language: cpp -dist: xenial -compiler: gcc - -addons: - apt: - packages: - - libboost-all-dev - -install: - - # Qt5 - - sudo apt-get install -qq qt5-qmake qt5-default - - # cppcheck - - sudo apt-get install -qq cppcheck - - # Codecov - - sudo pip install codecov - - # OCLint - - chmod +x ci/install_oclint - - ./ci/install_oclint - - # valgrind, for helgrind and memcheck - - sudo apt-get install -qq valgrind - - # SLOCcount - - sudo apt-get install sloccount - - # proselint - - sudo pip install proselint - -script: - - # Build and run the tests - - qmake ./speciome_test.pro - - make --silent debug - - travis_wait 60 ./speciome_test - - # helgrind - - travis_wait 60 valgrind --tool=helgrind --log-file=helgrind.log -v ./speciome_test - - cat helgrind.log - - # memcheck - - travis_wait 60 valgrind --leak-check=full -v --show-reachable=yes --log-file=memcheck.log ./speciome_test - - cat memcheck.log - - # Build and run and profile normal program - - qmake ./speciome.pro - - make --silent release - - echo "tend 1000" > parameters.txt - - echo "tsave 100" >> parameters.txt - - time ./speciome parameters.txt - - gprof ./speciome > gprof.log - - head gprof.log -n 1000 - - # Get code coverage - - chmod +x ci/get_code_cov - - ./ci/get_code_cov - - codecov - - # cppcheck - - cppcheck --quiet --error-exitcode=0 . - - # Call OCLint - - chmod +x ci/do_oclint - - ./ci/do_oclint - - # proselint - - proselint README.md || true - - # Get my folder - - my_github=${PWD##*/} - - # SLOCcount - # remove OCLint and .git, otherwise it will be counted as well - - rm -rf oclint-0.10.3 oclint-0.10.3-x86_64-linux-3.13.0-74-generic.tar.gz oclint.log - - rm -rf .git - - rm -rf debug - - rm -rf release - - cd ..; sloccount --overhead 1 --personcost 22614 $my_github; cd $my_github - -after_success: - - # Codecov - - bash <(curl -s https://codecov.io/bash) - From 747740272a14c2d987decf1c20acf6177f14b159 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Scherrer?= Date: Tue, 14 Feb 2023 14:39:59 +0100 Subject: [PATCH 70/71] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 68388fb..6dec0e0 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ Simulation program for evolutionary biologists to study speciation with a comple * A C++20 compiler (e.g. [GCC](https://gcc.gnu.org) or [Clang](https://clang.llvm.org)) * (optional) [CMake](docs/CMAKE.md) version 3.16 or higher -* (optional) R and speciomer to read and analyze the data +* (optional) R and [speciomer](https://github.com/rscherrer/speciomer) to read and analyze the data ## Build From 87d4154626b710c7d65edf19ec0527164495c28d Mon Sep 17 00:00:00 2001 From: rscherrer Date: Wed, 15 Feb 2023 17:09:21 +0100 Subject: [PATCH 71/71] removed .vscode --- .vscode/settings.json | 5 ----- 1 file changed, 5 deletions(-) delete mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 3ec79d0..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "files.associations": { - "fstream": "cpp" - } -} \ No newline at end of file

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