From 84ca2e8017476a6af25f997f1890e3b786377bee Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Thu, 3 Nov 2022 13:23:43 +0300 Subject: [PATCH 01/13] Add files via upload --- _BirthDeath.pyx | 2682 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2682 insertions(+) create mode 100644 _BirthDeath.pyx diff --git a/_BirthDeath.pyx b/_BirthDeath.pyx new file mode 100644 index 0000000..0ad0f59 --- /dev/null +++ b/_BirthDeath.pyx @@ -0,0 +1,2682 @@ +# cython: language_level=3 +# cython: initializedcheck = False +# distutils: language = c++ + +cimport cython + +from numpy cimport npy_int64 + +from libc.math cimport log, floor, abs +from libcpp.vector cimport vector +from mc_lib.rndm cimport RndmWrapper + +from prettytable import PrettyTable +import numpy as np +cimport numpy as np +import sys +import os +import time +import tskit + +from numpy.random.c_distributions cimport random_poisson, random_hypergeometric + +include "fast_choose.pxi" +include "models.pxi" +include "events.pxi" + +#pi - population ID, pn - popoulation number, spi - source population ID, tpi - target population ID +#hi - haplotype ID, hn - haplotype number, nhi - new haplotype number +#si - susceptibility ID, sn - susceptibility number, ssi - source susceptibility ID, tsi - target susceptibility ID +cdef class BirthDeathModel: + cdef: + RndmWrapper seed + + bint first_simulation, sampling_probability, memory_optimization + Py_ssize_t user_seed, sites, hapNum, currentHapNum, maxHapNum, addMemoryNum, popNum, susNum, bCounter, dCounter, sCounter, mCounter, iCounter, swapLockdown, migPlus, migNonPlus, globalInfectious, countsPerStep, good_attempt, conditionNum, cCounter + double currentTime, totalRate, totalMigrationRate, rn, tau_l + + Events events + multiEvents multievents + Mutations mut + Migrations mig + Lockdowns loc + + npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop + npy_int64[:,::1] susceptible, infectious, initial_susceptible, initial_infectious + npy_int64[:,::1] totalInfectiousByTypes, totalSusceptibleByTypes + npy_int64[:,:,::1] infectiousByTypes, susceptibleByTypes + + double[::1] bRate, dRate, sRate, tmRate, maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times + double[::1] totalRateByCondition, immunePopRateByConditionOneDem + double[:,::1] mRate, susceptibility, tEventHapPopRate, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration + double[:,::1] conditionRate, teventConditionRate, immunePopRateByCondition, infectPopRateByCondition, suscepCumulTransitionByCondition, popRateByCondition, massivForThree + double[:,:,::1] hapMutType, eventHapPopRate, susceptHapPopRate + double[:,:,::1] eventConditionRate,immuneSourcePopRateByCondition, suscepTransitionByCondition + + double[:,:,:,::1] PropensitiesMigr, PropensitiesMutatations + double[:,:,::1] PropensitiesSuscep, PropensitiesTransmission + double[:,::1] PropensitiesRecovery, PropensitiesSampling + + npy_int64[:,:,:,::1] eventsMigr, eventsMutatations + npy_int64[:,:,::1] eventsSuscep, eventsTransmission/ + npy_int64[:,::1] eventsRecovery, eventsSampling + + double[:,::1] infectiousAuxTau, susceptibleAuxTau + npy_int64[:,::1] infectiousDelta, susceptibleDelta + + + def __init__(self, number_of_sites, populations_number, number_of_susceptible_groups, seed, sampling_probability, memory_optimization): + self.user_seed = seed + self.seed = RndmWrapper(seed=(self.user_seed, 0)) + + self.first_simulation = False + self.sampling_probability = sampling_probability + + self.sites = number_of_sites + self.hapNum = 4**self.sites + self.susNum = number_of_susceptible_groups + self.popNum = populations_number + + if memory_optimization: + self.memory_optimization = True + if self.sites > 2: + self.maxHapNum = 4**(self.sites-2) + self.addMemoryNum = 4**(self.sites-2) + else: + self.maxHapNum = 4 + self.addMemoryNum = 4 + else: + self.memory_optimization = False + self.maxHapNum = self.hapNum + self.addMemoryNum = 0 + + self.hapToNum = np.zeros(self.hapNum, dtype=np.int64) # from haplotype to program number + self.numToHap = np.zeros(self.maxHapNum, dtype=np.int64) # from program number to haplotype + + if self.memory_optimization: + self.currentHapNum = 0 + else: + self.currentHapNum = self.hapNum + for hn in range(self.hapNum): + self.hapToNum[hn] = hn + self.numToHap[hn] = hn + + self.bCounter = 0 + self.dCounter = 0 + self.sCounter = 0 + self.mCounter = 0 + self.iCounter = 0 + self.cCounter = 0 + self.swapLockdown = 0 + self.migPlus = 0 + self.migNonPlus = 0 + self.globalInfectious = 0 + + self.currentTime = 0.0 + self.tau_l=0.01 + self.totalRate = 0.0 + self.totalMigrationRate = 0.0 + + self.events = Events() + self.multievents = multiEvents() + self.mut = Mutations() + self.mig = Migrations() + self.loc = Lockdowns() + + # memory_optimization + self.totalRateByCondition = np.zeros(self.conditionNum, dtype=float) + self.massivForThree = np.zeros((self.popNum, 3), dtype=float) + self.immunePopRateByConditionOneDem = np.zeros(self.popNum, dtype=float) + self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) + self.infectiousByTypes = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=np.int64) + self.tmRate = np.zeros(self.maxHapNum, dtype=float) + self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.hapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.eventHapPopRate = np.zeros((self.popNum, self.maxHapNum, 4), dtype=float) + self.susceptHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.susNum), dtype=float) + self.eventConditionRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) + + self.suscType = np.zeros(self.hapNum, dtype=np.int64) + self.bRate = np.zeros(self.hapNum, dtype=float) + self.dRate = np.zeros(self.hapNum, dtype=float) + self.sRate = np.zeros(self.hapNum, dtype=float) + self.mRate = np.zeros((self.hapNum, self.sites), dtype=float) + self.conditionRate = np.zeros((self.conditionNum, self.conditionNum), dtype=float) + self.susceptibility = np.zeros((self.hapNum, self.susNum), dtype=float) + self.hapMutType = np.ones((self.hapNum, self.sites, 3), dtype=float) + + for hn in range(self.hapNum): + self.bRate[hn] = 2.0 + self.dRate[hn] = 1.0 + self.sRate[hn] = 0.01 + for s in range(self.sites): + self.mRate[hn, s] = 0.01 + self.susceptibility[hn, 0] = 1.0 + + for i in range(self.conditionNum): + for j in range(self.conditionNum): + if i!=j: + self.conditionRate[i,j] = 2 + + self.sizes = np.zeros(self.popNum, dtype=np.int64) + self.totalSusceptible = np.zeros(self.popNum, dtype=np.int64) + self.totalSusceptibleByTypes = np.zeros((self.popNum, self.conditionNum), dtype=np.int64) + self.totalInfectious = np.zeros(self.popNum, dtype=np.int64) + self.totalInfectiousByTypes = np.zeros((self.popNum, self.conditionNum), dtype=np.int64) + self.lockdownON = np.zeros(self.popNum, dtype=np.int64) + + self.susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) + self.susceptibleByTypes = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=np.int64) + self.initial_susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) + self.initial_infectious = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + + self.maxEffectiveBirthMigration = np.zeros(self.popNum, dtype=float) + self.suscepCumulTransition = np.zeros(self.susNum, dtype=float) + self.suscepCumulTransitionByCondition = np.zeros((self.conditionNum, self.susNum), dtype=float) + self.infectPopRate = np.zeros(self.popNum, dtype=float) + self.infectPopRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) + self.immunePopRate = np.zeros(self.popNum, dtype=float) + self.immunePopRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) + self.popRate = np.zeros(self.popNum, dtype=float) + self.popRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) + self.migPopRate = np.zeros(self.popNum, dtype=float) + self.actualSizes = np.zeros(self.popNum, dtype=float) + self.contactDensity = np.ones(self.popNum, dtype=float) + self.contactDensityBeforeLockdown = np.ones(self.popNum, dtype=float) + self.contactDensityAfterLockdown = np.zeros(self.popNum, dtype=float) + self.startLD = np.ones(self.popNum, dtype=float) + self.endLD = np.ones(self.popNum, dtype=float) + self.samplingMultiplier = np.ones(self.popNum, dtype=float) + + self.suscepTransition = np.zeros( (self.susNum, self.susNum), dtype=float) + self.suscepTransitionByCondition = np.zeros((self.conditionNum, self.susNum, self.susNum), dtype=float) + self.immuneSourcePopRate = np.zeros((self.popNum, self.susNum), dtype=float) + self.immuneSourcePopRateByCondition = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=float) + self.migrationRates = np.zeros((self.popNum, self.popNum), dtype=float) + self.effectiveMigration = np.zeros((self.popNum, self.popNum), dtype=float) + + for pn in range(self.popNum): + self.sizes[pn] = 1000000 + self.totalSusceptible[pn] = 1000000 + self.susceptible[pn, 0] = 1000000 + + for pn in range(self.popNum): + for cn in range(self.conditionNum): + self.totalSusceptibleByTypes[pn, cn] = 1000000 + #self.susceptibleByTypes[pn, cn, 0] = 1000000 + for pn in range(self.popNum): + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + self.susceptibleByTypes[pn, hn, cn] = 1000000 + + self.tree = np.zeros(1, dtype=np.int64) + self.tree_pop = np.zeros(1, dtype=np.int64) + + #Init propensities + self.PropensitiesMigr = np.zeros((self.popNum, self.popNum, self.susNum, self.hapNum), dtype=float) + self.PropensitiesSuscep = np.zeros((self.popNum, self.susNum, self.susNum), dtype=float) + self.PropensitiesRecovery = np.zeros((self.popNum, self.hapNum), dtype=float) + self.PropensitiesSampling = np.zeros((self.popNum, self.hapNum), dtype=float) + self.PropensitiesMutatations = np.zeros((self.popNum, self.hapNum, self.sites, 3), dtype=float) + self.PropensitiesTransmission = np.zeros((self.popNum, self.hapNum, self.susNum), dtype=float) + + #Sampled number of events per step + self.eventsMigr = np.zeros((self.popNum, self.popNum, self.susNum, self.hapNum), dtype=np.int64) + self.eventsSuscep = np.zeros((self.popNum, self.susNum, self.susNum), dtype=np.int64) + self.eventsRecovery = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + self.eventsSampling = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + self.eventsMutatations = np.zeros((self.popNum, self.hapNum, self.sites, 3), dtype=np.int64) + self.eventsTransmission = np.zeros((self.popNum, self.hapNum, self.susNum), dtype=np.int64) + + self.infectiousAuxTau = np.zeros((self.popNum, self.hapNum), dtype=float) + self.susceptibleAuxTau = np.zeros((self.popNum, self.susNum), dtype=float) + + self.infectiousDelta = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + self.susceptibleDelta = np.zeros((self.popNum, self.susNum), dtype=np.int64) + + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void FirstInfection(self): + if self.globalInfectious == 0: + for sn in range(self.susNum): + if self.susceptible[0, sn] != 0: + if self.memory_optimization: + self.AddHaplotype(0, 0) + self.NewInfections(0, sn, 0) + return + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef inline void NewInfections(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t num=1): + self.susceptible[pi, si] -= num + self.totalSusceptible[pi] -= num + self.infectious[pi, hi] += num + self.totalInfectious[pi] += num + self.globalInfectious += num + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef inline void NewRecoveries(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t num=1): + self.susceptible[pi, si] += num + self.totalSusceptible[pi] += num + self.infectious[pi, hi] -= num + self.totalInfectious[pi] -= num + self.globalInfectious -= num + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void AddMemory(self): + if self.addMemoryNum + self.maxHapNum > self.hapNum: + self.addMemoryNum = self.hapNum - self.maxHapNum + self.numToHap = np.concatenate((self.numToHap, np.zeros(self.addMemoryNum, dtype=np.int64))) + self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum), dtype=np.int64)), axis=1) + self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, dtype=float))) + self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) + self.teventConditionRate = np.concatenate((self.teventConditionRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) + self.hapPopRate = np.concatenate((self.hapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) + self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, 4), dtype=float)), axis=1) + self.susceptHapPopRate = np.concatenate((self.susceptHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.susNum), dtype=float)), axis=1) + self.eventConditionRate = np.concatenate((self.eventConditionRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum)), axis=1)) + self.maxHapNum += self.addMemoryNum + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void UpdateAllRates(self): + cdef: + double maxEffectiveBirth + double[::1] maxEffectiveMigration + + for sn1 in range(self.susNum): + self.suscepCumulTransition[sn1] = 0 + for sn2 in range(self.susNum): + self.suscepCumulTransition[sn1] += self.suscepTransition[sn1, sn2] + + #for sn1 in range(self.susNum): + # for sn2 in range(self.susNum): + # self.suscepCumulTransitionByCondition[sn1, sn2] = 0 + # for cn in range(self.conditionNum): + # self.suscepCumulTransitionByCondition[sn1, sn2] += self.suscepTransitionByCondition[sn1, sn2, cn] + + for cn in range(self.conditionNum): + for sn1 in range(self.susNum): + self.suscepCumulTransitionByCondition[cn, sn1] = 0 + for sn2 in range(self.susNum): + self.suscepCumulTransitionByCondition[cn, sn1] += self.suscepTransitionByCondition[cn, sn1, sn2] + + for pn1 in range(self.popNum): + self.migrationRates[pn1, pn1] = 1.0 + self.actualSizes[pn1] = 0.0 + for pn2 in range(self.popNum): + if pn1 == pn2: + continue + self.migrationRates[pn1, pn1] -= self.migrationRates[pn1, pn2] + self.actualSizes[pn1] += self.migrationRates[pn2, pn1]*self.sizes[pn2] + self.actualSizes[pn1] += self.migrationRates[pn1, pn1]*self.sizes[pn1] + + self.totalRate = 0.0 + for cn in range(self.conditionNum): + self.totalRateByCondition[cn] = 0.0 + for pn in range(self.popNum): + self.infectPopRate[pn] = 0 + self.immunePopRate[pn] = 0 + self.popRate[pn] = 0.0 + for cn in range(self.conditionNum): + self.popRateByCondition[pn, cn] = 0 + + self.infectPopRateByCondition[pn, cn] = 0 + for hn in range(self.hapNum): + self.immunePopRateByCondition[pn, hn] = 0 + for pn in range(self.popNum): + for hn in range(self.currentHapNum): # hn - program number + self.tmRate[hn] = 0 + for s in range(self.sites): + self.tmRate[hn] += self.mRate[self.numToHap[hn], s] + + for ni in range(self.conditionNum): + self.eventConditionRate[pn, hn, ni] = self.conditionRate[hn, ni] + + self.eventHapPopRate[pn, hn, 0] = self.BirthRate(pn, hn) + self.eventHapPopRate[pn, hn, 1] = self.dRate[self.numToHap[hn]] + self.eventHapPopRate[pn, hn, 2] = self.sRate[self.numToHap[hn]]*self.samplingMultiplier[pn] + self.eventHapPopRate[pn, hn, 3] = self.tmRate[hn] + self.tEventHapPopRate[pn, hn] = 0 + self.teventConditionRate[pn, hn] = 0 + for i in range(4): + self.tEventHapPopRate[pn, hn] += self.eventHapPopRate[pn, hn, i] + for cn in range(self.conditionNum): + self.teventConditionRate[pn, hn] += self.eventConditionRate[pn, hn, cn] + self.hapPopRate[pn, hn] = self.tEventHapPopRate[pn, hn] * self.infectious[pn, hn] + self.infectPopRate[pn] += self.hapPopRate[pn, hn] + for cn in range(self.conditionNum): + self.infectPopRateByCondition[pn,cn] += self.teventConditionRate[pn, hn]*self.infectious[pn, hn] + for sn in range(self.susNum): + self.immuneSourcePopRate[pn, sn] = self.suscepCumulTransition[sn]*self.susceptible[pn, sn] + self.immunePopRate[pn] += self.immuneSourcePopRate[pn, sn] + #for cn in range(self.conditionNum): + # for sn in range(self.susNum): + # #self.immuneSourcePopRateByCondition[pn, cn, sn] = self.suscepCumulTransitionByCondition[cn, sn]*self.susceptibleByTypes[pn, cn, sn] + # self.immuneSourcePopRateByCondition[pn, cn, sn] = self.conditionRate[cn]*self.susceptibleByTypes[pn, cn, sn] + # self.immunePopRateByCondition[pn, cn] = self.immuneSourcePopRateByCondition[pn, cn, sn] + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + self.immuneSourcePopRateByCondition[pn, hn, cn] = self.conditionRate[cn] * self.susceptibleByTypes[pn, hn, cn] + self.immunePopRateByCondition[pn, hn] += immuneSourcePopRateByCondition[pn, hn, cn] + self.immunePopRateByConditionOneDem[pn] += self.immunePopRateByCondition[pn, hn] + + + + + + self.popRate[pn] = self.infectPopRate[pn] + self.immunePopRate[pn] + self.immunePopRateByConditionOneDem[pn] + self.massivForThree[pn][0] = self.infectPopRate[pn] + self.massivForThree[pn][1] = self.immunePopRate[pn] + self.massivForThree[pn][2] = self.immunePopRateByCondition[pn] + self.totalRate += self.popRate[pn] + #for cn in range(self.conditionNum): + # self.popRateByCondition[pn, cn] = self.immunePopRateByCondition[pn, cn] + self.infectPopRateByCondition[pn, cn] + # self.totalRateByCondition[cn] = self.popRateByCondition[pn, cn] + + maxEffectiveMigration = np.zeros(self.popNum, dtype=float) + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + if pn1 == pn2: + continue + self.effectiveMigration[pn1, pn2] = 0.0 + for pn3 in range(self.popNum): + self.effectiveMigration[pn1, pn2] += self.migrationRates[pn1, pn3]*self.migrationRates[pn2, pn3]*self.contactDensity[pn3]/self.actualSizes[pn3] + if self.effectiveMigration[pn1, pn2] > maxEffectiveMigration[pn2]: + maxEffectiveMigration[pn2] = self.effectiveMigration[pn1, pn2] + + maxEffectiveBirth = 0.0 + for hn in range(self.currentHapNum): + for sn in range(self.susNum): + if self.bRate[self.numToHap[hn]]*self.susceptibility[self.numToHap[hn], sn] > maxEffectiveBirth: + maxEffectiveBirth = self.bRate[self.numToHap[hn]]*self.susceptibility[self.numToHap[hn], sn] + + self.totalMigrationRate = 0.0 + for pn in range(self.popNum): + self.maxEffectiveBirthMigration[pn] = maxEffectiveMigration[pn]*maxEffectiveBirth + self.migPopRate[pn] = self.maxEffectiveBirthMigration[pn]*self.totalSusceptible[pn]*(self.globalInfectious-self.totalInfectious[pn]) + self.totalMigrationRate += self.migPopRate[pn] + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void AddHaplotype(self, Py_ssize_t nhi, Py_ssize_t pi): # nhi - haplotype + cdef: + bint check = False + Py_ssize_t mem_h, mem_inf + + if self.currentHapNum == self.maxHapNum and self.maxHapNum < self.hapNum: + self.AddMemory() + + for i in range(1, self.currentHapNum+1): + if check: + self.hapToNum[mem_h] += 1 + self.infectious[pi, i], mem_inf = mem_inf, self.infectious[pi, i] + self.numToHap[i], mem_h = mem_h, self.numToHap[i] + if (self.numToHap[i] > nhi or self.numToHap[i] == 0) and self.numToHap[i-1] < nhi: + mem_h = self.numToHap[i] + check = True + self.numToHap[i] = nhi + mem_inf = self.infectious[pi, i] + self.infectious[pi, i] = 0 + self.hapToNum[nhi] = i + + self.currentHapNum += 1 + self.UpdateAllRates() + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef inline double BirthRate(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number + cdef double ps = 0.0 + + for sn in range(self.susNum): + self.susceptHapPopRate[pi, hi, sn] = self.susceptible[pi, sn]*self.susceptibility[self.numToHap[hi], sn] + for pn in range(self.popNum): + ps += self.susceptHapPopRate[pi, hi, sn]*self.migrationRates[pi, pn]*self.migrationRates[pi, pn]*self.contactDensity[pn]/self.actualSizes[pn] + + return self.bRate[self.numToHap[hi]]*ps + + @cython.boundscheck(False) + @cython.wraparound(False) + cpdef void SimulatePopulation(self, Py_ssize_t iterations, Py_ssize_t sample_size, float time, Py_ssize_t attempts): + cdef Py_ssize_t pi + + self.PrepareParameters(iterations) + self.CheckSizes() + + for i in range(attempts): + self.seed = RndmWrapper(seed=(self.user_seed, i)) + if self.totalRate+self.totalMigrationRate != 0.0 and self.globalInfectious != 0: + while (self.events.ptr 100: + self.Restart() + else: + self.good_attempt = i+1 + break + + if self.totalRate == 0.0 or self.globalInfectious == 0: + print('Simulation finished because no infections individuals remain!') + if self.events.ptr>=self.events.size: + print("Achieved maximal number of iterations.") + if self.sCounter>sample_size and sample_size!=-1: + print("Achieved sample size.") + if self.currentTime>time and time != -1: + print("Achieved internal time limit.") + if self.sCounter <= 1: + print('\033[41m{}\033[0m'.format('WARNING!'), 'Simulated less 2 samples, so genealogy will not work!') + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void PrepareParameters(self, Py_ssize_t iterations): + self.events.CreateEvents(iterations) + if self.first_simulation == False: + self.FirstInfection() + for pn in range(self.popNum): + for sn in range(self.susNum): + self.initial_susceptible[pn, sn] = self.susceptible[pn, sn] + for hn in range(self.hapNum): + self.initial_infectious[pn, hn] = self.infectious[pn, hn] + self.first_simulation = True + for pn in range(self.popNum): + self.CheckLockdown(pn) + self.UpdateAllRates() + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void CheckSizes(self): + check = False + list_pop = [] + print('Actual sizes: ', end='') + for pn in range(self.popNum): + print(self.actualSizes[pn], end=' ') + if abs(self.actualSizes[pn]/self.sizes[pn]-1) >= 0.1: + check = True + list_pop.append(str(pn)) + print() + if check: + print('\033[41m{}\033[0m'.format('WARNING!'), 'Actual population size in deme: ', end='') + print(", ".join(list_pop) ) + print("\tis more than 10% different from the population size. The migration probabilities might be unrealistically high.") + print("\tWe recommend to check your model with print_populations() method before proceding to simulation.") + print("\tCheck the documentation file:https://vg-sim.readthedocs.io/en/latest/Migration.html for more details.") + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef inline void SampleTime(self): + cdef double tau = - log(self.seed.uniform()) / (self.totalRate + self.totalMigrationRate) + self.currentTime += tau + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef Py_ssize_t GenerateEvent(self): + cdef: + Py_ssize_t pi, hi, ei + double choose + + self.rn = self.seed.uniform() + choose = self.rn * (self.totalRate + self.totalMigrationRate) + if self.totalRate > choose: + self.rn = choose / self.totalRate + pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) + choose = self.rn * self.popRate[pi] + #if self.immunePopRate[pi] > choose: #delaem fastchoose1 is treh chisel kotory skladvutsy v popRate[pn], popRate[pn] vtoroy argument + ei, self.rn = fastChoose1(self.massivForThree[pn], self.popRate[pn], self.rn) + if ei == IMMUNITY: + self.rn = choose / self.immunePopRate[pi] + self.ImmunityTransition(pi) + elif ei == CONDITION: + self.rn = choose / self.immunePopRate[pi] + hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number + self.ImmunityTransition(pi, hi) + else: + self.rn = (choose - self.immunePopRate[pi]) / self.infectPopRate[pi] + hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number + ei, self.rn = fastChoose1(self.eventHapPopRate[pi, hi], self.tEventHapPopRate[pi, hi], self.rn) + if ei == BIRTH: + self.Birth(pi, hi) + elif ei == DEATH: + self.Death(pi, hi) + elif ei == SAMPLING: + self.Sampling(pi, hi) + else: + self.Mutation(pi, hi) + else: + self.rn = (choose - self.totalRate) / self.totalMigrationRate + pi = self.GenerateMigration() + return pi + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void UpdateRates(self, Py_ssize_t pi, bint infect, bint immune, bint migration): + cdef double tmp + if infect: + self.infectPopRate[pi] = 0.0 + for hn in range(self.currentHapNum): + self.eventHapPopRate[pi, hn, 0] = self.BirthRate(pi, hn) + tmp = (self.eventHapPopRate[pi, hn, 0] + + self.eventHapPopRate[pi, hn, 1] + + self.eventHapPopRate[pi, hn, 2] + + self.eventHapPopRate[pi, hn, 3] ) + self.tEventHapPopRate[pi, hn] = tmp + self.hapPopRate[pi, hn] = self.tEventHapPopRate[pi, hn] * self.infectious[pi, hn] + self.infectPopRate[pi] += self.hapPopRate[pi, hn] + + if immune: + self.immunePopRate[pi] = 0 + for sn in range(self.susNum): + self.immunePopRate[pi] += self.immuneSourcePopRate[pi, sn] + + if infect or immune: + self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi] + self.totalRate = 0.0 + for pn in range(self.popNum): + self.totalRate += self.popRate[pn] + + if migration: + self.totalMigrationRate = 0.0 + for pn in range(self.popNum): + self.migPopRate[pn] = self.maxEffectiveBirthMigration[pn]*self.totalSusceptible[pn]*(self.globalInfectious-self.totalInfectious[pn]) + self.totalMigrationRate += self.migPopRate[pn] + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void ImmunityTransition(self, Py_ssize_t pi): + cdef: + Py_ssize_t ssi, tsi + + ssi, self.rn = fastChoose1(self.immuneSourcePopRate[pi], self.immunePopRate[pi], self.rn) + tsi, self.rn = fastChoose1(self.suscepTransition[ssi], self.suscepCumulTransition[ssi], self.rn) + + self.susceptible[pi, ssi] -= 1 + self.susceptible[pi, tsi] += 1 + self.immuneSourcePopRate[pi, ssi] = self.susceptible[pi, ssi]*self.suscepCumulTransition[ssi] + self.immuneSourcePopRate[pi, tsi] = self.susceptible[pi, tsi]*self.suscepCumulTransition[tsi] + self.UpdateRates(pi, False, True, False) + + self.iCounter += 1 + self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void ConditionTransition(self, Py_ssize_t pi, Py_ssize_t hi): + cdef: + Py_ssize_t ssi, tsi, ci + + ci, self.rn = fastChoose1(self.popRateByCondition[pi], self.totalRateByCondition[pi], self.rn) + + ssi, self.rn = fastChoose1(self.immuneSourcePopRateByCondition[pi, ci], self.immunePopRateByCondition[pi, ci], self.rn) + tsi, self.rn = fastChoose1(self.suscepTransitionByCondition[ci, ssi], self.suscepCumulTransitionByCondition[ci, ssi], self.rn) + + self.susceptibleByTypes[pi, ci, ssi] -= 1 + self.susceptibleByTypes[pi, ci, tsi] += 1 + self.immuneSourcePopRateByCondition[pi, hi, ssi] = self.susceptibleByTypes[pi, ci, ssi]*self.suscepCumulTransitionByCondition[ci, ssi] + self.immuneSourcePopRateByCondition[pi, hi, tsi] = self.susceptibleByTypes[pi, ci, tsi]*self.suscepCumulTransitionByCondition[ci, tsi] + self.UpdateRates(pi, False, True, False) + + self.cCounter += 1 + #self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) + self.events.AddEvent(self.currentTime, CONDITION, ssi, pi, tsi, 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void Birth(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number + cdef double ws = 0.0 + cdef Py_ssize_t ei, ssi, tsi + + #ei, self.rn = fastChoose1(self.eventConditionRate[pi, hi], self.teventConditionRate[pi, hi], self.rn) + #for i in range(self.conditionNum): + #if ei == i: + #if i == 0: + + for sn in range(self.susNum): + ws += self.susceptHapPopRate[pi, hi, sn] + si, self.rn = fastChoose1(self.susceptHapPopRate[pi, hi], ws, self.rn) + + self.NewInfections(pi, si, hi) + self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] + self.UpdateRates(pi, True, True, True) + + self.bCounter += 1 + self.events.AddEvent(self.currentTime, BIRTH, self.numToHap[hi], pi, si, 0) + #else: + # self.rn = self.seed.uniform() + # choose = self.rn * (self.totalRate + self.totalMigrationRate) + # if self.totalRate > choose: + # self.rn = choose / self.totalRate + # pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) + # choose = self.rn * self.popRate[pi] + # if self.immunePopRate[pi] > choose: + # self.rn = choose / self.immunePopRate[pi] + # self.ConditionTransition(pi, hi) + + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void Death(self, Py_ssize_t pi, Py_ssize_t hi, bint add_event = True): # hi - program number + self.NewRecoveries(pi, self.suscType[self.numToHap[hi]], hi) + self.immuneSourcePopRate[pi, self.suscType[self.numToHap[hi]]] = self.susceptible[pi, self.suscType[self.numToHap[hi]]]*self.suscepCumulTransition[self.suscType[self.numToHap[hi]]] + self.UpdateRates(pi, True, True, True) + + if add_event: + self.dCounter += 1 + self.events.AddEvent(self.currentTime, DEATH, self.numToHap[hi], pi, self.suscType[self.numToHap[hi]], 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void Sampling(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number + self.Death(pi, hi, False) + + self.sCounter += 1 + self.events.AddEvent(self.currentTime, SAMPLING, self.numToHap[hi], pi, self.suscType[self.numToHap[hi]], 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void Mutation(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number + cdef: + bint check = True + Py_ssize_t ohi, mi, digit4, AS, DS, nhi + + ohi = self.numToHap[hi] # ohi - haplotype + mi, self.rn = fastChoose1(self.mRate[ohi], self.tmRate[hi], self.rn) + DS, self.rn = fastChoose1(self.hapMutType[ohi, mi], self.hapMutType[ohi, mi, 0] \ + + self.hapMutType[ohi, mi, 1] + self.hapMutType[ohi, mi, 2], self.rn) + nhi = self.Mutate(ohi, mi, DS) + + if self.memory_optimization: + #TODO binary search + for hn in range(self.currentHapNum+1): # hn - program number + if self.numToHap[hn] == nhi: + check = False + break + if check: + self.AddHaplotype(nhi, pi) + if nhi < ohi: + hi += 1 + + self.infectious[pi, self.hapToNum[nhi]] += 1 + self.infectious[pi, hi] -= 1 + self.UpdateRates(pi, True, False, False) + + self.mCounter += 1 + self.events.AddEvent(self.currentTime, MUTATION, ohi, pi, nhi, 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef Py_ssize_t GenerateMigration(self): + cdef: + Py_ssize_t tpi, spi, hi, si + double p_accept + + tpi, self.rn = fastChoose1(self.migPopRate, self.totalMigrationRate, self.rn) + spi, self.rn = fastChoose2_skip(self.totalInfectious, self.globalInfectious-self.totalInfectious[tpi], self.rn, skip=tpi) + hi, self.rn = fastChoose2(self.infectious[spi], self.totalInfectious[spi], self.rn) # hi - program number + si, self.rn = fastChoose2(self.susceptible[tpi], self.totalSusceptible[tpi], self.rn) + + p_accept = self.effectiveMigration[spi, tpi]*self.bRate[self.numToHap[hi]]*self.susceptibility[self.numToHap[hi], si]/self.maxEffectiveBirthMigration[tpi] + if self.rn < p_accept: + self.NewInfections(tpi, si, hi) + self.UpdateRates(tpi, True, True, True) + + self.migPlus += 1 + self.events.AddEvent(self.currentTime, MIGRATION, self.numToHap[hi], spi, si, tpi) + else: + self.migNonPlus += 1 + + return tpi + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void CheckLockdown(self, Py_ssize_t pi): + if self.totalInfectious[pi] > self.startLD[pi]*self.sizes[pi] and self.lockdownON[pi] == 0: + self.contactDensity[pi] = self.contactDensityAfterLockdown[pi] + self.swapLockdown += 1 + self.lockdownON[pi] = 1 + self.UpdateAllRates() + self.loc.AddLockdown(True, pi, self.currentTime) + if self.totalInfectious[pi] < self.endLD[pi]*self.sizes[pi] and self.lockdownON[pi] == 1: + self.contactDensity[pi] = self.contactDensityBeforeLockdown[pi] + self.swapLockdown += 1 + self.lockdownON[pi] = 0 + self.UpdateAllRates() + self.loc.AddLockdown(False, pi, self.currentTime) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void Restart(self): + self.events.ptr = 0 + self.multievents.ptr = 0 + self.bCounter = 0 + self.dCounter = 0 + self.sCounter = 0 + self.mCounter = 0 + self.iCounter = 0 + self.cCounter = 0 + self.migPlus = 0 + self.migNonPlus = 0 + self.currentTime = 0.0 + self.globalInfectious = 0 + for pn in range(self.popNum): + self.totalSusceptible[pn] = 0 + self.totalInfectious[pn] = 0 + for sn in range(self.susNum): + self.susceptible[pn, sn] = self.initial_susceptible[pn, sn] + self.totalSusceptible[pn] += self.initial_susceptible[pn, sn] + for hn in range(self.hapNum): + self.infectious[pn, hn] = self.initial_infectious[pn, hn] + self.totalInfectious[pn] += self.initial_infectious[pn, hn] + self.globalInfectious += self.initial_infectious[pn, hn] + for pn in range(self.popNum): + self.CheckLockdown(pn) + self.UpdateAllRates() + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cpdef GetGenealogy(self, seed): + cdef: + Py_ssize_t ptrTreeAndTime, n1, n2, id1, id2, id3, lbs, lbs_e, ns, nt, idt, ids, lbss, propNum + double p, p1 + vector[vector[vector[Py_ssize_t]]] liveBranchesS, newLineages + vector[vector[Py_ssize_t]] vecint2 + vector[Py_ssize_t] vecint1 + npy_int64[:,::1] liveBranches + + double e_time, me_time + Py_ssize_t e_type_, e_population, e_haplotype, e_newHaplotype, e_newPopulation + Py_ssize_t me_num, me_type_, me_population, me_haplotype, me_newHaplotype, me_newPopulation + Py_ssize_t mt_ev_num, mt_ev_num2 + + propNum = self.popNum*((self.popNum-1)*self.hapNum*self.susNum+self.susNum*(self.susNum-1)+self.hapNum*(2+self.sites*3+self.susNum)) + + if self.sCounter < 2: + print("Less than two cases were sampled...") + print("_________________________________") + sys.exit(0) + else: + if seed != None: + self.seed = RndmWrapper(seed=(seed, 0)) + + ptrTreeAndTime = 0 + self.tree = np.zeros(2 * self.sCounter - 1, dtype=np.int64) + self.tree_pop = np.zeros(2 * self.sCounter - 1, dtype=np.int64) + self.times = np.zeros(2 * self.sCounter - 1, dtype=float) + + for i in range( self.popNum ): + liveBranchesS.push_back(vecint2) + for _ in range( self.hapNum ): + liveBranchesS[i].push_back(vecint1) + + for i in range( self.popNum ): + newLineages.push_back(vecint2) + for _ in range( self.hapNum ): + newLineages[i].push_back(vecint1) + + for i in range( self.popNum ): + for j in range( self.hapNum ): + self.infectiousDelta[i, j] = 0 + + for e_id in range(self.events.ptr-1, -1, -1): + # this event + e_time = self.events.times[e_id] + e_type_ = self.events.types[e_id] + e_haplotype = self.events.haplotypes[e_id] + e_population = self.events.populations[e_id] + e_newHaplotype = self.events.newHaplotypes[e_id] + e_newPopulation = self.events.newPopulations[e_id] + if e_type_ == BIRTH: + lbs = liveBranchesS[e_population][e_haplotype].size() + lbs_e = self.infectious[e_population, self.hapToNum[e_haplotype]] + p = float(lbs)*(float(lbs)-1.0)/ float(lbs_e) / (float(lbs_e) - 1.0) + if self.seed.uniform() < p: + n1 = int(floor( lbs*self.seed.uniform() )) + n2 = int(floor( (lbs-1)*self.seed.uniform() )) + if n2 >= n1: + n2 += 1 + id1 = liveBranchesS[e_population][e_haplotype][n1] + id2 = liveBranchesS[e_population][e_haplotype][n2] + id3 = ptrTreeAndTime + liveBranchesS[e_population][e_haplotype][n1] = id3 + liveBranchesS[e_population][e_haplotype][n2] = liveBranchesS[e_population][e_haplotype][lbs-1] + liveBranchesS[e_population][e_haplotype].pop_back() + self.tree[id1] = id3 + self.tree[id2] = id3 + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = e_population + self.times[ptrTreeAndTime] = e_time + ptrTreeAndTime += 1 + self.infectious[e_population, self.hapToNum[e_haplotype]] -= 1 + elif e_type_ == DEATH: + self.infectious[e_population, self.hapToNum[e_haplotype]] += 1 + elif e_type_ == SAMPLING: + self.infectious[e_population, self.hapToNum[e_haplotype]] += 1 + liveBranchesS[e_population][e_haplotype].push_back( ptrTreeAndTime ) + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = e_population + self.times[ptrTreeAndTime] = e_time + ptrTreeAndTime += 1 + elif e_type_ == MUTATION: + lbs = liveBranchesS[e_population][e_newHaplotype].size() + p = float(lbs)/self.infectious[e_population, e_newHaplotype] + if self.seed.uniform() < p: + n1 = int(floor( lbs*self.seed.uniform() )) + id1 = liveBranchesS[e_population][e_newHaplotype][n1] + liveBranchesS[e_population][e_newHaplotype][n1] = liveBranchesS[e_population][e_newHaplotype][lbs-1] + liveBranchesS[e_population][e_newHaplotype].pop_back() + liveBranchesS[e_population][e_haplotype].push_back(id1) + self.mut.AddMutation(id1, e_haplotype, e_newHaplotype, e_time) + self.infectious[e_population, e_newHaplotype] -= 1 + self.infectious[e_population, e_haplotype] += 1 + elif e_type_ == SUSCCHANGE: + pass + elif e_type_ == MIGRATION: + lbs = liveBranchesS[e_newPopulation][e_haplotype].size() + p = float(lbs)/self.infectious[e_newPopulation, e_haplotype] + if self.seed.uniform() < p: + nt = int(floor( lbs*self.seed.uniform() )) + lbss = liveBranchesS[e_population][e_haplotype].size() + p1 = float(lbss)/self.infectious[e_population, e_haplotype] + if self.seed.uniform() < p1: + ns = int(floor( lbss*self.seed.uniform() )) + idt = liveBranchesS[e_newPopulation][e_haplotype][nt] + ids = liveBranchesS[e_population][e_haplotype][ns] + id3 = ptrTreeAndTime + liveBranchesS[e_population][e_haplotype][ns] = id3 + liveBranchesS[e_newPopulation][e_haplotype][nt] = liveBranchesS[e_newPopulation][e_haplotype][lbs-1] + liveBranchesS[e_newPopulation][e_haplotype].pop_back() + self.tree[idt] = id3 + self.tree[ids] = id3 + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = e_population + self.times[ptrTreeAndTime] = e_time + ptrTreeAndTime += 1 + self.mig.AddMigration(idt, e_time, e_population, e_newPopulation) + else: + liveBranchesS[e_population][e_haplotype].push_back(liveBranchesS[e_newPopulation][e_haplotype][nt]) + liveBranchesS[e_newPopulation][e_haplotype][nt] = liveBranchesS[e_newPopulation][e_haplotype][lbs-1] + liveBranchesS[e_newPopulation][e_haplotype].pop_back() + self.infectious[e_newPopulation, e_haplotype] -= 1 + elif e_type_ == MULTITYPE: + for me_id in range(e_haplotype, e_population): + me_num = self.multievents.num[me_id] + me_time = self.multievents.times[me_id] + me_type_ = self.multievents.types[me_id] + me_haplotype = self.multievents.haplotypes[me_id] + me_population = self.multievents.populations[me_id] + me_newHaplotype = self.multievents.newHaplotypes[me_id] + me_newPopulation = self.multievents.newPopulations[me_id] + + if me_type_ == BIRTH: + lbs = liveBranchesS[me_population][me_haplotype].size() + lbs_e = self.infectious[me_population, me_haplotype] + if me_num == 0 or lbs == 0: + mt_ev_num = 0 + else: + mt_ev_num = random_hypergeometric(self.seed.rng, int(lbs*(lbs-1.0)/2.0), int(lbs_e*(lbs_e-1)/2-lbs*(lbs-1)/2), me_num) + #print("me_num=", me_num, " mt_ev_num", mt_ev_num) + for i in range(mt_ev_num): + n1 = int(floor( lbs*self.seed.uniform() )) + n2 = int(floor( (lbs-1)*self.seed.uniform() )) + if n2 >= n1: + n2 += 1 + id1 = liveBranchesS[me_population][me_haplotype][n1] + id2 = liveBranchesS[me_population][me_haplotype][n2] + id3 = ptrTreeAndTime + newLineages[me_population][me_haplotype].push_back(id3) + if n1 == lbs-1:#TODO: need to check if we really need these if and elif. Most likely - no! + liveBranchesS[me_population][me_haplotype].pop_back() + liveBranchesS[me_population][me_haplotype][n2] = liveBranchesS[me_population][me_haplotype][lbs-2] + liveBranchesS[me_population][me_haplotype].pop_back() + elif n2 == lbs-1: + liveBranchesS[me_population][me_haplotype].pop_back() + liveBranchesS[me_population][me_haplotype][n1] = liveBranchesS[me_population][me_haplotype][lbs-2] + liveBranchesS[me_population][me_haplotype].pop_back() + else: + liveBranchesS[me_population][me_haplotype][n1] = liveBranchesS[me_population][me_haplotype][lbs-1] + liveBranchesS[me_population][me_haplotype].pop_back() + liveBranchesS[me_population][me_haplotype][n2] = liveBranchesS[me_population][me_haplotype][lbs-2] + liveBranchesS[me_population][me_haplotype].pop_back() + self.tree[id1] = id3 + self.tree[id2] = id3 + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = me_population + self.times[ptrTreeAndTime] = me_time + ptrTreeAndTime += 1 + lbs -= 2 + self.infectiousDelta[me_population, me_haplotype] -= me_num + elif me_type_ == DEATH: + self.infectiousDelta[me_population, me_haplotype] += me_num + elif me_type_ == SAMPLING: + self.infectiousDelta[me_population, me_haplotype] += me_num + for i in range(me_num): + #liveBranchesS[me_population][me_haplotype].push_back( ptrTreeAndTime ) + newLineages[me_population][me_haplotype].push_back( ptrTreeAndTime ) + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = me_population + self.times[ptrTreeAndTime] = me_time + ptrTreeAndTime += 1 + elif me_type_ == MUTATION: + lbs = liveBranchesS[me_population][me_newHaplotype].size() + if me_num == 0 or lbs == 0: + mt_ev_num = 0 + else: + mt_ev_num = random_hypergeometric(self.seed.rng, lbs, self.infectious[me_population, me_newHaplotype]-lbs, me_num) + for i in range(mt_ev_num): + n1 = int(floor( lbs*self.seed.uniform() )) + id1 = liveBranchesS[me_population][me_newHaplotype][n1] + liveBranchesS[me_population][me_newHaplotype][n1] = liveBranchesS[me_population][me_newHaplotype][lbs-1] + liveBranchesS[me_population][me_newHaplotype].pop_back() + #liveBranchesS[me_population][me_haplotype].push_back(id1) + newLineages[me_population][me_haplotype].push_back( id1 ) + self.mut.AddMutation(id1, me_haplotype, me_newHaplotype, me_time) + lbs-=1 + self.infectiousDelta[me_population, me_newHaplotype] -= me_num + self.infectiousDelta[me_population, me_haplotype] += me_num + elif me_type_ == SUSCCHANGE: + pass + elif me_type_ == MIGRATION: + lbs = liveBranchesS[me_newPopulation][me_haplotype].size() + if me_num == 0 or lbs == 0: + mt_ev_num = 0 + else: + mt_ev_num = random_hypergeometric(self.seed.rng, lbs, self.infectious[me_newPopulation, me_haplotype]-lbs, me_num) + #for i in range(mt_ev_num): + lbss = liveBranchesS[me_population][me_haplotype].size() + if mt_ev_num == 0 or lbss == 0: + mt_ev_num2 = 0 + else: + mt_ev_num2 = random_hypergeometric(self.seed.rng, lbss, self.infectious[me_population, me_haplotype]-lbss, mt_ev_num) + #print("me_num: ", me_num, "mt_ev_num: ", mt_ev_num, "mt_ev_num2: ", mt_ev_num2) + for i in range(mt_ev_num2): + nt = int(floor( lbs*self.seed.uniform() )) + ns = int(floor( lbss*self.seed.uniform() )) + idt = liveBranchesS[me_newPopulation][me_haplotype][nt] + ids = liveBranchesS[me_population][me_haplotype][ns] + id3 = ptrTreeAndTime + liveBranchesS[me_population][me_haplotype][ns] = liveBranchesS[me_population][me_haplotype][ liveBranchesS[me_population][me_haplotype].size()-1 ] + liveBranchesS[me_population][me_haplotype].pop_back() + liveBranchesS[me_newPopulation][me_haplotype][nt] = liveBranchesS[me_newPopulation][me_haplotype][lbs-1] + liveBranchesS[me_newPopulation][me_haplotype].pop_back() + newLineages[me_population][me_haplotype].push_back( id3 ) + self.tree[idt] = id3 + self.tree[ids] = id3 + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = me_population + self.times[ptrTreeAndTime] = me_time + ptrTreeAndTime += 1 + self.mig.AddMigration(idt, me_time, me_population, me_newPopulation) + lbss -= 1 + lbs -= 1 + for i in range(mt_ev_num-mt_ev_num2): + nt = int(floor( lbs*self.seed.uniform() )) + newLineages[me_population][me_haplotype].push_back(liveBranchesS[me_newPopulation][me_haplotype][nt]) + liveBranchesS[me_newPopulation][me_haplotype][nt] = liveBranchesS[me_newPopulation][me_haplotype][lbs-1] + liveBranchesS[me_newPopulation][me_haplotype].pop_back() + lbs -= 1 + self.infectiousDelta[me_newPopulation, me_haplotype] -= me_num + else: + print("Unknown event type: ", me_type_) + print("_________________________________") + sys.exit(0) + for pi in range(self.popNum): + for hi in range(self.hapNum): + self.infectious[pi, hi] += self.infectiousDelta[pi, hi] + self.infectiousDelta[pi, hi] = 0 + while newLineages[pi][hi].size() > 0: + liveBranchesS[pi][hi].push_back( newLineages[pi][hi][newLineages[pi][hi].size()-1] ) + newLineages[pi][hi].pop_back() + #for i in range(newLineages[pi][hi].size()-1, -1, -1): + # liveBranchesS[pi][hi].push_back(newLineages[pi][hi][i]) + # newLineages[pi][hi].pop_back() + else: + print("Unknown event type: ", e_type_) + print("_________________________________") + sys.exit(0) + for i in range(self.sCounter * 2 - 2): + if self.tree_pop[self.tree[i]] != self.tree_pop[i]: + self.mig.AddMigration(i, self.times[i], self.tree_pop[self.tree[i]], self.tree_pop[i]) + + #for i in range(self.sCounter * 2 - 1): + # print(self.tree[i], end=" ") + #print("") + #deg = [0 for i in range(self.sCounter * 2 - 1)] + #for i in range(self.sCounter * 2 - 1): + # deg[self.tree[i]] += 1 + #for i in range(self.sCounter * 2 - 1): + # print(deg[i], end=" ") + + #self.CheckTree() + + + def print_basic_parameters(self): + print("*****************") + print("***Basic rates***") + print("*****************") + table = PrettyTable() + + field = ["H", "TR", "RR", "SR", "ST"] + for s in range(self.sites): + field.append("M" + str(s)) + field.append("MW" + str(s)) + table.field_names = field + for hn in range(self.currentHapNum): + list = ["\n" + self.calculate_string(hn), "\n" + str(self.bRate[hn]), "\n" + str(self.dRate[hn]), "\n" + str(self.sRate[hn]), "\n" + str(self.suscType[hn])] + for s in range(self.sites): + list.append("\n" + str(self.mRate[hn, s])) + list.append(self.create_mutations(hn, s)) + table.add_row(list) + + print(table) + print("Legend:") + print("H - haplotype") + print("TR - transmission rate") + print("RR - recovery rate") + print("SR - sampling rate") + print("ST - susceptibility type") + for s in range(self.sites): + print("M" + str(s) + " - " + str(s) + " mutation rate") + print("MW" + str(s) + " - " + str(s) + " mutation weights") + print() + + def GetCurrentIndividuals(self): + current_susceptible, current_infectious = [[0 for _ in range(self.susNum)] for _ in range(self.popNum)], [[0 for _ in range(self.hapNum)] for _ in range(self.popNum)] + + for pi in range(self.popNum): + for si in range(self.susNum): + current_susceptible[pi][si] = self.initial_susceptible[pi, si] + for hi in range(self.hapNum): + current_infectious[pi][hi] = self.initial_infectious[pi, hi] + for i in range(self.events.ptr): + if self.events.types[i] == BIRTH: + current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] -= 1 + current_infectious[self.events.populations[i]][self.events.haplotypes[i]] += 1 + elif self.events.types[i] == DEATH: + current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 + current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 + elif self.events.types[i] == SAMPLING: + current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 + current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 + elif self.events.types[i] == MUTATION: + current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 + current_infectious[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 + elif self.events.types[i] == SUSCCHANGE: + current_susceptible[self.events.populations[i]][self.events.haplotypes[i]] -= 1 + current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 + else: + current_susceptible[self.events.newPopulations[i]][self.events.newHaplotypes[i]] -= 1 + current_infectious[self.events.newPopulations[i]][self.events.haplotypes[i]] += 1 + + return [current_susceptible, current_infectious] + + def print_populations(self): + current_susceptible, current_infectious = self.GetCurrentIndividuals() + + print("*****************") + print("***Populations***") + print("*****************") + table_populations = PrettyTable() + + table_populations.field_names = ["ID", "Size", 'Actual size', "CD",'CDBLC', "CDALD", "SLD", "ELD", "SM"] + for pn in range(self.popNum): + table_populations.add_row([pn, self.sizes[pn], self.actualSizes[pn], self.contactDensity[pn], self.contactDensityBeforeLockdown[pn], self.contactDensityAfterLockdown[pn], self.startLD[pn], self.endLD[pn], self.samplingMultiplier[pn]]) + + print(table_populations) + print("Legend:") + print("ID - number of population") + print("Size - size of population") + print("Actual size - actual size of population") + print("CD - contact density") + print("CDBLD - contact density without lockdown") + print("CDALD - contact density at lockdown") + print("SLD - start of lockdown") + print("ELD - end of lockdown") + print("SM - sampling multiplier") + print() + + print("*****************") + print("***Susceptible***") + print("*****************") + table_susceptible = PrettyTable() + + field = ["ST\\ID"] + for pn in range(self.popNum): + field.append(pn) + for sn in range(self.susNum): + row = [sn] + for pn in range(self.popNum): + row.append(current_susceptible[pn][sn]) + table_susceptible.add_row(row) + table_susceptible.field_names = field + + print(table_susceptible) + print("Legend:") + print("ID - ID population") + print("ST - susceptibility type") + print() + + print("****************") + print("***Infectious***") + print("****************") + table_infectious = PrettyTable() + + field = ["H\\ID"] + for pn in range(self.popNum): + field.append(pn) + for hn in range(self.hapNum): + row = [self.calculate_string(hn)] + for pn in range(self.popNum): + row.append(current_infectious[pn][hn]) + table_infectious.add_row(row) + table_infectious.field_names = field + + print(table_infectious) + print("Legend:") + print("ID - ID population") + print("H - haplotype") + print() + + print("**********************") + print("***Migration matrix***") + print("**********************") + table_migration = PrettyTable() + + field = ["S\\T"] + for pn1 in range(self.popNum): + field.append(pn1) + row = [pn1] + for pn2 in range(self.popNum): + row.append(self.migrationRates[pn1, pn2]) + table_migration.add_row(row) + table_migration.field_names = field + + print(table_migration) + print("Legend:") + print("S - ID source population") + print("T - ID target population") + print() + + def print_immunity_model(self): + print("********************") + print("***Immunity model***") + print("********************") + table_immunity = PrettyTable() + + field = ["H\\ST"] + for sn in range(self.susNum): + field.append("S" + str(sn)) + table_immunity.field_names = field + for hn in range(self.currentHapNum): + row = [self.calculate_string(hn)] + for sn in range(self.susNum): + row.append(self.susceptibility[hn, sn]) + table_immunity.add_row(row) + + print(table_immunity) + print("Legend:") + print("H - haplotype") + print("ST - susceptibility type") + print() + + print("*******************************") + print("***Immunity transition rates***") + print("*******************************") + table_immunity_transition = PrettyTable() + + field = ["ID"] + for sn1 in range(self.susNum): + field.append(sn1) + row = [sn1] + for sn2 in range(self.susNum): + row.append(self.suscepTransition[sn1, sn2]) + table_immunity_transition.add_row(row) + table_immunity_transition.field_names = field + + print(table_immunity_transition) + print("Legend:") + print("ID - ID susceptibility type") + print() + + + def Error(self, text): + print(text) + sys.exit(1) + + def create_list_haplotypes(self, haplotype): + haplotypes = [haplotype] + for s in range(self.sites): + for i in range(len(haplotypes)): + haplotype_old = haplotypes[i] + if haplotype_old[s] == "*": + haplotype = haplotype_old.replace("*", "A", 1) + haplotypes.append(haplotype) + haplotype = haplotype_old.replace("*", "T", 1) + haplotypes.append(haplotype) + haplotype = haplotype_old.replace("*", "C", 1) + haplotypes.append(haplotype) + haplotype = haplotype_old.replace("*", "G", 1) + haplotypes.append(haplotype) + for i in range(len(haplotypes)-1, -1, -1): + if haplotypes[i].count("*") != 0: + haplotypes.remove(haplotypes[i]) + for i in range(len(haplotypes)): + haplotypes[i] = self.calculate_haplotype(haplotypes[i]) + + return haplotypes + + def create_mutations(self, haplotype, site): + hap = self.calculate_string(haplotype) + haplotypes = [hap[:site] + "A" + hap[site+1:], hap[:site] + "T" + hap[site+1:], hap[:site] + "C" + hap[site+1:], hap[:site] + "G" + hap[site+1:]] + for i in range(4): + if haplotypes[i] == hap: + haplotypes.remove(haplotypes[i]) + haplotypes.append(hap) + break + color_hap=[] + for hapl in haplotypes: + a = "" + for s in range(self.sites): + if s == site: + a = a + "\033[31m{}\033[0m" .format(hapl[s:s+1]) + else: + a = a + hapl[s:s+1] + color_hap.append(a) + string = color_hap[3] + "->" + color_hap[0] + ": " + str(self.hapMutType[haplotype, site, 0]) + "\n" + color_hap[3] + "->" + color_hap[1] + ": " + str(self.hapMutType[haplotype, site, 1]) + "\n" + color_hap[3] + "->" + color_hap[2] + ": " + str(self.hapMutType[haplotype, site, 2]) + "\n" + return string + + def calculate_string(self, hapNum): + letters = ["A", "T", "C", "G"] + string = "" + for s in range(self.sites): + string = letters[hapNum%4] + string + hapNum = hapNum // 4 + return string + + def calculate_haplotype(self, string): + string = string[::-1] + haplotype = 0 + for s in range(self.sites): + if string[s]=="T": + haplotype += (4**s) + elif string[s]=="C": + haplotype += 2*(4**s) + elif string[s]=="G": + haplotype += 3*(4**s) + return haplotype + + def calculate_allele(self, haplotype, site): + for _ in range(self.sites-site): + allele = haplotype % 4 + haplotype = haplotype // 4 + return allele + + def get_sites(self): + return self.sites + + def get_hapNum(self): + return self.hapNum + + def get_popNum(self): + return self.popNum + + def get_susNum(self): + return self.susNum + + + def check_amount(self, amount, smth): + if isinstance(amount, int) == False: + raise TypeError('Incorrect type of ' + smth + '. Type should be int.') + elif amount < 0: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_rate_1(self, value, smth): + if isinstance(value, (int, float)) == False: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') + elif value < 0: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_rate_none_1(self, value, smth): + if isinstance(value, (int, float)) == False and value != None: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') + elif isinstance(value, (int, float)): + if value < 0: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_haplotype(self, haplotype): + if isinstance(haplotype, (int, str)) == False and haplotype != None: + raise TypeError('Incorrect type of haplotype. Type should be int or str or None.') + elif isinstance(haplotype, int): + if haplotype < 0 or haplotype >= self.hapNum: + raise IndexError('There are no such haplotype!') + elif isinstance(haplotype, str): + if haplotype.count("A") + haplotype.count("T") + haplotype.count("C") + haplotype.count("G") + haplotype.count("*") != self.sites: + raise ValueError('Incorrect haplotype. Haplotype should contain only \"A\", \"T\", \"C\", \"G\", \"*\" and lenght of haplotype should be equal number of mutations sites.') + + def check_rate_2(self, value, smth): + if isinstance(value, (int, float)) == False: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') + elif value < 0 and value > 1: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_rate_none_2(self, value, smth): + if isinstance(value, (int, float)) == False and value != None: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') + elif isinstance(value, (int, float)): + if value < 0 and value > 1: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_list(self, data, smth, lenght): + if isinstance(data, list) == False and data != None: + raise TypeError('Incorrect type of ' + smth + '. Type should be list or None.') + elif isinstance(data, list): + if len(data) != lenght: + raise ValueError('Incorrect lenght of ' + smth + '. Lenght should be equal ' + str(lenght) + '.') + + def check_index(self, value, edge, smth): + if isinstance(value, int) == False and value != None: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or None.') + elif isinstance(value, int): + if value < 0 or value >= edge: + raise IndexError('There are no such ' + smth + '!') + + def check_amount_sus(self, amount, source_type, target_type, population): + if self.initial_susceptible[population, source_type] - amount < 0: + raise ValueError('Number of susceptible minus amount should be more or equal 0.') + if self.initial_susceptible[population, target_type] + amount > self.sizes[population]: + raise ValueError('Number of susceptible plus amount should be equal or less population size.') + + def check_amount_inf(self, amount, source_type, target_haplotype, population): + if self.initial_susceptible[population, source_type] - amount < 0: + raise ValueError('Number of susceptible minus amount should be more or equal 0.') + if self.initial_infectious[population, target_haplotype] + amount > self.sizes[population]: + raise ValueError('Number of infectious plus amount should be equal or less population size.') + + def check_mig_rate(self): + for pn1 in range(self.popNum): + summa = 0 + self.migrationRates[pn1, pn1] = 1.0 + for pn2 in range(self.popNum): + if pn1 != pn2: + summa += self.migrationRates[pn1, pn2] + self.migrationRates[pn1, pn1] -= self.migrationRates[pn1, pn2] + if summa > 1: + raise ValueError('Incorrect the sum of migration probabilities. The sum of migration probabilities from each population should be equal or less 1.') + + + def set_initial_haplotype(self, amount): + if self.memory_optimization == False: + raise ValueError('Incorrect value of memory optimization. Value should be equal \'True\' for work this function.') + self.check_amount(amount, 'amount of initial haplotype') + + if amount > self.hapNum: + self.maxHapNum = self.hapNum + else: + self.maxHapNum = amount + + def set_step_haplotype(self, amount): + if self.memory_optimization == False: + raise ValueError('Incorrect value of memory optimization. Value should be equal \'True\' for work this function.') + self.check_amount(amount, 'amount of step haplotype') + + if amount > self.hapNum: + self.addMemoryNum = self.hapNum + else: + self.addMemoryNum = amount + + def set_transmission_rate(self, rate, haplotype): + self.check_rate_1(rate, 'transmission') + self.check_haplotype(haplotype) + + # for hn in self.get_list_haplotype(haplotype): + # self.bRate[hn] = rate + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + for haplotype in haplotypes: + self.bRate[haplotype] = rate + elif isinstance(haplotype, int): + self.bRate[haplotype] = rate + elif haplotype == None: + for hn in range(self.hapNum): + self.bRate[hn] = rate + + def set_recovery_rate(self, rate, haplotype): + self.check_rate_1(rate, 'recovery') + self.check_haplotype(haplotype) + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + for haplotype in haplotypes: + self.dRate[haplotype] = rate + elif isinstance(haplotype, int): + self.dRate[haplotype] = rate + elif haplotype == None: + for hn in range(self.hapNum): + self.dRate[hn] = rate + + def set_sampling_rate(self, rate, haplotype): + self.check_haplotype(haplotype) + + if self.sampling_probability == True: + self.check_rate_2(rate, 'sampling probability') + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + for haplotype in haplotypes: + deathRate = self.dRate[haplotype] + self.sRate[haplotype] + self.dRate[haplotype] = (1 - rate) * deathRate + self.sRate[haplotype] = rate * deathRate + elif isinstance(haplotype, int): + deathRate = self.dRate[haplotype] + self.sRate[haplotype] + self.dRate[haplotype] = (1 - rate) * deathRate + self.sRate[haplotype] = rate * deathRate + elif haplotype == None: + for hn in range(self.hapNum): + deathRate = self.dRate[hn] + self.sRate[hn] + self.dRate[hn] = (1 - rate) * deathRate + self.sRate[hn] = rate * deathRate + + elif self.sampling_probability == False: + self.check_rate_1(rate, 'sampling') + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + for haplotype in haplotypes: + self.sRate[haplotype] = rate + elif isinstance(haplotype, int): + self.sRate[haplotype] = rate + elif haplotype == None: + for hn in range(self.hapNum): + self.sRate[hn] = rate + + def set_mutation_rate(self, rate, probabilities, haplotype, mutation): + self.check_rate_none_1(rate, 'mutation rate') + self.check_list(probabilities, 'probabilities list', 4) + if isinstance(probabilities, list): + for i in range(4): + self.check_rate_1(probabilities[i], 'mutation probabilities') + self.check_haplotype(haplotype) + self.check_index(mutation, self.sites, 'mutation site') + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + if isinstance(mutation, int): + for hn in haplotypes: + self.set_probabilities(hn, mutation, rate, probabilities) + elif mutation == None: + for hn in haplotypes: + for s in range(self.sites): + self.set_probabilities(hn, s, rate, probabilities) + elif isinstance(haplotype, int): + if isinstance(mutation, int): + self.set_probabilities(haplotype, mutation, rate, probabilities) + elif mutation == None: + for s in range(self.sites): + self.set_probabilities(haplotype, s, rate, probabilities) + elif haplotype == None: + if isinstance(mutation, int): + for hn in range(self.hapNum): + self.set_probabilities(hn, mutation, rate, probabilities) + elif mutation == None: + for hn in range(self.hapNum): + for s in range(self.sites): + self.set_probabilities(hn, s, rate, probabilities) + + def set_probabilities(self, haplotype, mutation, rate, probabilities): + if isinstance(rate, (int, float)): + self.mRate[haplotype, mutation] = rate + if isinstance(probabilities, list): + probabilities_allele = list(probabilities) + del probabilities_allele[self.calculate_allele(haplotype, mutation)] + if sum(probabilities_allele) == 0: + raise ValueError('Incorrect probabilities list. The sum of three elements without mutation allele should be more 0.') + self.hapMutType[haplotype, mutation, 0] = probabilities_allele[0] + self.hapMutType[haplotype, mutation, 1] = probabilities_allele[1] + self.hapMutType[haplotype, mutation, 2] = probabilities_allele[2] + + def set_susceptibility_type(self, susceptibility_type, haplotype): + if isinstance(susceptibility_type, int) == False: + raise TypeError('Incorrect type of susceptibility type. Type should be int.') + elif susceptibility_type < 0 or susceptibility_type >= self.susNum: + raise IndexError('There are no such susceptibility type!') + self.check_haplotype(haplotype) + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + for hn in haplotypes: + self.suscType[hn] = susceptibility_type + elif isinstance(haplotype, int): + self.suscType[haplotype] = susceptibility_type + elif haplotype == None: + for hn in range(self.hapNum): + self.suscType[hn] = susceptibility_type + + def set_susceptibility(self, rate, haplotype, susceptibility_type): + self.check_rate_1(rate, 'rate of susceptibility') + self.check_haplotype(haplotype) + self.check_index(susceptibility_type, self.susNum, 'susceptibility type') + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + if isinstance(susceptibility_type, int): + for hn in haplotypes: + self.susceptibility[hn, susceptibility_type] = rate + else: + for hn in haplotypes: + for sn in range(self.susNum): + self.susceptibility[hn, sn] = rate + elif isinstance(haplotype, int): + if isinstance(susceptibility_type, int): + self.susceptibility[haplotype, susceptibility_type] + else: + for sn in range(self.susNum): + self.susceptibility[haplotype, sn] = rate + else: + if isinstance(susceptibility_type, int): + for hn in range(self.hapNum): + self.susceptibility[hn, susceptibility_type] = rate + else: + for hn in range(self.hapNum): + for sn in range(self.susNum): + self.susceptibility[hn, sn] = rate + + def set_immunity_transition(self, rate, source, target): + self.check_rate_1(rate, 'immunity transition rate') + self.check_index(source, self.susNum, 'susceptibility type') + self.check_index(target, self.susNum, 'susceptibility type') + + if isinstance(source, int): + if isinstance(target, int): + if source == target: + raise ValueError('Source and target susceptibility type shouldn\'t be equal!') + self.suscepTransition[source, target] = rate + else: + for sn1 in range(self.susNum): + if sn1 != target: + self.suscepTransition[sn1, target] = rate + else: + if isinstance(target, int): + for sn2 in range(self.susNum): + if source != sn2: + self.suscepTransition[source, sn2] = rate + else: + for sn1 in range(self.susNum): + for sn2 in range(self.susNum): + if sn1 != sn2: + self.suscepTransition[sn1, sn2] = rate + + def set_population_size(self, amount, population): + if self.first_simulation == True: + raise ValueError('Changing population size is available only before first simulation!') + self.check_amount(amount, 'population size') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.sizes[population] = amount + for sn in range(self.susNum): + self.initial_susceptible[population, 0] = amount + elif population == None: + for pn in range(self.popNum): + self.sizes[pn] = amount + for sn in range(self.susNum): + self.initial_susceptible[pn, 0] = amount + + def set_susceptible(self, amount, source_type, target_type, population): + if self.first_simulation: + raise ValueError('This function is available only before first simulation!') + self.check_amount(amount) + self.check_index(source_type, self.susNum, 'susceptibility type') + self.check_index(target_type, self.susNum, 'susceptibility type') + if source_type == target_type: + raise ValueError('Source and target susceptibility type shouldn\'t be equal!') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.check_amount_sus(amount, source_type, target_type, population) + + self.initial_susceptible[population, source_type] -= amount + self.initial_susceptible[population, target_type] += amount + elif population == None: + for pn in range(self.popNum): + self.check_amount_sus(amount, source_type, target_type, pn) + + self.initial_susceptible[pn, source_type] -= amount + self.initial_susceptible[pn, target_type] += amount + + def set_infectious(self, amount, source_type, target_haplotype, population): + if self.first_simulation: + raise ValueError('This function is available only before first simulation!') + self.check_amount(amount) + self.check_index(source_type, self.susNum, 'susceptibility type') + self.check_haplotype(target_haplotype) + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.check_amount_inf(amount, source_type, target_haplotype, population) + + self.initial_susceptible[population, source_type] -= amount + self.initial_infectious[population, target_haplotype] += amount + elif population == None: + for pn in range(self.popNum): + self.check_amount_inf(amount, source_type, target_haplotype, pn) + + self.initial_susceptible[pn, source_type] -= amount + self.initial_infectious[pn, target_haplotype] += amount + + def set_contact_density(self, value, population): + self.check_rate_1(value, 'contact density') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.contactDensity[population] = value + self.contactDensityBeforeLockdown[population] = value + elif population == None: + for pn in range(self.popNum): + self.contactDensity[pn] = value + self.contactDensityBeforeLockdown[pn] = value + + def set_npi(self, parameters, population): + self.check_list(parameters, 'parameters', 3) + if isinstance(parameters, list): + self.check_rate_1(parameters[0], 'first element of parameters') + self.check_rate_2(parameters[1], 'second element of parameters') + self.check_rate_2(parameters[2], 'third element of parameters') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.contactDensityAfterLockdown[population] = parameters[0] + self.startLD[population] = parameters[1] + self.endLD[population] = parameters[2] + elif population == None: + for pn in range(self.popNum): + self.contactDensityAfterLockdown[pn] = parameters[0] + self.startLD[pn] = parameters[1] + self.endLD[pn] = parameters[2] + + def set_sampling_multiplier(self, multiplier, population): + self.check_rate_1(multiplier, 'sampling multiplier') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.samplingMultiplier[population] = multiplier + elif population == None: + for pn in range(self.popNum): + self.samplingMultiplier[pn] = multiplier + + def set_migration_probability(self, probability, total_probability, source, target): + self.check_rate_none_2(probability, 'probability') + self.check_rate_none_2(total_probability, 'total probability') + self.check_index(source, self.popNum, 'population') + self.check_index(target, self.popNum, 'population') + + if isinstance(probability, float): + if isinstance(source, int): + if isinstance(target, int): + if source == target: + raise IndexError('Source and target population shouldn\'t be equal!') + + self.migrationRates[source, target] = probability + else: + for pn2 in range(self.popNum): + if source != pn2: + self.migrationRates[source, pn2] = probability + else: + if isinstance(target, int): + for pn1 in range(self.popNum): + if pn1 != target: + self.migrationRates[pn1, target] = probability + else: + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + if pn1 != pn2: + self.migrationRates[pn1, pn2] = probability + + self.check_mig_rate() + elif isinstance(total_probability, float): + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + if pn1 != pn2: + self.migrationRates[pn1, pn2] = total_probability/(self.popNum-1) + + self.check_mig_rate() + else: + raise TypeError('Incorrect type of probability or total_probability. Type should be float.') + + + def set_chain_events(self, name_file): + if isinstance(name_file, str) == False: + self.Error('#TODO') + tokens = np.load(name_file + '.npy') + #TODO Check sizes and resize if needed + self.size = tokens.shape[1] + self.ptr = tokens.shape[1] + + self.events.times = tokens[0] + self.events.types = tokens[1] + self.events.haplotypes = tokens[2] + self.events.populations = tokens[3] + self.events.newHaplotypes = tokens[4] + self.events.newPopulations = tokens[5] + + def set_settings(self, file_template): + pass + + + def output_tree_mutations(self): + if self.tree.shape[0] == 1: + print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') + sys.exit(1) + mut = [[], [], [], [], []] + for i in range(self.mut.nodeId.size()): + mut[0].append(self.mut.nodeId[i]) + mut[1].append(self.mut.AS[i]) + mut[2].append(self.mut.site[i]) + mut[3].append(self.mut.DS[i]) + mut[4].append(self.mut.time[i]) + times_dict = {self.events.times[i]: i for i in range(len(self.events.times))} + populations = {} + for time in self.times: + populations[time] = self.events.populations[times_dict[time]] + + return self.tree, self.times, mut, populations + + def output_migrations(self, name_file, file_path): + if self.tree.shape[0] == 1: + print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') + sys.exit(1) + if file_path != None: + f_mig = open(file_path + '/' + name_file + '.tsv', 'w') + else: + f_mig = open(name_file + '.tsv', 'w') + f_mig.write("Node\tTime\tOld_population\tNew_population\n") + for i in range(self.mig.nodeId.size()): + f_mig.write(str(self.mig.nodeId[i]) + '\t' + str(self.mig.time[i]) + '\t' + str(self.mig.oldPop[i]) + '\t' + str(self.mig.newPop[i]) + "\n") + f_mig.close() + + def output_sample_data(self): + time, pop, hap = [], [], [] + for i in range(self.events.ptr): + if self.events.types[i] == SAMPLING: + time.append(self.events.times[i]) + pop.append(self.events.populations[i]) + hap.append(self.events.haplotypes[i]) + return time, pop, hap + + def output_epidemiology_timelines(self, step_num, output_file): + time_points = [i*self.currentTime/step_num for i in range(step_num+1)] + suscepDate = np.zeros((self.popNum, self.susceptible_num), dtype=np.int64) + hapDate = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + for i in range(self.popNum): + for sn in range(self.susceptible_num): + if sn == 0: + suscepDate[i, sn] = self.sizes[i] + else: + suscepDate[i, sn] = 0 + for hn in range(self.hapNum): + hapDate[i, hn] = 0 + hapDate[0, 0] += 1 + suscepDate[0, 0] -= 1 + if output_file == True: + if not os.path.isdir("logs"): + os.mkdir("logs") + logDynamics = [] + for i in range(self.popNum): + logDynamics.append(open('logs/PID' + str(i) + '.log', 'w')) + logDynamics[i].write("time") + for sn in range(self.susceptible_num): + logDynamics[i].write(" S" + str(sn)) + for hn in range(self.hapNum): + logDynamics[i].write(" H" + str(hn)) + logDynamics[i].write("\n") + else: + log = dict() + log["time"] = list() + for i in range(self.popNum): + log["P" + str(i)] = dict() + for j in range(self.susceptible_num): + log["P" + str(i)]["S" + str(j)] = list() + for j in range(self.hapNum): + log["P" + str(i)]["H" + str(j)] = list() + + point = 0 + for j in range(self.events.ptr): + if self.events.types[j] == BIRTH: + hapDate[self.events.populations[j], self.events.haplotypes[j]] += 1 + suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] -= 1 + elif self.events.types[j] == DEATH: + hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 + suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 + elif self.events.types[j] == SAMPLING: + hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 + suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 + elif self.events.types[j] == MUTATION: + hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 + hapDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 + elif self.events.types[j] == SUSCCHANGE: + suscepDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 + suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 + elif self.events.types[j] == MIGRATION: + suscepDate[self.events.newPopulations[j], self.events.newHaplotypes[j]] -= 1 + hapDate[self.events.newPopulations[j], self.events.haplotypes[j]] += 1 + elif self.events.types[j] == MULTITYPE: + pass + #TODO + if time_points[point] <= self.events.times[j]: + if output_file == True: + for i in range(self.popNum): + logDynamics[i].write(str(time_points[point]) + " ") + for k in range(self.susceptible_num): + logDynamics[i].write(str(suscepDate[i, k]) + " ") + for k in range(self.hapNum): + logDynamics[i].write(str(hapDate[i, k]) + " ") + logDynamics[i].write("\n") + point += 1 + else: + log["time"].append(time_points[point]) + for i in range(self.popNum): + for j in range(self.susceptible_num): + log["P" + str(i)]["S" + str(j)].append(suscepDate[i, j]) + for j in range(self.hapNum): + log["P" + str(i)]["H" + str(j)].append(hapDate[i, j]) + point += 1 + + if output_file == True: + for i in range(self.popNum-1, -1, -1): + logDynamics[i].close() + else: + return log + + def output_chain_events(self, name_file): + np.save(name_file, [self.events.times, self.events.types, self.events.haplotypes, self.events.populations, self.events.newHaplotypes, self.events.newPopulations]) + + def output_settings(self, file_template): + if isinstance(file_template, str) == False: + self.Error('#TODO') + if not os.path.isdir(file_template): + os.mkdir(file_template) + os.chdir(file_template) + comand = 'Command line command: ' + with open(file_template + ".rt", "w") as file: + file.write("#Rates_format_version 0.0.1\nH B D S") + for s in range(self.sites): + file.write(" M" + str(s)) + file.write("\n") + for hn in range(self.hapNum): + file.write(self.calculate_string(hn) + " " + str(self.bRate[hn]) + " " + str(self.dRate[hn]) + " " + str(self.sRate[hn]) + ' ') + for s in range(self.sites): + file.write(str(self.mRate[hn, s]) + "," + str(self.hapMutType[hn, s, 0]) + "," + str(self.hapMutType[hn, s, 1]) + "," + str(self.hapMutType[hn, s, 2]) + " ") + file.write("\n") + comand += (file_template + '/' + file_template + '.rt ') + + with open(file_template + ".pp", "w") as file: + file.write("#Population_format_version 0.0.1\nid size contactDensity conDenAfterLD startLD endLD samplingMulriplier\n") + for pn in range(self.popNum): + file.write(str(pn) + " " + str(self.sizes[pn]) + " " + str(self.contactDensity[pn]) + " " + str(self.contactDensityAfterLockdown[pn]) + "," + str(self.startLD[pn]) + "," + str(self.endLD[pn]) + " " + str(self.samplingMultiplier[pn]) + "\n") + comand += ('-pm ' + file_template + '/' + file_template + '.pp ') + + with open(file_template + ".mg", "w") as file: + file.write("#Migration_format_version 0.0.1\n") + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + file.write(str(self.migrationRates[pn1, pn2]) + " ") + file.write("\n") + comand += (file_template + '/' + file_template + '.mg ') + + with open(file_template + ".su", "w") as file: + file.write("#Susceptibility_format_version 0.0.1\nH T") + for sn in range(self.susNum): + file.write(" S" + str(sn)) + file.write("\n") + for hn in range(self.hapNum): + file.write(self.calculate_string(hn) + " " + str(self.suscType[hn])) + for sn in range(self.susNum): + file.write(" " + str(self.susceptibility[hn, sn])) + file.write("\n") + comand += ('-su ' + file_template + '/' + file_template + '.su ') + + with open(file_template + ".st", "w") as file: + file.write("#Susceptibility_format_version 0.0.1\n") + for sn1 in range(self.susNum): + for sn2 in range(self.susNum): + file.write(str(self.suscepTransition[sn1, sn2]) + " ") + file.write("\n") + comand += ('-st ' + file_template + '/' + file_template + '.st ') + print(comand) + os.chdir('../') + + def get_ts_object(self): + tc = tskit.TableCollection() + tc.sequence_length = self.sites + 1.0 + + for i in range(self.mig.nodeId.size()): + mig_nodeId, mig_time, mig_oldPop, mig_newPop = self.mig.get_migration(i) + tc.migrations.add_row(0.0, 1.0, mig_nodeId, mig_oldPop, mig_newPop, self.times[0] - mig_time) + + for i in range(self.popNum): + tc.populations.add_row(None) + + for i in range(2 * self.sCounter - 2): + tc.edges.add_row(0.0, self.sites+1.0, self.tree[i], i) + + child_or_parent = [1 for _ in range(2 * self.sCounter - 1)] + for i in range(2 * self.sCounter - 2): + child_or_parent[self.tree[i]] = 0 + + for i in range(2 * self.sCounter - 1): + tc.nodes.add_row(child_or_parent[i], self.times[0] - self.times[i], self.tree_pop[i]) + + for i in range(self.sites): + tc.sites.add_row(i+1, 'A') + + allele = ['A', 'T', 'C', 'G'] + for i in range(self.mut.nodeId.size()): + mut_nodeId, mut_DS, mut_AS, mut_site, mut_time = self.mut.get_mutation(i) + tc.mutations.add_row(site=mut_site, node=mut_nodeId, derived_state=allele[mut_DS], time=self.times[0] - mut_time) + + tc.sort() + return tc.tree_sequence() + + def get_tree(self): + if self.tree.shape[0] == 1: + print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') + sys.exit(1) + return self.tree, self.times + + def get_data_infectious(self, pop, hap, step_num): + time_points = [i*self.currentTime/step_num for i in range(step_num+1)] + Data = np.zeros(step_num+1) + Sample = np.zeros(step_num+1) + Data[0] = self.initial_infectious[pop, hap] + + point = 0 + for i in range(self.events.ptr): + while point != step_num and time_points[point] < self.events.times[i]: + Data[point+1] = Data[point] + Sample[point+1] = Sample[point] + point += 1 + + if self.events.types[i] == BIRTH and self.events.populations[i] == pop and self.events.haplotypes[i] == hap: + Data[point] += 1 + elif self.events.types[i] == DEATH or self.events.types[i] == SAMPLING or self.events.types[i] == MUTATION and self.events.populations[i] == pop and self.events.haplotypes[i] == hap: + Data[point] -= 1 + if self.events.types[i] == SAMPLING: + Sample[point] += 1 + elif self.events.types[i] == MUTATION and self.events.newHaplotypes[i] == hap and self.events.populations[i] == pop: + Data[point] += 1 + elif self.events.types[i] == MIGRATION and self.events.newPopulations[i] == pop and self.events.haplotypes[i] == hap: + Data[point] += 1 + elif self.events.types[i] == MULTITYPE: + for j in range(self.events.haplotypes[i], self.events.populations[i]): + if self.multievents.types[j] == BIRTH and self.multievents.haplotypes[j] == hap and self.multievents.populations[j] == pop: + Data[point] += self.multievents.num[j] + elif self.multievents.types[j] == DEATH or self.multievents.types[j] == SAMPLING or self.multievents.types[j] == MUTATION and self.multievents.haplotypes[j] == hap and self.multievents.populations[j] == pop: + Data[point] -= self.multievents.num[j] + if self.multievents.types[j] == SAMPLING: + Sample[point] += self.multievents.num[j] + elif self.multievents.types[j] == MUTATION and self.multievents.newHaplotypes[j] == hap and self.multievents.populations[j] == pop: + Data[point] += self.multievents.num[j] + elif self.multievents.types[j] == MIGRATION and self.multievents.newPopulations[j] == pop and self.multievents.haplotypes[j] == hap: + Data[point] += self.multievents.num[j] + + Lockdowns = [] + for i in range(self.loc.times.size()): + if self.loc.populationsId[i] == pop: + Lockdowns.append([self.loc.states[i], self.loc.times[i]]) + + return Data, Sample, time_points, Lockdowns + + def get_data_susceptible(self, pop, sus, step_num): + time_points = [i*self.currentTime/step_num for i in range(step_num+1)] + Data = np.zeros(step_num+1) + Data[0] = self.initial_susceptible[pop, sus] + + point = 0 + for i in range(self.events.ptr): + while point != step_num and time_points[point] < self.events.times[i]: + Data[point+1] = Data[point] + point += 1 + + if self.events.types[i] == BIRTH and self.events.populations[i] == pop and self.events.newHaplotypes[i] == sus: + Data[point] -= 1 + elif (self.events.types[i] == DEATH or self.events.types[i] == SAMPLING or self.events.types[i] == SUSCCHANGE) and self.events.populations[i] == pop and self.events.newHaplotypes[i] == sus: + Data[point] += 1 + elif self.events.types[i] == SUSCCHANGE and self.events.haplotypes[i] == sus and self.events.populations[i] == pop: + Data[point] -= 1 + elif self.events.types[i] == MIGRATION and self.events.newPopulations[i] == pop and self.events.newHaplotypes[i] == sus: + Data[point] -= 1 + elif self.events.types[i] == MULTITYPE: + for j in range(self.events.haplotypes[i], self.events.populations[i]): + if self.multievents.types[j] == BIRTH and self.multievents.newHaplotypes[j] == sus and self.multievents.populations[j] == pop: + Data[point] -= self.multievents.num[j] + elif (self.multievents.types[j] == DEATH or self.multievents.types[j] == SAMPLING or self.multievents.types[j] == SUSCCHANGE) and self.multievents.newHaplotypes[j] == sus and self.multievents.populations[j] == pop: + Data[point] += self.multievents.num[j] + elif self.multievents.types[j] == SUSCCHANGE and self.multievents.haplotypes[j] == sus and self.multievents.populations[j] == pop: + Data[point] -= self.multievents.num[j] + elif self.multievents.types[j] == MIGRATION and self.multievents.newPopulations[j] == pop and self.multievents.haplotypes[j] == sus: + Data[point] -= self.multievents.num[j] + + Lockdowns = [] + for i in range(self.loc.times.size()): + if self.loc.populationsId[i] == pop: + Lockdowns.append([self.loc.states[i], self.loc.times[i]]) + + return Data, time_points, Lockdowns + + + def Stats(self, time_simulation): + print("Number of samples:", self.sCounter) + print("Total number of iterations:", self.events.ptr) + print('Success number:', self.good_attempt) + print("Epidemic time:", self.currentTime) + print('Simulation time:', time_simulation) + print('Number of infections:', self.bCounter) + print('Number of recoveries:', self.dCounter) + if self.sites >= 1: + print('Number of mutations:', self.mCounter) + if self.popNum >= 2: + print('Number of accepted migrations:', self.migPlus) + print('Number of rejected migrations:', self.migNonPlus) + check = False + for sn in range(self.susNum): + if self.suscepCumulTransition[sn] != 0.0: + check = True + + if check: + print('Number of immunity transitions:', self.iCounter) + print('----------------------------------') + + def Debug(self): + print("Parameters") + print('first_simulation(mutable): ', self.first_simulation) + print("sampling_probability(const): ", self.sampling_probability) + print("memory_optimization(const): ", self.memory_optimization) + print() + print("sites(const): ", self.sites) + print("hapNum(const): ", self.hapNum) + print("currentHapNum(mutable): ", self.currentHapNum) + print("maxHapNum(mutable): ", self.maxHapNum) + print("popNum(const): ", self.popNum) + print("susNum(const): ", self.susNum) + print("bCounter(mutable): ", self.bCounter) + print("dCounter(mutable): ", self.dCounter) + print("sCounter(mutable): ", self.sCounter) + print("mCounter(mutable): ", self.mCounter) + print("iCounter(mutable):", self.iCounter) + print("swapLockdown(mutable): ", self.swapLockdown) + print("migPlus(mutable): ", self.migPlus) + print("migNonPlus(mutable): ", self.migNonPlus) + print("globalInfectious(mutable): ", self.globalInfectious) + print() + print("currentTime(mutable): ", self.currentTime) + print("totalRate(mutable): ", self.totalRate) + print("totalMigrationTate(mutable): ", self.totalMigrationRate) + print("rn(mutable): ", self.rn) + print() + print("suscType(const): ", sep=" ", end="") + for hn in range(self.hapNum): + print(self.suscType[hn], end=" ") + print() + print() + print("hapToNum(mutable): ", sep="", end="") + for hn in range(self.hapNum): + print(self.hapToNum[hn], end=" ") + print() + print("numToHap(mutable): ", sep="", end="") + for hn in range(self.maxHapNum): + print(self.numToHap[hn], end=" ") + print() + print("sizes(const): ", end="") + for pn in range(self.popNum): + print(self.sizes[pn], end=" ") + print() + print("totalSusceptible(mutable): ", end="") + for pn in range(self.popNum): + print(self.totalSusceptible[pn], end=" ") + print() + print("totalInfectious(mutable): ", end="") + for pn in range(self.popNum): + print(self.totalInfectious[pn], end=" ") + print() + print("lockdownON(mutable): ", end="") + for pn in range(self.popNum): + print(self.lockdownON[pn], end=" ") + print() + print() + print("susceptible(mutable)----") + for pn in range(self.popNum): + for sn in range(self.susNum): + print(self.susceptible[pn, sn], end=" ") + print() + print() + print("infectious(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + print(self.infectious[pn, hn], end=" ") + print() + print() + print() + print("Birth rate(const): ", sep="", end="") + for hn in range(self.hapNum): + print(self.bRate[hn], end=" ") + print() + print("Death rate(const): ", sep="", end="") + for hn in range(self.hapNum): + print(self.dRate[hn], end=" ") + print() + print("Sampling rate(const): ", sep="", end="") + for hn in range(self.hapNum): + print(self.sRate[hn], end=" ") + print() + print("tmRate(const): ", sep="", end="") + for hn in range(self.currentHapNum): + print(self.tmRate[hn], end=" ") + print() + print("maxEffectiveBirthMigration(const): ", sep="", end="") + for pn in range(self.popNum): + print(self.maxEffectiveBirthMigration[pn], end=" ") + print() + print("suscepCumulTransition(const): ", sep="", end="") + for sn in range(self.susNum): + print(self.suscepCumulTransition[sn], end=" ") + print() + print("immunePopRate(mutable): ", sep="", end="") + for pn in range(self.popNum): + print(self.immunePopRate[pn], end=" ") + print() + print("infectPopRate(mutable): ", sep="", end="") + for pn in range(self.popNum): + print(self.infectPopRate[pn], end=" ") + print() + print("popRate(mutable): ", sep="", end="") + for pn in range(self.popNum): + print(self.popRate[pn], end=" ") + print() + print("migPopRate(mutable): ", sep="", end="") + for pn in range(self.popNum): + print(self.migPopRate[pn], end=" ") + print() + print("actualSizes(const): ", end=" ") + for pn in range(self.popNum): + print(self.actualSizes[pn], end=" ") + print() + print("contactDensity(const): ", end=" ") + for pn in range(self.popNum): + print(self.contactDensity[pn], end=" ") + print() + print("contactDensityBeforeLockdown(const): ", end=" ") + for pn in range(self.popNum): + print(self.contactDensityBeforeLockdown[pn], end=" ") + print() + print("contactDensityBeforeLockdown(const): ", end=" ") + for pn in range(self.popNum): + print(self.contactDensityAfterLockdown[pn], end=" ") + print() + print("startLD(const): ", end=" ") + for pn in range(self.popNum): + print(self.startLD[pn], end=" ") + print() + print("endLD(const): ", end=" ") + for pn in range(self.popNum): + print(self.endLD[pn], end=" ") + print() + print("samplingMultiplier(const): ", end=" ") + for pn in range(self.popNum): + print(self.samplingMultiplier[pn], end=" ") + print() + print() + print("mRate(const)----") + for hn in range(self.hapNum): + for s in range(self.sites): + print(self.mRate[hn, s], end=" ") + print() + print() + print("susceptibility(const)----") + for hn in range(self.currentHapNum): + for sn in range(self.susNum): + print(self.susceptibility[hn, sn], end=" ") + print() + print() + print("tEventHapPopRate(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + print(self.tEventHapPopRate[pn, hn], end=" ") + print() + print() + print("suscepTransition(const)----") + for sn1 in range(self.susNum): + for sn2 in range(self.susNum): + print(self.suscepTransition[sn1, sn2], end=" ") + print() + print() + print("immuneSourcePopRate(mutable)----") + for pn in range(self.popNum): + for sn in range(self.susNum): + print(self.immuneSourcePopRate[pn, sn], end=" ") + print() + print() + print("hapPopRate(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + print(self.hapPopRate[pn, hn], end=" ") + print() + print() + print("migrationRates(const)----") + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + print(self.migrationRates[pn1, pn2], end=" ") + print() + print() + print("effectiveMigration(const)----") + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + print(self.effectiveMigration[pn1, pn2], end=" ") + print() + print() + print() + print("hapMutType(const)----") + for hn in range(self.currentHapNum): + for s in range(self.sites): + for i in range(3): + print(self.hapMutType[hn, s, i], end=" ") + print() + print() + print() + print("eventHapPopRate(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + for i in range(4): + print(self.eventHapPopRate[pn, hn, i], end=" ") + print() + print() + print() + print("susceptHapPopRate(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + for sn in range(self.susNum): + print(self.susceptHapPopRate[pn, hn, sn], end=" ") + print() + print() + print() + + + def get_proportion(self): + return self.migNonPlus / (self.events.ptr-1) + + ############################# + ### TAU LEAPING ALGORITHM ### + ############################# + + @cython.boundscheck(False) + @cython.wraparound(False) + cpdef void SimulatePopulation_tau(self, Py_ssize_t iterations, Py_ssize_t sample_size, float time, Py_ssize_t attempts): + cdef: + Py_ssize_t i, pn, propNum + bint success + + self.PrepareParameters(iterations) + self.CheckSizes() + + propNum = self.popNum*((self.popNum-1)*self.hapNum*self.susNum+self.susNum*(self.susNum-1)+self.hapNum*(2+self.sites*3+self.susNum)) + if self.globalInfectious == 0: + self.FirstInfection() + #TODO less memory for multievents + self.multievents.CreateEvents(iterations*propNum) + self.events.CreateEvents(iterations) + self.UpdateAllRates() + + for i in range(attempts): + self.seed = RndmWrapper(seed=(self.user_seed, i)) + if self.totalRate+self.totalMigrationRate != 0.0 and self.globalInfectious != 0: + while ((self.events.ptr 100: + self.Restart() + else: + self.good_attempt = i+1 + break + + if self.totalRate == 0.0 or self.globalInfectious == 0: + print('Simulation finished because no infections individuals remain!') + if self.events.ptr>=self.events.size: + print("Achieved maximal number of iterations.") + if self.sCounter>sample_size and sample_size!=-1: + print("Achieved sample size.") + if self.currentTime>time and time != -1: + print("Achieved internal time limit.") + if self.sCounter <= 1: + print('\033[41m{}\033[0m'.format('WARNING!'), 'Simulated less 2 samples, so genealogy will not work!') + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void Propensities(self): + cdef Py_ssize_t pn, spn, tpn, hn, sn, ssn, tsn, s, i + + for pn in range(self.popNum): + for hn in range(self.hapNum): + self.infectiousAuxTau[pn, hn] = 0.0 + for sn in range(self.susNum): + self.susceptibleAuxTau[pn, sn] = 0.0 + + for spn in range(self.popNum): + for tpn in range(self.popNum): + if spn == tpn: + continue + for sn in range(self.susNum): + for hn in range(self.hapNum): + self.PropensitiesMigr[spn, tpn, sn, hn] = self.effectiveMigration[tpn, spn]*self.susceptible[tpn, sn]*\ + self.infectious[spn, hn]*self.bRate[hn]*self.susceptibility[hn, sn]*self.migrationRates[spn, spn] + + self.infectiousAuxTau[tpn, hn] += self.PropensitiesMigr[spn, tpn, sn, hn] + self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesMigr[spn, tpn, sn, hn] + + for pn in range(self.popNum): + #Susceptibility transition + for ssn in range(self.susNum): + for tsn in range(self.susNum): + if ssn == tsn: + continue + self.PropensitiesSuscep[pn, ssn, tsn] = self.suscepTransition[ssn, tsn]*self.susceptible[pn, ssn] + + self.susceptibleAuxTau[pn, tsn] += self.PropensitiesSuscep[pn, ssn, tsn] + self.susceptibleAuxTau[pn, ssn] -= self.PropensitiesSuscep[pn, ssn, tsn] + + #Infectious-realted event + for hn in range(self.hapNum): + #Recovery + self.PropensitiesRecovery[pn, hn] = self.dRate[hn]*self.infectious[pn, hn] + + self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesRecovery[pn, hn] + self.infectiousAuxTau[pn, hn] -= self.PropensitiesRecovery[pn, hn] + + #Sampling + self.PropensitiesSampling[pn, hn] = self.sRate[hn]*self.infectious[pn, hn]*self.samplingMultiplier[pn] + + self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesSampling[pn, hn] + self.infectiousAuxTau[pn, hn] -= self.PropensitiesSampling[pn, hn] + + #Mutation + for s in range(self.sites): + for i in range(3): + self.PropensitiesMutatations[pn, hn, s, i] = self.mRate[hn, s]*self.hapMutType[hn, s, i]/\ + (self.hapMutType[hn, s, 0] + self.hapMutType[hn, s, 1] + self.hapMutType[hn, s, 2])*self.infectious[pn, hn] + + self.infectiousAuxTau[pn, self.Mutate(hn, s, i)] += self.PropensitiesMutatations[pn, hn, s, i] + self.infectiousAuxTau[pn, hn] -= self.PropensitiesMutatations[pn, hn, s, i] + + #Transmission + for tpn in range(self.popNum): + for hn in range(self.hapNum): + for sn in range(self.susNum): + self.PropensitiesTransmission[tpn, hn, sn] = 0.0 + for spn in range(self.popNum): + self.PropensitiesTransmission[tpn, hn, sn] += self.bRate[hn]*self.susceptibility[hn, sn]*self.migrationRates[tpn, spn]*\ + self.migrationRates[tpn, spn]*self.contactDensity[spn]*self.susceptible[tpn, sn]*self.infectious[tpn, hn]/\ + self.actualSizes[spn] + + self.infectiousAuxTau[tpn, hn] += self.PropensitiesTransmission[tpn, hn, sn] + self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesTransmission[tpn, hn, sn] + + @cython.cdivision(True) + cdef Py_ssize_t Mutate(self, Py_ssize_t hi, Py_ssize_t s, Py_ssize_t DS): + cdef Py_ssize_t digit4, AS + + digit4 = 4**(self.sites-s-1) + AS = int(floor(hi/digit4) % 4) + if DS >= AS: + DS += 1 + return hi + (DS-AS)*digit4 + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void ChooseTau(self, float epsilon=0.03): + cdef: + Py_ssize_t pn, hn, sn + double tmp + + self.tau_l = 1.0 + for pn in range(self.popNum): + for hn in range(self.hapNum): + if abs(self.infectiousAuxTau[pn, hn]) < 1e-8: + continue + tmp = max(epsilon*self.infectious[pn,hn]/2.0,1.0)/abs(self.infectiousAuxTau[pn, hn]) + if tmp < self.tau_l: + self.tau_l = tmp + for sn in range(self.susNum): + if abs(self.susceptibleAuxTau[pn, sn]) < 1e-8: + continue + tmp = max(epsilon*self.susceptible[pn,sn]/2.0,1.0)/abs(self.susceptibleAuxTau[pn, sn]) + if tmp < self.tau_l: + self.tau_l = tmp + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef bint GenerateEvents_tau(self): + cdef Py_ssize_t event_num, pn, spn, tpn, hn, sn, ssn, tsn, s, i + + for pn in range(self.popNum): + for sn in range(self.susNum): + self.susceptibleDelta[pn, sn] = 0 + for hn in range(self.hapNum): + self.infectiousDelta[pn, hn] = 0 + + #Migrations + for spn in range(self.popNum): + for tpn in range(self.popNum): + if spn == tpn: + continue + for sn in range(self.susNum): + for hn in range(self.hapNum): + event_num = self.DrawEventsNum(self.PropensitiesMigr[spn, tpn, sn, hn]*self.tau_l) + self.eventsMigr[spn, tpn, sn, hn] = event_num + + self.infectiousDelta[spn, hn] += event_num + self.susceptibleDelta[tpn, sn] -= event_num + + + for pn in range(self.popNum): + #Susceptibility transition + for ssn in range(self.susNum): + for tsn in range(self.susNum): + if ssn == tsn: + continue + event_num = self.DrawEventsNum(self.PropensitiesSuscep[pn, ssn, tsn]*self.tau_l) + self.eventsSuscep[pn, ssn, tsn] = event_num + + self.susceptibleDelta[pn, tsn] += event_num + self.susceptibleDelta[pn, ssn] -= event_num + + #Infectious-realted event + for hn in range(self.hapNum): + #Recovery + event_num = self.DrawEventsNum(self.PropensitiesRecovery[pn, hn]*self.tau_l) + self.eventsRecovery[pn, hn] = event_num + + self.susceptibleDelta[pn, self.suscType[hn]] += event_num + self.infectiousDelta[pn, hn] -= event_num + + #Sampling + event_num = self.DrawEventsNum(self.PropensitiesSampling[pn, hn]*self.tau_l) + self.eventsSampling[pn, hn] = event_num + + self.susceptibleDelta[pn, self.suscType[hn]] += event_num + self.infectiousDelta[pn, hn] -= event_num + + #Mutation + for s in range(self.sites): + for i in range(3): + event_num = self.DrawEventsNum(self.PropensitiesMutatations[pn, hn, s, i]*self.tau_l) + self.eventsMutatations[pn, hn, s, i] = event_num + + self.infectiousDelta[pn, self.Mutate(hn, s, i)] += event_num + self.infectiousDelta[pn, hn] -= event_num + + #Transmission + for sn in range(self.susNum): + event_num = self.DrawEventsNum(self.PropensitiesTransmission[pn, hn, sn]*self.tau_l) + self.eventsTransmission[pn, hn, sn] = event_num + + self.infectiousDelta[pn, hn] += event_num + self.susceptibleDelta[pn, sn] -= event_num + + for pn in range(self.popNum): + for sn in range(self.susNum): + if self.susceptibleDelta[pn, sn] + self.susceptible[pn, sn] < 0 or self.susceptibleDelta[pn, sn] + self.susceptible[pn, sn] > self.sizes[pn]: + return False + for hn in range(self.hapNum): + if self.infectiousDelta[pn, hn] + self.infectious[pn, hn] < 0 or self.infectiousDelta[pn, hn] + self.infectious[pn, hn] > self.sizes[pn]: + return False + return True + + cdef inline Py_ssize_t DrawEventsNum(self, double tau): + return random_poisson(self.seed.rng, lam=tau) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void UpdateCompartmentCounts_tau(self): + #TODO not write events with 0 num + cdef Py_ssize_t event_num, pn, spn, tpn, hn, sn, ssn, tsn, s, i + + #Migrations + for spn in range(self.popNum): + for tpn in range(self.popNum): + if spn == tpn: + continue + for sn in range(self.susNum): + for hn in range(self.hapNum): + event_num = self.eventsMigr[spn, tpn, sn, hn] + self.NewInfections(tpn, sn, hn, event_num) + self.multievents.AddEvents(event_num, self.currentTime, MIGRATION, hn, spn, sn, tpn) + self.migPlus += event_num + + for pn in range(self.popNum): + #Susceptibility transition + for ssn in range(self.susNum): + for tsn in range(self.susNum): + if ssn == tsn: + continue + event_num = self.eventsSuscep[pn, ssn, tsn] + self.susceptible[pn, tsn] += event_num + self.susceptible[pn, ssn] -= event_num + self.multievents.AddEvents(event_num, self.currentTime, SUSCCHANGE, ssn, pn, tsn, 0) + self.iCounter += event_num + + #Infectious-realted event + for hn in range(self.hapNum): + #Recovery + event_num = self.eventsRecovery[pn, hn] + self.NewRecoveries(pn, self.suscType[hn], hn, event_num) + self.multievents.AddEvents(event_num, self.currentTime, DEATH, hn, pn, self.suscType[hn], 0) + self.dCounter += event_num + + #Sampling + event_num = self.eventsSampling[pn, hn] + self.NewRecoveries(pn, self.suscType[hn], hn, event_num) + self.multievents.AddEvents(event_num, self.currentTime, SAMPLING, hn, pn, self.suscType[hn], 0) + self.sCounter += event_num + + #Mutation + for s in range(self.sites): + for i in range(3): + nhn = self.Mutate(hn, s, i) + event_num = self.eventsMutatations[pn, hn, s, i] + self.infectious[pn, nhn] += event_num + self.infectious[pn, hn] -= event_num + self.multievents.AddEvents(event_num, self.currentTime, MUTATION, hn, pn, nhn, 0) + self.mCounter += event_num + + #Transmission + for sn in range(self.susNum): + event_num = self.eventsTransmission[pn, hn, sn] + self.NewInfections(pn, sn, hn, event_num) + self.multievents.AddEvents(event_num, self.currentTime, BIRTH, hn, pn, sn, 0) + self.bCounter += event_num + + @cython.boundscheck(False) + @cython.wraparound(False) + def Get_MultiEvents(self, id = None): + if id is None: + for i in range(self.multievents.ptr): + ev = self.multievents.GetEvent(i) + ev.PrintEvent() + else: + ev = self.multievents.GetEvent(id) + ev.PrintEvent() + + + def PrintCounters(self): + print("Birth counter(mutable): ", self.bCounter) + print("Death counter(mutable): ", self.dCounter) + print("Sampling counter(mutable): ", self.sCounter) + print("Mutation counter(mutable): ", self.mCounter) + print("Immunity transition counter(mutable):", self.iCounter) + print("Migration counter(mutable):", self.migPlus) + + def PrintPropensities(self): + #self.FirstInfection() + self.UpdateAllRates() + self.Propensities() + print("Migrations") + for s in range(self.popNum): + for r in range(self.popNum): + if s == r: + continue + for i in range(self.susNum): + for h in range(self.hapNum): + print (s, r, i, h, self.PropensitiesMigr[s, r, i, h]) + + + for s in range(self.popNum): + print("Susceptibility transition") + for i in range(self.susNum): + for j in range(self.susNum): + if i == j: + continue + print(s, i, j, self.PropensitiesSuscep[s, i, j]) + + #Infectious-realted event + for h in range(self.hapNum): + print("Recovery ", s, h, self.suscType[h], self.PropensitiesRecovery[s, h]) + print("Sampling ", s, h, self.suscType[h], self.PropensitiesSampling[s, h]) + + for site in range(self.sites): + for i in range(3): + #ht = self.Mutate(h, site, i) + print("Mutation", s, h, site, i, self.PropensitiesMutatations[s, h, site, i]) + #Transmission + for i in range(self.susNum): + print("Transmission", s, h, i, self.PropensitiesTransmission[s, h, i]) + #print("migr=", self.migrationRates[s, s], " prop", prop) From 08eb3971c7490cc3d58ab238d67339145980338b Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Thu, 3 Nov 2022 13:28:06 +0300 Subject: [PATCH 02/13] Add files via upload --- src/_BirthDeath.pyx | 142 ++++++++++++++++++++++++++++++++++++++++++-- src/events.pxi | 3 + 2 files changed, 140 insertions(+), 5 deletions(-) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index 028b7a9..0ad0f59 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -32,7 +32,7 @@ cdef class BirthDeathModel: RndmWrapper seed bint first_simulation, sampling_probability, memory_optimization - Py_ssize_t user_seed, sites, hapNum, currentHapNum, maxHapNum, addMemoryNum, popNum, susNum, bCounter, dCounter, sCounter, mCounter, iCounter, swapLockdown, migPlus, migNonPlus, globalInfectious, countsPerStep, good_attempt + Py_ssize_t user_seed, sites, hapNum, currentHapNum, maxHapNum, addMemoryNum, popNum, susNum, bCounter, dCounter, sCounter, mCounter, iCounter, swapLockdown, migPlus, migNonPlus, globalInfectious, countsPerStep, good_attempt, conditionNum, cCounter double currentTime, totalRate, totalMigrationRate, rn, tau_l Events events @@ -43,17 +43,22 @@ cdef class BirthDeathModel: npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop npy_int64[:,::1] susceptible, infectious, initial_susceptible, initial_infectious + npy_int64[:,::1] totalInfectiousByTypes, totalSusceptibleByTypes + npy_int64[:,:,::1] infectiousByTypes, susceptibleByTypes double[::1] bRate, dRate, sRate, tmRate, maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times + double[::1] totalRateByCondition, immunePopRateByConditionOneDem double[:,::1] mRate, susceptibility, tEventHapPopRate, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration + double[:,::1] conditionRate, teventConditionRate, immunePopRateByCondition, infectPopRateByCondition, suscepCumulTransitionByCondition, popRateByCondition, massivForThree double[:,:,::1] hapMutType, eventHapPopRate, susceptHapPopRate + double[:,:,::1] eventConditionRate,immuneSourcePopRateByCondition, suscepTransitionByCondition double[:,:,:,::1] PropensitiesMigr, PropensitiesMutatations double[:,:,::1] PropensitiesSuscep, PropensitiesTransmission double[:,::1] PropensitiesRecovery, PropensitiesSampling npy_int64[:,:,:,::1] eventsMigr, eventsMutatations - npy_int64[:,:,::1] eventsSuscep, eventsTransmission + npy_int64[:,:,::1] eventsSuscep, eventsTransmission/ npy_int64[:,::1] eventsRecovery, eventsSampling double[:,::1] infectiousAuxTau, susceptibleAuxTau @@ -101,6 +106,7 @@ cdef class BirthDeathModel: self.sCounter = 0 self.mCounter = 0 self.iCounter = 0 + self.cCounter = 0 self.swapLockdown = 0 self.migPlus = 0 self.migNonPlus = 0 @@ -118,18 +124,25 @@ cdef class BirthDeathModel: self.loc = Lockdowns() # memory_optimization + self.totalRateByCondition = np.zeros(self.conditionNum, dtype=float) + self.massivForThree = np.zeros((self.popNum, 3), dtype=float) + self.immunePopRateByConditionOneDem = np.zeros(self.popNum, dtype=float) self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) + self.infectiousByTypes = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=np.int64) self.tmRate = np.zeros(self.maxHapNum, dtype=float) self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) self.hapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) self.eventHapPopRate = np.zeros((self.popNum, self.maxHapNum, 4), dtype=float) self.susceptHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.susNum), dtype=float) + self.eventConditionRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) self.suscType = np.zeros(self.hapNum, dtype=np.int64) self.bRate = np.zeros(self.hapNum, dtype=float) self.dRate = np.zeros(self.hapNum, dtype=float) self.sRate = np.zeros(self.hapNum, dtype=float) self.mRate = np.zeros((self.hapNum, self.sites), dtype=float) + self.conditionRate = np.zeros((self.conditionNum, self.conditionNum), dtype=float) self.susceptibility = np.zeros((self.hapNum, self.susNum), dtype=float) self.hapMutType = np.ones((self.hapNum, self.sites, 3), dtype=float) @@ -140,21 +153,33 @@ cdef class BirthDeathModel: for s in range(self.sites): self.mRate[hn, s] = 0.01 self.susceptibility[hn, 0] = 1.0 + + for i in range(self.conditionNum): + for j in range(self.conditionNum): + if i!=j: + self.conditionRate[i,j] = 2 self.sizes = np.zeros(self.popNum, dtype=np.int64) self.totalSusceptible = np.zeros(self.popNum, dtype=np.int64) + self.totalSusceptibleByTypes = np.zeros((self.popNum, self.conditionNum), dtype=np.int64) self.totalInfectious = np.zeros(self.popNum, dtype=np.int64) + self.totalInfectiousByTypes = np.zeros((self.popNum, self.conditionNum), dtype=np.int64) self.lockdownON = np.zeros(self.popNum, dtype=np.int64) self.susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) + self.susceptibleByTypes = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=np.int64) self.initial_susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) self.initial_infectious = np.zeros((self.popNum, self.hapNum), dtype=np.int64) self.maxEffectiveBirthMigration = np.zeros(self.popNum, dtype=float) self.suscepCumulTransition = np.zeros(self.susNum, dtype=float) + self.suscepCumulTransitionByCondition = np.zeros((self.conditionNum, self.susNum), dtype=float) self.infectPopRate = np.zeros(self.popNum, dtype=float) + self.infectPopRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) self.immunePopRate = np.zeros(self.popNum, dtype=float) + self.immunePopRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) self.popRate = np.zeros(self.popNum, dtype=float) + self.popRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) self.migPopRate = np.zeros(self.popNum, dtype=float) self.actualSizes = np.zeros(self.popNum, dtype=float) self.contactDensity = np.ones(self.popNum, dtype=float) @@ -165,7 +190,9 @@ cdef class BirthDeathModel: self.samplingMultiplier = np.ones(self.popNum, dtype=float) self.suscepTransition = np.zeros( (self.susNum, self.susNum), dtype=float) + self.suscepTransitionByCondition = np.zeros((self.conditionNum, self.susNum, self.susNum), dtype=float) self.immuneSourcePopRate = np.zeros((self.popNum, self.susNum), dtype=float) + self.immuneSourcePopRateByCondition = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=float) self.migrationRates = np.zeros((self.popNum, self.popNum), dtype=float) self.effectiveMigration = np.zeros((self.popNum, self.popNum), dtype=float) @@ -173,6 +200,15 @@ cdef class BirthDeathModel: self.sizes[pn] = 1000000 self.totalSusceptible[pn] = 1000000 self.susceptible[pn, 0] = 1000000 + + for pn in range(self.popNum): + for cn in range(self.conditionNum): + self.totalSusceptibleByTypes[pn, cn] = 1000000 + #self.susceptibleByTypes[pn, cn, 0] = 1000000 + for pn in range(self.popNum): + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + self.susceptibleByTypes[pn, hn, cn] = 1000000 self.tree = np.zeros(1, dtype=np.int64) self.tree_pop = np.zeros(1, dtype=np.int64) @@ -238,9 +274,11 @@ cdef class BirthDeathModel: self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum), dtype=np.int64)), axis=1) self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, dtype=float))) self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) + self.teventConditionRate = np.concatenate((self.teventConditionRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) self.hapPopRate = np.concatenate((self.hapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, 4), dtype=float)), axis=1) self.susceptHapPopRate = np.concatenate((self.susceptHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.susNum), dtype=float)), axis=1) + self.eventConditionRate = np.concatenate((self.eventConditionRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum)), axis=1)) self.maxHapNum += self.addMemoryNum @cython.boundscheck(False) @@ -256,6 +294,18 @@ cdef class BirthDeathModel: for sn2 in range(self.susNum): self.suscepCumulTransition[sn1] += self.suscepTransition[sn1, sn2] + #for sn1 in range(self.susNum): + # for sn2 in range(self.susNum): + # self.suscepCumulTransitionByCondition[sn1, sn2] = 0 + # for cn in range(self.conditionNum): + # self.suscepCumulTransitionByCondition[sn1, sn2] += self.suscepTransitionByCondition[sn1, sn2, cn] + + for cn in range(self.conditionNum): + for sn1 in range(self.susNum): + self.suscepCumulTransitionByCondition[cn, sn1] = 0 + for sn2 in range(self.susNum): + self.suscepCumulTransitionByCondition[cn, sn1] += self.suscepTransitionByCondition[cn, sn1, sn2] + for pn1 in range(self.popNum): self.migrationRates[pn1, pn1] = 1.0 self.actualSizes[pn1] = 0.0 @@ -267,30 +317,67 @@ cdef class BirthDeathModel: self.actualSizes[pn1] += self.migrationRates[pn1, pn1]*self.sizes[pn1] self.totalRate = 0.0 + for cn in range(self.conditionNum): + self.totalRateByCondition[cn] = 0.0 for pn in range(self.popNum): self.infectPopRate[pn] = 0 self.immunePopRate[pn] = 0 - self.popRate[pn] = 0. + self.popRate[pn] = 0.0 + for cn in range(self.conditionNum): + self.popRateByCondition[pn, cn] = 0 + + self.infectPopRateByCondition[pn, cn] = 0 + for hn in range(self.hapNum): + self.immunePopRateByCondition[pn, hn] = 0 for pn in range(self.popNum): for hn in range(self.currentHapNum): # hn - program number self.tmRate[hn] = 0 for s in range(self.sites): self.tmRate[hn] += self.mRate[self.numToHap[hn], s] + for ni in range(self.conditionNum): + self.eventConditionRate[pn, hn, ni] = self.conditionRate[hn, ni] + self.eventHapPopRate[pn, hn, 0] = self.BirthRate(pn, hn) self.eventHapPopRate[pn, hn, 1] = self.dRate[self.numToHap[hn]] self.eventHapPopRate[pn, hn, 2] = self.sRate[self.numToHap[hn]]*self.samplingMultiplier[pn] self.eventHapPopRate[pn, hn, 3] = self.tmRate[hn] self.tEventHapPopRate[pn, hn] = 0 + self.teventConditionRate[pn, hn] = 0 for i in range(4): self.tEventHapPopRate[pn, hn] += self.eventHapPopRate[pn, hn, i] + for cn in range(self.conditionNum): + self.teventConditionRate[pn, hn] += self.eventConditionRate[pn, hn, cn] self.hapPopRate[pn, hn] = self.tEventHapPopRate[pn, hn] * self.infectious[pn, hn] self.infectPopRate[pn] += self.hapPopRate[pn, hn] + for cn in range(self.conditionNum): + self.infectPopRateByCondition[pn,cn] += self.teventConditionRate[pn, hn]*self.infectious[pn, hn] for sn in range(self.susNum): self.immuneSourcePopRate[pn, sn] = self.suscepCumulTransition[sn]*self.susceptible[pn, sn] self.immunePopRate[pn] += self.immuneSourcePopRate[pn, sn] - self.popRate[pn] = self.infectPopRate[pn] + self.immunePopRate[pn] + #for cn in range(self.conditionNum): + # for sn in range(self.susNum): + # #self.immuneSourcePopRateByCondition[pn, cn, sn] = self.suscepCumulTransitionByCondition[cn, sn]*self.susceptibleByTypes[pn, cn, sn] + # self.immuneSourcePopRateByCondition[pn, cn, sn] = self.conditionRate[cn]*self.susceptibleByTypes[pn, cn, sn] + # self.immunePopRateByCondition[pn, cn] = self.immuneSourcePopRateByCondition[pn, cn, sn] + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + self.immuneSourcePopRateByCondition[pn, hn, cn] = self.conditionRate[cn] * self.susceptibleByTypes[pn, hn, cn] + self.immunePopRateByCondition[pn, hn] += immuneSourcePopRateByCondition[pn, hn, cn] + self.immunePopRateByConditionOneDem[pn] += self.immunePopRateByCondition[pn, hn] + + + + + + self.popRate[pn] = self.infectPopRate[pn] + self.immunePopRate[pn] + self.immunePopRateByConditionOneDem[pn] + self.massivForThree[pn][0] = self.infectPopRate[pn] + self.massivForThree[pn][1] = self.immunePopRate[pn] + self.massivForThree[pn][2] = self.immunePopRateByCondition[pn] self.totalRate += self.popRate[pn] + #for cn in range(self.conditionNum): + # self.popRateByCondition[pn, cn] = self.immunePopRateByCondition[pn, cn] + self.infectPopRateByCondition[pn, cn] + # self.totalRateByCondition[cn] = self.popRateByCondition[pn, cn] maxEffectiveMigration = np.zeros(self.popNum, dtype=float) for pn1 in range(self.popNum): @@ -447,9 +534,15 @@ cdef class BirthDeathModel: self.rn = choose / self.totalRate pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) choose = self.rn * self.popRate[pi] - if self.immunePopRate[pi] > choose: + #if self.immunePopRate[pi] > choose: #delaem fastchoose1 is treh chisel kotory skladvutsy v popRate[pn], popRate[pn] vtoroy argument + ei, self.rn = fastChoose1(self.massivForThree[pn], self.popRate[pn], self.rn) + if ei == IMMUNITY: self.rn = choose / self.immunePopRate[pi] self.ImmunityTransition(pi) + elif ei == CONDITION: + self.rn = choose / self.immunePopRate[pi] + hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number + self.ImmunityTransition(pi, hi) else: self.rn = (choose - self.immunePopRate[pi]) / self.infectPopRate[pi] hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number @@ -517,11 +610,38 @@ cdef class BirthDeathModel: self.iCounter += 1 self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void ConditionTransition(self, Py_ssize_t pi, Py_ssize_t hi): + cdef: + Py_ssize_t ssi, tsi, ci + + ci, self.rn = fastChoose1(self.popRateByCondition[pi], self.totalRateByCondition[pi], self.rn) + + ssi, self.rn = fastChoose1(self.immuneSourcePopRateByCondition[pi, ci], self.immunePopRateByCondition[pi, ci], self.rn) + tsi, self.rn = fastChoose1(self.suscepTransitionByCondition[ci, ssi], self.suscepCumulTransitionByCondition[ci, ssi], self.rn) + + self.susceptibleByTypes[pi, ci, ssi] -= 1 + self.susceptibleByTypes[pi, ci, tsi] += 1 + self.immuneSourcePopRateByCondition[pi, hi, ssi] = self.susceptibleByTypes[pi, ci, ssi]*self.suscepCumulTransitionByCondition[ci, ssi] + self.immuneSourcePopRateByCondition[pi, hi, tsi] = self.susceptibleByTypes[pi, ci, tsi]*self.suscepCumulTransitionByCondition[ci, tsi] + self.UpdateRates(pi, False, True, False) + + self.cCounter += 1 + #self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) + self.events.AddEvent(self.currentTime, CONDITION, ssi, pi, tsi, 0) @cython.boundscheck(False) @cython.wraparound(False) cdef void Birth(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number cdef double ws = 0.0 + cdef Py_ssize_t ei, ssi, tsi + + #ei, self.rn = fastChoose1(self.eventConditionRate[pi, hi], self.teventConditionRate[pi, hi], self.rn) + #for i in range(self.conditionNum): + #if ei == i: + #if i == 0: for sn in range(self.susNum): ws += self.susceptHapPopRate[pi, hi, sn] @@ -533,6 +653,17 @@ cdef class BirthDeathModel: self.bCounter += 1 self.events.AddEvent(self.currentTime, BIRTH, self.numToHap[hi], pi, si, 0) + #else: + # self.rn = self.seed.uniform() + # choose = self.rn * (self.totalRate + self.totalMigrationRate) + # if self.totalRate > choose: + # self.rn = choose / self.totalRate + # pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) + # choose = self.rn * self.popRate[pi] + # if self.immunePopRate[pi] > choose: + # self.rn = choose / self.immunePopRate[pi] + # self.ConditionTransition(pi, hi) + @cython.boundscheck(False) @cython.wraparound(False) @@ -636,6 +767,7 @@ cdef class BirthDeathModel: self.sCounter = 0 self.mCounter = 0 self.iCounter = 0 + self.cCounter = 0 self.migPlus = 0 self.migNonPlus = 0 self.currentTime = 0.0 diff --git a/src/events.pxi b/src/events.pxi index 5a8cd7e..b8cdb19 100644 --- a/src/events.pxi +++ b/src/events.pxi @@ -6,6 +6,7 @@ DEF MUTATION = 3 DEF SUSCCHANGE = 4 DEF MIGRATION = 5 DEF MULTITYPE = 6 +DEF CONDITION = 7 cdef class Event: cdef: @@ -94,6 +95,8 @@ cdef class multiEvent: tn = "SUS" elif self.type_ == MIGRATION: tn = "MIG" + elif self.type_ == CONDITION: + tn = "CON" print("num=", self.num, " time=", self.time, " type=", tn, From eecf249fb382cea365475268f735c4c0878fa46f Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Sun, 6 Nov 2022 23:04:53 +0300 Subject: [PATCH 03/13] Add files via upload --- src/_BirthDeath.pyx | 178 +++++++++++++------------------------------- src/events.pxi | 2 +- 2 files changed, 54 insertions(+), 126 deletions(-) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index 0ad0f59..89c4fb2 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -32,7 +32,7 @@ cdef class BirthDeathModel: RndmWrapper seed bint first_simulation, sampling_probability, memory_optimization - Py_ssize_t user_seed, sites, hapNum, currentHapNum, maxHapNum, addMemoryNum, popNum, susNum, bCounter, dCounter, sCounter, mCounter, iCounter, swapLockdown, migPlus, migNonPlus, globalInfectious, countsPerStep, good_attempt, conditionNum, cCounter + Py_ssize_t user_seed, sites, hapNum, currentHapNum, maxHapNum, addMemoryNum, popNum, susNum, bCounter, dCounter, sCounter, mCounter, iCounter, swapLockdown, migPlus, migNonPlus, globalInfectious, countsPerStep, good_attempt, cCounter, conditionNum double currentTime, totalRate, totalMigrationRate, rn, tau_l Events events @@ -43,22 +43,21 @@ cdef class BirthDeathModel: npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop npy_int64[:,::1] susceptible, infectious, initial_susceptible, initial_infectious - npy_int64[:,::1] totalInfectiousByTypes, totalSusceptibleByTypes - npy_int64[:,:,::1] infectiousByTypes, susceptibleByTypes + npy_int64[:,:,::1] infectByCondition double[::1] bRate, dRate, sRate, tmRate, maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times - double[::1] totalRateByCondition, immunePopRateByConditionOneDem + double[::1] cumuConditionRate, conditionPopRate double[:,::1] mRate, susceptibility, tEventHapPopRate, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration - double[:,::1] conditionRate, teventConditionRate, immunePopRateByCondition, infectPopRateByCondition, suscepCumulTransitionByCondition, popRateByCondition, massivForThree + double[:,::1] conditionRate, massivForThree, conditionPopHapRate double[:,:,::1] hapMutType, eventHapPopRate, susceptHapPopRate - double[:,:,::1] eventConditionRate,immuneSourcePopRateByCondition, suscepTransitionByCondition + double[:,:,::1] conditionPopHapConRate double[:,:,:,::1] PropensitiesMigr, PropensitiesMutatations double[:,:,::1] PropensitiesSuscep, PropensitiesTransmission double[:,::1] PropensitiesRecovery, PropensitiesSampling npy_int64[:,:,:,::1] eventsMigr, eventsMutatations - npy_int64[:,:,::1] eventsSuscep, eventsTransmission/ + npy_int64[:,:,::1] eventsSuscep, eventsTransmission npy_int64[:,::1] eventsRecovery, eventsSampling double[:,::1] infectiousAuxTau, susceptibleAuxTau @@ -124,18 +123,14 @@ cdef class BirthDeathModel: self.loc = Lockdowns() # memory_optimization - self.totalRateByCondition = np.zeros(self.conditionNum, dtype=float) - self.massivForThree = np.zeros((self.popNum, 3), dtype=float) - self.immunePopRateByConditionOneDem = np.zeros(self.popNum, dtype=float) self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) - self.infectiousByTypes = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=np.int64) self.tmRate = np.zeros(self.maxHapNum, dtype=float) - self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.massivForThree = np.zeros((self.popNum, 3), dtype=float) + self.conditionPopRate = np.zeros(self.popNum, dtype=float) self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) self.hapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) self.eventHapPopRate = np.zeros((self.popNum, self.maxHapNum, 4), dtype=float) self.susceptHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.susNum), dtype=float) - self.eventConditionRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) self.suscType = np.zeros(self.hapNum, dtype=np.int64) self.bRate = np.zeros(self.hapNum, dtype=float) @@ -143,6 +138,10 @@ cdef class BirthDeathModel: self.sRate = np.zeros(self.hapNum, dtype=float) self.mRate = np.zeros((self.hapNum, self.sites), dtype=float) self.conditionRate = np.zeros((self.conditionNum, self.conditionNum), dtype=float) + self.cumuConditionRate = np.zeros(self.conditionNum, dtype=float) + self.conditionPopHapRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.conditionPopHapConRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum),dtype=float) + self.infectByCondition = np.zeros((self.popNum, self.maxHapNum, self.conditionNum),dtype=np.int64) self.susceptibility = np.zeros((self.hapNum, self.susNum), dtype=float) self.hapMutType = np.ones((self.hapNum, self.sites, 3), dtype=float) @@ -153,33 +152,34 @@ cdef class BirthDeathModel: for s in range(self.sites): self.mRate[hn, s] = 0.01 self.susceptibility[hn, 0] = 1.0 + + for cn1 in range(self.conditionNum): + for cn2 in range(self.conditionNum): + if cn1 != cn2: + conditionRate[cn1, cn2] = 2 + else: + conditionRate[cn1, cn2] = 0 - for i in range(self.conditionNum): - for j in range(self.conditionNum): - if i!=j: - self.conditionRate[i,j] = 2 + for cn1 in range(self.conditionNum): + self.cumuConditionRate[cn1] = 0 + for cn2 in range(self.conditionNum): + self.cumuConditionRate[cn1] += self.conditionRate[cn1, cn2] + self.sizes = np.zeros(self.popNum, dtype=np.int64) self.totalSusceptible = np.zeros(self.popNum, dtype=np.int64) - self.totalSusceptibleByTypes = np.zeros((self.popNum, self.conditionNum), dtype=np.int64) self.totalInfectious = np.zeros(self.popNum, dtype=np.int64) - self.totalInfectiousByTypes = np.zeros((self.popNum, self.conditionNum), dtype=np.int64) self.lockdownON = np.zeros(self.popNum, dtype=np.int64) self.susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) - self.susceptibleByTypes = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=np.int64) self.initial_susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) self.initial_infectious = np.zeros((self.popNum, self.hapNum), dtype=np.int64) self.maxEffectiveBirthMigration = np.zeros(self.popNum, dtype=float) self.suscepCumulTransition = np.zeros(self.susNum, dtype=float) - self.suscepCumulTransitionByCondition = np.zeros((self.conditionNum, self.susNum), dtype=float) self.infectPopRate = np.zeros(self.popNum, dtype=float) - self.infectPopRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) self.immunePopRate = np.zeros(self.popNum, dtype=float) - self.immunePopRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) self.popRate = np.zeros(self.popNum, dtype=float) - self.popRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) self.migPopRate = np.zeros(self.popNum, dtype=float) self.actualSizes = np.zeros(self.popNum, dtype=float) self.contactDensity = np.ones(self.popNum, dtype=float) @@ -190,9 +190,7 @@ cdef class BirthDeathModel: self.samplingMultiplier = np.ones(self.popNum, dtype=float) self.suscepTransition = np.zeros( (self.susNum, self.susNum), dtype=float) - self.suscepTransitionByCondition = np.zeros((self.conditionNum, self.susNum, self.susNum), dtype=float) self.immuneSourcePopRate = np.zeros((self.popNum, self.susNum), dtype=float) - self.immuneSourcePopRateByCondition = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=float) self.migrationRates = np.zeros((self.popNum, self.popNum), dtype=float) self.effectiveMigration = np.zeros((self.popNum, self.popNum), dtype=float) @@ -200,15 +198,6 @@ cdef class BirthDeathModel: self.sizes[pn] = 1000000 self.totalSusceptible[pn] = 1000000 self.susceptible[pn, 0] = 1000000 - - for pn in range(self.popNum): - for cn in range(self.conditionNum): - self.totalSusceptibleByTypes[pn, cn] = 1000000 - #self.susceptibleByTypes[pn, cn, 0] = 1000000 - for pn in range(self.popNum): - for hn in range(self.hapNum): - for cn in range(self.conditionNum): - self.susceptibleByTypes[pn, hn, cn] = 1000000 self.tree = np.zeros(1, dtype=np.int64) self.tree_pop = np.zeros(1, dtype=np.int64) @@ -274,11 +263,12 @@ cdef class BirthDeathModel: self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum), dtype=np.int64)), axis=1) self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, dtype=float))) self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) - self.teventConditionRate = np.concatenate((self.teventConditionRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) self.hapPopRate = np.concatenate((self.hapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, 4), dtype=float)), axis=1) self.susceptHapPopRate = np.concatenate((self.susceptHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.susNum), dtype=float)), axis=1) - self.eventConditionRate = np.concatenate((self.eventConditionRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum)), axis=1)) + self.conditionPopHapRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) + self.conditionPopHapRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) + self.infectByCondition = np.concatenate((self.infectByCondition, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum),dtype=np.int64)), axis=1) self.maxHapNum += self.addMemoryNum @cython.boundscheck(False) @@ -294,18 +284,6 @@ cdef class BirthDeathModel: for sn2 in range(self.susNum): self.suscepCumulTransition[sn1] += self.suscepTransition[sn1, sn2] - #for sn1 in range(self.susNum): - # for sn2 in range(self.susNum): - # self.suscepCumulTransitionByCondition[sn1, sn2] = 0 - # for cn in range(self.conditionNum): - # self.suscepCumulTransitionByCondition[sn1, sn2] += self.suscepTransitionByCondition[sn1, sn2, cn] - - for cn in range(self.conditionNum): - for sn1 in range(self.susNum): - self.suscepCumulTransitionByCondition[cn, sn1] = 0 - for sn2 in range(self.susNum): - self.suscepCumulTransitionByCondition[cn, sn1] += self.suscepTransitionByCondition[cn, sn1, sn2] - for pn1 in range(self.popNum): self.migrationRates[pn1, pn1] = 1.0 self.actualSizes[pn1] = 0.0 @@ -317,67 +295,40 @@ cdef class BirthDeathModel: self.actualSizes[pn1] += self.migrationRates[pn1, pn1]*self.sizes[pn1] self.totalRate = 0.0 - for cn in range(self.conditionNum): - self.totalRateByCondition[cn] = 0.0 for pn in range(self.popNum): self.infectPopRate[pn] = 0 self.immunePopRate[pn] = 0 - self.popRate[pn] = 0.0 - for cn in range(self.conditionNum): - self.popRateByCondition[pn, cn] = 0 - - self.infectPopRateByCondition[pn, cn] = 0 - for hn in range(self.hapNum): - self.immunePopRateByCondition[pn, hn] = 0 + self.popRate[pn] = 0. for pn in range(self.popNum): for hn in range(self.currentHapNum): # hn - program number self.tmRate[hn] = 0 for s in range(self.sites): self.tmRate[hn] += self.mRate[self.numToHap[hn], s] - for ni in range(self.conditionNum): - self.eventConditionRate[pn, hn, ni] = self.conditionRate[hn, ni] - self.eventHapPopRate[pn, hn, 0] = self.BirthRate(pn, hn) self.eventHapPopRate[pn, hn, 1] = self.dRate[self.numToHap[hn]] self.eventHapPopRate[pn, hn, 2] = self.sRate[self.numToHap[hn]]*self.samplingMultiplier[pn] self.eventHapPopRate[pn, hn, 3] = self.tmRate[hn] self.tEventHapPopRate[pn, hn] = 0 - self.teventConditionRate[pn, hn] = 0 for i in range(4): self.tEventHapPopRate[pn, hn] += self.eventHapPopRate[pn, hn, i] - for cn in range(self.conditionNum): - self.teventConditionRate[pn, hn] += self.eventConditionRate[pn, hn, cn] self.hapPopRate[pn, hn] = self.tEventHapPopRate[pn, hn] * self.infectious[pn, hn] self.infectPopRate[pn] += self.hapPopRate[pn, hn] - for cn in range(self.conditionNum): - self.infectPopRateByCondition[pn,cn] += self.teventConditionRate[pn, hn]*self.infectious[pn, hn] for sn in range(self.susNum): self.immuneSourcePopRate[pn, sn] = self.suscepCumulTransition[sn]*self.susceptible[pn, sn] self.immunePopRate[pn] += self.immuneSourcePopRate[pn, sn] - #for cn in range(self.conditionNum): - # for sn in range(self.susNum): - # #self.immuneSourcePopRateByCondition[pn, cn, sn] = self.suscepCumulTransitionByCondition[cn, sn]*self.susceptibleByTypes[pn, cn, sn] - # self.immuneSourcePopRateByCondition[pn, cn, sn] = self.conditionRate[cn]*self.susceptibleByTypes[pn, cn, sn] - # self.immunePopRateByCondition[pn, cn] = self.immuneSourcePopRateByCondition[pn, cn, sn] - for hn in range(self.hapNum): + for hn in range(self.currentHapNum): for cn in range(self.conditionNum): - self.immuneSourcePopRateByCondition[pn, hn, cn] = self.conditionRate[cn] * self.susceptibleByTypes[pn, hn, cn] - self.immunePopRateByCondition[pn, hn] += immuneSourcePopRateByCondition[pn, hn, cn] - self.immunePopRateByConditionOneDem[pn] += self.immunePopRateByCondition[pn, hn] - + self.conditionPopHapConRate[pn, hn, cn] = self.cumuConditionRate[cn] * self.infectByCondition[pn, hn, cn] + self.conditionPopHapRate[pn, hn] += self.conditionPopHapConRate[pn, hn, cn] + self.conditionPopRate[pn] += self.conditionPopHapRate[pn, hn] - - - self.popRate[pn] = self.infectPopRate[pn] + self.immunePopRate[pn] + self.immunePopRateByConditionOneDem[pn] - self.massivForThree[pn][0] = self.infectPopRate[pn] - self.massivForThree[pn][1] = self.immunePopRate[pn] - self.massivForThree[pn][2] = self.immunePopRateByCondition[pn] + self.popRate[pn] = self.infectPopRate[pn] + self.immunePopRate[pn] + self.conditionPopRate[pn] + self.massivForThree[0] = self.infectPopRate[pn] + self.massivForThree[1] = self.immunePopRate[pn] + self.conditionPopRate[2] = self.conditionPopRate[pn] self.totalRate += self.popRate[pn] - #for cn in range(self.conditionNum): - # self.popRateByCondition[pn, cn] = self.immunePopRateByCondition[pn, cn] + self.infectPopRateByCondition[pn, cn] - # self.totalRateByCondition[cn] = self.popRateByCondition[pn, cn] maxEffectiveMigration = np.zeros(self.popNum, dtype=float) for pn1 in range(self.popNum): @@ -533,16 +484,13 @@ cdef class BirthDeathModel: if self.totalRate > choose: self.rn = choose / self.totalRate pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) - choose = self.rn * self.popRate[pi] - #if self.immunePopRate[pi] > choose: #delaem fastchoose1 is treh chisel kotory skladvutsy v popRate[pn], popRate[pn] vtoroy argument - ei, self.rn = fastChoose1(self.massivForThree[pn], self.popRate[pn], self.rn) - if ei == IMMUNITY: - self.rn = choose / self.immunePopRate[pi] + #choose = self.rn * self.popRate[pi] + ei, self.rn = fastChoose1(self.massivForThree[pn], self.totalRate, self.rn) + if ei == 1: + #self.rn = choose / self.immunePopRate[pi] self.ImmunityTransition(pi) - elif ei == CONDITION: - self.rn = choose / self.immunePopRate[pi] - hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number - self.ImmunityTransition(pi, hi) + elif ei == 2: + self.ConditionTransition(pi) else: self.rn = (choose - self.immunePopRate[pi]) / self.infectPopRate[pi] hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number @@ -610,38 +558,30 @@ cdef class BirthDeathModel: self.iCounter += 1 self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) - + @cython.boundscheck(False) @cython.wraparound(False) - cdef void ConditionTransition(self, Py_ssize_t pi, Py_ssize_t hi): + cdef void ConditionTransition(self, Py_ssize_t pi): cdef: - Py_ssize_t ssi, tsi, ci - - ci, self.rn = fastChoose1(self.popRateByCondition[pi], self.totalRateByCondition[pi], self.rn) + Py_ssize_t ssi, tsi, hsi - ssi, self.rn = fastChoose1(self.immuneSourcePopRateByCondition[pi, ci], self.immunePopRateByCondition[pi, ci], self.rn) - tsi, self.rn = fastChoose1(self.suscepTransitionByCondition[ci, ssi], self.suscepCumulTransitionByCondition[ci, ssi], self.rn) + hsi, self.rn = fastChoose1(self.conditionPopHapRate[pi], self.immunePopRate[pi], self.rn) + ssi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self,conditionPopHapRate[pi, hsi], self.rn) + tsi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self.conditionPopHapRate[pi, hsi], self.rn) - self.susceptibleByTypes[pi, ci, ssi] -= 1 - self.susceptibleByTypes[pi, ci, tsi] += 1 - self.immuneSourcePopRateByCondition[pi, hi, ssi] = self.susceptibleByTypes[pi, ci, ssi]*self.suscepCumulTransitionByCondition[ci, ssi] - self.immuneSourcePopRateByCondition[pi, hi, tsi] = self.susceptibleByTypes[pi, ci, tsi]*self.suscepCumulTransitionByCondition[ci, tsi] + self.infectByCondition[pi, hsi, ssi] -= 1 + self.infectByCondition[pi, hsi, tsi] += 1 + self.conditionPopHapConRate[pi, hsi, ssi] = self.cumuConditionRate[ssi] * self.infectByCondition[pi, hsi, ssi] + self.conditionPopHapConRate[pi, hsi, tsi] = self.cumuConditionRate[tsi] * self.infectByCondition[pi, hsi, tsi] self.UpdateRates(pi, False, True, False) self.cCounter += 1 - #self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) - self.events.AddEvent(self.currentTime, CONDITION, ssi, pi, tsi, 0) + self.events.AddEvent(self.currentTime, CONDITION, ssi, pi, tsi, 0) #ssi, tsi -- condition, add haplotype hsi?? @cython.boundscheck(False) @cython.wraparound(False) cdef void Birth(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number cdef double ws = 0.0 - cdef Py_ssize_t ei, ssi, tsi - - #ei, self.rn = fastChoose1(self.eventConditionRate[pi, hi], self.teventConditionRate[pi, hi], self.rn) - #for i in range(self.conditionNum): - #if ei == i: - #if i == 0: for sn in range(self.susNum): ws += self.susceptHapPopRate[pi, hi, sn] @@ -653,17 +593,6 @@ cdef class BirthDeathModel: self.bCounter += 1 self.events.AddEvent(self.currentTime, BIRTH, self.numToHap[hi], pi, si, 0) - #else: - # self.rn = self.seed.uniform() - # choose = self.rn * (self.totalRate + self.totalMigrationRate) - # if self.totalRate > choose: - # self.rn = choose / self.totalRate - # pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) - # choose = self.rn * self.popRate[pi] - # if self.immunePopRate[pi] > choose: - # self.rn = choose / self.immunePopRate[pi] - # self.ConditionTransition(pi, hi) - @cython.boundscheck(False) @cython.wraparound(False) @@ -767,7 +696,6 @@ cdef class BirthDeathModel: self.sCounter = 0 self.mCounter = 0 self.iCounter = 0 - self.cCounter = 0 self.migPlus = 0 self.migNonPlus = 0 self.currentTime = 0.0 diff --git a/src/events.pxi b/src/events.pxi index b8cdb19..015b0f5 100644 --- a/src/events.pxi +++ b/src/events.pxi @@ -95,7 +95,7 @@ cdef class multiEvent: tn = "SUS" elif self.type_ == MIGRATION: tn = "MIG" - elif self.type_ == CONDITION: + elif self.type_ == CONDITION: tn = "CON" print("num=", self.num, " time=", self.time, From 38eac4ca4745aca35dfb760014f527713da6bda5 Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Sun, 6 Nov 2022 23:08:56 +0300 Subject: [PATCH 04/13] Add files via upload --- src/_BirthDeath.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index 89c4fb2..2ab0801 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -565,7 +565,7 @@ cdef class BirthDeathModel: cdef: Py_ssize_t ssi, tsi, hsi - hsi, self.rn = fastChoose1(self.conditionPopHapRate[pi], self.immunePopRate[pi], self.rn) + hsi, self.rn = fastChoose1(self.conditionPopHapRate[pi], self.conditionPopRate[pi], self.rn) ssi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self,conditionPopHapRate[pi, hsi], self.rn) tsi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self.conditionPopHapRate[pi, hsi], self.rn) From 6e312ca9e9cd22968285afb1e15e48b8a817f900 Mon Sep 17 00:00:00 2001 From: Spirin Vadim <61345894+spirinvadim@users.noreply.github.com> Date: Mon, 7 Nov 2022 14:50:48 +0300 Subject: [PATCH 05/13] Delete _BirthDeath.pyx --- _BirthDeath.pyx | 2682 ----------------------------------------------- 1 file changed, 2682 deletions(-) delete mode 100644 _BirthDeath.pyx diff --git a/_BirthDeath.pyx b/_BirthDeath.pyx deleted file mode 100644 index 0ad0f59..0000000 --- a/_BirthDeath.pyx +++ /dev/null @@ -1,2682 +0,0 @@ -# cython: language_level=3 -# cython: initializedcheck = False -# distutils: language = c++ - -cimport cython - -from numpy cimport npy_int64 - -from libc.math cimport log, floor, abs -from libcpp.vector cimport vector -from mc_lib.rndm cimport RndmWrapper - -from prettytable import PrettyTable -import numpy as np -cimport numpy as np -import sys -import os -import time -import tskit - -from numpy.random.c_distributions cimport random_poisson, random_hypergeometric - -include "fast_choose.pxi" -include "models.pxi" -include "events.pxi" - -#pi - population ID, pn - popoulation number, spi - source population ID, tpi - target population ID -#hi - haplotype ID, hn - haplotype number, nhi - new haplotype number -#si - susceptibility ID, sn - susceptibility number, ssi - source susceptibility ID, tsi - target susceptibility ID -cdef class BirthDeathModel: - cdef: - RndmWrapper seed - - bint first_simulation, sampling_probability, memory_optimization - Py_ssize_t user_seed, sites, hapNum, currentHapNum, maxHapNum, addMemoryNum, popNum, susNum, bCounter, dCounter, sCounter, mCounter, iCounter, swapLockdown, migPlus, migNonPlus, globalInfectious, countsPerStep, good_attempt, conditionNum, cCounter - double currentTime, totalRate, totalMigrationRate, rn, tau_l - - Events events - multiEvents multievents - Mutations mut - Migrations mig - Lockdowns loc - - npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop - npy_int64[:,::1] susceptible, infectious, initial_susceptible, initial_infectious - npy_int64[:,::1] totalInfectiousByTypes, totalSusceptibleByTypes - npy_int64[:,:,::1] infectiousByTypes, susceptibleByTypes - - double[::1] bRate, dRate, sRate, tmRate, maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times - double[::1] totalRateByCondition, immunePopRateByConditionOneDem - double[:,::1] mRate, susceptibility, tEventHapPopRate, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration - double[:,::1] conditionRate, teventConditionRate, immunePopRateByCondition, infectPopRateByCondition, suscepCumulTransitionByCondition, popRateByCondition, massivForThree - double[:,:,::1] hapMutType, eventHapPopRate, susceptHapPopRate - double[:,:,::1] eventConditionRate,immuneSourcePopRateByCondition, suscepTransitionByCondition - - double[:,:,:,::1] PropensitiesMigr, PropensitiesMutatations - double[:,:,::1] PropensitiesSuscep, PropensitiesTransmission - double[:,::1] PropensitiesRecovery, PropensitiesSampling - - npy_int64[:,:,:,::1] eventsMigr, eventsMutatations - npy_int64[:,:,::1] eventsSuscep, eventsTransmission/ - npy_int64[:,::1] eventsRecovery, eventsSampling - - double[:,::1] infectiousAuxTau, susceptibleAuxTau - npy_int64[:,::1] infectiousDelta, susceptibleDelta - - - def __init__(self, number_of_sites, populations_number, number_of_susceptible_groups, seed, sampling_probability, memory_optimization): - self.user_seed = seed - self.seed = RndmWrapper(seed=(self.user_seed, 0)) - - self.first_simulation = False - self.sampling_probability = sampling_probability - - self.sites = number_of_sites - self.hapNum = 4**self.sites - self.susNum = number_of_susceptible_groups - self.popNum = populations_number - - if memory_optimization: - self.memory_optimization = True - if self.sites > 2: - self.maxHapNum = 4**(self.sites-2) - self.addMemoryNum = 4**(self.sites-2) - else: - self.maxHapNum = 4 - self.addMemoryNum = 4 - else: - self.memory_optimization = False - self.maxHapNum = self.hapNum - self.addMemoryNum = 0 - - self.hapToNum = np.zeros(self.hapNum, dtype=np.int64) # from haplotype to program number - self.numToHap = np.zeros(self.maxHapNum, dtype=np.int64) # from program number to haplotype - - if self.memory_optimization: - self.currentHapNum = 0 - else: - self.currentHapNum = self.hapNum - for hn in range(self.hapNum): - self.hapToNum[hn] = hn - self.numToHap[hn] = hn - - self.bCounter = 0 - self.dCounter = 0 - self.sCounter = 0 - self.mCounter = 0 - self.iCounter = 0 - self.cCounter = 0 - self.swapLockdown = 0 - self.migPlus = 0 - self.migNonPlus = 0 - self.globalInfectious = 0 - - self.currentTime = 0.0 - self.tau_l=0.01 - self.totalRate = 0.0 - self.totalMigrationRate = 0.0 - - self.events = Events() - self.multievents = multiEvents() - self.mut = Mutations() - self.mig = Migrations() - self.loc = Lockdowns() - - # memory_optimization - self.totalRateByCondition = np.zeros(self.conditionNum, dtype=float) - self.massivForThree = np.zeros((self.popNum, 3), dtype=float) - self.immunePopRateByConditionOneDem = np.zeros(self.popNum, dtype=float) - self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) - self.infectiousByTypes = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=np.int64) - self.tmRate = np.zeros(self.maxHapNum, dtype=float) - self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) - self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) - self.hapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) - self.eventHapPopRate = np.zeros((self.popNum, self.maxHapNum, 4), dtype=float) - self.susceptHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.susNum), dtype=float) - self.eventConditionRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) - - self.suscType = np.zeros(self.hapNum, dtype=np.int64) - self.bRate = np.zeros(self.hapNum, dtype=float) - self.dRate = np.zeros(self.hapNum, dtype=float) - self.sRate = np.zeros(self.hapNum, dtype=float) - self.mRate = np.zeros((self.hapNum, self.sites), dtype=float) - self.conditionRate = np.zeros((self.conditionNum, self.conditionNum), dtype=float) - self.susceptibility = np.zeros((self.hapNum, self.susNum), dtype=float) - self.hapMutType = np.ones((self.hapNum, self.sites, 3), dtype=float) - - for hn in range(self.hapNum): - self.bRate[hn] = 2.0 - self.dRate[hn] = 1.0 - self.sRate[hn] = 0.01 - for s in range(self.sites): - self.mRate[hn, s] = 0.01 - self.susceptibility[hn, 0] = 1.0 - - for i in range(self.conditionNum): - for j in range(self.conditionNum): - if i!=j: - self.conditionRate[i,j] = 2 - - self.sizes = np.zeros(self.popNum, dtype=np.int64) - self.totalSusceptible = np.zeros(self.popNum, dtype=np.int64) - self.totalSusceptibleByTypes = np.zeros((self.popNum, self.conditionNum), dtype=np.int64) - self.totalInfectious = np.zeros(self.popNum, dtype=np.int64) - self.totalInfectiousByTypes = np.zeros((self.popNum, self.conditionNum), dtype=np.int64) - self.lockdownON = np.zeros(self.popNum, dtype=np.int64) - - self.susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) - self.susceptibleByTypes = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=np.int64) - self.initial_susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) - self.initial_infectious = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - - self.maxEffectiveBirthMigration = np.zeros(self.popNum, dtype=float) - self.suscepCumulTransition = np.zeros(self.susNum, dtype=float) - self.suscepCumulTransitionByCondition = np.zeros((self.conditionNum, self.susNum), dtype=float) - self.infectPopRate = np.zeros(self.popNum, dtype=float) - self.infectPopRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) - self.immunePopRate = np.zeros(self.popNum, dtype=float) - self.immunePopRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) - self.popRate = np.zeros(self.popNum, dtype=float) - self.popRateByCondition = np.zeros((self.popNum, self.conditionNum), dtype=float) - self.migPopRate = np.zeros(self.popNum, dtype=float) - self.actualSizes = np.zeros(self.popNum, dtype=float) - self.contactDensity = np.ones(self.popNum, dtype=float) - self.contactDensityBeforeLockdown = np.ones(self.popNum, dtype=float) - self.contactDensityAfterLockdown = np.zeros(self.popNum, dtype=float) - self.startLD = np.ones(self.popNum, dtype=float) - self.endLD = np.ones(self.popNum, dtype=float) - self.samplingMultiplier = np.ones(self.popNum, dtype=float) - - self.suscepTransition = np.zeros( (self.susNum, self.susNum), dtype=float) - self.suscepTransitionByCondition = np.zeros((self.conditionNum, self.susNum, self.susNum), dtype=float) - self.immuneSourcePopRate = np.zeros((self.popNum, self.susNum), dtype=float) - self.immuneSourcePopRateByCondition = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=float) - self.migrationRates = np.zeros((self.popNum, self.popNum), dtype=float) - self.effectiveMigration = np.zeros((self.popNum, self.popNum), dtype=float) - - for pn in range(self.popNum): - self.sizes[pn] = 1000000 - self.totalSusceptible[pn] = 1000000 - self.susceptible[pn, 0] = 1000000 - - for pn in range(self.popNum): - for cn in range(self.conditionNum): - self.totalSusceptibleByTypes[pn, cn] = 1000000 - #self.susceptibleByTypes[pn, cn, 0] = 1000000 - for pn in range(self.popNum): - for hn in range(self.hapNum): - for cn in range(self.conditionNum): - self.susceptibleByTypes[pn, hn, cn] = 1000000 - - self.tree = np.zeros(1, dtype=np.int64) - self.tree_pop = np.zeros(1, dtype=np.int64) - - #Init propensities - self.PropensitiesMigr = np.zeros((self.popNum, self.popNum, self.susNum, self.hapNum), dtype=float) - self.PropensitiesSuscep = np.zeros((self.popNum, self.susNum, self.susNum), dtype=float) - self.PropensitiesRecovery = np.zeros((self.popNum, self.hapNum), dtype=float) - self.PropensitiesSampling = np.zeros((self.popNum, self.hapNum), dtype=float) - self.PropensitiesMutatations = np.zeros((self.popNum, self.hapNum, self.sites, 3), dtype=float) - self.PropensitiesTransmission = np.zeros((self.popNum, self.hapNum, self.susNum), dtype=float) - - #Sampled number of events per step - self.eventsMigr = np.zeros((self.popNum, self.popNum, self.susNum, self.hapNum), dtype=np.int64) - self.eventsSuscep = np.zeros((self.popNum, self.susNum, self.susNum), dtype=np.int64) - self.eventsRecovery = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - self.eventsSampling = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - self.eventsMutatations = np.zeros((self.popNum, self.hapNum, self.sites, 3), dtype=np.int64) - self.eventsTransmission = np.zeros((self.popNum, self.hapNum, self.susNum), dtype=np.int64) - - self.infectiousAuxTau = np.zeros((self.popNum, self.hapNum), dtype=float) - self.susceptibleAuxTau = np.zeros((self.popNum, self.susNum), dtype=float) - - self.infectiousDelta = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - self.susceptibleDelta = np.zeros((self.popNum, self.susNum), dtype=np.int64) - - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void FirstInfection(self): - if self.globalInfectious == 0: - for sn in range(self.susNum): - if self.susceptible[0, sn] != 0: - if self.memory_optimization: - self.AddHaplotype(0, 0) - self.NewInfections(0, sn, 0) - return - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef inline void NewInfections(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t num=1): - self.susceptible[pi, si] -= num - self.totalSusceptible[pi] -= num - self.infectious[pi, hi] += num - self.totalInfectious[pi] += num - self.globalInfectious += num - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef inline void NewRecoveries(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t num=1): - self.susceptible[pi, si] += num - self.totalSusceptible[pi] += num - self.infectious[pi, hi] -= num - self.totalInfectious[pi] -= num - self.globalInfectious -= num - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void AddMemory(self): - if self.addMemoryNum + self.maxHapNum > self.hapNum: - self.addMemoryNum = self.hapNum - self.maxHapNum - self.numToHap = np.concatenate((self.numToHap, np.zeros(self.addMemoryNum, dtype=np.int64))) - self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum), dtype=np.int64)), axis=1) - self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, dtype=float))) - self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) - self.teventConditionRate = np.concatenate((self.teventConditionRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) - self.hapPopRate = np.concatenate((self.hapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) - self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, 4), dtype=float)), axis=1) - self.susceptHapPopRate = np.concatenate((self.susceptHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.susNum), dtype=float)), axis=1) - self.eventConditionRate = np.concatenate((self.eventConditionRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum)), axis=1)) - self.maxHapNum += self.addMemoryNum - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void UpdateAllRates(self): - cdef: - double maxEffectiveBirth - double[::1] maxEffectiveMigration - - for sn1 in range(self.susNum): - self.suscepCumulTransition[sn1] = 0 - for sn2 in range(self.susNum): - self.suscepCumulTransition[sn1] += self.suscepTransition[sn1, sn2] - - #for sn1 in range(self.susNum): - # for sn2 in range(self.susNum): - # self.suscepCumulTransitionByCondition[sn1, sn2] = 0 - # for cn in range(self.conditionNum): - # self.suscepCumulTransitionByCondition[sn1, sn2] += self.suscepTransitionByCondition[sn1, sn2, cn] - - for cn in range(self.conditionNum): - for sn1 in range(self.susNum): - self.suscepCumulTransitionByCondition[cn, sn1] = 0 - for sn2 in range(self.susNum): - self.suscepCumulTransitionByCondition[cn, sn1] += self.suscepTransitionByCondition[cn, sn1, sn2] - - for pn1 in range(self.popNum): - self.migrationRates[pn1, pn1] = 1.0 - self.actualSizes[pn1] = 0.0 - for pn2 in range(self.popNum): - if pn1 == pn2: - continue - self.migrationRates[pn1, pn1] -= self.migrationRates[pn1, pn2] - self.actualSizes[pn1] += self.migrationRates[pn2, pn1]*self.sizes[pn2] - self.actualSizes[pn1] += self.migrationRates[pn1, pn1]*self.sizes[pn1] - - self.totalRate = 0.0 - for cn in range(self.conditionNum): - self.totalRateByCondition[cn] = 0.0 - for pn in range(self.popNum): - self.infectPopRate[pn] = 0 - self.immunePopRate[pn] = 0 - self.popRate[pn] = 0.0 - for cn in range(self.conditionNum): - self.popRateByCondition[pn, cn] = 0 - - self.infectPopRateByCondition[pn, cn] = 0 - for hn in range(self.hapNum): - self.immunePopRateByCondition[pn, hn] = 0 - for pn in range(self.popNum): - for hn in range(self.currentHapNum): # hn - program number - self.tmRate[hn] = 0 - for s in range(self.sites): - self.tmRate[hn] += self.mRate[self.numToHap[hn], s] - - for ni in range(self.conditionNum): - self.eventConditionRate[pn, hn, ni] = self.conditionRate[hn, ni] - - self.eventHapPopRate[pn, hn, 0] = self.BirthRate(pn, hn) - self.eventHapPopRate[pn, hn, 1] = self.dRate[self.numToHap[hn]] - self.eventHapPopRate[pn, hn, 2] = self.sRate[self.numToHap[hn]]*self.samplingMultiplier[pn] - self.eventHapPopRate[pn, hn, 3] = self.tmRate[hn] - self.tEventHapPopRate[pn, hn] = 0 - self.teventConditionRate[pn, hn] = 0 - for i in range(4): - self.tEventHapPopRate[pn, hn] += self.eventHapPopRate[pn, hn, i] - for cn in range(self.conditionNum): - self.teventConditionRate[pn, hn] += self.eventConditionRate[pn, hn, cn] - self.hapPopRate[pn, hn] = self.tEventHapPopRate[pn, hn] * self.infectious[pn, hn] - self.infectPopRate[pn] += self.hapPopRate[pn, hn] - for cn in range(self.conditionNum): - self.infectPopRateByCondition[pn,cn] += self.teventConditionRate[pn, hn]*self.infectious[pn, hn] - for sn in range(self.susNum): - self.immuneSourcePopRate[pn, sn] = self.suscepCumulTransition[sn]*self.susceptible[pn, sn] - self.immunePopRate[pn] += self.immuneSourcePopRate[pn, sn] - #for cn in range(self.conditionNum): - # for sn in range(self.susNum): - # #self.immuneSourcePopRateByCondition[pn, cn, sn] = self.suscepCumulTransitionByCondition[cn, sn]*self.susceptibleByTypes[pn, cn, sn] - # self.immuneSourcePopRateByCondition[pn, cn, sn] = self.conditionRate[cn]*self.susceptibleByTypes[pn, cn, sn] - # self.immunePopRateByCondition[pn, cn] = self.immuneSourcePopRateByCondition[pn, cn, sn] - for hn in range(self.hapNum): - for cn in range(self.conditionNum): - self.immuneSourcePopRateByCondition[pn, hn, cn] = self.conditionRate[cn] * self.susceptibleByTypes[pn, hn, cn] - self.immunePopRateByCondition[pn, hn] += immuneSourcePopRateByCondition[pn, hn, cn] - self.immunePopRateByConditionOneDem[pn] += self.immunePopRateByCondition[pn, hn] - - - - - - self.popRate[pn] = self.infectPopRate[pn] + self.immunePopRate[pn] + self.immunePopRateByConditionOneDem[pn] - self.massivForThree[pn][0] = self.infectPopRate[pn] - self.massivForThree[pn][1] = self.immunePopRate[pn] - self.massivForThree[pn][2] = self.immunePopRateByCondition[pn] - self.totalRate += self.popRate[pn] - #for cn in range(self.conditionNum): - # self.popRateByCondition[pn, cn] = self.immunePopRateByCondition[pn, cn] + self.infectPopRateByCondition[pn, cn] - # self.totalRateByCondition[cn] = self.popRateByCondition[pn, cn] - - maxEffectiveMigration = np.zeros(self.popNum, dtype=float) - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - if pn1 == pn2: - continue - self.effectiveMigration[pn1, pn2] = 0.0 - for pn3 in range(self.popNum): - self.effectiveMigration[pn1, pn2] += self.migrationRates[pn1, pn3]*self.migrationRates[pn2, pn3]*self.contactDensity[pn3]/self.actualSizes[pn3] - if self.effectiveMigration[pn1, pn2] > maxEffectiveMigration[pn2]: - maxEffectiveMigration[pn2] = self.effectiveMigration[pn1, pn2] - - maxEffectiveBirth = 0.0 - for hn in range(self.currentHapNum): - for sn in range(self.susNum): - if self.bRate[self.numToHap[hn]]*self.susceptibility[self.numToHap[hn], sn] > maxEffectiveBirth: - maxEffectiveBirth = self.bRate[self.numToHap[hn]]*self.susceptibility[self.numToHap[hn], sn] - - self.totalMigrationRate = 0.0 - for pn in range(self.popNum): - self.maxEffectiveBirthMigration[pn] = maxEffectiveMigration[pn]*maxEffectiveBirth - self.migPopRate[pn] = self.maxEffectiveBirthMigration[pn]*self.totalSusceptible[pn]*(self.globalInfectious-self.totalInfectious[pn]) - self.totalMigrationRate += self.migPopRate[pn] - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void AddHaplotype(self, Py_ssize_t nhi, Py_ssize_t pi): # nhi - haplotype - cdef: - bint check = False - Py_ssize_t mem_h, mem_inf - - if self.currentHapNum == self.maxHapNum and self.maxHapNum < self.hapNum: - self.AddMemory() - - for i in range(1, self.currentHapNum+1): - if check: - self.hapToNum[mem_h] += 1 - self.infectious[pi, i], mem_inf = mem_inf, self.infectious[pi, i] - self.numToHap[i], mem_h = mem_h, self.numToHap[i] - if (self.numToHap[i] > nhi or self.numToHap[i] == 0) and self.numToHap[i-1] < nhi: - mem_h = self.numToHap[i] - check = True - self.numToHap[i] = nhi - mem_inf = self.infectious[pi, i] - self.infectious[pi, i] = 0 - self.hapToNum[nhi] = i - - self.currentHapNum += 1 - self.UpdateAllRates() - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef inline double BirthRate(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number - cdef double ps = 0.0 - - for sn in range(self.susNum): - self.susceptHapPopRate[pi, hi, sn] = self.susceptible[pi, sn]*self.susceptibility[self.numToHap[hi], sn] - for pn in range(self.popNum): - ps += self.susceptHapPopRate[pi, hi, sn]*self.migrationRates[pi, pn]*self.migrationRates[pi, pn]*self.contactDensity[pn]/self.actualSizes[pn] - - return self.bRate[self.numToHap[hi]]*ps - - @cython.boundscheck(False) - @cython.wraparound(False) - cpdef void SimulatePopulation(self, Py_ssize_t iterations, Py_ssize_t sample_size, float time, Py_ssize_t attempts): - cdef Py_ssize_t pi - - self.PrepareParameters(iterations) - self.CheckSizes() - - for i in range(attempts): - self.seed = RndmWrapper(seed=(self.user_seed, i)) - if self.totalRate+self.totalMigrationRate != 0.0 and self.globalInfectious != 0: - while (self.events.ptr 100: - self.Restart() - else: - self.good_attempt = i+1 - break - - if self.totalRate == 0.0 or self.globalInfectious == 0: - print('Simulation finished because no infections individuals remain!') - if self.events.ptr>=self.events.size: - print("Achieved maximal number of iterations.") - if self.sCounter>sample_size and sample_size!=-1: - print("Achieved sample size.") - if self.currentTime>time and time != -1: - print("Achieved internal time limit.") - if self.sCounter <= 1: - print('\033[41m{}\033[0m'.format('WARNING!'), 'Simulated less 2 samples, so genealogy will not work!') - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void PrepareParameters(self, Py_ssize_t iterations): - self.events.CreateEvents(iterations) - if self.first_simulation == False: - self.FirstInfection() - for pn in range(self.popNum): - for sn in range(self.susNum): - self.initial_susceptible[pn, sn] = self.susceptible[pn, sn] - for hn in range(self.hapNum): - self.initial_infectious[pn, hn] = self.infectious[pn, hn] - self.first_simulation = True - for pn in range(self.popNum): - self.CheckLockdown(pn) - self.UpdateAllRates() - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void CheckSizes(self): - check = False - list_pop = [] - print('Actual sizes: ', end='') - for pn in range(self.popNum): - print(self.actualSizes[pn], end=' ') - if abs(self.actualSizes[pn]/self.sizes[pn]-1) >= 0.1: - check = True - list_pop.append(str(pn)) - print() - if check: - print('\033[41m{}\033[0m'.format('WARNING!'), 'Actual population size in deme: ', end='') - print(", ".join(list_pop) ) - print("\tis more than 10% different from the population size. The migration probabilities might be unrealistically high.") - print("\tWe recommend to check your model with print_populations() method before proceding to simulation.") - print("\tCheck the documentation file:https://vg-sim.readthedocs.io/en/latest/Migration.html for more details.") - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef inline void SampleTime(self): - cdef double tau = - log(self.seed.uniform()) / (self.totalRate + self.totalMigrationRate) - self.currentTime += tau - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef Py_ssize_t GenerateEvent(self): - cdef: - Py_ssize_t pi, hi, ei - double choose - - self.rn = self.seed.uniform() - choose = self.rn * (self.totalRate + self.totalMigrationRate) - if self.totalRate > choose: - self.rn = choose / self.totalRate - pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) - choose = self.rn * self.popRate[pi] - #if self.immunePopRate[pi] > choose: #delaem fastchoose1 is treh chisel kotory skladvutsy v popRate[pn], popRate[pn] vtoroy argument - ei, self.rn = fastChoose1(self.massivForThree[pn], self.popRate[pn], self.rn) - if ei == IMMUNITY: - self.rn = choose / self.immunePopRate[pi] - self.ImmunityTransition(pi) - elif ei == CONDITION: - self.rn = choose / self.immunePopRate[pi] - hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number - self.ImmunityTransition(pi, hi) - else: - self.rn = (choose - self.immunePopRate[pi]) / self.infectPopRate[pi] - hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number - ei, self.rn = fastChoose1(self.eventHapPopRate[pi, hi], self.tEventHapPopRate[pi, hi], self.rn) - if ei == BIRTH: - self.Birth(pi, hi) - elif ei == DEATH: - self.Death(pi, hi) - elif ei == SAMPLING: - self.Sampling(pi, hi) - else: - self.Mutation(pi, hi) - else: - self.rn = (choose - self.totalRate) / self.totalMigrationRate - pi = self.GenerateMigration() - return pi - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void UpdateRates(self, Py_ssize_t pi, bint infect, bint immune, bint migration): - cdef double tmp - if infect: - self.infectPopRate[pi] = 0.0 - for hn in range(self.currentHapNum): - self.eventHapPopRate[pi, hn, 0] = self.BirthRate(pi, hn) - tmp = (self.eventHapPopRate[pi, hn, 0] + - self.eventHapPopRate[pi, hn, 1] + - self.eventHapPopRate[pi, hn, 2] + - self.eventHapPopRate[pi, hn, 3] ) - self.tEventHapPopRate[pi, hn] = tmp - self.hapPopRate[pi, hn] = self.tEventHapPopRate[pi, hn] * self.infectious[pi, hn] - self.infectPopRate[pi] += self.hapPopRate[pi, hn] - - if immune: - self.immunePopRate[pi] = 0 - for sn in range(self.susNum): - self.immunePopRate[pi] += self.immuneSourcePopRate[pi, sn] - - if infect or immune: - self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi] - self.totalRate = 0.0 - for pn in range(self.popNum): - self.totalRate += self.popRate[pn] - - if migration: - self.totalMigrationRate = 0.0 - for pn in range(self.popNum): - self.migPopRate[pn] = self.maxEffectiveBirthMigration[pn]*self.totalSusceptible[pn]*(self.globalInfectious-self.totalInfectious[pn]) - self.totalMigrationRate += self.migPopRate[pn] - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void ImmunityTransition(self, Py_ssize_t pi): - cdef: - Py_ssize_t ssi, tsi - - ssi, self.rn = fastChoose1(self.immuneSourcePopRate[pi], self.immunePopRate[pi], self.rn) - tsi, self.rn = fastChoose1(self.suscepTransition[ssi], self.suscepCumulTransition[ssi], self.rn) - - self.susceptible[pi, ssi] -= 1 - self.susceptible[pi, tsi] += 1 - self.immuneSourcePopRate[pi, ssi] = self.susceptible[pi, ssi]*self.suscepCumulTransition[ssi] - self.immuneSourcePopRate[pi, tsi] = self.susceptible[pi, tsi]*self.suscepCumulTransition[tsi] - self.UpdateRates(pi, False, True, False) - - self.iCounter += 1 - self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void ConditionTransition(self, Py_ssize_t pi, Py_ssize_t hi): - cdef: - Py_ssize_t ssi, tsi, ci - - ci, self.rn = fastChoose1(self.popRateByCondition[pi], self.totalRateByCondition[pi], self.rn) - - ssi, self.rn = fastChoose1(self.immuneSourcePopRateByCondition[pi, ci], self.immunePopRateByCondition[pi, ci], self.rn) - tsi, self.rn = fastChoose1(self.suscepTransitionByCondition[ci, ssi], self.suscepCumulTransitionByCondition[ci, ssi], self.rn) - - self.susceptibleByTypes[pi, ci, ssi] -= 1 - self.susceptibleByTypes[pi, ci, tsi] += 1 - self.immuneSourcePopRateByCondition[pi, hi, ssi] = self.susceptibleByTypes[pi, ci, ssi]*self.suscepCumulTransitionByCondition[ci, ssi] - self.immuneSourcePopRateByCondition[pi, hi, tsi] = self.susceptibleByTypes[pi, ci, tsi]*self.suscepCumulTransitionByCondition[ci, tsi] - self.UpdateRates(pi, False, True, False) - - self.cCounter += 1 - #self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) - self.events.AddEvent(self.currentTime, CONDITION, ssi, pi, tsi, 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void Birth(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number - cdef double ws = 0.0 - cdef Py_ssize_t ei, ssi, tsi - - #ei, self.rn = fastChoose1(self.eventConditionRate[pi, hi], self.teventConditionRate[pi, hi], self.rn) - #for i in range(self.conditionNum): - #if ei == i: - #if i == 0: - - for sn in range(self.susNum): - ws += self.susceptHapPopRate[pi, hi, sn] - si, self.rn = fastChoose1(self.susceptHapPopRate[pi, hi], ws, self.rn) - - self.NewInfections(pi, si, hi) - self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] - self.UpdateRates(pi, True, True, True) - - self.bCounter += 1 - self.events.AddEvent(self.currentTime, BIRTH, self.numToHap[hi], pi, si, 0) - #else: - # self.rn = self.seed.uniform() - # choose = self.rn * (self.totalRate + self.totalMigrationRate) - # if self.totalRate > choose: - # self.rn = choose / self.totalRate - # pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) - # choose = self.rn * self.popRate[pi] - # if self.immunePopRate[pi] > choose: - # self.rn = choose / self.immunePopRate[pi] - # self.ConditionTransition(pi, hi) - - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void Death(self, Py_ssize_t pi, Py_ssize_t hi, bint add_event = True): # hi - program number - self.NewRecoveries(pi, self.suscType[self.numToHap[hi]], hi) - self.immuneSourcePopRate[pi, self.suscType[self.numToHap[hi]]] = self.susceptible[pi, self.suscType[self.numToHap[hi]]]*self.suscepCumulTransition[self.suscType[self.numToHap[hi]]] - self.UpdateRates(pi, True, True, True) - - if add_event: - self.dCounter += 1 - self.events.AddEvent(self.currentTime, DEATH, self.numToHap[hi], pi, self.suscType[self.numToHap[hi]], 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void Sampling(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number - self.Death(pi, hi, False) - - self.sCounter += 1 - self.events.AddEvent(self.currentTime, SAMPLING, self.numToHap[hi], pi, self.suscType[self.numToHap[hi]], 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void Mutation(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number - cdef: - bint check = True - Py_ssize_t ohi, mi, digit4, AS, DS, nhi - - ohi = self.numToHap[hi] # ohi - haplotype - mi, self.rn = fastChoose1(self.mRate[ohi], self.tmRate[hi], self.rn) - DS, self.rn = fastChoose1(self.hapMutType[ohi, mi], self.hapMutType[ohi, mi, 0] \ - + self.hapMutType[ohi, mi, 1] + self.hapMutType[ohi, mi, 2], self.rn) - nhi = self.Mutate(ohi, mi, DS) - - if self.memory_optimization: - #TODO binary search - for hn in range(self.currentHapNum+1): # hn - program number - if self.numToHap[hn] == nhi: - check = False - break - if check: - self.AddHaplotype(nhi, pi) - if nhi < ohi: - hi += 1 - - self.infectious[pi, self.hapToNum[nhi]] += 1 - self.infectious[pi, hi] -= 1 - self.UpdateRates(pi, True, False, False) - - self.mCounter += 1 - self.events.AddEvent(self.currentTime, MUTATION, ohi, pi, nhi, 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef Py_ssize_t GenerateMigration(self): - cdef: - Py_ssize_t tpi, spi, hi, si - double p_accept - - tpi, self.rn = fastChoose1(self.migPopRate, self.totalMigrationRate, self.rn) - spi, self.rn = fastChoose2_skip(self.totalInfectious, self.globalInfectious-self.totalInfectious[tpi], self.rn, skip=tpi) - hi, self.rn = fastChoose2(self.infectious[spi], self.totalInfectious[spi], self.rn) # hi - program number - si, self.rn = fastChoose2(self.susceptible[tpi], self.totalSusceptible[tpi], self.rn) - - p_accept = self.effectiveMigration[spi, tpi]*self.bRate[self.numToHap[hi]]*self.susceptibility[self.numToHap[hi], si]/self.maxEffectiveBirthMigration[tpi] - if self.rn < p_accept: - self.NewInfections(tpi, si, hi) - self.UpdateRates(tpi, True, True, True) - - self.migPlus += 1 - self.events.AddEvent(self.currentTime, MIGRATION, self.numToHap[hi], spi, si, tpi) - else: - self.migNonPlus += 1 - - return tpi - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void CheckLockdown(self, Py_ssize_t pi): - if self.totalInfectious[pi] > self.startLD[pi]*self.sizes[pi] and self.lockdownON[pi] == 0: - self.contactDensity[pi] = self.contactDensityAfterLockdown[pi] - self.swapLockdown += 1 - self.lockdownON[pi] = 1 - self.UpdateAllRates() - self.loc.AddLockdown(True, pi, self.currentTime) - if self.totalInfectious[pi] < self.endLD[pi]*self.sizes[pi] and self.lockdownON[pi] == 1: - self.contactDensity[pi] = self.contactDensityBeforeLockdown[pi] - self.swapLockdown += 1 - self.lockdownON[pi] = 0 - self.UpdateAllRates() - self.loc.AddLockdown(False, pi, self.currentTime) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void Restart(self): - self.events.ptr = 0 - self.multievents.ptr = 0 - self.bCounter = 0 - self.dCounter = 0 - self.sCounter = 0 - self.mCounter = 0 - self.iCounter = 0 - self.cCounter = 0 - self.migPlus = 0 - self.migNonPlus = 0 - self.currentTime = 0.0 - self.globalInfectious = 0 - for pn in range(self.popNum): - self.totalSusceptible[pn] = 0 - self.totalInfectious[pn] = 0 - for sn in range(self.susNum): - self.susceptible[pn, sn] = self.initial_susceptible[pn, sn] - self.totalSusceptible[pn] += self.initial_susceptible[pn, sn] - for hn in range(self.hapNum): - self.infectious[pn, hn] = self.initial_infectious[pn, hn] - self.totalInfectious[pn] += self.initial_infectious[pn, hn] - self.globalInfectious += self.initial_infectious[pn, hn] - for pn in range(self.popNum): - self.CheckLockdown(pn) - self.UpdateAllRates() - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cpdef GetGenealogy(self, seed): - cdef: - Py_ssize_t ptrTreeAndTime, n1, n2, id1, id2, id3, lbs, lbs_e, ns, nt, idt, ids, lbss, propNum - double p, p1 - vector[vector[vector[Py_ssize_t]]] liveBranchesS, newLineages - vector[vector[Py_ssize_t]] vecint2 - vector[Py_ssize_t] vecint1 - npy_int64[:,::1] liveBranches - - double e_time, me_time - Py_ssize_t e_type_, e_population, e_haplotype, e_newHaplotype, e_newPopulation - Py_ssize_t me_num, me_type_, me_population, me_haplotype, me_newHaplotype, me_newPopulation - Py_ssize_t mt_ev_num, mt_ev_num2 - - propNum = self.popNum*((self.popNum-1)*self.hapNum*self.susNum+self.susNum*(self.susNum-1)+self.hapNum*(2+self.sites*3+self.susNum)) - - if self.sCounter < 2: - print("Less than two cases were sampled...") - print("_________________________________") - sys.exit(0) - else: - if seed != None: - self.seed = RndmWrapper(seed=(seed, 0)) - - ptrTreeAndTime = 0 - self.tree = np.zeros(2 * self.sCounter - 1, dtype=np.int64) - self.tree_pop = np.zeros(2 * self.sCounter - 1, dtype=np.int64) - self.times = np.zeros(2 * self.sCounter - 1, dtype=float) - - for i in range( self.popNum ): - liveBranchesS.push_back(vecint2) - for _ in range( self.hapNum ): - liveBranchesS[i].push_back(vecint1) - - for i in range( self.popNum ): - newLineages.push_back(vecint2) - for _ in range( self.hapNum ): - newLineages[i].push_back(vecint1) - - for i in range( self.popNum ): - for j in range( self.hapNum ): - self.infectiousDelta[i, j] = 0 - - for e_id in range(self.events.ptr-1, -1, -1): - # this event - e_time = self.events.times[e_id] - e_type_ = self.events.types[e_id] - e_haplotype = self.events.haplotypes[e_id] - e_population = self.events.populations[e_id] - e_newHaplotype = self.events.newHaplotypes[e_id] - e_newPopulation = self.events.newPopulations[e_id] - if e_type_ == BIRTH: - lbs = liveBranchesS[e_population][e_haplotype].size() - lbs_e = self.infectious[e_population, self.hapToNum[e_haplotype]] - p = float(lbs)*(float(lbs)-1.0)/ float(lbs_e) / (float(lbs_e) - 1.0) - if self.seed.uniform() < p: - n1 = int(floor( lbs*self.seed.uniform() )) - n2 = int(floor( (lbs-1)*self.seed.uniform() )) - if n2 >= n1: - n2 += 1 - id1 = liveBranchesS[e_population][e_haplotype][n1] - id2 = liveBranchesS[e_population][e_haplotype][n2] - id3 = ptrTreeAndTime - liveBranchesS[e_population][e_haplotype][n1] = id3 - liveBranchesS[e_population][e_haplotype][n2] = liveBranchesS[e_population][e_haplotype][lbs-1] - liveBranchesS[e_population][e_haplotype].pop_back() - self.tree[id1] = id3 - self.tree[id2] = id3 - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = e_population - self.times[ptrTreeAndTime] = e_time - ptrTreeAndTime += 1 - self.infectious[e_population, self.hapToNum[e_haplotype]] -= 1 - elif e_type_ == DEATH: - self.infectious[e_population, self.hapToNum[e_haplotype]] += 1 - elif e_type_ == SAMPLING: - self.infectious[e_population, self.hapToNum[e_haplotype]] += 1 - liveBranchesS[e_population][e_haplotype].push_back( ptrTreeAndTime ) - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = e_population - self.times[ptrTreeAndTime] = e_time - ptrTreeAndTime += 1 - elif e_type_ == MUTATION: - lbs = liveBranchesS[e_population][e_newHaplotype].size() - p = float(lbs)/self.infectious[e_population, e_newHaplotype] - if self.seed.uniform() < p: - n1 = int(floor( lbs*self.seed.uniform() )) - id1 = liveBranchesS[e_population][e_newHaplotype][n1] - liveBranchesS[e_population][e_newHaplotype][n1] = liveBranchesS[e_population][e_newHaplotype][lbs-1] - liveBranchesS[e_population][e_newHaplotype].pop_back() - liveBranchesS[e_population][e_haplotype].push_back(id1) - self.mut.AddMutation(id1, e_haplotype, e_newHaplotype, e_time) - self.infectious[e_population, e_newHaplotype] -= 1 - self.infectious[e_population, e_haplotype] += 1 - elif e_type_ == SUSCCHANGE: - pass - elif e_type_ == MIGRATION: - lbs = liveBranchesS[e_newPopulation][e_haplotype].size() - p = float(lbs)/self.infectious[e_newPopulation, e_haplotype] - if self.seed.uniform() < p: - nt = int(floor( lbs*self.seed.uniform() )) - lbss = liveBranchesS[e_population][e_haplotype].size() - p1 = float(lbss)/self.infectious[e_population, e_haplotype] - if self.seed.uniform() < p1: - ns = int(floor( lbss*self.seed.uniform() )) - idt = liveBranchesS[e_newPopulation][e_haplotype][nt] - ids = liveBranchesS[e_population][e_haplotype][ns] - id3 = ptrTreeAndTime - liveBranchesS[e_population][e_haplotype][ns] = id3 - liveBranchesS[e_newPopulation][e_haplotype][nt] = liveBranchesS[e_newPopulation][e_haplotype][lbs-1] - liveBranchesS[e_newPopulation][e_haplotype].pop_back() - self.tree[idt] = id3 - self.tree[ids] = id3 - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = e_population - self.times[ptrTreeAndTime] = e_time - ptrTreeAndTime += 1 - self.mig.AddMigration(idt, e_time, e_population, e_newPopulation) - else: - liveBranchesS[e_population][e_haplotype].push_back(liveBranchesS[e_newPopulation][e_haplotype][nt]) - liveBranchesS[e_newPopulation][e_haplotype][nt] = liveBranchesS[e_newPopulation][e_haplotype][lbs-1] - liveBranchesS[e_newPopulation][e_haplotype].pop_back() - self.infectious[e_newPopulation, e_haplotype] -= 1 - elif e_type_ == MULTITYPE: - for me_id in range(e_haplotype, e_population): - me_num = self.multievents.num[me_id] - me_time = self.multievents.times[me_id] - me_type_ = self.multievents.types[me_id] - me_haplotype = self.multievents.haplotypes[me_id] - me_population = self.multievents.populations[me_id] - me_newHaplotype = self.multievents.newHaplotypes[me_id] - me_newPopulation = self.multievents.newPopulations[me_id] - - if me_type_ == BIRTH: - lbs = liveBranchesS[me_population][me_haplotype].size() - lbs_e = self.infectious[me_population, me_haplotype] - if me_num == 0 or lbs == 0: - mt_ev_num = 0 - else: - mt_ev_num = random_hypergeometric(self.seed.rng, int(lbs*(lbs-1.0)/2.0), int(lbs_e*(lbs_e-1)/2-lbs*(lbs-1)/2), me_num) - #print("me_num=", me_num, " mt_ev_num", mt_ev_num) - for i in range(mt_ev_num): - n1 = int(floor( lbs*self.seed.uniform() )) - n2 = int(floor( (lbs-1)*self.seed.uniform() )) - if n2 >= n1: - n2 += 1 - id1 = liveBranchesS[me_population][me_haplotype][n1] - id2 = liveBranchesS[me_population][me_haplotype][n2] - id3 = ptrTreeAndTime - newLineages[me_population][me_haplotype].push_back(id3) - if n1 == lbs-1:#TODO: need to check if we really need these if and elif. Most likely - no! - liveBranchesS[me_population][me_haplotype].pop_back() - liveBranchesS[me_population][me_haplotype][n2] = liveBranchesS[me_population][me_haplotype][lbs-2] - liveBranchesS[me_population][me_haplotype].pop_back() - elif n2 == lbs-1: - liveBranchesS[me_population][me_haplotype].pop_back() - liveBranchesS[me_population][me_haplotype][n1] = liveBranchesS[me_population][me_haplotype][lbs-2] - liveBranchesS[me_population][me_haplotype].pop_back() - else: - liveBranchesS[me_population][me_haplotype][n1] = liveBranchesS[me_population][me_haplotype][lbs-1] - liveBranchesS[me_population][me_haplotype].pop_back() - liveBranchesS[me_population][me_haplotype][n2] = liveBranchesS[me_population][me_haplotype][lbs-2] - liveBranchesS[me_population][me_haplotype].pop_back() - self.tree[id1] = id3 - self.tree[id2] = id3 - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = me_population - self.times[ptrTreeAndTime] = me_time - ptrTreeAndTime += 1 - lbs -= 2 - self.infectiousDelta[me_population, me_haplotype] -= me_num - elif me_type_ == DEATH: - self.infectiousDelta[me_population, me_haplotype] += me_num - elif me_type_ == SAMPLING: - self.infectiousDelta[me_population, me_haplotype] += me_num - for i in range(me_num): - #liveBranchesS[me_population][me_haplotype].push_back( ptrTreeAndTime ) - newLineages[me_population][me_haplotype].push_back( ptrTreeAndTime ) - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = me_population - self.times[ptrTreeAndTime] = me_time - ptrTreeAndTime += 1 - elif me_type_ == MUTATION: - lbs = liveBranchesS[me_population][me_newHaplotype].size() - if me_num == 0 or lbs == 0: - mt_ev_num = 0 - else: - mt_ev_num = random_hypergeometric(self.seed.rng, lbs, self.infectious[me_population, me_newHaplotype]-lbs, me_num) - for i in range(mt_ev_num): - n1 = int(floor( lbs*self.seed.uniform() )) - id1 = liveBranchesS[me_population][me_newHaplotype][n1] - liveBranchesS[me_population][me_newHaplotype][n1] = liveBranchesS[me_population][me_newHaplotype][lbs-1] - liveBranchesS[me_population][me_newHaplotype].pop_back() - #liveBranchesS[me_population][me_haplotype].push_back(id1) - newLineages[me_population][me_haplotype].push_back( id1 ) - self.mut.AddMutation(id1, me_haplotype, me_newHaplotype, me_time) - lbs-=1 - self.infectiousDelta[me_population, me_newHaplotype] -= me_num - self.infectiousDelta[me_population, me_haplotype] += me_num - elif me_type_ == SUSCCHANGE: - pass - elif me_type_ == MIGRATION: - lbs = liveBranchesS[me_newPopulation][me_haplotype].size() - if me_num == 0 or lbs == 0: - mt_ev_num = 0 - else: - mt_ev_num = random_hypergeometric(self.seed.rng, lbs, self.infectious[me_newPopulation, me_haplotype]-lbs, me_num) - #for i in range(mt_ev_num): - lbss = liveBranchesS[me_population][me_haplotype].size() - if mt_ev_num == 0 or lbss == 0: - mt_ev_num2 = 0 - else: - mt_ev_num2 = random_hypergeometric(self.seed.rng, lbss, self.infectious[me_population, me_haplotype]-lbss, mt_ev_num) - #print("me_num: ", me_num, "mt_ev_num: ", mt_ev_num, "mt_ev_num2: ", mt_ev_num2) - for i in range(mt_ev_num2): - nt = int(floor( lbs*self.seed.uniform() )) - ns = int(floor( lbss*self.seed.uniform() )) - idt = liveBranchesS[me_newPopulation][me_haplotype][nt] - ids = liveBranchesS[me_population][me_haplotype][ns] - id3 = ptrTreeAndTime - liveBranchesS[me_population][me_haplotype][ns] = liveBranchesS[me_population][me_haplotype][ liveBranchesS[me_population][me_haplotype].size()-1 ] - liveBranchesS[me_population][me_haplotype].pop_back() - liveBranchesS[me_newPopulation][me_haplotype][nt] = liveBranchesS[me_newPopulation][me_haplotype][lbs-1] - liveBranchesS[me_newPopulation][me_haplotype].pop_back() - newLineages[me_population][me_haplotype].push_back( id3 ) - self.tree[idt] = id3 - self.tree[ids] = id3 - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = me_population - self.times[ptrTreeAndTime] = me_time - ptrTreeAndTime += 1 - self.mig.AddMigration(idt, me_time, me_population, me_newPopulation) - lbss -= 1 - lbs -= 1 - for i in range(mt_ev_num-mt_ev_num2): - nt = int(floor( lbs*self.seed.uniform() )) - newLineages[me_population][me_haplotype].push_back(liveBranchesS[me_newPopulation][me_haplotype][nt]) - liveBranchesS[me_newPopulation][me_haplotype][nt] = liveBranchesS[me_newPopulation][me_haplotype][lbs-1] - liveBranchesS[me_newPopulation][me_haplotype].pop_back() - lbs -= 1 - self.infectiousDelta[me_newPopulation, me_haplotype] -= me_num - else: - print("Unknown event type: ", me_type_) - print("_________________________________") - sys.exit(0) - for pi in range(self.popNum): - for hi in range(self.hapNum): - self.infectious[pi, hi] += self.infectiousDelta[pi, hi] - self.infectiousDelta[pi, hi] = 0 - while newLineages[pi][hi].size() > 0: - liveBranchesS[pi][hi].push_back( newLineages[pi][hi][newLineages[pi][hi].size()-1] ) - newLineages[pi][hi].pop_back() - #for i in range(newLineages[pi][hi].size()-1, -1, -1): - # liveBranchesS[pi][hi].push_back(newLineages[pi][hi][i]) - # newLineages[pi][hi].pop_back() - else: - print("Unknown event type: ", e_type_) - print("_________________________________") - sys.exit(0) - for i in range(self.sCounter * 2 - 2): - if self.tree_pop[self.tree[i]] != self.tree_pop[i]: - self.mig.AddMigration(i, self.times[i], self.tree_pop[self.tree[i]], self.tree_pop[i]) - - #for i in range(self.sCounter * 2 - 1): - # print(self.tree[i], end=" ") - #print("") - #deg = [0 for i in range(self.sCounter * 2 - 1)] - #for i in range(self.sCounter * 2 - 1): - # deg[self.tree[i]] += 1 - #for i in range(self.sCounter * 2 - 1): - # print(deg[i], end=" ") - - #self.CheckTree() - - - def print_basic_parameters(self): - print("*****************") - print("***Basic rates***") - print("*****************") - table = PrettyTable() - - field = ["H", "TR", "RR", "SR", "ST"] - for s in range(self.sites): - field.append("M" + str(s)) - field.append("MW" + str(s)) - table.field_names = field - for hn in range(self.currentHapNum): - list = ["\n" + self.calculate_string(hn), "\n" + str(self.bRate[hn]), "\n" + str(self.dRate[hn]), "\n" + str(self.sRate[hn]), "\n" + str(self.suscType[hn])] - for s in range(self.sites): - list.append("\n" + str(self.mRate[hn, s])) - list.append(self.create_mutations(hn, s)) - table.add_row(list) - - print(table) - print("Legend:") - print("H - haplotype") - print("TR - transmission rate") - print("RR - recovery rate") - print("SR - sampling rate") - print("ST - susceptibility type") - for s in range(self.sites): - print("M" + str(s) + " - " + str(s) + " mutation rate") - print("MW" + str(s) + " - " + str(s) + " mutation weights") - print() - - def GetCurrentIndividuals(self): - current_susceptible, current_infectious = [[0 for _ in range(self.susNum)] for _ in range(self.popNum)], [[0 for _ in range(self.hapNum)] for _ in range(self.popNum)] - - for pi in range(self.popNum): - for si in range(self.susNum): - current_susceptible[pi][si] = self.initial_susceptible[pi, si] - for hi in range(self.hapNum): - current_infectious[pi][hi] = self.initial_infectious[pi, hi] - for i in range(self.events.ptr): - if self.events.types[i] == BIRTH: - current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] -= 1 - current_infectious[self.events.populations[i]][self.events.haplotypes[i]] += 1 - elif self.events.types[i] == DEATH: - current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 - current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 - elif self.events.types[i] == SAMPLING: - current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 - current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 - elif self.events.types[i] == MUTATION: - current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 - current_infectious[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 - elif self.events.types[i] == SUSCCHANGE: - current_susceptible[self.events.populations[i]][self.events.haplotypes[i]] -= 1 - current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 - else: - current_susceptible[self.events.newPopulations[i]][self.events.newHaplotypes[i]] -= 1 - current_infectious[self.events.newPopulations[i]][self.events.haplotypes[i]] += 1 - - return [current_susceptible, current_infectious] - - def print_populations(self): - current_susceptible, current_infectious = self.GetCurrentIndividuals() - - print("*****************") - print("***Populations***") - print("*****************") - table_populations = PrettyTable() - - table_populations.field_names = ["ID", "Size", 'Actual size', "CD",'CDBLC', "CDALD", "SLD", "ELD", "SM"] - for pn in range(self.popNum): - table_populations.add_row([pn, self.sizes[pn], self.actualSizes[pn], self.contactDensity[pn], self.contactDensityBeforeLockdown[pn], self.contactDensityAfterLockdown[pn], self.startLD[pn], self.endLD[pn], self.samplingMultiplier[pn]]) - - print(table_populations) - print("Legend:") - print("ID - number of population") - print("Size - size of population") - print("Actual size - actual size of population") - print("CD - contact density") - print("CDBLD - contact density without lockdown") - print("CDALD - contact density at lockdown") - print("SLD - start of lockdown") - print("ELD - end of lockdown") - print("SM - sampling multiplier") - print() - - print("*****************") - print("***Susceptible***") - print("*****************") - table_susceptible = PrettyTable() - - field = ["ST\\ID"] - for pn in range(self.popNum): - field.append(pn) - for sn in range(self.susNum): - row = [sn] - for pn in range(self.popNum): - row.append(current_susceptible[pn][sn]) - table_susceptible.add_row(row) - table_susceptible.field_names = field - - print(table_susceptible) - print("Legend:") - print("ID - ID population") - print("ST - susceptibility type") - print() - - print("****************") - print("***Infectious***") - print("****************") - table_infectious = PrettyTable() - - field = ["H\\ID"] - for pn in range(self.popNum): - field.append(pn) - for hn in range(self.hapNum): - row = [self.calculate_string(hn)] - for pn in range(self.popNum): - row.append(current_infectious[pn][hn]) - table_infectious.add_row(row) - table_infectious.field_names = field - - print(table_infectious) - print("Legend:") - print("ID - ID population") - print("H - haplotype") - print() - - print("**********************") - print("***Migration matrix***") - print("**********************") - table_migration = PrettyTable() - - field = ["S\\T"] - for pn1 in range(self.popNum): - field.append(pn1) - row = [pn1] - for pn2 in range(self.popNum): - row.append(self.migrationRates[pn1, pn2]) - table_migration.add_row(row) - table_migration.field_names = field - - print(table_migration) - print("Legend:") - print("S - ID source population") - print("T - ID target population") - print() - - def print_immunity_model(self): - print("********************") - print("***Immunity model***") - print("********************") - table_immunity = PrettyTable() - - field = ["H\\ST"] - for sn in range(self.susNum): - field.append("S" + str(sn)) - table_immunity.field_names = field - for hn in range(self.currentHapNum): - row = [self.calculate_string(hn)] - for sn in range(self.susNum): - row.append(self.susceptibility[hn, sn]) - table_immunity.add_row(row) - - print(table_immunity) - print("Legend:") - print("H - haplotype") - print("ST - susceptibility type") - print() - - print("*******************************") - print("***Immunity transition rates***") - print("*******************************") - table_immunity_transition = PrettyTable() - - field = ["ID"] - for sn1 in range(self.susNum): - field.append(sn1) - row = [sn1] - for sn2 in range(self.susNum): - row.append(self.suscepTransition[sn1, sn2]) - table_immunity_transition.add_row(row) - table_immunity_transition.field_names = field - - print(table_immunity_transition) - print("Legend:") - print("ID - ID susceptibility type") - print() - - - def Error(self, text): - print(text) - sys.exit(1) - - def create_list_haplotypes(self, haplotype): - haplotypes = [haplotype] - for s in range(self.sites): - for i in range(len(haplotypes)): - haplotype_old = haplotypes[i] - if haplotype_old[s] == "*": - haplotype = haplotype_old.replace("*", "A", 1) - haplotypes.append(haplotype) - haplotype = haplotype_old.replace("*", "T", 1) - haplotypes.append(haplotype) - haplotype = haplotype_old.replace("*", "C", 1) - haplotypes.append(haplotype) - haplotype = haplotype_old.replace("*", "G", 1) - haplotypes.append(haplotype) - for i in range(len(haplotypes)-1, -1, -1): - if haplotypes[i].count("*") != 0: - haplotypes.remove(haplotypes[i]) - for i in range(len(haplotypes)): - haplotypes[i] = self.calculate_haplotype(haplotypes[i]) - - return haplotypes - - def create_mutations(self, haplotype, site): - hap = self.calculate_string(haplotype) - haplotypes = [hap[:site] + "A" + hap[site+1:], hap[:site] + "T" + hap[site+1:], hap[:site] + "C" + hap[site+1:], hap[:site] + "G" + hap[site+1:]] - for i in range(4): - if haplotypes[i] == hap: - haplotypes.remove(haplotypes[i]) - haplotypes.append(hap) - break - color_hap=[] - for hapl in haplotypes: - a = "" - for s in range(self.sites): - if s == site: - a = a + "\033[31m{}\033[0m" .format(hapl[s:s+1]) - else: - a = a + hapl[s:s+1] - color_hap.append(a) - string = color_hap[3] + "->" + color_hap[0] + ": " + str(self.hapMutType[haplotype, site, 0]) + "\n" + color_hap[3] + "->" + color_hap[1] + ": " + str(self.hapMutType[haplotype, site, 1]) + "\n" + color_hap[3] + "->" + color_hap[2] + ": " + str(self.hapMutType[haplotype, site, 2]) + "\n" - return string - - def calculate_string(self, hapNum): - letters = ["A", "T", "C", "G"] - string = "" - for s in range(self.sites): - string = letters[hapNum%4] + string - hapNum = hapNum // 4 - return string - - def calculate_haplotype(self, string): - string = string[::-1] - haplotype = 0 - for s in range(self.sites): - if string[s]=="T": - haplotype += (4**s) - elif string[s]=="C": - haplotype += 2*(4**s) - elif string[s]=="G": - haplotype += 3*(4**s) - return haplotype - - def calculate_allele(self, haplotype, site): - for _ in range(self.sites-site): - allele = haplotype % 4 - haplotype = haplotype // 4 - return allele - - def get_sites(self): - return self.sites - - def get_hapNum(self): - return self.hapNum - - def get_popNum(self): - return self.popNum - - def get_susNum(self): - return self.susNum - - - def check_amount(self, amount, smth): - if isinstance(amount, int) == False: - raise TypeError('Incorrect type of ' + smth + '. Type should be int.') - elif amount < 0: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_rate_1(self, value, smth): - if isinstance(value, (int, float)) == False: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') - elif value < 0: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_rate_none_1(self, value, smth): - if isinstance(value, (int, float)) == False and value != None: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') - elif isinstance(value, (int, float)): - if value < 0: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_haplotype(self, haplotype): - if isinstance(haplotype, (int, str)) == False and haplotype != None: - raise TypeError('Incorrect type of haplotype. Type should be int or str or None.') - elif isinstance(haplotype, int): - if haplotype < 0 or haplotype >= self.hapNum: - raise IndexError('There are no such haplotype!') - elif isinstance(haplotype, str): - if haplotype.count("A") + haplotype.count("T") + haplotype.count("C") + haplotype.count("G") + haplotype.count("*") != self.sites: - raise ValueError('Incorrect haplotype. Haplotype should contain only \"A\", \"T\", \"C\", \"G\", \"*\" and lenght of haplotype should be equal number of mutations sites.') - - def check_rate_2(self, value, smth): - if isinstance(value, (int, float)) == False: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') - elif value < 0 and value > 1: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_rate_none_2(self, value, smth): - if isinstance(value, (int, float)) == False and value != None: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') - elif isinstance(value, (int, float)): - if value < 0 and value > 1: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_list(self, data, smth, lenght): - if isinstance(data, list) == False and data != None: - raise TypeError('Incorrect type of ' + smth + '. Type should be list or None.') - elif isinstance(data, list): - if len(data) != lenght: - raise ValueError('Incorrect lenght of ' + smth + '. Lenght should be equal ' + str(lenght) + '.') - - def check_index(self, value, edge, smth): - if isinstance(value, int) == False and value != None: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or None.') - elif isinstance(value, int): - if value < 0 or value >= edge: - raise IndexError('There are no such ' + smth + '!') - - def check_amount_sus(self, amount, source_type, target_type, population): - if self.initial_susceptible[population, source_type] - amount < 0: - raise ValueError('Number of susceptible minus amount should be more or equal 0.') - if self.initial_susceptible[population, target_type] + amount > self.sizes[population]: - raise ValueError('Number of susceptible plus amount should be equal or less population size.') - - def check_amount_inf(self, amount, source_type, target_haplotype, population): - if self.initial_susceptible[population, source_type] - amount < 0: - raise ValueError('Number of susceptible minus amount should be more or equal 0.') - if self.initial_infectious[population, target_haplotype] + amount > self.sizes[population]: - raise ValueError('Number of infectious plus amount should be equal or less population size.') - - def check_mig_rate(self): - for pn1 in range(self.popNum): - summa = 0 - self.migrationRates[pn1, pn1] = 1.0 - for pn2 in range(self.popNum): - if pn1 != pn2: - summa += self.migrationRates[pn1, pn2] - self.migrationRates[pn1, pn1] -= self.migrationRates[pn1, pn2] - if summa > 1: - raise ValueError('Incorrect the sum of migration probabilities. The sum of migration probabilities from each population should be equal or less 1.') - - - def set_initial_haplotype(self, amount): - if self.memory_optimization == False: - raise ValueError('Incorrect value of memory optimization. Value should be equal \'True\' for work this function.') - self.check_amount(amount, 'amount of initial haplotype') - - if amount > self.hapNum: - self.maxHapNum = self.hapNum - else: - self.maxHapNum = amount - - def set_step_haplotype(self, amount): - if self.memory_optimization == False: - raise ValueError('Incorrect value of memory optimization. Value should be equal \'True\' for work this function.') - self.check_amount(amount, 'amount of step haplotype') - - if amount > self.hapNum: - self.addMemoryNum = self.hapNum - else: - self.addMemoryNum = amount - - def set_transmission_rate(self, rate, haplotype): - self.check_rate_1(rate, 'transmission') - self.check_haplotype(haplotype) - - # for hn in self.get_list_haplotype(haplotype): - # self.bRate[hn] = rate - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for haplotype in haplotypes: - self.bRate[haplotype] = rate - elif isinstance(haplotype, int): - self.bRate[haplotype] = rate - elif haplotype == None: - for hn in range(self.hapNum): - self.bRate[hn] = rate - - def set_recovery_rate(self, rate, haplotype): - self.check_rate_1(rate, 'recovery') - self.check_haplotype(haplotype) - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for haplotype in haplotypes: - self.dRate[haplotype] = rate - elif isinstance(haplotype, int): - self.dRate[haplotype] = rate - elif haplotype == None: - for hn in range(self.hapNum): - self.dRate[hn] = rate - - def set_sampling_rate(self, rate, haplotype): - self.check_haplotype(haplotype) - - if self.sampling_probability == True: - self.check_rate_2(rate, 'sampling probability') - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for haplotype in haplotypes: - deathRate = self.dRate[haplotype] + self.sRate[haplotype] - self.dRate[haplotype] = (1 - rate) * deathRate - self.sRate[haplotype] = rate * deathRate - elif isinstance(haplotype, int): - deathRate = self.dRate[haplotype] + self.sRate[haplotype] - self.dRate[haplotype] = (1 - rate) * deathRate - self.sRate[haplotype] = rate * deathRate - elif haplotype == None: - for hn in range(self.hapNum): - deathRate = self.dRate[hn] + self.sRate[hn] - self.dRate[hn] = (1 - rate) * deathRate - self.sRate[hn] = rate * deathRate - - elif self.sampling_probability == False: - self.check_rate_1(rate, 'sampling') - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for haplotype in haplotypes: - self.sRate[haplotype] = rate - elif isinstance(haplotype, int): - self.sRate[haplotype] = rate - elif haplotype == None: - for hn in range(self.hapNum): - self.sRate[hn] = rate - - def set_mutation_rate(self, rate, probabilities, haplotype, mutation): - self.check_rate_none_1(rate, 'mutation rate') - self.check_list(probabilities, 'probabilities list', 4) - if isinstance(probabilities, list): - for i in range(4): - self.check_rate_1(probabilities[i], 'mutation probabilities') - self.check_haplotype(haplotype) - self.check_index(mutation, self.sites, 'mutation site') - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - if isinstance(mutation, int): - for hn in haplotypes: - self.set_probabilities(hn, mutation, rate, probabilities) - elif mutation == None: - for hn in haplotypes: - for s in range(self.sites): - self.set_probabilities(hn, s, rate, probabilities) - elif isinstance(haplotype, int): - if isinstance(mutation, int): - self.set_probabilities(haplotype, mutation, rate, probabilities) - elif mutation == None: - for s in range(self.sites): - self.set_probabilities(haplotype, s, rate, probabilities) - elif haplotype == None: - if isinstance(mutation, int): - for hn in range(self.hapNum): - self.set_probabilities(hn, mutation, rate, probabilities) - elif mutation == None: - for hn in range(self.hapNum): - for s in range(self.sites): - self.set_probabilities(hn, s, rate, probabilities) - - def set_probabilities(self, haplotype, mutation, rate, probabilities): - if isinstance(rate, (int, float)): - self.mRate[haplotype, mutation] = rate - if isinstance(probabilities, list): - probabilities_allele = list(probabilities) - del probabilities_allele[self.calculate_allele(haplotype, mutation)] - if sum(probabilities_allele) == 0: - raise ValueError('Incorrect probabilities list. The sum of three elements without mutation allele should be more 0.') - self.hapMutType[haplotype, mutation, 0] = probabilities_allele[0] - self.hapMutType[haplotype, mutation, 1] = probabilities_allele[1] - self.hapMutType[haplotype, mutation, 2] = probabilities_allele[2] - - def set_susceptibility_type(self, susceptibility_type, haplotype): - if isinstance(susceptibility_type, int) == False: - raise TypeError('Incorrect type of susceptibility type. Type should be int.') - elif susceptibility_type < 0 or susceptibility_type >= self.susNum: - raise IndexError('There are no such susceptibility type!') - self.check_haplotype(haplotype) - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for hn in haplotypes: - self.suscType[hn] = susceptibility_type - elif isinstance(haplotype, int): - self.suscType[haplotype] = susceptibility_type - elif haplotype == None: - for hn in range(self.hapNum): - self.suscType[hn] = susceptibility_type - - def set_susceptibility(self, rate, haplotype, susceptibility_type): - self.check_rate_1(rate, 'rate of susceptibility') - self.check_haplotype(haplotype) - self.check_index(susceptibility_type, self.susNum, 'susceptibility type') - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - if isinstance(susceptibility_type, int): - for hn in haplotypes: - self.susceptibility[hn, susceptibility_type] = rate - else: - for hn in haplotypes: - for sn in range(self.susNum): - self.susceptibility[hn, sn] = rate - elif isinstance(haplotype, int): - if isinstance(susceptibility_type, int): - self.susceptibility[haplotype, susceptibility_type] - else: - for sn in range(self.susNum): - self.susceptibility[haplotype, sn] = rate - else: - if isinstance(susceptibility_type, int): - for hn in range(self.hapNum): - self.susceptibility[hn, susceptibility_type] = rate - else: - for hn in range(self.hapNum): - for sn in range(self.susNum): - self.susceptibility[hn, sn] = rate - - def set_immunity_transition(self, rate, source, target): - self.check_rate_1(rate, 'immunity transition rate') - self.check_index(source, self.susNum, 'susceptibility type') - self.check_index(target, self.susNum, 'susceptibility type') - - if isinstance(source, int): - if isinstance(target, int): - if source == target: - raise ValueError('Source and target susceptibility type shouldn\'t be equal!') - self.suscepTransition[source, target] = rate - else: - for sn1 in range(self.susNum): - if sn1 != target: - self.suscepTransition[sn1, target] = rate - else: - if isinstance(target, int): - for sn2 in range(self.susNum): - if source != sn2: - self.suscepTransition[source, sn2] = rate - else: - for sn1 in range(self.susNum): - for sn2 in range(self.susNum): - if sn1 != sn2: - self.suscepTransition[sn1, sn2] = rate - - def set_population_size(self, amount, population): - if self.first_simulation == True: - raise ValueError('Changing population size is available only before first simulation!') - self.check_amount(amount, 'population size') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.sizes[population] = amount - for sn in range(self.susNum): - self.initial_susceptible[population, 0] = amount - elif population == None: - for pn in range(self.popNum): - self.sizes[pn] = amount - for sn in range(self.susNum): - self.initial_susceptible[pn, 0] = amount - - def set_susceptible(self, amount, source_type, target_type, population): - if self.first_simulation: - raise ValueError('This function is available only before first simulation!') - self.check_amount(amount) - self.check_index(source_type, self.susNum, 'susceptibility type') - self.check_index(target_type, self.susNum, 'susceptibility type') - if source_type == target_type: - raise ValueError('Source and target susceptibility type shouldn\'t be equal!') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.check_amount_sus(amount, source_type, target_type, population) - - self.initial_susceptible[population, source_type] -= amount - self.initial_susceptible[population, target_type] += amount - elif population == None: - for pn in range(self.popNum): - self.check_amount_sus(amount, source_type, target_type, pn) - - self.initial_susceptible[pn, source_type] -= amount - self.initial_susceptible[pn, target_type] += amount - - def set_infectious(self, amount, source_type, target_haplotype, population): - if self.first_simulation: - raise ValueError('This function is available only before first simulation!') - self.check_amount(amount) - self.check_index(source_type, self.susNum, 'susceptibility type') - self.check_haplotype(target_haplotype) - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.check_amount_inf(amount, source_type, target_haplotype, population) - - self.initial_susceptible[population, source_type] -= amount - self.initial_infectious[population, target_haplotype] += amount - elif population == None: - for pn in range(self.popNum): - self.check_amount_inf(amount, source_type, target_haplotype, pn) - - self.initial_susceptible[pn, source_type] -= amount - self.initial_infectious[pn, target_haplotype] += amount - - def set_contact_density(self, value, population): - self.check_rate_1(value, 'contact density') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.contactDensity[population] = value - self.contactDensityBeforeLockdown[population] = value - elif population == None: - for pn in range(self.popNum): - self.contactDensity[pn] = value - self.contactDensityBeforeLockdown[pn] = value - - def set_npi(self, parameters, population): - self.check_list(parameters, 'parameters', 3) - if isinstance(parameters, list): - self.check_rate_1(parameters[0], 'first element of parameters') - self.check_rate_2(parameters[1], 'second element of parameters') - self.check_rate_2(parameters[2], 'third element of parameters') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.contactDensityAfterLockdown[population] = parameters[0] - self.startLD[population] = parameters[1] - self.endLD[population] = parameters[2] - elif population == None: - for pn in range(self.popNum): - self.contactDensityAfterLockdown[pn] = parameters[0] - self.startLD[pn] = parameters[1] - self.endLD[pn] = parameters[2] - - def set_sampling_multiplier(self, multiplier, population): - self.check_rate_1(multiplier, 'sampling multiplier') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.samplingMultiplier[population] = multiplier - elif population == None: - for pn in range(self.popNum): - self.samplingMultiplier[pn] = multiplier - - def set_migration_probability(self, probability, total_probability, source, target): - self.check_rate_none_2(probability, 'probability') - self.check_rate_none_2(total_probability, 'total probability') - self.check_index(source, self.popNum, 'population') - self.check_index(target, self.popNum, 'population') - - if isinstance(probability, float): - if isinstance(source, int): - if isinstance(target, int): - if source == target: - raise IndexError('Source and target population shouldn\'t be equal!') - - self.migrationRates[source, target] = probability - else: - for pn2 in range(self.popNum): - if source != pn2: - self.migrationRates[source, pn2] = probability - else: - if isinstance(target, int): - for pn1 in range(self.popNum): - if pn1 != target: - self.migrationRates[pn1, target] = probability - else: - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - if pn1 != pn2: - self.migrationRates[pn1, pn2] = probability - - self.check_mig_rate() - elif isinstance(total_probability, float): - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - if pn1 != pn2: - self.migrationRates[pn1, pn2] = total_probability/(self.popNum-1) - - self.check_mig_rate() - else: - raise TypeError('Incorrect type of probability or total_probability. Type should be float.') - - - def set_chain_events(self, name_file): - if isinstance(name_file, str) == False: - self.Error('#TODO') - tokens = np.load(name_file + '.npy') - #TODO Check sizes and resize if needed - self.size = tokens.shape[1] - self.ptr = tokens.shape[1] - - self.events.times = tokens[0] - self.events.types = tokens[1] - self.events.haplotypes = tokens[2] - self.events.populations = tokens[3] - self.events.newHaplotypes = tokens[4] - self.events.newPopulations = tokens[5] - - def set_settings(self, file_template): - pass - - - def output_tree_mutations(self): - if self.tree.shape[0] == 1: - print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') - sys.exit(1) - mut = [[], [], [], [], []] - for i in range(self.mut.nodeId.size()): - mut[0].append(self.mut.nodeId[i]) - mut[1].append(self.mut.AS[i]) - mut[2].append(self.mut.site[i]) - mut[3].append(self.mut.DS[i]) - mut[4].append(self.mut.time[i]) - times_dict = {self.events.times[i]: i for i in range(len(self.events.times))} - populations = {} - for time in self.times: - populations[time] = self.events.populations[times_dict[time]] - - return self.tree, self.times, mut, populations - - def output_migrations(self, name_file, file_path): - if self.tree.shape[0] == 1: - print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') - sys.exit(1) - if file_path != None: - f_mig = open(file_path + '/' + name_file + '.tsv', 'w') - else: - f_mig = open(name_file + '.tsv', 'w') - f_mig.write("Node\tTime\tOld_population\tNew_population\n") - for i in range(self.mig.nodeId.size()): - f_mig.write(str(self.mig.nodeId[i]) + '\t' + str(self.mig.time[i]) + '\t' + str(self.mig.oldPop[i]) + '\t' + str(self.mig.newPop[i]) + "\n") - f_mig.close() - - def output_sample_data(self): - time, pop, hap = [], [], [] - for i in range(self.events.ptr): - if self.events.types[i] == SAMPLING: - time.append(self.events.times[i]) - pop.append(self.events.populations[i]) - hap.append(self.events.haplotypes[i]) - return time, pop, hap - - def output_epidemiology_timelines(self, step_num, output_file): - time_points = [i*self.currentTime/step_num for i in range(step_num+1)] - suscepDate = np.zeros((self.popNum, self.susceptible_num), dtype=np.int64) - hapDate = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - for i in range(self.popNum): - for sn in range(self.susceptible_num): - if sn == 0: - suscepDate[i, sn] = self.sizes[i] - else: - suscepDate[i, sn] = 0 - for hn in range(self.hapNum): - hapDate[i, hn] = 0 - hapDate[0, 0] += 1 - suscepDate[0, 0] -= 1 - if output_file == True: - if not os.path.isdir("logs"): - os.mkdir("logs") - logDynamics = [] - for i in range(self.popNum): - logDynamics.append(open('logs/PID' + str(i) + '.log', 'w')) - logDynamics[i].write("time") - for sn in range(self.susceptible_num): - logDynamics[i].write(" S" + str(sn)) - for hn in range(self.hapNum): - logDynamics[i].write(" H" + str(hn)) - logDynamics[i].write("\n") - else: - log = dict() - log["time"] = list() - for i in range(self.popNum): - log["P" + str(i)] = dict() - for j in range(self.susceptible_num): - log["P" + str(i)]["S" + str(j)] = list() - for j in range(self.hapNum): - log["P" + str(i)]["H" + str(j)] = list() - - point = 0 - for j in range(self.events.ptr): - if self.events.types[j] == BIRTH: - hapDate[self.events.populations[j], self.events.haplotypes[j]] += 1 - suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] -= 1 - elif self.events.types[j] == DEATH: - hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 - suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 - elif self.events.types[j] == SAMPLING: - hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 - suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 - elif self.events.types[j] == MUTATION: - hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 - hapDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 - elif self.events.types[j] == SUSCCHANGE: - suscepDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 - suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 - elif self.events.types[j] == MIGRATION: - suscepDate[self.events.newPopulations[j], self.events.newHaplotypes[j]] -= 1 - hapDate[self.events.newPopulations[j], self.events.haplotypes[j]] += 1 - elif self.events.types[j] == MULTITYPE: - pass - #TODO - if time_points[point] <= self.events.times[j]: - if output_file == True: - for i in range(self.popNum): - logDynamics[i].write(str(time_points[point]) + " ") - for k in range(self.susceptible_num): - logDynamics[i].write(str(suscepDate[i, k]) + " ") - for k in range(self.hapNum): - logDynamics[i].write(str(hapDate[i, k]) + " ") - logDynamics[i].write("\n") - point += 1 - else: - log["time"].append(time_points[point]) - for i in range(self.popNum): - for j in range(self.susceptible_num): - log["P" + str(i)]["S" + str(j)].append(suscepDate[i, j]) - for j in range(self.hapNum): - log["P" + str(i)]["H" + str(j)].append(hapDate[i, j]) - point += 1 - - if output_file == True: - for i in range(self.popNum-1, -1, -1): - logDynamics[i].close() - else: - return log - - def output_chain_events(self, name_file): - np.save(name_file, [self.events.times, self.events.types, self.events.haplotypes, self.events.populations, self.events.newHaplotypes, self.events.newPopulations]) - - def output_settings(self, file_template): - if isinstance(file_template, str) == False: - self.Error('#TODO') - if not os.path.isdir(file_template): - os.mkdir(file_template) - os.chdir(file_template) - comand = 'Command line command: ' - with open(file_template + ".rt", "w") as file: - file.write("#Rates_format_version 0.0.1\nH B D S") - for s in range(self.sites): - file.write(" M" + str(s)) - file.write("\n") - for hn in range(self.hapNum): - file.write(self.calculate_string(hn) + " " + str(self.bRate[hn]) + " " + str(self.dRate[hn]) + " " + str(self.sRate[hn]) + ' ') - for s in range(self.sites): - file.write(str(self.mRate[hn, s]) + "," + str(self.hapMutType[hn, s, 0]) + "," + str(self.hapMutType[hn, s, 1]) + "," + str(self.hapMutType[hn, s, 2]) + " ") - file.write("\n") - comand += (file_template + '/' + file_template + '.rt ') - - with open(file_template + ".pp", "w") as file: - file.write("#Population_format_version 0.0.1\nid size contactDensity conDenAfterLD startLD endLD samplingMulriplier\n") - for pn in range(self.popNum): - file.write(str(pn) + " " + str(self.sizes[pn]) + " " + str(self.contactDensity[pn]) + " " + str(self.contactDensityAfterLockdown[pn]) + "," + str(self.startLD[pn]) + "," + str(self.endLD[pn]) + " " + str(self.samplingMultiplier[pn]) + "\n") - comand += ('-pm ' + file_template + '/' + file_template + '.pp ') - - with open(file_template + ".mg", "w") as file: - file.write("#Migration_format_version 0.0.1\n") - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - file.write(str(self.migrationRates[pn1, pn2]) + " ") - file.write("\n") - comand += (file_template + '/' + file_template + '.mg ') - - with open(file_template + ".su", "w") as file: - file.write("#Susceptibility_format_version 0.0.1\nH T") - for sn in range(self.susNum): - file.write(" S" + str(sn)) - file.write("\n") - for hn in range(self.hapNum): - file.write(self.calculate_string(hn) + " " + str(self.suscType[hn])) - for sn in range(self.susNum): - file.write(" " + str(self.susceptibility[hn, sn])) - file.write("\n") - comand += ('-su ' + file_template + '/' + file_template + '.su ') - - with open(file_template + ".st", "w") as file: - file.write("#Susceptibility_format_version 0.0.1\n") - for sn1 in range(self.susNum): - for sn2 in range(self.susNum): - file.write(str(self.suscepTransition[sn1, sn2]) + " ") - file.write("\n") - comand += ('-st ' + file_template + '/' + file_template + '.st ') - print(comand) - os.chdir('../') - - def get_ts_object(self): - tc = tskit.TableCollection() - tc.sequence_length = self.sites + 1.0 - - for i in range(self.mig.nodeId.size()): - mig_nodeId, mig_time, mig_oldPop, mig_newPop = self.mig.get_migration(i) - tc.migrations.add_row(0.0, 1.0, mig_nodeId, mig_oldPop, mig_newPop, self.times[0] - mig_time) - - for i in range(self.popNum): - tc.populations.add_row(None) - - for i in range(2 * self.sCounter - 2): - tc.edges.add_row(0.0, self.sites+1.0, self.tree[i], i) - - child_or_parent = [1 for _ in range(2 * self.sCounter - 1)] - for i in range(2 * self.sCounter - 2): - child_or_parent[self.tree[i]] = 0 - - for i in range(2 * self.sCounter - 1): - tc.nodes.add_row(child_or_parent[i], self.times[0] - self.times[i], self.tree_pop[i]) - - for i in range(self.sites): - tc.sites.add_row(i+1, 'A') - - allele = ['A', 'T', 'C', 'G'] - for i in range(self.mut.nodeId.size()): - mut_nodeId, mut_DS, mut_AS, mut_site, mut_time = self.mut.get_mutation(i) - tc.mutations.add_row(site=mut_site, node=mut_nodeId, derived_state=allele[mut_DS], time=self.times[0] - mut_time) - - tc.sort() - return tc.tree_sequence() - - def get_tree(self): - if self.tree.shape[0] == 1: - print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') - sys.exit(1) - return self.tree, self.times - - def get_data_infectious(self, pop, hap, step_num): - time_points = [i*self.currentTime/step_num for i in range(step_num+1)] - Data = np.zeros(step_num+1) - Sample = np.zeros(step_num+1) - Data[0] = self.initial_infectious[pop, hap] - - point = 0 - for i in range(self.events.ptr): - while point != step_num and time_points[point] < self.events.times[i]: - Data[point+1] = Data[point] - Sample[point+1] = Sample[point] - point += 1 - - if self.events.types[i] == BIRTH and self.events.populations[i] == pop and self.events.haplotypes[i] == hap: - Data[point] += 1 - elif self.events.types[i] == DEATH or self.events.types[i] == SAMPLING or self.events.types[i] == MUTATION and self.events.populations[i] == pop and self.events.haplotypes[i] == hap: - Data[point] -= 1 - if self.events.types[i] == SAMPLING: - Sample[point] += 1 - elif self.events.types[i] == MUTATION and self.events.newHaplotypes[i] == hap and self.events.populations[i] == pop: - Data[point] += 1 - elif self.events.types[i] == MIGRATION and self.events.newPopulations[i] == pop and self.events.haplotypes[i] == hap: - Data[point] += 1 - elif self.events.types[i] == MULTITYPE: - for j in range(self.events.haplotypes[i], self.events.populations[i]): - if self.multievents.types[j] == BIRTH and self.multievents.haplotypes[j] == hap and self.multievents.populations[j] == pop: - Data[point] += self.multievents.num[j] - elif self.multievents.types[j] == DEATH or self.multievents.types[j] == SAMPLING or self.multievents.types[j] == MUTATION and self.multievents.haplotypes[j] == hap and self.multievents.populations[j] == pop: - Data[point] -= self.multievents.num[j] - if self.multievents.types[j] == SAMPLING: - Sample[point] += self.multievents.num[j] - elif self.multievents.types[j] == MUTATION and self.multievents.newHaplotypes[j] == hap and self.multievents.populations[j] == pop: - Data[point] += self.multievents.num[j] - elif self.multievents.types[j] == MIGRATION and self.multievents.newPopulations[j] == pop and self.multievents.haplotypes[j] == hap: - Data[point] += self.multievents.num[j] - - Lockdowns = [] - for i in range(self.loc.times.size()): - if self.loc.populationsId[i] == pop: - Lockdowns.append([self.loc.states[i], self.loc.times[i]]) - - return Data, Sample, time_points, Lockdowns - - def get_data_susceptible(self, pop, sus, step_num): - time_points = [i*self.currentTime/step_num for i in range(step_num+1)] - Data = np.zeros(step_num+1) - Data[0] = self.initial_susceptible[pop, sus] - - point = 0 - for i in range(self.events.ptr): - while point != step_num and time_points[point] < self.events.times[i]: - Data[point+1] = Data[point] - point += 1 - - if self.events.types[i] == BIRTH and self.events.populations[i] == pop and self.events.newHaplotypes[i] == sus: - Data[point] -= 1 - elif (self.events.types[i] == DEATH or self.events.types[i] == SAMPLING or self.events.types[i] == SUSCCHANGE) and self.events.populations[i] == pop and self.events.newHaplotypes[i] == sus: - Data[point] += 1 - elif self.events.types[i] == SUSCCHANGE and self.events.haplotypes[i] == sus and self.events.populations[i] == pop: - Data[point] -= 1 - elif self.events.types[i] == MIGRATION and self.events.newPopulations[i] == pop and self.events.newHaplotypes[i] == sus: - Data[point] -= 1 - elif self.events.types[i] == MULTITYPE: - for j in range(self.events.haplotypes[i], self.events.populations[i]): - if self.multievents.types[j] == BIRTH and self.multievents.newHaplotypes[j] == sus and self.multievents.populations[j] == pop: - Data[point] -= self.multievents.num[j] - elif (self.multievents.types[j] == DEATH or self.multievents.types[j] == SAMPLING or self.multievents.types[j] == SUSCCHANGE) and self.multievents.newHaplotypes[j] == sus and self.multievents.populations[j] == pop: - Data[point] += self.multievents.num[j] - elif self.multievents.types[j] == SUSCCHANGE and self.multievents.haplotypes[j] == sus and self.multievents.populations[j] == pop: - Data[point] -= self.multievents.num[j] - elif self.multievents.types[j] == MIGRATION and self.multievents.newPopulations[j] == pop and self.multievents.haplotypes[j] == sus: - Data[point] -= self.multievents.num[j] - - Lockdowns = [] - for i in range(self.loc.times.size()): - if self.loc.populationsId[i] == pop: - Lockdowns.append([self.loc.states[i], self.loc.times[i]]) - - return Data, time_points, Lockdowns - - - def Stats(self, time_simulation): - print("Number of samples:", self.sCounter) - print("Total number of iterations:", self.events.ptr) - print('Success number:', self.good_attempt) - print("Epidemic time:", self.currentTime) - print('Simulation time:', time_simulation) - print('Number of infections:', self.bCounter) - print('Number of recoveries:', self.dCounter) - if self.sites >= 1: - print('Number of mutations:', self.mCounter) - if self.popNum >= 2: - print('Number of accepted migrations:', self.migPlus) - print('Number of rejected migrations:', self.migNonPlus) - check = False - for sn in range(self.susNum): - if self.suscepCumulTransition[sn] != 0.0: - check = True - - if check: - print('Number of immunity transitions:', self.iCounter) - print('----------------------------------') - - def Debug(self): - print("Parameters") - print('first_simulation(mutable): ', self.first_simulation) - print("sampling_probability(const): ", self.sampling_probability) - print("memory_optimization(const): ", self.memory_optimization) - print() - print("sites(const): ", self.sites) - print("hapNum(const): ", self.hapNum) - print("currentHapNum(mutable): ", self.currentHapNum) - print("maxHapNum(mutable): ", self.maxHapNum) - print("popNum(const): ", self.popNum) - print("susNum(const): ", self.susNum) - print("bCounter(mutable): ", self.bCounter) - print("dCounter(mutable): ", self.dCounter) - print("sCounter(mutable): ", self.sCounter) - print("mCounter(mutable): ", self.mCounter) - print("iCounter(mutable):", self.iCounter) - print("swapLockdown(mutable): ", self.swapLockdown) - print("migPlus(mutable): ", self.migPlus) - print("migNonPlus(mutable): ", self.migNonPlus) - print("globalInfectious(mutable): ", self.globalInfectious) - print() - print("currentTime(mutable): ", self.currentTime) - print("totalRate(mutable): ", self.totalRate) - print("totalMigrationTate(mutable): ", self.totalMigrationRate) - print("rn(mutable): ", self.rn) - print() - print("suscType(const): ", sep=" ", end="") - for hn in range(self.hapNum): - print(self.suscType[hn], end=" ") - print() - print() - print("hapToNum(mutable): ", sep="", end="") - for hn in range(self.hapNum): - print(self.hapToNum[hn], end=" ") - print() - print("numToHap(mutable): ", sep="", end="") - for hn in range(self.maxHapNum): - print(self.numToHap[hn], end=" ") - print() - print("sizes(const): ", end="") - for pn in range(self.popNum): - print(self.sizes[pn], end=" ") - print() - print("totalSusceptible(mutable): ", end="") - for pn in range(self.popNum): - print(self.totalSusceptible[pn], end=" ") - print() - print("totalInfectious(mutable): ", end="") - for pn in range(self.popNum): - print(self.totalInfectious[pn], end=" ") - print() - print("lockdownON(mutable): ", end="") - for pn in range(self.popNum): - print(self.lockdownON[pn], end=" ") - print() - print() - print("susceptible(mutable)----") - for pn in range(self.popNum): - for sn in range(self.susNum): - print(self.susceptible[pn, sn], end=" ") - print() - print() - print("infectious(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - print(self.infectious[pn, hn], end=" ") - print() - print() - print() - print("Birth rate(const): ", sep="", end="") - for hn in range(self.hapNum): - print(self.bRate[hn], end=" ") - print() - print("Death rate(const): ", sep="", end="") - for hn in range(self.hapNum): - print(self.dRate[hn], end=" ") - print() - print("Sampling rate(const): ", sep="", end="") - for hn in range(self.hapNum): - print(self.sRate[hn], end=" ") - print() - print("tmRate(const): ", sep="", end="") - for hn in range(self.currentHapNum): - print(self.tmRate[hn], end=" ") - print() - print("maxEffectiveBirthMigration(const): ", sep="", end="") - for pn in range(self.popNum): - print(self.maxEffectiveBirthMigration[pn], end=" ") - print() - print("suscepCumulTransition(const): ", sep="", end="") - for sn in range(self.susNum): - print(self.suscepCumulTransition[sn], end=" ") - print() - print("immunePopRate(mutable): ", sep="", end="") - for pn in range(self.popNum): - print(self.immunePopRate[pn], end=" ") - print() - print("infectPopRate(mutable): ", sep="", end="") - for pn in range(self.popNum): - print(self.infectPopRate[pn], end=" ") - print() - print("popRate(mutable): ", sep="", end="") - for pn in range(self.popNum): - print(self.popRate[pn], end=" ") - print() - print("migPopRate(mutable): ", sep="", end="") - for pn in range(self.popNum): - print(self.migPopRate[pn], end=" ") - print() - print("actualSizes(const): ", end=" ") - for pn in range(self.popNum): - print(self.actualSizes[pn], end=" ") - print() - print("contactDensity(const): ", end=" ") - for pn in range(self.popNum): - print(self.contactDensity[pn], end=" ") - print() - print("contactDensityBeforeLockdown(const): ", end=" ") - for pn in range(self.popNum): - print(self.contactDensityBeforeLockdown[pn], end=" ") - print() - print("contactDensityBeforeLockdown(const): ", end=" ") - for pn in range(self.popNum): - print(self.contactDensityAfterLockdown[pn], end=" ") - print() - print("startLD(const): ", end=" ") - for pn in range(self.popNum): - print(self.startLD[pn], end=" ") - print() - print("endLD(const): ", end=" ") - for pn in range(self.popNum): - print(self.endLD[pn], end=" ") - print() - print("samplingMultiplier(const): ", end=" ") - for pn in range(self.popNum): - print(self.samplingMultiplier[pn], end=" ") - print() - print() - print("mRate(const)----") - for hn in range(self.hapNum): - for s in range(self.sites): - print(self.mRate[hn, s], end=" ") - print() - print() - print("susceptibility(const)----") - for hn in range(self.currentHapNum): - for sn in range(self.susNum): - print(self.susceptibility[hn, sn], end=" ") - print() - print() - print("tEventHapPopRate(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - print(self.tEventHapPopRate[pn, hn], end=" ") - print() - print() - print("suscepTransition(const)----") - for sn1 in range(self.susNum): - for sn2 in range(self.susNum): - print(self.suscepTransition[sn1, sn2], end=" ") - print() - print() - print("immuneSourcePopRate(mutable)----") - for pn in range(self.popNum): - for sn in range(self.susNum): - print(self.immuneSourcePopRate[pn, sn], end=" ") - print() - print() - print("hapPopRate(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - print(self.hapPopRate[pn, hn], end=" ") - print() - print() - print("migrationRates(const)----") - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - print(self.migrationRates[pn1, pn2], end=" ") - print() - print() - print("effectiveMigration(const)----") - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - print(self.effectiveMigration[pn1, pn2], end=" ") - print() - print() - print() - print("hapMutType(const)----") - for hn in range(self.currentHapNum): - for s in range(self.sites): - for i in range(3): - print(self.hapMutType[hn, s, i], end=" ") - print() - print() - print() - print("eventHapPopRate(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - for i in range(4): - print(self.eventHapPopRate[pn, hn, i], end=" ") - print() - print() - print() - print("susceptHapPopRate(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - for sn in range(self.susNum): - print(self.susceptHapPopRate[pn, hn, sn], end=" ") - print() - print() - print() - - - def get_proportion(self): - return self.migNonPlus / (self.events.ptr-1) - - ############################# - ### TAU LEAPING ALGORITHM ### - ############################# - - @cython.boundscheck(False) - @cython.wraparound(False) - cpdef void SimulatePopulation_tau(self, Py_ssize_t iterations, Py_ssize_t sample_size, float time, Py_ssize_t attempts): - cdef: - Py_ssize_t i, pn, propNum - bint success - - self.PrepareParameters(iterations) - self.CheckSizes() - - propNum = self.popNum*((self.popNum-1)*self.hapNum*self.susNum+self.susNum*(self.susNum-1)+self.hapNum*(2+self.sites*3+self.susNum)) - if self.globalInfectious == 0: - self.FirstInfection() - #TODO less memory for multievents - self.multievents.CreateEvents(iterations*propNum) - self.events.CreateEvents(iterations) - self.UpdateAllRates() - - for i in range(attempts): - self.seed = RndmWrapper(seed=(self.user_seed, i)) - if self.totalRate+self.totalMigrationRate != 0.0 and self.globalInfectious != 0: - while ((self.events.ptr 100: - self.Restart() - else: - self.good_attempt = i+1 - break - - if self.totalRate == 0.0 or self.globalInfectious == 0: - print('Simulation finished because no infections individuals remain!') - if self.events.ptr>=self.events.size: - print("Achieved maximal number of iterations.") - if self.sCounter>sample_size and sample_size!=-1: - print("Achieved sample size.") - if self.currentTime>time and time != -1: - print("Achieved internal time limit.") - if self.sCounter <= 1: - print('\033[41m{}\033[0m'.format('WARNING!'), 'Simulated less 2 samples, so genealogy will not work!') - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void Propensities(self): - cdef Py_ssize_t pn, spn, tpn, hn, sn, ssn, tsn, s, i - - for pn in range(self.popNum): - for hn in range(self.hapNum): - self.infectiousAuxTau[pn, hn] = 0.0 - for sn in range(self.susNum): - self.susceptibleAuxTau[pn, sn] = 0.0 - - for spn in range(self.popNum): - for tpn in range(self.popNum): - if spn == tpn: - continue - for sn in range(self.susNum): - for hn in range(self.hapNum): - self.PropensitiesMigr[spn, tpn, sn, hn] = self.effectiveMigration[tpn, spn]*self.susceptible[tpn, sn]*\ - self.infectious[spn, hn]*self.bRate[hn]*self.susceptibility[hn, sn]*self.migrationRates[spn, spn] - - self.infectiousAuxTau[tpn, hn] += self.PropensitiesMigr[spn, tpn, sn, hn] - self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesMigr[spn, tpn, sn, hn] - - for pn in range(self.popNum): - #Susceptibility transition - for ssn in range(self.susNum): - for tsn in range(self.susNum): - if ssn == tsn: - continue - self.PropensitiesSuscep[pn, ssn, tsn] = self.suscepTransition[ssn, tsn]*self.susceptible[pn, ssn] - - self.susceptibleAuxTau[pn, tsn] += self.PropensitiesSuscep[pn, ssn, tsn] - self.susceptibleAuxTau[pn, ssn] -= self.PropensitiesSuscep[pn, ssn, tsn] - - #Infectious-realted event - for hn in range(self.hapNum): - #Recovery - self.PropensitiesRecovery[pn, hn] = self.dRate[hn]*self.infectious[pn, hn] - - self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesRecovery[pn, hn] - self.infectiousAuxTau[pn, hn] -= self.PropensitiesRecovery[pn, hn] - - #Sampling - self.PropensitiesSampling[pn, hn] = self.sRate[hn]*self.infectious[pn, hn]*self.samplingMultiplier[pn] - - self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesSampling[pn, hn] - self.infectiousAuxTau[pn, hn] -= self.PropensitiesSampling[pn, hn] - - #Mutation - for s in range(self.sites): - for i in range(3): - self.PropensitiesMutatations[pn, hn, s, i] = self.mRate[hn, s]*self.hapMutType[hn, s, i]/\ - (self.hapMutType[hn, s, 0] + self.hapMutType[hn, s, 1] + self.hapMutType[hn, s, 2])*self.infectious[pn, hn] - - self.infectiousAuxTau[pn, self.Mutate(hn, s, i)] += self.PropensitiesMutatations[pn, hn, s, i] - self.infectiousAuxTau[pn, hn] -= self.PropensitiesMutatations[pn, hn, s, i] - - #Transmission - for tpn in range(self.popNum): - for hn in range(self.hapNum): - for sn in range(self.susNum): - self.PropensitiesTransmission[tpn, hn, sn] = 0.0 - for spn in range(self.popNum): - self.PropensitiesTransmission[tpn, hn, sn] += self.bRate[hn]*self.susceptibility[hn, sn]*self.migrationRates[tpn, spn]*\ - self.migrationRates[tpn, spn]*self.contactDensity[spn]*self.susceptible[tpn, sn]*self.infectious[tpn, hn]/\ - self.actualSizes[spn] - - self.infectiousAuxTau[tpn, hn] += self.PropensitiesTransmission[tpn, hn, sn] - self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesTransmission[tpn, hn, sn] - - @cython.cdivision(True) - cdef Py_ssize_t Mutate(self, Py_ssize_t hi, Py_ssize_t s, Py_ssize_t DS): - cdef Py_ssize_t digit4, AS - - digit4 = 4**(self.sites-s-1) - AS = int(floor(hi/digit4) % 4) - if DS >= AS: - DS += 1 - return hi + (DS-AS)*digit4 - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void ChooseTau(self, float epsilon=0.03): - cdef: - Py_ssize_t pn, hn, sn - double tmp - - self.tau_l = 1.0 - for pn in range(self.popNum): - for hn in range(self.hapNum): - if abs(self.infectiousAuxTau[pn, hn]) < 1e-8: - continue - tmp = max(epsilon*self.infectious[pn,hn]/2.0,1.0)/abs(self.infectiousAuxTau[pn, hn]) - if tmp < self.tau_l: - self.tau_l = tmp - for sn in range(self.susNum): - if abs(self.susceptibleAuxTau[pn, sn]) < 1e-8: - continue - tmp = max(epsilon*self.susceptible[pn,sn]/2.0,1.0)/abs(self.susceptibleAuxTau[pn, sn]) - if tmp < self.tau_l: - self.tau_l = tmp - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef bint GenerateEvents_tau(self): - cdef Py_ssize_t event_num, pn, spn, tpn, hn, sn, ssn, tsn, s, i - - for pn in range(self.popNum): - for sn in range(self.susNum): - self.susceptibleDelta[pn, sn] = 0 - for hn in range(self.hapNum): - self.infectiousDelta[pn, hn] = 0 - - #Migrations - for spn in range(self.popNum): - for tpn in range(self.popNum): - if spn == tpn: - continue - for sn in range(self.susNum): - for hn in range(self.hapNum): - event_num = self.DrawEventsNum(self.PropensitiesMigr[spn, tpn, sn, hn]*self.tau_l) - self.eventsMigr[spn, tpn, sn, hn] = event_num - - self.infectiousDelta[spn, hn] += event_num - self.susceptibleDelta[tpn, sn] -= event_num - - - for pn in range(self.popNum): - #Susceptibility transition - for ssn in range(self.susNum): - for tsn in range(self.susNum): - if ssn == tsn: - continue - event_num = self.DrawEventsNum(self.PropensitiesSuscep[pn, ssn, tsn]*self.tau_l) - self.eventsSuscep[pn, ssn, tsn] = event_num - - self.susceptibleDelta[pn, tsn] += event_num - self.susceptibleDelta[pn, ssn] -= event_num - - #Infectious-realted event - for hn in range(self.hapNum): - #Recovery - event_num = self.DrawEventsNum(self.PropensitiesRecovery[pn, hn]*self.tau_l) - self.eventsRecovery[pn, hn] = event_num - - self.susceptibleDelta[pn, self.suscType[hn]] += event_num - self.infectiousDelta[pn, hn] -= event_num - - #Sampling - event_num = self.DrawEventsNum(self.PropensitiesSampling[pn, hn]*self.tau_l) - self.eventsSampling[pn, hn] = event_num - - self.susceptibleDelta[pn, self.suscType[hn]] += event_num - self.infectiousDelta[pn, hn] -= event_num - - #Mutation - for s in range(self.sites): - for i in range(3): - event_num = self.DrawEventsNum(self.PropensitiesMutatations[pn, hn, s, i]*self.tau_l) - self.eventsMutatations[pn, hn, s, i] = event_num - - self.infectiousDelta[pn, self.Mutate(hn, s, i)] += event_num - self.infectiousDelta[pn, hn] -= event_num - - #Transmission - for sn in range(self.susNum): - event_num = self.DrawEventsNum(self.PropensitiesTransmission[pn, hn, sn]*self.tau_l) - self.eventsTransmission[pn, hn, sn] = event_num - - self.infectiousDelta[pn, hn] += event_num - self.susceptibleDelta[pn, sn] -= event_num - - for pn in range(self.popNum): - for sn in range(self.susNum): - if self.susceptibleDelta[pn, sn] + self.susceptible[pn, sn] < 0 or self.susceptibleDelta[pn, sn] + self.susceptible[pn, sn] > self.sizes[pn]: - return False - for hn in range(self.hapNum): - if self.infectiousDelta[pn, hn] + self.infectious[pn, hn] < 0 or self.infectiousDelta[pn, hn] + self.infectious[pn, hn] > self.sizes[pn]: - return False - return True - - cdef inline Py_ssize_t DrawEventsNum(self, double tau): - return random_poisson(self.seed.rng, lam=tau) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void UpdateCompartmentCounts_tau(self): - #TODO not write events with 0 num - cdef Py_ssize_t event_num, pn, spn, tpn, hn, sn, ssn, tsn, s, i - - #Migrations - for spn in range(self.popNum): - for tpn in range(self.popNum): - if spn == tpn: - continue - for sn in range(self.susNum): - for hn in range(self.hapNum): - event_num = self.eventsMigr[spn, tpn, sn, hn] - self.NewInfections(tpn, sn, hn, event_num) - self.multievents.AddEvents(event_num, self.currentTime, MIGRATION, hn, spn, sn, tpn) - self.migPlus += event_num - - for pn in range(self.popNum): - #Susceptibility transition - for ssn in range(self.susNum): - for tsn in range(self.susNum): - if ssn == tsn: - continue - event_num = self.eventsSuscep[pn, ssn, tsn] - self.susceptible[pn, tsn] += event_num - self.susceptible[pn, ssn] -= event_num - self.multievents.AddEvents(event_num, self.currentTime, SUSCCHANGE, ssn, pn, tsn, 0) - self.iCounter += event_num - - #Infectious-realted event - for hn in range(self.hapNum): - #Recovery - event_num = self.eventsRecovery[pn, hn] - self.NewRecoveries(pn, self.suscType[hn], hn, event_num) - self.multievents.AddEvents(event_num, self.currentTime, DEATH, hn, pn, self.suscType[hn], 0) - self.dCounter += event_num - - #Sampling - event_num = self.eventsSampling[pn, hn] - self.NewRecoveries(pn, self.suscType[hn], hn, event_num) - self.multievents.AddEvents(event_num, self.currentTime, SAMPLING, hn, pn, self.suscType[hn], 0) - self.sCounter += event_num - - #Mutation - for s in range(self.sites): - for i in range(3): - nhn = self.Mutate(hn, s, i) - event_num = self.eventsMutatations[pn, hn, s, i] - self.infectious[pn, nhn] += event_num - self.infectious[pn, hn] -= event_num - self.multievents.AddEvents(event_num, self.currentTime, MUTATION, hn, pn, nhn, 0) - self.mCounter += event_num - - #Transmission - for sn in range(self.susNum): - event_num = self.eventsTransmission[pn, hn, sn] - self.NewInfections(pn, sn, hn, event_num) - self.multievents.AddEvents(event_num, self.currentTime, BIRTH, hn, pn, sn, 0) - self.bCounter += event_num - - @cython.boundscheck(False) - @cython.wraparound(False) - def Get_MultiEvents(self, id = None): - if id is None: - for i in range(self.multievents.ptr): - ev = self.multievents.GetEvent(i) - ev.PrintEvent() - else: - ev = self.multievents.GetEvent(id) - ev.PrintEvent() - - - def PrintCounters(self): - print("Birth counter(mutable): ", self.bCounter) - print("Death counter(mutable): ", self.dCounter) - print("Sampling counter(mutable): ", self.sCounter) - print("Mutation counter(mutable): ", self.mCounter) - print("Immunity transition counter(mutable):", self.iCounter) - print("Migration counter(mutable):", self.migPlus) - - def PrintPropensities(self): - #self.FirstInfection() - self.UpdateAllRates() - self.Propensities() - print("Migrations") - for s in range(self.popNum): - for r in range(self.popNum): - if s == r: - continue - for i in range(self.susNum): - for h in range(self.hapNum): - print (s, r, i, h, self.PropensitiesMigr[s, r, i, h]) - - - for s in range(self.popNum): - print("Susceptibility transition") - for i in range(self.susNum): - for j in range(self.susNum): - if i == j: - continue - print(s, i, j, self.PropensitiesSuscep[s, i, j]) - - #Infectious-realted event - for h in range(self.hapNum): - print("Recovery ", s, h, self.suscType[h], self.PropensitiesRecovery[s, h]) - print("Sampling ", s, h, self.suscType[h], self.PropensitiesSampling[s, h]) - - for site in range(self.sites): - for i in range(3): - #ht = self.Mutate(h, site, i) - print("Mutation", s, h, site, i, self.PropensitiesMutatations[s, h, site, i]) - #Transmission - for i in range(self.susNum): - print("Transmission", s, h, i, self.PropensitiesTransmission[s, h, i]) - #print("migr=", self.migrationRates[s, s], " prop", prop) From 1c8ff9264922bea5038619bbf96647a5544fa5bc Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Sat, 12 Nov 2022 11:09:58 +0300 Subject: [PATCH 06/13] Add files via upload --- src/_BirthDeath.pyx | 91 +++++++++++++++++++++++++-------------------- 1 file changed, 51 insertions(+), 40 deletions(-) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index 2ab0801..6235abf 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -42,15 +42,16 @@ cdef class BirthDeathModel: Lockdowns loc npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop - npy_int64[:,::1] susceptible, infectious, initial_susceptible, initial_infectious - npy_int64[:,:,::1] infectByCondition + npy_int64[:,::1] susceptible, initial_susceptible, initial_infectious + npy_int64[:,:,::1] infectByCondition, infectious double[::1] bRate, dRate, sRate, tmRate, maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times double[::1] cumuConditionRate, conditionPopRate - double[:,::1] mRate, susceptibility, tEventHapPopRate, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration + double[:,::1] mRate, susceptibility, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration double[:,::1] conditionRate, massivForThree, conditionPopHapRate - double[:,:,::1] hapMutType, eventHapPopRate, susceptHapPopRate - double[:,:,::1] conditionPopHapConRate + double[:,:,::1] hapMutType, susceptHapPopRate, tEventHapPopRate, + double[:,:,::1] conditionPopHapConRate, tEventConHapPopRate + double[:,:,:,::1] eventHapPopRate double[:,:,:,::1] PropensitiesMigr, PropensitiesMutatations double[:,:,::1] PropensitiesSuscep, PropensitiesTransmission @@ -123,13 +124,13 @@ cdef class BirthDeathModel: self.loc = Lockdowns() # memory_optimization - self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) + self.infectious = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=np.int64) self.tmRate = np.zeros(self.maxHapNum, dtype=float) self.massivForThree = np.zeros((self.popNum, 3), dtype=float) self.conditionPopRate = np.zeros(self.popNum, dtype=float) - self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) self.hapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) - self.eventHapPopRate = np.zeros((self.popNum, self.maxHapNum, 4), dtype=float) + self.eventHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum, 4), dtype=float) self.susceptHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.susNum), dtype=float) self.suscType = np.zeros(self.hapNum, dtype=np.int64) @@ -142,6 +143,7 @@ cdef class BirthDeathModel: self.conditionPopHapRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) self.conditionPopHapConRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum),dtype=float) self.infectByCondition = np.zeros((self.popNum, self.maxHapNum, self.conditionNum),dtype=np.int64) + self.tEventConHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) self.susceptibility = np.zeros((self.hapNum, self.susNum), dtype=float) self.hapMutType = np.ones((self.hapNum, self.sites, 3), dtype=float) @@ -233,24 +235,24 @@ cdef class BirthDeathModel: if self.susceptible[0, sn] != 0: if self.memory_optimization: self.AddHaplotype(0, 0) - self.NewInfections(0, sn, 0) + self.NewInfections(0, sn, 0, 0) return @cython.boundscheck(False) @cython.wraparound(False) - cdef inline void NewInfections(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t num=1): + cdef inline void NewInfections(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t ci, Py_ssize_t num=1): self.susceptible[pi, si] -= num self.totalSusceptible[pi] -= num - self.infectious[pi, hi] += num + self.infectious[pi, hi, ci] += num self.totalInfectious[pi] += num self.globalInfectious += num @cython.boundscheck(False) @cython.wraparound(False) - cdef inline void NewRecoveries(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t num=1): + cdef inline void NewRecoveries(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t ci, Py_ssize_t num=1): self.susceptible[pi, si] += num self.totalSusceptible[pi] += num - self.infectious[pi, hi] -= num + self.infectious[pi, hi, ci] -= num self.totalInfectious[pi] -= num self.globalInfectious -= num @@ -260,15 +262,16 @@ cdef class BirthDeathModel: if self.addMemoryNum + self.maxHapNum > self.hapNum: self.addMemoryNum = self.hapNum - self.maxHapNum self.numToHap = np.concatenate((self.numToHap, np.zeros(self.addMemoryNum, dtype=np.int64))) - self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum), dtype=np.int64)), axis=1) + self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=np.int64)), axis=1) self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, dtype=float))) - self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) + self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) self.hapPopRate = np.concatenate((self.hapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) - self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, 4), dtype=float)), axis=1) + self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum ,4), dtype=float)), axis=1) self.susceptHapPopRate = np.concatenate((self.susceptHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.susNum), dtype=float)), axis=1) self.conditionPopHapRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) - self.conditionPopHapRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) + self.conditionPopHapConRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) self.infectByCondition = np.concatenate((self.infectByCondition, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum),dtype=np.int64)), axis=1) + self.tEventConHapPopRate = np.concatenate((self.tEventConHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)),axis=1) self.maxHapNum += self.addMemoryNum @cython.boundscheck(False) @@ -305,14 +308,16 @@ cdef class BirthDeathModel: for s in range(self.sites): self.tmRate[hn] += self.mRate[self.numToHap[hn], s] - self.eventHapPopRate[pn, hn, 0] = self.BirthRate(pn, hn) - self.eventHapPopRate[pn, hn, 1] = self.dRate[self.numToHap[hn]] - self.eventHapPopRate[pn, hn, 2] = self.sRate[self.numToHap[hn]]*self.samplingMultiplier[pn] - self.eventHapPopRate[pn, hn, 3] = self.tmRate[hn] - self.tEventHapPopRate[pn, hn] = 0 - for i in range(4): - self.tEventHapPopRate[pn, hn] += self.eventHapPopRate[pn, hn, i] - self.hapPopRate[pn, hn] = self.tEventHapPopRate[pn, hn] * self.infectious[pn, hn] + for cn in range(self.conditionNum): + self.eventHapPopRate[pn, hn, cn, 0] = self.BirthRate(pn, hn) + self.eventHapPopRate[pn, hn, cn, 1] = self.dRate[self.numToHap[hn]] + self.eventHapPopRate[pn, hn, cn, 2] = self.sRate[self.numToHap[hn]]*self.samplingMultiplier[pn] + self.eventHapPopRate[pn, hn, cn, 3] = self.tmRate[hn] + self.tEventHapPopRate[pn, hn, cn] = 0 + for i in range(4): + self.tEventHapPopRate[pn, hn, cn] += self.eventHapPopRate[pn, hn, cn, i] + self.tEventConHapPopRate[pn, hn, cn] = self.tEventHapPopRate[pn, hn, cn] * self.infectious[pn, hn, cn] + self.hapPopRate[pn, hn] += tEventConHapPopRate[pn, hn, cn] self.infectPopRate[pn] += self.hapPopRate[pn, hn] for sn in range(self.susNum): self.immuneSourcePopRate[pn, sn] = self.suscepCumulTransition[sn]*self.susceptible[pn, sn] @@ -494,11 +499,12 @@ cdef class BirthDeathModel: else: self.rn = (choose - self.immunePopRate[pi]) / self.infectPopRate[pi] hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number - ei, self.rn = fastChoose1(self.eventHapPopRate[pi, hi], self.tEventHapPopRate[pi, hi], self.rn) + ci, self.rn = fastChoose1(self.tEventConHapPopRate[pi, hi], self.hapPopRate[pi, hi], self.rn) + ei, self.rn = fastChoose1(self.tEventHapPopRate[pi, hi, ci], self.tEvenContHapPopRate[pi, hi, ci], self.rn) if ei == BIRTH: - self.Birth(pi, hi) + self.Birth(pi, hi, ci) elif ei == DEATH: - self.Death(pi, hi) + self.Death(pi, hi, ci) elif ei == SAMPLING: self.Sampling(pi, hi) else: @@ -515,13 +521,15 @@ cdef class BirthDeathModel: if infect: self.infectPopRate[pi] = 0.0 for hn in range(self.currentHapNum): - self.eventHapPopRate[pi, hn, 0] = self.BirthRate(pi, hn) - tmp = (self.eventHapPopRate[pi, hn, 0] + - self.eventHapPopRate[pi, hn, 1] + - self.eventHapPopRate[pi, hn, 2] + - self.eventHapPopRate[pi, hn, 3] ) - self.tEventHapPopRate[pi, hn] = tmp - self.hapPopRate[pi, hn] = self.tEventHapPopRate[pi, hn] * self.infectious[pi, hn] + for cn in range(self.conditionNum): + self.eventHapPopRate[pi, hn, cn, 0] = self.BirthRate(pi, hn) + tmp = (self.eventHapPopRate[pi, hn, cn, 0] + + self.eventHapPopRate[pi, hn, cn, 1] + + self.eventHapPopRate[pi, hn, cn, 2] + + self.eventHapPopRate[pi, hn, cn, 3] ) + self.tEventHapPopRate[pi, hn, cn] = tmp + self.tEventConHapPopRate[pn, hn, cn] = self.tEventHapPopRate[pn, hn, cn] * self.infectious[pn, hn, cn] + self.hapPopRate[pn, hn] += tEventConHapPopRate[pn, hn, cn] self.infectPopRate[pi] += self.hapPopRate[pi, hn] if immune: @@ -530,7 +538,10 @@ cdef class BirthDeathModel: self.immunePopRate[pi] += self.immuneSourcePopRate[pi, sn] if infect or immune: - self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi] + self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi]+ self.conditionPopRate[pn] + self.massivForThree[0] = self.infectPopRate[pn] + self.massivForThree[1] = self.immunePopRate[pn] + self.conditionPopRate[2] = self.conditionPopRate[pn] self.totalRate = 0.0 for pn in range(self.popNum): self.totalRate += self.popRate[pn] @@ -580,14 +591,14 @@ cdef class BirthDeathModel: @cython.boundscheck(False) @cython.wraparound(False) - cdef void Birth(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number + cdef void Birth(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number cdef double ws = 0.0 for sn in range(self.susNum): ws += self.susceptHapPopRate[pi, hi, sn] si, self.rn = fastChoose1(self.susceptHapPopRate[pi, hi], ws, self.rn) - self.NewInfections(pi, si, hi) + self.NewInfections(pi, si, hi, ci) self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] self.UpdateRates(pi, True, True, True) @@ -596,8 +607,8 @@ cdef class BirthDeathModel: @cython.boundscheck(False) @cython.wraparound(False) - cdef void Death(self, Py_ssize_t pi, Py_ssize_t hi, bint add_event = True): # hi - program number - self.NewRecoveries(pi, self.suscType[self.numToHap[hi]], hi) + cdef void Death(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci, bint add_event = True): # hi - program number + self.NewRecoveries(pi, self.suscType[self.numToHap[hi]], hi, ci) self.immuneSourcePopRate[pi, self.suscType[self.numToHap[hi]]] = self.susceptible[pi, self.suscType[self.numToHap[hi]]]*self.suscepCumulTransition[self.suscType[self.numToHap[hi]]] self.UpdateRates(pi, True, True, True) From 11eba9e5885b7195114b81e820c058989e7dc257 Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Wed, 30 Nov 2022 13:24:43 +0300 Subject: [PATCH 07/13] Add files via upload --- src/_BirthDeath.pyx | 53 +++++++++++++++++++++++++-------------------- 1 file changed, 29 insertions(+), 24 deletions(-) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index 6235abf..315e3f3 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -42,8 +42,8 @@ cdef class BirthDeathModel: Lockdowns loc npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop - npy_int64[:,::1] susceptible, initial_susceptible, initial_infectious - npy_int64[:,:,::1] infectByCondition, infectious + npy_int64[:,::1] susceptible, initial_susceptible, initial_infectious, infectious + npy_int64[:,:,::1] infectByCondition double[::1] bRate, dRate, sRate, tmRate, maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times double[::1] cumuConditionRate, conditionPopRate @@ -124,7 +124,7 @@ cdef class BirthDeathModel: self.loc = Lockdowns() # memory_optimization - self.infectious = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=np.int64) + self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) self.tmRate = np.zeros(self.maxHapNum, dtype=float) self.massivForThree = np.zeros((self.popNum, 3), dtype=float) self.conditionPopRate = np.zeros(self.popNum, dtype=float) @@ -148,11 +148,12 @@ cdef class BirthDeathModel: self.hapMutType = np.ones((self.hapNum, self.sites, 3), dtype=float) for hn in range(self.hapNum): - self.bRate[hn] = 2.0 - self.dRate[hn] = 1.0 - self.sRate[hn] = 0.01 - for s in range(self.sites): - self.mRate[hn, s] = 0.01 + for cn in range(self.conditionNum): + self.bRate[hn, cn] = 2.0 + self.dRate[hn, cn] = 1.0 + self.sRate[hn, cn] = 0.01 + for s in range(self.sites): + self.mRate[hn, cn, s] = 0.01 self.susceptibility[hn, 0] = 1.0 for cn1 in range(self.conditionNum): @@ -243,7 +244,8 @@ cdef class BirthDeathModel: cdef inline void NewInfections(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t ci, Py_ssize_t num=1): self.susceptible[pi, si] -= num self.totalSusceptible[pi] -= num - self.infectious[pi, hi, ci] += num + self.infectious[pi, hi] += num + self.infectByCondition[pi, hi, ci] += num self.totalInfectious[pi] += num self.globalInfectious += num @@ -252,7 +254,8 @@ cdef class BirthDeathModel: cdef inline void NewRecoveries(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t ci, Py_ssize_t num=1): self.susceptible[pi, si] += num self.totalSusceptible[pi] += num - self.infectious[pi, hi, ci] -= num + self.infectious[pi, hi] -= num + self.infectByCondition[pi, hi, ci] -= num self.totalInfectious[pi] -= num self.globalInfectious -= num @@ -305,18 +308,19 @@ cdef class BirthDeathModel: for pn in range(self.popNum): for hn in range(self.currentHapNum): # hn - program number self.tmRate[hn] = 0 - for s in range(self.sites): - self.tmRate[hn] += self.mRate[self.numToHap[hn], s] + for cn in range(self.conditionNum): - self.eventHapPopRate[pn, hn, cn, 0] = self.BirthRate(pn, hn) - self.eventHapPopRate[pn, hn, cn, 1] = self.dRate[self.numToHap[hn]] - self.eventHapPopRate[pn, hn, cn, 2] = self.sRate[self.numToHap[hn]]*self.samplingMultiplier[pn] - self.eventHapPopRate[pn, hn, cn, 3] = self.tmRate[hn] + for s in range(self.sites): + self.tmRate[hn] += self.mRate[self.numToHap[hn], cn, s] + self.eventHapPopRate[pn, hn, cn, 0] = self.BirthRate(pn, hn, cn) + self.eventHapPopRate[pn, hn, cn, 1] = self.dRate[self.numToHap[hn], cn] + self.eventHapPopRate[pn, hn, cn, 2] = self.sRate[self.numToHap[hn], cn]*self.samplingMultiplier[pn] + self.eventHapPopRate[pn, hn, cn, 3] = self.tmRate[hn, cn] self.tEventHapPopRate[pn, hn, cn] = 0 for i in range(4): self.tEventHapPopRate[pn, hn, cn] += self.eventHapPopRate[pn, hn, cn, i] - self.tEventConHapPopRate[pn, hn, cn] = self.tEventHapPopRate[pn, hn, cn] * self.infectious[pn, hn, cn] + self.tEventConHapPopRate[pn, hn, cn] = self.tEventHapPopRate[pn, hn, cn] * self.infectByCondition[pn, hn, cn] self.hapPopRate[pn, hn] += tEventConHapPopRate[pn, hn, cn] self.infectPopRate[pn] += self.hapPopRate[pn, hn] for sn in range(self.susNum): @@ -387,7 +391,7 @@ cdef class BirthDeathModel: @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) - cdef inline double BirthRate(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number + cdef inline double BirthRate(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number cdef double ps = 0.0 for sn in range(self.susNum): @@ -506,11 +510,10 @@ cdef class BirthDeathModel: elif ei == DEATH: self.Death(pi, hi, ci) elif ei == SAMPLING: - self.Sampling(pi, hi) + self.Sampling(pi, hi, ci) else: self.Mutation(pi, hi) else: - self.rn = (choose - self.totalRate) / self.totalMigrationRate pi = self.GenerateMigration() return pi @@ -538,10 +541,11 @@ cdef class BirthDeathModel: self.immunePopRate[pi] += self.immuneSourcePopRate[pi, sn] if infect or immune: + self.popRate[pi] = 0 self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi]+ self.conditionPopRate[pn] self.massivForThree[0] = self.infectPopRate[pn] self.massivForThree[1] = self.immunePopRate[pn] - self.conditionPopRate[2] = self.conditionPopRate[pn] + self.massivForThree[2] = self.conditionPopRate[pn] self.totalRate = 0.0 for pn in range(self.popNum): self.totalRate += self.popRate[pn] @@ -598,8 +602,9 @@ cdef class BirthDeathModel: ws += self.susceptHapPopRate[pi, hi, sn] si, self.rn = fastChoose1(self.susceptHapPopRate[pi, hi], ws, self.rn) - self.NewInfections(pi, si, hi, ci) - self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] + self.NewInfections(pi, si, hi, 0) + #self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] + self.immuneSourcePopRate[pi, 0] = self.suscepCumulTransition[0]*self.susceptible[pi, 0] self.UpdateRates(pi, True, True, True) self.bCounter += 1 @@ -618,7 +623,7 @@ cdef class BirthDeathModel: @cython.boundscheck(False) @cython.wraparound(False) - cdef void Sampling(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number + cdef void Sampling(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number self.Death(pi, hi, False) self.sCounter += 1 From 86ee5c4bee0b4d63c2ea38a6618a0c03847d746c Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Wed, 30 Nov 2022 13:33:13 +0300 Subject: [PATCH 08/13] Add files via upload --- src/_BirthDeath.pyx | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index 315e3f3..063e8f4 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -45,10 +45,10 @@ cdef class BirthDeathModel: npy_int64[:,::1] susceptible, initial_susceptible, initial_infectious, infectious npy_int64[:,:,::1] infectByCondition - double[::1] bRate, dRate, sRate, tmRate, maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times - double[::1] cumuConditionRate, conditionPopRate - double[:,::1] mRate, susceptibility, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration - double[:,::1] conditionRate, massivForThree, conditionPopHapRate + double[::1] maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times + double[::1] cumuConditionRate, conditionPopRate, bRate, dRate, sRate, tmRate + double[:,::1] susceptibility, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration + double[:,::1] conditionRate, massivForThree, conditionPopHapRate, mRate double[:,:,::1] hapMutType, susceptHapPopRate, tEventHapPopRate, double[:,:,::1] conditionPopHapConRate, tEventConHapPopRate double[:,:,:,::1] eventHapPopRate @@ -134,10 +134,10 @@ cdef class BirthDeathModel: self.susceptHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.susNum), dtype=float) self.suscType = np.zeros(self.hapNum, dtype=np.int64) - self.bRate = np.zeros(self.hapNum, dtype=float) - self.dRate = np.zeros(self.hapNum, dtype=float) - self.sRate = np.zeros(self.hapNum, dtype=float) - self.mRate = np.zeros((self.hapNum, self.sites), dtype=float) + self.bRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) + self.dRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) + self.sRate = np.zeros(self.hapNum, self.conditionNUm, dtype=float) + self.mRate = np.zeros((self.hapNum, self.conditionNum, self.sites), dtype=float) self.conditionRate = np.zeros((self.conditionNum, self.conditionNum), dtype=float) self.cumuConditionRate = np.zeros(self.conditionNum, dtype=float) self.conditionPopHapRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) @@ -266,7 +266,7 @@ cdef class BirthDeathModel: self.addMemoryNum = self.hapNum - self.maxHapNum self.numToHap = np.concatenate((self.numToHap, np.zeros(self.addMemoryNum, dtype=np.int64))) self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=np.int64)), axis=1) - self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, dtype=float))) + self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, self.conditionNum, dtype=float))) self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) self.hapPopRate = np.concatenate((self.hapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum ,4), dtype=float)), axis=1) @@ -307,12 +307,13 @@ cdef class BirthDeathModel: self.popRate[pn] = 0. for pn in range(self.popNum): for hn in range(self.currentHapNum): # hn - program number - self.tmRate[hn] = 0 + for cn in range(self.conditionNum): + self.tmRate[hn, cn] = 0 for cn in range(self.conditionNum): for s in range(self.sites): - self.tmRate[hn] += self.mRate[self.numToHap[hn], cn, s] + self.tmRate[hn, cn] += self.mRate[self.numToHap[hn], cn, s] self.eventHapPopRate[pn, hn, cn, 0] = self.BirthRate(pn, hn, cn) self.eventHapPopRate[pn, hn, cn, 1] = self.dRate[self.numToHap[hn], cn] self.eventHapPopRate[pn, hn, cn, 2] = self.sRate[self.numToHap[hn], cn]*self.samplingMultiplier[pn] From 6511f8881e9c671e04c2ff96f06de9a1070fc76b Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Tue, 6 Dec 2022 11:27:40 +0300 Subject: [PATCH 09/13] Add files via upload --- src/_BirthDeath.pyx | 176 +++++++++++++++++++------------------------- src/_interface.py | 27 ++++--- 2 files changed, 91 insertions(+), 112 deletions(-) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index 063e8f4..b106f78 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -42,8 +42,8 @@ cdef class BirthDeathModel: Lockdowns loc npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop - npy_int64[:,::1] susceptible, initial_susceptible, initial_infectious, infectious - npy_int64[:,:,::1] infectByCondition + npy_int64[:,::1] susceptible, initial_susceptible, infectious + npy_int64[:,:,::1] infectByCondition, initial_infectious double[::1] maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times double[::1] cumuConditionRate, conditionPopRate, bRate, dRate, sRate, tmRate @@ -65,7 +65,7 @@ cdef class BirthDeathModel: npy_int64[:,::1] infectiousDelta, susceptibleDelta - def __init__(self, number_of_sites, populations_number, number_of_susceptible_groups, seed, sampling_probability, memory_optimization): + def __init__(self, number_of_sites, populations_number, number_of_susceptible_groups, seed, sampling_probability, memory_optimizationm, conditionNum): self.user_seed = seed self.seed = RndmWrapper(seed=(self.user_seed, 0)) @@ -125,7 +125,7 @@ cdef class BirthDeathModel: # memory_optimization self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) - self.tmRate = np.zeros(self.maxHapNum, dtype=float) + self.tmRate = np.zeros((self.maxHapNum, self.conditionNum), dtype=float) self.massivForThree = np.zeros((self.popNum, 3), dtype=float) self.conditionPopRate = np.zeros(self.popNum, dtype=float) self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) @@ -136,7 +136,7 @@ cdef class BirthDeathModel: self.suscType = np.zeros(self.hapNum, dtype=np.int64) self.bRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) self.dRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) - self.sRate = np.zeros(self.hapNum, self.conditionNUm, dtype=float) + self.sRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) self.mRate = np.zeros((self.hapNum, self.conditionNum, self.sites), dtype=float) self.conditionRate = np.zeros((self.conditionNum, self.conditionNum), dtype=float) self.cumuConditionRate = np.zeros(self.conditionNum, dtype=float) @@ -176,7 +176,7 @@ cdef class BirthDeathModel: self.susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) self.initial_susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) - self.initial_infectious = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + self.initial_infectious = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=np.int64) self.maxEffectiveBirthMigration = np.zeros(self.popNum, dtype=float) self.suscepCumulTransition = np.zeros(self.susNum, dtype=float) @@ -353,9 +353,10 @@ cdef class BirthDeathModel: maxEffectiveBirth = 0.0 for hn in range(self.currentHapNum): - for sn in range(self.susNum): - if self.bRate[self.numToHap[hn]]*self.susceptibility[self.numToHap[hn], sn] > maxEffectiveBirth: - maxEffectiveBirth = self.bRate[self.numToHap[hn]]*self.susceptibility[self.numToHap[hn], sn] + for cn in range(self.conditionNum): + for sn in range(self.susNum): + if self.bRate[self.numToHap[hn], cn]*self.susceptibility[self.numToHap[hn], sn] > maxEffectiveBirth: + maxEffectiveBirth = self.bRate[self.numToHap[hn], cn]*self.susceptibility[self.numToHap[hn], sn] self.totalMigrationRate = 0.0 for pn in range(self.popNum): @@ -448,7 +449,8 @@ cdef class BirthDeathModel: for sn in range(self.susNum): self.initial_susceptible[pn, sn] = self.susceptible[pn, sn] for hn in range(self.hapNum): - self.initial_infectious[pn, hn] = self.infectious[pn, hn] + for cn in range(self.conditionNum): + self.initial_infectious[pn, hn, cn] = self.infectByCondition[pn, hn, cn] self.first_simulation = True for pn in range(self.popNum): self.CheckLockdown(pn) @@ -583,7 +585,7 @@ cdef class BirthDeathModel: hsi, self.rn = fastChoose1(self.conditionPopHapRate[pi], self.conditionPopRate[pi], self.rn) ssi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self,conditionPopHapRate[pi, hsi], self.rn) - tsi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self.conditionPopHapRate[pi, hsi], self.rn) + tsi, self.rn = fastChoose2_skip(self.conditionRate[ssi], self.cumuConditionRate[ssi], self.rn) self.infectByCondition[pi, hsi, ssi] -= 1 self.infectByCondition[pi, hsi, tsi] += 1 @@ -604,8 +606,7 @@ cdef class BirthDeathModel: si, self.rn = fastChoose1(self.susceptHapPopRate[pi, hi], ws, self.rn) self.NewInfections(pi, si, hi, 0) - #self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] - self.immuneSourcePopRate[pi, 0] = self.suscepCumulTransition[0]*self.susceptible[pi, 0] + self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] self.UpdateRates(pi, True, True, True) self.bCounter += 1 @@ -633,13 +634,13 @@ cdef class BirthDeathModel: @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) - cdef void Mutation(self, Py_ssize_t pi, Py_ssize_t hi): # hi - program number + cdef void Mutation(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number cdef: bint check = True Py_ssize_t ohi, mi, digit4, AS, DS, nhi ohi = self.numToHap[hi] # ohi - haplotype - mi, self.rn = fastChoose1(self.mRate[ohi], self.tmRate[hi], self.rn) + mi, self.rn = fastChoose1(self.mRate[ohi, ci], self.tmRate[hi, ci], self.rn) DS, self.rn = fastChoose1(self.hapMutType[ohi, mi], self.hapMutType[ohi, mi, 0] \ + self.hapMutType[ohi, mi, 1] + self.hapMutType[ohi, mi, 2], self.rn) nhi = self.Mutate(ohi, mi, DS) @@ -673,9 +674,10 @@ cdef class BirthDeathModel: tpi, self.rn = fastChoose1(self.migPopRate, self.totalMigrationRate, self.rn) spi, self.rn = fastChoose2_skip(self.totalInfectious, self.globalInfectious-self.totalInfectious[tpi], self.rn, skip=tpi) hi, self.rn = fastChoose2(self.infectious[spi], self.totalInfectious[spi], self.rn) # hi - program number + ci, self.rn = fastChoose1(self.conditionRate[ssi], self.cumuConditionRate[ssi], self.rn) si, self.rn = fastChoose2(self.susceptible[tpi], self.totalSusceptible[tpi], self.rn) - p_accept = self.effectiveMigration[spi, tpi]*self.bRate[self.numToHap[hi]]*self.susceptibility[self.numToHap[hi], si]/self.maxEffectiveBirthMigration[tpi] + p_accept = self.effectiveMigration[spi, tpi]*self.bRate[self.numToHap[hi], ci]*self.susceptibility[self.numToHap[hi], si]/self.maxEffectiveBirthMigration[tpi] if self.rn < p_accept: self.NewInfections(tpi, si, hi) self.UpdateRates(tpi, True, True, True) @@ -1383,104 +1385,74 @@ cdef class BirthDeathModel: else: self.addMemoryNum = amount - def set_transmission_rate(self, rate, haplotype): - self.check_rate_1(rate, 'transmission') - self.check_haplotype(haplotype) + def set_transmission_rate(self, rate, haplotype, condition): + self.check_value(rate, 'transmission rate') + self.check_index(haplotype, self.hapNum, 'haplotype', True) + self.check_index(condition, self.conditionNum, 'number of states') + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + conditions = self.create_list_for_cycles(condition, self.conditionNum) - # for hn in self.get_list_haplotype(haplotype): - # self.bRate[hn] = rate + for hn in haplotypes: + for cn in conditions: + self.bRate[hn, cn] = rate - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for haplotype in haplotypes: - self.bRate[haplotype] = rate - elif isinstance(haplotype, int): - self.bRate[haplotype] = rate - elif haplotype == None: - for hn in range(self.hapNum): - self.bRate[hn] = rate - def set_recovery_rate(self, rate, haplotype): - self.check_rate_1(rate, 'recovery') - self.check_haplotype(haplotype) + def set_recovery_rate(self, rate, haplotype, condition): + self.check_value(rate, 'recovery rate') + self.check_index(haplotype, self.hapNum, 'haplotype', True) + self.check_index(condition. self.conditionNum, 'number of states') - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for haplotype in haplotypes: - self.dRate[haplotype] = rate - elif isinstance(haplotype, int): - self.dRate[haplotype] = rate - elif haplotype == None: - for hn in range(self.hapNum): - self.dRate[hn] = rate + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + conditions = self.create_list_for_cycles(condition, self.conditionNum) - def set_sampling_rate(self, rate, haplotype): - self.check_haplotype(haplotype) + for hn in haplotypes: + for cn in conditions: + self.dRate[hn, cn] = rate + + def set_sampling_rate(self, rate, haplotype, condition): + self.check_index(haplotype, self.hapNum, 'haplotype', True) + self.check_value(condition, self.conditionNum, 'number of states') + conditions = self.create_list_for_cycles(condition, self.conditionNum) if self.sampling_probability == True: - self.check_rate_2(rate, 'sampling probability') + self.check_value(rate, 'sampling probability', edge=1) - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for haplotype in haplotypes: - deathRate = self.dRate[haplotype] + self.sRate[haplotype] - self.dRate[haplotype] = (1 - rate) * deathRate - self.sRate[haplotype] = rate * deathRate - elif isinstance(haplotype, int): - deathRate = self.dRate[haplotype] + self.sRate[haplotype] - self.dRate[haplotype] = (1 - rate) * deathRate - self.sRate[haplotype] = rate * deathRate - elif haplotype == None: - for hn in range(self.hapNum): - deathRate = self.dRate[hn] + self.sRate[hn] - self.dRate[hn] = (1 - rate) * deathRate - self.sRate[hn] = rate * deathRate + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + for hn in haplotypes: + for cn in conditions: + deathRate = self.dRate[hn, cn] + self.sRate[hn, cn] + self.dRate[hn, cn] = (1 - rate) * deathRate + self.sRate[hn, cn] = rate * deathRate elif self.sampling_probability == False: - self.check_rate_1(rate, 'sampling') - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for haplotype in haplotypes: - self.sRate[haplotype] = rate - elif isinstance(haplotype, int): - self.sRate[haplotype] = rate - elif haplotype == None: - for hn in range(self.hapNum): - self.sRate[hn] = rate + self.check_value(rate, 'sampling rate') - def set_mutation_rate(self, rate, probabilities, haplotype, mutation): - self.check_rate_none_1(rate, 'mutation rate') - self.check_list(probabilities, 'probabilities list', 4) - if isinstance(probabilities, list): - for i in range(4): - self.check_rate_1(probabilities[i], 'mutation probabilities') - self.check_haplotype(haplotype) - self.check_index(mutation, self.sites, 'mutation site') + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + for hn in haplotypes: + for cn in conditions: + self.sRate[hn, cn] = rate - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - if isinstance(mutation, int): - for hn in haplotypes: - self.set_probabilities(hn, mutation, rate, probabilities) - elif mutation == None: - for hn in haplotypes: - for s in range(self.sites): - self.set_probabilities(hn, s, rate, probabilities) - elif isinstance(haplotype, int): - if isinstance(mutation, int): - self.set_probabilities(haplotype, mutation, rate, probabilities) - elif mutation == None: - for s in range(self.sites): - self.set_probabilities(haplotype, s, rate, probabilities) - elif haplotype == None: - if isinstance(mutation, int): - for hn in range(self.hapNum): - self.set_probabilities(hn, mutation, rate, probabilities) - elif mutation == None: - for hn in range(self.hapNum): - for s in range(self.sites): - self.set_probabilities(hn, s, rate, probabilities) + def set_mutation_rate(self, rate, haplotype, mutation, condition): + self.check_value(rate, 'mutation rate') + self.check_index(haplotype, self.hapNum, 'haplotype', True) + self.check_index(mutation, self.sites, 'mutation site') + self.check_value(condition, self.conditionNum, 'number of states') + + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + conditions = self.create_list_for_cycles(condition, self.conditionNum) + sites = self.create_list_for_cycles(mutation, self.sites) + for hn in haplotypes: + for cn in conditions: + for s in sites: + self.mRate[hn, cn, s] = rate + + @property + def condition_rate(self): + return self.conditionRate + + def set_condition_rate(self, rate, c1, c2): + self.check_value(rate, 'condition_rate') + self.conditionRate[c1, c2] = rate def set_probabilities(self, haplotype, mutation, rate, probabilities): if isinstance(rate, (int, float)): diff --git a/src/_interface.py b/src/_interface.py index be6dba3..ca2eb3b 100644 --- a/src/_interface.py +++ b/src/_interface.py @@ -7,7 +7,7 @@ import time class Simulator: - def __init__(self, number_of_sites=0, populations_number=1, number_of_susceptible_groups=1, seed=None, sampling_probability=False, memory_optimization=False): + def __init__(self, number_of_sites=0, populations_number=1, number_of_susceptible_groups=1, seed=None, sampling_probability=False, memory_optimization=False, conditionNum=1): self.fig = None if seed == None: seed = int(randrange(sys.maxsize)) @@ -15,7 +15,7 @@ def __init__(self, number_of_sites=0, populations_number=1, number_of_susceptibl self.simulation = BirthDeathModel(number_of_sites=number_of_sites, populations_number=populations_number, \ number_of_susceptible_groups=number_of_susceptible_groups, seed=seed, sampling_probability=sampling_probability, \ - memory_optimization=memory_optimization) + memory_optimization=memory_optimization, conditionNum=conditionNum) def print_basic_parameters(self): @@ -43,17 +43,24 @@ def set_step_haplotype(self, amount): self.simulation.set_step_haplotype(amount) - def set_transmission_rate(self, rate, haplotype=None): - self.simulation.set_transmission_rate(rate, haplotype) + def set_transmission_rate(self, rate, haplotype=None, condition=None): + self.simulation.set_transmission_rate(rate, haplotype, condition) - def set_recovery_rate(self, rate, haplotype=None): - self.simulation.set_recovery_rate(rate, haplotype) + def set_recovery_rate(self, rate, haplotype=None, condition=None): + self.simulation.set_recovery_rate(rate, haplotype, condition) - def set_sampling_rate(self, rate, haplotype=None): - self.simulation.set_sampling_rate(rate, haplotype) + def set_sampling_rate(self, rate, haplotype=None, condition=None): + self.simulation.set_sampling_rate(rate, haplotype, condition) - def set_mutation_rate(self, rate=None, probabilities=None, haplotype=None, mutation=None): - self.simulation.set_mutation_rate(rate, probabilities, haplotype, mutation) + def set_mutation_rate(self, rate=None, probabilities=None, haplotype=None, mutation=None, condition=None): + self.simulation.set_mutation_rate(rate, probabilities, haplotype, mutation, condition) + + @property + def condition_rate(self): + return self.simulation.condition_rate + + def set_condition_rate(self, rate, c1=0, c2=0): + self.simulation.set_condition_rate(rate, c1, c2) def set_susceptibility_type(self, susceptibility_type, haplotype=None): From 9c21615783d86e433b013d221a5a70caa5b7074a Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Tue, 6 Dec 2022 11:30:27 +0300 Subject: [PATCH 10/13] Add files via upload --- src/_BirthDeath.pyx | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index b106f78..cf72c03 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -1452,6 +1452,8 @@ cdef class BirthDeathModel: def set_condition_rate(self, rate, c1, c2): self.check_value(rate, 'condition_rate') + self.check_index(c1, self.conditionNum, 'first_state') + self.check_index(c2, self.conditionNum, 'second_state') self.conditionRate[c1, c2] = rate def set_probabilities(self, haplotype, mutation, rate, probabilities): From aa9dfbb8cf181d97e0427afa2c60793630794a9e Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Sun, 19 Mar 2023 15:33:29 +0300 Subject: [PATCH 11/13] Add files via upload --- VGsim/LICENSE | 674 +++++ VGsim/README.md | 112 + VGsim/Simulator.py | 492 ++++ 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VGsim/testing/cmd_example/example.pp create mode 100644 VGsim/testing/cmd_example/example.rt create mode 100644 VGsim/testing/cmd_example/example.st create mode 100644 VGsim/testing/cmd_example/example.su create mode 100644 VGsim/testing/example.ipynb create mode 100644 VGsim/testing/example.py create mode 100644 VGsim/testing/getting_reference.py create mode 100644 VGsim/testing/reference_1.npy create mode 100644 VGsim/testing/reference_2.npy create mode 100644 VGsim/testing/reference_3.npy create mode 100644 VGsim/testing/reference_4.npy create mode 100644 VGsim/testing/reference_5.npy create mode 100644 VGsim/testing/reference_6.npy create mode 100644 VGsim/testing/reference_7.npy create mode 100644 VGsim/testing/reference_8.npy create mode 100644 VGsim/testing/reference_9.npy create mode 100644 VGsim/testing/tests.py diff --git a/VGsim/LICENSE b/VGsim/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/VGsim/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/VGsim/README.md b/VGsim/README.md new file mode 100644 index 0000000..07aa52a --- /dev/null +++ b/VGsim/README.md @@ -0,0 +1,112 @@ +# VGsim +Fast simulator of viral genealogies in the world-scale pandemic scenarios. + +[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) +[![pip](https://github.com/Genomics-HSE/VGsim/actions/workflows/pip.yml/badge.svg)](https://github.com/Genomics-HSE/VGsim/actions/workflows/pip.yml) +[![dev](https://github.com/Genomics-HSE/VGsim/actions/workflows/dev.yml/badge.svg)](https://github.com/Genomics-HSE/VGsim/actions/workflows/dev.yml) +[![windows](https://github.com/Genomics-HSE/VGsim/actions/workflows/windows.yml/badge.svg)](https://github.com/Genomics-HSE/VGsim/actions/workflows/windows.yml) +[![Documentation Status](https://readthedocs.org/projects/vg-sim/badge/?version=latest)](https://vg-sim.readthedocs.io/en/latest/?badge=latest) + +Documentation +------------- + +We keep on updating the detailed documentation (https://vg-sim.readthedocs.io/). In particular, there is a Python API which allows to build complicated simulations, which are currently not supported from the command line. + +Building the package +-------------------- + +For performance reasons, the package includes C extensions, which need to be +built. In the future, we plan to provide prebuilt packages, but for now you need +to build them yourself. For this, you need a working toolchain for building C++ +code (gcc and clang are known to work). Since you are going to build Python extensions, +you will need python development headers (e.g. on ubuntu linux the package name is `python-dev`). + +The simplest quick-start cross-platform way is to use `conda`. To do this, create a fresh conda environment: + +``` +$ conda create -n conda_vgsim +$ conda activate conda_vgsim +$ conda install python=3.9 # or other python version of your choice (any `python >= 3.7` should work). +``` + +Note that we install python *with conda, inside the conda environment*. Mixing system-install python and conda may lead to build- or runtime errors. + +Get the source code --- here we clone it from GitHub + +``` +$ git clone https://github.com/Genomics-HSE/VGsim.git +$ cd VGsim +``` + +Then build the package: + +``` +$ python -m pip install . +``` + +That's it! +You may now run simulations: + +``` +$ python ./testing/example.py +``` + +Running this script should create a directory ``testing/example_output`` with five files ``tree.nwk`` (genealogy tree), ``mutations.tsv`` (mutations on the genealogy), ``migrations.tsv`` (migrations of the lineages in the genealogy), ``sample_population.tsv`` (population of each sample), ``plot.png`` (visualization of epidemiological curves). There will be a warning in the output. This is an expected behaviour in this example (see https://vg-sim.readthedocs.io/en/latest/Migration.html for detailed explanation of inflated number of individuals in a deme due to migration). This example is also provided as a jupyter notebook ``testing/example.ipynb``. + +If you prefer not to use ``conda``, the package builds fine with just pip. We recommend using virtual environments, via the standard library ``venv`` package (or a third-party ``virtualenv`` package). For MacOS users: please make sure to use Python with Homebrew. Avoid system Python. Manually installed Python (e.g. downloaded from python.org) currently does not work with meson too. If there are still errors, try to install pkg-config with brew install. YMMV. +We tested this procedure on python 3.7-3.9 on Ubuntu linux and MacOS. +On Apple Silicon, you need to have ``numpy >= 1.21`` (which is the first NumPy +version to support this hardware). + +If you encounter problems with either of these steps, please file an issue at +``https://github.com/Genomics-HSE/VGsim``: please rerun with the ``-v`` flag, +``$ python -mpip install . -v`` and include the output. + + + +Adding neutral mutations +------------------------ + +We suggest to pipe the tree and non-neautral sites into phastSim (https://github.com/NicolaDM/phastSim) by Nicola de Maio to add neutral mutations and to obtain full sequences. + +Planned features +---------------- +Here we list the features which we are adding or consider for the VGsim package. +- Tau-leaping: done (beta-version). +- Memory usage optimisation to enable larger number of mutable sites: done (beta-version). +- Population-level susceptibility transitions (e.g. to simulate different uneven efforts across the world): in work. +- Advanced immunity model: in work. +- Advanced sampling schemes: medium priority. +- Super-spreading events: medium priority. +- Life-cycle: low priority. +- Recombinations: low priority. + + +Citation +-------- + +Please cite our preprint (https://www.medrxiv.org/content/10.1101/2021.04.21.21255891) when using VGsim in your research. +``` +Shchur et al. VGsim: scalable viral genealogy simulator for global pandemic. +medRxiv 2021.04.21.21255891; doi: https://doi.org/10.1101/2021.04.21.21255891 +``` + +Fast build +---------- + +For developers, it is recommended to manually build the package. This process is much faster then using pip, so it is handy while actively working with the code. + +From the `VGsim` directory, first let us configure `PYTHOPATH`. +``` +ver=$(python -c'from sys import version_info as v; print("%s.%s"%(v.major, v.minor))') +export PYTHONPATH=$PWD/installdir/lib/python$ver/site-packages/ +``` +Remember to do it in every terminal window/tab where you work on the project (e.g. with your `jupyter notebook`)! Now we are ready to build `VGsim`. +``` +meson setup build --prefix=$PWD/installdir +meson install -C build +``` +That's it! For subsequent rebuilds (in the same terminal session) you need to run only the last command +``` +meson install -C build +``` diff --git a/VGsim/Simulator.py b/VGsim/Simulator.py new file mode 100644 index 0000000..88190d7 --- /dev/null +++ b/VGsim/Simulator.py @@ -0,0 +1,492 @@ +def print_basic_parameters(): + """ + This methods prints the basic parameters of the epidemiological model. + """ + +def print_populations(): + """ + This methods prints parameters of the population model. + """ + +def print_immunity_model(): + """ + This methods prints the basic parameters of the immunity model. + """ + +def print_all(basic_parameters=True, populations=True, immunity_model=True): + """ + This methods prints all the parameters of the simulation. + + :param basic_parameters: whether to output the basic parameters of the epidemiological model. + :type basic_parameters: bool + + :param populations: whether to output parameters of the population model. + :type populations: bool + + :param immunity_model: whether to output parameters of the immunity model. + :type immunity_model: bool + """ + +def set_transmission_rate(rate, haplotype=None): + """ + .. _Wikipedia: https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology + + Transmission rate is the the expected number of new infections from a single infected individual per time unit in the beginning of the epidemics when all but one of the hosts are susceptible. See `Wikipedia`_ - that is parameter beta in SIR model. + + :param rate: transmission rate value. + :type rate: float + + :param haplotype: haplotypes for which the new value is being set. `See for details `_. + :type haplotype: int or string or None + """ + + +def set_recovery_rate(rate, haplotype=None): + """ + .. _Wikipedia: https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology + + Recovery rate is the inverse of the expected recovery time - non-negative float value. See `Wikipedia`_ - that is parameter gamma in SIR model. + + :param rate: recovery rate value. + :type rate: float + + :param haplotype: haplotypes for which the new value is being set. `See for details `_. + :type haplotype: int or string or None + """ + +def set_sampling_rate(rate, haplotype=None): + """ + Sampling rate: the rate at which infected individuals are sampled. The genealogy will be generated only for these samples. Alternatively, if *sampling_probability* is **True**, it will specify the fraction of recovered individuals which are sampled. + + :param rate: sampling rate value. + :type rate: float + + :param haplotype: haplotypes for which the new value is being set. `See for details `_. + :type haplotype: int or string or None + """ + +def set_mutation_rate(rate=None, probabilities=None, haplotype=None, mutation=None): + """ + This method allows setting the mutation rate and the weights for each single nucleotide substitution (given the mutation happened). + + :param rate: mutation rate value (None would not change the old value). + :type rate: float or None + + :param probabilities: weights of each single nucleotide substitution given mutation occured (None would not change the old values). `See for example `_. + :type probabilities: list of four non-negative integers or None + + :param haplotype: the ancestral haplotype for the mutation. `See for details `_. + :type haplotype: int or string or None + + :param mutation: the id of position on the haplotype where mutation arises. + :type mutation: int or None + """ + +def set_susceptibility_type(susceptibility_type, haplotype=None): + """ + The type of immunity (or the susceptibility group) which an individual gets after being infected with a pathogen of a haplotype. + + :param susceptibility_type: immunity group id. + :type susceptibility_type: int + + :param haplotype: haplotypes for which the new value is being set. `See for details `_. + :type haplotype: int or string or None + """ + +def set_susceptibility(rate, haplotype=None, susceptibility_type=None): + """ + Susceptibility is a multiplicative modifier of the transmission rate based on the susceptible host immunity with a certain *susceptibility_type*. It can decrease or increase the transmission rate of a particular haplotype to the individuals of said *susceptibility_type*. + + :param rate: susceptibility value. + :type rate: float + + :param haplotype: haplotypes for which the new value is being set. `See for details `_. + :type haplotype: int or string or None + + :param susceptibility_type: immunity group id for which the new susceptibility value is being set. + :type susceptibility_type: int or None + """ + +def set_immunity_transition(rate, source=None, target=None): + """ + The change of immunity without infection (e.g. due to vaccination or immunity loss with time). + + :param rate: transition rate value. + :type rate: float + + :param source: source immunity group id (None means that the new value will be set to all immunity groups as source). + :type source: int or None + + :param target: target immunity group id (None means that the new value will be set to all immunity groups as source). + :type target: int or None + """ + + +def set_population_size(size, population=None): + """ + Set the number of individuals in the population. + + :param size: total number of individuals in the population. + :type size: int + + :param population: population for which the new population size is being set (in case of None the value will be updated for all populations). + :type population: int or None + """ + +def set_contact_density(value, population=None): + """ + The relative number of contacts per time units. + + :param value: contact density value. + :type value: float + + :param population: population for which the new contact density size is being set (in case of None the value will be updated for all populations). + :type population: int or None + """ + +def set_npi(parameters, population=None): + """ + Setting conditions when lockdown in a population is imposed and lifted with the certain contact density during the lockdown. + + :param parameters: list with three elements: contact density value during lockdown, fraction of infectious population when the lockdown is set, fraction of infectious population when lockdown is lifted. + :type parameters: list of length 3 with float values + + :param population: population for which the lockdown parameters are being set (in case of None the value will be updated for all populations). + :type population: int or None + """ + +def set_sampling_multiplier(multiplier, population=None): + """ + The relative sampling in the population (multiplicative modifier). Sampling rate of each haplotype is modified by this factor. + + :param value: sampling multiplier value. + :type value: float + + :param population: population for which the new sampling multiplier is being set (in case of None the value will be updated for all populations). + :type population: int or None + """ + +def set_migration_probability(probability=None, total_probability=None, source=None, target=None): + """ + The probability that an individual from the population source is travelling to the population target. + + :param probability: probability of migration value. + :type probability: float + + :param total_probability: if True, all the entries (except for the diagonal element) of row corresponding to source population is filled with probabilitie/(K-1). That represents individual travelling out of its population to a random destination. + :type total_probability: bool + + :param source: source population with infectious individual (None means that the new value will be set to all populations as source). + :type source: int or None + + :param target: target population with infectious individual (None means that the new value will be set to all populations as source). + :type target: int or None + """ + +def set_susceptible_individuals(amount, source_type, target_type, population=None): + """ + The method allows moving susceptible individuals between susceptibility groups. + + :param amount: the number of individuals to be moved. + :type amount: int + + :param source_type: source susceptibility group from which individuals are moved. + :type source_type: int + + :param target_type: target susceptibility group into which individuals are moved. + :type target_type: int + + :param population: population for which the new value is being set (in case of None the value will be updated for all populations). + :type population: int or None + """ + +def set_infected_individuals(amount, source_haplotype, target_haplotype, population=None): + """ + The method allows moving infected individuals between susceptibility groups. + + :param amount: the number of individuals to be moved. + :type amount: int + + :param source_haplotype: source haplotype from which individuals are moved. + :type source_haplotype: int + + :param target_haplotype: target haplotype into which individuals are moved. + :type target_haplotype: int + + :param population: population for which the new value is being set (in case of None the value will be updated for all populations). + :type population: int or None + """ + +def set_infection(amount, source_type, target_haplotype, population=None): + """ + The method allows moving individuals from susceptibility group to target haplotype. + + :param amount: the number of individuals to be moved. + :type amount: int + + :param source_type: source susceptibility group from which individuals are moved. + :type source_type: int + + :param target_haplotype: target haplotype into which individuals are moved. + :type target_haplotype: int + + :param population: population for which the new value is being set (in case of None the value will be updated for all populations). + :type population: int or None + """ + +def set_chain_events(file_name): + """ + Allows to import chain of events directly from the .npy file + + :param file_name: a name of the file (without the .npy extension) + :type file_name: str + """ + +def set_settings(file_template): + """ + Currently is a placeholder and does nothing. Do not include in actual docs until it does something. + """ + +def set_state(set_state): + """ + Calls set_chain_events and set_setting. Now identical to set_events. Do not include in docs until it starts to differ. + """ + + +def output_newick(file_template="newick_output", file_path = None): + """ + Outputs the result of simulation in the newick format. + + :param file_template: template for the file name + :type file_template: str + + :param file_path: path to output file + :type file_path: str + """ + +def output_mutations(file_template="mutation_output", file_path = None): + """ + Outputs the information about all mutations + + :param file_template: template for the file name + :type file_template: str + + :param file_path: path to output file + :type file_path: str + """ + +def output_migrations(file_template="migrations", file_path = None): + """ + Outputs the information about all migrations + + :param file_template: template for the file name + :type file_template: str + + :param file_path: path to output file + :type file_path: str + """ + +def output_sample_data(output_print=False): + """ + Show all information about sampling, time, place, etc. + + :param output_print: if *Thue*, it outputs data about each sample to the console. Data includes time of sampling, population and haplotype of sampled individual. + :type output_print: bool + + :return: 3 lists with times, population ids and haplotypes of the sampled individuals + """ + + +def output_epidemiology_timelines(step=1000, output_file=False): + """ + Records simulation state changes over some period of time. + + :param step: a number of steps which split the epidemiology timelines. + :type step: str + + :param output_file: if **True**, the timeline is written in the output file + :type output_file: bool + """ + +def output_chain_events(file_name="chain_events"): + """ + Outputs event chain. + + :param file_name: file name for the output + :param file_name: str + """ + +def output_settings(self, file_template="parameters"): + """ + Outputs settings. + + :param file_template: file name for the output + :param file_template: str + """ + +def output_state(): + """ + Outputs event chain and settings. + """ + + +def get_data_susceptible(population, susceptibility_type, step_num): + """ + Returns the list with the amount of sucseptible individuals over some period of time. + + :param population: population to retrieve data from + :type population: int + + :param susceptibility_type: susceptibility type to retrieve data from + :type susceptibility_type: int + + :param step_num: number of steps which split the epidemiology timeline. + :type step_num: int + + :return: 3 lists - amounts of susceptible individuals on the time points, time points themselves at which we retrieve amounts of individuals, and lockdowns data for the current epidemiology + """ + +def get_data_infectious(self, population, haplotype, step_num): + """ + Returns the list with the amount of infectious individuals over some period of time. + + :param population: population to retrieve data from + :type population: int + + :param haplotype: haplotype to retrieve data from + :type haplotype: int + + :param step_num: number of steps which split the epidemiology timeline + :type step_num: int + + :return: 3 lists - amounts of infectious individuals on the time points, time points themselves at which we retrieve amounts of individuals, and lockdowns data for the current epidemiology + """ + + +def plot_infectious(population, haplotype, step_num, label_infectious, label_samples): + """ + Creates plots for infectious and sampled individuals. + + :param population: population id for the plot + :type population: int + + :param haplotype: haplotype for the plot + :type haplotype: int or str + + :param step_num: number of steps which split the epidemiology timeline + :type step_num: int + + :param label_infectious: the label for the infectious plot in the plot legend + :type label_infectious: str + + :param label_samples: the label for the samples plot in the plot legend + :type label_samples: str + """ + +def add_plot_infectious(population, haplotype, step_num=100, label_infectious=None, label_samples=None): + """ + Add to plot the trajectories of the change of the number of infectious individuals over time. + + :param population: population id for the plot + :type population: int + + :param haplotype: haplotype for the plot + :type haplotype: int or str + + :param step_num: number of steps which split the epidemiology timeline + :type step_num: int + + :param label_infectious: the label for the infectious plot + :type label_infectious: str or None + + :param label_samples: the label for the samples plot + :type label_samples: str or None + """ + +def add_plot_susceptible(population, susceptibility_type, step_num=100, label=None): + """ + Add to plot the trajectories of the change of the number of susceptible individuals over time. + + :param population: population id for the plot + :type population: int + + :param haplotype: haplotype for the plot + :type haplotype: int or str + + :param step_num: number of steps which split the epidemiology timeline + :type step_num: int + + :param label: the label for the susceptible plot + :type label: str + """ + + +def add_legend(): + """ + Add legend to the plot. + """ + +def add_title(name="Plot"): + """ + Add plot title. + + :param name: the title for the plot + :type name: str + """ + +def plot(): + """ + Show plot with all the trajectories added by other methods. + """ + +def simulate(iterations=1000, sample_size=None, epidemic_time=None, method='direct'): + """ + This methods performs the simulation. The simulation interrupts when either one of the following conditions is satisfied: the number of iterations equals *iterations*, the number of collected samples equals *sample_size*, ot the total virtual time of the epidemic exceeds *time* + It can be called multiple times, changes of most parameters are allowed between simulation runs. + + :param iterations: maximal number of iterations. + :type iterations: int + + :param sample_size: desired sample size. + :type sample_size: int or None + + :param epidemic_time: virtual (model) time to be simulated. + :type epidemic_time: float or None + + :param method: 'direct' for the exact algorithm, 'tau' for tau-leaping approximated algorithm. + :type method: string + """ + + +def genealogy(seed=None): + """ + Generates a genealogy based on the chain of events generated during all the instances of simulate() method. + + :param seed: seed value (None for random seed). + :type seed: int or None + """ + +def citation(): + """ + Prints a citation of the paper in the console + """ + +def debug(): + """ + Returns all of the available information about simulation. Mainly used for the debug purposes. Not documented. + """ + +def get_proportion(): + """ + Used for paper preparation. Not documented. + """ + +def print_counters(): + """ + Used for paper preparation. Not documented. + """ + +def print_propensities(): + """ + Used for paper preparation. Not documented. + """ diff --git a/VGsim/VGsim_cmd.py b/VGsim/VGsim_cmd.py new file mode 100644 index 0000000..b89df1f --- /dev/null +++ b/VGsim/VGsim_cmd.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 + +import argparse +import sys +import time +import VGsim +from random import randrange +import numpy as np +import math + +parser = argparse.ArgumentParser(description='Migration inference from PSMC.') + +parser.add_argument('--iterations', '-it', nargs=1, type=int, default=1000, + help='number of iterations (default is 1000)') +parser.add_argument('--sampleSize', '-s', nargs=1, type=int, default=None, + help='number of sample (default is None)') +parser.add_argument('--time', '-t', nargs=1, type=float, default=None, + help='time for stopping simulation (default is None)') +parser.add_argument('--max_haplotypes_number', '-mhn', nargs=1, type=int, default=None, + help='#TODO') +parser.add_argument('--seed', '-seed', nargs=1, type=float, default=None, + help='random seed') + +parser.add_argument('--rates', '-rt', nargs=1, default=None, + help='rate: a file with rates for each haplotype') +parser.add_argument('--populationModel', '-pm', nargs=2, default=None, + help='population model: a file with population sizes etc, and a file with migration rate matrix') +parser.add_argument('--susceptibility', '-su', nargs=1, default=None, + help='susceptibility file') +parser.add_argument('--suscepTransition', '-st', nargs=1, default=None, + help='susceptibility transition file') + +parser.add_argument('--sampling_probability', help="#TODO", action="store_true") + +parser.add_argument("--createNewick", '-nwk', nargs=1, default=False, + help="Create a newick file of tree *.nwk ") +parser.add_argument("--writeMutations", '-tsv', nargs=1, default=False, + help="Create a mutation file *.tsv ") +parser.add_argument("--writeMigrations", nargs=1, default=False, + help="Create a migration file *.tsv ") +parser.add_argument("--output_chain_events", nargs=1, default=False, + help="#TODO") + +parser.add_argument("-citation", '-c', help="Information for citation.") + +clargs = parser.parse_args() + +if clargs.citation != None: + print("VGsim: scalable viral genealogy simulator for global pandemic") + print("Vladimir Shchur, Vadim Spirin, Victor Pokrovskii, Evgeni Burovski, Nicola De Maio, Russell Corbett-Detig") + print("medRxiv 2021.04.21.21255891; doi: https://doi.org/10.1101/2021.04.21.21255891") + sys.exit(0) + +if isinstance(clargs.rates, list): + clargs.rates = clargs.rates[0] +if isinstance(clargs.iterations, list): + clargs.iterations = clargs.iterations[0] +if isinstance(clargs.sampleSize, list): + clargs.sampleSize = clargs.sampleSize[0] +if isinstance(clargs.time, list): + clargs.time = clargs.time[0] +if isinstance(clargs.susceptibility, list): + clargs.susceptibility = clargs.susceptibility[0] +if isinstance(clargs.suscepTransition, list): + clargs.suscepTransition = clargs.suscepTransition[0] +if isinstance(clargs.seed, list): + clargs.seed = clargs.seed[0] +if isinstance(clargs.output_chain_events, list): + clargs.output_chain_events = clargs.output_chain_events[0] + +if clargs.rates == None: + bRate, dRate, sRate, mRate = [2], [1], [0.1], [[]] +else: + bRate, dRate, sRate, mRate = VGsim.IO.read_rates(clargs.rates) + +if clargs.sampleSize == None: + clargs.sampleSize = clargs.iterations +if clargs.time == None: + clargs.time = -1 + +if clargs.populationModel == None: + sizes, contactDensity, contactAfter, startLD, endLD, samplingMultiplier = [1000000], [1], [1], [1], [1], [1] + migrationRates = [[0.0]] +else: + sizes, contactDensity, contactAfter, startLD, endLD, samplingMultiplier = VGsim.IO.read_populations(clargs.populationModel[0]) + migrationRates = VGsim.IO.read_matrix(clargs.populationModel[1]) + +if clargs.susceptibility == None: + susceptible = [[1.0] for _ in range(len(bRate))] + susType = [0 for _ in range(len(bRate))] +else: + susceptible, susType = VGsim.IO.read_susceptibility(clargs.susceptibility) + +if clargs.suscepTransition == None: + suscepTransition = [[0.0]] +else: + suscepTransition = VGsim.IO.read_matrix(clargs.suscepTransition) + +if clargs.seed == None: + seed = randrange(sys.maxsize) +else: + seed = clargs.seed + +simulator = VGsim.Simulator(number_of_sites=int(math.log(len(bRate), 4)), populations_number=len(sizes), number_of_susceptible_groups=len(susceptible[0]), seed=int(seed), sampling_probability=clargs.sampling_probability) + +for i in range(len(bRate)): + simulator.set_transmission_rate(bRate[i], i) + simulator.set_recovery_rate(dRate[i], i) + simulator.set_sampling_rate(sRate[i], i) + for j in range(len(mRate[0])): + simulator.set_mutation_rate(mRate[i][j][0], [mRate[i][j][1], mRate[i][j][2], mRate[i][j][3], mRate[i][j][4]], i, j) + +for i in range(len(sizes)): + simulator.set_population_size(sizes[i], i) + simulator.set_contact_density(contactDensity[i], i) + simulator.set_npi([contactAfter[i], startLD[i], endLD[i]], i) + simulator.set_sampling_multiplier(samplingMultiplier[i], i) + for j in range(len(sizes)): + if i != j: + simulator.set_migration_probability(probability=migrationRates[i][j], source=i, target=j) +for i in range(len(susceptible)): + for j in range(len(susceptible[i])): + simulator.set_susceptibility(float(susceptible[i][j]), i, j) + +for i in range(len(susType)): + simulator.set_susceptibility_type(susType[i], i) + +for i in range(len(suscepTransition)): + for j in range(len(suscepTransition[i])): + if i != j: + simulator.set_immunity_transition(suscepTransition[i][j], i, j) + +simulator.simulate(clargs.iterations, clargs.sampleSize, clargs.time) +simulator.genealogy(int(seed)) + +if clargs.createNewick: + simulator.output_newick(clargs.createNewick) +if clargs.writeMutations: + simulator.output_mutations(clargs.writeMutations) +if clargs.writeMigrations: + simulator.output_migrations(clargs.writeMigrations) +if clargs.output_chain_events: + simulator.output_chain_events(clargs.output_chain_events) \ No newline at end of file diff --git a/VGsim/data/Table 3/Table 3.ipynb b/VGsim/data/Table 3/Table 3.ipynb new file mode 100644 index 0000000..8cf503b --- /dev/null +++ b/VGsim/data/Table 3/Table 3.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "id": "07023925", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 288\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.668513393756367\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 281\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.6670835908470565\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 284\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.665982508283415\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 279\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.664658152957584\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 277\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.665755126964471\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 277\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.664815285090734\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 288\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.696113516237194\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 280\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.699337971323137\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 278\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.7003684985309535\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 288\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.699360261198045\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 272\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.697479261542758\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 274\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.692345292808055\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 265\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.913211277905174\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 290\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.914098068672664\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 281\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.946369973301594\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 275\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.959552629825298\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 287\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.965133379383973\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 266\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.928729686794892\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 277\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.876683982516927\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 277\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.925576407521985\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 262\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.854080162915341\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 285\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.917866703259873\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 267\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.890442395401363\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 269\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.872726366798063\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 265\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.028753464035603\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 281\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.600252675503189\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 273\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.337184147856449\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 237\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.386193897044432\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 274\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 6.047362981795651\n", + "Iterations\n", + "Seed: 2023.0\n", + "Number of samples: 255\n", + "Total number of iterations: 10000\n", + "Size events: 10000\n", + "Current time: 5.1490976751889335\n" + ] + } + ], + "source": [ + "from VGsim import Simulator\n", + "import time\n", + "import os \n", + "\n", + "def get_data(num_pop, total_migration_rate):\n", + " simulator = Simulator(number_of_sites=2, populations_number=num_pop, number_of_susceptible_groups=3, seed=2023)\n", + " simulator.set_transmission_rate(2.5)\n", + " simulator.set_transmission_rate(4.0, haplotype='GG')\n", + " simulator.set_recovery_rate(0.9)\n", + " simulator.set_sampling_rate(0.1)\n", + " simulator.set_migration_probability(total_probability=total_migration_rate)\n", + " simulator.set_population_size(int(2*10**9/num_pop))\n", + " simulator.set_susceptibility(1.0, susceptibility_type=0)\n", + " simulator.set_susceptibility(0.0, susceptibility_type=1)\n", + " simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype=\"C*\")\n", + " simulator.set_susceptibility(1.0, susceptibility_type=1, haplotype=\"G*\")\n", + " simulator.set_susceptibility(0.0, susceptibility_type=2)\n", + " simulator.set_susceptibility_type(1)\n", + " simulator.set_susceptibility_type(2, haplotype=\"C*\")\n", + " simulator.set_susceptibility_type(2, haplotype=\"G*\")\n", + " simulator.set_immunity_transition(0.01, target=0)\n", + " simulator.set_lockdown([0.1, 0.02, 0.01])\n", + " time_1 = time.time()\n", + " simulator.simulate(iterations=10000000)\n", + " time_2 = time.time()\n", + " \n", + " return [str(round(time_2 - time_1, 1)), str(round(simulator.get_proportion()*100, 2))]\n", + "\n", + "population_numbers = [2, 5, 10, 20, 50, 100]\n", + "total_migration_rates = [0.001, 0.002, 0.005, 0.01, 0.1]\n", + "\n", + "with open('table.txt', 'w') as table:\n", + " table.write('\\\\begin{table}[ht]\\n')\n", + " table.write('\\t\\\\resizebox{\\\\linewidth}{!}{\\n')\n", + " table.write('\\t\\t\\\\begin{tabular}{|c||c|c|c|c|c|c|}\\n')\n", + " table.write('\\t\\t\\t\\\\hline\\n')\n", + " table.write('\\t\\t\\t\\t\\\\multirow{2}{*}{\\\\shortstack[c]{Cumulative\\\\\\\\migration\\\\\\\\probability $M$}} & \\\\multicolumn{6}{c|}{\\\\makecell{Number of demes $K$\\\\\\\\\\\\,}} \\\\\\\\\\n')\n", + " table.write('\\t\\t\\t\\\\cline{2-7}\\n')\n", + " table.write('\\t\\t\\t\\t& 2 & 5 & 10 & 20 & 50 & 100\\\\\\\\\\n')\n", + " table.write('\\t\\t\\t\\\\hline\\n')\n", + " for rate in total_migration_rates:\n", + " c = []\n", + " for number in population_numbers:\n", + " c.append(get_data(number, rate))\n", + " table.write('\\t\\t\\t\\t'+str(rate)+' & '+c[0][0]+'s & '+c[1][0]+'s & '+c[2][0]+'s & '+c[3][0]+'s & '+c[4][0]+'s & '+c[5][0]+'s \\\\\\\\')\n", + " table.write('\\t\\t\\t\\t& '+c[0][1]+'\\\\% & '+c[1][1]+'\\\\% & '+c[2][1]+'\\\\% & '+c[3][1]+'\\\\% & '+c[4][1]+'\\\\% & '+c[5][1]+'\\\\% \\\\\\\\\\n')\n", + " table.write('\\t\\t\\t\\\\hline\\n')\n", + " table.write('\\t\\t\\\\end{tabular}\\n')\n", + " table.write('\\t}\\n')\n", + " table.write('\\\\end{table}\\n')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8906f6df", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/VGsim/data/Table 3/Table 3.py b/VGsim/data/Table 3/Table 3.py new file mode 100644 index 0000000..5dcf6aa --- /dev/null +++ b/VGsim/data/Table 3/Table 3.py @@ -0,0 +1,50 @@ +from VGsim import Simulator +import time +import os + +def get_data(num_pop, total_migration_rate): + simulator = Simulator(number_of_sites=2, populations_number=num_pop, number_of_susceptible_groups=3, seed=2023) + simulator.set_transmission_rate(2.5) + simulator.set_transmission_rate(4.0, haplotype='GG') + simulator.set_recovery_rate(0.9) + simulator.set_sampling_rate(0.1) + simulator.set_migration_probability(total_probability=total_migration_rate) + simulator.set_population_size(int(2*10**9/num_pop)) + simulator.set_susceptibility(1.0, susceptibility_type=0) + simulator.set_susceptibility(0.0, susceptibility_type=1) + simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype='C*') + simulator.set_susceptibility(1.0, susceptibility_type=1, haplotype='G*') + simulator.set_susceptibility(0.0, susceptibility_type=2) + simulator.set_susceptibility_type(1) + simulator.set_susceptibility_type(2, haplotype='C*') + simulator.set_susceptibility_type(2, haplotype='G*') + simulator.set_immunity_transition(0.01, target=0) + simulator.set_lockdown([0.1, 0.02, 0.01]) + time_1 = time.time() + simulator.simulate(iterations=100000000) + time_2 = time.time() + + return [str(round(time_2 - time_1, 1)), str(round(simulator.get_proportion()*100, 2))] + +population_numbers = [2, 5, 10, 20, 50, 100] +total_migration_rates = [0.001, 0.002, 0.005, 0.01, 0.1] + +with open('table.txt', 'w') as table: + table.write('\\begin{table}[ht]\n') + table.write('\t\\resizebox{\\linewidth}{!}{\n') + table.write('\t\t\\begin{tabular}{|c||c|c|c|c|c|c|}\n') + table.write('\t\t\t\\hline\n') + table.write('\t\t\t\t\\multirow{2}{*}{\\shortstack[c]{Cumulative\\\\migration\\\\probability $M$}} & \\multicolumn{6}{c|}{\\makecell{Number of demes $K$\\\\\\,}} \\\\\n') + table.write('\t\t\t\\cline{2-7}\n') + table.write('\t\t\t\t& 2 & 5 & 10 & 20 & 50 & 100\\\\\n') + table.write('\t\t\t\\hline\n') + for rate in total_migration_rates: + c = [] + for number in population_numbers: + c.append(get_data(number, rate)) + table.write('\t\t\t\t'+str(rate)+' & '+c[0][0]+'s & '+c[1][0]+'s & '+c[2][0]+'s & '+c[3][0]+'s & '+c[4][0]+'s & '+c[5][0]+'s \\\\') + table.write('\t\t\t\t& '+c[0][1]+'\\% & '+c[1][1]+'\\% & '+c[2][1]+'\\% & '+c[3][1]+'\\% & '+c[4][1]+'\\% & '+c[5][1]+'\\% \\\\\n') + table.write('\t\t\t\\hline\n') + table.write('\t\t\\end{tabular}\n') + table.write('\t}\n') + table.write('\\end{table}\n') diff --git a/VGsim/data/Table 3/table.txt b/VGsim/data/Table 3/table.txt new file mode 100644 index 0000000..9759851 --- /dev/null +++ b/VGsim/data/Table 3/table.txt @@ -0,0 +1,21 @@ +\begin{table}[ht] + \resizebox{\linewidth}{!}{ + \begin{tabular}{|c||c|c|c|c|c|c|} + \hline + \multirow{2}{*}{\shortstack[c]{Cumulative\\migration\\probability $M$}} & \multicolumn{6}{c|}{\makecell{Number of demes $K$\\\,}} \\ + \cline{2-7} + & 2 & 5 & 10 & 20 & 50 & 100\\ + \hline + 0.001 & 28.7s & 30.0s & 31.9s & 35.1s & 47.2s & 69.3s \\& 0.09\% & 0.12\% & 0.11\% & 0.11\% & 0.11\% & 0.12\% \\ + \hline + 0.002 & 29.2s & 30.4s & 32.3s & 35.3s & 47.0s & 70.1s \\& 0.17\% & 0.21\% & 0.16\% & 0.2\% & 0.21\% & 0.2\% \\ + \hline + 0.005 & 29.4s & 30.7s & 32.5s & 35.6s & 48.1s & 70.4s \\& 0.33\% & 0.51\% & 0.25\% & 0.46\% & 0.52\% & 0.43\% \\ + \hline + 0.01 & 29.4s & 30.6s & 32.9s & 35.5s & 48.0s & 70.9s \\& 0.75\% & 1.16\% & 0.85\% & 0.78\% & 0.93\% & 0.95\% \\ + \hline + 0.1 & 30.3s & 31.9s & 33.8s & 37.0s & 50.3s & 73.0s \\& 2.04\% & 6.56\% & 6.15\% & 6.94\% & 5.98\% & 5.08\% \\ + \hline + \end{tabular} + } +\end{table} diff --git a/VGsim/data/Table 4/Table 4.ipynb b/VGsim/data/Table 4/Table 4.ipynb new file mode 100644 index 0000000..93a3391 --- /dev/null +++ b/VGsim/data/Table 4/Table 4.ipynb @@ -0,0 +1,84 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "d756fcb3", + "metadata": {}, + "outputs": [], + "source": [ + "from VGsim import Simulator\n", + "import time\n", + "import os \n", + "\n", + "def get_data(rate, size):\n", + " simulator = Simulator(number_of_sites=2, populations_number=100, number_of_susceptible_groups=1, seed=2023)\n", + " simulator.set_transmission_rate(2.5)\n", + " simulator.set_recovery_rate(1.0-rate)\n", + " simulator.set_sampling_rate(rate)\n", + " simulator.set_migration_probability(total_probability=0.005)\n", + " simulator.set_population_size(200000000)\n", + " time_1 = time.time()\n", + " simulator.simulate(iterations=size*300, sample_size=size)\n", + " time_2 = time.time()\n", + " simulator.genealogy(seed=2023)\n", + " time_3 = time.time()\n", + " \n", + " return [str(round(time_2 - time_1, 2)), str(round(time_3 - time_2, 2)), str(simulator.simulation.events.ptr), str(simulator.simulation.bCounter)]\n", + "\n", + "\n", + "sampling_rate = [0.01, 0.1, 1.0]\n", + "sample_size = [10**5, 10**6, 5*10**6, 10**7, 5*10**7, 1.5*10**8]\n", + "\n", + "with open('table.txt', 'w') as table:\n", + " table.write('\\\\begin{table}[ht]\\n')\n", + " table.write('\\t\\\\resizebox{\\\\linewidth}{!}{\\n')\n", + " table.write('\\t\\t\\\\begin{tabular}{|c||c|c|c|c|c|c|}\\n')\n", + " table.write('\\t\\t\\t\\\\hline\\n')\n", + " table.write('\\t\\t\\t\\t\\\\multirow{2}{*}{\\\\shortstack[c]{Cumulative\\\\\\\\migration\\\\\\\\probability $M$}} & \\\\multicolumn{6}{c|}{\\\\makecell{Number of demes $K$\\\\\\\\\\\\,}} \\\\\\\\\\n')\n", + " table.write('\\t\\t\\t\\\\cline{2-7}\\n')\n", + " table.write('\\t\\t\\t\\t& 2 & 5 & 10 & 20 & 50 & 100\\\\\\\\\\n')\n", + " table.write('\\t\\t\\t\\\\hline\\n')\n", + " for rate in samping_rates:\n", + " c = []\n", + " for size in sample_size:\n", + " c.append(get_data(number, rate))\n", + " table.write('\\t\\t\\t\\t'+str(rate)+' & '+c[0][0]+'s & '+c[1][0]+'s & '+c[2][0]+'s & '+c[3][0]+'s & '+c[4][0]+'s & '+c[5][0]+'s \\\\\\\\')\n", + " table.write('\\t\\t\\t\\t& '+c[0][1]+'\\\\% & '+c[1][1]+'\\\\% & '+c[2][1]+'\\\\% & '+c[3][1]+'\\\\% & '+c[4][1]+'\\\\% & '+c[5][1]+'\\\\% \\\\\\\\\\n')\n", + " table.write('\\t\\t\\t\\\\hline\\n')\n", + " table.write('\\t\\t\\\\end{tabular}\\n')\n", + " table.write('\\t}\\n')\n", + " table.write('\\\\end{table}\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e06d2c6e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/VGsim/data/VGsim and TiPS/test_TiPS.R b/VGsim/data/VGsim and TiPS/test_TiPS.R new file mode 100644 index 0000000..bb3c647 --- /dev/null +++ b/VGsim/data/VGsim and TiPS/test_TiPS.R @@ -0,0 +1,62 @@ +library(TiPS) +library(ape) + +reactions <- c("S [beta*S*I] -> I", + "I [gamma*I] -> R") + +sir_simu <- build_simulator(reactions) + +N <- 10000000 + +initialStates <- c(I = 1, S = N - 1, R = 0) + +time_start <- 0 +time_finish <- 20 + +time <- c(time_start, time_finish) + +theta <- list(gamma = 1, beta = 2.5 / N) + +safe_run <- function(f, ...) { + out <- list() + while(! length(out)) {out <- f(...)} + out +} + +safe_sir_simu <- function(...) safe_run(sir_simu, ...) + +time1 <- Sys.time() + + +traj_dm <- safe_sir_simu( + paramValues = theta, + initialStates = initialStates, + method = "exact", + times = time) + +time2 <- Sys.time() +print("Forward time exact") +print(time2-time1) + + + +number_of_samples <- 10000 + +time1 <- Sys.time() + +sir_tree <- simulate_tree( + simuResults = traj_dm, + #dates = dates, + dates = seq(from = 12, to = 12, length.out = number_of_samples), + deme = c("I"), # the type of individuals that contribute to the phylogeny + sampled = c(I = 1), # the type of individuals that are sampled and their proportion of sampling + root = "I", # type of individual at the root of the tree + isFullTrajectory = FALSE, # deads do not generate leaves + nTrials = 1, + addInfos = FALSE) # additional info for each node + +time2 <- Sys.time() +print("Backward time") +print(time2-time1) + +#ape::plot.phylo(sir_tree, cex = .5) diff --git a/VGsim/data/VGsim and TiPS/test_VGsim.py b/VGsim/data/VGsim and TiPS/test_VGsim.py new file mode 100644 index 0000000..7d6b88c --- /dev/null +++ b/VGsim/data/VGsim and TiPS/test_VGsim.py @@ -0,0 +1,29 @@ +import time +import VGsim + +number_of_sites = 0 +populations_number = 1 +number_of_susceptible_groups = 2 +simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, seed=12312) + +simulator.set_transmission_rate(0.25) +simulator.set_recovery_rate(0.099) +simulator.set_sampling_rate(0.001) +simulator.set_susceptibility_type(1, haplotype=None) + +simulator.set_population_size(1000000000, population=0) +t1 = time.time() + +simulator.simulate(100000000) + +all_time_forward = 0 +t2 = time.time() +all_time_forward += t2-t1 +print("Forward time is", all_time_forward) + +all_time_backward = 0 +t1 = time.time() +simulator.genealogy() +t2 = time.time() +all_time_backward += t2 - t1 +print("Backward time is", all_time_backward) \ No newline at end of file diff --git a/VGsim/data/model_example.py b/VGsim/data/model_example.py new file mode 100644 index 0000000..dfacd90 --- /dev/null +++ b/VGsim/data/model_example.py @@ -0,0 +1,39 @@ +import VGsim +import random +import sys + +number_of_sites = 1 +populations_number = 2 +number_of_susceptible_groups = 3 +simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, + seed=random.randint(0, sys.maxsize)) + +simulator.set_transmission_rate(0.25) +simulator.set_transmission_rate(0.4, haplotype="G") + +mutation_rate = 0.003 +substitution_weights = [1, 1, 1, 1] # ATCG +simulator.set_mutation_rate(mutation_rate, substitution_weights) + +simulator.set_recovery_rate(0.09) +simulator.set_sampling_rate(0.01) + +simulator.set_susceptibility_type(0) # default +simulator.set_susceptibility_type(1, haplotype="A") +simulator.set_susceptibility_type(1, haplotype="T") +simulator.set_susceptibility_type(1, haplotype="C") +simulator.set_susceptibility_type(2, haplotype="G") + +simulator.set_susceptibility(1.0, susceptibility_type=0) # no resist +simulator.set_susceptibility(0.0, susceptibility_type=1, haplotype="A") # resist +simulator.set_susceptibility(0.0, susceptibility_type=1, haplotype="T") # resist +simulator.set_susceptibility(0.0, susceptibility_type=1, haplotype="C") # resist +simulator.set_susceptibility(0.2, susceptibility_type=1, haplotype="G") # partial resist +simulator.set_susceptibility(0.0, susceptibility_type=2) # resist + +simulator.set_immunity_transition(0.01, source=1, target=0) +simulator.set_immunity_transition(0.01, source=2, target=0) + +simulator.set_population_size(100000, population=0) +simulator.set_population_size(100000, population=1) +simulator.set_migration_probability(0.1) \ No newline at end of file diff --git a/VGsim/docs/Makefile b/VGsim/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/VGsim/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/VGsim/docs/make.bat b/VGsim/docs/make.bat new file mode 100644 index 0000000..6fcf05b --- /dev/null +++ b/VGsim/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. 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zxM`hx9)R$b01ZCw3P{6G1O!H5BjD2-cU!R`VM${%7xE5ZwfU|zgG{_j(cTZG6BY>gB7XU>yeLdwCaUEWNH@tS3s&%{m)cVE-vx1I} zPW@Vb`gK$W=yH3o$|yo02Fe0~_iW7#h-<~aUufPIzNQv*ngFbF5P(Rf4bBRPFQecF z1qnEE_F#b}9C%(UesQ3sFm#P9=a;mwWRL+2^X#X<&8wLm3OA3&+BOV4RP4 z=T3o@Q7Kr{{@Pkb;z2lVuK#|yabRE*I9L3DfCrXP8^DL;6a@I|(FQrBVHgKuBz?V} zodhv;9TT`, `A->C`, `A->G` occur with probabilities 3/8, 4/8 and 1/8 respectively, and for the second site substitutions `T->A`, `T->C`, `T->G` occur with probabilities 0.4, 0.6 and 0.0 respectively). + +Setting population model +------------------------ + +In order to set population model use `--populationModel` or `-pm` flag followed by two files. The first file contains information abouch each population, and the second file contains the matrix of "visit" rates. + +**Part 1 - setting populations** + +Here is an example of the file with populations. + +.. code-block:: python + + #Population_format_version 0.0.1 + id size contactDensity conDenAfterLD startLD endLD samplingMultiplier + 0 20000000 1.0 0.1,2,1 5 + 1 10000000 1.0 0.1,2,1 1 + +`id` is the population number (for convinience). `size` is the total number of individuals in the population. Contact density in the relative number of contacts per time unit. It can be used to model different social, cultural, economical and other aspects (e.g. population density in a city or a country side, holiday times etc.) Currently we provide only one out-of-the-box solution to change contact density during simulation. Those are optinal fields (included in the example) to tune lockdowns. `conDenAfterLD` is the contact density during lockdown, `startLD` and `endLD` are condition to impose and lift the lockdown. These two numbers are the percentage of individuals which are simultaneously infected. If the percentage of simultanelously infected individuals in a population becomes larger than `startLD`, lockdown is imposed. As soon as the percentage of simultaneously infected individuals drops below `endLD` the lockdown is lifted. + +**Part 2 - setting migration matrix** + +In the model we consider migration as some individuals spending some time in other population and then returning to their home population. This is modelled by the probabilities that an individual from their home population is found in a destination population. During such visits individuals are assumed to contact with individuals of another population according to contact density of the destination population (so lockdown in a destination country also changes the contact rate of a visitor). Example: + +.. code-block:: python + + #Migration_format_version 0.0.1 + 0.0 0.002 0.002 0.001 + 0.0 0.0 0.002 0.0 + 0.0 0.005 0.0 0.001 + 0.0 0.002 0.001 0.0 + +Susceptibility types (immunity) +------------------------------- + +Multiple susceptibility classes can be modelled by adding `--susceptibility` or, `-su` flag followed by the file with susceptibility constants (non-negative `float`). Example: + +.. code-block:: python + + #Susceptibility_format_version 0.0.1 + H T S0 S1 S2 + AA 1 1.0 0.0 0.0 + AT 2 1.0 0.4 0.0 + AC 2 1.0 1.1 0.0 + +There are three susceptibility types `S0`, `S1` and `S2` in this example. All the individuals start in `S0`, and they have susceptibility of `1.0` to all strains. `T` is the type of susceptibility (immunity) caused by recovering from a particular strain. Inficting by haplotype `AA` leads to susceptibility type `S1`, which gives total resistance (susceptibility 0.0) to haplotype `AA`, partial resitance to `AT` (susceptibility 0.4) and increases susceptibility (1.1) to strain `AC`. + +*NB* There is no "immunity memory" - the immunity does not depend on the whole illness history of an individual, but only on the **latest** infection. + +**Susceptibility transition** + +The user can specify the rates of direct transitions between susceptibility types. This can be used for example to model vaccination or immunity loss. Use `--suscepTransition` or, `-st` flag followed by the file with susceptibility transition rate matrix (non-negative `float` entrys). Example: + +.. code-block:: python + + #Susceptibility_format_version 0.0.1 + 0.0 0.0 0.0001 + 0.001 0.0 0.0001 + 0.0 0.0 0.0 + +Flags +----- + +.. list-table:: + :widths: 15 15 70 + :header-rows: 1 + + * - Flag + - Parameter + - Meaning + * - -it or --iterations + - int + - maximal number of iterations (default is 1000) + * - -s or --sampleSize + - int + - maximal number of samples (default is None) + * - -t or --time + - float + - maximal simulated time for epidemics, do not confuse with the run time (default is None) + * - -seed or --seed + - float + - random seed + * - -pm or --populationModel + - two files + - population model: a file with population sizes etc, and a file with migration rate matrix + * - -su or --susceptibility + - file + - susceptibility file + * - -st or --suscepTransition + - file + - susceptibility transition file + * - --sampling_probability + - float [0.0, 1.0] + - the probability that an infection case is sampled + * - -nwk or --createNewick + - None + - Create a newick file of tree *.nwk + * - -tsv or --writeMutations + - None + - Create a mutation file *.tsv + * - --writeMigrations + - None + - Create a migration file *.txt + * - -c or -citation + - None + - Information for citation + +Output +------ + +The final genealogical tree can be exported into Newick format with `--createNewick` or `-nwk`, and mutations can be exported in a tsv file with `--writeMutations` or `-tsv`. This format is compatible with phastSim (https://github.com/NicolaDM/phastSim) and Usher (REF). diff --git a/VGsim/docs/source/Documentation.rst b/VGsim/docs/source/Documentation.rst new file mode 100644 index 0000000..9dd29d6 --- /dev/null +++ b/VGsim/docs/source/Documentation.rst @@ -0,0 +1,84 @@ +API +=== + +.. autoclass:: sim.Simulator + :members: + + .. automethod:: __init__ + +Epidemiological model +--------------------- + +.. autofunction:: Simulator.set_transmission_rate +.. note:: + In many methods skipping an argument (or setting it to None) allows filling in multiple entries. Methods will apply the settings to all possible values of this argument. +.. autofunction:: Simulator.set_recovery_rate +.. autofunction:: Simulator.set_sampling_rate +.. autofunction:: Simulator.set_mutation_rate +.. note:: + Haplotypes can be set either by their id (which can be calculated by assuming that the haplotype is a quaternary number with A=0, T=1, C=2, G=3), or directly by the string. E.g. haplotype “AGT” can be referred by the same string or by id=0*4\ :sup:`2`\+3*4\ :sup:`1`\+1*4\ :sup:`0`\=13. One can also use “*” to indicate “any nucleotide”. In this case the changes will be applied to all the haplotypes matching the pattern. E.g. “A*T” corresponds to four haplotypes “AAT”, “ATT”, “ACT”, “AGT”. `See for details `_. + +Host immunity +------------- + +.. autofunction:: Simulator.set_susceptibility_type +.. autofunction:: Simulator.set_susceptibility +.. autofunction:: Simulator.set_immunity_transition + +Population model +---------------- + +.. autofunction:: Simulator.set_population_size +.. autofunction:: Simulator.set_contact_density +.. autofunction:: Simulator.set_npi +.. autofunction:: Simulator.set_sampling_multiplier +.. autofunction:: Simulator.set_migration_probability +.. note:: + total_probability=float means that probability is the total probability to find an individual from population source outside of its population. All the entries (except for the diagonal) of the corresponding row of the migration probability matrix will be set to total_probability/(K-1), where K is the number of populations in the simulation. + +Simulate +-------- + +.. autofunction:: Simulator.simulate +.. autofunction:: Simulator.genealogy +.. autofunction:: Simulator.set_chain_events + +Output +------ + +.. autofunction:: Simulator.output_newick +.. autofunction:: Simulator.output_mutations +.. autofunction:: Simulator.output_migrations +.. autofunction:: Simulator.output_sample_data +.. autofunction:: Simulator.output_epidemiology_timelines +.. autofunction:: Simulator.output_chain_events +.. autofunction:: Simulator.output_state + +Visualization +------------- + +.. autofunction:: Simulator.plot_infectious +.. autofunction:: Simulator.add_plot_infectious +.. autofunction:: Simulator.add_plot_susceptible +.. autofunction:: Simulator.add_legend +.. autofunction:: Simulator.add_title +.. autofunction:: Simulator.plot + +Data retrieval +-------------- + +.. autofunction:: Simulator.get_data_susceptible +.. autofunction:: Simulator.get_data_infectious + +Parameters +---------- + +.. autofunction:: Simulator.print_basic_parameters +.. autofunction:: Simulator.print_populations +.. autofunction:: Simulator.print_immunity_model +.. autofunction:: Simulator.print_all + +Miscellaneous +------------- + +.. autofunction:: Simulator.citation \ No newline at end of file diff --git a/VGsim/docs/source/Haplotypes.rst b/VGsim/docs/source/Haplotypes.rst new file mode 100644 index 0000000..de46e60 --- /dev/null +++ b/VGsim/docs/source/Haplotypes.rst @@ -0,0 +1,40 @@ +Haplotypes +========== + +One of the key features of VGsim are pathogen haplotypes (or strains). Haplotype determines haplotype properties: transmissibility, mutation rates, interaction with immunity etc. Currently VGsim is optimized for a relatively small number of haplotypes within a simulation. This number is equal to 4\ :sup:`U`\, where U is the number of genetic sites. Each site is assumed to have a single nucleotide with four possible alleles (A, T, C or G). + +.. note:: + If you have many haplotypes in your simulation, we encourage you to set *memory_optimisation* field in **Simulator** class to *True*. That might help with the memory performance for your simulation. + +Single nucleotide mutations lead to different haplotypes arising in the population. The rates of such mutations can be finely tuned: the user can set them independently for each site and each derived state of each haplotype. + +VGsim’s API provides a convenient tool to set values of different parameters to a particular haplotype or to groups of haplotypes. Every API function with a parameter ``haplotype`` supports the following parameter values. + +``haplotype`` default value is None: the setting will be applied to all possible haplotypes in the simulation. + +First possibility to refer a haplotype or a group of haplotypes is to use a string of length U (U is the number of genetic sites in the simulation) over five possible symbols ‘ATCG*’. Here * asterisk corresponds to any nucleotide. For example, ‘\*A\*’ corresponds to 16 different haplotypes ‘AAA’, ‘AAT’, ‘AAC’, ‘AAG’, ‘TAA’, ‘TAT’, ‘TAC’, ‘TAG’ etc. + +Another possibility is to use haplotype id, which is an integer. It can be obtained by converting a number in the quaternary numeral system (we assume the following bijection ATCG->0123) into the decimal system. In other words, haplotype CAG corresponds to a quaternary number 203. In decimal system it is 2*4\ :sup:`2`\+0^4\ :sup:`1`\+3*4\ :sup:`0`\=32+0+3=35. + +Example +------- + +Let us consider a handy way to set mutation rates. In this example we change the parameters for the mutation arising at the second site of four haplotypes AAA, ATA, ACA, AGA. The mutation rate is set to 2.5 for all cases. + +.. code-block:: python + + Simulator.set_mutation_rate(2.5, [2, 2, 3, 1], haplotype=”A*A”, site_id=1) + +The mutation rate and substitution weights will be the following: + +=========================== =========================== =========================== =========================== ========================== +Haplotype Rate Weights +--------------------------- --------------------------- ---------------------------------------------------------------------------------- +AAA 2.5 A\ **A**\ A->A\ **T**\ A: 2 A\ **A**\ A->A\ **C**\ A: 3 A\ **A**\ A->A\ **G**\ A:1 + +ATA 2.5 A\ **T**\ A->A\ **A**\ A: 2 A\ **T**\ A->A\ **C**\ A: 3 A\ **T**\ A->A\ **G**\ A:1 + +ACA 2.5 A\ **C**\ A->A\ **A**\ A: 2 A\ **C**\ A->A\ **T**\ A: 2 A\ **C**\ A->A\ **G**\ A:1 + +AGA 2.5 A\ **G**\ A->A\ **A**\ A: 2 A\ **G**\ A->A\ **T**\ A: 3 A\ **G**\ A->A\ **C**\ A:3 +=========================== =========================== =========================== =========================== ========================== diff --git a/VGsim/docs/source/Installation.rst b/VGsim/docs/source/Installation.rst new file mode 100644 index 0000000..d3abbcf --- /dev/null +++ b/VGsim/docs/source/Installation.rst @@ -0,0 +1,49 @@ +Building the package +-------------------- + +For performance reasons, the package includes C extensions, which need to be +built. In the future, we plan to provide prebuilt packages, but for now you need +to build them yourself. For this, you need a working toolchain for building C++ +code (gcc and clang are known to work). Since you are going to build Python extensions, +you will need python development headers (e.g. on ubuntu linux the package name is `python-dev`). + +The simplest quick-start cross-platform way is to use `conda`. To do this, create a fresh conda environment: + +.. code-block:: bash + + $ conda create -n conda_vgsim + $ conda activate conda_vgsim + $ conda install python=3.9 # or other python version of your choice (any `python >= 3.7` should work). + +Note that we install python *with conda, inside the conda environment*. Mixing system-install python and conda may lead to build- or runtime errors. + +Get the source code --- here we clone it from GitHub + +.. code-block:: bash + + $ git clone https://github.com/Genomics-HSE/VGsim.git + $ cd VGsim + +Then build the package: + +.. code-block:: bash + + $ python -m pip install . + +That's it! +You may now run simulations: + +.. code-block:: bash + + $ python ./testing/example.py + +Running this script should create a directory ``testing/example_output`` with five files ``tree.nwk`` (genealogy tree), ``mutations.tsv`` (mutations on the genealogy), ``migrations.tsv`` (migrations of the lineages in the genealogy), ``sample_population.tsv`` (population of each sample), ``plot.png`` (visualization of epidemiological curves). There will be a warning in the output. This is an expected behaviour in this example (`see for detailed explanation `_ of inflated number of individuals in a deme due to migration). This example is also provided as a jupyter notebook ``testing/example.ipynb``. + +If you prefer to not use ``conda``, the package builds fine with just pip. We recommend using virtual environments, via the standard library ``venv`` package (or a third-party ``virtualenv`` package). For MacOS users: please make sure to use Python with Homebrew. Avoid system Python. Manually installed Python (e.g. downloaded from python.org) currently does not work with meson too. If there are still errors, try to install pkg-config with brew install. YMMV. +We tested this procedure on python 3.7-3.9 on Ubuntu linux and MacOS. +On Apple Silicon, you need to have ``numpy >= 1.21`` (which is the first NumPy +version to support this hardware). + +If you encounter problems with either of these steps, please file an issue at +``https://github.com/Genomics-HSE/VGsim``: please rerun with the ``-v`` flag, +``$ python -mpip install . -v`` and include the output. diff --git a/VGsim/docs/source/Migration.rst b/VGsim/docs/source/Migration.rst new file mode 100644 index 0000000..e835638 --- /dev/null +++ b/VGsim/docs/source/Migration.rst @@ -0,0 +1,49 @@ +Migration +--------- + +The migrations are modelled as between-population transmissions: an infected individual from the source population `s` contacts a susceptible individual from the recipient population `r`. So, the individuals should contact each other while being in the same population. That can happen in three ways: an infected individual travels to population `r`, susceptible individual travels to population `s`, or both individuals travel to a third population `q` simultaneously. These travels are not modelled directly, but instead there is a matrix of probabilities to find an individual from one population into another. + +For example, if an individuals from population 0 spends on average 7 days per year in population 1 and 1 day per year in population 2, the corresponding matrix entries row will be + +.. code-block:: python + + Simulator.set_migration_probability(probability=7/365, source=0, target=1) + Simulator.set_migration_probability(probability=1/365, source=0, target=2) + +=========== =========== =========== +1-(7+1)/365 7/365 1/365 +=========== =========== =========== + +Assume that individuals from population 1 do not travel at all: + += = = +0 1 0 += = = + +And travelers from the last population 2 travel to population 0 and 1 symmetrically: + +.. code-block:: python + + Simualator.set_migration_probability(probability=1/365, source=2, target=0) + +======= ======= ======= +1/365 0 1-1/365 +======= ======= ======= + +The resulting migration matrix is + +===== ===== ===== +0.978 0.019 0.003 +0 1 0 +0.003 0 0.997 +===== ===== ===== + +Notice, that travels might considerably change the number of individuals which present in a deme at a given time. For example, assume that population sizes are `N0=1,000,000,000`, `N1=100,000,000` and `N2=1,000,000`, respectively. Then the expected number of individuals in each deme is + +* D0=357/365*10\ :sup:`9`\+1/365*10\ :sup:`6`\=978,084,932 (98% of N0) +* D1=7/365*10\ :sup:`9`\+10\ :sup:`8`\=119,178,082 (119% of N1) +* D2=1/365*10\ :sup:`9`\+364/365*10\ :sup:`6`\=3,736,986 (373% of N2) + +So, when the population sizes are considerably different, even small migration probabilities might lead to dramatic effects of the number of individuals in a deme. It might be unrealistic, and in most cases will probably be a sign to verify the simulation parameters. + +One can check the number of individuals in each deme and their changes relative to corresponding population sizes by the following command print_populations() and pay attention to the `Size` and `Actual size` collums. \ No newline at end of file diff --git a/VGsim/docs/source/Susceptible_group_sizes.png b/VGsim/docs/source/Susceptible_group_sizes.png new file mode 100644 index 0000000000000000000000000000000000000000..6f35b7acfebf8c038a9a32dda937ae0f4cf04b36 GIT binary patch literal 33586 zcmeFZcTm;owk?X93YA*O0gMMbSc{#h3z6m3jJbvsP_ z;&}y!m!qBb4m1XNrIX#`Q5P<(zx$`eq4R&ZSx=GtJmRm>+t34Nrpq@uLnGT z`wd?_iv2vuUvKRB`%m(9hsgi^<^TSI*?ZWIG?nXf8h7T}W~N?#v~Q@(d7@w_WfL{s z({lZ~+Z(r9alGTZC?n&SYcuqH^d!~0SZofBgZuKNS6ile{8V3MWpS~HLB&&9|6@|z zdY`r#c|GeftPYiWLq%0qukrFA6;*GPw)=`AwZODrMF30mcxN8Vp+mx=j}O#(Zr{&s z-o`yM*52HDxId;f&8V`vnxj~|mEPg&I?Y@gaSIEJs?FOd5dzM$WoG@B?o{JuI0IYK z&vd)U3knJ@j%9WCj*OUQahyKg#Cx9iR;$@FFE6jO;`w@nzzTPr07ol z_#vaB^1N_$(KPSe)?KWASU_o^m5AM14(Cg}cPl?Ra;YK5+9-tA`jZmJi4$V(D>H#^ z%afu(SB~NTJSGida{YS3b>^+9m$vWY_KS{=zJ2EokNtQDtDBT_>jMFn=&K=hY1M-9 zM=m`qYi?G4^5jWdik_&ab4{e=ZbwH)V|+A|uKb+*f`Z0a`Qzfo56WJhzeBTU&k1{b zd&MLTRm-B4%x%SQ54y9=*-j7Eat2*#D{#(g%d$+yE5k(Z{`h)cy=AD5vIvtk?r zH8nLYhnB`%r+rvcf~s|W%er?@uCH4s^D2NtTWh2_xjD>zH7Idib8Bsyku;lTu7d00 zw5*ue{i`A8BDLKXF6G&c$t9{~Fo>HrCCFAjV^fS%V49ie)?T-6U0a4};4=>Gq}7!r zNy_Tt2nUZ@iwq84ZOv;D#q1k8uCuL1CY`w%f@{mx0+Szi+BJ{WNBg?Cy7D+pUmt5t z6SeODT>DzY%dEVlAy&RI%TgO_Hq@xB9l#L4sygM_-S;_&i>x16kjDr4hOyN0$yx=y z{ryomFzl5*7Cf`2s)o2Z*o^h&w$^!wc=_8WH>7Tmslo7GArW)rqxL z4w2R$-->DX?`PrQ;1F+JOBD5`OT5|hDONqpB2GOksVYn;`AhH_(~+h`le&m<_f-L^ zL#2DEWISB4Rh}ifr&xD+|3ybVcI-TUm78I&w(Cql;%KPz_$fM`rR&Yh25@R;sfU0uyIHJEMJKQsH;1xxA4pf}tQo0;0spwRQpi@xecNLiVf z2<4Q`piG`Mt9s^VKmGj4Py6lq%-b`0t{n4EvFt8n)swbgaXrXm^7L@I!)QwB=y1XO zcW=B>>EnZ{ZqfM3y%Zlt$!8ru@&Y-uyY6FCt=ty{RtCd;%?w&s5=|RobkeWae)nwN zUzxLP z&y>Q7)_APjz-hCB+eeAOR*!hmyU8rgj?vQ7C;K0l9_}soKW97qln_)gT+$hP;&aRfCTjIV*N9q0k{SBha`^+(%Q4(fSQc~Jd4J4yVmjhdpaZZ_Y za&zOH=f*QR(^?a{3Y?7`ui(^Nc8bG~|Tg+4EdS6ArndPoCR;~wz zcG7kX+F!N&rc&UP@%P_?-W)dlpZA)#rHdDPZkKm)$tS5IgwMWfYT?+`moY+X%ksBw-6H4m zGLEVH>OyIzJAxO*rkGtDOX07xXWuE^yAn@N=*;cIZbkUq>19!j)6W{~>x-0&lxTfK zo2?ir8CZ4l74F)^vk2DeC2ifJCR}a>#0y$xg|ei4-I9kvP4+i$%C@E&aJ>w-8LFF_ zoy}xPR?CPpUvsxznQ&>=v@bV1-TlLU$BrFJ3JOn=`2-dg7GjO-qj2J$b0BS|TMPP|Hai=j}`S zRVC`)s{X;2&d$!YpA8vVR@#;`2bzPqjVZ|6Z;}faCAwYbLKbI6jP{FKxvsZO#Qn}N zZ&yNYTpUfS?)`2x;HbJu>VU?nVEh6{M2_FRkD;~==^yN&qLYm&vg0_QmiiQp8bXx$>E zg{i&_yD>FJ28Nc5+oSDS6VIw9aS2%j1-p`H_2fyV(bCZKA6gtPK|zz9DMdl+e0;ZO_x^&tsIeT@}OJ7GtYI1>%9Nly@Gx^7hNV z!M;9&;NLD#>_%H&<7OB-dkS|P(0ofpbKSH#DI@q9kEWCRD)16kkA~@pkPr@5y|mp7 zkd=8>$cq;*UaLPUDk_o)WKTq95GOg4=D>l3Sovr}haXbDCEeZHkM{A@@lE9GN#0JP z-Mu^TwOLobqr}y#nda3&T%t@&Om=G`>e)BGZEMbd^7JW9!Otqb%rTq_@&{(wH+M~% zlR}10tufA;@|d?NAjLc37@8X>#UqJ*sj%z7@++H|#2Hq-G*%WgsGtFaX}6?wE;%KR z^gKVM=gH9d<5vIj!cPft@f4PLEb3>&w6P-Z&j|LLoCZSdxlNl`8xz&f6tCh=Nr#*> z$f~Zal%!=*%KtWVi%&L8&^6s{#feQVT@2fshvXkEO7F^DIVnF;Fc&&rxMb0Z$g|^d z`qwube-pmdgmjM09(-^-R3?B~@#)j25o80scXKEa>}oe=65ozDdqgj!1*9tvnC7-P zbX?t3ICp;DA%IO?3Bg|xE7pq$Gf}jrRkSi2jM&b=WG+1OlGnN+PLZWCK~)}CRWVjB zGTnL3Vr8aTTMYp+B7IXQ$@oZm@;FzDuP!5>B|gy9+G;C{|q+M1*U)yLqP#U{)t$ zfLgvoN~UFZ5>ROCc88*_#7}OCO^NF97M;2KxUc)oj85E(?%=C zD>)6m_L?4!PagjI{tmgicwat(SoWQ~=8;G%QPJgE&~$6VCW2Ci05}>EB4#E_=|_g5 z!<4Wdsr>yUKVdf-k~I0qV_-jP*H*gM%5_s>5PE{>tfzX#$$j3kdGiyXX2I2Yy}T7^ z{>i(*$uGyc&oMAu#0$v*0x2e_oHFmqZ#}Ka?ZNFuP2aSM-sM%X7j-;>Go>z91BY0G z0EJJF51J~kfm$r~`b%>oqZ{2-zrkz0g`KL*oJBc_n}nZmi2!ELz`($7{r!dt2`aUN z?ODk;zkl4Em%DlM=1)JXg)Hai=l67VbzMe6dDPOPk~gHb6IcS#dOx7{Nr2=>AMS1a z^=DkFkZ)~8%TwO9!29Y2z(4!B3_rh(PeUXOM*zGmEbQ@Zc-Z7All-Uat*MbIHbZji zA=Ac$28n_>&|R*5b7?z}SGaui)layDqgA_i?$iTT`0!(P_B2ClRPPs)eO=DuIi)T6 zj%fsFIXF1%>8lJpL++1#RYFwim#d?y54^p(LfsaPi`(1Ve*kD9cIz%rxUX?B?DgKo zmfGm;b%^(SPtPN4+diC;(;AwZkFXkhEemFzbt;s4J}Fo-*`pl&tux0ux;E2X zoxIu!@Rr(WY5E={rZdZc$Ul82JFKhs+IQLpe*5-KhlWMzvjdKb4-S7ZJ9rZ8VOy$9^_x|=q5<1$hiHglvJb7UM__Q zvA^0)rl|btFNlZ)ym%4U($W(AcAEzQwTPQ%ZXhi%^OU4J#2?;6666&p^tOrZP%UyRICA93Xaxbc>o)w3oT{t)rgKXM zvJqi->o;sLI9-2sBNdfjvr3{838SxHzqY#hO?I*9t8(SxQ>RWbtENV1s+Ln2ZC95U zw$)2T?LTAj@{`Zro|r$O5&4OnHgAgnd_K$P2rY zFZb-lLvdO!&)@u&g6Kn^xH25#Wq>Ym{OZfMEPOY=-$mM`^u3$~K(ey8&oW<{pBJOx zmPMu-RQ7AV#jj_x*#xdl%=cM|IXGm$2@fBu04@vv@#DtvOGKaQ6<|1|iu`(;#O$&5 zEXmy5hyCA*y%-~H!-hL@(h}XOLj`84h1Lf90=1irGanjWzaDe>@c|~L#5HXv&LiUQ-@jj+ZMV!Y{IahxMplFzA$-p0 zD5>CcjBa>|LmavX{rG!vm$q!%*6`%0=$;Mhsj?YkV`BjY?Ckak7Y+;$Gkcd%`rJaB zE$HvmtfML`-Bv7f<%$nBaAZ`*V|_8C2GCCMm8u<)!bhOR9io?%7KA#Y7$b9-?zu<% zwLPAo2{a&|;;i0&lH}f$MFYl~my1q*F9|ugL*`HfN7$eEzPtO{e!J2S?!|{}@bO}& zPtgP7Sy?d%zo)EPKD?=za|haN(E9c38`6#CR}ej|?XI`lA&C-%a=T;*YZJ9` z>#nnm$BcMg=HIHNUw?h!-j;G8CeU&vaQL+1)HK`saK9rsB2km{Q_Wk~P1GGq! 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Let’s also assume that having allele G at the first site increases the mutation rate at the second site by three times. + +.. code-block:: python + + mutation_rate=0.0003 + substitution_weights=[1,1,1,2]#ATCG + simulator.set_mutation_rate(mutation_rate, substitution_weights) + simulator.set_mutation_rate(3*mutation_rate, haplotype="G*", mutation=1) + +Susceptibility types +-------------------- + +At the beginning of the epidemics all the individuals do not have any special immunity, and thus we assign them by default susceptibility type 0. Now let us assume that after recovery individuals get some immunity. If an individual was infected with haplotype carrying G at the first site (we will denote them by G*), the susceptibility type after recovery will be 2. For all other haplotypes it will be 1. + +.. code-block:: python + + simulator.set_susceptibility_type(1) + simulator.set_susceptibility_type(2, haplotype="G*") + +Now for each susceptibility type we will specify how it changes the risk of infection. We will assume that susceptibility type 1 gives strong (but not perfect) protection against all haplotypes except G* by reducing the risk of a new infection by ten times. And it gives only some protection against haplotypes G*. The immunity is lost on average after 90 days, and the individual is hence moved to the susceptibility type 0. + +.. code-block:: python + + simulator.set_susceptibility(0.1, susceptibility_type=1) + simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype="G*") + simulator.set_immunity_transition(1/90, source=1, target=0) + +Susceptibility type 2 will be a result of recovery from haplotype G* or vaccination. It gives complete protection from all the haplotypes, and it is lost after 180 days on average. At the beginning of the epidemics there is no vaccine, we will add it a bit later. + +.. code-block:: python + + simulator.set_susceptibility(0.0, susceptibility_type=2) + simulator.set_immunity_transition(1/180, source=2, target=0) + +Population model +---------------- + +First, we should define the population sizes. Let's create populations with sizes 10000000, 5000000 and 1000000 respectively. + +.. code-block:: python + + simulator.set_population_size(10000000, population=0) + simulator.set_population_size(5000000, population=1) + simulator.set_population_size(1000000, population=2) + +Now let us add some more heterogeneity. First of all assume that population 1 has a three times higher sampling rate than population 0, while population 2 does not sequence at all. + +.. code-block:: python + + simulator.set_sampling_multiplier(3, population=1) + simulator.set_sampling_multiplier(0, population=2) + +Now, all the countries impose lockdowns when 20% of its population is infected simultaneously. The lockdowns are lifted if this number drops to 2%. The amount of contacts is ten times less during the lockdown. + +.. code-block:: python + + simulator.set_npi([0.1, 0.2, 0.02]) + +Migration +--------- + +There are 3 populations in our scenario. Assume that an average individual from one population spends on average 10 days per year outside of its population of origin. The chance to travel to any destination is the same. The cumulative migration probability is 10/365. The probability to travel to a particular destination is 10 (days)/365 (days)/2 (the number of possible destinations). So, we can set + +.. code-block:: python + + simulator.set_migration_probability(10/365/2) + + + +Running the simulation +---------------------- + +Let us simulate the first 110 days. The first argument is the maximal number of iterations to be performed. Notice that the simulation will stop if this number is achieved even if 110 days (of virtual epidemics) did not pass. + +.. code-block:: python + + simulator.simulate(10000000, epidemic_time=110) + +Example of output + +.. image:: output.png + +After these 90 days, the vaccine was developed, and the susceptible individuals of types 0 and 1 can move to type 2 with the rate 0.05 (average waiting time of 20 days to get vaccinated). + +.. code-block:: python + + simulator.set_immunity_transition(0.05, source=0, target=1) + simulator.set_immunity_transition(0.05, source=0, target=2) + +Also, the rising awareness in the population reduces the contact density (e.g. because of masks) in populations 0 and 1. + +.. code-block:: python + + simulator.set_contact_density(0.7, population=0) + simulator.set_contact_density(0.7, population=1) + + +And the amount of travels is reduced with population 2 + +.. code-block:: python + + simulator.set_migration_probability(2/365/2, source=0, target=2) + simulator.set_migration_probability(2/365/2, source=1, target=2) + + +#TODO + +.. code-block:: python + + simulator.simulate(1000, method='tau') + +Visualizing epidemiological trajectories +---------------------------------------- + +Now let us plot how the haplotypes appear and spread in each population. + +.. code-block:: python + + simulator.add_plot_infectious(population=0, haplotype='AA', step_num=100) + simulator.add_plot_infectious(population=1, haplotype='AA', step_num=100) + simulator.add_plot_infectious(population=2, haplotype='AA', step_num=100) + simulator.add_title(name="AA haplotype") + simulator.add_legend() + simulator.plot() + +.. image:: AA_haplotype.png + +And finally let us look how the susceptible group sizes change. + +.. code-block:: python + + simulator.add_plot_susceptible(population=0, susceptibility_type=0, step_num=100) + simulator.add_plot_susceptible(population=0, susceptibility_type=1, step_num=100) + simulator.add_plot_susceptible(population=0, susceptibility_type=2, step_num=100) + simulator.add_title(name="Susceptible group sizes") + simulator.add_legend() + simulator.plot() + +.. image:: Susceptible_group_sizes.png + +Extracting the genealogy +------------------------ + +Finally, we extract the genealogy of the sampled cases. We write the genealogy and mutations on it into a file in MAT format. These files can be used as phastSim input to add neutral mutations. + +.. code-block:: python + + simulator.genealogy() + file_name = "example" + simulator.output_newick(file_name) + simulator.output_mutations(file_name) + +The user can also output the migrations of the genealogy lineages to have the detailed information about population structure. + +.. code-block:: python + + simulator.output_migrations(file_name) + +Print all the parameters +------------------------ +One can check the parametrization of the model by printing all the parameters. The basic parameters (transmission, recovery, sampling, mutation rates, substitution weights, susceptibility type after recovery) + +.. code-block:: python + + simulator.print_basic_parameters() + +Susceptibility matrix (how each immunity type affects susceptibility to each of the haplotypes) and the susceptibility type transition matrix + +.. code-block:: python + + simulator.print_immunity_model() + +Finally, let’s print population information (size, contact density, sampling modifier, lockdown settings) and migration matrix + +.. code-block:: python + + simulator.print_populations() + +Resulting code +-------------- + +This code can be found `here `_. + +.. code-block:: python + + import VGsim + number_of_sites = 2 + populations_number = 3 + number_of_susceptible_groups = 3 + simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, seed=1234) + + simulator.set_transmission_rate(0.25) + simulator.set_recovery_rate(0.099) + simulator.set_sampling_rate(0.001) + simulator.set_transmission_rate(0.5, haplotype="GG") + mutation_rate=0.000003 + substitution_weights=[1,1,1,2]#ATCG + simulator.set_mutation_rate(mutation_rate, substitution_weights) + simulator.set_mutation_rate(3*mutation_rate, haplotype='G*', mutation=1) + + simulator.set_susceptibility_type(1) + simulator.set_susceptibility_type(2, haplotype='G*') + simulator.set_susceptibility(0.1, susceptibility_type=1) + simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype='G*') + simulator.set_immunity_transition(1/90, source=1, target=0) + simulator.set_susceptibility(0.0, susceptibility_type=2) + simulator.set_immunity_transition(1/180, source=2, target=0) + + simulator.set_population_size(10000000, population=0) + simulator.set_population_size(5000000, population=1) + simulator.set_population_size(1000000, population=2) + simulator.set_sampling_multiplier(3, population=1) + simulator.set_sampling_multiplier(0, population=2) + simulator.set_npi([0.1, 0.01, 0.002]) + simulator.set_migration_probability(10/365/2) + + simulator.simulate(10000000, epidemic_time=110) + + simulator.set_immunity_transition(0.05, source=0, target=1) + simulator.set_immunity_transition(0.05, source=0, target=2) + simulator.set_contact_density(0.7, population=0) + simulator.set_contact_density(0.7, population=1) + simulator.set_migration_probability(2/365/2, source=0, target=2) + simulator.set_migration_probability(2/365/2, source=1, target=2) + + simulator.simulate(1000, method='tau') + + simulator.add_plot_infectious(population=0, haplotype='AA', step_num=100) + simulator.add_plot_infectious(population=1, haplotype='AA', step_num=100) + simulator.add_plot_infectious(population=2, haplotype='AA', step_num=100) + simulator.add_title(name="AA haplotype") + simulator.add_legend() + simulator.plot() + + simulator.add_plot_susceptible(population=0, susceptibility_type=0, step_num=100) + simulator.add_plot_susceptible(population=0, susceptibility_type=1, step_num=100) + simulator.add_plot_susceptible(population=0, susceptibility_type=2, step_num=100) + simulator.add_title(name="Susceptible group sizes") + simulator.add_legend() + simulator.plot() + + simulator.genealogy() + file_name = "example" + simulator.output_newick(file_name) + simulator.output_mutations(file_name) + simulator.output_migrations(file_name) + + simulator.print_basic_parameters() + simulator.print_immunity_model() + simulator.print_populations() diff --git a/VGsim/docs/source/conf.py b/VGsim/docs/source/conf.py new file mode 100644 index 0000000..ed6634f --- /dev/null +++ b/VGsim/docs/source/conf.py @@ -0,0 +1,57 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('../..')) + + +# -- Project information ----------------------------------------------------- + +project = 'VGsim' +copyright = '2021, Test' +author = 'Test' + +# The full version, including alpha/beta/rc tags +release = '1.0.0' + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ +'sphinx.ext.napoleon', +'sphinx.ext.autodoc' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] diff --git a/VGsim/docs/source/index.rst b/VGsim/docs/source/index.rst new file mode 100644 index 0000000..94a1625 --- /dev/null +++ b/VGsim/docs/source/index.rst @@ -0,0 +1,21 @@ +.. VGsim documentation master file, created by + sphinx-quickstart on Wed Sep 29 16:30:04 2021. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to VGsim's documentation! +================================= + +VGsim is the Python package for simulation of viral genealogies in the world-scale pandemic scenarios designed primarily for research purposes. Key VGsim features include + +* scalability and efficiency: optimized Guillespe algorithm over compartmental model generates epidemiological process; genealogy (transmission chain) is simulated only for sampled cases; +* pathogen evolution and epistasis (through single nucleotide substitutions): each haplotype properties (e.g. transmission rate, recovery rate, triggered host immunity) can be set independently; +* host immunity: different immunity types can have lower or higher susceptilibities to different haplotypes; +* population model: demic model with migration (migration is modelled as short-term travels); +* contact density (to reflect social, cultural and other population differences) and lockdowns. + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + modules diff --git a/VGsim/docs/source/modules.rst b/VGsim/docs/source/modules.rst new file mode 100644 index 0000000..8d8a3d0 --- /dev/null +++ b/VGsim/docs/source/modules.rst @@ -0,0 +1,26 @@ +Installation +============ + +.. toctree:: + :maxdepth: 1 + + Installation + +Tutorials and examples +====================== + +.. toctree:: + :maxdepth: 1 + + Tutorials and examples + Haplotypes + Migration + +Documentation +============= + +.. toctree:: + :maxdepth: 1 + + Documentation + Command line diff --git a/VGsim/docs/source/output.png b/VGsim/docs/source/output.png new file mode 100644 index 0000000000000000000000000000000000000000..2e832068c933cb50e68933b381c612e220b4cc06 GIT binary patch literal 132657 zcmafa1ymf_)-~?#1ZyP0f(Q3NaQ6Vg-QC??f(Q2y+#$GoAh^2|-2E@+&6}6^zghG3 z>RNrfx~jVB-gD2{XP~Fz!DmCG#n8;ciG#Ac zlhKrZef_#R{{f2jJ5A26I@m|0ZY`XiWn4rW+5GTX_1p~Iy9w&AV4x+h8R6Ku;z=Nyzi5dxQofRQ1 zzq<*GLnt&A#sXtw4ow?eAaZa^q8eM z{Yi840D>k;B~eY10MZ8dGX9FK(PJdPen{rf$g9r5W*A{QVT!m-yTa$p{M(h!Nwg#v zx{w6QRFNU7o2ce4+{SkC5Q?Nn7{=Y*G-k-Q_@Cl|-89`2K z_Ch%zwjd6vGy=@habtt9P7buLDL)y^Eqe@7HXoNt0h}*cRdZzNgym;}(}ogrZxfyf zRk>DG*AB9IC`0qty~(j`lpc~rvKg76^$@yMpmR3yLRdjgiCYxHMHV912y;ySYznT; zWL~r(8_??~Ifk=4;bHT|Fi1tnO~Z2cY@l?b+IN(}b(iD` 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package name +# programming languages +project( + 'VGsim', + ['cpp', 'cython'], + default_options : ['buildtype=debugoptimized'], + version: '0.3', + license: 'GPL-3.0 License', + meson_version: '>= 0.54', +) + +# Special check for MacOS, cause of cpp version +if build_machine.system() == 'darwin' + add_global_arguments('-std=c++17', language : 'cpp') + message('found OS X, add special argument to compiler') +endif + +# Seek the backend +if meson.backend() != 'ninja' + error('Ninja backend required') +endif + +# Seek for Cython installation +cython = find_program('cython', required : true) +if not cython.found() + error('Cython not found.') +endif + + +# Python default installton path and basic dependecies +py_mod = import('python') +py3 = py_mod.find_installation() +py3_dep = py3.dependency() + +# Thx rgommers "https://github.com/rgommers/scipy/blob/meson/scipy/meson.build" +# NumPy include directory - needed in all submodules +incdir_numpy = run_command(py3, + ['-c', 'import os; os.chdir(".."); import numpy; print(numpy.get_include())'], + check : true +).stdout().strip() +# Declare numpy as dependency +inc_np = include_directories(incdir_numpy) + +numpy_nodepr_api = '-DNPY_NO_DEPRECATED_API=NPY_1_9_API_VERSION' + +subdir('src') diff --git a/VGsim/pyproject.toml b/VGsim/pyproject.toml new file mode 100644 index 0000000..ad6e69b --- /dev/null +++ b/VGsim/pyproject.toml @@ -0,0 +1,38 @@ +[build-system] +requires = [ + "wheel", + "meson>=0.60.3", + "mesonpep517", + "ninja", + "numpy>=1.19.5", + "Cython>=0.29.18", + "mc_lib @ git+http://github.com/ev-br/mc_lib.git@v0.4.1" +] + +build-backend = "mesonpep517.buildapi" + +[tool.mesonpep517.metadata] +author="Vladimir Shchur et al" +author-email="vshchur@hse.ru" +home-page="https://scg.hse.ru" +project-urls=[ + "Source, https://github.com/Genomics-HSE/VGsim", +] +summary = "VGsim is the fast viral genealogy simulator for world-wide pandemic scenarios." +requires-python="py3" +description-file="README.md" +platforms="any" +classifiers = [ + "Intended Audience :: Developers", + "Programming Language :: Python :: 3", + "Topic :: Software Development :: Libraries :: Python Modules", +] + +requires = [ + "numpy>=1.19.5", + "mc_lib @ git+http://github.com/ev-br/mc_lib.git@v0.4.1", + "prettytable", + "matplotlib", + "pytest", + "tskit" +] diff --git a/VGsim/sim.py b/VGsim/sim.py new file mode 100644 index 0000000..f50d785 --- /dev/null +++ b/VGsim/sim.py @@ -0,0 +1,26 @@ +class Simulator(): + """ + The class which creates and runs simulations. + """ + + def __init__(number_of_sites=0, populations_number=1, number_of_susceptible_groups=1, seed=None, sampling_probability=False, memory_optimization=None): + """ + + :param number_of_sites: the number of mutable sites + :type number_of_sites: int + + :param populations_number: the number of populations (demes) + :type populations_number: int + + :param number_of_susceptible_groups: the number of susceptible groups (groups with different immunity response) + :type number_of_susceptible_groups: int + + :param seed: seed to generate simulation from. If **None**, then chosen at random + :type seed: float or None + + :param sampling_probability: whether we set sampling probability as a share of recovered individuals (**True** value) or we will set it explicitly. Default is **False**. + :type sampling_probability: bool or None + + :param memory_optimization: if True, then memory optimization is conducted (useful for large number of possible haplotypes) + :type memory_optimization: bool or None + """ diff --git a/VGsim/src/IO.py b/VGsim/src/IO.py new file mode 100644 index 0000000..4ca64c3 --- /dev/null +++ b/VGsim/src/IO.py @@ -0,0 +1,255 @@ +import sys +import math + +def read_rates(fn): + hapFilled = False + flag = False + bRate = [] + dRate = [] + sRate = [] + mRate = [] + with open(fn) as f: + line = next(f).rstrip().split(" ")#header with version etc + line = next(f).rstrip().split(" ") + if line[0] == "H": + hapFilled = True + shift = int(hapFilled) + if line[2+shift] == "SP": + flag = True + samProbability = int(flag) + dim = len(line) - shift + hapNum = int( 4**(dim - 3) ) + if dim < 3: + print("At least three rates (B, D, S) are expected") + sys.exit(1) + + for line in f: + if line[0] == "#": + next + line = line.rstrip().split(" ")[shift:] + bRate.append(float(line[0])) + if flag == 0: + dRate.append(float(line[1])) + sRate.append(float(line[2])) + else: + dRate.append(float(line[1]) * (1 - float(line[2]))) + sRate.append(float(line[1]) * float(line[2])) + + mRate.append( [] ) + mutations = line[3:] + for mut in mutations: + a = mut.split(',') + if len(a) == 1: + mRate[-1].append( [float(a[0]), 1.0/3.0, 1.0/3.0, 1.0/3.0] ) + elif len(a) == 4: + mRate[-1].append( [float(a[0]), float(a[1]), float(a[2]), float(a[3])] ) + else: + print("Error in mutations!!!") + sys.exit(1) + mRate = update_mRate(mRate) + return bRate, dRate, sRate, mRate + +def update_mRate(mRate): + if math.log(len(mRate), 4) != int(math.log(len(mRate), 4)): + print("Error!") + sys.exit(1) + + for i in range(len(mRate)): + for j in range(len(mRate[0])): + mRate[i][j].insert(calculate_allele(i, j, len(mRate[0]))+1, 0) + return mRate + +def calculate_allele(haplotype, site, sites): + for _ in range(sites-site): + allele = haplotype % 4 + haplotype = haplotype // 4 + return allele + +def read_susceptibility(fn): + hapFilled = False + susceptibility = [] + sType = [] + with open(fn) as f: + line = next(f).rstrip().split(" ")#header with version etc + line = next(f).rstrip().split(" ") + if line[0] == "H": + hapFilled = True + shift = int(hapFilled) + + for line in f: + if line[0] == "#": + next + line = line.rstrip().split(" ")[shift:] + susceptibility.append( line[1:] ) + sType.append( int( line[0] ) ) + return susceptibility, sType + +def read_populations(fn): + sizes = [] + contactDensity = [] + contactAfter = [] + startLD = [] + endLD = [] + samplingMultiplier = [] + with open(fn) as f: + line = next(f).rstrip().split(" ")#header with version etc + line = next(f).rstrip().split(" ") + + for line in f: + if line[0] == "#": + next + line = line.rstrip().split(" ") + sizes.append(int(line[1])) + contactDensity.append(float(line[2])) + if len(line) == 4: + part_line = line[3].split(",") + if len(part_line) == 1: + contactAfter.append(0) + startLD.append(1.0) + endLD.append(1.0) + samplingMultiplier.append(float(part_line[0])) + elif len(part_line) == 3: + contactAfter.append(float(part_line[0])) + startLD.append(float(part_line[1])) + endLD.append(float(part_line[2])) + samplingMultiplier.append(1) + elif len(line) == 5: + part_line1 = line[3].split(",") + part_line2 = line[4].split(",") + if len(part_line1) == 1: + samplingMultiplier.append(float(part_line1[0])) + contactAfter.append(float(part_line2[0])) + startLD.append(float(part_line2[1])) + endLD.append(float(part_line2[2])) + elif len(part_line1) == 3: + samplingMultiplier.append(float(part_line2[0])) + contactAfter.append(float(part_line1[0])) + startLD.append(float(part_line1[1])) + endLD.append(float(part_line1[2])) + return sizes, contactDensity, contactAfter, startLD, endLD, samplingMultiplier + +def read_matrix(fn): + with open(fn) as f: + line = next(f).rstrip()#header with version etc + line = line.split(" ") + matrix = [] + for line in f: + if line[0] == "#": + next + line = line.rstrip() + line = line.split(" ") + matrix.append( [float(v) for v in line] ) + return matrix + +def writeMutations(mut, len_prufer, name_file, file_path): + #digits replacement + alleles = ["A","T","C","G"] + for i in [1,3]: + for j in range(len(mut[i])): + mut[i][j] = alleles[mut[i][j]] + + mutations_dict = {} + for nodeId in mut[0]: + if nodeId in mutations_dict: #adding mutation for existing node + mutations_dict[nodeId] += str(mut[1][mut[0].index(nodeId)]) \ + + str(mut[2][mut[0].index(nodeId)]) \ + + str(mut[3][mut[0].index(nodeId)])+',' + else: + mutations_dict[nodeId] = str(mut[1][mut[0].index(nodeId)]) \ + + str(mut[2][mut[0].index(nodeId)]) \ + + str(mut[3][mut[0].index(nodeId)])+',' + #removing extra comma + for nodeId in mutations_dict: + mutations_dict[nodeId] = mutations_dict[nodeId][:-1] + + if file_path != None: + f_mut = open(file_path + '/' + name_file + ".tsv", 'w') + else: + f_mut = open(name_file + ".tsv", 'w') + + for i in range(len_prufer): + if i in mutations_dict: + f_mut.write(str(i)+'\t'+str(mutations_dict[i])+'\n') + f_mut.close() + +class Vertex(): + def __init__(self, root, root_time, children, populations): + self.__children = children + self.__root = root + self.__root_time = root_time + self.__root_population_id = populations[self.__root_time] + left_node = self.__children[root][0][0] + left_time = self.__children[root][0][1] + right_node = self.__children[root][1][0] + right_time = self.__children[root][1][1] + + if left_node in self.__children: + self.__left_child = Vertex(left_node, left_time, self.__children, populations) + else: + self.__left_child = Leaf(left_node, left_time, populations) + + if right_node in self.__children: + self.__right_child = Vertex(right_node, right_time, self.__children, populations) + else: + self.__right_child = Leaf(right_node, right_time, populations) + + def get_children(self, base_time): + return '({0},{1}){2}:{3}'.format(self.__left_child.get_children(self.__root_time), self.__right_child.get_children(self.__root_time), self.__root, self.__root_time - base_time) + + def write_population(self): + return '{0}\t{1}\n'.format(self.__root, self.__root_population_id) + self.__left_child.write_population() + self.__right_child.write_population() + +class Leaf(Vertex): + def __init__(self, leaf, times, populations): + self.__leaf = leaf + self.__times = times + self.__leaf_population_id = populations[times] + + def get_children(self, base_time): + return '{0}:{1}'.format(self.__leaf, self.__times - base_time) + + def write_population(self): + return '{0}\t{1}\n'.format(self.__leaf, self.__leaf_population_id) + +#find list with childrens +def find_children(pruferSeq, times): + children = {} + for index in range(len(pruferSeq)): + add_list = [] + add_list = [index, times[index]] + if pruferSeq[index] in children: + children[pruferSeq[index]].append(add_list) + else: + children[pruferSeq[index]] = [] + children[pruferSeq[index]].append(add_list) + return children + +def get_last(output_string): + try: + return output_string[-1] + except: + return "notDigit" + +def writeGenomeNewick(pruferSeq, times, populations, name_file, file_path): + children = find_children(pruferSeq, times) + root = children[-1][0][0] + root_time = children[-1][0][1] + + result = Vertex(root, root_time, children, populations) + + if file_path != None: + f_nwk = open(file_path + '/' + name_file + '_tree.nwk', 'w') + f_pop = open(file_path + '/' + name_file + '_sample_population.tsv', 'w') + elif name_file != None: + f_nwk = open(name_file + '_tree.nwk', 'w') + f_pop = open(name_file + '_sample_population.tsv', 'w') + else: + f_nwk = open('tree.nwk', 'w') + f_pop = open('sample_population.tsv', 'w') + + f_nwk.write(result.get_children(root_time)) + f_nwk.write(';') + f_pop.write(result.write_population()) + + f_nwk.close() + f_pop.close() diff --git a/VGsim/src/_BirthDeath.pyx b/VGsim/src/_BirthDeath.pyx new file mode 100644 index 0000000..f4590ef --- /dev/null +++ b/VGsim/src/_BirthDeath.pyx @@ -0,0 +1,2613 @@ +# cython: language_level=3 +# cython: initializedcheck = False +# distutils: language = c++ + +cimport cython + +from numpy cimport npy_int64 + +from libc.math cimport log, floor, abs +from libcpp.vector cimport vector +from mc_lib.rndm cimport RndmWrapper + +from prettytable import PrettyTable +import numpy as np +cimport numpy as np +import sys +import os +import time +import tskit + +from numpy.random.c_distributions cimport random_poisson, random_hypergeometric + +include "fast_choose.pxi" +include "models.pxi" +include "events.pxi" + +#pi - population ID, pn - popoulation number, spi - source population ID, tpi - target population ID +#hi - haplotype ID, hn - haplotype number, nhi - new haplotype number +#si - susceptibility ID, sn - susceptibility number, ssi - source susceptibility ID, tsi - target susceptibility ID +cdef class BirthDeathModel: + cdef: + RndmWrapper seed + + bint first_simulation, sampling_probability, memory_optimization + Py_ssize_t user_seed, sites, hapNum, currentHapNum, maxHapNum, addMemoryNum, popNum, susNum, bCounter, dCounter, sCounter, mCounter, iCounter, swapLockdown, migPlus, migNonPlus, globalInfectious, countsPerStep, good_attempt, cCounter, conditionNum + double currentTime, totalRate, totalMigrationRate, rn, tau_l + + Events events + multiEvents multievents + Mutations mut + Migrations mig + Lockdowns loc + + npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop + npy_int64[:,::1] susceptible, initial_susceptible, infectious, + npy_int64[:,:,::1] infectByCondition, initial_infectious + + double[::1] maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times + double[::1] conditionPopRate, cumuConditionRate + double[:,::1] susceptibility, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration + double[:,::1] conditionRate,massivForThree, conditionPopHapRate, bRate, dRate, sRate, tmRate + double[:,:,::1] hapMutType, susceptHapPopRate, tEventHapPopRate, mRate + double[:,:,::1] conditionPopHapConRate, tEventConHapPopRate + double[:,:,:,::1] eventHapPopRate + + double[:,:,:,::1] PropensitiesMigr, PropensitiesMutatations + double[:,:,::1] PropensitiesSuscep, PropensitiesTransmission + double[:,::1] PropensitiesRecovery, PropensitiesSampling + + npy_int64[:,:,:,::1] eventsMigr, eventsMutatations + npy_int64[:,:,::1] eventsSuscep, eventsTransmission + npy_int64[:,::1] eventsRecovery, eventsSampling + + double[:,::1] infectiousAuxTau, susceptibleAuxTau + npy_int64[:,::1] infectiousDelta, susceptibleDelta + + + def __init__(self, number_of_sites, populations_number, number_of_susceptible_groups, seed, sampling_probability, memory_optimization, conditionNum): + self.user_seed = seed + self.seed = RndmWrapper(seed=(self.user_seed, 0)) + + self.first_simulation = False + self.sampling_probability = sampling_probability + + self.sites = number_of_sites + self.hapNum = 4**self.sites + self.susNum = number_of_susceptible_groups + self.popNum = populations_number + + if memory_optimization: + self.memory_optimization = True + if self.sites > 2: + self.maxHapNum = 4**(self.sites-2) + self.addMemoryNum = 4**(self.sites-2) + else: + self.maxHapNum = 4 + self.addMemoryNum = 4 + else: + self.memory_optimization = False + self.maxHapNum = self.hapNum + self.addMemoryNum = 0 + + self.hapToNum = np.zeros(self.hapNum, dtype=np.int64) # from haplotype to program number + self.numToHap = np.zeros(self.maxHapNum, dtype=np.int64) # from program number to haplotype + + if self.memory_optimization: + self.currentHapNum = 0 + else: + self.currentHapNum = self.hapNum + for hn in range(self.hapNum): + self.hapToNum[hn] = hn + self.numToHap[hn] = hn + + self.bCounter = 0 + self.dCounter = 0 + self.sCounter = 0 + self.mCounter = 0 + self.iCounter = 0 + self.cCounter = 0 + self.swapLockdown = 0 + self.migPlus = 0 + self.migNonPlus = 0 + self.globalInfectious = 0 + + self.currentTime = 0.0 + self.tau_l=0.01 + self.totalRate = 0.0 + self.totalMigrationRate = 0.0 + + self.events = Events() + self.multievents = multiEvents() + self.mut = Mutations() + self.mig = Migrations() + self.loc = Lockdowns() + + # memory_optimization + self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) + self.tmRate = np.zeros((self.maxHapNum, self.conditionNum), dtype=float) + self.massivForThree = np.zeros((self.popNum, 3), dtype=float) + self.conditionPopRate = np.zeros(self.popNum, dtype=float) + self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) + self.hapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.eventHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum, 4), dtype=float) + self.susceptHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.susNum), dtype=float) + + self.suscType = np.zeros(self.hapNum, dtype=np.int64) + self.bRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) + self.dRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) + self.sRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) + self.mRate = np.zeros((self.hapNum, self.conditionNum, self.sites), dtype=float) + self.conditionRate = np.zeros((self.conditionNum, self.conditionNum), dtype=float) + self.cumuConditionRate = np.zeros(self.conditionNum, dtype=float) + self.conditionPopHapRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) + self.conditionPopHapConRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum),dtype=float) + self.infectByCondition = np.zeros((self.popNum, self.maxHapNum, self.conditionNum),dtype=np.int64) + self.tEventConHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) + self.susceptibility = np.zeros((self.hapNum, self.susNum), dtype=float) + self.hapMutType = np.ones((self.hapNum, self.sites, 3), dtype=float) + + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + self.bRate[hn, cn] = 2.0 + self.dRate[hn, cn] = 1.0 + self.sRate[hn, cn] = 0.01 + for s in range(self.sites): + self.mRate[hn, cn, s] = 0.01 + self.susceptibility[hn, 0] = 1.0 + + for cn1 in range(self.conditionNum): + for cn2 in range(self.conditionNum): + if cn1 != cn2: + self.conditionRate[cn1, cn2] = 2 + else: + self.conditionRate[cn1, cn2] = 0 + + for cn1 in range(self.conditionNum): + self.cumuConditionRate[cn1] = 0 + for cn2 in range(self.conditionNum): + self.cumuConditionRate[cn1] += self.conditionRate[cn1, cn2] + + + self.sizes = np.zeros(self.popNum, dtype=np.int64) + self.totalSusceptible = np.zeros(self.popNum, dtype=np.int64) + self.totalInfectious = np.zeros(self.popNum, dtype=np.int64) + self.lockdownON = np.zeros(self.popNum, dtype=np.int64) + + self.susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) + self.initial_susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) + self.initial_infectious = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=np.int64) + + self.maxEffectiveBirthMigration = np.zeros(self.popNum, dtype=float) + self.suscepCumulTransition = np.zeros(self.susNum, dtype=float) + self.infectPopRate = np.zeros(self.popNum, dtype=float) + self.immunePopRate = np.zeros(self.popNum, dtype=float) + self.popRate = np.zeros(self.popNum, dtype=float) + self.migPopRate = np.zeros(self.popNum, dtype=float) + self.actualSizes = np.zeros(self.popNum, dtype=float) + self.contactDensity = np.ones(self.popNum, dtype=float) + self.contactDensityBeforeLockdown = np.ones(self.popNum, dtype=float) + self.contactDensityAfterLockdown = np.zeros(self.popNum, dtype=float) + self.startLD = np.ones(self.popNum, dtype=float) + self.endLD = np.ones(self.popNum, dtype=float) + self.samplingMultiplier = np.ones(self.popNum, dtype=float) + + self.suscepTransition = np.zeros( (self.susNum, self.susNum), dtype=float) + self.immuneSourcePopRate = np.zeros((self.popNum, self.susNum), dtype=float) + self.migrationRates = np.zeros((self.popNum, self.popNum), dtype=float) + self.effectiveMigration = np.zeros((self.popNum, self.popNum), dtype=float) + + for pn in range(self.popNum): + self.sizes[pn] = 1000000 + self.totalSusceptible[pn] = 1000000 + self.susceptible[pn, 0] = 1000000 + + self.tree = np.zeros(1, dtype=np.int64) + self.tree_pop = np.zeros(1, dtype=np.int64) + + #Init propensities + self.PropensitiesMigr = np.zeros((self.popNum, self.popNum, self.susNum, self.hapNum), dtype=float) + self.PropensitiesSuscep = np.zeros((self.popNum, self.susNum, self.susNum), dtype=float) + self.PropensitiesRecovery = np.zeros((self.popNum, self.hapNum), dtype=float) + self.PropensitiesSampling = np.zeros((self.popNum, self.hapNum), dtype=float) + self.PropensitiesMutatations = np.zeros((self.popNum, self.hapNum, self.sites, 3), dtype=float) + self.PropensitiesTransmission = np.zeros((self.popNum, self.hapNum, self.susNum), dtype=float) + + #Sampled number of events per step + self.eventsMigr = np.zeros((self.popNum, self.popNum, self.susNum, self.hapNum), dtype=np.int64) + self.eventsSuscep = np.zeros((self.popNum, self.susNum, self.susNum), dtype=np.int64) + self.eventsRecovery = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + self.eventsSampling = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + self.eventsMutatations = np.zeros((self.popNum, self.hapNum, self.sites, 3), dtype=np.int64) + self.eventsTransmission = np.zeros((self.popNum, self.hapNum, self.susNum), dtype=np.int64) + + self.infectiousAuxTau = np.zeros((self.popNum, self.hapNum), dtype=float) + self.susceptibleAuxTau = np.zeros((self.popNum, self.susNum), dtype=float) + + self.infectiousDelta = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + self.susceptibleDelta = np.zeros((self.popNum, self.susNum), dtype=np.int64) + + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void FirstInfection(self): + if self.globalInfectious == 0: + for sn in range(self.susNum): + if self.susceptible[0, sn] != 0: + if self.memory_optimization: + self.AddHaplotype(0, 0) + self.NewInfections(0, sn, 0, 0) + return + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef inline void NewInfections(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t ci, Py_ssize_t num=1): + self.susceptible[pi, si] -= num + self.totalSusceptible[pi] -= num + self.infectious[pi, hi] += num + self.infectByCondition[pi, hi, ci] += num + self.totalInfectious[pi] += num + self.globalInfectious += num + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef inline void NewRecoveries(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t ci, Py_ssize_t num=1): + self.susceptible[pi, si] += num + self.totalSusceptible[pi] += num + self.infectious[pi, hi] -= num + self.infectByCondition[pi, hi, ci] -= num + self.totalInfectious[pi] -= num + self.globalInfectious -= num + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void AddMemory(self): + if self.addMemoryNum + self.maxHapNum > self.hapNum: + self.addMemoryNum = self.hapNum - self.maxHapNum + self.numToHap = np.concatenate((self.numToHap, np.zeros(self.addMemoryNum, dtype=np.int64))) + self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=np.int64)), axis=1) + self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, self.conditionNum, dtype=float))) + self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) + self.hapPopRate = np.concatenate((self.hapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) + self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum ,4), dtype=float)), axis=1) + self.susceptHapPopRate = np.concatenate((self.susceptHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.susNum), dtype=float)), axis=1) + self.conditionPopHapRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) + self.conditionPopHapConRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) + self.infectByCondition = np.concatenate((self.infectByCondition, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum),dtype=np.int64)), axis=1) + self.tEventConHapPopRate = np.concatenate((self.tEventConHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)),axis=1) + self.maxHapNum += self.addMemoryNum + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void UpdateAllRates(self): + cdef: + double maxEffectiveBirth + double[::1] maxEffectiveMigration + + for sn1 in range(self.susNum): + self.suscepCumulTransition[sn1] = 0 + for sn2 in range(self.susNum): + self.suscepCumulTransition[sn1] += self.suscepTransition[sn1, sn2] + + for pn1 in range(self.popNum): + self.migrationRates[pn1, pn1] = 1.0 + self.actualSizes[pn1] = 0.0 + for pn2 in range(self.popNum): + if pn1 == pn2: + continue + self.migrationRates[pn1, pn1] -= self.migrationRates[pn1, pn2] + self.actualSizes[pn1] += self.migrationRates[pn2, pn1]*self.sizes[pn2] + self.actualSizes[pn1] += self.migrationRates[pn1, pn1]*self.sizes[pn1] + + self.totalRate = 0.0 + for pn in range(self.popNum): + self.infectPopRate[pn] = 0 + self.immunePopRate[pn] = 0 + self.popRate[pn] = 0. + for pn in range(self.popNum): + for hn in range(self.currentHapNum): # hn - program number + for cn in range(self.conditionNum): + self.tmRate[hn, cn] = 0 + + + for cn in range(self.conditionNum): + for s in range(self.sites): + self.tmRate[hn, cn] += self.mRate[self.numToHap[hn], cn, s] + self.eventHapPopRate[pn, hn, cn, 0] = self.BirthRate(pn, hn, cn) + self.eventHapPopRate[pn, hn, cn, 1] = self.dRate[self.numToHap[hn], cn] + self.eventHapPopRate[pn, hn, cn, 2] = self.sRate[self.numToHap[hn], cn]*self.samplingMultiplier[pn] + self.eventHapPopRate[pn, hn, cn, 3] = self.tmRate[hn, cn] + self.tEventHapPopRate[pn, hn, cn] = 0 + for i in range(4): + self.tEventHapPopRate[pn, hn, cn] += self.eventHapPopRate[pn, hn, cn, i] + self.tEventConHapPopRate[pn, hn, cn] = self.tEventHapPopRate[pn, hn, cn] * self.infectByCondition[pn, hn, cn] + self.hapPopRate[pn, hn] += self.tEventConHapPopRate[pn, hn, cn] + self.infectPopRate[pn] += self.hapPopRate[pn, hn] + for sn in range(self.susNum): + self.immuneSourcePopRate[pn, sn] = self.suscepCumulTransition[sn]*self.susceptible[pn, sn] + self.immunePopRate[pn] += self.immuneSourcePopRate[pn, sn] + for hn in range(self.currentHapNum): + for cn in range(self.conditionNum): + self.conditionPopHapConRate[pn, hn, cn] = self.cumuConditionRate[cn] * self.infectByCondition[pn, hn, cn] + self.conditionPopHapRate[pn, hn] += self.conditionPopHapConRate[pn, hn, cn] + self.conditionPopRate[pn] += self.conditionPopHapRate[pn, hn] + + + self.popRate[pn] = self.infectPopRate[pn] + self.immunePopRate[pn] + self.conditionPopRate[pn] + self.massivForThree[0] = self.infectPopRate[pn] + self.massivForThree[1] = self.immunePopRate[pn] + self.conditionPopRate[2] = self.conditionPopRate[pn] + self.totalRate += self.popRate[pn] + + maxEffectiveMigration = np.zeros(self.popNum, dtype=float) + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + if pn1 == pn2: + continue + self.effectiveMigration[pn1, pn2] = 0.0 + for pn3 in range(self.popNum): + self.effectiveMigration[pn1, pn2] += self.migrationRates[pn1, pn3]*self.migrationRates[pn2, pn3]*self.contactDensity[pn3]/self.actualSizes[pn3] + if self.effectiveMigration[pn1, pn2] > maxEffectiveMigration[pn2]: + maxEffectiveMigration[pn2] = self.effectiveMigration[pn1, pn2] + + maxEffectiveBirth = 0.0 + for hn in range(self.currentHapNum): + for cn in range(self.conditionNum): + for sn in range(self.susNum): + if self.bRate[self.numToHap[hn], cn]*self.susceptibility[self.numToHap[hn], sn] > maxEffectiveBirth: + maxEffectiveBirth = self.bRate[self.numToHap[hn], cn]*self.susceptibility[self.numToHap[hn], sn] + + self.totalMigrationRate = 0.0 + for pn in range(self.popNum): + self.maxEffectiveBirthMigration[pn] = maxEffectiveMigration[pn]*maxEffectiveBirth + self.migPopRate[pn] = self.maxEffectiveBirthMigration[pn]*self.totalSusceptible[pn]*(self.globalInfectious-self.totalInfectious[pn]) + self.totalMigrationRate += self.migPopRate[pn] + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void AddHaplotype(self, Py_ssize_t nhi, Py_ssize_t pi): # nhi - haplotype + cdef: + bint check = False + Py_ssize_t mem_h, mem_inf + + if self.currentHapNum == self.maxHapNum and self.maxHapNum < self.hapNum: + self.AddMemory() + + for i in range(1, self.currentHapNum+1): + if check: + self.hapToNum[mem_h] += 1 + self.infectious[pi, i], mem_inf = mem_inf, self.infectious[pi, i] + self.numToHap[i], mem_h = mem_h, self.numToHap[i] + if (self.numToHap[i] > nhi or self.numToHap[i] == 0) and self.numToHap[i-1] < nhi: + mem_h = self.numToHap[i] + check = True + self.numToHap[i] = nhi + mem_inf = self.infectious[pi, i] + self.infectious[pi, i] = 0 + self.hapToNum[nhi] = i + + self.currentHapNum += 1 + self.UpdateAllRates() + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef inline double BirthRate(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number + cdef double ps = 0.0 + + for sn in range(self.susNum): + self.susceptHapPopRate[pi, hi, sn] = self.susceptible[pi, sn]*self.susceptibility[self.numToHap[hi], sn] + for pn in range(self.popNum): + ps += self.susceptHapPopRate[pi, hi, sn]*self.migrationRates[pi, pn]*self.migrationRates[pi, pn]*self.contactDensity[pn]/self.actualSizes[pn] + + for cn in range(self.conditionNum): + self.bRate[self.numToHap[hi], cn] = self.bRate[self.numToHap[hi], cn] * ps + cdef double hi_ps = 0.0 + for cn in range(self.conditionNum): + hi_ps += self.bRate[self.numToHap[hi], cn] + return hi_ps + + @cython.boundscheck(False) + @cython.wraparound(False) + cpdef void SimulatePopulation(self, Py_ssize_t iterations, Py_ssize_t sample_size, float time, Py_ssize_t attempts): + cdef Py_ssize_t pi + + self.PrepareParameters(iterations) + self.CheckSizes() + + for i in range(attempts): + self.seed = RndmWrapper(seed=(self.user_seed, i)) + if self.totalRate+self.totalMigrationRate != 0.0 and self.globalInfectious != 0: + while (self.events.ptr 100: + self.Restart() + else: + self.good_attempt = i+1 + break + + if self.totalRate == 0.0 or self.globalInfectious == 0: + print('Simulation finished because no infections individuals remain!') + if self.events.ptr>=self.events.size: + print("Achieved maximal number of iterations.") + if self.sCounter>sample_size and sample_size!=-1: + print("Achieved sample size.") + if self.currentTime>time and time != -1: + print("Achieved internal time limit.") + if self.sCounter <= 1: + print('\033[41m{}\033[0m'.format('WARNING!'), 'Simulated less 2 samples, so genealogy will not work!') + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void PrepareParameters(self, Py_ssize_t iterations): + self.events.CreateEvents(iterations) + if self.first_simulation == False: + self.FirstInfection() + for pn in range(self.popNum): + for sn in range(self.susNum): + self.initial_susceptible[pn, sn] = self.susceptible[pn, sn] + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + self.initial_infectious[pn, hn, cn] = self.infectByCondition[pn, hn, cn] + self.first_simulation = True + for pn in range(self.popNum): + self.CheckLockdown(pn) + self.UpdateAllRates() + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void CheckSizes(self): + check = False + list_pop = [] + print('Actual sizes: ', end='') + for pn in range(self.popNum): + print(self.actualSizes[pn], end=' ') + if abs(self.actualSizes[pn]/self.sizes[pn]-1) >= 0.1: + check = True + list_pop.append(str(pn)) + print() + if check: + print('\033[41m{}\033[0m'.format('WARNING!'), 'Actual population size in deme: ', end='') + print(", ".join(list_pop) ) + print("\tis more than 10% different from the population size. The migration probabilities might be unrealistically high.") + print("\tWe recommend to check your model with print_populations() method before proceding to simulation.") + print("\tCheck the documentation file:https://vg-sim.readthedocs.io/en/latest/Migration.html for more details.") + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef inline void SampleTime(self): + cdef double tau = - log(self.seed.uniform()) / (self.totalRate + self.totalMigrationRate) + self.currentTime += tau + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef Py_ssize_t GenerateEvent(self): + cdef: + Py_ssize_t pi, hi, ei + double choose + + self.rn = self.seed.uniform() + choose = self.rn * (self.totalRate + self.totalMigrationRate) + if self.totalRate > choose: + self.rn = choose / self.totalRate + pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) + #choose = self.rn * self.popRate[pi] + ei, self.rn = fastChoose1(self.massivForThree[pi], self.totalRate, self.rn) + if ei == 1: + #self.rn = choose / self.immunePopRate[pi] + self.ImmunityTransition(pi) + elif ei == 2: + self.ConditionTransition(pi) + else: + self.rn = (choose - self.immunePopRate[pi]) / self.infectPopRate[pi] + hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number + ci, self.rn = fastChoose1(self.tEventConHapPopRate[pi, hi], self.hapPopRate[pi, hi], self.rn) + ei, self.rn = fastChoose1(self.tEventHapPopRate[pi, hi], self.tEvenContHapPopRate[pi, hi, ci], self.rn) + if ei == BIRTH: + self.Birth(pi, hi, ci) + elif ei == DEATH: + self.Death(pi, hi, ci) + elif ei == SAMPLING: + self.Sampling(pi, hi, ci) + else: + self.Mutation(pi, hi, ci) + else: + pi = self.GenerateMigration() + return pi + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void UpdateRates(self, Py_ssize_t pi, bint infect, bint immune, bint migration): + cdef double tmp + if infect: + self.infectPopRate[pi] = 0.0 + for hn in range(self.currentHapNum): + for cn in range(self.conditionNum): + self.eventHapPopRate[pi, hn, cn, 0] = self.BirthRate(pi, hn, cn) + tmp = (self.eventHapPopRate[pi, hn, cn, 0] + + self.eventHapPopRate[pi, hn, cn, 1] + + self.eventHapPopRate[pi, hn, cn, 2] + + self.eventHapPopRate[pi, hn, cn, 3] ) + self.tEventHapPopRate[pi, hn, cn] = tmp + self.tEventConHapPopRate[pi, hn, cn] = self.tEventHapPopRate[pi, hn, cn] * self.infectByCondition[pi, hn, cn] + self.hapPopRate[pi, hn] += self.tEventConHapPopRate[pi, hn, cn] + self.infectPopRate[pi] += self.hapPopRate[pi, hn] + + if immune: + self.immunePopRate[pi] = 0 + for sn in range(self.susNum): + self.immunePopRate[pi] += self.immuneSourcePopRate[pi, sn] + + if infect or immune: + self.popRate[pi] = 0 + self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi]+ self.conditionPopRate[pi] + self.massivForThree[0] = self.infectPopRate[pi] + self.massivForThree[1] = self.immunePopRate[pi] + self.massivForThree[2] = self.conditionPopRate[pi] + self.totalRate = 0.0 + for pn in range(self.popNum): + self.totalRate += self.popRate[pn] + + if migration: + self.totalMigrationRate = 0.0 + for pn in range(self.popNum): + self.migPopRate[pn] = self.maxEffectiveBirthMigration[pn]*self.totalSusceptible[pn]*(self.globalInfectious-self.totalInfectious[pn]) + self.totalMigrationRate += self.migPopRate[pn] + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void ImmunityTransition(self, Py_ssize_t pi): + cdef: + Py_ssize_t ssi, tsi + + ssi, self.rn = fastChoose1(self.immuneSourcePopRate[pi], self.immunePopRate[pi], self.rn) + tsi, self.rn = fastChoose1(self.suscepTransition[ssi], self.suscepCumulTransition[ssi], self.rn) + + self.susceptible[pi, ssi] -= 1 + self.susceptible[pi, tsi] += 1 + self.immuneSourcePopRate[pi, ssi] = self.susceptible[pi, ssi]*self.suscepCumulTransition[ssi] + self.immuneSourcePopRate[pi, tsi] = self.susceptible[pi, tsi]*self.suscepCumulTransition[tsi] + self.UpdateRates(pi, False, True, False) + + self.iCounter += 1 + self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void ConditionTransition(self, Py_ssize_t pi): + cdef: + Py_ssize_t ssi, tsi, hsi + + hsi, self.rn = fastChoose1(self.conditionPopHapRate[pi], self.conditionPopRate[pi], self.rn) + ssi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self.conditionPopHapRate[pi, hsi], self.rn) + tsi, self.rn = fastChoose1_skip(self.conditionRate[ssi], self.cumuConditionRate[ssi], self.rn, ssi) + + self.infectByCondition[pi, hsi, ssi] -= 1 + self.infectByCondition[pi, hsi, tsi] += 1 + self.conditionPopHapConRate[pi, hsi, ssi] = self.cumuConditionRate[ssi] * self.infectByCondition[pi, hsi, ssi] + self.conditionPopHapConRate[pi, hsi, tsi] = self.cumuConditionRate[tsi] * self.infectByCondition[pi, hsi, tsi] + self.UpdateRates(pi, False, True, False) + + self.cCounter += 1 + self.events.AddEvent(self.currentTime, CONDITION, ssi, pi, tsi, 0) #ssi, tsi -- condition, add haplotype hsi?? + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void Birth(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number + cdef double ws = 0.0 + + for sn in range(self.susNum): + ws += self.susceptHapPopRate[pi, hi, sn] + si, self.rn = fastChoose1(self.susceptHapPopRate[pi, hi], ws, self.rn) + + self.NewInfections(pi, si, hi, 0) + self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] + self.UpdateRates(pi, True, True, True) + + self.bCounter += 1 + self.events.AddEvent(self.currentTime, BIRTH, self.numToHap[hi], pi, si, 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void Death(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci, bint add_event = True): # hi - program number + self.NewRecoveries(pi, self.suscType[self.numToHap[hi]], hi, ci) + self.immuneSourcePopRate[pi, self.suscType[self.numToHap[hi]]] = self.susceptible[pi, self.suscType[self.numToHap[hi]]]*self.suscepCumulTransition[self.suscType[self.numToHap[hi]]] + self.UpdateRates(pi, True, True, True) + + if add_event: + self.dCounter += 1 + self.events.AddEvent(self.currentTime, DEATH, self.numToHap[hi], pi, self.suscType[self.numToHap[hi]], 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void Sampling(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number + self.Death(pi, hi, False) + + self.sCounter += 1 + self.events.AddEvent(self.currentTime, SAMPLING, self.numToHap[hi], pi, self.suscType[self.numToHap[hi]], 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void Mutation(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number + cdef: + bint check = True + Py_ssize_t ohi, mi, digit4, AS, DS, nhi + + ohi = self.numToHap[hi] # ohi - haplotype + mi, self.rn = fastChoose1(self.mRate[ohi, ci], self.tmRate[hi, ci], self.rn) + DS, self.rn = fastChoose1(self.hapMutType[ohi, mi], self.hapMutType[ohi, mi, 0] \ + + self.hapMutType[ohi, mi, 1] + self.hapMutType[ohi, mi, 2], self.rn) + nhi = self.Mutate(ohi, mi, DS) + + if self.memory_optimization: + #TODO binary search + for hn in range(self.currentHapNum+1): # hn - program number + if self.numToHap[hn] == nhi: + check = False + break + if check: + self.AddHaplotype(nhi, pi) + if nhi < ohi: + hi += 1 + + self.infectious[pi, self.hapToNum[nhi]] += 1 + self.infectious[pi, hi] -= 1 + self.UpdateRates(pi, True, False, False) + + self.mCounter += 1 + self.events.AddEvent(self.currentTime, MUTATION, ohi, pi, nhi, 0) + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef Py_ssize_t GenerateMigration(self): + cdef: + Py_ssize_t tpi, spi, hi, si + double p_accept + + tpi, self.rn = fastChoose1(self.migPopRate, self.totalMigrationRate, self.rn) + spi, self.rn = fastChoose2_skip(self.totalInfectious, self.globalInfectious-self.totalInfectious[tpi], self.rn, skip=tpi) + hi, self.rn = fastChoose2(self.infectious[spi], self.totalInfectious[spi], self.rn) # hi - program number + ci, self.rn = fastChoose1(self.conditionRate[spi], self.cumuConditionRate[spi], self.rn) + si, self.rn = fastChoose2(self.susceptible[tpi], self.totalSusceptible[tpi], self.rn) + + p_accept = self.effectiveMigration[spi, tpi]*self.bRate[self.numToHap[hi], ci]*self.susceptibility[self.numToHap[hi], si]/self.maxEffectiveBirthMigration[tpi] + if self.rn < p_accept: + self.NewInfections(tpi, si, hi, ci) + self.UpdateRates(tpi, True, True, True) + + self.migPlus += 1 + self.events.AddEvent(self.currentTime, MIGRATION, self.numToHap[hi], spi, si, tpi) + else: + self.migNonPlus += 1 + + return tpi + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void CheckLockdown(self, Py_ssize_t pi): + if self.totalInfectious[pi] > self.startLD[pi]*self.sizes[pi] and self.lockdownON[pi] == 0: + self.contactDensity[pi] = self.contactDensityAfterLockdown[pi] + self.swapLockdown += 1 + self.lockdownON[pi] = 1 + self.UpdateAllRates() + self.loc.AddLockdown(True, pi, self.currentTime) + if self.totalInfectious[pi] < self.endLD[pi]*self.sizes[pi] and self.lockdownON[pi] == 1: + self.contactDensity[pi] = self.contactDensityBeforeLockdown[pi] + self.swapLockdown += 1 + self.lockdownON[pi] = 0 + self.UpdateAllRates() + self.loc.AddLockdown(False, pi, self.currentTime) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void Restart(self): + self.events.ptr = 0 + self.multievents.ptr = 0 + self.bCounter = 0 + self.dCounter = 0 + self.sCounter = 0 + self.mCounter = 0 + self.iCounter = 0 + self.migPlus = 0 + self.migNonPlus = 0 + self.currentTime = 0.0 + self.globalInfectious = 0 + for pn in range(self.popNum): + self.totalSusceptible[pn] = 0 + self.totalInfectious[pn] = 0 + for sn in range(self.susNum): + self.susceptible[pn, sn] = self.initial_susceptible[pn, sn] + self.totalSusceptible[pn] += self.initial_susceptible[pn, sn] + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + self.infectious[pn, hn] += self.initial_infectious[pn, hn, cn] + self.totalInfectious[pn] += self.initial_infectious[pn, hn, cn] + self.globalInfectious += self.initial_infectious[pn, hn, cn] + for pn in range(self.popNum): + self.CheckLockdown(pn) + self.UpdateAllRates() + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cpdef GetGenealogy(self, seed): + cdef: + Py_ssize_t ptrTreeAndTime, n1, n2, id1, id2, id3, lbs, lbs_e, ns, nt, idt, ids, lbss, propNum + double p, p1 + vector[vector[vector[Py_ssize_t]]] liveBranchesS, newLineages + vector[vector[Py_ssize_t]] vecint2 + vector[Py_ssize_t] vecint1 + npy_int64[:,::1] liveBranches + + double e_time, me_time + Py_ssize_t e_type_, e_population, e_haplotype, e_newHaplotype, e_newPopulation + Py_ssize_t me_num, me_type_, me_population, me_haplotype, me_newHaplotype, me_newPopulation + Py_ssize_t mt_ev_num, mt_ev_num2 + + propNum = self.popNum*((self.popNum-1)*self.hapNum*self.susNum+self.susNum*(self.susNum-1)+self.hapNum*(2+self.sites*3+self.susNum)) + + if self.sCounter < 2: + print("Less than two cases were sampled...") + print("_________________________________") + sys.exit(0) + else: + if seed != None: + self.seed = RndmWrapper(seed=(seed, 0)) + + ptrTreeAndTime = 0 + self.tree = np.zeros(2 * self.sCounter - 1, dtype=np.int64) + self.tree_pop = np.zeros(2 * self.sCounter - 1, dtype=np.int64) + self.times = np.zeros(2 * self.sCounter - 1, dtype=float) + + for i in range( self.popNum ): + liveBranchesS.push_back(vecint2) + for _ in range( self.hapNum ): + liveBranchesS[i].push_back(vecint1) + + for i in range( self.popNum ): + newLineages.push_back(vecint2) + for _ in range( self.hapNum ): + newLineages[i].push_back(vecint1) + + for i in range( self.popNum ): + for j in range( self.hapNum ): + self.infectiousDelta[i, j] = 0 + + for e_id in range(self.events.ptr-1, -1, -1): + # this event + e_time = self.events.times[e_id] + e_type_ = self.events.types[e_id] + e_haplotype = self.events.haplotypes[e_id] + e_population = self.events.populations[e_id] + e_newHaplotype = self.events.newHaplotypes[e_id] + e_newPopulation = self.events.newPopulations[e_id] + if e_type_ == BIRTH: + lbs = liveBranchesS[e_population][e_haplotype].size() + lbs_e = self.infectious[e_population, self.hapToNum[e_haplotype]] + p = float(lbs)*(float(lbs)-1.0)/ float(lbs_e) / (float(lbs_e) - 1.0) + if self.seed.uniform() < p: + n1 = int(floor( lbs*self.seed.uniform() )) + n2 = int(floor( (lbs-1)*self.seed.uniform() )) + if n2 >= n1: + n2 += 1 + id1 = liveBranchesS[e_population][e_haplotype][n1] + id2 = liveBranchesS[e_population][e_haplotype][n2] + id3 = ptrTreeAndTime + liveBranchesS[e_population][e_haplotype][n1] = id3 + liveBranchesS[e_population][e_haplotype][n2] = liveBranchesS[e_population][e_haplotype][lbs-1] + liveBranchesS[e_population][e_haplotype].pop_back() + self.tree[id1] = id3 + self.tree[id2] = id3 + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = e_population + self.times[ptrTreeAndTime] = e_time + ptrTreeAndTime += 1 + self.infectious[e_population, self.hapToNum[e_haplotype]] -= 1 + elif e_type_ == DEATH: + self.infectious[e_population, self.hapToNum[e_haplotype]] += 1 + elif e_type_ == SAMPLING: + self.infectious[e_population, self.hapToNum[e_haplotype]] += 1 + liveBranchesS[e_population][e_haplotype].push_back( ptrTreeAndTime ) + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = e_population + self.times[ptrTreeAndTime] = e_time + ptrTreeAndTime += 1 + elif e_type_ == MUTATION: + lbs = liveBranchesS[e_population][e_newHaplotype].size() + p = float(lbs)/self.infectious[e_population, e_newHaplotype] + if self.seed.uniform() < p: + n1 = int(floor( lbs*self.seed.uniform() )) + id1 = liveBranchesS[e_population][e_newHaplotype][n1] + liveBranchesS[e_population][e_newHaplotype][n1] = liveBranchesS[e_population][e_newHaplotype][lbs-1] + liveBranchesS[e_population][e_newHaplotype].pop_back() + liveBranchesS[e_population][e_haplotype].push_back(id1) + self.mut.AddMutation(id1, e_haplotype, e_newHaplotype, e_time) + self.infectious[e_population, e_newHaplotype] -= 1 + self.infectious[e_population, e_haplotype] += 1 + elif e_type_ == SUSCCHANGE: + pass + elif e_type_ == MIGRATION: + lbs = liveBranchesS[e_newPopulation][e_haplotype].size() + p = float(lbs)/self.infectious[e_newPopulation, e_haplotype] + if self.seed.uniform() < p: + nt = int(floor( lbs*self.seed.uniform() )) + lbss = liveBranchesS[e_population][e_haplotype].size() + p1 = float(lbss)/self.infectious[e_population, e_haplotype] + if self.seed.uniform() < p1: + ns = int(floor( lbss*self.seed.uniform() )) + idt = liveBranchesS[e_newPopulation][e_haplotype][nt] + ids = liveBranchesS[e_population][e_haplotype][ns] + id3 = ptrTreeAndTime + liveBranchesS[e_population][e_haplotype][ns] = id3 + liveBranchesS[e_newPopulation][e_haplotype][nt] = liveBranchesS[e_newPopulation][e_haplotype][lbs-1] + liveBranchesS[e_newPopulation][e_haplotype].pop_back() + self.tree[idt] = id3 + self.tree[ids] = id3 + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = e_population + self.times[ptrTreeAndTime] = e_time + ptrTreeAndTime += 1 + self.mig.AddMigration(idt, e_time, e_population, e_newPopulation) + else: + liveBranchesS[e_population][e_haplotype].push_back(liveBranchesS[e_newPopulation][e_haplotype][nt]) + liveBranchesS[e_newPopulation][e_haplotype][nt] = liveBranchesS[e_newPopulation][e_haplotype][lbs-1] + liveBranchesS[e_newPopulation][e_haplotype].pop_back() + self.infectious[e_newPopulation, e_haplotype] -= 1 + elif e_type_ == MULTITYPE: + for me_id in range(e_haplotype, e_population): + me_num = self.multievents.num[me_id] + me_time = self.multievents.times[me_id] + me_type_ = self.multievents.types[me_id] + me_haplotype = self.multievents.haplotypes[me_id] + me_population = self.multievents.populations[me_id] + me_newHaplotype = self.multievents.newHaplotypes[me_id] + me_newPopulation = self.multievents.newPopulations[me_id] + + if me_type_ == BIRTH: + lbs = liveBranchesS[me_population][me_haplotype].size() + lbs_e = self.infectious[me_population, me_haplotype] + if me_num == 0 or lbs == 0: + mt_ev_num = 0 + else: + mt_ev_num = random_hypergeometric(self.seed.rng, int(lbs*(lbs-1.0)/2.0), int(lbs_e*(lbs_e-1)/2-lbs*(lbs-1)/2), me_num) + #print("me_num=", me_num, " mt_ev_num", mt_ev_num) + for i in range(mt_ev_num): + n1 = int(floor( lbs*self.seed.uniform() )) + n2 = int(floor( (lbs-1)*self.seed.uniform() )) + if n2 >= n1: + n2 += 1 + id1 = liveBranchesS[me_population][me_haplotype][n1] + id2 = liveBranchesS[me_population][me_haplotype][n2] + id3 = ptrTreeAndTime + newLineages[me_population][me_haplotype].push_back(id3) + if n1 == lbs-1:#TODO: need to check if we really need these if and elif. Most likely - no! + liveBranchesS[me_population][me_haplotype].pop_back() + liveBranchesS[me_population][me_haplotype][n2] = liveBranchesS[me_population][me_haplotype][lbs-2] + liveBranchesS[me_population][me_haplotype].pop_back() + elif n2 == lbs-1: + liveBranchesS[me_population][me_haplotype].pop_back() + liveBranchesS[me_population][me_haplotype][n1] = liveBranchesS[me_population][me_haplotype][lbs-2] + liveBranchesS[me_population][me_haplotype].pop_back() + else: + liveBranchesS[me_population][me_haplotype][n1] = liveBranchesS[me_population][me_haplotype][lbs-1] + liveBranchesS[me_population][me_haplotype].pop_back() + liveBranchesS[me_population][me_haplotype][n2] = liveBranchesS[me_population][me_haplotype][lbs-2] + liveBranchesS[me_population][me_haplotype].pop_back() + self.tree[id1] = id3 + self.tree[id2] = id3 + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = me_population + self.times[ptrTreeAndTime] = me_time + ptrTreeAndTime += 1 + lbs -= 2 + self.infectiousDelta[me_population, me_haplotype] -= me_num + elif me_type_ == DEATH: + self.infectiousDelta[me_population, me_haplotype] += me_num + elif me_type_ == SAMPLING: + self.infectiousDelta[me_population, me_haplotype] += me_num + for i in range(me_num): + #liveBranchesS[me_population][me_haplotype].push_back( ptrTreeAndTime ) + newLineages[me_population][me_haplotype].push_back( ptrTreeAndTime ) + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = me_population + self.times[ptrTreeAndTime] = me_time + ptrTreeAndTime += 1 + elif me_type_ == MUTATION: + lbs = liveBranchesS[me_population][me_newHaplotype].size() + if me_num == 0 or lbs == 0: + mt_ev_num = 0 + else: + mt_ev_num = random_hypergeometric(self.seed.rng, lbs, self.infectious[me_population, me_newHaplotype]-lbs, me_num) + for i in range(mt_ev_num): + n1 = int(floor( lbs*self.seed.uniform() )) + id1 = liveBranchesS[me_population][me_newHaplotype][n1] + liveBranchesS[me_population][me_newHaplotype][n1] = liveBranchesS[me_population][me_newHaplotype][lbs-1] + liveBranchesS[me_population][me_newHaplotype].pop_back() + #liveBranchesS[me_population][me_haplotype].push_back(id1) + newLineages[me_population][me_haplotype].push_back( id1 ) + self.mut.AddMutation(id1, me_haplotype, me_newHaplotype, me_time) + lbs-=1 + self.infectiousDelta[me_population, me_newHaplotype] -= me_num + self.infectiousDelta[me_population, me_haplotype] += me_num + elif me_type_ == SUSCCHANGE: + pass + elif me_type_ == MIGRATION: + lbs = liveBranchesS[me_newPopulation][me_haplotype].size() + if me_num == 0 or lbs == 0: + mt_ev_num = 0 + else: + mt_ev_num = random_hypergeometric(self.seed.rng, lbs, self.infectious[me_newPopulation, me_haplotype]-lbs, me_num) + #for i in range(mt_ev_num): + lbss = liveBranchesS[me_population][me_haplotype].size() + if mt_ev_num == 0 or lbss == 0: + mt_ev_num2 = 0 + else: + mt_ev_num2 = random_hypergeometric(self.seed.rng, lbss, self.infectious[me_population, me_haplotype]-lbss, mt_ev_num) + #print("me_num: ", me_num, "mt_ev_num: ", mt_ev_num, "mt_ev_num2: ", mt_ev_num2) + for i in range(mt_ev_num2): + nt = int(floor( lbs*self.seed.uniform() )) + ns = int(floor( lbss*self.seed.uniform() )) + idt = liveBranchesS[me_newPopulation][me_haplotype][nt] + ids = liveBranchesS[me_population][me_haplotype][ns] + id3 = ptrTreeAndTime + liveBranchesS[me_population][me_haplotype][ns] = liveBranchesS[me_population][me_haplotype][ liveBranchesS[me_population][me_haplotype].size()-1 ] + liveBranchesS[me_population][me_haplotype].pop_back() + liveBranchesS[me_newPopulation][me_haplotype][nt] = liveBranchesS[me_newPopulation][me_haplotype][lbs-1] + liveBranchesS[me_newPopulation][me_haplotype].pop_back() + newLineages[me_population][me_haplotype].push_back( id3 ) + self.tree[idt] = id3 + self.tree[ids] = id3 + self.tree[ptrTreeAndTime] = -1 + self.tree_pop[ptrTreeAndTime] = me_population + self.times[ptrTreeAndTime] = me_time + ptrTreeAndTime += 1 + self.mig.AddMigration(idt, me_time, me_population, me_newPopulation) + lbss -= 1 + lbs -= 1 + for i in range(mt_ev_num-mt_ev_num2): + nt = int(floor( lbs*self.seed.uniform() )) + newLineages[me_population][me_haplotype].push_back(liveBranchesS[me_newPopulation][me_haplotype][nt]) + liveBranchesS[me_newPopulation][me_haplotype][nt] = liveBranchesS[me_newPopulation][me_haplotype][lbs-1] + liveBranchesS[me_newPopulation][me_haplotype].pop_back() + lbs -= 1 + self.infectiousDelta[me_newPopulation, me_haplotype] -= me_num + else: + print("Unknown event type: ", me_type_) + print("_________________________________") + sys.exit(0) + for pi in range(self.popNum): + for hi in range(self.hapNum): + self.infectious[pi, hi] += self.infectiousDelta[pi, hi] + self.infectiousDelta[pi, hi] = 0 + while newLineages[pi][hi].size() > 0: + liveBranchesS[pi][hi].push_back( newLineages[pi][hi][newLineages[pi][hi].size()-1] ) + newLineages[pi][hi].pop_back() + #for i in range(newLineages[pi][hi].size()-1, -1, -1): + # liveBranchesS[pi][hi].push_back(newLineages[pi][hi][i]) + # newLineages[pi][hi].pop_back() + else: + print("Unknown event type: ", e_type_) + print("_________________________________") + sys.exit(0) + for i in range(self.sCounter * 2 - 2): + if self.tree_pop[self.tree[i]] != self.tree_pop[i]: + self.mig.AddMigration(i, self.times[i], self.tree_pop[self.tree[i]], self.tree_pop[i]) + + #for i in range(self.sCounter * 2 - 1): + # print(self.tree[i], end=" ") + #print("") + #deg = [0 for i in range(self.sCounter * 2 - 1)] + #for i in range(self.sCounter * 2 - 1): + # deg[self.tree[i]] += 1 + #for i in range(self.sCounter * 2 - 1): + # print(deg[i], end=" ") + + #self.CheckTree() + + + def print_basic_parameters(self): + print("*****************") + print("***Basic rates***") + print("*****************") + table = PrettyTable() + + field = ["H", "TR", "RR", "SR", "ST"] + for s in range(self.sites): + field.append("M" + str(s)) + field.append("MW" + str(s)) + table.field_names = field + for hn in range(self.currentHapNum): + list = ["\n" + self.calculate_string(hn), "\n" + str(self.bRate[hn]), "\n" + str(self.dRate[hn]), "\n" + str(self.sRate[hn]), "\n" + str(self.suscType[hn])] + for s in range(self.sites): + list.append("\n" + str(self.mRate[hn, s])) + list.append(self.create_mutations(hn, s)) + table.add_row(list) + + print(table) + print("Legend:") + print("H - haplotype") + print("TR - transmission rate") + print("RR - recovery rate") + print("SR - sampling rate") + print("ST - susceptibility type") + for s in range(self.sites): + print("M" + str(s) + " - " + str(s) + " mutation rate") + print("MW" + str(s) + " - " + str(s) + " mutation weights") + print() + + def GetCurrentIndividuals(self): + current_susceptible, current_infectious = [[0 for _ in range(self.susNum)] for _ in range(self.popNum)], [[0 for _ in range(self.hapNum)] for _ in range(self.popNum)] + + for pi in range(self.popNum): + for si in range(self.susNum): + current_susceptible[pi][si] = self.initial_susceptible[pi, si] + for hi in range(self.hapNum): + current_infectious[pi][hi] = self.initial_infectious[pi, hi] + for i in range(self.events.ptr): + if self.events.types[i] == BIRTH: + current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] -= 1 + current_infectious[self.events.populations[i]][self.events.haplotypes[i]] += 1 + elif self.events.types[i] == DEATH: + current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 + current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 + elif self.events.types[i] == SAMPLING: + current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 + current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 + elif self.events.types[i] == MUTATION: + current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 + current_infectious[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 + elif self.events.types[i] == SUSCCHANGE: + current_susceptible[self.events.populations[i]][self.events.haplotypes[i]] -= 1 + current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 + else: + current_susceptible[self.events.newPopulations[i]][self.events.newHaplotypes[i]] -= 1 + current_infectious[self.events.newPopulations[i]][self.events.haplotypes[i]] += 1 + + return [current_susceptible, current_infectious] + + def print_populations(self): + current_susceptible, current_infectious = self.GetCurrentIndividuals() + + print("*****************") + print("***Populations***") + print("*****************") + table_populations = PrettyTable() + + table_populations.field_names = ["ID", "Size", 'Actual size', "CD",'CDBLC', "CDALD", "SLD", "ELD", "SM"] + for pn in range(self.popNum): + table_populations.add_row([pn, self.sizes[pn], self.actualSizes[pn], self.contactDensity[pn], self.contactDensityBeforeLockdown[pn], self.contactDensityAfterLockdown[pn], self.startLD[pn], self.endLD[pn], self.samplingMultiplier[pn]]) + + print(table_populations) + print("Legend:") + print("ID - number of population") + print("Size - size of population") + print("Actual size - actual size of population") + print("CD - contact density") + print("CDBLD - contact density without lockdown") + print("CDALD - contact density at lockdown") + print("SLD - start of lockdown") + print("ELD - end of lockdown") + print("SM - sampling multiplier") + print() + + print("*****************") + print("***Susceptible***") + print("*****************") + table_susceptible = PrettyTable() + + field = ["ST\\ID"] + for pn in range(self.popNum): + field.append(pn) + for sn in range(self.susNum): + row = [sn] + for pn in range(self.popNum): + row.append(current_susceptible[pn][sn]) + table_susceptible.add_row(row) + table_susceptible.field_names = field + + print(table_susceptible) + print("Legend:") + print("ID - ID population") + print("ST - susceptibility type") + print() + + print("****************") + print("***Infectious***") + print("****************") + table_infectious = PrettyTable() + + field = ["H\\ID"] + for pn in range(self.popNum): + field.append(pn) + for hn in range(self.hapNum): + row = [self.calculate_string(hn)] + for pn in range(self.popNum): + row.append(current_infectious[pn][hn]) + table_infectious.add_row(row) + table_infectious.field_names = field + + print(table_infectious) + print("Legend:") + print("ID - ID population") + print("H - haplotype") + print() + + print("**********************") + print("***Migration matrix***") + print("**********************") + table_migration = PrettyTable() + + field = ["S\\T"] + for pn1 in range(self.popNum): + field.append(pn1) + row = [pn1] + for pn2 in range(self.popNum): + row.append(self.migrationRates[pn1, pn2]) + table_migration.add_row(row) + table_migration.field_names = field + + print(table_migration) + print("Legend:") + print("S - ID source population") + print("T - ID target population") + print() + + def print_immunity_model(self): + print("********************") + print("***Immunity model***") + print("********************") + table_immunity = PrettyTable() + + field = ["H\\ST"] + for sn in range(self.susNum): + field.append("S" + str(sn)) + table_immunity.field_names = field + for hn in range(self.currentHapNum): + row = [self.calculate_string(hn)] + for sn in range(self.susNum): + row.append(self.susceptibility[hn, sn]) + table_immunity.add_row(row) + + print(table_immunity) + print("Legend:") + print("H - haplotype") + print("ST - susceptibility type") + print() + + print("*******************************") + print("***Immunity transition rates***") + print("*******************************") + table_immunity_transition = PrettyTable() + + field = ["ID"] + for sn1 in range(self.susNum): + field.append(sn1) + row = [sn1] + for sn2 in range(self.susNum): + row.append(self.suscepTransition[sn1, sn2]) + table_immunity_transition.add_row(row) + table_immunity_transition.field_names = field + + print(table_immunity_transition) + print("Legend:") + print("ID - ID susceptibility type") + print() + + + def Error(self, text): + print(text) + sys.exit(1) + + def create_list_haplotypes(self, haplotype): + haplotypes = [haplotype] + for s in range(self.sites): + for i in range(len(haplotypes)): + haplotype_old = haplotypes[i] + if haplotype_old[s] == "*": + haplotype = haplotype_old.replace("*", "A", 1) + haplotypes.append(haplotype) + haplotype = haplotype_old.replace("*", "T", 1) + haplotypes.append(haplotype) + haplotype = haplotype_old.replace("*", "C", 1) + haplotypes.append(haplotype) + haplotype = haplotype_old.replace("*", "G", 1) + haplotypes.append(haplotype) + for i in range(len(haplotypes)-1, -1, -1): + if haplotypes[i].count("*") != 0: + haplotypes.remove(haplotypes[i]) + for i in range(len(haplotypes)): + haplotypes[i] = self.calculate_haplotype(haplotypes[i]) + + return haplotypes + + def create_mutations(self, haplotype, site): + hap = self.calculate_string(haplotype) + haplotypes = [hap[:site] + "A" + hap[site+1:], hap[:site] + "T" + hap[site+1:], hap[:site] + "C" + hap[site+1:], hap[:site] + "G" + hap[site+1:]] + for i in range(4): + if haplotypes[i] == hap: + haplotypes.remove(haplotypes[i]) + haplotypes.append(hap) + break + color_hap=[] + for hapl in haplotypes: + a = "" + for s in range(self.sites): + if s == site: + a = a + "\033[31m{}\033[0m" .format(hapl[s:s+1]) + else: + a = a + hapl[s:s+1] + color_hap.append(a) + string = color_hap[3] + "->" + color_hap[0] + ": " + str(self.hapMutType[haplotype, site, 0]) + "\n" + color_hap[3] + "->" + color_hap[1] + ": " + str(self.hapMutType[haplotype, site, 1]) + "\n" + color_hap[3] + "->" + color_hap[2] + ": " + str(self.hapMutType[haplotype, site, 2]) + "\n" + return string + + def calculate_string(self, hapNum): + letters = ["A", "T", "C", "G"] + string = "" + for s in range(self.sites): + string = letters[hapNum%4] + string + hapNum = hapNum // 4 + return string + + def calculate_haplotype(self, string): + string = string[::-1] + haplotype = 0 + for s in range(self.sites): + if string[s]=="T": + haplotype += (4**s) + elif string[s]=="C": + haplotype += 2*(4**s) + elif string[s]=="G": + haplotype += 3*(4**s) + return haplotype + + def calculate_allele(self, haplotype, site): + for _ in range(self.sites-site): + allele = haplotype % 4 + haplotype = haplotype // 4 + return allele + + def get_sites(self): + return self.sites + + def get_hapNum(self): + return self.hapNum + + def get_popNum(self): + return self.popNum + + def get_susNum(self): + return self.susNum + + + def check_amount(self, amount, smth): + if isinstance(amount, int) == False: + raise TypeError('Incorrect type of ' + smth + '. Type should be int.') + elif amount < 0: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_rate_1(self, value, smth): + if isinstance(value, (int, float)) == False: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') + elif value < 0: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_rate_none_1(self, value, smth): + if isinstance(value, (int, float)) == False and value != None: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') + elif isinstance(value, (int, float)): + if value < 0: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_haplotype(self, haplotype): + if isinstance(haplotype, (int, str)) == False and haplotype != None: + raise TypeError('Incorrect type of haplotype. Type should be int or str or None.') + elif isinstance(haplotype, int): + if haplotype < 0 or haplotype >= self.hapNum: + raise IndexError('There are no such haplotype!') + elif isinstance(haplotype, str): + if haplotype.count("A") + haplotype.count("T") + haplotype.count("C") + haplotype.count("G") + haplotype.count("*") != self.sites: + raise ValueError('Incorrect haplotype. Haplotype should contain only \"A\", \"T\", \"C\", \"G\", \"*\" and lenght of haplotype should be equal number of mutations sites.') + + def check_rate_2(self, value, smth): + if isinstance(value, (int, float)) == False: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') + elif value < 0 and value > 1: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_rate_none_2(self, value, smth): + if isinstance(value, (int, float)) == False and value != None: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') + elif isinstance(value, (int, float)): + if value < 0 and value > 1: + raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') + + def check_list(self, data, smth, lenght): + if isinstance(data, list) == False and data != None: + raise TypeError('Incorrect type of ' + smth + '. Type should be list or None.') + elif isinstance(data, list): + if len(data) != lenght: + raise ValueError('Incorrect lenght of ' + smth + '. Lenght should be equal ' + str(lenght) + '.') + + def check_index(self, value, edge, smth): + if isinstance(value, int) == False and value != None: + raise TypeError('Incorrect type of ' + smth + '. Type should be int or None.') + elif isinstance(value, int): + if value < 0 or value >= edge: + raise IndexError('There are no such ' + smth + '!') + + def check_amount_sus(self, amount, source_type, target_type, population): + if self.initial_susceptible[population, source_type] - amount < 0: + raise ValueError('Number of susceptible minus amount should be more or equal 0.') + if self.initial_susceptible[population, target_type] + amount > self.sizes[population]: + raise ValueError('Number of susceptible plus amount should be equal or less population size.') + + def check_amount_inf(self, amount, source_type, target_haplotype, population): + if self.initial_susceptible[population, source_type] - amount < 0: + raise ValueError('Number of susceptible minus amount should be more or equal 0.') + if self.initial_infectious[population, target_haplotype] + amount > self.sizes[population]: + raise ValueError('Number of infectious plus amount should be equal or less population size.') + + def check_mig_rate(self): + for pn1 in range(self.popNum): + summa = 0 + self.migrationRates[pn1, pn1] = 1.0 + for pn2 in range(self.popNum): + if pn1 != pn2: + summa += self.migrationRates[pn1, pn2] + self.migrationRates[pn1, pn1] -= self.migrationRates[pn1, pn2] + if summa > 1: + raise ValueError('Incorrect the sum of migration probabilities. The sum of migration probabilities from each population should be equal or less 1.') + + + def set_initial_haplotype(self, amount): + if self.memory_optimization == False: + raise ValueError('Incorrect value of memory optimization. Value should be equal \'True\' for work this function.') + self.check_amount(amount, 'amount of initial haplotype') + + if amount > self.hapNum: + self.maxHapNum = self.hapNum + else: + self.maxHapNum = amount + + def set_step_haplotype(self, amount): + if self.memory_optimization == False: + raise ValueError('Incorrect value of memory optimization. Value should be equal \'True\' for work this function.') + self.check_amount(amount, 'amount of step haplotype') + + if amount > self.hapNum: + self.addMemoryNum = self.hapNum + else: + self.addMemoryNum = amount + + def set_transmission_rate(self, rate, haplotype, condition): + self.check_value(rate, 'transmission rate') + self.check_index(haplotype, self.hapNum, 'haplotype', True) + self.check_index(condition, self.conditionNum, 'number of states') + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + conditions = self.create_list_for_cycles(condition, self.conditionNum) + + for hn in haplotypes: + for cn in conditions: + self.bRate[hn, cn] = rate + + + def set_recovery_rate(self, rate, haplotype, condition): + self.check_value(rate, 'recovery rate') + self.check_index(haplotype, self.hapNum, 'haplotype', True) + self.check_index(condition. self.conditionNum, 'number of states') + + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + conditions = self.create_list_for_cycles(condition, self.conditionNum) + + for hn in haplotypes: + for cn in conditions: + self.dRate[hn, cn] = rate + + def set_sampling_rate(self, rate, haplotype, condition): + self.check_index(haplotype, self.hapNum, 'haplotype', True) + self.check_value(condition, self.conditionNum, 'number of states') + conditions = self.create_list_for_cycles(condition, self.conditionNum) + + if self.sampling_probability == True: + self.check_value(rate, 'sampling probability', edge=1) + + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + for hn in haplotypes: + for cn in conditions: + deathRate = self.dRate[hn, cn] + self.sRate[hn, cn] + self.dRate[hn, cn] = (1 - rate) * deathRate + self.sRate[hn, cn] = rate * deathRate + + elif self.sampling_probability == False: + self.check_value(rate, 'sampling rate') + + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + for hn in haplotypes: + for cn in conditions: + self.sRate[hn, cn] = rate + + def set_mutation_rate(self, rate, haplotype, mutation, condition): + self.check_value(rate, 'mutation rate') + self.check_index(haplotype, self.hapNum, 'haplotype', True) + self.check_index(mutation, self.sites, 'mutation site') + self.check_value(condition, self.conditionNum, 'number of states') + + haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) + conditions = self.create_list_for_cycles(condition, self.conditionNum) + sites = self.create_list_for_cycles(mutation, self.sites) + for hn in haplotypes: + for cn in conditions: + for s in sites: + self.mRate[hn, cn, s] = rate + + @property + def condition_rate(self): + return self.conditionRate + + def set_condition_rate(self, rate, c1, c2): + self.check_value(rate, 'condition_rate') + self.check_index(c1, self.conditionNum, 'first_state') + self.check_index(c2, self.conditionNum, 'second_state') + self.conditionRate[c1, c2] = rate + + def set_probabilities(self, haplotype, mutation, rate, probabilities): + if isinstance(rate, (int, float)): + self.mRate[haplotype, mutation] = rate + if isinstance(probabilities, list): + probabilities_allele = list(probabilities) + del probabilities_allele[self.calculate_allele(haplotype, mutation)] + if sum(probabilities_allele) == 0: + raise ValueError('Incorrect probabilities list. The sum of three elements without mutation allele should be more 0.') + self.hapMutType[haplotype, mutation, 0] = probabilities_allele[0] + self.hapMutType[haplotype, mutation, 1] = probabilities_allele[1] + self.hapMutType[haplotype, mutation, 2] = probabilities_allele[2] + + def set_susceptibility_type(self, susceptibility_type, haplotype): + if isinstance(susceptibility_type, int) == False: + raise TypeError('Incorrect type of susceptibility type. Type should be int.') + elif susceptibility_type < 0 or susceptibility_type >= self.susNum: + raise IndexError('There are no such susceptibility type!') + self.check_haplotype(haplotype) + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + for hn in haplotypes: + self.suscType[hn] = susceptibility_type + elif isinstance(haplotype, int): + self.suscType[haplotype] = susceptibility_type + elif haplotype == None: + for hn in range(self.hapNum): + self.suscType[hn] = susceptibility_type + + def set_susceptibility(self, rate, haplotype, susceptibility_type): + self.check_rate_1(rate, 'rate of susceptibility') + self.check_haplotype(haplotype) + self.check_index(susceptibility_type, self.susNum, 'susceptibility type') + + if isinstance(haplotype, str): + haplotypes = self.create_list_haplotypes(haplotype) + if isinstance(susceptibility_type, int): + for hn in haplotypes: + self.susceptibility[hn, susceptibility_type] = rate + else: + for hn in haplotypes: + for sn in range(self.susNum): + self.susceptibility[hn, sn] = rate + elif isinstance(haplotype, int): + if isinstance(susceptibility_type, int): + self.susceptibility[haplotype, susceptibility_type] + else: + for sn in range(self.susNum): + self.susceptibility[haplotype, sn] = rate + else: + if isinstance(susceptibility_type, int): + for hn in range(self.hapNum): + self.susceptibility[hn, susceptibility_type] = rate + else: + for hn in range(self.hapNum): + for sn in range(self.susNum): + self.susceptibility[hn, sn] = rate + + def set_immunity_transition(self, rate, source, target): + self.check_rate_1(rate, 'immunity transition rate') + self.check_index(source, self.susNum, 'susceptibility type') + self.check_index(target, self.susNum, 'susceptibility type') + + if isinstance(source, int): + if isinstance(target, int): + if source == target: + raise ValueError('Source and target susceptibility type shouldn\'t be equal!') + self.suscepTransition[source, target] = rate + else: + for sn1 in range(self.susNum): + if sn1 != target: + self.suscepTransition[sn1, target] = rate + else: + if isinstance(target, int): + for sn2 in range(self.susNum): + if source != sn2: + self.suscepTransition[source, sn2] = rate + else: + for sn1 in range(self.susNum): + for sn2 in range(self.susNum): + if sn1 != sn2: + self.suscepTransition[sn1, sn2] = rate + + def set_population_size(self, amount, population): + if self.first_simulation == True: + raise ValueError('Changing population size is available only before first simulation!') + self.check_amount(amount, 'population size') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.sizes[population] = amount + for sn in range(self.susNum): + self.initial_susceptible[population, 0] = amount + elif population == None: + for pn in range(self.popNum): + self.sizes[pn] = amount + for sn in range(self.susNum): + self.initial_susceptible[pn, 0] = amount + + def set_susceptible(self, amount, source_type, target_type, population): + if self.first_simulation: + raise ValueError('This function is available only before first simulation!') + self.check_amount(amount) + self.check_index(source_type, self.susNum, 'susceptibility type') + self.check_index(target_type, self.susNum, 'susceptibility type') + if source_type == target_type: + raise ValueError('Source and target susceptibility type shouldn\'t be equal!') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.check_amount_sus(amount, source_type, target_type, population) + + self.initial_susceptible[population, source_type] -= amount + self.initial_susceptible[population, target_type] += amount + elif population == None: + for pn in range(self.popNum): + self.check_amount_sus(amount, source_type, target_type, pn) + + self.initial_susceptible[pn, source_type] -= amount + self.initial_susceptible[pn, target_type] += amount + + def set_infectious(self, amount, source_type, target_haplotype, population, condition): + if self.first_simulation: + raise ValueError('This function is available only before first simulation!') + self.check_amount(amount) + self.check_index(source_type, self.susNum, 'susceptibility type') + self.check_haplotype(target_haplotype) + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.check_amount_inf(amount, source_type, target_haplotype, population) + + self.initial_susceptible[population, source_type] -= amount + + self.initial_infectious[population, target_haplotype, condition] += amount + elif population == None: + for pn in range(self.popNum): + self.check_amount_inf(amount, source_type, target_haplotype, pn) + + self.initial_susceptible[pn, source_type] -= amount + self.initial_infectious[pn, target_haplotype, condition] += amount + + def set_contact_density(self, value, population): + self.check_rate_1(value, 'contact density') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.contactDensity[population] = value + self.contactDensityBeforeLockdown[population] = value + elif population == None: + for pn in range(self.popNum): + self.contactDensity[pn] = value + self.contactDensityBeforeLockdown[pn] = value + + def set_npi(self, parameters, population): + self.check_list(parameters, 'parameters', 3) + if isinstance(parameters, list): + self.check_rate_1(parameters[0], 'first element of parameters') + self.check_rate_2(parameters[1], 'second element of parameters') + self.check_rate_2(parameters[2], 'third element of parameters') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.contactDensityAfterLockdown[population] = parameters[0] + self.startLD[population] = parameters[1] + self.endLD[population] = parameters[2] + elif population == None: + for pn in range(self.popNum): + self.contactDensityAfterLockdown[pn] = parameters[0] + self.startLD[pn] = parameters[1] + self.endLD[pn] = parameters[2] + + def set_sampling_multiplier(self, multiplier, population): + self.check_rate_1(multiplier, 'sampling multiplier') + self.check_index(population, self.popNum, 'population') + + if isinstance(population, int): + self.samplingMultiplier[population] = multiplier + elif population == None: + for pn in range(self.popNum): + self.samplingMultiplier[pn] = multiplier + + def set_migration_probability(self, probability, total_probability, source, target): + self.check_rate_none_2(probability, 'probability') + self.check_rate_none_2(total_probability, 'total probability') + self.check_index(source, self.popNum, 'population') + self.check_index(target, self.popNum, 'population') + + if isinstance(probability, float): + if isinstance(source, int): + if isinstance(target, int): + if source == target: + raise IndexError('Source and target population shouldn\'t be equal!') + + self.migrationRates[source, target] = probability + else: + for pn2 in range(self.popNum): + if source != pn2: + self.migrationRates[source, pn2] = probability + else: + if isinstance(target, int): + for pn1 in range(self.popNum): + if pn1 != target: + self.migrationRates[pn1, target] = probability + else: + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + if pn1 != pn2: + self.migrationRates[pn1, pn2] = probability + + self.check_mig_rate() + elif isinstance(total_probability, float): + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + if pn1 != pn2: + self.migrationRates[pn1, pn2] = total_probability/(self.popNum-1) + + self.check_mig_rate() + else: + raise TypeError('Incorrect type of probability or total_probability. Type should be float.') + + + def set_chain_events(self, name_file): + if isinstance(name_file, str) == False: + self.Error('#TODO') + tokens = np.load(name_file + '.npy') + #TODO Check sizes and resize if needed + self.size = tokens.shape[1] + self.ptr = tokens.shape[1] + + self.events.times = tokens[0] + self.events.types = tokens[1] + self.events.haplotypes = tokens[2] + self.events.populations = tokens[3] + self.events.newHaplotypes = tokens[4] + self.events.newPopulations = tokens[5] + + def set_settings(self, file_template): + pass + + + def output_tree_mutations(self): + if self.tree.shape[0] == 1: + print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') + sys.exit(1) + mut = [[], [], [], [], []] + for i in range(self.mut.nodeId.size()): + mut[0].append(self.mut.nodeId[i]) + mut[1].append(self.mut.AS[i]) + mut[2].append(self.mut.site[i]) + mut[3].append(self.mut.DS[i]) + mut[4].append(self.mut.time[i]) + times_dict = {self.events.times[i]: i for i in range(len(self.events.times))} + populations = {} + for time in self.times: + populations[time] = self.events.populations[times_dict[time]] + + return self.tree, self.times, mut, populations + + def output_migrations(self, name_file, file_path): + if self.tree.shape[0] == 1: + print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') + sys.exit(1) + if file_path != None: + f_mig = open(file_path + '/' + name_file + '.tsv', 'w') + else: + f_mig = open(name_file + '.tsv', 'w') + f_mig.write("Node\tTime\tOld_population\tNew_population\n") + for i in range(self.mig.nodeId.size()): + f_mig.write(str(self.mig.nodeId[i]) + '\t' + str(self.mig.time[i]) + '\t' + str(self.mig.oldPop[i]) + '\t' + str(self.mig.newPop[i]) + "\n") + f_mig.close() + + def output_sample_data(self): + time, pop, hap = [], [], [] + for i in range(self.events.ptr): + if self.events.types[i] == SAMPLING: + time.append(self.events.times[i]) + pop.append(self.events.populations[i]) + hap.append(self.events.haplotypes[i]) + return time, pop, hap + + def output_epidemiology_timelines(self, step_num, output_file): + time_points = [i*self.currentTime/step_num for i in range(step_num+1)] + suscepDate = np.zeros((self.popNum, self.susceptible_num), dtype=np.int64) + hapDate = np.zeros((self.popNum, self.hapNum), dtype=np.int64) + for i in range(self.popNum): + for sn in range(self.susceptible_num): + if sn == 0: + suscepDate[i, sn] = self.sizes[i] + else: + suscepDate[i, sn] = 0 + for hn in range(self.hapNum): + hapDate[i, hn] = 0 + hapDate[0, 0] += 1 + suscepDate[0, 0] -= 1 + if output_file == True: + if not os.path.isdir("logs"): + os.mkdir("logs") + logDynamics = [] + for i in range(self.popNum): + logDynamics.append(open('logs/PID' + str(i) + '.log', 'w')) + logDynamics[i].write("time") + for sn in range(self.susceptible_num): + logDynamics[i].write(" S" + str(sn)) + for hn in range(self.hapNum): + logDynamics[i].write(" H" + str(hn)) + logDynamics[i].write("\n") + else: + log = dict() + log["time"] = list() + for i in range(self.popNum): + log["P" + str(i)] = dict() + for j in range(self.susceptible_num): + log["P" + str(i)]["S" + str(j)] = list() + for j in range(self.hapNum): + log["P" + str(i)]["H" + str(j)] = list() + + point = 0 + for j in range(self.events.ptr): + if self.events.types[j] == BIRTH: + hapDate[self.events.populations[j], self.events.haplotypes[j]] += 1 + suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] -= 1 + elif self.events.types[j] == DEATH: + hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 + suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 + elif self.events.types[j] == SAMPLING: + hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 + suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 + elif self.events.types[j] == MUTATION: + hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 + hapDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 + elif self.events.types[j] == SUSCCHANGE: + suscepDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 + suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 + elif self.events.types[j] == MIGRATION: + suscepDate[self.events.newPopulations[j], self.events.newHaplotypes[j]] -= 1 + hapDate[self.events.newPopulations[j], self.events.haplotypes[j]] += 1 + elif self.events.types[j] == MULTITYPE: + pass + #TODO + if time_points[point] <= self.events.times[j]: + if output_file == True: + for i in range(self.popNum): + logDynamics[i].write(str(time_points[point]) + " ") + for k in range(self.susceptible_num): + logDynamics[i].write(str(suscepDate[i, k]) + " ") + for k in range(self.hapNum): + logDynamics[i].write(str(hapDate[i, k]) + " ") + logDynamics[i].write("\n") + point += 1 + else: + log["time"].append(time_points[point]) + for i in range(self.popNum): + for j in range(self.susceptible_num): + log["P" + str(i)]["S" + str(j)].append(suscepDate[i, j]) + for j in range(self.hapNum): + log["P" + str(i)]["H" + str(j)].append(hapDate[i, j]) + point += 1 + + if output_file == True: + for i in range(self.popNum-1, -1, -1): + logDynamics[i].close() + else: + return log + + def output_chain_events(self, name_file): + np.save(name_file, [self.events.times, self.events.types, self.events.haplotypes, self.events.populations, self.events.newHaplotypes, self.events.newPopulations]) + + def output_settings(self, file_template): + if isinstance(file_template, str) == False: + self.Error('#TODO') + if not os.path.isdir(file_template): + os.mkdir(file_template) + os.chdir(file_template) + comand = 'Command line command: ' + with open(file_template + ".rt", "w") as file: + file.write("#Rates_format_version 0.0.1\nH B D S") + for s in range(self.sites): + file.write(" M" + str(s)) + file.write("\n") + for hn in range(self.hapNum): + file.write(self.calculate_string(hn) + " " + str(self.bRate[hn]) + " " + str(self.dRate[hn]) + " " + str(self.sRate[hn]) + ' ') + for s in range(self.sites): + file.write(str(self.mRate[hn, s]) + "," + str(self.hapMutType[hn, s, 0]) + "," + str(self.hapMutType[hn, s, 1]) + "," + str(self.hapMutType[hn, s, 2]) + " ") + file.write("\n") + comand += (file_template + '/' + file_template + '.rt ') + + with open(file_template + ".pp", "w") as file: + file.write("#Population_format_version 0.0.1\nid size contactDensity conDenAfterLD startLD endLD samplingMulriplier\n") + for pn in range(self.popNum): + file.write(str(pn) + " " + str(self.sizes[pn]) + " " + str(self.contactDensity[pn]) + " " + str(self.contactDensityAfterLockdown[pn]) + "," + str(self.startLD[pn]) + "," + str(self.endLD[pn]) + " " + str(self.samplingMultiplier[pn]) + "\n") + comand += ('-pm ' + file_template + '/' + file_template + '.pp ') + + with open(file_template + ".mg", "w") as file: + file.write("#Migration_format_version 0.0.1\n") + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + file.write(str(self.migrationRates[pn1, pn2]) + " ") + file.write("\n") + comand += (file_template + '/' + file_template + '.mg ') + + with open(file_template + ".su", "w") as file: + file.write("#Susceptibility_format_version 0.0.1\nH T") + for sn in range(self.susNum): + file.write(" S" + str(sn)) + file.write("\n") + for hn in range(self.hapNum): + file.write(self.calculate_string(hn) + " " + str(self.suscType[hn])) + for sn in range(self.susNum): + file.write(" " + str(self.susceptibility[hn, sn])) + file.write("\n") + comand += ('-su ' + file_template + '/' + file_template + '.su ') + + with open(file_template + ".st", "w") as file: + file.write("#Susceptibility_format_version 0.0.1\n") + for sn1 in range(self.susNum): + for sn2 in range(self.susNum): + file.write(str(self.suscepTransition[sn1, sn2]) + " ") + file.write("\n") + comand += ('-st ' + file_template + '/' + file_template + '.st ') + print(comand) + os.chdir('../') + + def get_ts_object(self): + tc = tskit.TableCollection() + tc.sequence_length = self.sites + 1.0 + + for i in range(self.mig.nodeId.size()): + mig_nodeId, mig_time, mig_oldPop, mig_newPop = self.mig.get_migration(i) + tc.migrations.add_row(0.0, 1.0, mig_nodeId, mig_oldPop, mig_newPop, self.times[0] - mig_time) + + for i in range(self.popNum): + tc.populations.add_row(None) + + for i in range(2 * self.sCounter - 2): + tc.edges.add_row(0.0, self.sites+1.0, self.tree[i], i) + + child_or_parent = [1 for _ in range(2 * self.sCounter - 1)] + for i in range(2 * self.sCounter - 2): + child_or_parent[self.tree[i]] = 0 + + for i in range(2 * self.sCounter - 1): + tc.nodes.add_row(child_or_parent[i], self.times[0] - self.times[i], self.tree_pop[i]) + + for i in range(self.sites): + tc.sites.add_row(i+1, 'A') + + allele = ['A', 'T', 'C', 'G'] + for i in range(self.mut.nodeId.size()): + mut_nodeId, mut_DS, mut_AS, mut_site, mut_time = self.mut.get_mutation(i) + tc.mutations.add_row(site=mut_site, node=mut_nodeId, derived_state=allele[mut_DS], time=self.times[0] - mut_time) + + tc.sort() + return tc.tree_sequence() + + def get_tree(self): + if self.tree.shape[0] == 1: + print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') + sys.exit(1) + return self.tree, self.times + + def get_data_infectious(self, pop, hap, step_num): + time_points = [i*self.currentTime/step_num for i in range(step_num+1)] + Data = np.zeros(step_num+1) + Sample = np.zeros(step_num+1) + Data[0] = self.initial_infectious[pop, hap] + + point = 0 + for i in range(self.events.ptr): + while point != step_num and time_points[point] < self.events.times[i]: + Data[point+1] = Data[point] + Sample[point+1] = Sample[point] + point += 1 + + if self.events.types[i] == BIRTH and self.events.populations[i] == pop and self.events.haplotypes[i] == hap: + Data[point] += 1 + elif self.events.types[i] == DEATH or self.events.types[i] == SAMPLING or self.events.types[i] == MUTATION and self.events.populations[i] == pop and self.events.haplotypes[i] == hap: + Data[point] -= 1 + if self.events.types[i] == SAMPLING: + Sample[point] += 1 + elif self.events.types[i] == MUTATION and self.events.newHaplotypes[i] == hap and self.events.populations[i] == pop: + Data[point] += 1 + elif self.events.types[i] == MIGRATION and self.events.newPopulations[i] == pop and self.events.haplotypes[i] == hap: + Data[point] += 1 + elif self.events.types[i] == MULTITYPE: + for j in range(self.events.haplotypes[i], self.events.populations[i]): + if self.multievents.types[j] == BIRTH and self.multievents.haplotypes[j] == hap and self.multievents.populations[j] == pop: + Data[point] += self.multievents.num[j] + elif self.multievents.types[j] == DEATH or self.multievents.types[j] == SAMPLING or self.multievents.types[j] == MUTATION and self.multievents.haplotypes[j] == hap and self.multievents.populations[j] == pop: + Data[point] -= self.multievents.num[j] + if self.multievents.types[j] == SAMPLING: + Sample[point] += self.multievents.num[j] + elif self.multievents.types[j] == MUTATION and self.multievents.newHaplotypes[j] == hap and self.multievents.populations[j] == pop: + Data[point] += self.multievents.num[j] + elif self.multievents.types[j] == MIGRATION and self.multievents.newPopulations[j] == pop and self.multievents.haplotypes[j] == hap: + Data[point] += self.multievents.num[j] + + Lockdowns = [] + for i in range(self.loc.times.size()): + if self.loc.populationsId[i] == pop: + Lockdowns.append([self.loc.states[i], self.loc.times[i]]) + + return Data, Sample, time_points, Lockdowns + + def get_data_susceptible(self, pop, sus, step_num): + time_points = [i*self.currentTime/step_num for i in range(step_num+1)] + Data = np.zeros(step_num+1) + Data[0] = self.initial_susceptible[pop, sus] + + point = 0 + for i in range(self.events.ptr): + while point != step_num and time_points[point] < self.events.times[i]: + Data[point+1] = Data[point] + point += 1 + + if self.events.types[i] == BIRTH and self.events.populations[i] == pop and self.events.newHaplotypes[i] == sus: + Data[point] -= 1 + elif (self.events.types[i] == DEATH or self.events.types[i] == SAMPLING or self.events.types[i] == SUSCCHANGE) and self.events.populations[i] == pop and self.events.newHaplotypes[i] == sus: + Data[point] += 1 + elif self.events.types[i] == SUSCCHANGE and self.events.haplotypes[i] == sus and self.events.populations[i] == pop: + Data[point] -= 1 + elif self.events.types[i] == MIGRATION and self.events.newPopulations[i] == pop and self.events.newHaplotypes[i] == sus: + Data[point] -= 1 + elif self.events.types[i] == MULTITYPE: + for j in range(self.events.haplotypes[i], self.events.populations[i]): + if self.multievents.types[j] == BIRTH and self.multievents.newHaplotypes[j] == sus and self.multievents.populations[j] == pop: + Data[point] -= self.multievents.num[j] + elif (self.multievents.types[j] == DEATH or self.multievents.types[j] == SAMPLING or self.multievents.types[j] == SUSCCHANGE) and self.multievents.newHaplotypes[j] == sus and self.multievents.populations[j] == pop: + Data[point] += self.multievents.num[j] + elif self.multievents.types[j] == SUSCCHANGE and self.multievents.haplotypes[j] == sus and self.multievents.populations[j] == pop: + Data[point] -= self.multievents.num[j] + elif self.multievents.types[j] == MIGRATION and self.multievents.newPopulations[j] == pop and self.multievents.haplotypes[j] == sus: + Data[point] -= self.multievents.num[j] + + Lockdowns = [] + for i in range(self.loc.times.size()): + if self.loc.populationsId[i] == pop: + Lockdowns.append([self.loc.states[i], self.loc.times[i]]) + + return Data, time_points, Lockdowns + + + def Stats(self, time_simulation): + print("Number of samples:", self.sCounter) + print("Total number of iterations:", self.events.ptr) + print('Success number:', self.good_attempt) + print("Epidemic time:", self.currentTime) + print('Simulation time:', time_simulation) + print('Number of infections:', self.bCounter) + print('Number of recoveries:', self.dCounter) + if self.sites >= 1: + print('Number of mutations:', self.mCounter) + if self.popNum >= 2: + print('Number of accepted migrations:', self.migPlus) + print('Number of rejected migrations:', self.migNonPlus) + check = False + for sn in range(self.susNum): + if self.suscepCumulTransition[sn] != 0.0: + check = True + + if check: + print('Number of immunity transitions:', self.iCounter) + print('----------------------------------') + + def Debug(self): + print("Parameters") + print('first_simulation(mutable): ', self.first_simulation) + print("sampling_probability(const): ", self.sampling_probability) + print("memory_optimization(const): ", self.memory_optimization) + print() + print("sites(const): ", self.sites) + print("hapNum(const): ", self.hapNum) + print("currentHapNum(mutable): ", self.currentHapNum) + print("maxHapNum(mutable): ", self.maxHapNum) + print("popNum(const): ", self.popNum) + print("susNum(const): ", self.susNum) + print("bCounter(mutable): ", self.bCounter) + print("dCounter(mutable): ", self.dCounter) + print("sCounter(mutable): ", self.sCounter) + print("mCounter(mutable): ", self.mCounter) + print("iCounter(mutable):", self.iCounter) + print("swapLockdown(mutable): ", self.swapLockdown) + print("migPlus(mutable): ", self.migPlus) + print("migNonPlus(mutable): ", self.migNonPlus) + print("globalInfectious(mutable): ", self.globalInfectious) + print() + print("currentTime(mutable): ", self.currentTime) + print("totalRate(mutable): ", self.totalRate) + print("totalMigrationTate(mutable): ", self.totalMigrationRate) + print("rn(mutable): ", self.rn) + print() + print("suscType(const): ", sep=" ", end="") + for hn in range(self.hapNum): + print(self.suscType[hn], end=" ") + print() + print() + print("hapToNum(mutable): ", sep="", end="") + for hn in range(self.hapNum): + print(self.hapToNum[hn], end=" ") + print() + print("numToHap(mutable): ", sep="", end="") + for hn in range(self.maxHapNum): + print(self.numToHap[hn], end=" ") + print() + print("sizes(const): ", end="") + for pn in range(self.popNum): + print(self.sizes[pn], end=" ") + print() + print("totalSusceptible(mutable): ", end="") + for pn in range(self.popNum): + print(self.totalSusceptible[pn], end=" ") + print() + print("totalInfectious(mutable): ", end="") + for pn in range(self.popNum): + print(self.totalInfectious[pn], end=" ") + print() + print("lockdownON(mutable): ", end="") + for pn in range(self.popNum): + print(self.lockdownON[pn], end=" ") + print() + print() + print("susceptible(mutable)----") + for pn in range(self.popNum): + for sn in range(self.susNum): + print(self.susceptible[pn, sn], end=" ") + print() + print() + print("infectious(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + print(self.infectious[pn, hn], end=" ") + print() + print() + print() + print("Birth rate(const): ", sep="", end="") + for hn in range(self.hapNum): + print(self.bRate[hn], end=" ") + print() + print("Death rate(const): ", sep="", end="") + for hn in range(self.hapNum): + print(self.dRate[hn], end=" ") + print() + print("Sampling rate(const): ", sep="", end="") + for hn in range(self.hapNum): + print(self.sRate[hn], end=" ") + print() + print("tmRate(const): ", sep="", end="") + for hn in range(self.currentHapNum): + print(self.tmRate[hn], end=" ") + print() + print("maxEffectiveBirthMigration(const): ", sep="", end="") + for pn in range(self.popNum): + print(self.maxEffectiveBirthMigration[pn], end=" ") + print() + print("suscepCumulTransition(const): ", sep="", end="") + for sn in range(self.susNum): + print(self.suscepCumulTransition[sn], end=" ") + print() + print("immunePopRate(mutable): ", sep="", end="") + for pn in range(self.popNum): + print(self.immunePopRate[pn], end=" ") + print() + print("infectPopRate(mutable): ", sep="", end="") + for pn in range(self.popNum): + print(self.infectPopRate[pn], end=" ") + print() + print("popRate(mutable): ", sep="", end="") + for pn in range(self.popNum): + print(self.popRate[pn], end=" ") + print() + print("migPopRate(mutable): ", sep="", end="") + for pn in range(self.popNum): + print(self.migPopRate[pn], end=" ") + print() + print("actualSizes(const): ", end=" ") + for pn in range(self.popNum): + print(self.actualSizes[pn], end=" ") + print() + print("contactDensity(const): ", end=" ") + for pn in range(self.popNum): + print(self.contactDensity[pn], end=" ") + print() + print("contactDensityBeforeLockdown(const): ", end=" ") + for pn in range(self.popNum): + print(self.contactDensityBeforeLockdown[pn], end=" ") + print() + print("contactDensityBeforeLockdown(const): ", end=" ") + for pn in range(self.popNum): + print(self.contactDensityAfterLockdown[pn], end=" ") + print() + print("startLD(const): ", end=" ") + for pn in range(self.popNum): + print(self.startLD[pn], end=" ") + print() + print("endLD(const): ", end=" ") + for pn in range(self.popNum): + print(self.endLD[pn], end=" ") + print() + print("samplingMultiplier(const): ", end=" ") + for pn in range(self.popNum): + print(self.samplingMultiplier[pn], end=" ") + print() + print() + print("mRate(const)----") + for hn in range(self.hapNum): + for s in range(self.sites): + print(self.mRate[hn, s], end=" ") + print() + print() + print("susceptibility(const)----") + for hn in range(self.currentHapNum): + for sn in range(self.susNum): + print(self.susceptibility[hn, sn], end=" ") + print() + print() + print("tEventHapPopRate(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + print(self.tEventHapPopRate[pn, hn], end=" ") + print() + print() + print("suscepTransition(const)----") + for sn1 in range(self.susNum): + for sn2 in range(self.susNum): + print(self.suscepTransition[sn1, sn2], end=" ") + print() + print() + print("immuneSourcePopRate(mutable)----") + for pn in range(self.popNum): + for sn in range(self.susNum): + print(self.immuneSourcePopRate[pn, sn], end=" ") + print() + print() + print("hapPopRate(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + print(self.hapPopRate[pn, hn], end=" ") + print() + print() + print("migrationRates(const)----") + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + print(self.migrationRates[pn1, pn2], end=" ") + print() + print() + print("effectiveMigration(const)----") + for pn1 in range(self.popNum): + for pn2 in range(self.popNum): + print(self.effectiveMigration[pn1, pn2], end=" ") + print() + print() + print() + print("hapMutType(const)----") + for hn in range(self.currentHapNum): + for s in range(self.sites): + for i in range(3): + print(self.hapMutType[hn, s, i], end=" ") + print() + print() + print() + print("eventHapPopRate(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + for i in range(4): + print(self.eventHapPopRate[pn, hn, i], end=" ") + print() + print() + print() + print("susceptHapPopRate(mutable)----") + for pn in range(self.popNum): + for hn in range(self.currentHapNum): + for sn in range(self.susNum): + print(self.susceptHapPopRate[pn, hn, sn], end=" ") + print() + print() + print() + + + def get_proportion(self): + return self.migNonPlus / (self.events.ptr-1) + + ############################# + ### TAU LEAPING ALGORITHM ### + ############################# + + @cython.boundscheck(False) + @cython.wraparound(False) + cpdef void SimulatePopulation_tau(self, Py_ssize_t iterations, Py_ssize_t sample_size, float time, Py_ssize_t attempts): + cdef: + Py_ssize_t i, pn, propNum + bint success + + self.PrepareParameters(iterations) + self.CheckSizes() + + propNum = self.popNum*((self.popNum-1)*self.hapNum*self.susNum+self.susNum*(self.susNum-1)+self.hapNum*(2+self.sites*3+self.susNum)) + if self.globalInfectious == 0: + self.FirstInfection() + #TODO less memory for multievents + self.multievents.CreateEvents(iterations*propNum) + self.events.CreateEvents(iterations) + self.UpdateAllRates() + + for i in range(attempts): + self.seed = RndmWrapper(seed=(self.user_seed, i)) + if self.totalRate+self.totalMigrationRate != 0.0 and self.globalInfectious != 0: + while ((self.events.ptr 100: + self.Restart() + else: + self.good_attempt = i+1 + break + + if self.totalRate == 0.0 or self.globalInfectious == 0: + print('Simulation finished because no infections individuals remain!') + if self.events.ptr>=self.events.size: + print("Achieved maximal number of iterations.") + if self.sCounter>sample_size and sample_size!=-1: + print("Achieved sample size.") + if self.currentTime>time and time != -1: + print("Achieved internal time limit.") + if self.sCounter <= 1: + print('\033[41m{}\033[0m'.format('WARNING!'), 'Simulated less 2 samples, so genealogy will not work!') + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void Propensities(self): + cdef Py_ssize_t pn, spn, tpn, hn, sn, ssn, tsn, s, i + + for pn in range(self.popNum): + for hn in range(self.hapNum): + self.infectiousAuxTau[pn, hn] = 0.0 + for sn in range(self.susNum): + self.susceptibleAuxTau[pn, sn] = 0.0 + + for spn in range(self.popNum): + for tpn in range(self.popNum): + if spn == tpn: + continue + for sn in range(self.susNum): + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + self.PropensitiesMigr[spn, tpn, sn, hn] = self.effectiveMigration[tpn, spn]*self.susceptible[tpn, sn]*\ + self.infectious[spn, hn]*self.bRate[hn, cn]*self.susceptibility[hn, sn]*self.migrationRates[spn, spn] + + self.infectiousAuxTau[tpn, hn] += self.PropensitiesMigr[spn, tpn, sn, hn] + self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesMigr[spn, tpn, sn, hn] + + for pn in range(self.popNum): + #Susceptibility transition + for ssn in range(self.susNum): + for tsn in range(self.susNum): + if ssn == tsn: + continue + self.PropensitiesSuscep[pn, ssn, tsn] = self.suscepTransition[ssn, tsn]*self.susceptible[pn, ssn] + + self.susceptibleAuxTau[pn, tsn] += self.PropensitiesSuscep[pn, ssn, tsn] + self.susceptibleAuxTau[pn, ssn] -= self.PropensitiesSuscep[pn, ssn, tsn] + + #Infectious-realted event + for hn in range(self.hapNum): + #Recovery + for cn in range(self.conditionNum): + self.PropensitiesRecovery[pn, hn] = self.dRate[hn, cn]*self.infectious[pn, hn] + + self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesRecovery[pn, hn] + self.infectiousAuxTau[pn, hn] -= self.PropensitiesRecovery[pn, hn] + + #Sampling + for cn in range(self.conditionNum): + self.PropensitiesSampling[pn, hn] = self.sRate[hn, cn]*self.infectious[pn, hn]*self.samplingMultiplier[pn] + + self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesSampling[pn, hn] + self.infectiousAuxTau[pn, hn] -= self.PropensitiesSampling[pn, hn] + + #Mutation + for s in range(self.sites): + for cn in range(self.conditionNum): + for i in range(3): + self.PropensitiesMutatations[pn, hn, s, i] = self.mRate[hn, cn, s]*self.hapMutType[hn, s, i]/\ + (self.hapMutType[hn, s, 0] + self.hapMutType[hn, s, 1] + self.hapMutType[hn, s, 2])*self.infectious[pn, hn] + + self.infectiousAuxTau[pn, self.Mutate(hn, s, i)] += self.PropensitiesMutatations[pn, hn, s, i] + self.infectiousAuxTau[pn, hn] -= self.PropensitiesMutatations[pn, hn, s, i] + + #Transmission + for tpn in range(self.popNum): + for hn in range(self.hapNum): + for cn in range(self.conditionNum): + for sn in range(self.susNum): + self.PropensitiesTransmission[tpn, hn, sn] = 0.0 + for spn in range(self.popNum): + self.PropensitiesTransmission[tpn, hn, sn] += self.bRate[hn, cn]*self.susceptibility[hn, sn]*self.migrationRates[tpn, spn]*\ + self.migrationRates[tpn, spn]*self.contactDensity[spn]*self.susceptible[tpn, sn]*self.infectious[tpn, hn]/\ + self.actualSizes[spn] + + self.infectiousAuxTau[tpn, hn] += self.PropensitiesTransmission[tpn, hn, sn] + self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesTransmission[tpn, hn, sn] + + @cython.cdivision(True) + cdef Py_ssize_t Mutate(self, Py_ssize_t hi, Py_ssize_t s, Py_ssize_t DS): + cdef Py_ssize_t digit4, AS + + digit4 = 4**(self.sites-s-1) + AS = int(floor(hi/digit4) % 4) + if DS >= AS: + DS += 1 + return hi + (DS-AS)*digit4 + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void ChooseTau(self, float epsilon=0.03): + cdef: + Py_ssize_t pn, hn, sn + double tmp + + self.tau_l = 1.0 + for pn in range(self.popNum): + for hn in range(self.hapNum): + if abs(self.infectiousAuxTau[pn, hn]) < 1e-8: + continue + tmp = max(epsilon*self.infectious[pn,hn]/2.0,1.0)/abs(self.infectiousAuxTau[pn, hn]) + if tmp < self.tau_l: + self.tau_l = tmp + for sn in range(self.susNum): + if abs(self.susceptibleAuxTau[pn, sn]) < 1e-8: + continue + tmp = max(epsilon*self.susceptible[pn,sn]/2.0,1.0)/abs(self.susceptibleAuxTau[pn, sn]) + if tmp < self.tau_l: + self.tau_l = tmp + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef bint GenerateEvents_tau(self): + cdef Py_ssize_t event_num, pn, spn, tpn, hn, sn, ssn, tsn, s, i + + for pn in range(self.popNum): + for sn in range(self.susNum): + self.susceptibleDelta[pn, sn] = 0 + for hn in range(self.hapNum): + self.infectiousDelta[pn, hn] = 0 + + #Migrations + for spn in range(self.popNum): + for tpn in range(self.popNum): + if spn == tpn: + continue + for sn in range(self.susNum): + for hn in range(self.hapNum): + event_num = self.DrawEventsNum(self.PropensitiesMigr[spn, tpn, sn, hn]*self.tau_l) + self.eventsMigr[spn, tpn, sn, hn] = event_num + + self.infectiousDelta[spn, hn] += event_num + self.susceptibleDelta[tpn, sn] -= event_num + + + for pn in range(self.popNum): + #Susceptibility transition + for ssn in range(self.susNum): + for tsn in range(self.susNum): + if ssn == tsn: + continue + event_num = self.DrawEventsNum(self.PropensitiesSuscep[pn, ssn, tsn]*self.tau_l) + self.eventsSuscep[pn, ssn, tsn] = event_num + + self.susceptibleDelta[pn, tsn] += event_num + self.susceptibleDelta[pn, ssn] -= event_num + + #Infectious-realted event + for hn in range(self.hapNum): + #Recovery + event_num = self.DrawEventsNum(self.PropensitiesRecovery[pn, hn]*self.tau_l) + self.eventsRecovery[pn, hn] = event_num + + self.susceptibleDelta[pn, self.suscType[hn]] += event_num + self.infectiousDelta[pn, hn] -= event_num + + #Sampling + event_num = self.DrawEventsNum(self.PropensitiesSampling[pn, hn]*self.tau_l) + self.eventsSampling[pn, hn] = event_num + + self.susceptibleDelta[pn, self.suscType[hn]] += event_num + self.infectiousDelta[pn, hn] -= event_num + + #Mutation + for s in range(self.sites): + for i in range(3): + event_num = self.DrawEventsNum(self.PropensitiesMutatations[pn, hn, s, i]*self.tau_l) + self.eventsMutatations[pn, hn, s, i] = event_num + + self.infectiousDelta[pn, self.Mutate(hn, s, i)] += event_num + self.infectiousDelta[pn, hn] -= event_num + + #Transmission + for sn in range(self.susNum): + event_num = self.DrawEventsNum(self.PropensitiesTransmission[pn, hn, sn]*self.tau_l) + self.eventsTransmission[pn, hn, sn] = event_num + + self.infectiousDelta[pn, hn] += event_num + self.susceptibleDelta[pn, sn] -= event_num + + for pn in range(self.popNum): + for sn in range(self.susNum): + if self.susceptibleDelta[pn, sn] + self.susceptible[pn, sn] < 0 or self.susceptibleDelta[pn, sn] + self.susceptible[pn, sn] > self.sizes[pn]: + return False + for hn in range(self.hapNum): + if self.infectiousDelta[pn, hn] + self.infectious[pn, hn] < 0 or self.infectiousDelta[pn, hn] + self.infectious[pn, hn] > self.sizes[pn]: + return False + return True + + cdef inline Py_ssize_t DrawEventsNum(self, double tau): + return random_poisson(self.seed.rng, lam=tau) + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void UpdateCompartmentCounts_tau(self): + #TODO not write events with 0 num + cdef Py_ssize_t event_num, pn, spn, tpn, hn, sn, ssn, tsn, s, i + + #Migrations + for spn in range(self.popNum): + for tpn in range(self.popNum): + if spn == tpn: + continue + for sn in range(self.susNum): + for hn in range(self.hapNum): + event_num = self.eventsMigr[spn, tpn, sn, hn] + self.NewInfections(tpn, sn, hn, event_num) + self.multievents.AddEvents(event_num, self.currentTime, MIGRATION, hn, spn, sn, tpn) + self.migPlus += event_num + + for pn in range(self.popNum): + #Susceptibility transition + for ssn in range(self.susNum): + for tsn in range(self.susNum): + if ssn == tsn: + continue + event_num = self.eventsSuscep[pn, ssn, tsn] + self.susceptible[pn, tsn] += event_num + self.susceptible[pn, ssn] -= event_num + self.multievents.AddEvents(event_num, self.currentTime, SUSCCHANGE, ssn, pn, tsn, 0) + self.iCounter += event_num + + #Infectious-realted event + for hn in range(self.hapNum): + #Recovery + event_num = self.eventsRecovery[pn, hn] + self.NewRecoveries(pn, self.suscType[hn], hn, event_num) + self.multievents.AddEvents(event_num, self.currentTime, DEATH, hn, pn, self.suscType[hn], 0) + self.dCounter += event_num + + #Sampling + event_num = self.eventsSampling[pn, hn] + self.NewRecoveries(pn, self.suscType[hn], hn, event_num) + self.multievents.AddEvents(event_num, self.currentTime, SAMPLING, hn, pn, self.suscType[hn], 0) + self.sCounter += event_num + + #Mutation + for s in range(self.sites): + for i in range(3): + nhn = self.Mutate(hn, s, i) + event_num = self.eventsMutatations[pn, hn, s, i] + self.infectious[pn, nhn] += event_num + self.infectious[pn, hn] -= event_num + self.multievents.AddEvents(event_num, self.currentTime, MUTATION, hn, pn, nhn, 0) + self.mCounter += event_num + + #Transmission + for sn in range(self.susNum): + event_num = self.eventsTransmission[pn, hn, sn] + self.NewInfections(pn, sn, hn, event_num) + self.multievents.AddEvents(event_num, self.currentTime, BIRTH, hn, pn, sn, 0) + self.bCounter += event_num + + @cython.boundscheck(False) + @cython.wraparound(False) + def Get_MultiEvents(self, id = None): + if id is None: + for i in range(self.multievents.ptr): + ev = self.multievents.GetEvent(i) + ev.PrintEvent() + else: + ev = self.multievents.GetEvent(id) + ev.PrintEvent() + + + def PrintCounters(self): + print("Birth counter(mutable): ", self.bCounter) + print("Death counter(mutable): ", self.dCounter) + print("Sampling counter(mutable): ", self.sCounter) + print("Mutation counter(mutable): ", self.mCounter) + print("Immunity transition counter(mutable):", self.iCounter) + print("Migration counter(mutable):", self.migPlus) + + def PrintPropensities(self): + #self.FirstInfection() + self.UpdateAllRates() + self.Propensities() + print("Migrations") + for s in range(self.popNum): + for r in range(self.popNum): + if s == r: + continue + for i in range(self.susNum): + for h in range(self.hapNum): + print (s, r, i, h, self.PropensitiesMigr[s, r, i, h]) + + + for s in range(self.popNum): + print("Susceptibility transition") + for i in range(self.susNum): + for j in range(self.susNum): + if i == j: + continue + print(s, i, j, self.PropensitiesSuscep[s, i, j]) + + #Infectious-realted event + for h in range(self.hapNum): + print("Recovery ", s, h, self.suscType[h], self.PropensitiesRecovery[s, h]) + print("Sampling ", s, h, self.suscType[h], self.PropensitiesSampling[s, h]) + + for site in range(self.sites): + for i in range(3): + #ht = self.Mutate(h, site, i) + print("Mutation", s, h, site, i, self.PropensitiesMutatations[s, h, site, i]) + #Transmission + for i in range(self.susNum): + print("Transmission", s, h, i, self.PropensitiesTransmission[s, h, i]) + #print("migr=", self.migrationRates[s, s], " prop", prop) diff --git a/VGsim/src/__init__.py b/VGsim/src/__init__.py new file mode 100644 index 0000000..4824c80 --- /dev/null +++ b/VGsim/src/__init__.py @@ -0,0 +1,3 @@ +from ._interface import Simulator + +__version__ = "0.2" diff --git a/VGsim/src/_interface.py b/VGsim/src/_interface.py new file mode 100644 index 0000000..ca2eb3b --- /dev/null +++ b/VGsim/src/_interface.py @@ -0,0 +1,295 @@ +from ._BirthDeath import BirthDeathModel +from .IO import writeGenomeNewick, writeMutations +from random import randrange +import sys +import matplotlib.pyplot as plt +import numpy as np +import time + +class Simulator: + def __init__(self, number_of_sites=0, populations_number=1, number_of_susceptible_groups=1, seed=None, sampling_probability=False, memory_optimization=False, conditionNum=1): + self.fig = None + if seed == None: + seed = int(randrange(sys.maxsize)) + print('User seed:', seed) + + self.simulation = BirthDeathModel(number_of_sites=number_of_sites, populations_number=populations_number, \ + number_of_susceptible_groups=number_of_susceptible_groups, seed=seed, sampling_probability=sampling_probability, \ + memory_optimization=memory_optimization, conditionNum=conditionNum) + + + def print_basic_parameters(self): + self.simulation.print_basic_parameters() + + def print_populations(self): + self.simulation.print_populations() + + def print_immunity_model(self): + self.simulation.print_immunity_model() + + def print_all(self, basic_parameters=True, populations=True, immunity_model=True): + if basic_parameters: + self.simulation.print_basic_parameters() + if populations: + self.simulation.print_populations() + if immunity_model: + self.simulation.print_immunity_model() + + + def set_initial_haplotype(self, amount): + self.simulation.set_initial_haplotype(amount) + + def set_step_haplotype(self, amount): + self.simulation.set_step_haplotype(amount) + + + def set_transmission_rate(self, rate, haplotype=None, condition=None): + self.simulation.set_transmission_rate(rate, haplotype, condition) + + def set_recovery_rate(self, rate, haplotype=None, condition=None): + self.simulation.set_recovery_rate(rate, haplotype, condition) + + def set_sampling_rate(self, rate, haplotype=None, condition=None): + self.simulation.set_sampling_rate(rate, haplotype, condition) + + def set_mutation_rate(self, rate=None, probabilities=None, haplotype=None, mutation=None, condition=None): + self.simulation.set_mutation_rate(rate, probabilities, haplotype, mutation, condition) + + @property + def condition_rate(self): + return self.simulation.condition_rate + + def set_condition_rate(self, rate, c1=0, c2=0): + self.simulation.set_condition_rate(rate, c1, c2) + + + def set_susceptibility_type(self, susceptibility_type, haplotype=None): + self.simulation.set_susceptibility_type(susceptibility_type, haplotype) + + def set_susceptibility(self, rate, haplotype=None, susceptibility_type=None): + self.simulation.set_susceptibility(rate, haplotype, susceptibility_type) + + def set_immunity_transition(self, rate, source=None, target=None): + self.simulation.set_immunity_transition(rate, source, target) + + + def set_population_size(self, size, population=None): + self.simulation.set_population_size(size, population) + + def set_contact_density(self, value, population=None): + self.simulation.set_contact_density(value, population) + + def set_npi(self, parameters, population=None): + self.simulation.set_npi(parameters, population) + + def set_sampling_multiplier(self, multiplier, population=None): + self.simulation.set_sampling_multiplier(multiplier, population) + + def set_migration_probability(self, probability=None, total_probability=None, source=None, target=None): + self.simulation.set_migration_probability(probability, total_probability, source, target) + + def set_susceptible(self, amount, source_type, target_type, population=None): + self.simulation.set_susceptible(amount, source_type, target_type, population) + + def set_infectious(self, amount, source_type, target_haplotype, population=None): + self.simulation.set_infectious(amount, source_type, target_haplotype, population) + + + def set_chain_events(self, file_name): + self.simulation.set_chain_events(file_name) + + def set_settings(self, file_template): + self.simulation.set_settings(file_template) + + def set_state(self, file_template): + self.set_chain_events(file_template) + self.set_settings(file_template) + + + def output_newick(self, file_template=None, file_path = None): + pruferSeq, times, mut, populations = self.simulation.output_tree_mutations() + writeGenomeNewick(pruferSeq, times, populations, file_template, file_path) + + def output_mutations(self, file_template=None, file_path = None): + pruferSeq, times, mut, populations = self.simulation.output_tree_mutations() + writeMutations(mut, len(pruferSeq), file_template, file_path) + + def output_migrations(self, file_template=None, file_path = None): + self.simulation.output_migrations(file_template, file_path) + + def output_sample_data(self, output_print=False): + time, pop, hap = self.simulation.output_sample_data() + if output_print: + return time, pop, hap + else: + print(time) + print(pop) + print(hap) + + def output_epidemiology_timelines(self, step=1000, output_file=False): + if output_file: + self.simulation.output_epidemiology_timelines(step, output_file) + else: + return self.simulation.output_epidemiology_timelines(step, output_file) + + def output_chain_events(self, file_name="chain_events"): + self.simulation.output_chain_events(file_name) + + def output_settings(self, file_template="parameters"): + self.simulation.output_settings(file_template) + + def output_state(self): + self.output_chain_events() + self.output_settings() + + def get_ts_object(self): + return self.simulation.get_ts_object() + + def get_tree(self): + return self.simulation.get_tree() + + def get_data_susceptible(self, population, susceptibility_type, + step_num): # returns susceptible, time_points, lockdowns + return self.simulation.get_data_susceptible(population, susceptibility_type, step_num) + + def get_data_infectious(self, population, haplotype, + step_num): # returns infections, sample, time_points, lockdowns + return self.simulation.get_data_infectious(population, haplotype, step_num) + + def add_plot_infectious(self, population, haplotype, step_num=100, label_infectious=None, label_samples=None): + if self.fig == None: + self.fig, self.ax = plt.subplots(figsize=(8, 6)) + self.ax.set_ylabel('Number of samples') + self.ax.set_xlabel('Time') + self.ax_2 = self.ax.twinx() + self.ax_2.set_ylabel('Number of individuals') + + if isinstance(haplotype, int) == True: + self.plot_infectious(population, haplotype, step_num, label_infectious, label_samples) + elif isinstance(haplotype, str) == True: + haplotypes = self.simulation.create_list_haplotypes(haplotype) + for hi in haplotypes: + self.plot_infectious(population, hi, step_num, label_infectious, label_samples) + else: + print("#TODO") + + def plot_infectious(self, population, haplotype, step_num, label_infectious, label_samples): + infections, sample, time_points, lockdowns = self.simulation.get_data_infectious(population, haplotype, step_num) + if label_infectious == None: + self.ax_2.plot(time_points, infections, label='Infectious pop:' + str(population) + ' hap:' + self.simulation.calculate_string(haplotype)) + elif isinstance(label_infectious, str) == True: + self.ax_2.plot(time_points, infections, label=label_infectious) + else: + print("#TODO") + + if label_samples == None: + self.ax.plot(time_points, sample, "--", label='Samples pop:' + str(population) + ' hap:' + self.simulation.calculate_string(haplotype)) + elif isinstance(label_samples, str) == True: + self.ax.plot(time_points, sample, "--", label=label_samples) + else: + print("#TODO") + + if len(lockdowns) != 0: + point = 0 + pointEnd = 0 + for ld in range(0, len(lockdowns), 2): + if ld+1 == len(lockdowns): + while time_points[point] < lockdowns[ld][1]: + point += 1 + plt.fill_between(time_points[point:], infections[point:], alpha=0.2) + else: + while time_points[point] < lockdowns[ld][1]: + point += 1 + while time_points[pointEnd] < lockdowns[ld+1][1]: + pointEnd += 1 + if pointEnd == point: + continue + plt.fill_between(time_points[point:pointEnd+1], infections[point:pointEnd+1], alpha=0.2) + + def add_plot_susceptible(self, population, susceptibility_type, step_num=100, label_susceptible=None): + if self.fig == None: + self.fig, self.ax = plt.subplots(figsize=(8, 6)) + self.ax.set_ylabel('Number of samples') + self.ax.set_xlabel('Time') + self.ax_2 = self.ax.twinx() + self.ax_2.set_ylabel('Number of individuals') + + susceptible, time_points, lockdowns = self.simulation.get_data_susceptible(population, susceptibility_type, step_num) + if label_susceptible == None: + self.ax_2.plot(time_points, susceptible, label='Susceptible pop:' + str(population) + ' sus:' + str(susceptibility_type)) + elif isinstance(label_susceptible, str) == True: + self.ax_2.plot(time_points, susceptible, label=label_susceptible) + else: + print("#TODO") + + if len(lockdowns) != 0: + point = 0 + pointEnd = 0 + for ld in range(0, len(lockdowns), 2): + if ld+1 == len(lockdowns): + while time_points[point] < lockdowns[ld][1]: + point += 1 + plt.fill_between(time_points[point:], susceptible[point:], alpha=0.2) + else: + while time_points[point] < lockdowns[ld][1]: + point += 1 + while time_points[pointEnd] < lockdowns[ld+1][1]: + pointEnd += 1 + if pointEnd == point: + continue + plt.fill_between(time_points[point:pointEnd+1], susceptible[point:pointEnd+1], alpha=0.2) + + def add_legend(self): + lines_1, labels_1 = self.ax.get_legend_handles_labels() + lines_2, labels_2 = self.ax_2.get_legend_handles_labels() + lines = lines_1 + lines_2 + labels = labels_1 + labels_2 + self.ax.legend(lines, labels, loc=0) + + def add_title(self, name="Plot"): + self.ax.set_title(name) + + def plot(self, name_file=None): + if name_file: + plt.savefig(name_file) + else: + plt.show() + self.fig = None + + + def simulate(self, iterations=1000, sample_size=None, epidemic_time=-1, method='direct', attempts=200): + if sample_size is None: + sample_size = iterations + if epidemic_time is None: + epidemic_time = -1 + + start_time = time.time() + if method == 'direct': + self.simulation.SimulatePopulation(iterations, sample_size, epidemic_time, attempts) + self.simulation.Stats(time.time() - start_time) + elif method == 'tau': + self.simulation.SimulatePopulation_tau(iterations, sample_size, epidemic_time, attempts) + self.simulation.Stats(time.time() - start_time) + else: + print("Unknown method. Choose between 'direct' and 'tau'.") + + def genealogy(self, seed=None): + self.simulation.GetGenealogy(seed) + + + def citation(self): + print("VGsim: scalable viral genealogy simulator for global pandemic") + print("Vladimir Shchur, Vadim Spirin, Dmitry Sirotkin, EvgeniBurovski, Nicola De Maio, Russell Corbett-Detig") + print("medRxiv 2021.04.21.21255891; doi: https://doi.org/10.1101/2021.04.21.21255891") + + def debug(self): + self.simulation.Debug() + + def get_proportion(self): + return self.simulation.get_proportion() + + def print_counters(self): + self.simulation.PrintCounters() + + def print_propensities(self): + self.simulation.PrintPropensities() diff --git a/VGsim/src/events.pxi b/VGsim/src/events.pxi new file mode 100644 index 0000000..015b0f5 --- /dev/null +++ b/VGsim/src/events.pxi @@ -0,0 +1,155 @@ +# use named constants for event types +DEF BIRTH = 0 +DEF DEATH = 1 +DEF SAMPLING = 2 +DEF MUTATION = 3 +DEF SUSCCHANGE = 4 +DEF MIGRATION = 5 +DEF MULTITYPE = 6 +DEF CONDITION = 7 + +cdef class Event: + cdef: + Py_ssize_t type_, haplotype, population, newHaplotype, newPopulation + double time + + def __init__(self, double time, Py_ssize_t type_, Py_ssize_t haplotype, Py_ssize_t population, Py_ssize_t newHaplotype, Py_ssize_t newPopulation): + self.time = time + self.type_ = type_ + self.haplotype = haplotype + self.population = population + self.newHaplotype = newHaplotype + self.newPopulation = newPopulation + + +cdef class Events: + cdef: + Py_ssize_t size, ptr + + Py_ssize_t[::1] types, haplotypes, populations, newHaplotypes, newPopulations + double[::1] times + + def __init__(self): + self.size = 0 + self.ptr = 0#pointer to the first empty cell + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void AddEvent(self, double time_, Py_ssize_t type_, Py_ssize_t haplotype, Py_ssize_t population, Py_ssize_t newHaplotype, Py_ssize_t newPopulation): + self.times[ self.ptr ] = time_ + self.types[ self.ptr ] = type_ + self.haplotypes[ self.ptr ] = haplotype + self.populations[ self.ptr ] = population + self.newHaplotypes[ self.ptr ] = newHaplotype + self.newPopulations[ self.ptr ] = newPopulation + self.ptr += 1 + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef Event GetEvent(self, Py_ssize_t e_id): + ev = Event( self.times[ e_id ], self.types[ e_id ], self.haplotypes[ e_id ], self.populations[ e_id ], self.newHaplotypes[ e_id ], self.newPopulations[ e_id ]) + return( ev ) + + cdef void CreateEvents(self, Py_ssize_t iterations): + if self.ptr == 0: + self.size += iterations + self.times = np.zeros(self.size, dtype=float) + self.types = np.zeros(self.size, dtype=np.int64) + self.haplotypes = np.zeros(self.size, dtype=np.int64) + self.populations = np.zeros(self.size, dtype=np.int64) + self.newHaplotypes = np.zeros(self.size, dtype=np.int64) + self.newPopulations = np.zeros(self.size, dtype=np.int64) + elif iterations + self.ptr - self.size > 0: + self.times = np.concatenate((self.times, np.zeros(iterations + self.ptr - self.size, dtype=float))) + self.types = np.concatenate((self.types, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.haplotypes = np.concatenate((self.haplotypes, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.populations = np.concatenate((self.populations, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.newHaplotypes = np.concatenate((self.newHaplotypes, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.newPopulations = np.concatenate((self.newPopulations, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.size = iterations + self.ptr + +cdef class multiEvent: + cdef: + Py_ssize_t num, type_, haplotype, population, newHaplotype, newPopulation + double time + + def __init__(self, Py_ssize_t num, double time, Py_ssize_t type_, Py_ssize_t haplotype, Py_ssize_t population, Py_ssize_t newHaplotype, Py_ssize_t newPopulation): + self.num = num + self.time = time + self.type_ = type_ + self.haplotype = haplotype + self.population = population + self.newHaplotype = newHaplotype + self.newPopulation = newPopulation + + def PrintEvent(self): + if self.type_ == BIRTH: + tn = "B" + elif self.type_ == DEATH: + tn = "D" + elif self.type_ == SAMPLING: + tn = "S" + elif self.type_ == MUTATION: + tn = "MUT" + elif self.type_ == SUSCCHANGE: + tn = "SUS" + elif self.type_ == MIGRATION: + tn = "MIG" + elif self.type_ == CONDITION: + tn = "CON" + print("num=", self.num, + " time=", self.time, + " type=", tn, + " hap=", self.haplotype, + " pop=", self.population, + " dest1=", self.newHaplotype, + " dest2=", self.newPopulation) + +cdef class multiEvents: + cdef: + Py_ssize_t size, ptr + + Py_ssize_t[::1] num, types, haplotypes, populations, newHaplotypes, newPopulations + double[::1] times + + def __init__(self): + self.size = 0 + self.ptr = 0#pointer to the first empty cell + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void AddEvents(self, Py_ssize_t num, double time_, Py_ssize_t type_, Py_ssize_t haplotype, Py_ssize_t population, Py_ssize_t newHaplotype, Py_ssize_t newPopulation): + self.num[ self.ptr ] = num + self.times[ self.ptr ] = time_ + self.types[ self.ptr ] = type_ + self.haplotypes[ self.ptr ] = haplotype + self.populations[ self.ptr ] = population + self.newHaplotypes[ self.ptr ] = newHaplotype + self.newPopulations[ self.ptr ] = newPopulation + self.ptr += 1 + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef multiEvent GetEvent(self, Py_ssize_t e_id): + ev = multiEvent( self.num[ e_id ], self.times[ e_id ], self.types[ e_id ], self.haplotypes[ e_id ], self.populations[ e_id ], self.newHaplotypes[ e_id ], self.newPopulations[ e_id ]) + return( ev ) + + cdef void CreateEvents(self, Py_ssize_t iterations): + if self.ptr == 0: + self.size = iterations + self.num = np.zeros(self.size, dtype=np.int64) + self.times = np.zeros(self.size, dtype=float) + self.types = np.zeros(self.size, dtype=np.int64) + self.haplotypes = np.zeros(self.size, dtype=np.int64) + self.populations = np.zeros(self.size, dtype=np.int64) + self.newHaplotypes = np.zeros(self.size, dtype=np.int64) + self.newPopulations = np.zeros(self.size, dtype=np.int64) + else: + self.num = np.concatenate((self.num, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.times = np.concatenate((self.times, np.zeros(iterations + self.ptr - self.size, dtype=float))) + self.types = np.concatenate((self.types, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.haplotypes = np.concatenate((self.haplotypes, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.populations = np.concatenate((self.populations, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.newHaplotypes = np.concatenate((self.newHaplotypes, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.newPopulations = np.concatenate((self.newPopulations, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) + self.size = iterations + self.ptr diff --git a/VGsim/src/fast_choose.pxi b/VGsim/src/fast_choose.pxi new file mode 100644 index 0000000..309b53c --- /dev/null +++ b/VGsim/src/fast_choose.pxi @@ -0,0 +1,110 @@ +def print_err(*args, **kwargs): + print(*args, file=sys.stderr, **kwargs) + +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.cdivision(True) +cdef inline (Py_ssize_t, double) fastChoose1(double[::1] w, double tw, double rn): + cdef: + Py_ssize_t i + double total + + rn = tw*rn + i = 0 + total = w[0] + while total < rn and i < w.shape[0] - 1: + i += 1 + total += w[i] + if w[i] == 0.0: + print_err("fastChoose1() alert: 0-weight sampled") + for i in range(w.shape[0]): + print_err(w[i], end=", ") + print_err() + print_err(tw) + print_err(rn) + sys.exit(1) + return [ i, ( rn-(total-w[i]) )/w[i] ] + +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.cdivision(True) +cdef inline (Py_ssize_t, double) fastChoose2(npy_int64[::1] w, npy_int64 tw, double rn): + cdef: + Py_ssize_t i + double total + + rn = tw*rn + i = 0 + total = w[0] + while total < rn and i < w.shape[0] - 1: + i += 1 + total += w[i] + if w[i] == 0.0: + print_err("fastChoose2() alert: 0-weight sampled") + for i in range(w.shape[0]): + print_err(w[i], end=", ") + print_err() + print_err(tw) + print_err(rn) + sys.exit(1) + return [ i, ( rn-(total-w[i]) )/w[i] ] + +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.cdivision(True) +cdef inline (Py_ssize_t, double) fastChoose1_skip(double[::1] w, double tw, double rn, Py_ssize_t skip): + cdef: + Py_ssize_t i + double total + + rn = tw*rn + if skip > 0: + i = 0 + total = w[0] + else: + i = 1 + total = w[1] + while total < rn and i < w.shape[0] - 1: + i += 1 + if i == skip: + i += 1 + total += w[i] + if w[i] == 0.0: + print_err("fastChoose1_skip() alert: 0-weight sampled") + for i in range(w.shape[0]): + print_err(w[i], end=", ") + print_err() + print_err(tw) + print_err(rn) + sys.exit(1) + return [ i, ( rn-(total-w[i]) )/w[i] ] + +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.cdivision(True) +cdef inline (Py_ssize_t, double) fastChoose2_skip(npy_int64[::1] w, npy_int64 tw, double rn, Py_ssize_t skip): + cdef: + Py_ssize_t i + double total + + rn = tw*rn + if skip > 0: + i = 0 + total = w[0] + else: + i = 1 + total = w[1] + while total < rn and i < w.shape[0] - 1: + i += 1 + if i == skip: + i += 1 + total += w[i] + if w[i] == 0.0: + print_err("fastChoose2_skip() alert: 0-weight sampled") + for i in range(w.shape[0]): + print_err(w[i], end=", ") + print_err() + print_err(tw) + print_err(rn) + sys.exit(1) + return [ i, ( rn-(total-w[i]) )/w[i] ] diff --git a/VGsim/src/meson.build b/VGsim/src/meson.build new file mode 100644 index 0000000..23ec3cb --- /dev/null +++ b/VGsim/src/meson.build @@ -0,0 +1,40 @@ +### Build the cython extension ### + +# compile .cpp for _BirthDeath.pyx +_birth_death = custom_target('_BirthDeath', + output : '_BirthDeath.cxx', + input : '_BirthDeath.pyx', + depend_files: ['models.pxi', 'fast_choose.pxi'], + command : [cython, '--cplus', '-3', '--fast-fail', '@INPUT@', '-o', '@OUTPUT@'] +) + +npymath_path = incdir_numpy / '..' / 'lib' +npymath_lib = meson.get_compiler('cpp').find_library('npymath', dirs: npymath_path) +npyrandom_path = incdir_numpy / '..' / '..' / 'random' / 'lib' +npyrandom_lib = meson.get_compiler('cpp').find_library('npyrandom', dirs: npyrandom_path) + +# generate .so for rndm +py3.extension_module( + '_BirthDeath', _birth_death, + include_directories: [inc_np, npymath_path, npyrandom_path], + dependencies : [py3_dep, npymath_lib, npyrandom_lib], + install: true, + subdir: 'VGsim' +) + + +### Install stuff: python files + the extension just built ### + +#create a list of files which will be installed as source +python_sources = [ + '__init__.py', + 'IO.py', + '_interface.py', +] + +# install source files in current dir +py3.install_sources( + python_sources, + pure: false, + subdir: 'VGsim' +) diff --git a/VGsim/src/models.pxi b/VGsim/src/models.pxi new file mode 100644 index 0000000..c21c893 --- /dev/null +++ b/VGsim/src/models.pxi @@ -0,0 +1,65 @@ +cdef class Mutations: + cdef: + vector[Py_ssize_t] nodeId, AS, DS, site + vector[double] time + + def __init__(self):#AS = ancestral state, DS = derived state + pass + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void AddMutation(self, Py_ssize_t nodeId, Py_ssize_t haplotype, Py_ssize_t newHaplotype, double time): + cdef: + Py_ssize_t ASDSdigit4, site, digit4 + + ASDSdigit4 = int(abs(newHaplotype - haplotype)) + site = 0 + while ASDSdigit4 >= 4: + ASDSdigit4 = ASDSdigit4 / 4 + site += 1 + digit4 = int(4**site) + self.nodeId.push_back(nodeId) + self.DS.push_back(int(floor(newHaplotype/digit4) % 4)) + self.AS.push_back(int(floor(haplotype/digit4) % 4)) + self.site.push_back(int(site)) + self.time.push_back(time) + + def get_mutation(self, id_mut): + return self.nodeId[id_mut], self.DS[id_mut], self.AS[id_mut], self.site[id_mut], self.time[id_mut] + + +cdef class Migrations: + cdef: + vector[Py_ssize_t] nodeId, oldPop, newPop + vector[double] time + + def __init__(self): + pass + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void AddMigration(self, Py_ssize_t nodeId, double time, Py_ssize_t oldPop, Py_ssize_t newPop): + self.nodeId.push_back(nodeId) + self.time.push_back(time) + self.oldPop.push_back(oldPop) + self.newPop.push_back(newPop) + + def get_migration(self, id_mig): + return self.nodeId[id_mig], self.time[id_mig], self.oldPop[id_mig], self.newPop[id_mig] + +cdef class Lockdowns: + cdef: + vector[bint] states + vector[Py_ssize_t] populationsId + vector[double] times + + def __init__(self): + pass + + @cython.boundscheck(False) + @cython.wraparound(False) + cdef void AddLockdown(self, bint state, Py_ssize_t populationId, double time): + self.states.push_back(state) + self.populationsId.push_back(populationId) + self.times.push_back(time) diff --git a/VGsim/testing/check_simulator.py b/VGsim/testing/check_simulator.py new file mode 100644 index 0000000..9492949 --- /dev/null +++ b/VGsim/testing/check_simulator.py @@ -0,0 +1,179 @@ +import VGsim +import sys +import os +import numpy as np + +def check_data(num): + ref = np.load("reference_" + str(num) + ".npy") + test = np.load("test_" + str(num) + ".npy") + + for i in range(ref.shape[1]): + if ref[0, i] != test[0, i] or ref[1, i] != test[1, i] or ref[2, i] != test[2, i] or ref[3, i] != test[3, i] or ref[4, i] != test[4, i] or ref[5, i] != test[5, i]: + print("Error!") + print(num) + print("--------------") + print('ref-time: ' + str(ref[0, i])) + print('ref-type: ' + str(ref[1, i])) + print('ref-haplotype: ' + str(ref[2, i])) + print('ref-population: ' + str(ref[3, i])) + print('ref-newHaplotype: ' + str(ref[4, i])) + print('ref-newPopulation: ' + str(ref[5, i])) + print("--------------") + print('test-time: ' + str(test[0, i])) + print('test-type: ' + str(test[1, i])) + print('test-haplotype: ' + str(test[2, i])) + print('test-population: ' + str(test[3, i])) + print('test-newHaplotype: ' + str(test[4, i])) + print('test-newPopulation: ' + str(test[5, i])) + print("--------------") + sys.exit(1) + + os.remove("test_" + str(num) + ".npy") + print('Correct data!') + +my_seed = 2020 +iterations = 100000 + +#1 model +simulator = VGsim.Simulator(seed=my_seed) + +name = 1 +simulator.set_transmission_rate(4.0) +simulator.set_recovery_rate(1.5) +simulator.set_sampling_rate(0.3) +simulator.simulate(iterations) +simulator.output_chain_events('test_' + str(name)) +check_data(name) + +#2 model +simulator = VGsim.Simulator(number_of_sites=1, seed=my_seed) + +name = 2 +simulator.set_transmission_rate(4, haplotype=3) +simulator.simulate(iterations) +simulator.output_chain_events('test_' + str(name)) +check_data(name) + +#3 model +simulator = VGsim.Simulator(number_of_susceptible_groups=2, seed=my_seed) + +name = 3 +simulator.set_susceptibility_type(1) +simulator.simulate(iterations) +simulator.output_chain_events('test_' + str(name)) +check_data(name) + +# #4 model +simulator = VGsim.Simulator(number_of_sites=1, number_of_susceptible_groups=3, seed=my_seed) + +name = 4 +simulator.set_mutation_rate(0.01) +simulator.set_susceptibility_type(1, haplotype=0) +simulator.set_susceptibility_type(2, haplotype=1) +simulator.set_susceptibility_type(2, haplotype=2) +simulator.set_susceptibility_type(2, haplotype=3) +simulator.set_immunity_transition(0.01, source=0, target=1) +simulator.set_immunity_transition(0.01, source=1, target=2) +simulator.set_immunity_transition(0.02, source=2, target=1) +simulator.simulate(iterations) +simulator.output_chain_events('test_' + str(name)) +check_data(name) + +#5 model +# simulator = VGsim.Simulator(populations_number=2, seed=my_seed) + +# name = 5 +# simulator.set_population_size(2000000) +# simulator.set_contact_density(1.3, population=0) +# simulator.set_contact_density(0.8, population=1) +# simulator.set_migration_probability(probability=0.01, source=0, target=1) +# simulator.set_migration_probability(probability=0.005, source=1, target=0) +# simulator.simulate(iterations) +# simulator.output_chain_events('test_' + str(name)) +# check_data(name) + +#6 model +simulator = VGsim.Simulator(populations_number=3, seed=my_seed) + +name = 6 +simulator.set_migration_probability(0.01, source=0, target=1) +simulator.set_migration_probability(0.005, source=2, target=1) +simulator.simulate(iterations) +simulator.output_chain_events('test_' + str(name)) +check_data(name) + +#7 model +simulator = VGsim.Simulator(populations_number=3, seed=my_seed) + +name = 7 +simulator.set_migration_probability(0.01, source=0, target=1) +simulator.set_migration_probability(0.005, source=2, target=1) +simulator.set_sampling_multiplier(2.5, population=1) +simulator.set_sampling_multiplier(2, population=2) +simulator.set_npi([0.5, 0.30, 0.15], population=0) +simulator.simulate(iterations) +simulator.output_chain_events('test_' + str(name)) +check_data(name) + +#8 model +simulator = VGsim.Simulator(number_of_sites=2, seed=my_seed) + +name = 8 +simulator.set_mutation_rate(0.01, probabilities=[1, 0, 0, 1]) +simulator.simulate(iterations) +simulator.output_chain_events('test_' + str(name)) +check_data(name) + +#9 model +simulator = VGsim.Simulator(number_of_sites=2, populations_number=3, number_of_susceptible_groups=3, seed=my_seed) + +name = 9 +simulator.set_transmission_rate(5.0, haplotype=12) +simulator.set_recovery_rate(1.5) +simulator.set_sampling_rate(0.3) +simulator.set_mutation_rate(0.01, probabilities=[1, 0, 0, 1]) +simulator.set_migration_probability(0.01, source=0, target=1) +simulator.set_migration_probability(0.005, source=2, target=1) +simulator.set_sampling_multiplier(2.5, population=1) +simulator.set_sampling_multiplier(2, population=2) +simulator.set_npi([0.5, 0.30, 0.15], population=1) +simulator.set_susceptibility_type(1, haplotype=0) +simulator.set_susceptibility_type(2, haplotype=1) +simulator.set_susceptibility_type(2, haplotype=2) +simulator.set_susceptibility_type(2, haplotype=3) +simulator.set_immunity_transition(0.000001, source=0, target=1) +simulator.set_immunity_transition(0.000001, source=1, target=2) +simulator.set_immunity_transition(0.000002, source=2, target=1) +simulator.simulate(iterations) +simulator.output_chain_events('test_' + str(name)) +check_data(name) + +# #10 model +# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=True) + +# name = 10 +# simulator.set_mutation_rate(0.01) +# simulator.simulate(iterations) +# simulator.output_chain_events('test_' + str(name)) +# check_data(name) + + +# #11 model +# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=20) + +# name = 11 +# simulator.set_mutation_rate(0.01) +# simulator.simulate(iterations) +# simulator.output_chain_events('test_' + str(name)) +# check_data(name) + + +# #12 model +# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=(20, 2)) + +# name = 12 +# simulator.set_mutation_rate(0.01) +# simulator.simulate(iterations) +# simulator.output_chain_events('test_' + str(name)) +# check_data(name) + diff --git a/VGsim/testing/cmd_example/example.mg b/VGsim/testing/cmd_example/example.mg new file mode 100644 index 0000000..4da7c07 --- /dev/null +++ b/VGsim/testing/cmd_example/example.mg @@ -0,0 +1,4 @@ +#Migration_format_version 0.0.1 +0.0 0.0136986301369863 0.0136986301369863 +0.0136986301369863 0.0 0.0136986301369863 +0.0136986301369863 0.0136986301369863 0.0 diff --git a/VGsim/testing/cmd_example/example.pp b/VGsim/testing/cmd_example/example.pp new file mode 100644 index 0000000..bdc2c83 --- /dev/null +++ b/VGsim/testing/cmd_example/example.pp @@ -0,0 +1,5 @@ +#Population_format_version 0.0.1 +id size contactDensity conDenAfterLD startLD endLD samplingMulriplier +0 10000000 1.0 0.1,0.01,0.002 1.0 +1 5000000 1.0 0.1,0.01,0.002 3.0 +2 1000000 1.0 0.1,0.01,0.002 0.0 diff --git a/VGsim/testing/cmd_example/example.rt b/VGsim/testing/cmd_example/example.rt new file mode 100644 index 0000000..7b6dcb3 --- /dev/null +++ b/VGsim/testing/cmd_example/example.rt @@ -0,0 +1,18 @@ +#Rates_format_version 0.0.1 +H B D S M0 M1 +AA 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +AT 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +AC 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +AG 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,1.0 +TA 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +TT 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +TC 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +TG 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,1.0 +CA 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +CT 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +CC 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 +CG 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,1.0 +GA 0.25 0.149 0.001 3e-06,1.0,1.0,1.0 9e-06,1.0,1.0,2.0 +GT 0.25 0.149 0.001 3e-06,1.0,1.0,1.0 9e-06,1.0,1.0,2.0 +GC 0.25 0.149 0.001 3e-06,1.0,1.0,1.0 9e-06,1.0,1.0,2.0 +GG 0.5 0.149 0.001 3e-06,1.0,1.0,1.0 9e-06,1.0,1.0,1.0 diff --git a/VGsim/testing/cmd_example/example.st b/VGsim/testing/cmd_example/example.st new file mode 100644 index 0000000..bb7c6d5 --- /dev/null +++ b/VGsim/testing/cmd_example/example.st @@ -0,0 +1,4 @@ +#Susceptibility_format_version 0.0.1 +0.0 0.0 0.0 +0.011111111111111112 0.0 0.0 +0.005555555555555556 0.0 0.0 diff --git a/VGsim/testing/cmd_example/example.su b/VGsim/testing/cmd_example/example.su new file mode 100644 index 0000000..4081a2d --- /dev/null +++ b/VGsim/testing/cmd_example/example.su @@ -0,0 +1,18 @@ +#Susceptibility_format_version 0.0.1 +H T S0 S1 S2 +AA 1 1.0 0.1 0.0 +AT 1 1.0 0.1 0.0 +AC 1 1.0 0.1 0.0 +AG 1 1.0 0.1 0.0 +TA 1 1.0 0.1 0.0 +TT 1 1.0 0.1 0.0 +TC 1 1.0 0.1 0.0 +TG 1 1.0 0.1 0.0 +CA 1 1.0 0.1 0.0 +CT 1 1.0 0.1 0.0 +CC 1 1.0 0.1 0.0 +CG 1 1.0 0.1 0.0 +GA 2 1.0 0.5 0.0 +GT 2 1.0 0.5 0.0 +GC 2 1.0 0.5 0.0 +GG 2 1.0 0.5 0.0 diff --git a/VGsim/testing/example.ipynb b/VGsim/testing/example.ipynb new file mode 100644 index 0000000..a4e3872 --- /dev/null +++ b/VGsim/testing/example.ipynb @@ -0,0 +1,482 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "id": "7a8cbe89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User seed: 1234\n" + ] + } + ], + "source": [ + "#This is a notebook with the example from https://vg-sim.readthedocs.io/en/latest/Tutorials%20and%20examples.html\n", + "\n", + "import VGsim\n", + "number_of_sites = 2\n", + "populations_number = 3\n", + "number_of_susceptible_groups = 3\n", + "simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, seed=1234)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b65b865c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual sizes: 9808219.17808219 5013698.630136985 1178082.191780822 \n", + "\u001b[41mWARNING!\u001b[0m Actual population size in deme \n", + "2\n", + "is more than 10% different from the population size. The migration probabilities might be unrealistically high.\n", + "We recommend to check your model with print_populations() method before proceding to simulation.\n", + "Check the documentation file:https://vg-sim.readthedocs.io/en/latest/Migration.html for more details.\n", + "Achieved internal time limit.\n", + "Number of samples: 5426\n", + "Total number of iterations: 943958\n", + "Success number: 1\n", + "Epidemic time: 110.00008313493404\n", + "Number of infections: 478628\n", + "Number of recoveries: 353747\n", + "Number of mutations: 21\n", + "Number of accepted migrations: 25676\n", + "Number of rejected migrations: 84643\n", + "Number of immunity transitions: 80460\n", + "----------------------------------\n" + ] + } + ], + "source": [ + "simulator.set_transmission_rate(0.25)\n", + "simulator.set_recovery_rate(0.099)\n", + "simulator.set_sampling_rate(0.001)\n", + "simulator.set_transmission_rate(0.5, haplotype=\"GG\")\n", + "mutation_rate=0.000003\n", + "substitution_weights=[1,1,1,2]#ATCG\n", + "simulator.set_mutation_rate(mutation_rate, substitution_weights)\n", + "simulator.set_mutation_rate(3*mutation_rate, haplotype='G*', mutation=1)\n", + "\n", + "simulator.set_susceptibility_type(1)\n", + "simulator.set_susceptibility_type(2, haplotype='G*')\n", + "simulator.set_susceptibility(0.1, susceptibility_type=1)\n", + "simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype='G*')\n", + "simulator.set_immunity_transition(1/90, source=1, target=0)\n", + "simulator.set_susceptibility(0.0, susceptibility_type=2)\n", + "simulator.set_immunity_transition(1/180, source=2, target=0)\n", + "\n", + "simulator.set_population_size(10000000, population=0)\n", + "simulator.set_population_size(5000000, population=1)\n", + "simulator.set_population_size(1000000, population=2)\n", + "simulator.set_sampling_multiplier(3, population=1)\n", + "simulator.set_sampling_multiplier(0, population=2)\n", + "simulator.set_npi([0.1, 0.01, 0.002])\n", + "simulator.set_migration_probability(10/365/2)\n", + "\n", + "simulator.simulate(10000000, epidemic_time=110)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "89474358", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of iterations: 0\n", + "Simulation model time: 110.00008313493404\n", + "Number of samples: 5426\n", + "Total number of iterations: 943958\n", + "Success number: 1\n", + "Epidemic time: 110.00008313493404\n", + "Number of infections: 478628\n", + "Number of recoveries: 353747\n", + "Number of mutations: 21\n", + "Number of accepted migrations: 25676\n", + "Number of rejected migrations: 84643\n", + "Number of immunity transitions: 80460\n", + "----------------------------------\n" + ] + } + ], + "source": [ + "\n", + "simulator.set_immunity_transition(0.05, source=0, target=1)\n", + "simulator.set_immunity_transition(0.05, source=0, target=2)\n", + "simulator.set_contact_density(0.7, population=0)\n", + "simulator.set_contact_density(0.7, population=1)\n", + "simulator.set_migration_probability(2/365/2, source=0, target=2)\n", + "simulator.set_migration_probability(2/365/2, source=1, target=2)\n", + "\n", + "simulator.simulate(1000, method='tau', time=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5698f399", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "simulator.add_plot_infectious(population=0, haplotype='AA', step_num=100)\n", + "simulator.add_plot_infectious(population=1, haplotype='AA', step_num=100)\n", + "simulator.add_plot_infectious(population=2, haplotype='AA', step_num=100)\n", + "simulator.add_title(name=\"AA haplotype\")\n", + "simulator.add_legend()\n", + "simulator.plot()\n", + "\n", + "simulator.add_plot_susceptible(population=0, susceptibility_type=0, step_num=100)\n", + "simulator.add_plot_susceptible(population=0, susceptibility_type=1, step_num=100)\n", + "simulator.add_plot_susceptible(population=0, susceptibility_type=2, step_num=100)\n", + "simulator.add_title(name=\"Susceptible group sizes\")\n", + "simulator.add_legend()\n", + "simulator.plot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7dd885ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*****************\n", + "***Basic rates***\n", + "*****************\n", + "+----+------+-------+-------+----+-------+-------------+-------+-------------+\n", + "| H | TR | RR | SR | ST | M0 | MW0 | M1 | MW1 |\n", + "+----+------+-------+-------+----+-------+-------------+-------+-------------+\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mA\u001b[0mA->\u001b[31mT\u001b[0mA: 1.0 | | A\u001b[31mA\u001b[0m->A\u001b[31mT\u001b[0m: 1.0 |\n", + "| AA | | | | | 3e-06 | \u001b[31mA\u001b[0mA->\u001b[31mC\u001b[0mA: 1.0 | 3e-06 | A\u001b[31mA\u001b[0m->A\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mA\u001b[0mA->\u001b[31mG\u001b[0mA: 2.0 | | A\u001b[31mA\u001b[0m->A\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mA\u001b[0mT->\u001b[31mT\u001b[0mT: 1.0 | | A\u001b[31mT\u001b[0m->A\u001b[31mA\u001b[0m: 1.0 |\n", + "| AT | | | | | 3e-06 | \u001b[31mA\u001b[0mT->\u001b[31mC\u001b[0mT: 1.0 | 3e-06 | A\u001b[31mT\u001b[0m->A\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mA\u001b[0mT->\u001b[31mG\u001b[0mT: 2.0 | | A\u001b[31mT\u001b[0m->A\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mA\u001b[0mC->\u001b[31mT\u001b[0mC: 1.0 | | A\u001b[31mC\u001b[0m->A\u001b[31mA\u001b[0m: 1.0 |\n", + "| AC | | | | | 3e-06 | \u001b[31mA\u001b[0mC->\u001b[31mC\u001b[0mC: 1.0 | 3e-06 | A\u001b[31mC\u001b[0m->A\u001b[31mT\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mA\u001b[0mC->\u001b[31mG\u001b[0mC: 2.0 | | A\u001b[31mC\u001b[0m->A\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mA\u001b[0mG->\u001b[31mT\u001b[0mG: 1.0 | | A\u001b[31mG\u001b[0m->A\u001b[31mA\u001b[0m: 1.0 |\n", + "| AG | | | | | 3e-06 | \u001b[31mA\u001b[0mG->\u001b[31mC\u001b[0mG: 1.0 | 3e-06 | A\u001b[31mG\u001b[0m->A\u001b[31mT\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mA\u001b[0mG->\u001b[31mG\u001b[0mG: 2.0 | | A\u001b[31mG\u001b[0m->A\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mT\u001b[0mA->\u001b[31mA\u001b[0mA: 1.0 | | T\u001b[31mA\u001b[0m->T\u001b[31mT\u001b[0m: 1.0 |\n", + "| TA | | | | | 3e-06 | \u001b[31mT\u001b[0mA->\u001b[31mC\u001b[0mA: 1.0 | 3e-06 | T\u001b[31mA\u001b[0m->T\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mT\u001b[0mA->\u001b[31mG\u001b[0mA: 2.0 | | T\u001b[31mA\u001b[0m->T\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mT\u001b[0mT->\u001b[31mA\u001b[0mT: 1.0 | | T\u001b[31mT\u001b[0m->T\u001b[31mA\u001b[0m: 1.0 |\n", + "| TT | | | | | 3e-06 | \u001b[31mT\u001b[0mT->\u001b[31mC\u001b[0mT: 1.0 | 3e-06 | T\u001b[31mT\u001b[0m->T\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mT\u001b[0mT->\u001b[31mG\u001b[0mT: 2.0 | | T\u001b[31mT\u001b[0m->T\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mT\u001b[0mC->\u001b[31mA\u001b[0mC: 1.0 | | T\u001b[31mC\u001b[0m->T\u001b[31mA\u001b[0m: 1.0 |\n", + "| TC | | | | | 3e-06 | \u001b[31mT\u001b[0mC->\u001b[31mC\u001b[0mC: 1.0 | 3e-06 | T\u001b[31mC\u001b[0m->T\u001b[31mT\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mT\u001b[0mC->\u001b[31mG\u001b[0mC: 2.0 | | T\u001b[31mC\u001b[0m->T\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mT\u001b[0mG->\u001b[31mA\u001b[0mG: 1.0 | | T\u001b[31mG\u001b[0m->T\u001b[31mA\u001b[0m: 1.0 |\n", + "| TG | | | | | 3e-06 | \u001b[31mT\u001b[0mG->\u001b[31mC\u001b[0mG: 1.0 | 3e-06 | T\u001b[31mG\u001b[0m->T\u001b[31mT\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mT\u001b[0mG->\u001b[31mG\u001b[0mG: 2.0 | | T\u001b[31mG\u001b[0m->T\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mC\u001b[0mA->\u001b[31mA\u001b[0mA: 1.0 | | C\u001b[31mA\u001b[0m->C\u001b[31mT\u001b[0m: 1.0 |\n", + "| CA | | | | | 3e-06 | \u001b[31mC\u001b[0mA->\u001b[31mT\u001b[0mA: 1.0 | 3e-06 | C\u001b[31mA\u001b[0m->C\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mC\u001b[0mA->\u001b[31mG\u001b[0mA: 2.0 | | C\u001b[31mA\u001b[0m->C\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mC\u001b[0mT->\u001b[31mA\u001b[0mT: 1.0 | | C\u001b[31mT\u001b[0m->C\u001b[31mA\u001b[0m: 1.0 |\n", + "| CT | | | | | 3e-06 | \u001b[31mC\u001b[0mT->\u001b[31mT\u001b[0mT: 1.0 | 3e-06 | C\u001b[31mT\u001b[0m->C\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mC\u001b[0mT->\u001b[31mG\u001b[0mT: 2.0 | | C\u001b[31mT\u001b[0m->C\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mC\u001b[0mC->\u001b[31mA\u001b[0mC: 1.0 | | C\u001b[31mC\u001b[0m->C\u001b[31mA\u001b[0m: 1.0 |\n", + "| CC | | | | | 3e-06 | \u001b[31mC\u001b[0mC->\u001b[31mT\u001b[0mC: 1.0 | 3e-06 | C\u001b[31mC\u001b[0m->C\u001b[31mT\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mC\u001b[0mC->\u001b[31mG\u001b[0mC: 2.0 | | C\u001b[31mC\u001b[0m->C\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mC\u001b[0mG->\u001b[31mA\u001b[0mG: 1.0 | | C\u001b[31mG\u001b[0m->C\u001b[31mA\u001b[0m: 1.0 |\n", + "| CG | | | | | 3e-06 | \u001b[31mC\u001b[0mG->\u001b[31mT\u001b[0mG: 1.0 | 3e-06 | C\u001b[31mG\u001b[0m->C\u001b[31mT\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mC\u001b[0mG->\u001b[31mG\u001b[0mG: 2.0 | | C\u001b[31mG\u001b[0m->C\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 2 | | \u001b[31mG\u001b[0mA->\u001b[31mA\u001b[0mA: 1.0 | | G\u001b[31mA\u001b[0m->G\u001b[31mT\u001b[0m: 1.0 |\n", + "| GA | | | | | 3e-06 | \u001b[31mG\u001b[0mA->\u001b[31mT\u001b[0mA: 1.0 | 9e-06 | G\u001b[31mA\u001b[0m->G\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mG\u001b[0mA->\u001b[31mC\u001b[0mA: 1.0 | | G\u001b[31mA\u001b[0m->G\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 2 | | \u001b[31mG\u001b[0mT->\u001b[31mA\u001b[0mT: 1.0 | | G\u001b[31mT\u001b[0m->G\u001b[31mA\u001b[0m: 1.0 |\n", + "| GT | | | | | 3e-06 | \u001b[31mG\u001b[0mT->\u001b[31mT\u001b[0mT: 1.0 | 9e-06 | G\u001b[31mT\u001b[0m->G\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mG\u001b[0mT->\u001b[31mC\u001b[0mT: 1.0 | | G\u001b[31mT\u001b[0m->G\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.25 | 0.099 | 0.001 | 2 | | \u001b[31mG\u001b[0mC->\u001b[31mA\u001b[0mC: 1.0 | | G\u001b[31mC\u001b[0m->G\u001b[31mA\u001b[0m: 1.0 |\n", + "| GC | | | | | 3e-06 | \u001b[31mG\u001b[0mC->\u001b[31mT\u001b[0mC: 1.0 | 9e-06 | G\u001b[31mC\u001b[0m->G\u001b[31mT\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mG\u001b[0mC->\u001b[31mC\u001b[0mC: 1.0 | | G\u001b[31mC\u001b[0m->G\u001b[31mG\u001b[0m: 2.0 |\n", + "| | | | | | | | | |\n", + "| | 0.5 | 0.099 | 0.001 | 2 | | \u001b[31mG\u001b[0mG->\u001b[31mA\u001b[0mG: 1.0 | | G\u001b[31mG\u001b[0m->G\u001b[31mA\u001b[0m: 1.0 |\n", + "| GG | | | | | 3e-06 | \u001b[31mG\u001b[0mG->\u001b[31mT\u001b[0mG: 1.0 | 9e-06 | G\u001b[31mG\u001b[0m->G\u001b[31mT\u001b[0m: 1.0 |\n", + "| | | | | | | \u001b[31mG\u001b[0mG->\u001b[31mC\u001b[0mG: 1.0 | | G\u001b[31mG\u001b[0m->G\u001b[31mC\u001b[0m: 1.0 |\n", + "| | | | | | | | | |\n", + "+----+------+-------+-------+----+-------+-------------+-------+-------------+\n", + "Legend:\n", + "H - haplotype\n", + "TR - transmission rate\n", + "RR - recovery rate\n", + "SR - sampling rate\n", + "ST - susceptibility type\n", + "M0 - 0 mutation rate\n", + "MW0 - 0 mutation weights\n", + "M1 - 1 mutation rate\n", + "MW1 - 1 mutation weights\n", + "\n", + "********************\n", + "***Immunity model***\n", + "********************\n", + "+------+-----+-----+-----+\n", + "| H\\ST | S0 | S1 | S2 |\n", + "+------+-----+-----+-----+\n", + "| AA | 1.0 | 0.1 | 0.0 |\n", + "| AT | 1.0 | 0.1 | 0.0 |\n", + "| AC | 1.0 | 0.1 | 0.0 |\n", + "| AG | 1.0 | 0.1 | 0.0 |\n", + "| TA | 1.0 | 0.1 | 0.0 |\n", + "| TT | 1.0 | 0.1 | 0.0 |\n", + "| TC | 1.0 | 0.1 | 0.0 |\n", + "| TG | 1.0 | 0.1 | 0.0 |\n", + "| CA | 1.0 | 0.1 | 0.0 |\n", + "| CT | 1.0 | 0.1 | 0.0 |\n", + "| CC | 1.0 | 0.1 | 0.0 |\n", + "| CG | 1.0 | 0.1 | 0.0 |\n", + "| GA | 1.0 | 0.5 | 0.0 |\n", + "| GT | 1.0 | 0.5 | 0.0 |\n", + "| GC | 1.0 | 0.5 | 0.0 |\n", + "| GG | 1.0 | 0.5 | 0.0 |\n", + "+------+-----+-----+-----+\n", + "Legend:\n", + "H - haplotype\n", + "ST - susceptibility type\n", + "\n", + "*******************************\n", + "***Immunity transition rates***\n", + "*******************************\n", + "+----+----------------------+------+------+\n", + "| ID | 0 | 1 | 2 |\n", + "+----+----------------------+------+------+\n", + "| 0 | 0.0 | 0.05 | 0.05 |\n", + "| 1 | 0.011111111111111112 | 0.0 | 0.0 |\n", + "| 2 | 0.005555555555555556 | 0.0 | 0.0 |\n", + "+----+----------------------+------+------+\n", + "Legend:\n", + "ID - ID susceptibility type\n", + "\n", + "*****************\n", + "***Populations***\n", + "*****************\n", + "+----+----------+--------------------+-----+-------+-------+------+-------+-----+\n", + "| ID | Size | Actual size | CD | CDBLC | CDALD | SLD | ELD | SM |\n", + "+----+----------+--------------------+-----+-------+-------+------+-------+-----+\n", + "| 0 | 10000000 | 9917808.219178082 | 0.7 | 0.7 | 0.1 | 0.01 | 0.002 | 1.0 |\n", + "| 1 | 5000000 | 5068493.150684931 | 0.7 | 0.7 | 0.1 | 0.01 | 0.002 | 3.0 |\n", + "| 2 | 1000000 | 1013698.6301369861 | 1.0 | 1.0 | 0.1 | 0.01 | 0.002 | 0.0 |\n", + "+----+----------+--------------------+-----+-------+-------+------+-------+-----+\n", + "Legend:\n", + "ID - number of population\n", + "Size - size of population\n", + "Actual size - actual size of population\n", + "CD - contact density\n", + "CDBLD - contact density without lockdown\n", + "CDALD - contact density at lockdown\n", + "SLD - start of lockdown\n", + "ELD - end of lockdown\n", + "SM - sampling multiplier\n", + "\n", + "*****************\n", + "***Susceptible***\n", + "*****************\n", + "+-------+---------+---------+--------+\n", + "| ST\\ID | 0 | 1 | 2 |\n", + "+-------+---------+---------+--------+\n", + "| 0 | 9720799 | 4881625 | 974187 |\n", + "| 1 | 179371 | 81927 | 16943 |\n", + "| 2 | 14 | 1 | 1 |\n", + "+-------+---------+---------+--------+\n", + "Legend:\n", + "ID - ID population\n", + "ST - susceptibility type\n", + "\n", + "****************\n", + "***Infectious***\n", + "****************\n", + "+------+-------+-------+------+\n", + "| H\\ID | 0 | 1 | 2 |\n", + "+------+-------+-------+------+\n", + "| AA | 99787 | 36444 | 8869 |\n", + "| AT | 0 | 0 | 0 |\n", + "| AC | 6 | 0 | 0 |\n", + "| AG | 3 | 0 | 0 |\n", + "| TA | 0 | 0 | 0 |\n", + "| TT | 0 | 0 | 0 |\n", + "| TC | 0 | 0 | 0 |\n", + "| TG | 0 | 0 | 0 |\n", + "| CA | 12 | 0 | 0 |\n", + "| CT | 0 | 0 | 0 |\n", + "| CC | 0 | 0 | 0 |\n", + "| CG | 0 | 0 | 0 |\n", + "| GA | 8 | 3 | 0 |\n", + "| GT | 0 | 0 | 0 |\n", + "| GC | 0 | 0 | 0 |\n", + "| GG | 0 | 0 | 0 |\n", + "+------+-------+-------+------+\n", + "Legend:\n", + "ID - ID population\n", + "H - haplotype\n", + "\n", + "**********************\n", + "***Migration matrix***\n", + "**********************\n", + "+-----+--------------------+--------------------+-----------------------+\n", + "| S\\T | 0 | 1 | 2 |\n", + "+-----+--------------------+--------------------+-----------------------+\n", + "| 0 | 0.9835616438356164 | 0.0136986301369863 | 0.0027397260273972603 |\n", + "| 1 | 0.0136986301369863 | 0.9835616438356164 | 0.0027397260273972603 |\n", + "| 2 | 0.0136986301369863 | 0.0136986301369863 | 0.9726027397260273 |\n", + "+-----+--------------------+--------------------+-----------------------+\n", + "Legend:\n", + "S - ID source population\n", + "T - ID target population\n", + "\n" + ] + } + ], + "source": [ + "simulator.genealogy()\n", + "file_name = \"example\"\n", + "simulator.output_newick(file_name)\n", + "simulator.output_mutations(file_name)\n", + "simulator.output_migrations(file_name)\n", + "\n", + "simulator.print_basic_parameters()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "921d9d31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "********************\n", + "***Immunity model***\n", + "********************\n", + "+------+-----+-----+-----+\n", + "| H\\ST | S0 | S1 | S2 |\n", + "+------+-----+-----+-----+\n", + "| AA | 1.0 | 0.1 | 0.0 |\n", + "| AT | 1.0 | 0.1 | 0.0 |\n", + "| AC | 1.0 | 0.1 | 0.0 |\n", + "| AG | 1.0 | 0.1 | 0.0 |\n", + "| TA | 1.0 | 0.1 | 0.0 |\n", + "| TT | 1.0 | 0.1 | 0.0 |\n", + "| TC | 1.0 | 0.1 | 0.0 |\n", + "| TG | 1.0 | 0.1 | 0.0 |\n", + "| CA | 1.0 | 0.1 | 0.0 |\n", + "| CT | 1.0 | 0.1 | 0.0 |\n", + "| CC | 1.0 | 0.1 | 0.0 |\n", + "| CG | 1.0 | 0.1 | 0.0 |\n", + "| GA | 1.0 | 0.5 | 0.0 |\n", + "| GT | 1.0 | 0.5 | 0.0 |\n", + "| GC | 1.0 | 0.5 | 0.0 |\n", + "| GG | 1.0 | 0.5 | 0.0 |\n", + "+------+-----+-----+-----+\n", + "Legend:\n", + "H - haplotype\n", + "ST - susceptibility type\n", + "\n", + "*******************************\n", + "***Immunity transition rates***\n", + "*******************************\n", + "+----+----------------------+------+------+\n", + "| ID | 0 | 1 | 2 |\n", + "+----+----------------------+------+------+\n", + "| 0 | 0.0 | 0.05 | 0.05 |\n", + "| 1 | 0.011111111111111112 | 0.0 | 0.0 |\n", + "| 2 | 0.005555555555555556 | 0.0 | 0.0 |\n", + "+----+----------------------+------+------+\n", + "Legend:\n", + "ID - ID susceptibility type\n", + "\n" + ] + } + ], + "source": [ + "simulator.print_immunity_model()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35d66dd0", + "metadata": {}, + "outputs": [], + "source": [ + "simulator.print_populations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/VGsim/testing/example.py b/VGsim/testing/example.py new file mode 100644 index 0000000..bd3b2da --- /dev/null +++ b/VGsim/testing/example.py @@ -0,0 +1,82 @@ +import VGsim +import os +import os.path + +number_of_sites = 2 +populations_number = 3 +number_of_susceptible_groups = 3 +simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, seed=1234) + +#Set epidemiological parameters +simulator.set_transmission_rate(0.25) +simulator.set_transmission_rate(0.5, haplotype="GG") +simulator.set_recovery_rate(0.099) +simulator.set_sampling_rate(0.001) + +#Set mutation rates +mutation_rate=0.00003 +substitution_weights=[1,1,1,2]#ATCG +simulator.set_mutation_rate(mutation_rate, substitution_weights) +simulator.set_mutation_rate(3*mutation_rate, haplotype='G*', mutation=1) + +#Set host immunity types triggered by infection +simulator.set_susceptibility_type(1) +simulator.set_susceptibility_type(2, haplotype='G*') +simulator.set_susceptibility(0.1, susceptibility_type=1) +simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype='G*') +simulator.set_susceptibility(0.0, susceptibility_type=2) + +#Set loss of immunity +simulator.set_immunity_transition(1/90, source=1, target=0) +simulator.set_immunity_transition(1/180, source=2, target=0) + +#Set host population structure with three populations and migration +simulator.set_population_size(10000000, population=0) +simulator.set_population_size(5000000, population=1) +simulator.set_population_size(1000000, population=2) +simulator.set_migration_probability(10/365/2) + +#Set specific sampling efforts in different populations +simulator.set_sampling_multiplier(3, population=1) +simulator.set_sampling_multiplier(0, population=2) + +#Set non-pharmasutical interventions (same for each population) +simulator.set_npi([0.1, 0.01, 0.002]) + +#Run simulation with the exact algorithm +simulator.simulate(10000000, epidemic_time=110) + +#Change some of parameters +##simulator.set_immunity_transition(0.05, source=0, target=1) +##simulator.set_immunity_transition(0.05, source=0, target=2) +##simulator.set_contact_density(0.7, population=0) +##simulator.set_contact_density(0.7, population=1) +##simulator.set_migration_probability(2/365/2, source=0, target=2) +##simulator.set_migration_probability(2/365/2, source=1, target=2) + +##print("\nSwitching to tau-leaping algorithm.\n") + +#Run simulation with the approximate tau-leaping algorithm +##simulator.simulate(1000, epidemic_time=210, method='tau') + +#Simulate genealogy of sampled individuals +simulator.genealogy() + +#Create directory to save results +os.chdir('testing') +if os.path.exists('example_output') == False: + os.mkdir('example_output') +os.chdir('example_output') + +#Generate a plot with epidemiological curves +simulator.add_plot_infectious(population=0, haplotype='AA', step_num=100) +simulator.add_plot_infectious(population=1, haplotype='AA', step_num=100) +simulator.add_plot_infectious(population=2, haplotype='AA', step_num=100) +simulator.add_title(name="AA haplotype") +simulator.add_legend() +simulator.plot('plot') + +#Save the genealogy with mutations and migrations +simulator.output_newick() +simulator.output_mutations('mutations') +simulator.output_migrations('migrations') diff --git a/VGsim/testing/getting_reference.py b/VGsim/testing/getting_reference.py new file mode 100644 index 0000000..df2e1b2 --- /dev/null +++ b/VGsim/testing/getting_reference.py @@ -0,0 +1,131 @@ +import VGsim + +#1 model +simulator = VGsim.Simulator(seed=2020) + +name = 1 +simulator.set_transmission_rate(4.0) +simulator.set_recovery_rate(1.5) +simulator.set_sampling_rate(0.3) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#2 model +simulator = VGsim.Simulator(number_of_sites=1, seed=2020) + +name = 2 +simulator.set_transmission_rate(4, haplotype=3) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#3 model +simulator = VGsim.Simulator(number_of_susceptible_groups=2, seed=2020) + +name = 3 +simulator.set_susceptibility_type(1) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#4 model +simulator = VGsim.Simulator(number_of_sites=1, number_of_susceptible_groups=3, seed=2020) + +name = 4 +simulator.set_mutation_rate(0.01) +simulator.set_susceptibility_type(1, haplotype=0) +simulator.set_susceptibility_type(2, haplotype=1) +simulator.set_susceptibility_type(2, haplotype=2) +simulator.set_susceptibility_type(2, haplotype=3) +simulator.set_immunity_transition(0.01, source=0, target=1) +simulator.set_immunity_transition(0.01, source=1, target=2) +simulator.set_immunity_transition(0.02, source=2, target=1) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#5 model +simulator = VGsim.Simulator(populations_number=2, seed=2020) + +name = 5 +simulator.set_population_size(2000000) +simulator.set_contact_density(1.3, population=0) +simulator.set_contact_density(0.8, population=1) +simulator.set_migration_probability(0.01, source=0, target=1) +simulator.set_migration_probability(0.005, source=1, target=0) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#6 model +simulator = VGsim.Simulator(populations_number=3, seed=2020) + +name = 6 +simulator.set_migration_probability(0.01, source=0, target=1) +simulator.set_migration_probability(0.005, source=2, target=1) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#7 model +simulator = VGsim.Simulator(populations_number=3, seed=2020) + +name = 7 +simulator.set_migration_probability(0.01, source=0, target=1) +simulator.set_migration_probability(0.005, source=2, target=1) +simulator.set_sampling_multiplier(2.5, population=1) +simulator.set_sampling_multiplier(2, population=2) +simulator.set_npi([0.5, 0.30, 0.15], population=0) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#8 model +simulator = VGsim.Simulator(number_of_sites=2, seed=2020) + +name = 8 +simulator.set_mutation_rate(probabilities=[1, 0, 0, 1]) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#9 model +simulator = VGsim.Simulator(number_of_sites=2, populations_number=3, number_of_susceptible_groups=3, seed=2020) + +name = 9 +simulator.set_transmission_rate(5.0, haplotype=12) +simulator.set_recovery_rate(1.5) +simulator.set_sampling_rate(0.3) +simulator.set_mutation_rate(0.01, probabilities=[1, 0, 0, 1]) +simulator.set_migration_probability(0.01, source=0, target=1) +simulator.set_migration_probability(0.005, source=2, target=1) +simulator.set_sampling_multiplier(2.5, population=1) +simulator.set_sampling_multiplier(2, population=2) +simulator.set_npi([0.5, 0.30, 0.15], population=1) +simulator.set_susceptibility_type(1, haplotype=0) +simulator.set_susceptibility_type(2, haplotype=1) +simulator.set_susceptibility_type(2, haplotype=2) +simulator.set_susceptibility_type(2, haplotype=3) +simulator.set_immunity_transition(0.000001, source=0, target=1) +simulator.set_immunity_transition(0.000001, source=1, target=2) +simulator.set_immunity_transition(0.000002, source=2, target=1) +simulator.simulate(100000) +simulator.output_chain_events('reference_' + str(name)) + +#10 model +# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=True) + +# name = 10 +# simulator.set_mutation_rate(0.01) +# simulator.simulate(iterations) +# simulator.output_chain_events('reference_' + str(name)) + +# #11 model +# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=20) + +# name = 11 +# simulator.set_mutation_rate(0.01) +# simulator.simulate(iterations) +# simulator.output_chain_events('reference_' + str(name)) + +# #12 model +# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=(20, 2)) + +# name = 12 +# simulator.set_mutation_rate(0.01) +# simulator.simulate(iterations) +# simulator.output_chain_events('reference_' + str(name)) + diff --git a/VGsim/testing/reference_1.npy b/VGsim/testing/reference_1.npy new file mode 100644 index 0000000000000000000000000000000000000000..ced633d01a7b98ed7407fcd3890dbf58d0461d0c GIT binary patch literal 4800128 zcmbT-_dnL%8vyWo5)Fiu>=9+9NHWebvr?3bXc(DMMoNhi6$)i!g(yUJ_7)|ZR3tO9 z_nzPD`4_(Z@_v1|ea^VXIeB_s=^C6mV@M?2CfpM;w{S9Z6gt5pB!B(5kSLGPbq7ah 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zfWXQE?#pXb_vt!5z1|vCvI_zP2oNAZfB*pk1PBlya7|#>&*8i7``T4v76AeT2oNAZ zU|fNxp6i}kXIyES5g1Rv>vFuB$cVt`0`425S4e*U5pbQl4hRq+Kww7!?|(a1-JW+5 z@cMqX(!8Jbw%WS_rR%+P{@v`YPJjRb0tDU_@ILvj|1PV$&UUG^eG(u*fB*pk1PBly zK!5-N0t5&UAV7e?N&>#$U#Xh*LVy5)aRj`-j8hAl5FkK+0D)BmJYTF*$v*pVee`h^ vbuIy~gSqM@TLJ_K>?YuSYLAMm^B#4!O9BK45FjwJfaj5st0Ol8slfjOKV`)M literal 0 HcmV?d00001 diff --git a/VGsim/testing/tests.py b/VGsim/testing/tests.py new file mode 100644 index 0000000..48c7453 --- /dev/null +++ b/VGsim/testing/tests.py @@ -0,0 +1,302 @@ +import VGsim + +# set_initial_haplotype +model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) +model.set_initial_haplotype(0) +model.set_initial_haplotype(1) +model.set_initial_haplotype(64) +model.set_initial_haplotype(65) + +try: + model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) + model.set_initial_haplotype(1/2) +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) + model.set_initial_haplotype(-1) +except ValueError: + pass + + +# set_step_haplotype +model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) +model.set_step_haplotype(0) +model.set_step_haplotype(1) +model.set_step_haplotype(64) +model.set_step_haplotype(65) + +try: + model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) + model.set_step_haplotype(1/2) +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) + model.set_step_haplotype(-1) +except ValueError: + pass + + +# set_transmission_rate +model = VGsim.Simulator(number_of_sites=2) +model.set_transmission_rate(0) +model.set_transmission_rate(0.002, 'A*') +model.set_transmission_rate(0.003, 'AT') +model.set_transmission_rate(0.004, 0) +model.set_transmission_rate(0.005, 15) + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_transmission_rate('str') +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_transmission_rate(-1) +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_transmission_rate(0.01, haplotype=[1, 2]) +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_transmission_rate(0.01, haplotype=-1) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_transmission_rate(0.01, haplotype=16) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_transmission_rate(0.01, haplotype='str') +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_transmission_rate(0.01, haplotype='AAA') +except ValueError: + pass + + +# set_recovery_rate +model = VGsim.Simulator(number_of_sites=2) +model.set_recovery_rate(0) +model.set_recovery_rate(0.002, 'A*') +model.set_recovery_rate(0.003, 'AT') +model.set_recovery_rate(0.004, 0) +model.set_recovery_rate(0.005, 15) + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_recovery_rate('str') +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_recovery_rate(-1) +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_recovery_rate(0.01, haplotype=[1, 2]) +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_recovery_rate(0.01, haplotype=-1) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_recovery_rate(0.01, haplotype=16) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_recovery_rate(0.01, haplotype='str') +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_recovery_rate(0.01, haplotype='AAA') +except ValueError: + pass + + +# set_sampling_rate +model = VGsim.Simulator(number_of_sites=2) +model.set_sampling_rate(0) +model.set_sampling_rate(0.002, 'A*') +model.set_sampling_rate(0.003, 'AT') +model.set_sampling_rate(0.004, 0) +model.set_sampling_rate(0.005, 15) + +model = VGsim.Simulator(number_of_sites=2, sampling_probability=True) +model.set_sampling_rate(0) +model.set_sampling_rate(0.002, 'A*') +model.set_sampling_rate(0.003, 'AT') +model.set_sampling_rate(0.004, 0) +model.set_sampling_rate(0.005, 15) +model.set_sampling_rate(1) + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_sampling_rate('str') +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_sampling_rate(-1) +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2, sampling_probability=True) + model.set_sampling_rate('str') +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2, sampling_probability=True) + model.set_sampling_rate(-1) +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2, sampling_probability=True) + model.set_sampling_rate(2) +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_sampling_rate(0.01, haplotype=[1, 2]) +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_sampling_rate(0.01, haplotype=-1) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_sampling_rate(0.01, haplotype=16) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_sampling_rate(0.01, haplotype='str') +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_sampling_rate(0.01, haplotype='AAA') +except ValueError: + pass + + +model = VGsim.Simulator(number_of_sites=2) +model.set_mutation_rate(rate=0) +model.set_mutation_rate(rate=0.002, haplotype='A*') +model.set_mutation_rate(rate=0.003, haplotype='AT') +model.set_mutation_rate(rate=0.004, haplotype=0) +model.set_mutation_rate(rate=0.005, haplotype=15) +model.set_mutation_rate(rate=0.006, mutation=0) +model.set_mutation_rate(rate=0.007, mutation=1) +model.set_mutation_rate(probabilities=[5, 4, 8, 2], haplotype='A*') +model.set_mutation_rate(probabilities=[5, 4, 8, 2], haplotype='AT') +model.set_mutation_rate(probabilities=[5, 4, 8, 2], haplotype=0) +model.set_mutation_rate(probabilities=[5, 4, 8, 2], haplotype=15) +model.set_mutation_rate(probabilities=[5, 4, 8, 2], mutation=0) +model.set_mutation_rate(probabilities=[5, 4, 8, 2], mutation=1) + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(rate='str') +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(rate=-1) +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(probabilities=[5, -1, 8, 2]) +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(probabilities=[0, 0, 0, 0]) +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(probabilities=2) +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(rate=0.002, haplotype='R*') +except ValueError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(rate=0.002, haplotype=-1) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(rate=0.002, haplotype=[1, 2]) +except TypeError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(rate=0.002, haplotype=16) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(rate=0.002, mutation=-1) +except IndexError: + pass + +try: + model = VGsim.Simulator(number_of_sites=2) + model.set_mutation_rate(rate=0.002, mutation=2) +except IndexError: + pass + From 804c6d6a4409ed8a43f33b4a8d52cae8ea92b550 Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Sun, 19 Mar 2023 15:35:50 +0300 Subject: [PATCH 12/13] Delete VGsim directory --- VGsim/LICENSE | 674 ----- VGsim/README.md | 112 - VGsim/Simulator.py | 492 ---- VGsim/VGsim_cmd.py | 143 - VGsim/data/Table 3/Table 3.ipynb | 279 -- VGsim/data/Table 3/Table 3.py | 50 - VGsim/data/Table 3/table.txt | 21 - VGsim/data/Table 4/Table 4.ipynb | 84 - VGsim/data/VGsim and TiPS/test_TiPS.R | 62 - VGsim/data/VGsim and TiPS/test_VGsim.py | 29 - VGsim/data/model_example.py | 39 - VGsim/docs/Makefile | 20 - VGsim/docs/make.bat | 35 - VGsim/docs/source/AA_haplotype.png | Bin 73473 -> 0 bytes VGsim/docs/source/Command line.rst | 146 - VGsim/docs/source/Documentation.rst | 84 - VGsim/docs/source/Haplotypes.rst | 40 - VGsim/docs/source/Installation.rst | 49 - VGsim/docs/source/Migration.rst | 49 - VGsim/docs/source/Susceptible_group_sizes.png | Bin 33586 -> 0 bytes VGsim/docs/source/Tutorials and examples.rst | 299 -- VGsim/docs/source/conf.py | 57 - VGsim/docs/source/index.rst | 21 - VGsim/docs/source/modules.rst | 26 - VGsim/docs/source/output.png | Bin 132657 -> 0 bytes VGsim/meson.build | 47 - VGsim/pyproject.toml | 38 - VGsim/sim.py | 26 - VGsim/src/IO.py | 255 -- VGsim/src/_BirthDeath.pyx | 2613 ----------------- VGsim/src/__init__.py | 3 - VGsim/src/_interface.py | 295 -- VGsim/src/events.pxi | 155 - VGsim/src/fast_choose.pxi | 110 - VGsim/src/meson.build | 40 - VGsim/src/models.pxi | 65 - VGsim/testing/check_simulator.py | 179 -- VGsim/testing/cmd_example/example.mg | 4 - VGsim/testing/cmd_example/example.pp | 5 - VGsim/testing/cmd_example/example.rt | 18 - VGsim/testing/cmd_example/example.st | 4 - VGsim/testing/cmd_example/example.su | 18 - VGsim/testing/example.ipynb | 482 --- VGsim/testing/example.py | 82 - VGsim/testing/getting_reference.py | 131 - VGsim/testing/reference_1.npy | Bin 4800128 -> 0 bytes VGsim/testing/reference_2.npy | Bin 4800128 -> 0 bytes VGsim/testing/reference_3.npy | Bin 4800128 -> 0 bytes VGsim/testing/reference_4.npy | Bin 4800128 -> 0 bytes VGsim/testing/reference_5.npy | Bin 4800128 -> 0 bytes VGsim/testing/reference_6.npy | Bin 4800128 -> 0 bytes VGsim/testing/reference_7.npy | Bin 4800128 -> 0 bytes VGsim/testing/reference_8.npy | Bin 4800128 -> 0 bytes VGsim/testing/reference_9.npy | Bin 4800128 -> 0 bytes VGsim/testing/tests.py | 302 -- 55 files changed, 7683 deletions(-) delete mode 100644 VGsim/LICENSE delete mode 100644 VGsim/README.md delete mode 100644 VGsim/Simulator.py delete mode 100644 VGsim/VGsim_cmd.py delete mode 100644 VGsim/data/Table 3/Table 3.ipynb delete mode 100644 VGsim/data/Table 3/Table 3.py delete mode 100644 VGsim/data/Table 3/table.txt delete mode 100644 VGsim/data/Table 4/Table 4.ipynb delete mode 100644 VGsim/data/VGsim and TiPS/test_TiPS.R delete mode 100644 VGsim/data/VGsim and TiPS/test_VGsim.py delete mode 100644 VGsim/data/model_example.py delete mode 100644 VGsim/docs/Makefile delete mode 100644 VGsim/docs/make.bat delete mode 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100644 VGsim/testing/tests.py diff --git a/VGsim/LICENSE b/VGsim/LICENSE deleted file mode 100644 index f288702..0000000 --- a/VGsim/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. diff --git a/VGsim/README.md b/VGsim/README.md deleted file mode 100644 index 07aa52a..0000000 --- a/VGsim/README.md +++ /dev/null @@ -1,112 +0,0 @@ -# VGsim -Fast simulator of viral genealogies in the world-scale pandemic scenarios. - -[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) -[![pip](https://github.com/Genomics-HSE/VGsim/actions/workflows/pip.yml/badge.svg)](https://github.com/Genomics-HSE/VGsim/actions/workflows/pip.yml) -[![dev](https://github.com/Genomics-HSE/VGsim/actions/workflows/dev.yml/badge.svg)](https://github.com/Genomics-HSE/VGsim/actions/workflows/dev.yml) -[![windows](https://github.com/Genomics-HSE/VGsim/actions/workflows/windows.yml/badge.svg)](https://github.com/Genomics-HSE/VGsim/actions/workflows/windows.yml) -[![Documentation Status](https://readthedocs.org/projects/vg-sim/badge/?version=latest)](https://vg-sim.readthedocs.io/en/latest/?badge=latest) - -Documentation -------------- - -We keep on updating the detailed documentation (https://vg-sim.readthedocs.io/). In particular, there is a Python API which allows to build complicated simulations, which are currently not supported from the command line. - -Building the package --------------------- - -For performance reasons, the package includes C extensions, which need to be -built. In the future, we plan to provide prebuilt packages, but for now you need -to build them yourself. For this, you need a working toolchain for building C++ -code (gcc and clang are known to work). Since you are going to build Python extensions, -you will need python development headers (e.g. on ubuntu linux the package name is `python-dev`). - -The simplest quick-start cross-platform way is to use `conda`. To do this, create a fresh conda environment: - -``` -$ conda create -n conda_vgsim -$ conda activate conda_vgsim -$ conda install python=3.9 # or other python version of your choice (any `python >= 3.7` should work). -``` - -Note that we install python *with conda, inside the conda environment*. Mixing system-install python and conda may lead to build- or runtime errors. - -Get the source code --- here we clone it from GitHub - -``` -$ git clone https://github.com/Genomics-HSE/VGsim.git -$ cd VGsim -``` - -Then build the package: - -``` -$ python -m pip install . -``` - -That's it! -You may now run simulations: - -``` -$ python ./testing/example.py -``` - -Running this script should create a directory ``testing/example_output`` with five files ``tree.nwk`` (genealogy tree), ``mutations.tsv`` (mutations on the genealogy), ``migrations.tsv`` (migrations of the lineages in the genealogy), ``sample_population.tsv`` (population of each sample), ``plot.png`` (visualization of epidemiological curves). There will be a warning in the output. This is an expected behaviour in this example (see https://vg-sim.readthedocs.io/en/latest/Migration.html for detailed explanation of inflated number of individuals in a deme due to migration). This example is also provided as a jupyter notebook ``testing/example.ipynb``. - -If you prefer not to use ``conda``, the package builds fine with just pip. We recommend using virtual environments, via the standard library ``venv`` package (or a third-party ``virtualenv`` package). For MacOS users: please make sure to use Python with Homebrew. Avoid system Python. Manually installed Python (e.g. downloaded from python.org) currently does not work with meson too. If there are still errors, try to install pkg-config with brew install. YMMV. -We tested this procedure on python 3.7-3.9 on Ubuntu linux and MacOS. -On Apple Silicon, you need to have ``numpy >= 1.21`` (which is the first NumPy -version to support this hardware). - -If you encounter problems with either of these steps, please file an issue at -``https://github.com/Genomics-HSE/VGsim``: please rerun with the ``-v`` flag, -``$ python -mpip install . -v`` and include the output. - - - -Adding neutral mutations ------------------------- - -We suggest to pipe the tree and non-neautral sites into phastSim (https://github.com/NicolaDM/phastSim) by Nicola de Maio to add neutral mutations and to obtain full sequences. - -Planned features ----------------- -Here we list the features which we are adding or consider for the VGsim package. -- Tau-leaping: done (beta-version). -- Memory usage optimisation to enable larger number of mutable sites: done (beta-version). -- Population-level susceptibility transitions (e.g. to simulate different uneven efforts across the world): in work. -- Advanced immunity model: in work. -- Advanced sampling schemes: medium priority. -- Super-spreading events: medium priority. -- Life-cycle: low priority. -- Recombinations: low priority. - - -Citation --------- - -Please cite our preprint (https://www.medrxiv.org/content/10.1101/2021.04.21.21255891) when using VGsim in your research. -``` -Shchur et al. VGsim: scalable viral genealogy simulator for global pandemic. -medRxiv 2021.04.21.21255891; doi: https://doi.org/10.1101/2021.04.21.21255891 -``` - -Fast build ----------- - -For developers, it is recommended to manually build the package. This process is much faster then using pip, so it is handy while actively working with the code. - -From the `VGsim` directory, first let us configure `PYTHOPATH`. -``` -ver=$(python -c'from sys import version_info as v; print("%s.%s"%(v.major, v.minor))') -export PYTHONPATH=$PWD/installdir/lib/python$ver/site-packages/ -``` -Remember to do it in every terminal window/tab where you work on the project (e.g. with your `jupyter notebook`)! Now we are ready to build `VGsim`. -``` -meson setup build --prefix=$PWD/installdir -meson install -C build -``` -That's it! For subsequent rebuilds (in the same terminal session) you need to run only the last command -``` -meson install -C build -``` diff --git a/VGsim/Simulator.py b/VGsim/Simulator.py deleted file mode 100644 index 88190d7..0000000 --- a/VGsim/Simulator.py +++ /dev/null @@ -1,492 +0,0 @@ -def print_basic_parameters(): - """ - This methods prints the basic parameters of the epidemiological model. - """ - -def print_populations(): - """ - This methods prints parameters of the population model. - """ - -def print_immunity_model(): - """ - This methods prints the basic parameters of the immunity model. - """ - -def print_all(basic_parameters=True, populations=True, immunity_model=True): - """ - This methods prints all the parameters of the simulation. - - :param basic_parameters: whether to output the basic parameters of the epidemiological model. - :type basic_parameters: bool - - :param populations: whether to output parameters of the population model. - :type populations: bool - - :param immunity_model: whether to output parameters of the immunity model. - :type immunity_model: bool - """ - -def set_transmission_rate(rate, haplotype=None): - """ - .. _Wikipedia: https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology - - Transmission rate is the the expected number of new infections from a single infected individual per time unit in the beginning of the epidemics when all but one of the hosts are susceptible. See `Wikipedia`_ - that is parameter beta in SIR model. - - :param rate: transmission rate value. - :type rate: float - - :param haplotype: haplotypes for which the new value is being set. `See for details `_. - :type haplotype: int or string or None - """ - - -def set_recovery_rate(rate, haplotype=None): - """ - .. _Wikipedia: https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology - - Recovery rate is the inverse of the expected recovery time - non-negative float value. See `Wikipedia`_ - that is parameter gamma in SIR model. - - :param rate: recovery rate value. - :type rate: float - - :param haplotype: haplotypes for which the new value is being set. `See for details `_. - :type haplotype: int or string or None - """ - -def set_sampling_rate(rate, haplotype=None): - """ - Sampling rate: the rate at which infected individuals are sampled. The genealogy will be generated only for these samples. Alternatively, if *sampling_probability* is **True**, it will specify the fraction of recovered individuals which are sampled. - - :param rate: sampling rate value. - :type rate: float - - :param haplotype: haplotypes for which the new value is being set. `See for details `_. - :type haplotype: int or string or None - """ - -def set_mutation_rate(rate=None, probabilities=None, haplotype=None, mutation=None): - """ - This method allows setting the mutation rate and the weights for each single nucleotide substitution (given the mutation happened). - - :param rate: mutation rate value (None would not change the old value). - :type rate: float or None - - :param probabilities: weights of each single nucleotide substitution given mutation occured (None would not change the old values). `See for example `_. - :type probabilities: list of four non-negative integers or None - - :param haplotype: the ancestral haplotype for the mutation. `See for details `_. - :type haplotype: int or string or None - - :param mutation: the id of position on the haplotype where mutation arises. - :type mutation: int or None - """ - -def set_susceptibility_type(susceptibility_type, haplotype=None): - """ - The type of immunity (or the susceptibility group) which an individual gets after being infected with a pathogen of a haplotype. - - :param susceptibility_type: immunity group id. - :type susceptibility_type: int - - :param haplotype: haplotypes for which the new value is being set. `See for details `_. - :type haplotype: int or string or None - """ - -def set_susceptibility(rate, haplotype=None, susceptibility_type=None): - """ - Susceptibility is a multiplicative modifier of the transmission rate based on the susceptible host immunity with a certain *susceptibility_type*. It can decrease or increase the transmission rate of a particular haplotype to the individuals of said *susceptibility_type*. - - :param rate: susceptibility value. - :type rate: float - - :param haplotype: haplotypes for which the new value is being set. `See for details `_. - :type haplotype: int or string or None - - :param susceptibility_type: immunity group id for which the new susceptibility value is being set. - :type susceptibility_type: int or None - """ - -def set_immunity_transition(rate, source=None, target=None): - """ - The change of immunity without infection (e.g. due to vaccination or immunity loss with time). - - :param rate: transition rate value. - :type rate: float - - :param source: source immunity group id (None means that the new value will be set to all immunity groups as source). - :type source: int or None - - :param target: target immunity group id (None means that the new value will be set to all immunity groups as source). - :type target: int or None - """ - - -def set_population_size(size, population=None): - """ - Set the number of individuals in the population. - - :param size: total number of individuals in the population. - :type size: int - - :param population: population for which the new population size is being set (in case of None the value will be updated for all populations). - :type population: int or None - """ - -def set_contact_density(value, population=None): - """ - The relative number of contacts per time units. - - :param value: contact density value. - :type value: float - - :param population: population for which the new contact density size is being set (in case of None the value will be updated for all populations). - :type population: int or None - """ - -def set_npi(parameters, population=None): - """ - Setting conditions when lockdown in a population is imposed and lifted with the certain contact density during the lockdown. - - :param parameters: list with three elements: contact density value during lockdown, fraction of infectious population when the lockdown is set, fraction of infectious population when lockdown is lifted. - :type parameters: list of length 3 with float values - - :param population: population for which the lockdown parameters are being set (in case of None the value will be updated for all populations). - :type population: int or None - """ - -def set_sampling_multiplier(multiplier, population=None): - """ - The relative sampling in the population (multiplicative modifier). Sampling rate of each haplotype is modified by this factor. - - :param value: sampling multiplier value. - :type value: float - - :param population: population for which the new sampling multiplier is being set (in case of None the value will be updated for all populations). - :type population: int or None - """ - -def set_migration_probability(probability=None, total_probability=None, source=None, target=None): - """ - The probability that an individual from the population source is travelling to the population target. - - :param probability: probability of migration value. - :type probability: float - - :param total_probability: if True, all the entries (except for the diagonal element) of row corresponding to source population is filled with probabilitie/(K-1). That represents individual travelling out of its population to a random destination. - :type total_probability: bool - - :param source: source population with infectious individual (None means that the new value will be set to all populations as source). - :type source: int or None - - :param target: target population with infectious individual (None means that the new value will be set to all populations as source). - :type target: int or None - """ - -def set_susceptible_individuals(amount, source_type, target_type, population=None): - """ - The method allows moving susceptible individuals between susceptibility groups. - - :param amount: the number of individuals to be moved. - :type amount: int - - :param source_type: source susceptibility group from which individuals are moved. - :type source_type: int - - :param target_type: target susceptibility group into which individuals are moved. - :type target_type: int - - :param population: population for which the new value is being set (in case of None the value will be updated for all populations). - :type population: int or None - """ - -def set_infected_individuals(amount, source_haplotype, target_haplotype, population=None): - """ - The method allows moving infected individuals between susceptibility groups. - - :param amount: the number of individuals to be moved. - :type amount: int - - :param source_haplotype: source haplotype from which individuals are moved. - :type source_haplotype: int - - :param target_haplotype: target haplotype into which individuals are moved. - :type target_haplotype: int - - :param population: population for which the new value is being set (in case of None the value will be updated for all populations). - :type population: int or None - """ - -def set_infection(amount, source_type, target_haplotype, population=None): - """ - The method allows moving individuals from susceptibility group to target haplotype. - - :param amount: the number of individuals to be moved. - :type amount: int - - :param source_type: source susceptibility group from which individuals are moved. - :type source_type: int - - :param target_haplotype: target haplotype into which individuals are moved. - :type target_haplotype: int - - :param population: population for which the new value is being set (in case of None the value will be updated for all populations). - :type population: int or None - """ - -def set_chain_events(file_name): - """ - Allows to import chain of events directly from the .npy file - - :param file_name: a name of the file (without the .npy extension) - :type file_name: str - """ - -def set_settings(file_template): - """ - Currently is a placeholder and does nothing. Do not include in actual docs until it does something. - """ - -def set_state(set_state): - """ - Calls set_chain_events and set_setting. Now identical to set_events. Do not include in docs until it starts to differ. - """ - - -def output_newick(file_template="newick_output", file_path = None): - """ - Outputs the result of simulation in the newick format. - - :param file_template: template for the file name - :type file_template: str - - :param file_path: path to output file - :type file_path: str - """ - -def output_mutations(file_template="mutation_output", file_path = None): - """ - Outputs the information about all mutations - - :param file_template: template for the file name - :type file_template: str - - :param file_path: path to output file - :type file_path: str - """ - -def output_migrations(file_template="migrations", file_path = None): - """ - Outputs the information about all migrations - - :param file_template: template for the file name - :type file_template: str - - :param file_path: path to output file - :type file_path: str - """ - -def output_sample_data(output_print=False): - """ - Show all information about sampling, time, place, etc. - - :param output_print: if *Thue*, it outputs data about each sample to the console. Data includes time of sampling, population and haplotype of sampled individual. - :type output_print: bool - - :return: 3 lists with times, population ids and haplotypes of the sampled individuals - """ - - -def output_epidemiology_timelines(step=1000, output_file=False): - """ - Records simulation state changes over some period of time. - - :param step: a number of steps which split the epidemiology timelines. - :type step: str - - :param output_file: if **True**, the timeline is written in the output file - :type output_file: bool - """ - -def output_chain_events(file_name="chain_events"): - """ - Outputs event chain. - - :param file_name: file name for the output - :param file_name: str - """ - -def output_settings(self, file_template="parameters"): - """ - Outputs settings. - - :param file_template: file name for the output - :param file_template: str - """ - -def output_state(): - """ - Outputs event chain and settings. - """ - - -def get_data_susceptible(population, susceptibility_type, step_num): - """ - Returns the list with the amount of sucseptible individuals over some period of time. - - :param population: population to retrieve data from - :type population: int - - :param susceptibility_type: susceptibility type to retrieve data from - :type susceptibility_type: int - - :param step_num: number of steps which split the epidemiology timeline. - :type step_num: int - - :return: 3 lists - amounts of susceptible individuals on the time points, time points themselves at which we retrieve amounts of individuals, and lockdowns data for the current epidemiology - """ - -def get_data_infectious(self, population, haplotype, step_num): - """ - Returns the list with the amount of infectious individuals over some period of time. - - :param population: population to retrieve data from - :type population: int - - :param haplotype: haplotype to retrieve data from - :type haplotype: int - - :param step_num: number of steps which split the epidemiology timeline - :type step_num: int - - :return: 3 lists - amounts of infectious individuals on the time points, time points themselves at which we retrieve amounts of individuals, and lockdowns data for the current epidemiology - """ - - -def plot_infectious(population, haplotype, step_num, label_infectious, label_samples): - """ - Creates plots for infectious and sampled individuals. - - :param population: population id for the plot - :type population: int - - :param haplotype: haplotype for the plot - :type haplotype: int or str - - :param step_num: number of steps which split the epidemiology timeline - :type step_num: int - - :param label_infectious: the label for the infectious plot in the plot legend - :type label_infectious: str - - :param label_samples: the label for the samples plot in the plot legend - :type label_samples: str - """ - -def add_plot_infectious(population, haplotype, step_num=100, label_infectious=None, label_samples=None): - """ - Add to plot the trajectories of the change of the number of infectious individuals over time. - - :param population: population id for the plot - :type population: int - - :param haplotype: haplotype for the plot - :type haplotype: int or str - - :param step_num: number of steps which split the epidemiology timeline - :type step_num: int - - :param label_infectious: the label for the infectious plot - :type label_infectious: str or None - - :param label_samples: the label for the samples plot - :type label_samples: str or None - """ - -def add_plot_susceptible(population, susceptibility_type, step_num=100, label=None): - """ - Add to plot the trajectories of the change of the number of susceptible individuals over time. - - :param population: population id for the plot - :type population: int - - :param haplotype: haplotype for the plot - :type haplotype: int or str - - :param step_num: number of steps which split the epidemiology timeline - :type step_num: int - - :param label: the label for the susceptible plot - :type label: str - """ - - -def add_legend(): - """ - Add legend to the plot. - """ - -def add_title(name="Plot"): - """ - Add plot title. - - :param name: the title for the plot - :type name: str - """ - -def plot(): - """ - Show plot with all the trajectories added by other methods. - """ - -def simulate(iterations=1000, sample_size=None, epidemic_time=None, method='direct'): - """ - This methods performs the simulation. The simulation interrupts when either one of the following conditions is satisfied: the number of iterations equals *iterations*, the number of collected samples equals *sample_size*, ot the total virtual time of the epidemic exceeds *time* - It can be called multiple times, changes of most parameters are allowed between simulation runs. - - :param iterations: maximal number of iterations. - :type iterations: int - - :param sample_size: desired sample size. - :type sample_size: int or None - - :param epidemic_time: virtual (model) time to be simulated. - :type epidemic_time: float or None - - :param method: 'direct' for the exact algorithm, 'tau' for tau-leaping approximated algorithm. - :type method: string - """ - - -def genealogy(seed=None): - """ - Generates a genealogy based on the chain of events generated during all the instances of simulate() method. - - :param seed: seed value (None for random seed). - :type seed: int or None - """ - -def citation(): - """ - Prints a citation of the paper in the console - """ - -def debug(): - """ - Returns all of the available information about simulation. Mainly used for the debug purposes. Not documented. - """ - -def get_proportion(): - """ - Used for paper preparation. Not documented. - """ - -def print_counters(): - """ - Used for paper preparation. Not documented. - """ - -def print_propensities(): - """ - Used for paper preparation. Not documented. - """ diff --git a/VGsim/VGsim_cmd.py b/VGsim/VGsim_cmd.py deleted file mode 100644 index b89df1f..0000000 --- a/VGsim/VGsim_cmd.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import sys -import time -import VGsim -from random import randrange -import numpy as np -import math - -parser = argparse.ArgumentParser(description='Migration inference from PSMC.') - -parser.add_argument('--iterations', '-it', nargs=1, type=int, default=1000, - help='number of iterations (default is 1000)') -parser.add_argument('--sampleSize', '-s', nargs=1, type=int, default=None, - help='number of sample (default is None)') -parser.add_argument('--time', '-t', nargs=1, type=float, default=None, - help='time for stopping simulation (default is None)') -parser.add_argument('--max_haplotypes_number', '-mhn', nargs=1, type=int, default=None, - help='#TODO') -parser.add_argument('--seed', '-seed', nargs=1, type=float, default=None, - help='random seed') - -parser.add_argument('--rates', '-rt', nargs=1, default=None, - help='rate: a file with rates for each haplotype') -parser.add_argument('--populationModel', '-pm', nargs=2, default=None, - help='population model: a file with population sizes etc, and a file with migration rate matrix') -parser.add_argument('--susceptibility', '-su', nargs=1, default=None, - help='susceptibility file') -parser.add_argument('--suscepTransition', '-st', nargs=1, default=None, - help='susceptibility transition file') - -parser.add_argument('--sampling_probability', help="#TODO", action="store_true") - -parser.add_argument("--createNewick", '-nwk', nargs=1, default=False, - help="Create a newick file of tree *.nwk ") -parser.add_argument("--writeMutations", '-tsv', nargs=1, default=False, - help="Create a mutation file *.tsv ") -parser.add_argument("--writeMigrations", nargs=1, default=False, - help="Create a migration file *.tsv ") -parser.add_argument("--output_chain_events", nargs=1, default=False, - help="#TODO") - -parser.add_argument("-citation", '-c', help="Information for citation.") - -clargs = parser.parse_args() - -if clargs.citation != None: - print("VGsim: scalable viral genealogy simulator for global pandemic") - print("Vladimir Shchur, Vadim Spirin, Victor Pokrovskii, Evgeni Burovski, Nicola De Maio, Russell Corbett-Detig") - print("medRxiv 2021.04.21.21255891; doi: https://doi.org/10.1101/2021.04.21.21255891") - sys.exit(0) - -if isinstance(clargs.rates, list): - clargs.rates = clargs.rates[0] -if isinstance(clargs.iterations, list): - clargs.iterations = clargs.iterations[0] -if isinstance(clargs.sampleSize, list): - clargs.sampleSize = clargs.sampleSize[0] -if isinstance(clargs.time, list): - clargs.time = clargs.time[0] -if isinstance(clargs.susceptibility, list): - clargs.susceptibility = clargs.susceptibility[0] -if isinstance(clargs.suscepTransition, list): - clargs.suscepTransition = clargs.suscepTransition[0] -if isinstance(clargs.seed, list): - clargs.seed = clargs.seed[0] -if isinstance(clargs.output_chain_events, list): - clargs.output_chain_events = clargs.output_chain_events[0] - -if clargs.rates == None: - bRate, dRate, sRate, mRate = [2], [1], [0.1], [[]] -else: - bRate, dRate, sRate, mRate = VGsim.IO.read_rates(clargs.rates) - -if clargs.sampleSize == None: - clargs.sampleSize = clargs.iterations -if clargs.time == None: - clargs.time = -1 - -if clargs.populationModel == None: - sizes, contactDensity, contactAfter, startLD, endLD, samplingMultiplier = [1000000], [1], [1], [1], [1], [1] - migrationRates = [[0.0]] -else: - sizes, contactDensity, contactAfter, startLD, endLD, samplingMultiplier = VGsim.IO.read_populations(clargs.populationModel[0]) - migrationRates = VGsim.IO.read_matrix(clargs.populationModel[1]) - -if clargs.susceptibility == None: - susceptible = [[1.0] for _ in range(len(bRate))] - susType = [0 for _ in range(len(bRate))] -else: - susceptible, susType = VGsim.IO.read_susceptibility(clargs.susceptibility) - -if clargs.suscepTransition == None: - suscepTransition = [[0.0]] -else: - suscepTransition = VGsim.IO.read_matrix(clargs.suscepTransition) - -if clargs.seed == None: - seed = randrange(sys.maxsize) -else: - seed = clargs.seed - -simulator = VGsim.Simulator(number_of_sites=int(math.log(len(bRate), 4)), populations_number=len(sizes), number_of_susceptible_groups=len(susceptible[0]), seed=int(seed), sampling_probability=clargs.sampling_probability) - -for i in range(len(bRate)): - simulator.set_transmission_rate(bRate[i], i) - simulator.set_recovery_rate(dRate[i], i) - simulator.set_sampling_rate(sRate[i], i) - for j in range(len(mRate[0])): - simulator.set_mutation_rate(mRate[i][j][0], [mRate[i][j][1], mRate[i][j][2], mRate[i][j][3], mRate[i][j][4]], i, j) - -for i in range(len(sizes)): - simulator.set_population_size(sizes[i], i) - simulator.set_contact_density(contactDensity[i], i) - simulator.set_npi([contactAfter[i], startLD[i], endLD[i]], i) - simulator.set_sampling_multiplier(samplingMultiplier[i], i) - for j in range(len(sizes)): - if i != j: - simulator.set_migration_probability(probability=migrationRates[i][j], source=i, target=j) -for i in range(len(susceptible)): - for j in range(len(susceptible[i])): - simulator.set_susceptibility(float(susceptible[i][j]), i, j) - -for i in range(len(susType)): - simulator.set_susceptibility_type(susType[i], i) - -for i in range(len(suscepTransition)): - for j in range(len(suscepTransition[i])): - if i != j: - simulator.set_immunity_transition(suscepTransition[i][j], i, j) - -simulator.simulate(clargs.iterations, clargs.sampleSize, clargs.time) -simulator.genealogy(int(seed)) - -if clargs.createNewick: - simulator.output_newick(clargs.createNewick) -if clargs.writeMutations: - simulator.output_mutations(clargs.writeMutations) -if clargs.writeMigrations: - simulator.output_migrations(clargs.writeMigrations) -if clargs.output_chain_events: - simulator.output_chain_events(clargs.output_chain_events) \ No newline at end of file diff --git a/VGsim/data/Table 3/Table 3.ipynb b/VGsim/data/Table 3/Table 3.ipynb deleted file mode 100644 index 8cf503b..0000000 --- a/VGsim/data/Table 3/Table 3.ipynb +++ /dev/null @@ -1,279 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 27, - "id": "07023925", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 288\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.668513393756367\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 281\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.6670835908470565\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 284\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.665982508283415\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 279\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.664658152957584\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 277\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.665755126964471\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 277\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.664815285090734\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 288\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.696113516237194\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 280\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.699337971323137\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 278\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.7003684985309535\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 288\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.699360261198045\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 272\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.697479261542758\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 274\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.692345292808055\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 265\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.913211277905174\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 290\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.914098068672664\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 281\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.946369973301594\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 275\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.959552629825298\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 287\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.965133379383973\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 266\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.928729686794892\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 277\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.876683982516927\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 277\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.925576407521985\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 262\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.854080162915341\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 285\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.917866703259873\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 267\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.890442395401363\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 269\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.872726366798063\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 265\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.028753464035603\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 281\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.600252675503189\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 273\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.337184147856449\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 237\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.386193897044432\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 274\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 6.047362981795651\n", - "Iterations\n", - "Seed: 2023.0\n", - "Number of samples: 255\n", - "Total number of iterations: 10000\n", - "Size events: 10000\n", - "Current time: 5.1490976751889335\n" - ] - } - ], - "source": [ - "from VGsim import Simulator\n", - "import time\n", - "import os \n", - "\n", - "def get_data(num_pop, total_migration_rate):\n", - " simulator = Simulator(number_of_sites=2, populations_number=num_pop, number_of_susceptible_groups=3, seed=2023)\n", - " simulator.set_transmission_rate(2.5)\n", - " simulator.set_transmission_rate(4.0, haplotype='GG')\n", - " simulator.set_recovery_rate(0.9)\n", - " simulator.set_sampling_rate(0.1)\n", - " simulator.set_migration_probability(total_probability=total_migration_rate)\n", - " simulator.set_population_size(int(2*10**9/num_pop))\n", - " simulator.set_susceptibility(1.0, susceptibility_type=0)\n", - " simulator.set_susceptibility(0.0, susceptibility_type=1)\n", - " simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype=\"C*\")\n", - " simulator.set_susceptibility(1.0, susceptibility_type=1, haplotype=\"G*\")\n", - " simulator.set_susceptibility(0.0, susceptibility_type=2)\n", - " simulator.set_susceptibility_type(1)\n", - " simulator.set_susceptibility_type(2, haplotype=\"C*\")\n", - " simulator.set_susceptibility_type(2, haplotype=\"G*\")\n", - " simulator.set_immunity_transition(0.01, target=0)\n", - " simulator.set_lockdown([0.1, 0.02, 0.01])\n", - " time_1 = time.time()\n", - " simulator.simulate(iterations=10000000)\n", - " time_2 = time.time()\n", - " \n", - " return [str(round(time_2 - time_1, 1)), str(round(simulator.get_proportion()*100, 2))]\n", - "\n", - "population_numbers = [2, 5, 10, 20, 50, 100]\n", - "total_migration_rates = [0.001, 0.002, 0.005, 0.01, 0.1]\n", - "\n", - "with open('table.txt', 'w') as table:\n", - " table.write('\\\\begin{table}[ht]\\n')\n", - " table.write('\\t\\\\resizebox{\\\\linewidth}{!}{\\n')\n", - " table.write('\\t\\t\\\\begin{tabular}{|c||c|c|c|c|c|c|}\\n')\n", - " table.write('\\t\\t\\t\\\\hline\\n')\n", - " table.write('\\t\\t\\t\\t\\\\multirow{2}{*}{\\\\shortstack[c]{Cumulative\\\\\\\\migration\\\\\\\\probability $M$}} & \\\\multicolumn{6}{c|}{\\\\makecell{Number of demes $K$\\\\\\\\\\\\,}} \\\\\\\\\\n')\n", - " table.write('\\t\\t\\t\\\\cline{2-7}\\n')\n", - " table.write('\\t\\t\\t\\t& 2 & 5 & 10 & 20 & 50 & 100\\\\\\\\\\n')\n", - " table.write('\\t\\t\\t\\\\hline\\n')\n", - " for rate in total_migration_rates:\n", - " c = []\n", - " for number in population_numbers:\n", - " c.append(get_data(number, rate))\n", - " table.write('\\t\\t\\t\\t'+str(rate)+' & '+c[0][0]+'s & '+c[1][0]+'s & '+c[2][0]+'s & '+c[3][0]+'s & '+c[4][0]+'s & '+c[5][0]+'s \\\\\\\\')\n", - " table.write('\\t\\t\\t\\t& '+c[0][1]+'\\\\% & '+c[1][1]+'\\\\% & '+c[2][1]+'\\\\% & '+c[3][1]+'\\\\% & '+c[4][1]+'\\\\% & '+c[5][1]+'\\\\% \\\\\\\\\\n')\n", - " table.write('\\t\\t\\t\\\\hline\\n')\n", - " table.write('\\t\\t\\\\end{tabular}\\n')\n", - " table.write('\\t}\\n')\n", - " table.write('\\\\end{table}\\n')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8906f6df", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/VGsim/data/Table 3/Table 3.py b/VGsim/data/Table 3/Table 3.py deleted file mode 100644 index 5dcf6aa..0000000 --- a/VGsim/data/Table 3/Table 3.py +++ /dev/null @@ -1,50 +0,0 @@ -from VGsim import Simulator -import time -import os - -def get_data(num_pop, total_migration_rate): - simulator = Simulator(number_of_sites=2, populations_number=num_pop, number_of_susceptible_groups=3, seed=2023) - simulator.set_transmission_rate(2.5) - simulator.set_transmission_rate(4.0, haplotype='GG') - simulator.set_recovery_rate(0.9) - simulator.set_sampling_rate(0.1) - simulator.set_migration_probability(total_probability=total_migration_rate) - simulator.set_population_size(int(2*10**9/num_pop)) - simulator.set_susceptibility(1.0, susceptibility_type=0) - simulator.set_susceptibility(0.0, susceptibility_type=1) - simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype='C*') - simulator.set_susceptibility(1.0, susceptibility_type=1, haplotype='G*') - simulator.set_susceptibility(0.0, susceptibility_type=2) - simulator.set_susceptibility_type(1) - simulator.set_susceptibility_type(2, haplotype='C*') - simulator.set_susceptibility_type(2, haplotype='G*') - simulator.set_immunity_transition(0.01, target=0) - simulator.set_lockdown([0.1, 0.02, 0.01]) - time_1 = time.time() - simulator.simulate(iterations=100000000) - time_2 = time.time() - - return [str(round(time_2 - time_1, 1)), str(round(simulator.get_proportion()*100, 2))] - -population_numbers = [2, 5, 10, 20, 50, 100] -total_migration_rates = [0.001, 0.002, 0.005, 0.01, 0.1] - -with open('table.txt', 'w') as table: - table.write('\\begin{table}[ht]\n') - table.write('\t\\resizebox{\\linewidth}{!}{\n') - table.write('\t\t\\begin{tabular}{|c||c|c|c|c|c|c|}\n') - table.write('\t\t\t\\hline\n') - table.write('\t\t\t\t\\multirow{2}{*}{\\shortstack[c]{Cumulative\\\\migration\\\\probability $M$}} & \\multicolumn{6}{c|}{\\makecell{Number of demes $K$\\\\\\,}} \\\\\n') - table.write('\t\t\t\\cline{2-7}\n') - table.write('\t\t\t\t& 2 & 5 & 10 & 20 & 50 & 100\\\\\n') - table.write('\t\t\t\\hline\n') - for rate in total_migration_rates: - c = [] - for number in population_numbers: - c.append(get_data(number, rate)) - table.write('\t\t\t\t'+str(rate)+' & '+c[0][0]+'s & '+c[1][0]+'s & '+c[2][0]+'s & '+c[3][0]+'s & '+c[4][0]+'s & '+c[5][0]+'s \\\\') - table.write('\t\t\t\t& '+c[0][1]+'\\% & '+c[1][1]+'\\% & '+c[2][1]+'\\% & '+c[3][1]+'\\% & '+c[4][1]+'\\% & '+c[5][1]+'\\% \\\\\n') - table.write('\t\t\t\\hline\n') - table.write('\t\t\\end{tabular}\n') - table.write('\t}\n') - table.write('\\end{table}\n') diff --git a/VGsim/data/Table 3/table.txt b/VGsim/data/Table 3/table.txt deleted file mode 100644 index 9759851..0000000 --- a/VGsim/data/Table 3/table.txt +++ /dev/null @@ -1,21 +0,0 @@ -\begin{table}[ht] - \resizebox{\linewidth}{!}{ - \begin{tabular}{|c||c|c|c|c|c|c|} - \hline - \multirow{2}{*}{\shortstack[c]{Cumulative\\migration\\probability $M$}} & \multicolumn{6}{c|}{\makecell{Number of demes $K$\\\,}} \\ - \cline{2-7} - & 2 & 5 & 10 & 20 & 50 & 100\\ - \hline - 0.001 & 28.7s & 30.0s & 31.9s & 35.1s & 47.2s & 69.3s \\& 0.09\% & 0.12\% & 0.11\% & 0.11\% & 0.11\% & 0.12\% \\ - \hline - 0.002 & 29.2s & 30.4s & 32.3s & 35.3s & 47.0s & 70.1s \\& 0.17\% & 0.21\% & 0.16\% & 0.2\% & 0.21\% & 0.2\% \\ - \hline - 0.005 & 29.4s & 30.7s & 32.5s & 35.6s & 48.1s & 70.4s \\& 0.33\% & 0.51\% & 0.25\% & 0.46\% & 0.52\% & 0.43\% \\ - \hline - 0.01 & 29.4s & 30.6s & 32.9s & 35.5s & 48.0s & 70.9s \\& 0.75\% & 1.16\% & 0.85\% & 0.78\% & 0.93\% & 0.95\% \\ - \hline - 0.1 & 30.3s & 31.9s & 33.8s & 37.0s & 50.3s & 73.0s \\& 2.04\% & 6.56\% & 6.15\% & 6.94\% & 5.98\% & 5.08\% \\ - \hline - \end{tabular} - } -\end{table} diff --git a/VGsim/data/Table 4/Table 4.ipynb b/VGsim/data/Table 4/Table 4.ipynb deleted file mode 100644 index 93a3391..0000000 --- a/VGsim/data/Table 4/Table 4.ipynb +++ /dev/null @@ -1,84 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 7, - "id": "d756fcb3", - "metadata": {}, - "outputs": [], - "source": [ - "from VGsim import Simulator\n", - "import time\n", - "import os \n", - "\n", - "def get_data(rate, size):\n", - " simulator = Simulator(number_of_sites=2, populations_number=100, number_of_susceptible_groups=1, seed=2023)\n", - " simulator.set_transmission_rate(2.5)\n", - " simulator.set_recovery_rate(1.0-rate)\n", - " simulator.set_sampling_rate(rate)\n", - " simulator.set_migration_probability(total_probability=0.005)\n", - " simulator.set_population_size(200000000)\n", - " time_1 = time.time()\n", - " simulator.simulate(iterations=size*300, sample_size=size)\n", - " time_2 = time.time()\n", - " simulator.genealogy(seed=2023)\n", - " time_3 = time.time()\n", - " \n", - " return [str(round(time_2 - time_1, 2)), str(round(time_3 - time_2, 2)), str(simulator.simulation.events.ptr), str(simulator.simulation.bCounter)]\n", - "\n", - "\n", - "sampling_rate = [0.01, 0.1, 1.0]\n", - "sample_size = [10**5, 10**6, 5*10**6, 10**7, 5*10**7, 1.5*10**8]\n", - "\n", - "with open('table.txt', 'w') as table:\n", - " table.write('\\\\begin{table}[ht]\\n')\n", - " table.write('\\t\\\\resizebox{\\\\linewidth}{!}{\\n')\n", - " table.write('\\t\\t\\\\begin{tabular}{|c||c|c|c|c|c|c|}\\n')\n", - " table.write('\\t\\t\\t\\\\hline\\n')\n", - " table.write('\\t\\t\\t\\t\\\\multirow{2}{*}{\\\\shortstack[c]{Cumulative\\\\\\\\migration\\\\\\\\probability $M$}} & \\\\multicolumn{6}{c|}{\\\\makecell{Number of demes $K$\\\\\\\\\\\\,}} \\\\\\\\\\n')\n", - " table.write('\\t\\t\\t\\\\cline{2-7}\\n')\n", - " table.write('\\t\\t\\t\\t& 2 & 5 & 10 & 20 & 50 & 100\\\\\\\\\\n')\n", - " table.write('\\t\\t\\t\\\\hline\\n')\n", - " for rate in samping_rates:\n", - " c = []\n", - " for size in sample_size:\n", - " c.append(get_data(number, rate))\n", - " table.write('\\t\\t\\t\\t'+str(rate)+' & '+c[0][0]+'s & '+c[1][0]+'s & '+c[2][0]+'s & '+c[3][0]+'s & '+c[4][0]+'s & '+c[5][0]+'s \\\\\\\\')\n", - " table.write('\\t\\t\\t\\t& '+c[0][1]+'\\\\% & '+c[1][1]+'\\\\% & '+c[2][1]+'\\\\% & '+c[3][1]+'\\\\% & '+c[4][1]+'\\\\% & '+c[5][1]+'\\\\% \\\\\\\\\\n')\n", - " table.write('\\t\\t\\t\\\\hline\\n')\n", - " table.write('\\t\\t\\\\end{tabular}\\n')\n", - " table.write('\\t}\\n')\n", - " table.write('\\\\end{table}\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e06d2c6e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/VGsim/data/VGsim and TiPS/test_TiPS.R b/VGsim/data/VGsim and TiPS/test_TiPS.R deleted file mode 100644 index bb3c647..0000000 --- a/VGsim/data/VGsim and TiPS/test_TiPS.R +++ /dev/null @@ -1,62 +0,0 @@ -library(TiPS) -library(ape) - -reactions <- c("S [beta*S*I] -> I", - "I [gamma*I] -> R") - -sir_simu <- build_simulator(reactions) - -N <- 10000000 - -initialStates <- c(I = 1, S = N - 1, R = 0) - -time_start <- 0 -time_finish <- 20 - -time <- c(time_start, time_finish) - -theta <- list(gamma = 1, beta = 2.5 / N) - -safe_run <- function(f, ...) { - out <- list() - while(! length(out)) {out <- f(...)} - out -} - -safe_sir_simu <- function(...) safe_run(sir_simu, ...) - -time1 <- Sys.time() - - -traj_dm <- safe_sir_simu( - paramValues = theta, - initialStates = initialStates, - method = "exact", - times = time) - -time2 <- Sys.time() -print("Forward time exact") -print(time2-time1) - - - -number_of_samples <- 10000 - -time1 <- Sys.time() - -sir_tree <- simulate_tree( - simuResults = traj_dm, - #dates = dates, - dates = seq(from = 12, to = 12, length.out = number_of_samples), - deme = c("I"), # the type of individuals that contribute to the phylogeny - sampled = c(I = 1), # the type of individuals that are sampled and their proportion of sampling - root = "I", # type of individual at the root of the tree - isFullTrajectory = FALSE, # deads do not generate leaves - nTrials = 1, - addInfos = FALSE) # additional info for each node - -time2 <- Sys.time() -print("Backward time") -print(time2-time1) - -#ape::plot.phylo(sir_tree, cex = .5) diff --git a/VGsim/data/VGsim and TiPS/test_VGsim.py b/VGsim/data/VGsim and TiPS/test_VGsim.py deleted file mode 100644 index 7d6b88c..0000000 --- a/VGsim/data/VGsim and TiPS/test_VGsim.py +++ /dev/null @@ -1,29 +0,0 @@ -import time -import VGsim - -number_of_sites = 0 -populations_number = 1 -number_of_susceptible_groups = 2 -simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, seed=12312) - -simulator.set_transmission_rate(0.25) -simulator.set_recovery_rate(0.099) -simulator.set_sampling_rate(0.001) -simulator.set_susceptibility_type(1, haplotype=None) - -simulator.set_population_size(1000000000, population=0) -t1 = time.time() - -simulator.simulate(100000000) - -all_time_forward = 0 -t2 = time.time() -all_time_forward += t2-t1 -print("Forward time is", all_time_forward) - -all_time_backward = 0 -t1 = time.time() -simulator.genealogy() -t2 = time.time() -all_time_backward += t2 - t1 -print("Backward time is", all_time_backward) \ No newline at end of file diff --git a/VGsim/data/model_example.py b/VGsim/data/model_example.py deleted file mode 100644 index dfacd90..0000000 --- a/VGsim/data/model_example.py +++ /dev/null @@ -1,39 +0,0 @@ -import VGsim -import random -import sys - -number_of_sites = 1 -populations_number = 2 -number_of_susceptible_groups = 3 -simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, - seed=random.randint(0, sys.maxsize)) - -simulator.set_transmission_rate(0.25) -simulator.set_transmission_rate(0.4, haplotype="G") - -mutation_rate = 0.003 -substitution_weights = [1, 1, 1, 1] # ATCG -simulator.set_mutation_rate(mutation_rate, substitution_weights) - -simulator.set_recovery_rate(0.09) -simulator.set_sampling_rate(0.01) - -simulator.set_susceptibility_type(0) # default -simulator.set_susceptibility_type(1, haplotype="A") -simulator.set_susceptibility_type(1, haplotype="T") -simulator.set_susceptibility_type(1, haplotype="C") -simulator.set_susceptibility_type(2, haplotype="G") - -simulator.set_susceptibility(1.0, susceptibility_type=0) # no resist -simulator.set_susceptibility(0.0, susceptibility_type=1, haplotype="A") # resist -simulator.set_susceptibility(0.0, susceptibility_type=1, haplotype="T") # resist -simulator.set_susceptibility(0.0, susceptibility_type=1, haplotype="C") # resist -simulator.set_susceptibility(0.2, susceptibility_type=1, haplotype="G") # partial resist -simulator.set_susceptibility(0.0, susceptibility_type=2) # resist - -simulator.set_immunity_transition(0.01, source=1, target=0) -simulator.set_immunity_transition(0.01, source=2, target=0) - -simulator.set_population_size(100000, population=0) -simulator.set_population_size(100000, population=1) -simulator.set_migration_probability(0.1) \ No newline at end of file diff --git a/VGsim/docs/Makefile b/VGsim/docs/Makefile deleted file mode 100644 index d0c3cbf..0000000 --- a/VGsim/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/VGsim/docs/make.bat b/VGsim/docs/make.bat deleted file mode 100644 index 6fcf05b..0000000 --- a/VGsim/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. 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zxM`hx9)R$b01ZCw3P{6G1O!H5BjD2-cU!R`VM${%7xE5ZwfU|zgG{_j(cTZG6BY>gB7XU>yeLdwCaUEWNH@tS3s&%{m)cVE-vx1I} zPW@Vb`gK$W=yH3o$|yo02Fe0~_iW7#h-<~aUufPIzNQv*ngFbF5P(Rf4bBRPFQecF z1qnEE_F#b}9C%(UesQ3sFm#P9=a;mwWRL+2^X#X<&8wLm3OA3&+BOV4RP4 z=T3o@Q7Kr{{@Pkb;z2lVuK#|yabRE*I9L3DfCrXP8^DL;6a@I|(FQrBVHgKuBz?V} zodhv;9TT`, `A->C`, `A->G` occur with probabilities 3/8, 4/8 and 1/8 respectively, and for the second site substitutions `T->A`, `T->C`, `T->G` occur with probabilities 0.4, 0.6 and 0.0 respectively). - -Setting population model ------------------------- - -In order to set population model use `--populationModel` or `-pm` flag followed by two files. The first file contains information abouch each population, and the second file contains the matrix of "visit" rates. - -**Part 1 - setting populations** - -Here is an example of the file with populations. - -.. code-block:: python - - #Population_format_version 0.0.1 - id size contactDensity conDenAfterLD startLD endLD samplingMultiplier - 0 20000000 1.0 0.1,2,1 5 - 1 10000000 1.0 0.1,2,1 1 - -`id` is the population number (for convinience). `size` is the total number of individuals in the population. Contact density in the relative number of contacts per time unit. It can be used to model different social, cultural, economical and other aspects (e.g. population density in a city or a country side, holiday times etc.) Currently we provide only one out-of-the-box solution to change contact density during simulation. Those are optinal fields (included in the example) to tune lockdowns. `conDenAfterLD` is the contact density during lockdown, `startLD` and `endLD` are condition to impose and lift the lockdown. These two numbers are the percentage of individuals which are simultaneously infected. If the percentage of simultanelously infected individuals in a population becomes larger than `startLD`, lockdown is imposed. As soon as the percentage of simultaneously infected individuals drops below `endLD` the lockdown is lifted. - -**Part 2 - setting migration matrix** - -In the model we consider migration as some individuals spending some time in other population and then returning to their home population. This is modelled by the probabilities that an individual from their home population is found in a destination population. During such visits individuals are assumed to contact with individuals of another population according to contact density of the destination population (so lockdown in a destination country also changes the contact rate of a visitor). Example: - -.. code-block:: python - - #Migration_format_version 0.0.1 - 0.0 0.002 0.002 0.001 - 0.0 0.0 0.002 0.0 - 0.0 0.005 0.0 0.001 - 0.0 0.002 0.001 0.0 - -Susceptibility types (immunity) -------------------------------- - -Multiple susceptibility classes can be modelled by adding `--susceptibility` or, `-su` flag followed by the file with susceptibility constants (non-negative `float`). Example: - -.. code-block:: python - - #Susceptibility_format_version 0.0.1 - H T S0 S1 S2 - AA 1 1.0 0.0 0.0 - AT 2 1.0 0.4 0.0 - AC 2 1.0 1.1 0.0 - -There are three susceptibility types `S0`, `S1` and `S2` in this example. All the individuals start in `S0`, and they have susceptibility of `1.0` to all strains. `T` is the type of susceptibility (immunity) caused by recovering from a particular strain. Inficting by haplotype `AA` leads to susceptibility type `S1`, which gives total resistance (susceptibility 0.0) to haplotype `AA`, partial resitance to `AT` (susceptibility 0.4) and increases susceptibility (1.1) to strain `AC`. - -*NB* There is no "immunity memory" - the immunity does not depend on the whole illness history of an individual, but only on the **latest** infection. - -**Susceptibility transition** - -The user can specify the rates of direct transitions between susceptibility types. This can be used for example to model vaccination or immunity loss. Use `--suscepTransition` or, `-st` flag followed by the file with susceptibility transition rate matrix (non-negative `float` entrys). Example: - -.. code-block:: python - - #Susceptibility_format_version 0.0.1 - 0.0 0.0 0.0001 - 0.001 0.0 0.0001 - 0.0 0.0 0.0 - -Flags ------ - -.. list-table:: - :widths: 15 15 70 - :header-rows: 1 - - * - Flag - - Parameter - - Meaning - * - -it or --iterations - - int - - maximal number of iterations (default is 1000) - * - -s or --sampleSize - - int - - maximal number of samples (default is None) - * - -t or --time - - float - - maximal simulated time for epidemics, do not confuse with the run time (default is None) - * - -seed or --seed - - float - - random seed - * - -pm or --populationModel - - two files - - population model: a file with population sizes etc, and a file with migration rate matrix - * - -su or --susceptibility - - file - - susceptibility file - * - -st or --suscepTransition - - file - - susceptibility transition file - * - --sampling_probability - - float [0.0, 1.0] - - the probability that an infection case is sampled - * - -nwk or --createNewick - - None - - Create a newick file of tree *.nwk - * - -tsv or --writeMutations - - None - - Create a mutation file *.tsv - * - --writeMigrations - - None - - Create a migration file *.txt - * - -c or -citation - - None - - Information for citation - -Output ------- - -The final genealogical tree can be exported into Newick format with `--createNewick` or `-nwk`, and mutations can be exported in a tsv file with `--writeMutations` or `-tsv`. This format is compatible with phastSim (https://github.com/NicolaDM/phastSim) and Usher (REF). diff --git a/VGsim/docs/source/Documentation.rst b/VGsim/docs/source/Documentation.rst deleted file mode 100644 index 9dd29d6..0000000 --- a/VGsim/docs/source/Documentation.rst +++ /dev/null @@ -1,84 +0,0 @@ -API -=== - -.. autoclass:: sim.Simulator - :members: - - .. automethod:: __init__ - -Epidemiological model ---------------------- - -.. autofunction:: Simulator.set_transmission_rate -.. note:: - In many methods skipping an argument (or setting it to None) allows filling in multiple entries. Methods will apply the settings to all possible values of this argument. -.. autofunction:: Simulator.set_recovery_rate -.. autofunction:: Simulator.set_sampling_rate -.. autofunction:: Simulator.set_mutation_rate -.. note:: - Haplotypes can be set either by their id (which can be calculated by assuming that the haplotype is a quaternary number with A=0, T=1, C=2, G=3), or directly by the string. E.g. haplotype “AGT” can be referred by the same string or by id=0*4\ :sup:`2`\+3*4\ :sup:`1`\+1*4\ :sup:`0`\=13. One can also use “*” to indicate “any nucleotide”. In this case the changes will be applied to all the haplotypes matching the pattern. E.g. “A*T” corresponds to four haplotypes “AAT”, “ATT”, “ACT”, “AGT”. `See for details `_. - -Host immunity -------------- - -.. autofunction:: Simulator.set_susceptibility_type -.. autofunction:: Simulator.set_susceptibility -.. autofunction:: Simulator.set_immunity_transition - -Population model ----------------- - -.. autofunction:: Simulator.set_population_size -.. autofunction:: Simulator.set_contact_density -.. autofunction:: Simulator.set_npi -.. autofunction:: Simulator.set_sampling_multiplier -.. autofunction:: Simulator.set_migration_probability -.. note:: - total_probability=float means that probability is the total probability to find an individual from population source outside of its population. All the entries (except for the diagonal) of the corresponding row of the migration probability matrix will be set to total_probability/(K-1), where K is the number of populations in the simulation. - -Simulate --------- - -.. autofunction:: Simulator.simulate -.. autofunction:: Simulator.genealogy -.. autofunction:: Simulator.set_chain_events - -Output ------- - -.. autofunction:: Simulator.output_newick -.. autofunction:: Simulator.output_mutations -.. autofunction:: Simulator.output_migrations -.. autofunction:: Simulator.output_sample_data -.. autofunction:: Simulator.output_epidemiology_timelines -.. autofunction:: Simulator.output_chain_events -.. autofunction:: Simulator.output_state - -Visualization -------------- - -.. autofunction:: Simulator.plot_infectious -.. autofunction:: Simulator.add_plot_infectious -.. autofunction:: Simulator.add_plot_susceptible -.. autofunction:: Simulator.add_legend -.. autofunction:: Simulator.add_title -.. autofunction:: Simulator.plot - -Data retrieval --------------- - -.. autofunction:: Simulator.get_data_susceptible -.. autofunction:: Simulator.get_data_infectious - -Parameters ----------- - -.. autofunction:: Simulator.print_basic_parameters -.. autofunction:: Simulator.print_populations -.. autofunction:: Simulator.print_immunity_model -.. autofunction:: Simulator.print_all - -Miscellaneous -------------- - -.. autofunction:: Simulator.citation \ No newline at end of file diff --git a/VGsim/docs/source/Haplotypes.rst b/VGsim/docs/source/Haplotypes.rst deleted file mode 100644 index de46e60..0000000 --- a/VGsim/docs/source/Haplotypes.rst +++ /dev/null @@ -1,40 +0,0 @@ -Haplotypes -========== - -One of the key features of VGsim are pathogen haplotypes (or strains). Haplotype determines haplotype properties: transmissibility, mutation rates, interaction with immunity etc. Currently VGsim is optimized for a relatively small number of haplotypes within a simulation. This number is equal to 4\ :sup:`U`\, where U is the number of genetic sites. Each site is assumed to have a single nucleotide with four possible alleles (A, T, C or G). - -.. note:: - If you have many haplotypes in your simulation, we encourage you to set *memory_optimisation* field in **Simulator** class to *True*. That might help with the memory performance for your simulation. - -Single nucleotide mutations lead to different haplotypes arising in the population. The rates of such mutations can be finely tuned: the user can set them independently for each site and each derived state of each haplotype. - -VGsim’s API provides a convenient tool to set values of different parameters to a particular haplotype or to groups of haplotypes. Every API function with a parameter ``haplotype`` supports the following parameter values. - -``haplotype`` default value is None: the setting will be applied to all possible haplotypes in the simulation. - -First possibility to refer a haplotype or a group of haplotypes is to use a string of length U (U is the number of genetic sites in the simulation) over five possible symbols ‘ATCG*’. Here * asterisk corresponds to any nucleotide. For example, ‘\*A\*’ corresponds to 16 different haplotypes ‘AAA’, ‘AAT’, ‘AAC’, ‘AAG’, ‘TAA’, ‘TAT’, ‘TAC’, ‘TAG’ etc. - -Another possibility is to use haplotype id, which is an integer. It can be obtained by converting a number in the quaternary numeral system (we assume the following bijection ATCG->0123) into the decimal system. In other words, haplotype CAG corresponds to a quaternary number 203. In decimal system it is 2*4\ :sup:`2`\+0^4\ :sup:`1`\+3*4\ :sup:`0`\=32+0+3=35. - -Example -------- - -Let us consider a handy way to set mutation rates. In this example we change the parameters for the mutation arising at the second site of four haplotypes AAA, ATA, ACA, AGA. The mutation rate is set to 2.5 for all cases. - -.. code-block:: python - - Simulator.set_mutation_rate(2.5, [2, 2, 3, 1], haplotype=”A*A”, site_id=1) - -The mutation rate and substitution weights will be the following: - -=========================== =========================== =========================== =========================== ========================== -Haplotype Rate Weights ---------------------------- --------------------------- ---------------------------------------------------------------------------------- -AAA 2.5 A\ **A**\ A->A\ **T**\ A: 2 A\ **A**\ A->A\ **C**\ A: 3 A\ **A**\ A->A\ **G**\ A:1 - -ATA 2.5 A\ **T**\ A->A\ **A**\ A: 2 A\ **T**\ A->A\ **C**\ A: 3 A\ **T**\ A->A\ **G**\ A:1 - -ACA 2.5 A\ **C**\ A->A\ **A**\ A: 2 A\ **C**\ A->A\ **T**\ A: 2 A\ **C**\ A->A\ **G**\ A:1 - -AGA 2.5 A\ **G**\ A->A\ **A**\ A: 2 A\ **G**\ A->A\ **T**\ A: 3 A\ **G**\ A->A\ **C**\ A:3 -=========================== =========================== =========================== =========================== ========================== diff --git a/VGsim/docs/source/Installation.rst b/VGsim/docs/source/Installation.rst deleted file mode 100644 index d3abbcf..0000000 --- a/VGsim/docs/source/Installation.rst +++ /dev/null @@ -1,49 +0,0 @@ -Building the package --------------------- - -For performance reasons, the package includes C extensions, which need to be -built. In the future, we plan to provide prebuilt packages, but for now you need -to build them yourself. For this, you need a working toolchain for building C++ -code (gcc and clang are known to work). Since you are going to build Python extensions, -you will need python development headers (e.g. on ubuntu linux the package name is `python-dev`). - -The simplest quick-start cross-platform way is to use `conda`. To do this, create a fresh conda environment: - -.. code-block:: bash - - $ conda create -n conda_vgsim - $ conda activate conda_vgsim - $ conda install python=3.9 # or other python version of your choice (any `python >= 3.7` should work). - -Note that we install python *with conda, inside the conda environment*. Mixing system-install python and conda may lead to build- or runtime errors. - -Get the source code --- here we clone it from GitHub - -.. code-block:: bash - - $ git clone https://github.com/Genomics-HSE/VGsim.git - $ cd VGsim - -Then build the package: - -.. code-block:: bash - - $ python -m pip install . - -That's it! -You may now run simulations: - -.. code-block:: bash - - $ python ./testing/example.py - -Running this script should create a directory ``testing/example_output`` with five files ``tree.nwk`` (genealogy tree), ``mutations.tsv`` (mutations on the genealogy), ``migrations.tsv`` (migrations of the lineages in the genealogy), ``sample_population.tsv`` (population of each sample), ``plot.png`` (visualization of epidemiological curves). There will be a warning in the output. This is an expected behaviour in this example (`see for detailed explanation `_ of inflated number of individuals in a deme due to migration). This example is also provided as a jupyter notebook ``testing/example.ipynb``. - -If you prefer to not use ``conda``, the package builds fine with just pip. We recommend using virtual environments, via the standard library ``venv`` package (or a third-party ``virtualenv`` package). For MacOS users: please make sure to use Python with Homebrew. Avoid system Python. Manually installed Python (e.g. downloaded from python.org) currently does not work with meson too. If there are still errors, try to install pkg-config with brew install. YMMV. -We tested this procedure on python 3.7-3.9 on Ubuntu linux and MacOS. -On Apple Silicon, you need to have ``numpy >= 1.21`` (which is the first NumPy -version to support this hardware). - -If you encounter problems with either of these steps, please file an issue at -``https://github.com/Genomics-HSE/VGsim``: please rerun with the ``-v`` flag, -``$ python -mpip install . -v`` and include the output. diff --git a/VGsim/docs/source/Migration.rst b/VGsim/docs/source/Migration.rst deleted file mode 100644 index e835638..0000000 --- a/VGsim/docs/source/Migration.rst +++ /dev/null @@ -1,49 +0,0 @@ -Migration ---------- - -The migrations are modelled as between-population transmissions: an infected individual from the source population `s` contacts a susceptible individual from the recipient population `r`. So, the individuals should contact each other while being in the same population. That can happen in three ways: an infected individual travels to population `r`, susceptible individual travels to population `s`, or both individuals travel to a third population `q` simultaneously. These travels are not modelled directly, but instead there is a matrix of probabilities to find an individual from one population into another. - -For example, if an individuals from population 0 spends on average 7 days per year in population 1 and 1 day per year in population 2, the corresponding matrix entries row will be - -.. code-block:: python - - Simulator.set_migration_probability(probability=7/365, source=0, target=1) - Simulator.set_migration_probability(probability=1/365, source=0, target=2) - -=========== =========== =========== -1-(7+1)/365 7/365 1/365 -=========== =========== =========== - -Assume that individuals from population 1 do not travel at all: - -= = = -0 1 0 -= = = - -And travelers from the last population 2 travel to population 0 and 1 symmetrically: - -.. code-block:: python - - Simualator.set_migration_probability(probability=1/365, source=2, target=0) - -======= ======= ======= -1/365 0 1-1/365 -======= ======= ======= - -The resulting migration matrix is - -===== ===== ===== -0.978 0.019 0.003 -0 1 0 -0.003 0 0.997 -===== ===== ===== - -Notice, that travels might considerably change the number of individuals which present in a deme at a given time. For example, assume that population sizes are `N0=1,000,000,000`, `N1=100,000,000` and `N2=1,000,000`, respectively. Then the expected number of individuals in each deme is - -* D0=357/365*10\ :sup:`9`\+1/365*10\ :sup:`6`\=978,084,932 (98% of N0) -* D1=7/365*10\ :sup:`9`\+10\ :sup:`8`\=119,178,082 (119% of N1) -* D2=1/365*10\ :sup:`9`\+364/365*10\ :sup:`6`\=3,736,986 (373% of N2) - -So, when the population sizes are considerably different, even small migration probabilities might lead to dramatic effects of the number of individuals in a deme. It might be unrealistic, and in most cases will probably be a sign to verify the simulation parameters. - -One can check the number of individuals in each deme and their changes relative to corresponding population sizes by the following command print_populations() and pay attention to the `Size` and `Actual size` collums. \ No newline at end of file diff --git a/VGsim/docs/source/Susceptible_group_sizes.png b/VGsim/docs/source/Susceptible_group_sizes.png deleted file mode 100644 index 6f35b7acfebf8c038a9a32dda937ae0f4cf04b36..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 33586 zcmeFZcTm;owk?X93YA*O0gMMbSc{#h3z6m3jJbvsP_ z;&}y!m!qBb4m1XNrIX#`Q5P<(zx$`eq4R&ZSx=GtJmRm>+t34Nrpq@uLnGT z`wd?_iv2vuUvKRB`%m(9hsgi^<^TSI*?ZWIG?nXf8h7T}W~N?#v~Q@(d7@w_WfL{s z({lZ~+Z(r9alGTZC?n&SYcuqH^d!~0SZofBgZuKNS6ile{8V3MWpS~HLB&&9|6@|z zdY`r#c|GeftPYiWLq%0qukrFA6;*GPw)=`AwZODrMF30mcxN8Vp+mx=j}O#(Zr{&s z-o`yM*52HDxId;f&8V`vnxj~|mEPg&I?Y@gaSIEJs?FOd5dzM$WoG@B?o{JuI0IYK z&vd)U3knJ@j%9WCj*OUQahyKg#Cx9iR;$@FFE6jO;`w@nzzTPr07ol z_#vaB^1N_$(KPSe)?KWASU_o^m5AM14(Cg}cPl?Ra;YK5+9-tA`jZmJi4$V(D>H#^ z%afu(SB~NTJSGida{YS3b>^+9m$vWY_KS{=zJ2EokNtQDtDBT_>jMFn=&K=hY1M-9 zM=m`qYi?G4^5jWdik_&ab4{e=ZbwH)V|+A|uKb+*f`Z0a`Qzfo56WJhzeBTU&k1{b zd&MLTRm-B4%x%SQ54y9=*-j7Eat2*#D{#(g%d$+yE5k(Z{`h)cy=AD5vIvtk?r zH8nLYhnB`%r+rvcf~s|W%er?@uCH4s^D2NtTWh2_xjD>zH7Idib8Bsyku;lTu7d00 zw5*ue{i`A8BDLKXF6G&c$t9{~Fo>HrCCFAjV^fS%V49ie)?T-6U0a4};4=>Gq}7!r zNy_Tt2nUZ@iwq84ZOv;D#q1k8uCuL1CY`w%f@{mx0+Szi+BJ{WNBg?Cy7D+pUmt5t z6SeODT>DzY%dEVlAy&RI%TgO_Hq@xB9l#L4sygM_-S;_&i>x16kjDr4hOyN0$yx=y z{ryomFzl5*7Cf`2s)o2Z*o^h&w$^!wc=_8WH>7Tmslo7GArW)rqxL z4w2R$-->DX?`PrQ;1F+JOBD5`OT5|hDONqpB2GOksVYn;`AhH_(~+h`le&m<_f-L^ zL#2DEWISB4Rh}ifr&xD+|3ybVcI-TUm78I&w(Cql;%KPz_$fM`rR&Yh25@R;sfU0uyIHJEMJKQsH;1xxA4pf}tQo0;0spwRQpi@xecNLiVf z2<4Q`piG`Mt9s^VKmGj4Py6lq%-b`0t{n4EvFt8n)swbgaXrXm^7L@I!)QwB=y1XO zcW=B>>EnZ{ZqfM3y%Zlt$!8ru@&Y-uyY6FCt=ty{RtCd;%?w&s5=|RobkeWae)nwN zUzxLP z&y>Q7)_APjz-hCB+eeAOR*!hmyU8rgj?vQ7C;K0l9_}soKW97qln_)gT+$hP;&aRfCTjIV*N9q0k{SBha`^+(%Q4(fSQc~Jd4J4yVmjhdpaZZ_Y za&zOH=f*QR(^?a{3Y?7`ui(^Nc8bG~|Tg+4EdS6ArndPoCR;~wz zcG7kX+F!N&rc&UP@%P_?-W)dlpZA)#rHdDPZkKm)$tS5IgwMWfYT?+`moY+X%ksBw-6H4m zGLEVH>OyIzJAxO*rkGtDOX07xXWuE^yAn@N=*;cIZbkUq>19!j)6W{~>x-0&lxTfK zo2?ir8CZ4l74F)^vk2DeC2ifJCR}a>#0y$xg|ei4-I9kvP4+i$%C@E&aJ>w-8LFF_ zoy}xPR?CPpUvsxznQ&>=v@bV1-TlLU$BrFJ3JOn=`2-dg7GjO-qj2J$b0BS|TMPP|Hai=j}`S zRVC`)s{X;2&d$!YpA8vVR@#;`2bzPqjVZ|6Z;}faCAwYbLKbI6jP{FKxvsZO#Qn}N zZ&yNYTpUfS?)`2x;HbJu>VU?nVEh6{M2_FRkD;~==^yN&qLYm&vg0_QmiiQp8bXx$>E zg{i&_yD>FJ28Nc5+oSDS6VIw9aS2%j1-p`H_2fyV(bCZKA6gtPK|zz9DMdl+e0;ZO_x^&tsIeT@}OJ7GtYI1>%9Nly@Gx^7hNV z!M;9&;NLD#>_%H&<7OB-dkS|P(0ofpbKSH#DI@q9kEWCRD)16kkA~@pkPr@5y|mp7 zkd=8>$cq;*UaLPUDk_o)WKTq95GOg4=D>l3Sovr}haXbDCEeZHkM{A@@lE9GN#0JP z-Mu^TwOLobqr}y#nda3&T%t@&Om=G`>e)BGZEMbd^7JW9!Otqb%rTq_@&{(wH+M~% zlR}10tufA;@|d?NAjLc37@8X>#UqJ*sj%z7@++H|#2Hq-G*%WgsGtFaX}6?wE;%KR z^gKVM=gH9d<5vIj!cPft@f4PLEb3>&w6P-Z&j|LLoCZSdxlNl`8xz&f6tCh=Nr#*> z$f~Zal%!=*%KtWVi%&L8&^6s{#feQVT@2fshvXkEO7F^DIVnF;Fc&&rxMb0Z$g|^d z`qwube-pmdgmjM09(-^-R3?B~@#)j25o80scXKEa>}oe=65ozDdqgj!1*9tvnC7-P zbX?t3ICp;DA%IO?3Bg|xE7pq$Gf}jrRkSi2jM&b=WG+1OlGnN+PLZWCK~)}CRWVjB zGTnL3Vr8aTTMYp+B7IXQ$@oZm@;FzDuP!5>B|gy9+G;C{|q+M1*U)yLqP#U{)t$ zfLgvoN~UFZ5>ROCc88*_#7}OCO^NF97M;2KxUc)oj85E(?%=C zD>)6m_L?4!PagjI{tmgicwat(SoWQ~=8;G%QPJgE&~$6VCW2Ci05}>EB4#E_=|_g5 z!<4Wdsr>yUKVdf-k~I0qV_-jP*H*gM%5_s>5PE{>tfzX#$$j3kdGiyXX2I2Yy}T7^ z{>i(*$uGyc&oMAu#0$v*0x2e_oHFmqZ#}Ka?ZNFuP2aSM-sM%X7j-;>Go>z91BY0G z0EJJF51J~kfm$r~`b%>oqZ{2-zrkz0g`KL*oJBc_n}nZmi2!ELz`($7{r!dt2`aUN z?ODk;zkl4Em%DlM=1)JXg)Hai=l67VbzMe6dDPOPk~gHb6IcS#dOx7{Nr2=>AMS1a z^=DkFkZ)~8%TwO9!29Y2z(4!B3_rh(PeUXOM*zGmEbQ@Zc-Z7All-Uat*MbIHbZji zA=Ac$28n_>&|R*5b7?z}SGaui)layDqgA_i?$iTT`0!(P_B2ClRPPs)eO=DuIi)T6 zj%fsFIXF1%>8lJpL++1#RYFwim#d?y54^p(LfsaPi`(1Ve*kD9cIz%rxUX?B?DgKo zmfGm;b%^(SPtPN4+diC;(;AwZkFXkhEemFzbt;s4J}Fo-*`pl&tux0ux;E2X zoxIu!@Rr(WY5E={rZdZc$Ul82JFKhs+IQLpe*5-KhlWMzvjdKb4-S7ZJ9rZ8VOy$9^_x|=q5<1$hiHglvJb7UM__Q zvA^0)rl|btFNlZ)ym%4U($W(AcAEzQwTPQ%ZXhi%^OU4J#2?;6666&p^tOrZP%UyRICA93Xaxbc>o)w3oT{t)rgKXM zvJqi->o;sLI9-2sBNdfjvr3{838SxHzqY#hO?I*9t8(SxQ>RWbtENV1s+Ln2ZC95U zw$)2T?LTAj@{`Zro|r$O5&4OnHgAgnd_K$P2rY zFZb-lLvdO!&)@u&g6Kn^xH25#Wq>Ym{OZfMEPOY=-$mM`^u3$~K(ey8&oW<{pBJOx zmPMu-RQ7AV#jj_x*#xdl%=cM|IXGm$2@fBu04@vv@#DtvOGKaQ6<|1|iu`(;#O$&5 zEXmy5hyCA*y%-~H!-hL@(h}XOLj`84h1Lf90=1irGanjWzaDe>@c|~L#5HXv&LiUQ-@jj+ZMV!Y{IahxMplFzA$-p0 zD5>CcjBa>|LmavX{rG!vm$q!%*6`%0=$;Mhsj?YkV`BjY?Ckak7Y+;$Gkcd%`rJaB zE$HvmtfML`-Bv7f<%$nBaAZ`*V|_8C2GCCMm8u<)!bhOR9io?%7KA#Y7$b9-?zu<% zwLPAo2{a&|;;i0&lH}f$MFYl~my1q*F9|ugL*`HfN7$eEzPtO{e!J2S?!|{}@bO}& zPtgP7Sy?d%zo)EPKD?=za|haN(E9c38`6#CR}ej|?XI`lA&C-%a=T;*YZJ9` z>#nnm$BcMg=HIHNUw?h!-j;G8CeU&vaQL+1)HK`saK9rsB2km{Q_Wk~P1GGq! 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Let’s also assume that having allele G at the first site increases the mutation rate at the second site by three times. - -.. code-block:: python - - mutation_rate=0.0003 - substitution_weights=[1,1,1,2]#ATCG - simulator.set_mutation_rate(mutation_rate, substitution_weights) - simulator.set_mutation_rate(3*mutation_rate, haplotype="G*", mutation=1) - -Susceptibility types --------------------- - -At the beginning of the epidemics all the individuals do not have any special immunity, and thus we assign them by default susceptibility type 0. Now let us assume that after recovery individuals get some immunity. If an individual was infected with haplotype carrying G at the first site (we will denote them by G*), the susceptibility type after recovery will be 2. For all other haplotypes it will be 1. - -.. code-block:: python - - simulator.set_susceptibility_type(1) - simulator.set_susceptibility_type(2, haplotype="G*") - -Now for each susceptibility type we will specify how it changes the risk of infection. We will assume that susceptibility type 1 gives strong (but not perfect) protection against all haplotypes except G* by reducing the risk of a new infection by ten times. And it gives only some protection against haplotypes G*. The immunity is lost on average after 90 days, and the individual is hence moved to the susceptibility type 0. - -.. code-block:: python - - simulator.set_susceptibility(0.1, susceptibility_type=1) - simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype="G*") - simulator.set_immunity_transition(1/90, source=1, target=0) - -Susceptibility type 2 will be a result of recovery from haplotype G* or vaccination. It gives complete protection from all the haplotypes, and it is lost after 180 days on average. At the beginning of the epidemics there is no vaccine, we will add it a bit later. - -.. code-block:: python - - simulator.set_susceptibility(0.0, susceptibility_type=2) - simulator.set_immunity_transition(1/180, source=2, target=0) - -Population model ----------------- - -First, we should define the population sizes. Let's create populations with sizes 10000000, 5000000 and 1000000 respectively. - -.. code-block:: python - - simulator.set_population_size(10000000, population=0) - simulator.set_population_size(5000000, population=1) - simulator.set_population_size(1000000, population=2) - -Now let us add some more heterogeneity. First of all assume that population 1 has a three times higher sampling rate than population 0, while population 2 does not sequence at all. - -.. code-block:: python - - simulator.set_sampling_multiplier(3, population=1) - simulator.set_sampling_multiplier(0, population=2) - -Now, all the countries impose lockdowns when 20% of its population is infected simultaneously. The lockdowns are lifted if this number drops to 2%. The amount of contacts is ten times less during the lockdown. - -.. code-block:: python - - simulator.set_npi([0.1, 0.2, 0.02]) - -Migration ---------- - -There are 3 populations in our scenario. Assume that an average individual from one population spends on average 10 days per year outside of its population of origin. The chance to travel to any destination is the same. The cumulative migration probability is 10/365. The probability to travel to a particular destination is 10 (days)/365 (days)/2 (the number of possible destinations). So, we can set - -.. code-block:: python - - simulator.set_migration_probability(10/365/2) - - - -Running the simulation ----------------------- - -Let us simulate the first 110 days. The first argument is the maximal number of iterations to be performed. Notice that the simulation will stop if this number is achieved even if 110 days (of virtual epidemics) did not pass. - -.. code-block:: python - - simulator.simulate(10000000, epidemic_time=110) - -Example of output - -.. image:: output.png - -After these 90 days, the vaccine was developed, and the susceptible individuals of types 0 and 1 can move to type 2 with the rate 0.05 (average waiting time of 20 days to get vaccinated). - -.. code-block:: python - - simulator.set_immunity_transition(0.05, source=0, target=1) - simulator.set_immunity_transition(0.05, source=0, target=2) - -Also, the rising awareness in the population reduces the contact density (e.g. because of masks) in populations 0 and 1. - -.. code-block:: python - - simulator.set_contact_density(0.7, population=0) - simulator.set_contact_density(0.7, population=1) - - -And the amount of travels is reduced with population 2 - -.. code-block:: python - - simulator.set_migration_probability(2/365/2, source=0, target=2) - simulator.set_migration_probability(2/365/2, source=1, target=2) - - -#TODO - -.. code-block:: python - - simulator.simulate(1000, method='tau') - -Visualizing epidemiological trajectories ----------------------------------------- - -Now let us plot how the haplotypes appear and spread in each population. - -.. code-block:: python - - simulator.add_plot_infectious(population=0, haplotype='AA', step_num=100) - simulator.add_plot_infectious(population=1, haplotype='AA', step_num=100) - simulator.add_plot_infectious(population=2, haplotype='AA', step_num=100) - simulator.add_title(name="AA haplotype") - simulator.add_legend() - simulator.plot() - -.. image:: AA_haplotype.png - -And finally let us look how the susceptible group sizes change. - -.. code-block:: python - - simulator.add_plot_susceptible(population=0, susceptibility_type=0, step_num=100) - simulator.add_plot_susceptible(population=0, susceptibility_type=1, step_num=100) - simulator.add_plot_susceptible(population=0, susceptibility_type=2, step_num=100) - simulator.add_title(name="Susceptible group sizes") - simulator.add_legend() - simulator.plot() - -.. image:: Susceptible_group_sizes.png - -Extracting the genealogy ------------------------- - -Finally, we extract the genealogy of the sampled cases. We write the genealogy and mutations on it into a file in MAT format. These files can be used as phastSim input to add neutral mutations. - -.. code-block:: python - - simulator.genealogy() - file_name = "example" - simulator.output_newick(file_name) - simulator.output_mutations(file_name) - -The user can also output the migrations of the genealogy lineages to have the detailed information about population structure. - -.. code-block:: python - - simulator.output_migrations(file_name) - -Print all the parameters ------------------------- -One can check the parametrization of the model by printing all the parameters. The basic parameters (transmission, recovery, sampling, mutation rates, substitution weights, susceptibility type after recovery) - -.. code-block:: python - - simulator.print_basic_parameters() - -Susceptibility matrix (how each immunity type affects susceptibility to each of the haplotypes) and the susceptibility type transition matrix - -.. code-block:: python - - simulator.print_immunity_model() - -Finally, let’s print population information (size, contact density, sampling modifier, lockdown settings) and migration matrix - -.. code-block:: python - - simulator.print_populations() - -Resulting code --------------- - -This code can be found `here `_. - -.. code-block:: python - - import VGsim - number_of_sites = 2 - populations_number = 3 - number_of_susceptible_groups = 3 - simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, seed=1234) - - simulator.set_transmission_rate(0.25) - simulator.set_recovery_rate(0.099) - simulator.set_sampling_rate(0.001) - simulator.set_transmission_rate(0.5, haplotype="GG") - mutation_rate=0.000003 - substitution_weights=[1,1,1,2]#ATCG - simulator.set_mutation_rate(mutation_rate, substitution_weights) - simulator.set_mutation_rate(3*mutation_rate, haplotype='G*', mutation=1) - - simulator.set_susceptibility_type(1) - simulator.set_susceptibility_type(2, haplotype='G*') - simulator.set_susceptibility(0.1, susceptibility_type=1) - simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype='G*') - simulator.set_immunity_transition(1/90, source=1, target=0) - simulator.set_susceptibility(0.0, susceptibility_type=2) - simulator.set_immunity_transition(1/180, source=2, target=0) - - simulator.set_population_size(10000000, population=0) - simulator.set_population_size(5000000, population=1) - simulator.set_population_size(1000000, population=2) - simulator.set_sampling_multiplier(3, population=1) - simulator.set_sampling_multiplier(0, population=2) - simulator.set_npi([0.1, 0.01, 0.002]) - simulator.set_migration_probability(10/365/2) - - simulator.simulate(10000000, epidemic_time=110) - - simulator.set_immunity_transition(0.05, source=0, target=1) - simulator.set_immunity_transition(0.05, source=0, target=2) - simulator.set_contact_density(0.7, population=0) - simulator.set_contact_density(0.7, population=1) - simulator.set_migration_probability(2/365/2, source=0, target=2) - simulator.set_migration_probability(2/365/2, source=1, target=2) - - simulator.simulate(1000, method='tau') - - simulator.add_plot_infectious(population=0, haplotype='AA', step_num=100) - simulator.add_plot_infectious(population=1, haplotype='AA', step_num=100) - simulator.add_plot_infectious(population=2, haplotype='AA', step_num=100) - simulator.add_title(name="AA haplotype") - simulator.add_legend() - simulator.plot() - - simulator.add_plot_susceptible(population=0, susceptibility_type=0, step_num=100) - simulator.add_plot_susceptible(population=0, susceptibility_type=1, step_num=100) - simulator.add_plot_susceptible(population=0, susceptibility_type=2, step_num=100) - simulator.add_title(name="Susceptible group sizes") - simulator.add_legend() - simulator.plot() - - simulator.genealogy() - file_name = "example" - simulator.output_newick(file_name) - simulator.output_mutations(file_name) - simulator.output_migrations(file_name) - - simulator.print_basic_parameters() - simulator.print_immunity_model() - simulator.print_populations() diff --git a/VGsim/docs/source/conf.py b/VGsim/docs/source/conf.py deleted file mode 100644 index ed6634f..0000000 --- a/VGsim/docs/source/conf.py +++ /dev/null @@ -1,57 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys -sys.path.insert(0, os.path.abspath('../..')) - - -# -- Project information ----------------------------------------------------- - -project = 'VGsim' -copyright = '2021, Test' -author = 'Test' - -# The full version, including alpha/beta/rc tags -release = '1.0.0' - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ -'sphinx.ext.napoleon', -'sphinx.ext.autodoc' -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'sphinx_rtd_theme' - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] diff --git a/VGsim/docs/source/index.rst b/VGsim/docs/source/index.rst deleted file mode 100644 index 94a1625..0000000 --- a/VGsim/docs/source/index.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. VGsim documentation master file, created by - sphinx-quickstart on Wed Sep 29 16:30:04 2021. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to VGsim's documentation! -================================= - -VGsim is the Python package for simulation of viral genealogies in the world-scale pandemic scenarios designed primarily for research purposes. Key VGsim features include - -* scalability and efficiency: optimized Guillespe algorithm over compartmental model generates epidemiological process; genealogy (transmission chain) is simulated only for sampled cases; -* pathogen evolution and epistasis (through single nucleotide substitutions): each haplotype properties (e.g. transmission rate, recovery rate, triggered host immunity) can be set independently; -* host immunity: different immunity types can have lower or higher susceptilibities to different haplotypes; -* population model: demic model with migration (migration is modelled as short-term travels); -* contact density (to reflect social, cultural and other population differences) and lockdowns. - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - modules diff --git a/VGsim/docs/source/modules.rst b/VGsim/docs/source/modules.rst deleted file mode 100644 index 8d8a3d0..0000000 --- a/VGsim/docs/source/modules.rst +++ /dev/null @@ -1,26 +0,0 @@ -Installation -============ - -.. toctree:: - :maxdepth: 1 - - Installation - -Tutorials and examples -====================== - -.. toctree:: - :maxdepth: 1 - - Tutorials and examples - Haplotypes - Migration - -Documentation -============= - -.. toctree:: - :maxdepth: 1 - - Documentation - Command line diff --git a/VGsim/docs/source/output.png b/VGsim/docs/source/output.png deleted file mode 100644 index 2e832068c933cb50e68933b381c612e220b4cc06..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 132657 zcmafa1ymf_)-~?#1ZyP0f(Q3NaQ6Vg-QC??f(Q2y+#$GoAh^2|-2E@+&6}6^zghG3 z>RNrfx~jVB-gD2{XP~Fz!DmCG#n8;ciG#Ac zlhKrZef_#R{{f2jJ5A26I@m|0ZY`XiWn4rW+5GTX_1p~Iy9w&AV4x+h8R6Ku;z=Nyzi5dxQofRQ1 zzq<*GLnt&A#sXtw4ow?eAaZa^q8eM z{Yi840D>k;B~eY10MZ8dGX9FK(PJdPen{rf$g9r5W*A{QVT!m-yTa$p{M(h!Nwg#v zx{w6QRFNU7o2ce4+{SkC5Q?Nn7{=Y*G-k-Q_@Cl|-89`2K z_Ch%zwjd6vGy=@habtt9P7buLDL)y^Eqe@7HXoNt0h}*cRdZzNgym;}(}ogrZxfyf zRk>DG*AB9IC`0qty~(j`lpc~rvKg76^$@yMpmR3yLRdjgiCYxHMHV912y;ySYznT; 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programming languages -project( - 'VGsim', - ['cpp', 'cython'], - default_options : ['buildtype=debugoptimized'], - version: '0.3', - license: 'GPL-3.0 License', - meson_version: '>= 0.54', -) - -# Special check for MacOS, cause of cpp version -if build_machine.system() == 'darwin' - add_global_arguments('-std=c++17', language : 'cpp') - message('found OS X, add special argument to compiler') -endif - -# Seek the backend -if meson.backend() != 'ninja' - error('Ninja backend required') -endif - -# Seek for Cython installation -cython = find_program('cython', required : true) -if not cython.found() - error('Cython not found.') -endif - - -# Python default installton path and basic dependecies -py_mod = import('python') -py3 = py_mod.find_installation() -py3_dep = py3.dependency() - -# Thx rgommers "https://github.com/rgommers/scipy/blob/meson/scipy/meson.build" -# NumPy include directory - needed in all submodules -incdir_numpy = run_command(py3, - ['-c', 'import os; os.chdir(".."); import numpy; print(numpy.get_include())'], - check : true -).stdout().strip() -# Declare numpy as dependency -inc_np = include_directories(incdir_numpy) - -numpy_nodepr_api = '-DNPY_NO_DEPRECATED_API=NPY_1_9_API_VERSION' - -subdir('src') diff --git a/VGsim/pyproject.toml b/VGsim/pyproject.toml deleted file mode 100644 index ad6e69b..0000000 --- a/VGsim/pyproject.toml +++ /dev/null @@ -1,38 +0,0 @@ -[build-system] -requires = [ - "wheel", - "meson>=0.60.3", - "mesonpep517", - "ninja", - "numpy>=1.19.5", - "Cython>=0.29.18", - "mc_lib @ git+http://github.com/ev-br/mc_lib.git@v0.4.1" -] - -build-backend = "mesonpep517.buildapi" - -[tool.mesonpep517.metadata] -author="Vladimir Shchur et al" -author-email="vshchur@hse.ru" -home-page="https://scg.hse.ru" -project-urls=[ - "Source, https://github.com/Genomics-HSE/VGsim", -] -summary = "VGsim is the fast viral genealogy simulator for world-wide pandemic scenarios." -requires-python="py3" -description-file="README.md" -platforms="any" -classifiers = [ - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Topic :: Software Development :: Libraries :: Python Modules", -] - -requires = [ - "numpy>=1.19.5", - "mc_lib @ git+http://github.com/ev-br/mc_lib.git@v0.4.1", - "prettytable", - "matplotlib", - "pytest", - "tskit" -] diff --git a/VGsim/sim.py b/VGsim/sim.py deleted file mode 100644 index f50d785..0000000 --- a/VGsim/sim.py +++ /dev/null @@ -1,26 +0,0 @@ -class Simulator(): - """ - The class which creates and runs simulations. - """ - - def __init__(number_of_sites=0, populations_number=1, number_of_susceptible_groups=1, seed=None, sampling_probability=False, memory_optimization=None): - """ - - :param number_of_sites: the number of mutable sites - :type number_of_sites: int - - :param populations_number: the number of populations (demes) - :type populations_number: int - - :param number_of_susceptible_groups: the number of susceptible groups (groups with different immunity response) - :type number_of_susceptible_groups: int - - :param seed: seed to generate simulation from. If **None**, then chosen at random - :type seed: float or None - - :param sampling_probability: whether we set sampling probability as a share of recovered individuals (**True** value) or we will set it explicitly. Default is **False**. - :type sampling_probability: bool or None - - :param memory_optimization: if True, then memory optimization is conducted (useful for large number of possible haplotypes) - :type memory_optimization: bool or None - """ diff --git a/VGsim/src/IO.py b/VGsim/src/IO.py deleted file mode 100644 index 4ca64c3..0000000 --- a/VGsim/src/IO.py +++ /dev/null @@ -1,255 +0,0 @@ -import sys -import math - -def read_rates(fn): - hapFilled = False - flag = False - bRate = [] - dRate = [] - sRate = [] - mRate = [] - with open(fn) as f: - line = next(f).rstrip().split(" ")#header with version etc - line = next(f).rstrip().split(" ") - if line[0] == "H": - hapFilled = True - shift = int(hapFilled) - if line[2+shift] == "SP": - flag = True - samProbability = int(flag) - dim = len(line) - shift - hapNum = int( 4**(dim - 3) ) - if dim < 3: - print("At least three rates (B, D, S) are expected") - sys.exit(1) - - for line in f: - if line[0] == "#": - next - line = line.rstrip().split(" ")[shift:] - bRate.append(float(line[0])) - if flag == 0: - dRate.append(float(line[1])) - sRate.append(float(line[2])) - else: - dRate.append(float(line[1]) * (1 - float(line[2]))) - sRate.append(float(line[1]) * float(line[2])) - - mRate.append( [] ) - mutations = line[3:] - for mut in mutations: - a = mut.split(',') - if len(a) == 1: - mRate[-1].append( [float(a[0]), 1.0/3.0, 1.0/3.0, 1.0/3.0] ) - elif len(a) == 4: - mRate[-1].append( [float(a[0]), float(a[1]), float(a[2]), float(a[3])] ) - else: - print("Error in mutations!!!") - sys.exit(1) - mRate = update_mRate(mRate) - return bRate, dRate, sRate, mRate - -def update_mRate(mRate): - if math.log(len(mRate), 4) != int(math.log(len(mRate), 4)): - print("Error!") - sys.exit(1) - - for i in range(len(mRate)): - for j in range(len(mRate[0])): - mRate[i][j].insert(calculate_allele(i, j, len(mRate[0]))+1, 0) - return mRate - -def calculate_allele(haplotype, site, sites): - for _ in range(sites-site): - allele = haplotype % 4 - haplotype = haplotype // 4 - return allele - -def read_susceptibility(fn): - hapFilled = False - susceptibility = [] - sType = [] - with open(fn) as f: - line = next(f).rstrip().split(" ")#header with version etc - line = next(f).rstrip().split(" ") - if line[0] == "H": - hapFilled = True - shift = int(hapFilled) - - for line in f: - if line[0] == "#": - next - line = line.rstrip().split(" ")[shift:] - susceptibility.append( line[1:] ) - sType.append( int( line[0] ) ) - return susceptibility, sType - -def read_populations(fn): - sizes = [] - contactDensity = [] - contactAfter = [] - startLD = [] - endLD = [] - samplingMultiplier = [] - with open(fn) as f: - line = next(f).rstrip().split(" ")#header with version etc - line = next(f).rstrip().split(" ") - - for line in f: - if line[0] == "#": - next - line = line.rstrip().split(" ") - sizes.append(int(line[1])) - contactDensity.append(float(line[2])) - if len(line) == 4: - part_line = line[3].split(",") - if len(part_line) == 1: - contactAfter.append(0) - startLD.append(1.0) - endLD.append(1.0) - samplingMultiplier.append(float(part_line[0])) - elif len(part_line) == 3: - contactAfter.append(float(part_line[0])) - startLD.append(float(part_line[1])) - endLD.append(float(part_line[2])) - samplingMultiplier.append(1) - elif len(line) == 5: - part_line1 = line[3].split(",") - part_line2 = line[4].split(",") - if len(part_line1) == 1: - samplingMultiplier.append(float(part_line1[0])) - contactAfter.append(float(part_line2[0])) - startLD.append(float(part_line2[1])) - endLD.append(float(part_line2[2])) - elif len(part_line1) == 3: - samplingMultiplier.append(float(part_line2[0])) - contactAfter.append(float(part_line1[0])) - startLD.append(float(part_line1[1])) - endLD.append(float(part_line1[2])) - return sizes, contactDensity, contactAfter, startLD, endLD, samplingMultiplier - -def read_matrix(fn): - with open(fn) as f: - line = next(f).rstrip()#header with version etc - line = line.split(" ") - matrix = [] - for line in f: - if line[0] == "#": - next - line = line.rstrip() - line = line.split(" ") - matrix.append( [float(v) for v in line] ) - return matrix - -def writeMutations(mut, len_prufer, name_file, file_path): - #digits replacement - alleles = ["A","T","C","G"] - for i in [1,3]: - for j in range(len(mut[i])): - mut[i][j] = alleles[mut[i][j]] - - mutations_dict = {} - for nodeId in mut[0]: - if nodeId in mutations_dict: #adding mutation for existing node - mutations_dict[nodeId] += str(mut[1][mut[0].index(nodeId)]) \ - + str(mut[2][mut[0].index(nodeId)]) \ - + str(mut[3][mut[0].index(nodeId)])+',' - else: - mutations_dict[nodeId] = str(mut[1][mut[0].index(nodeId)]) \ - + str(mut[2][mut[0].index(nodeId)]) \ - + str(mut[3][mut[0].index(nodeId)])+',' - #removing extra comma - for nodeId in mutations_dict: - mutations_dict[nodeId] = mutations_dict[nodeId][:-1] - - if file_path != None: - f_mut = open(file_path + '/' + name_file + ".tsv", 'w') - else: - f_mut = open(name_file + ".tsv", 'w') - - for i in range(len_prufer): - if i in mutations_dict: - f_mut.write(str(i)+'\t'+str(mutations_dict[i])+'\n') - f_mut.close() - -class Vertex(): - def __init__(self, root, root_time, children, populations): - self.__children = children - self.__root = root - self.__root_time = root_time - self.__root_population_id = populations[self.__root_time] - left_node = self.__children[root][0][0] - left_time = self.__children[root][0][1] - right_node = self.__children[root][1][0] - right_time = self.__children[root][1][1] - - if left_node in self.__children: - self.__left_child = Vertex(left_node, left_time, self.__children, populations) - else: - self.__left_child = Leaf(left_node, left_time, populations) - - if right_node in self.__children: - self.__right_child = Vertex(right_node, right_time, self.__children, populations) - else: - self.__right_child = Leaf(right_node, right_time, populations) - - def get_children(self, base_time): - return '({0},{1}){2}:{3}'.format(self.__left_child.get_children(self.__root_time), self.__right_child.get_children(self.__root_time), self.__root, self.__root_time - base_time) - - def write_population(self): - return '{0}\t{1}\n'.format(self.__root, self.__root_population_id) + self.__left_child.write_population() + self.__right_child.write_population() - -class Leaf(Vertex): - def __init__(self, leaf, times, populations): - self.__leaf = leaf - self.__times = times - self.__leaf_population_id = populations[times] - - def get_children(self, base_time): - return '{0}:{1}'.format(self.__leaf, self.__times - base_time) - - def write_population(self): - return '{0}\t{1}\n'.format(self.__leaf, self.__leaf_population_id) - -#find list with childrens -def find_children(pruferSeq, times): - children = {} - for index in range(len(pruferSeq)): - add_list = [] - add_list = [index, times[index]] - if pruferSeq[index] in children: - children[pruferSeq[index]].append(add_list) - else: - children[pruferSeq[index]] = [] - children[pruferSeq[index]].append(add_list) - return children - -def get_last(output_string): - try: - return output_string[-1] - except: - return "notDigit" - -def writeGenomeNewick(pruferSeq, times, populations, name_file, file_path): - children = find_children(pruferSeq, times) - root = children[-1][0][0] - root_time = children[-1][0][1] - - result = Vertex(root, root_time, children, populations) - - if file_path != None: - f_nwk = open(file_path + '/' + name_file + '_tree.nwk', 'w') - f_pop = open(file_path + '/' + name_file + '_sample_population.tsv', 'w') - elif name_file != None: - f_nwk = open(name_file + '_tree.nwk', 'w') - f_pop = open(name_file + '_sample_population.tsv', 'w') - else: - f_nwk = open('tree.nwk', 'w') - f_pop = open('sample_population.tsv', 'w') - - f_nwk.write(result.get_children(root_time)) - f_nwk.write(';') - f_pop.write(result.write_population()) - - f_nwk.close() - f_pop.close() diff --git a/VGsim/src/_BirthDeath.pyx b/VGsim/src/_BirthDeath.pyx deleted file mode 100644 index f4590ef..0000000 --- a/VGsim/src/_BirthDeath.pyx +++ /dev/null @@ -1,2613 +0,0 @@ -# cython: language_level=3 -# cython: initializedcheck = False -# distutils: language = c++ - -cimport cython - -from numpy cimport npy_int64 - -from libc.math cimport log, floor, abs -from libcpp.vector cimport vector -from mc_lib.rndm cimport RndmWrapper - -from prettytable import PrettyTable -import numpy as np -cimport numpy as np -import sys -import os -import time -import tskit - -from numpy.random.c_distributions cimport random_poisson, random_hypergeometric - -include "fast_choose.pxi" -include "models.pxi" -include "events.pxi" - -#pi - population ID, pn - popoulation number, spi - source population ID, tpi - target population ID -#hi - haplotype ID, hn - haplotype number, nhi - new haplotype number -#si - susceptibility ID, sn - susceptibility number, ssi - source susceptibility ID, tsi - target susceptibility ID -cdef class BirthDeathModel: - cdef: - RndmWrapper seed - - bint first_simulation, sampling_probability, memory_optimization - Py_ssize_t user_seed, sites, hapNum, currentHapNum, maxHapNum, addMemoryNum, popNum, susNum, bCounter, dCounter, sCounter, mCounter, iCounter, swapLockdown, migPlus, migNonPlus, globalInfectious, countsPerStep, good_attempt, cCounter, conditionNum - double currentTime, totalRate, totalMigrationRate, rn, tau_l - - Events events - multiEvents multievents - Mutations mut - Migrations mig - Lockdowns loc - - npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop - npy_int64[:,::1] susceptible, initial_susceptible, infectious, - npy_int64[:,:,::1] infectByCondition, initial_infectious - - double[::1] maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times - double[::1] conditionPopRate, cumuConditionRate - double[:,::1] susceptibility, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration - double[:,::1] conditionRate,massivForThree, conditionPopHapRate, bRate, dRate, sRate, tmRate - double[:,:,::1] hapMutType, susceptHapPopRate, tEventHapPopRate, mRate - double[:,:,::1] conditionPopHapConRate, tEventConHapPopRate - double[:,:,:,::1] eventHapPopRate - - double[:,:,:,::1] PropensitiesMigr, PropensitiesMutatations - double[:,:,::1] PropensitiesSuscep, PropensitiesTransmission - double[:,::1] PropensitiesRecovery, PropensitiesSampling - - npy_int64[:,:,:,::1] eventsMigr, eventsMutatations - npy_int64[:,:,::1] eventsSuscep, eventsTransmission - npy_int64[:,::1] eventsRecovery, eventsSampling - - double[:,::1] infectiousAuxTau, susceptibleAuxTau - npy_int64[:,::1] infectiousDelta, susceptibleDelta - - - def __init__(self, number_of_sites, populations_number, number_of_susceptible_groups, seed, sampling_probability, memory_optimization, conditionNum): - self.user_seed = seed - self.seed = RndmWrapper(seed=(self.user_seed, 0)) - - self.first_simulation = False - self.sampling_probability = sampling_probability - - self.sites = number_of_sites - self.hapNum = 4**self.sites - self.susNum = number_of_susceptible_groups - self.popNum = populations_number - - if memory_optimization: - self.memory_optimization = True - if self.sites > 2: - self.maxHapNum = 4**(self.sites-2) - self.addMemoryNum = 4**(self.sites-2) - else: - self.maxHapNum = 4 - self.addMemoryNum = 4 - else: - self.memory_optimization = False - self.maxHapNum = self.hapNum - self.addMemoryNum = 0 - - self.hapToNum = np.zeros(self.hapNum, dtype=np.int64) # from haplotype to program number - self.numToHap = np.zeros(self.maxHapNum, dtype=np.int64) # from program number to haplotype - - if self.memory_optimization: - self.currentHapNum = 0 - else: - self.currentHapNum = self.hapNum - for hn in range(self.hapNum): - self.hapToNum[hn] = hn - self.numToHap[hn] = hn - - self.bCounter = 0 - self.dCounter = 0 - self.sCounter = 0 - self.mCounter = 0 - self.iCounter = 0 - self.cCounter = 0 - self.swapLockdown = 0 - self.migPlus = 0 - self.migNonPlus = 0 - self.globalInfectious = 0 - - self.currentTime = 0.0 - self.tau_l=0.01 - self.totalRate = 0.0 - self.totalMigrationRate = 0.0 - - self.events = Events() - self.multievents = multiEvents() - self.mut = Mutations() - self.mig = Migrations() - self.loc = Lockdowns() - - # memory_optimization - self.infectious = np.zeros((self.popNum, self.maxHapNum), dtype=np.int64) - self.tmRate = np.zeros((self.maxHapNum, self.conditionNum), dtype=float) - self.massivForThree = np.zeros((self.popNum, 3), dtype=float) - self.conditionPopRate = np.zeros(self.popNum, dtype=float) - self.tEventHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) - self.hapPopRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) - self.eventHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum, 4), dtype=float) - self.susceptHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.susNum), dtype=float) - - self.suscType = np.zeros(self.hapNum, dtype=np.int64) - self.bRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) - self.dRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) - self.sRate = np.zeros(self.hapNum, self.conditionNum, dtype=float) - self.mRate = np.zeros((self.hapNum, self.conditionNum, self.sites), dtype=float) - self.conditionRate = np.zeros((self.conditionNum, self.conditionNum), dtype=float) - self.cumuConditionRate = np.zeros(self.conditionNum, dtype=float) - self.conditionPopHapRate = np.zeros((self.popNum, self.maxHapNum), dtype=float) - self.conditionPopHapConRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum),dtype=float) - self.infectByCondition = np.zeros((self.popNum, self.maxHapNum, self.conditionNum),dtype=np.int64) - self.tEventConHapPopRate = np.zeros((self.popNum, self.maxHapNum, self.conditionNum), dtype=float) - self.susceptibility = np.zeros((self.hapNum, self.susNum), dtype=float) - self.hapMutType = np.ones((self.hapNum, self.sites, 3), dtype=float) - - for hn in range(self.hapNum): - for cn in range(self.conditionNum): - self.bRate[hn, cn] = 2.0 - self.dRate[hn, cn] = 1.0 - self.sRate[hn, cn] = 0.01 - for s in range(self.sites): - self.mRate[hn, cn, s] = 0.01 - self.susceptibility[hn, 0] = 1.0 - - for cn1 in range(self.conditionNum): - for cn2 in range(self.conditionNum): - if cn1 != cn2: - self.conditionRate[cn1, cn2] = 2 - else: - self.conditionRate[cn1, cn2] = 0 - - for cn1 in range(self.conditionNum): - self.cumuConditionRate[cn1] = 0 - for cn2 in range(self.conditionNum): - self.cumuConditionRate[cn1] += self.conditionRate[cn1, cn2] - - - self.sizes = np.zeros(self.popNum, dtype=np.int64) - self.totalSusceptible = np.zeros(self.popNum, dtype=np.int64) - self.totalInfectious = np.zeros(self.popNum, dtype=np.int64) - self.lockdownON = np.zeros(self.popNum, dtype=np.int64) - - self.susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) - self.initial_susceptible = np.zeros((self.popNum, self.susNum), dtype=np.int64) - self.initial_infectious = np.zeros((self.popNum, self.hapNum, self.conditionNum), dtype=np.int64) - - self.maxEffectiveBirthMigration = np.zeros(self.popNum, dtype=float) - self.suscepCumulTransition = np.zeros(self.susNum, dtype=float) - self.infectPopRate = np.zeros(self.popNum, dtype=float) - self.immunePopRate = np.zeros(self.popNum, dtype=float) - self.popRate = np.zeros(self.popNum, dtype=float) - self.migPopRate = np.zeros(self.popNum, dtype=float) - self.actualSizes = np.zeros(self.popNum, dtype=float) - self.contactDensity = np.ones(self.popNum, dtype=float) - self.contactDensityBeforeLockdown = np.ones(self.popNum, dtype=float) - self.contactDensityAfterLockdown = np.zeros(self.popNum, dtype=float) - self.startLD = np.ones(self.popNum, dtype=float) - self.endLD = np.ones(self.popNum, dtype=float) - self.samplingMultiplier = np.ones(self.popNum, dtype=float) - - self.suscepTransition = np.zeros( (self.susNum, self.susNum), dtype=float) - self.immuneSourcePopRate = np.zeros((self.popNum, self.susNum), dtype=float) - self.migrationRates = np.zeros((self.popNum, self.popNum), dtype=float) - self.effectiveMigration = np.zeros((self.popNum, self.popNum), dtype=float) - - for pn in range(self.popNum): - self.sizes[pn] = 1000000 - self.totalSusceptible[pn] = 1000000 - self.susceptible[pn, 0] = 1000000 - - self.tree = np.zeros(1, dtype=np.int64) - self.tree_pop = np.zeros(1, dtype=np.int64) - - #Init propensities - self.PropensitiesMigr = np.zeros((self.popNum, self.popNum, self.susNum, self.hapNum), dtype=float) - self.PropensitiesSuscep = np.zeros((self.popNum, self.susNum, self.susNum), dtype=float) - self.PropensitiesRecovery = np.zeros((self.popNum, self.hapNum), dtype=float) - self.PropensitiesSampling = np.zeros((self.popNum, self.hapNum), dtype=float) - self.PropensitiesMutatations = np.zeros((self.popNum, self.hapNum, self.sites, 3), dtype=float) - self.PropensitiesTransmission = np.zeros((self.popNum, self.hapNum, self.susNum), dtype=float) - - #Sampled number of events per step - self.eventsMigr = np.zeros((self.popNum, self.popNum, self.susNum, self.hapNum), dtype=np.int64) - self.eventsSuscep = np.zeros((self.popNum, self.susNum, self.susNum), dtype=np.int64) - self.eventsRecovery = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - self.eventsSampling = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - self.eventsMutatations = np.zeros((self.popNum, self.hapNum, self.sites, 3), dtype=np.int64) - self.eventsTransmission = np.zeros((self.popNum, self.hapNum, self.susNum), dtype=np.int64) - - self.infectiousAuxTau = np.zeros((self.popNum, self.hapNum), dtype=float) - self.susceptibleAuxTau = np.zeros((self.popNum, self.susNum), dtype=float) - - self.infectiousDelta = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - self.susceptibleDelta = np.zeros((self.popNum, self.susNum), dtype=np.int64) - - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void FirstInfection(self): - if self.globalInfectious == 0: - for sn in range(self.susNum): - if self.susceptible[0, sn] != 0: - if self.memory_optimization: - self.AddHaplotype(0, 0) - self.NewInfections(0, sn, 0, 0) - return - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef inline void NewInfections(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t ci, Py_ssize_t num=1): - self.susceptible[pi, si] -= num - self.totalSusceptible[pi] -= num - self.infectious[pi, hi] += num - self.infectByCondition[pi, hi, ci] += num - self.totalInfectious[pi] += num - self.globalInfectious += num - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef inline void NewRecoveries(self, Py_ssize_t pi, Py_ssize_t si, Py_ssize_t hi, Py_ssize_t ci, Py_ssize_t num=1): - self.susceptible[pi, si] += num - self.totalSusceptible[pi] += num - self.infectious[pi, hi] -= num - self.infectByCondition[pi, hi, ci] -= num - self.totalInfectious[pi] -= num - self.globalInfectious -= num - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void AddMemory(self): - if self.addMemoryNum + self.maxHapNum > self.hapNum: - self.addMemoryNum = self.hapNum - self.maxHapNum - self.numToHap = np.concatenate((self.numToHap, np.zeros(self.addMemoryNum, dtype=np.int64))) - self.infectious = np.concatenate((self.infectious, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=np.int64)), axis=1) - self.tmRate = np.concatenate((self.tmRate, np.zeros(self.addMemoryNum, self.conditionNum, dtype=float))) - self.tEventHapPopRate = np.concatenate((self.tEventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) - self.hapPopRate = np.concatenate((self.hapPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) - self.eventHapPopRate = np.concatenate((self.eventHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum ,4), dtype=float)), axis=1) - self.susceptHapPopRate = np.concatenate((self.susceptHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.susNum), dtype=float)), axis=1) - self.conditionPopHapRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum), dtype=float)), axis=1) - self.conditionPopHapConRate = np.concatenate((self.conditionPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)), axis=1) - self.infectByCondition = np.concatenate((self.infectByCondition, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum),dtype=np.int64)), axis=1) - self.tEventConHapPopRate = np.concatenate((self.tEventConHapPopRate, np.zeros((self.popNum, self.addMemoryNum, self.conditionNum), dtype=float)),axis=1) - self.maxHapNum += self.addMemoryNum - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void UpdateAllRates(self): - cdef: - double maxEffectiveBirth - double[::1] maxEffectiveMigration - - for sn1 in range(self.susNum): - self.suscepCumulTransition[sn1] = 0 - for sn2 in range(self.susNum): - self.suscepCumulTransition[sn1] += self.suscepTransition[sn1, sn2] - - for pn1 in range(self.popNum): - self.migrationRates[pn1, pn1] = 1.0 - self.actualSizes[pn1] = 0.0 - for pn2 in range(self.popNum): - if pn1 == pn2: - continue - self.migrationRates[pn1, pn1] -= self.migrationRates[pn1, pn2] - self.actualSizes[pn1] += self.migrationRates[pn2, pn1]*self.sizes[pn2] - self.actualSizes[pn1] += self.migrationRates[pn1, pn1]*self.sizes[pn1] - - self.totalRate = 0.0 - for pn in range(self.popNum): - self.infectPopRate[pn] = 0 - self.immunePopRate[pn] = 0 - self.popRate[pn] = 0. - for pn in range(self.popNum): - for hn in range(self.currentHapNum): # hn - program number - for cn in range(self.conditionNum): - self.tmRate[hn, cn] = 0 - - - for cn in range(self.conditionNum): - for s in range(self.sites): - self.tmRate[hn, cn] += self.mRate[self.numToHap[hn], cn, s] - self.eventHapPopRate[pn, hn, cn, 0] = self.BirthRate(pn, hn, cn) - self.eventHapPopRate[pn, hn, cn, 1] = self.dRate[self.numToHap[hn], cn] - self.eventHapPopRate[pn, hn, cn, 2] = self.sRate[self.numToHap[hn], cn]*self.samplingMultiplier[pn] - self.eventHapPopRate[pn, hn, cn, 3] = self.tmRate[hn, cn] - self.tEventHapPopRate[pn, hn, cn] = 0 - for i in range(4): - self.tEventHapPopRate[pn, hn, cn] += self.eventHapPopRate[pn, hn, cn, i] - self.tEventConHapPopRate[pn, hn, cn] = self.tEventHapPopRate[pn, hn, cn] * self.infectByCondition[pn, hn, cn] - self.hapPopRate[pn, hn] += self.tEventConHapPopRate[pn, hn, cn] - self.infectPopRate[pn] += self.hapPopRate[pn, hn] - for sn in range(self.susNum): - self.immuneSourcePopRate[pn, sn] = self.suscepCumulTransition[sn]*self.susceptible[pn, sn] - self.immunePopRate[pn] += self.immuneSourcePopRate[pn, sn] - for hn in range(self.currentHapNum): - for cn in range(self.conditionNum): - self.conditionPopHapConRate[pn, hn, cn] = self.cumuConditionRate[cn] * self.infectByCondition[pn, hn, cn] - self.conditionPopHapRate[pn, hn] += self.conditionPopHapConRate[pn, hn, cn] - self.conditionPopRate[pn] += self.conditionPopHapRate[pn, hn] - - - self.popRate[pn] = self.infectPopRate[pn] + self.immunePopRate[pn] + self.conditionPopRate[pn] - self.massivForThree[0] = self.infectPopRate[pn] - self.massivForThree[1] = self.immunePopRate[pn] - self.conditionPopRate[2] = self.conditionPopRate[pn] - self.totalRate += self.popRate[pn] - - maxEffectiveMigration = np.zeros(self.popNum, dtype=float) - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - if pn1 == pn2: - continue - self.effectiveMigration[pn1, pn2] = 0.0 - for pn3 in range(self.popNum): - self.effectiveMigration[pn1, pn2] += self.migrationRates[pn1, pn3]*self.migrationRates[pn2, pn3]*self.contactDensity[pn3]/self.actualSizes[pn3] - if self.effectiveMigration[pn1, pn2] > maxEffectiveMigration[pn2]: - maxEffectiveMigration[pn2] = self.effectiveMigration[pn1, pn2] - - maxEffectiveBirth = 0.0 - for hn in range(self.currentHapNum): - for cn in range(self.conditionNum): - for sn in range(self.susNum): - if self.bRate[self.numToHap[hn], cn]*self.susceptibility[self.numToHap[hn], sn] > maxEffectiveBirth: - maxEffectiveBirth = self.bRate[self.numToHap[hn], cn]*self.susceptibility[self.numToHap[hn], sn] - - self.totalMigrationRate = 0.0 - for pn in range(self.popNum): - self.maxEffectiveBirthMigration[pn] = maxEffectiveMigration[pn]*maxEffectiveBirth - self.migPopRate[pn] = self.maxEffectiveBirthMigration[pn]*self.totalSusceptible[pn]*(self.globalInfectious-self.totalInfectious[pn]) - self.totalMigrationRate += self.migPopRate[pn] - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void AddHaplotype(self, Py_ssize_t nhi, Py_ssize_t pi): # nhi - haplotype - cdef: - bint check = False - Py_ssize_t mem_h, mem_inf - - if self.currentHapNum == self.maxHapNum and self.maxHapNum < self.hapNum: - self.AddMemory() - - for i in range(1, self.currentHapNum+1): - if check: - self.hapToNum[mem_h] += 1 - self.infectious[pi, i], mem_inf = mem_inf, self.infectious[pi, i] - self.numToHap[i], mem_h = mem_h, self.numToHap[i] - if (self.numToHap[i] > nhi or self.numToHap[i] == 0) and self.numToHap[i-1] < nhi: - mem_h = self.numToHap[i] - check = True - self.numToHap[i] = nhi - mem_inf = self.infectious[pi, i] - self.infectious[pi, i] = 0 - self.hapToNum[nhi] = i - - self.currentHapNum += 1 - self.UpdateAllRates() - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef inline double BirthRate(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number - cdef double ps = 0.0 - - for sn in range(self.susNum): - self.susceptHapPopRate[pi, hi, sn] = self.susceptible[pi, sn]*self.susceptibility[self.numToHap[hi], sn] - for pn in range(self.popNum): - ps += self.susceptHapPopRate[pi, hi, sn]*self.migrationRates[pi, pn]*self.migrationRates[pi, pn]*self.contactDensity[pn]/self.actualSizes[pn] - - for cn in range(self.conditionNum): - self.bRate[self.numToHap[hi], cn] = self.bRate[self.numToHap[hi], cn] * ps - cdef double hi_ps = 0.0 - for cn in range(self.conditionNum): - hi_ps += self.bRate[self.numToHap[hi], cn] - return hi_ps - - @cython.boundscheck(False) - @cython.wraparound(False) - cpdef void SimulatePopulation(self, Py_ssize_t iterations, Py_ssize_t sample_size, float time, Py_ssize_t attempts): - cdef Py_ssize_t pi - - self.PrepareParameters(iterations) - self.CheckSizes() - - for i in range(attempts): - self.seed = RndmWrapper(seed=(self.user_seed, i)) - if self.totalRate+self.totalMigrationRate != 0.0 and self.globalInfectious != 0: - while (self.events.ptr 100: - self.Restart() - else: - self.good_attempt = i+1 - break - - if self.totalRate == 0.0 or self.globalInfectious == 0: - print('Simulation finished because no infections individuals remain!') - if self.events.ptr>=self.events.size: - print("Achieved maximal number of iterations.") - if self.sCounter>sample_size and sample_size!=-1: - print("Achieved sample size.") - if self.currentTime>time and time != -1: - print("Achieved internal time limit.") - if self.sCounter <= 1: - print('\033[41m{}\033[0m'.format('WARNING!'), 'Simulated less 2 samples, so genealogy will not work!') - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void PrepareParameters(self, Py_ssize_t iterations): - self.events.CreateEvents(iterations) - if self.first_simulation == False: - self.FirstInfection() - for pn in range(self.popNum): - for sn in range(self.susNum): - self.initial_susceptible[pn, sn] = self.susceptible[pn, sn] - for hn in range(self.hapNum): - for cn in range(self.conditionNum): - self.initial_infectious[pn, hn, cn] = self.infectByCondition[pn, hn, cn] - self.first_simulation = True - for pn in range(self.popNum): - self.CheckLockdown(pn) - self.UpdateAllRates() - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void CheckSizes(self): - check = False - list_pop = [] - print('Actual sizes: ', end='') - for pn in range(self.popNum): - print(self.actualSizes[pn], end=' ') - if abs(self.actualSizes[pn]/self.sizes[pn]-1) >= 0.1: - check = True - list_pop.append(str(pn)) - print() - if check: - print('\033[41m{}\033[0m'.format('WARNING!'), 'Actual population size in deme: ', end='') - print(", ".join(list_pop) ) - print("\tis more than 10% different from the population size. The migration probabilities might be unrealistically high.") - print("\tWe recommend to check your model with print_populations() method before proceding to simulation.") - print("\tCheck the documentation file:https://vg-sim.readthedocs.io/en/latest/Migration.html for more details.") - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef inline void SampleTime(self): - cdef double tau = - log(self.seed.uniform()) / (self.totalRate + self.totalMigrationRate) - self.currentTime += tau - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef Py_ssize_t GenerateEvent(self): - cdef: - Py_ssize_t pi, hi, ei - double choose - - self.rn = self.seed.uniform() - choose = self.rn * (self.totalRate + self.totalMigrationRate) - if self.totalRate > choose: - self.rn = choose / self.totalRate - pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) - #choose = self.rn * self.popRate[pi] - ei, self.rn = fastChoose1(self.massivForThree[pi], self.totalRate, self.rn) - if ei == 1: - #self.rn = choose / self.immunePopRate[pi] - self.ImmunityTransition(pi) - elif ei == 2: - self.ConditionTransition(pi) - else: - self.rn = (choose - self.immunePopRate[pi]) / self.infectPopRate[pi] - hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number - ci, self.rn = fastChoose1(self.tEventConHapPopRate[pi, hi], self.hapPopRate[pi, hi], self.rn) - ei, self.rn = fastChoose1(self.tEventHapPopRate[pi, hi], self.tEvenContHapPopRate[pi, hi, ci], self.rn) - if ei == BIRTH: - self.Birth(pi, hi, ci) - elif ei == DEATH: - self.Death(pi, hi, ci) - elif ei == SAMPLING: - self.Sampling(pi, hi, ci) - else: - self.Mutation(pi, hi, ci) - else: - pi = self.GenerateMigration() - return pi - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void UpdateRates(self, Py_ssize_t pi, bint infect, bint immune, bint migration): - cdef double tmp - if infect: - self.infectPopRate[pi] = 0.0 - for hn in range(self.currentHapNum): - for cn in range(self.conditionNum): - self.eventHapPopRate[pi, hn, cn, 0] = self.BirthRate(pi, hn, cn) - tmp = (self.eventHapPopRate[pi, hn, cn, 0] + - self.eventHapPopRate[pi, hn, cn, 1] + - self.eventHapPopRate[pi, hn, cn, 2] + - self.eventHapPopRate[pi, hn, cn, 3] ) - self.tEventHapPopRate[pi, hn, cn] = tmp - self.tEventConHapPopRate[pi, hn, cn] = self.tEventHapPopRate[pi, hn, cn] * self.infectByCondition[pi, hn, cn] - self.hapPopRate[pi, hn] += self.tEventConHapPopRate[pi, hn, cn] - self.infectPopRate[pi] += self.hapPopRate[pi, hn] - - if immune: - self.immunePopRate[pi] = 0 - for sn in range(self.susNum): - self.immunePopRate[pi] += self.immuneSourcePopRate[pi, sn] - - if infect or immune: - self.popRate[pi] = 0 - self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi]+ self.conditionPopRate[pi] - self.massivForThree[0] = self.infectPopRate[pi] - self.massivForThree[1] = self.immunePopRate[pi] - self.massivForThree[2] = self.conditionPopRate[pi] - self.totalRate = 0.0 - for pn in range(self.popNum): - self.totalRate += self.popRate[pn] - - if migration: - self.totalMigrationRate = 0.0 - for pn in range(self.popNum): - self.migPopRate[pn] = self.maxEffectiveBirthMigration[pn]*self.totalSusceptible[pn]*(self.globalInfectious-self.totalInfectious[pn]) - self.totalMigrationRate += self.migPopRate[pn] - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void ImmunityTransition(self, Py_ssize_t pi): - cdef: - Py_ssize_t ssi, tsi - - ssi, self.rn = fastChoose1(self.immuneSourcePopRate[pi], self.immunePopRate[pi], self.rn) - tsi, self.rn = fastChoose1(self.suscepTransition[ssi], self.suscepCumulTransition[ssi], self.rn) - - self.susceptible[pi, ssi] -= 1 - self.susceptible[pi, tsi] += 1 - self.immuneSourcePopRate[pi, ssi] = self.susceptible[pi, ssi]*self.suscepCumulTransition[ssi] - self.immuneSourcePopRate[pi, tsi] = self.susceptible[pi, tsi]*self.suscepCumulTransition[tsi] - self.UpdateRates(pi, False, True, False) - - self.iCounter += 1 - self.events.AddEvent(self.currentTime, SUSCCHANGE, ssi, pi, tsi, 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void ConditionTransition(self, Py_ssize_t pi): - cdef: - Py_ssize_t ssi, tsi, hsi - - hsi, self.rn = fastChoose1(self.conditionPopHapRate[pi], self.conditionPopRate[pi], self.rn) - ssi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self.conditionPopHapRate[pi, hsi], self.rn) - tsi, self.rn = fastChoose1_skip(self.conditionRate[ssi], self.cumuConditionRate[ssi], self.rn, ssi) - - self.infectByCondition[pi, hsi, ssi] -= 1 - self.infectByCondition[pi, hsi, tsi] += 1 - self.conditionPopHapConRate[pi, hsi, ssi] = self.cumuConditionRate[ssi] * self.infectByCondition[pi, hsi, ssi] - self.conditionPopHapConRate[pi, hsi, tsi] = self.cumuConditionRate[tsi] * self.infectByCondition[pi, hsi, tsi] - self.UpdateRates(pi, False, True, False) - - self.cCounter += 1 - self.events.AddEvent(self.currentTime, CONDITION, ssi, pi, tsi, 0) #ssi, tsi -- condition, add haplotype hsi?? - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void Birth(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number - cdef double ws = 0.0 - - for sn in range(self.susNum): - ws += self.susceptHapPopRate[pi, hi, sn] - si, self.rn = fastChoose1(self.susceptHapPopRate[pi, hi], ws, self.rn) - - self.NewInfections(pi, si, hi, 0) - self.immuneSourcePopRate[pi, si] = self.suscepCumulTransition[si]*self.susceptible[pi, si] - self.UpdateRates(pi, True, True, True) - - self.bCounter += 1 - self.events.AddEvent(self.currentTime, BIRTH, self.numToHap[hi], pi, si, 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void Death(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci, bint add_event = True): # hi - program number - self.NewRecoveries(pi, self.suscType[self.numToHap[hi]], hi, ci) - self.immuneSourcePopRate[pi, self.suscType[self.numToHap[hi]]] = self.susceptible[pi, self.suscType[self.numToHap[hi]]]*self.suscepCumulTransition[self.suscType[self.numToHap[hi]]] - self.UpdateRates(pi, True, True, True) - - if add_event: - self.dCounter += 1 - self.events.AddEvent(self.currentTime, DEATH, self.numToHap[hi], pi, self.suscType[self.numToHap[hi]], 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void Sampling(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number - self.Death(pi, hi, False) - - self.sCounter += 1 - self.events.AddEvent(self.currentTime, SAMPLING, self.numToHap[hi], pi, self.suscType[self.numToHap[hi]], 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void Mutation(self, Py_ssize_t pi, Py_ssize_t hi, Py_ssize_t ci): # hi - program number - cdef: - bint check = True - Py_ssize_t ohi, mi, digit4, AS, DS, nhi - - ohi = self.numToHap[hi] # ohi - haplotype - mi, self.rn = fastChoose1(self.mRate[ohi, ci], self.tmRate[hi, ci], self.rn) - DS, self.rn = fastChoose1(self.hapMutType[ohi, mi], self.hapMutType[ohi, mi, 0] \ - + self.hapMutType[ohi, mi, 1] + self.hapMutType[ohi, mi, 2], self.rn) - nhi = self.Mutate(ohi, mi, DS) - - if self.memory_optimization: - #TODO binary search - for hn in range(self.currentHapNum+1): # hn - program number - if self.numToHap[hn] == nhi: - check = False - break - if check: - self.AddHaplotype(nhi, pi) - if nhi < ohi: - hi += 1 - - self.infectious[pi, self.hapToNum[nhi]] += 1 - self.infectious[pi, hi] -= 1 - self.UpdateRates(pi, True, False, False) - - self.mCounter += 1 - self.events.AddEvent(self.currentTime, MUTATION, ohi, pi, nhi, 0) - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef Py_ssize_t GenerateMigration(self): - cdef: - Py_ssize_t tpi, spi, hi, si - double p_accept - - tpi, self.rn = fastChoose1(self.migPopRate, self.totalMigrationRate, self.rn) - spi, self.rn = fastChoose2_skip(self.totalInfectious, self.globalInfectious-self.totalInfectious[tpi], self.rn, skip=tpi) - hi, self.rn = fastChoose2(self.infectious[spi], self.totalInfectious[spi], self.rn) # hi - program number - ci, self.rn = fastChoose1(self.conditionRate[spi], self.cumuConditionRate[spi], self.rn) - si, self.rn = fastChoose2(self.susceptible[tpi], self.totalSusceptible[tpi], self.rn) - - p_accept = self.effectiveMigration[spi, tpi]*self.bRate[self.numToHap[hi], ci]*self.susceptibility[self.numToHap[hi], si]/self.maxEffectiveBirthMigration[tpi] - if self.rn < p_accept: - self.NewInfections(tpi, si, hi, ci) - self.UpdateRates(tpi, True, True, True) - - self.migPlus += 1 - self.events.AddEvent(self.currentTime, MIGRATION, self.numToHap[hi], spi, si, tpi) - else: - self.migNonPlus += 1 - - return tpi - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void CheckLockdown(self, Py_ssize_t pi): - if self.totalInfectious[pi] > self.startLD[pi]*self.sizes[pi] and self.lockdownON[pi] == 0: - self.contactDensity[pi] = self.contactDensityAfterLockdown[pi] - self.swapLockdown += 1 - self.lockdownON[pi] = 1 - self.UpdateAllRates() - self.loc.AddLockdown(True, pi, self.currentTime) - if self.totalInfectious[pi] < self.endLD[pi]*self.sizes[pi] and self.lockdownON[pi] == 1: - self.contactDensity[pi] = self.contactDensityBeforeLockdown[pi] - self.swapLockdown += 1 - self.lockdownON[pi] = 0 - self.UpdateAllRates() - self.loc.AddLockdown(False, pi, self.currentTime) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void Restart(self): - self.events.ptr = 0 - self.multievents.ptr = 0 - self.bCounter = 0 - self.dCounter = 0 - self.sCounter = 0 - self.mCounter = 0 - self.iCounter = 0 - self.migPlus = 0 - self.migNonPlus = 0 - self.currentTime = 0.0 - self.globalInfectious = 0 - for pn in range(self.popNum): - self.totalSusceptible[pn] = 0 - self.totalInfectious[pn] = 0 - for sn in range(self.susNum): - self.susceptible[pn, sn] = self.initial_susceptible[pn, sn] - self.totalSusceptible[pn] += self.initial_susceptible[pn, sn] - for hn in range(self.hapNum): - for cn in range(self.conditionNum): - self.infectious[pn, hn] += self.initial_infectious[pn, hn, cn] - self.totalInfectious[pn] += self.initial_infectious[pn, hn, cn] - self.globalInfectious += self.initial_infectious[pn, hn, cn] - for pn in range(self.popNum): - self.CheckLockdown(pn) - self.UpdateAllRates() - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cpdef GetGenealogy(self, seed): - cdef: - Py_ssize_t ptrTreeAndTime, n1, n2, id1, id2, id3, lbs, lbs_e, ns, nt, idt, ids, lbss, propNum - double p, p1 - vector[vector[vector[Py_ssize_t]]] liveBranchesS, newLineages - vector[vector[Py_ssize_t]] vecint2 - vector[Py_ssize_t] vecint1 - npy_int64[:,::1] liveBranches - - double e_time, me_time - Py_ssize_t e_type_, e_population, e_haplotype, e_newHaplotype, e_newPopulation - Py_ssize_t me_num, me_type_, me_population, me_haplotype, me_newHaplotype, me_newPopulation - Py_ssize_t mt_ev_num, mt_ev_num2 - - propNum = self.popNum*((self.popNum-1)*self.hapNum*self.susNum+self.susNum*(self.susNum-1)+self.hapNum*(2+self.sites*3+self.susNum)) - - if self.sCounter < 2: - print("Less than two cases were sampled...") - print("_________________________________") - sys.exit(0) - else: - if seed != None: - self.seed = RndmWrapper(seed=(seed, 0)) - - ptrTreeAndTime = 0 - self.tree = np.zeros(2 * self.sCounter - 1, dtype=np.int64) - self.tree_pop = np.zeros(2 * self.sCounter - 1, dtype=np.int64) - self.times = np.zeros(2 * self.sCounter - 1, dtype=float) - - for i in range( self.popNum ): - liveBranchesS.push_back(vecint2) - for _ in range( self.hapNum ): - liveBranchesS[i].push_back(vecint1) - - for i in range( self.popNum ): - newLineages.push_back(vecint2) - for _ in range( self.hapNum ): - newLineages[i].push_back(vecint1) - - for i in range( self.popNum ): - for j in range( self.hapNum ): - self.infectiousDelta[i, j] = 0 - - for e_id in range(self.events.ptr-1, -1, -1): - # this event - e_time = self.events.times[e_id] - e_type_ = self.events.types[e_id] - e_haplotype = self.events.haplotypes[e_id] - e_population = self.events.populations[e_id] - e_newHaplotype = self.events.newHaplotypes[e_id] - e_newPopulation = self.events.newPopulations[e_id] - if e_type_ == BIRTH: - lbs = liveBranchesS[e_population][e_haplotype].size() - lbs_e = self.infectious[e_population, self.hapToNum[e_haplotype]] - p = float(lbs)*(float(lbs)-1.0)/ float(lbs_e) / (float(lbs_e) - 1.0) - if self.seed.uniform() < p: - n1 = int(floor( lbs*self.seed.uniform() )) - n2 = int(floor( (lbs-1)*self.seed.uniform() )) - if n2 >= n1: - n2 += 1 - id1 = liveBranchesS[e_population][e_haplotype][n1] - id2 = liveBranchesS[e_population][e_haplotype][n2] - id3 = ptrTreeAndTime - liveBranchesS[e_population][e_haplotype][n1] = id3 - liveBranchesS[e_population][e_haplotype][n2] = liveBranchesS[e_population][e_haplotype][lbs-1] - liveBranchesS[e_population][e_haplotype].pop_back() - self.tree[id1] = id3 - self.tree[id2] = id3 - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = e_population - self.times[ptrTreeAndTime] = e_time - ptrTreeAndTime += 1 - self.infectious[e_population, self.hapToNum[e_haplotype]] -= 1 - elif e_type_ == DEATH: - self.infectious[e_population, self.hapToNum[e_haplotype]] += 1 - elif e_type_ == SAMPLING: - self.infectious[e_population, self.hapToNum[e_haplotype]] += 1 - liveBranchesS[e_population][e_haplotype].push_back( ptrTreeAndTime ) - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = e_population - self.times[ptrTreeAndTime] = e_time - ptrTreeAndTime += 1 - elif e_type_ == MUTATION: - lbs = liveBranchesS[e_population][e_newHaplotype].size() - p = float(lbs)/self.infectious[e_population, e_newHaplotype] - if self.seed.uniform() < p: - n1 = int(floor( lbs*self.seed.uniform() )) - id1 = liveBranchesS[e_population][e_newHaplotype][n1] - liveBranchesS[e_population][e_newHaplotype][n1] = liveBranchesS[e_population][e_newHaplotype][lbs-1] - liveBranchesS[e_population][e_newHaplotype].pop_back() - liveBranchesS[e_population][e_haplotype].push_back(id1) - self.mut.AddMutation(id1, e_haplotype, e_newHaplotype, e_time) - self.infectious[e_population, e_newHaplotype] -= 1 - self.infectious[e_population, e_haplotype] += 1 - elif e_type_ == SUSCCHANGE: - pass - elif e_type_ == MIGRATION: - lbs = liveBranchesS[e_newPopulation][e_haplotype].size() - p = float(lbs)/self.infectious[e_newPopulation, e_haplotype] - if self.seed.uniform() < p: - nt = int(floor( lbs*self.seed.uniform() )) - lbss = liveBranchesS[e_population][e_haplotype].size() - p1 = float(lbss)/self.infectious[e_population, e_haplotype] - if self.seed.uniform() < p1: - ns = int(floor( lbss*self.seed.uniform() )) - idt = liveBranchesS[e_newPopulation][e_haplotype][nt] - ids = liveBranchesS[e_population][e_haplotype][ns] - id3 = ptrTreeAndTime - liveBranchesS[e_population][e_haplotype][ns] = id3 - liveBranchesS[e_newPopulation][e_haplotype][nt] = liveBranchesS[e_newPopulation][e_haplotype][lbs-1] - liveBranchesS[e_newPopulation][e_haplotype].pop_back() - self.tree[idt] = id3 - self.tree[ids] = id3 - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = e_population - self.times[ptrTreeAndTime] = e_time - ptrTreeAndTime += 1 - self.mig.AddMigration(idt, e_time, e_population, e_newPopulation) - else: - liveBranchesS[e_population][e_haplotype].push_back(liveBranchesS[e_newPopulation][e_haplotype][nt]) - liveBranchesS[e_newPopulation][e_haplotype][nt] = liveBranchesS[e_newPopulation][e_haplotype][lbs-1] - liveBranchesS[e_newPopulation][e_haplotype].pop_back() - self.infectious[e_newPopulation, e_haplotype] -= 1 - elif e_type_ == MULTITYPE: - for me_id in range(e_haplotype, e_population): - me_num = self.multievents.num[me_id] - me_time = self.multievents.times[me_id] - me_type_ = self.multievents.types[me_id] - me_haplotype = self.multievents.haplotypes[me_id] - me_population = self.multievents.populations[me_id] - me_newHaplotype = self.multievents.newHaplotypes[me_id] - me_newPopulation = self.multievents.newPopulations[me_id] - - if me_type_ == BIRTH: - lbs = liveBranchesS[me_population][me_haplotype].size() - lbs_e = self.infectious[me_population, me_haplotype] - if me_num == 0 or lbs == 0: - mt_ev_num = 0 - else: - mt_ev_num = random_hypergeometric(self.seed.rng, int(lbs*(lbs-1.0)/2.0), int(lbs_e*(lbs_e-1)/2-lbs*(lbs-1)/2), me_num) - #print("me_num=", me_num, " mt_ev_num", mt_ev_num) - for i in range(mt_ev_num): - n1 = int(floor( lbs*self.seed.uniform() )) - n2 = int(floor( (lbs-1)*self.seed.uniform() )) - if n2 >= n1: - n2 += 1 - id1 = liveBranchesS[me_population][me_haplotype][n1] - id2 = liveBranchesS[me_population][me_haplotype][n2] - id3 = ptrTreeAndTime - newLineages[me_population][me_haplotype].push_back(id3) - if n1 == lbs-1:#TODO: need to check if we really need these if and elif. Most likely - no! - liveBranchesS[me_population][me_haplotype].pop_back() - liveBranchesS[me_population][me_haplotype][n2] = liveBranchesS[me_population][me_haplotype][lbs-2] - liveBranchesS[me_population][me_haplotype].pop_back() - elif n2 == lbs-1: - liveBranchesS[me_population][me_haplotype].pop_back() - liveBranchesS[me_population][me_haplotype][n1] = liveBranchesS[me_population][me_haplotype][lbs-2] - liveBranchesS[me_population][me_haplotype].pop_back() - else: - liveBranchesS[me_population][me_haplotype][n1] = liveBranchesS[me_population][me_haplotype][lbs-1] - liveBranchesS[me_population][me_haplotype].pop_back() - liveBranchesS[me_population][me_haplotype][n2] = liveBranchesS[me_population][me_haplotype][lbs-2] - liveBranchesS[me_population][me_haplotype].pop_back() - self.tree[id1] = id3 - self.tree[id2] = id3 - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = me_population - self.times[ptrTreeAndTime] = me_time - ptrTreeAndTime += 1 - lbs -= 2 - self.infectiousDelta[me_population, me_haplotype] -= me_num - elif me_type_ == DEATH: - self.infectiousDelta[me_population, me_haplotype] += me_num - elif me_type_ == SAMPLING: - self.infectiousDelta[me_population, me_haplotype] += me_num - for i in range(me_num): - #liveBranchesS[me_population][me_haplotype].push_back( ptrTreeAndTime ) - newLineages[me_population][me_haplotype].push_back( ptrTreeAndTime ) - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = me_population - self.times[ptrTreeAndTime] = me_time - ptrTreeAndTime += 1 - elif me_type_ == MUTATION: - lbs = liveBranchesS[me_population][me_newHaplotype].size() - if me_num == 0 or lbs == 0: - mt_ev_num = 0 - else: - mt_ev_num = random_hypergeometric(self.seed.rng, lbs, self.infectious[me_population, me_newHaplotype]-lbs, me_num) - for i in range(mt_ev_num): - n1 = int(floor( lbs*self.seed.uniform() )) - id1 = liveBranchesS[me_population][me_newHaplotype][n1] - liveBranchesS[me_population][me_newHaplotype][n1] = liveBranchesS[me_population][me_newHaplotype][lbs-1] - liveBranchesS[me_population][me_newHaplotype].pop_back() - #liveBranchesS[me_population][me_haplotype].push_back(id1) - newLineages[me_population][me_haplotype].push_back( id1 ) - self.mut.AddMutation(id1, me_haplotype, me_newHaplotype, me_time) - lbs-=1 - self.infectiousDelta[me_population, me_newHaplotype] -= me_num - self.infectiousDelta[me_population, me_haplotype] += me_num - elif me_type_ == SUSCCHANGE: - pass - elif me_type_ == MIGRATION: - lbs = liveBranchesS[me_newPopulation][me_haplotype].size() - if me_num == 0 or lbs == 0: - mt_ev_num = 0 - else: - mt_ev_num = random_hypergeometric(self.seed.rng, lbs, self.infectious[me_newPopulation, me_haplotype]-lbs, me_num) - #for i in range(mt_ev_num): - lbss = liveBranchesS[me_population][me_haplotype].size() - if mt_ev_num == 0 or lbss == 0: - mt_ev_num2 = 0 - else: - mt_ev_num2 = random_hypergeometric(self.seed.rng, lbss, self.infectious[me_population, me_haplotype]-lbss, mt_ev_num) - #print("me_num: ", me_num, "mt_ev_num: ", mt_ev_num, "mt_ev_num2: ", mt_ev_num2) - for i in range(mt_ev_num2): - nt = int(floor( lbs*self.seed.uniform() )) - ns = int(floor( lbss*self.seed.uniform() )) - idt = liveBranchesS[me_newPopulation][me_haplotype][nt] - ids = liveBranchesS[me_population][me_haplotype][ns] - id3 = ptrTreeAndTime - liveBranchesS[me_population][me_haplotype][ns] = liveBranchesS[me_population][me_haplotype][ liveBranchesS[me_population][me_haplotype].size()-1 ] - liveBranchesS[me_population][me_haplotype].pop_back() - liveBranchesS[me_newPopulation][me_haplotype][nt] = liveBranchesS[me_newPopulation][me_haplotype][lbs-1] - liveBranchesS[me_newPopulation][me_haplotype].pop_back() - newLineages[me_population][me_haplotype].push_back( id3 ) - self.tree[idt] = id3 - self.tree[ids] = id3 - self.tree[ptrTreeAndTime] = -1 - self.tree_pop[ptrTreeAndTime] = me_population - self.times[ptrTreeAndTime] = me_time - ptrTreeAndTime += 1 - self.mig.AddMigration(idt, me_time, me_population, me_newPopulation) - lbss -= 1 - lbs -= 1 - for i in range(mt_ev_num-mt_ev_num2): - nt = int(floor( lbs*self.seed.uniform() )) - newLineages[me_population][me_haplotype].push_back(liveBranchesS[me_newPopulation][me_haplotype][nt]) - liveBranchesS[me_newPopulation][me_haplotype][nt] = liveBranchesS[me_newPopulation][me_haplotype][lbs-1] - liveBranchesS[me_newPopulation][me_haplotype].pop_back() - lbs -= 1 - self.infectiousDelta[me_newPopulation, me_haplotype] -= me_num - else: - print("Unknown event type: ", me_type_) - print("_________________________________") - sys.exit(0) - for pi in range(self.popNum): - for hi in range(self.hapNum): - self.infectious[pi, hi] += self.infectiousDelta[pi, hi] - self.infectiousDelta[pi, hi] = 0 - while newLineages[pi][hi].size() > 0: - liveBranchesS[pi][hi].push_back( newLineages[pi][hi][newLineages[pi][hi].size()-1] ) - newLineages[pi][hi].pop_back() - #for i in range(newLineages[pi][hi].size()-1, -1, -1): - # liveBranchesS[pi][hi].push_back(newLineages[pi][hi][i]) - # newLineages[pi][hi].pop_back() - else: - print("Unknown event type: ", e_type_) - print("_________________________________") - sys.exit(0) - for i in range(self.sCounter * 2 - 2): - if self.tree_pop[self.tree[i]] != self.tree_pop[i]: - self.mig.AddMigration(i, self.times[i], self.tree_pop[self.tree[i]], self.tree_pop[i]) - - #for i in range(self.sCounter * 2 - 1): - # print(self.tree[i], end=" ") - #print("") - #deg = [0 for i in range(self.sCounter * 2 - 1)] - #for i in range(self.sCounter * 2 - 1): - # deg[self.tree[i]] += 1 - #for i in range(self.sCounter * 2 - 1): - # print(deg[i], end=" ") - - #self.CheckTree() - - - def print_basic_parameters(self): - print("*****************") - print("***Basic rates***") - print("*****************") - table = PrettyTable() - - field = ["H", "TR", "RR", "SR", "ST"] - for s in range(self.sites): - field.append("M" + str(s)) - field.append("MW" + str(s)) - table.field_names = field - for hn in range(self.currentHapNum): - list = ["\n" + self.calculate_string(hn), "\n" + str(self.bRate[hn]), "\n" + str(self.dRate[hn]), "\n" + str(self.sRate[hn]), "\n" + str(self.suscType[hn])] - for s in range(self.sites): - list.append("\n" + str(self.mRate[hn, s])) - list.append(self.create_mutations(hn, s)) - table.add_row(list) - - print(table) - print("Legend:") - print("H - haplotype") - print("TR - transmission rate") - print("RR - recovery rate") - print("SR - sampling rate") - print("ST - susceptibility type") - for s in range(self.sites): - print("M" + str(s) + " - " + str(s) + " mutation rate") - print("MW" + str(s) + " - " + str(s) + " mutation weights") - print() - - def GetCurrentIndividuals(self): - current_susceptible, current_infectious = [[0 for _ in range(self.susNum)] for _ in range(self.popNum)], [[0 for _ in range(self.hapNum)] for _ in range(self.popNum)] - - for pi in range(self.popNum): - for si in range(self.susNum): - current_susceptible[pi][si] = self.initial_susceptible[pi, si] - for hi in range(self.hapNum): - current_infectious[pi][hi] = self.initial_infectious[pi, hi] - for i in range(self.events.ptr): - if self.events.types[i] == BIRTH: - current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] -= 1 - current_infectious[self.events.populations[i]][self.events.haplotypes[i]] += 1 - elif self.events.types[i] == DEATH: - current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 - current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 - elif self.events.types[i] == SAMPLING: - current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 - current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 - elif self.events.types[i] == MUTATION: - current_infectious[self.events.populations[i]][self.events.haplotypes[i]] -= 1 - current_infectious[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 - elif self.events.types[i] == SUSCCHANGE: - current_susceptible[self.events.populations[i]][self.events.haplotypes[i]] -= 1 - current_susceptible[self.events.populations[i]][self.events.newHaplotypes[i]] += 1 - else: - current_susceptible[self.events.newPopulations[i]][self.events.newHaplotypes[i]] -= 1 - current_infectious[self.events.newPopulations[i]][self.events.haplotypes[i]] += 1 - - return [current_susceptible, current_infectious] - - def print_populations(self): - current_susceptible, current_infectious = self.GetCurrentIndividuals() - - print("*****************") - print("***Populations***") - print("*****************") - table_populations = PrettyTable() - - table_populations.field_names = ["ID", "Size", 'Actual size', "CD",'CDBLC', "CDALD", "SLD", "ELD", "SM"] - for pn in range(self.popNum): - table_populations.add_row([pn, self.sizes[pn], self.actualSizes[pn], self.contactDensity[pn], self.contactDensityBeforeLockdown[pn], self.contactDensityAfterLockdown[pn], self.startLD[pn], self.endLD[pn], self.samplingMultiplier[pn]]) - - print(table_populations) - print("Legend:") - print("ID - number of population") - print("Size - size of population") - print("Actual size - actual size of population") - print("CD - contact density") - print("CDBLD - contact density without lockdown") - print("CDALD - contact density at lockdown") - print("SLD - start of lockdown") - print("ELD - end of lockdown") - print("SM - sampling multiplier") - print() - - print("*****************") - print("***Susceptible***") - print("*****************") - table_susceptible = PrettyTable() - - field = ["ST\\ID"] - for pn in range(self.popNum): - field.append(pn) - for sn in range(self.susNum): - row = [sn] - for pn in range(self.popNum): - row.append(current_susceptible[pn][sn]) - table_susceptible.add_row(row) - table_susceptible.field_names = field - - print(table_susceptible) - print("Legend:") - print("ID - ID population") - print("ST - susceptibility type") - print() - - print("****************") - print("***Infectious***") - print("****************") - table_infectious = PrettyTable() - - field = ["H\\ID"] - for pn in range(self.popNum): - field.append(pn) - for hn in range(self.hapNum): - row = [self.calculate_string(hn)] - for pn in range(self.popNum): - row.append(current_infectious[pn][hn]) - table_infectious.add_row(row) - table_infectious.field_names = field - - print(table_infectious) - print("Legend:") - print("ID - ID population") - print("H - haplotype") - print() - - print("**********************") - print("***Migration matrix***") - print("**********************") - table_migration = PrettyTable() - - field = ["S\\T"] - for pn1 in range(self.popNum): - field.append(pn1) - row = [pn1] - for pn2 in range(self.popNum): - row.append(self.migrationRates[pn1, pn2]) - table_migration.add_row(row) - table_migration.field_names = field - - print(table_migration) - print("Legend:") - print("S - ID source population") - print("T - ID target population") - print() - - def print_immunity_model(self): - print("********************") - print("***Immunity model***") - print("********************") - table_immunity = PrettyTable() - - field = ["H\\ST"] - for sn in range(self.susNum): - field.append("S" + str(sn)) - table_immunity.field_names = field - for hn in range(self.currentHapNum): - row = [self.calculate_string(hn)] - for sn in range(self.susNum): - row.append(self.susceptibility[hn, sn]) - table_immunity.add_row(row) - - print(table_immunity) - print("Legend:") - print("H - haplotype") - print("ST - susceptibility type") - print() - - print("*******************************") - print("***Immunity transition rates***") - print("*******************************") - table_immunity_transition = PrettyTable() - - field = ["ID"] - for sn1 in range(self.susNum): - field.append(sn1) - row = [sn1] - for sn2 in range(self.susNum): - row.append(self.suscepTransition[sn1, sn2]) - table_immunity_transition.add_row(row) - table_immunity_transition.field_names = field - - print(table_immunity_transition) - print("Legend:") - print("ID - ID susceptibility type") - print() - - - def Error(self, text): - print(text) - sys.exit(1) - - def create_list_haplotypes(self, haplotype): - haplotypes = [haplotype] - for s in range(self.sites): - for i in range(len(haplotypes)): - haplotype_old = haplotypes[i] - if haplotype_old[s] == "*": - haplotype = haplotype_old.replace("*", "A", 1) - haplotypes.append(haplotype) - haplotype = haplotype_old.replace("*", "T", 1) - haplotypes.append(haplotype) - haplotype = haplotype_old.replace("*", "C", 1) - haplotypes.append(haplotype) - haplotype = haplotype_old.replace("*", "G", 1) - haplotypes.append(haplotype) - for i in range(len(haplotypes)-1, -1, -1): - if haplotypes[i].count("*") != 0: - haplotypes.remove(haplotypes[i]) - for i in range(len(haplotypes)): - haplotypes[i] = self.calculate_haplotype(haplotypes[i]) - - return haplotypes - - def create_mutations(self, haplotype, site): - hap = self.calculate_string(haplotype) - haplotypes = [hap[:site] + "A" + hap[site+1:], hap[:site] + "T" + hap[site+1:], hap[:site] + "C" + hap[site+1:], hap[:site] + "G" + hap[site+1:]] - for i in range(4): - if haplotypes[i] == hap: - haplotypes.remove(haplotypes[i]) - haplotypes.append(hap) - break - color_hap=[] - for hapl in haplotypes: - a = "" - for s in range(self.sites): - if s == site: - a = a + "\033[31m{}\033[0m" .format(hapl[s:s+1]) - else: - a = a + hapl[s:s+1] - color_hap.append(a) - string = color_hap[3] + "->" + color_hap[0] + ": " + str(self.hapMutType[haplotype, site, 0]) + "\n" + color_hap[3] + "->" + color_hap[1] + ": " + str(self.hapMutType[haplotype, site, 1]) + "\n" + color_hap[3] + "->" + color_hap[2] + ": " + str(self.hapMutType[haplotype, site, 2]) + "\n" - return string - - def calculate_string(self, hapNum): - letters = ["A", "T", "C", "G"] - string = "" - for s in range(self.sites): - string = letters[hapNum%4] + string - hapNum = hapNum // 4 - return string - - def calculate_haplotype(self, string): - string = string[::-1] - haplotype = 0 - for s in range(self.sites): - if string[s]=="T": - haplotype += (4**s) - elif string[s]=="C": - haplotype += 2*(4**s) - elif string[s]=="G": - haplotype += 3*(4**s) - return haplotype - - def calculate_allele(self, haplotype, site): - for _ in range(self.sites-site): - allele = haplotype % 4 - haplotype = haplotype // 4 - return allele - - def get_sites(self): - return self.sites - - def get_hapNum(self): - return self.hapNum - - def get_popNum(self): - return self.popNum - - def get_susNum(self): - return self.susNum - - - def check_amount(self, amount, smth): - if isinstance(amount, int) == False: - raise TypeError('Incorrect type of ' + smth + '. Type should be int.') - elif amount < 0: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_rate_1(self, value, smth): - if isinstance(value, (int, float)) == False: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') - elif value < 0: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_rate_none_1(self, value, smth): - if isinstance(value, (int, float)) == False and value != None: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') - elif isinstance(value, (int, float)): - if value < 0: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_haplotype(self, haplotype): - if isinstance(haplotype, (int, str)) == False and haplotype != None: - raise TypeError('Incorrect type of haplotype. Type should be int or str or None.') - elif isinstance(haplotype, int): - if haplotype < 0 or haplotype >= self.hapNum: - raise IndexError('There are no such haplotype!') - elif isinstance(haplotype, str): - if haplotype.count("A") + haplotype.count("T") + haplotype.count("C") + haplotype.count("G") + haplotype.count("*") != self.sites: - raise ValueError('Incorrect haplotype. Haplotype should contain only \"A\", \"T\", \"C\", \"G\", \"*\" and lenght of haplotype should be equal number of mutations sites.') - - def check_rate_2(self, value, smth): - if isinstance(value, (int, float)) == False: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') - elif value < 0 and value > 1: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_rate_none_2(self, value, smth): - if isinstance(value, (int, float)) == False and value != None: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or float.') - elif isinstance(value, (int, float)): - if value < 0 and value > 1: - raise ValueError('Incorrect value of ' + smth + '. Value should be more or equal 0.') - - def check_list(self, data, smth, lenght): - if isinstance(data, list) == False and data != None: - raise TypeError('Incorrect type of ' + smth + '. Type should be list or None.') - elif isinstance(data, list): - if len(data) != lenght: - raise ValueError('Incorrect lenght of ' + smth + '. Lenght should be equal ' + str(lenght) + '.') - - def check_index(self, value, edge, smth): - if isinstance(value, int) == False and value != None: - raise TypeError('Incorrect type of ' + smth + '. Type should be int or None.') - elif isinstance(value, int): - if value < 0 or value >= edge: - raise IndexError('There are no such ' + smth + '!') - - def check_amount_sus(self, amount, source_type, target_type, population): - if self.initial_susceptible[population, source_type] - amount < 0: - raise ValueError('Number of susceptible minus amount should be more or equal 0.') - if self.initial_susceptible[population, target_type] + amount > self.sizes[population]: - raise ValueError('Number of susceptible plus amount should be equal or less population size.') - - def check_amount_inf(self, amount, source_type, target_haplotype, population): - if self.initial_susceptible[population, source_type] - amount < 0: - raise ValueError('Number of susceptible minus amount should be more or equal 0.') - if self.initial_infectious[population, target_haplotype] + amount > self.sizes[population]: - raise ValueError('Number of infectious plus amount should be equal or less population size.') - - def check_mig_rate(self): - for pn1 in range(self.popNum): - summa = 0 - self.migrationRates[pn1, pn1] = 1.0 - for pn2 in range(self.popNum): - if pn1 != pn2: - summa += self.migrationRates[pn1, pn2] - self.migrationRates[pn1, pn1] -= self.migrationRates[pn1, pn2] - if summa > 1: - raise ValueError('Incorrect the sum of migration probabilities. The sum of migration probabilities from each population should be equal or less 1.') - - - def set_initial_haplotype(self, amount): - if self.memory_optimization == False: - raise ValueError('Incorrect value of memory optimization. Value should be equal \'True\' for work this function.') - self.check_amount(amount, 'amount of initial haplotype') - - if amount > self.hapNum: - self.maxHapNum = self.hapNum - else: - self.maxHapNum = amount - - def set_step_haplotype(self, amount): - if self.memory_optimization == False: - raise ValueError('Incorrect value of memory optimization. Value should be equal \'True\' for work this function.') - self.check_amount(amount, 'amount of step haplotype') - - if amount > self.hapNum: - self.addMemoryNum = self.hapNum - else: - self.addMemoryNum = amount - - def set_transmission_rate(self, rate, haplotype, condition): - self.check_value(rate, 'transmission rate') - self.check_index(haplotype, self.hapNum, 'haplotype', True) - self.check_index(condition, self.conditionNum, 'number of states') - haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) - conditions = self.create_list_for_cycles(condition, self.conditionNum) - - for hn in haplotypes: - for cn in conditions: - self.bRate[hn, cn] = rate - - - def set_recovery_rate(self, rate, haplotype, condition): - self.check_value(rate, 'recovery rate') - self.check_index(haplotype, self.hapNum, 'haplotype', True) - self.check_index(condition. self.conditionNum, 'number of states') - - haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) - conditions = self.create_list_for_cycles(condition, self.conditionNum) - - for hn in haplotypes: - for cn in conditions: - self.dRate[hn, cn] = rate - - def set_sampling_rate(self, rate, haplotype, condition): - self.check_index(haplotype, self.hapNum, 'haplotype', True) - self.check_value(condition, self.conditionNum, 'number of states') - conditions = self.create_list_for_cycles(condition, self.conditionNum) - - if self.sampling_probability == True: - self.check_value(rate, 'sampling probability', edge=1) - - haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) - for hn in haplotypes: - for cn in conditions: - deathRate = self.dRate[hn, cn] + self.sRate[hn, cn] - self.dRate[hn, cn] = (1 - rate) * deathRate - self.sRate[hn, cn] = rate * deathRate - - elif self.sampling_probability == False: - self.check_value(rate, 'sampling rate') - - haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) - for hn in haplotypes: - for cn in conditions: - self.sRate[hn, cn] = rate - - def set_mutation_rate(self, rate, haplotype, mutation, condition): - self.check_value(rate, 'mutation rate') - self.check_index(haplotype, self.hapNum, 'haplotype', True) - self.check_index(mutation, self.sites, 'mutation site') - self.check_value(condition, self.conditionNum, 'number of states') - - haplotypes = self.create_list_for_cycles(haplotype, self.hapNum) - conditions = self.create_list_for_cycles(condition, self.conditionNum) - sites = self.create_list_for_cycles(mutation, self.sites) - for hn in haplotypes: - for cn in conditions: - for s in sites: - self.mRate[hn, cn, s] = rate - - @property - def condition_rate(self): - return self.conditionRate - - def set_condition_rate(self, rate, c1, c2): - self.check_value(rate, 'condition_rate') - self.check_index(c1, self.conditionNum, 'first_state') - self.check_index(c2, self.conditionNum, 'second_state') - self.conditionRate[c1, c2] = rate - - def set_probabilities(self, haplotype, mutation, rate, probabilities): - if isinstance(rate, (int, float)): - self.mRate[haplotype, mutation] = rate - if isinstance(probabilities, list): - probabilities_allele = list(probabilities) - del probabilities_allele[self.calculate_allele(haplotype, mutation)] - if sum(probabilities_allele) == 0: - raise ValueError('Incorrect probabilities list. The sum of three elements without mutation allele should be more 0.') - self.hapMutType[haplotype, mutation, 0] = probabilities_allele[0] - self.hapMutType[haplotype, mutation, 1] = probabilities_allele[1] - self.hapMutType[haplotype, mutation, 2] = probabilities_allele[2] - - def set_susceptibility_type(self, susceptibility_type, haplotype): - if isinstance(susceptibility_type, int) == False: - raise TypeError('Incorrect type of susceptibility type. Type should be int.') - elif susceptibility_type < 0 or susceptibility_type >= self.susNum: - raise IndexError('There are no such susceptibility type!') - self.check_haplotype(haplotype) - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - for hn in haplotypes: - self.suscType[hn] = susceptibility_type - elif isinstance(haplotype, int): - self.suscType[haplotype] = susceptibility_type - elif haplotype == None: - for hn in range(self.hapNum): - self.suscType[hn] = susceptibility_type - - def set_susceptibility(self, rate, haplotype, susceptibility_type): - self.check_rate_1(rate, 'rate of susceptibility') - self.check_haplotype(haplotype) - self.check_index(susceptibility_type, self.susNum, 'susceptibility type') - - if isinstance(haplotype, str): - haplotypes = self.create_list_haplotypes(haplotype) - if isinstance(susceptibility_type, int): - for hn in haplotypes: - self.susceptibility[hn, susceptibility_type] = rate - else: - for hn in haplotypes: - for sn in range(self.susNum): - self.susceptibility[hn, sn] = rate - elif isinstance(haplotype, int): - if isinstance(susceptibility_type, int): - self.susceptibility[haplotype, susceptibility_type] - else: - for sn in range(self.susNum): - self.susceptibility[haplotype, sn] = rate - else: - if isinstance(susceptibility_type, int): - for hn in range(self.hapNum): - self.susceptibility[hn, susceptibility_type] = rate - else: - for hn in range(self.hapNum): - for sn in range(self.susNum): - self.susceptibility[hn, sn] = rate - - def set_immunity_transition(self, rate, source, target): - self.check_rate_1(rate, 'immunity transition rate') - self.check_index(source, self.susNum, 'susceptibility type') - self.check_index(target, self.susNum, 'susceptibility type') - - if isinstance(source, int): - if isinstance(target, int): - if source == target: - raise ValueError('Source and target susceptibility type shouldn\'t be equal!') - self.suscepTransition[source, target] = rate - else: - for sn1 in range(self.susNum): - if sn1 != target: - self.suscepTransition[sn1, target] = rate - else: - if isinstance(target, int): - for sn2 in range(self.susNum): - if source != sn2: - self.suscepTransition[source, sn2] = rate - else: - for sn1 in range(self.susNum): - for sn2 in range(self.susNum): - if sn1 != sn2: - self.suscepTransition[sn1, sn2] = rate - - def set_population_size(self, amount, population): - if self.first_simulation == True: - raise ValueError('Changing population size is available only before first simulation!') - self.check_amount(amount, 'population size') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.sizes[population] = amount - for sn in range(self.susNum): - self.initial_susceptible[population, 0] = amount - elif population == None: - for pn in range(self.popNum): - self.sizes[pn] = amount - for sn in range(self.susNum): - self.initial_susceptible[pn, 0] = amount - - def set_susceptible(self, amount, source_type, target_type, population): - if self.first_simulation: - raise ValueError('This function is available only before first simulation!') - self.check_amount(amount) - self.check_index(source_type, self.susNum, 'susceptibility type') - self.check_index(target_type, self.susNum, 'susceptibility type') - if source_type == target_type: - raise ValueError('Source and target susceptibility type shouldn\'t be equal!') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.check_amount_sus(amount, source_type, target_type, population) - - self.initial_susceptible[population, source_type] -= amount - self.initial_susceptible[population, target_type] += amount - elif population == None: - for pn in range(self.popNum): - self.check_amount_sus(amount, source_type, target_type, pn) - - self.initial_susceptible[pn, source_type] -= amount - self.initial_susceptible[pn, target_type] += amount - - def set_infectious(self, amount, source_type, target_haplotype, population, condition): - if self.first_simulation: - raise ValueError('This function is available only before first simulation!') - self.check_amount(amount) - self.check_index(source_type, self.susNum, 'susceptibility type') - self.check_haplotype(target_haplotype) - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.check_amount_inf(amount, source_type, target_haplotype, population) - - self.initial_susceptible[population, source_type] -= amount - - self.initial_infectious[population, target_haplotype, condition] += amount - elif population == None: - for pn in range(self.popNum): - self.check_amount_inf(amount, source_type, target_haplotype, pn) - - self.initial_susceptible[pn, source_type] -= amount - self.initial_infectious[pn, target_haplotype, condition] += amount - - def set_contact_density(self, value, population): - self.check_rate_1(value, 'contact density') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.contactDensity[population] = value - self.contactDensityBeforeLockdown[population] = value - elif population == None: - for pn in range(self.popNum): - self.contactDensity[pn] = value - self.contactDensityBeforeLockdown[pn] = value - - def set_npi(self, parameters, population): - self.check_list(parameters, 'parameters', 3) - if isinstance(parameters, list): - self.check_rate_1(parameters[0], 'first element of parameters') - self.check_rate_2(parameters[1], 'second element of parameters') - self.check_rate_2(parameters[2], 'third element of parameters') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.contactDensityAfterLockdown[population] = parameters[0] - self.startLD[population] = parameters[1] - self.endLD[population] = parameters[2] - elif population == None: - for pn in range(self.popNum): - self.contactDensityAfterLockdown[pn] = parameters[0] - self.startLD[pn] = parameters[1] - self.endLD[pn] = parameters[2] - - def set_sampling_multiplier(self, multiplier, population): - self.check_rate_1(multiplier, 'sampling multiplier') - self.check_index(population, self.popNum, 'population') - - if isinstance(population, int): - self.samplingMultiplier[population] = multiplier - elif population == None: - for pn in range(self.popNum): - self.samplingMultiplier[pn] = multiplier - - def set_migration_probability(self, probability, total_probability, source, target): - self.check_rate_none_2(probability, 'probability') - self.check_rate_none_2(total_probability, 'total probability') - self.check_index(source, self.popNum, 'population') - self.check_index(target, self.popNum, 'population') - - if isinstance(probability, float): - if isinstance(source, int): - if isinstance(target, int): - if source == target: - raise IndexError('Source and target population shouldn\'t be equal!') - - self.migrationRates[source, target] = probability - else: - for pn2 in range(self.popNum): - if source != pn2: - self.migrationRates[source, pn2] = probability - else: - if isinstance(target, int): - for pn1 in range(self.popNum): - if pn1 != target: - self.migrationRates[pn1, target] = probability - else: - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - if pn1 != pn2: - self.migrationRates[pn1, pn2] = probability - - self.check_mig_rate() - elif isinstance(total_probability, float): - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - if pn1 != pn2: - self.migrationRates[pn1, pn2] = total_probability/(self.popNum-1) - - self.check_mig_rate() - else: - raise TypeError('Incorrect type of probability or total_probability. Type should be float.') - - - def set_chain_events(self, name_file): - if isinstance(name_file, str) == False: - self.Error('#TODO') - tokens = np.load(name_file + '.npy') - #TODO Check sizes and resize if needed - self.size = tokens.shape[1] - self.ptr = tokens.shape[1] - - self.events.times = tokens[0] - self.events.types = tokens[1] - self.events.haplotypes = tokens[2] - self.events.populations = tokens[3] - self.events.newHaplotypes = tokens[4] - self.events.newPopulations = tokens[5] - - def set_settings(self, file_template): - pass - - - def output_tree_mutations(self): - if self.tree.shape[0] == 1: - print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') - sys.exit(1) - mut = [[], [], [], [], []] - for i in range(self.mut.nodeId.size()): - mut[0].append(self.mut.nodeId[i]) - mut[1].append(self.mut.AS[i]) - mut[2].append(self.mut.site[i]) - mut[3].append(self.mut.DS[i]) - mut[4].append(self.mut.time[i]) - times_dict = {self.events.times[i]: i for i in range(len(self.events.times))} - populations = {} - for time in self.times: - populations[time] = self.events.populations[times_dict[time]] - - return self.tree, self.times, mut, populations - - def output_migrations(self, name_file, file_path): - if self.tree.shape[0] == 1: - print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') - sys.exit(1) - if file_path != None: - f_mig = open(file_path + '/' + name_file + '.tsv', 'w') - else: - f_mig = open(name_file + '.tsv', 'w') - f_mig.write("Node\tTime\tOld_population\tNew_population\n") - for i in range(self.mig.nodeId.size()): - f_mig.write(str(self.mig.nodeId[i]) + '\t' + str(self.mig.time[i]) + '\t' + str(self.mig.oldPop[i]) + '\t' + str(self.mig.newPop[i]) + "\n") - f_mig.close() - - def output_sample_data(self): - time, pop, hap = [], [], [] - for i in range(self.events.ptr): - if self.events.types[i] == SAMPLING: - time.append(self.events.times[i]) - pop.append(self.events.populations[i]) - hap.append(self.events.haplotypes[i]) - return time, pop, hap - - def output_epidemiology_timelines(self, step_num, output_file): - time_points = [i*self.currentTime/step_num for i in range(step_num+1)] - suscepDate = np.zeros((self.popNum, self.susceptible_num), dtype=np.int64) - hapDate = np.zeros((self.popNum, self.hapNum), dtype=np.int64) - for i in range(self.popNum): - for sn in range(self.susceptible_num): - if sn == 0: - suscepDate[i, sn] = self.sizes[i] - else: - suscepDate[i, sn] = 0 - for hn in range(self.hapNum): - hapDate[i, hn] = 0 - hapDate[0, 0] += 1 - suscepDate[0, 0] -= 1 - if output_file == True: - if not os.path.isdir("logs"): - os.mkdir("logs") - logDynamics = [] - for i in range(self.popNum): - logDynamics.append(open('logs/PID' + str(i) + '.log', 'w')) - logDynamics[i].write("time") - for sn in range(self.susceptible_num): - logDynamics[i].write(" S" + str(sn)) - for hn in range(self.hapNum): - logDynamics[i].write(" H" + str(hn)) - logDynamics[i].write("\n") - else: - log = dict() - log["time"] = list() - for i in range(self.popNum): - log["P" + str(i)] = dict() - for j in range(self.susceptible_num): - log["P" + str(i)]["S" + str(j)] = list() - for j in range(self.hapNum): - log["P" + str(i)]["H" + str(j)] = list() - - point = 0 - for j in range(self.events.ptr): - if self.events.types[j] == BIRTH: - hapDate[self.events.populations[j], self.events.haplotypes[j]] += 1 - suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] -= 1 - elif self.events.types[j] == DEATH: - hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 - suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 - elif self.events.types[j] == SAMPLING: - hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 - suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 - elif self.events.types[j] == MUTATION: - hapDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 - hapDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 - elif self.events.types[j] == SUSCCHANGE: - suscepDate[self.events.populations[j], self.events.haplotypes[j]] -= 1 - suscepDate[self.events.populations[j], self.events.newHaplotypes[j]] += 1 - elif self.events.types[j] == MIGRATION: - suscepDate[self.events.newPopulations[j], self.events.newHaplotypes[j]] -= 1 - hapDate[self.events.newPopulations[j], self.events.haplotypes[j]] += 1 - elif self.events.types[j] == MULTITYPE: - pass - #TODO - if time_points[point] <= self.events.times[j]: - if output_file == True: - for i in range(self.popNum): - logDynamics[i].write(str(time_points[point]) + " ") - for k in range(self.susceptible_num): - logDynamics[i].write(str(suscepDate[i, k]) + " ") - for k in range(self.hapNum): - logDynamics[i].write(str(hapDate[i, k]) + " ") - logDynamics[i].write("\n") - point += 1 - else: - log["time"].append(time_points[point]) - for i in range(self.popNum): - for j in range(self.susceptible_num): - log["P" + str(i)]["S" + str(j)].append(suscepDate[i, j]) - for j in range(self.hapNum): - log["P" + str(i)]["H" + str(j)].append(hapDate[i, j]) - point += 1 - - if output_file == True: - for i in range(self.popNum-1, -1, -1): - logDynamics[i].close() - else: - return log - - def output_chain_events(self, name_file): - np.save(name_file, [self.events.times, self.events.types, self.events.haplotypes, self.events.populations, self.events.newHaplotypes, self.events.newPopulations]) - - def output_settings(self, file_template): - if isinstance(file_template, str) == False: - self.Error('#TODO') - if not os.path.isdir(file_template): - os.mkdir(file_template) - os.chdir(file_template) - comand = 'Command line command: ' - with open(file_template + ".rt", "w") as file: - file.write("#Rates_format_version 0.0.1\nH B D S") - for s in range(self.sites): - file.write(" M" + str(s)) - file.write("\n") - for hn in range(self.hapNum): - file.write(self.calculate_string(hn) + " " + str(self.bRate[hn]) + " " + str(self.dRate[hn]) + " " + str(self.sRate[hn]) + ' ') - for s in range(self.sites): - file.write(str(self.mRate[hn, s]) + "," + str(self.hapMutType[hn, s, 0]) + "," + str(self.hapMutType[hn, s, 1]) + "," + str(self.hapMutType[hn, s, 2]) + " ") - file.write("\n") - comand += (file_template + '/' + file_template + '.rt ') - - with open(file_template + ".pp", "w") as file: - file.write("#Population_format_version 0.0.1\nid size contactDensity conDenAfterLD startLD endLD samplingMulriplier\n") - for pn in range(self.popNum): - file.write(str(pn) + " " + str(self.sizes[pn]) + " " + str(self.contactDensity[pn]) + " " + str(self.contactDensityAfterLockdown[pn]) + "," + str(self.startLD[pn]) + "," + str(self.endLD[pn]) + " " + str(self.samplingMultiplier[pn]) + "\n") - comand += ('-pm ' + file_template + '/' + file_template + '.pp ') - - with open(file_template + ".mg", "w") as file: - file.write("#Migration_format_version 0.0.1\n") - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - file.write(str(self.migrationRates[pn1, pn2]) + " ") - file.write("\n") - comand += (file_template + '/' + file_template + '.mg ') - - with open(file_template + ".su", "w") as file: - file.write("#Susceptibility_format_version 0.0.1\nH T") - for sn in range(self.susNum): - file.write(" S" + str(sn)) - file.write("\n") - for hn in range(self.hapNum): - file.write(self.calculate_string(hn) + " " + str(self.suscType[hn])) - for sn in range(self.susNum): - file.write(" " + str(self.susceptibility[hn, sn])) - file.write("\n") - comand += ('-su ' + file_template + '/' + file_template + '.su ') - - with open(file_template + ".st", "w") as file: - file.write("#Susceptibility_format_version 0.0.1\n") - for sn1 in range(self.susNum): - for sn2 in range(self.susNum): - file.write(str(self.suscepTransition[sn1, sn2]) + " ") - file.write("\n") - comand += ('-st ' + file_template + '/' + file_template + '.st ') - print(comand) - os.chdir('../') - - def get_ts_object(self): - tc = tskit.TableCollection() - tc.sequence_length = self.sites + 1.0 - - for i in range(self.mig.nodeId.size()): - mig_nodeId, mig_time, mig_oldPop, mig_newPop = self.mig.get_migration(i) - tc.migrations.add_row(0.0, 1.0, mig_nodeId, mig_oldPop, mig_newPop, self.times[0] - mig_time) - - for i in range(self.popNum): - tc.populations.add_row(None) - - for i in range(2 * self.sCounter - 2): - tc.edges.add_row(0.0, self.sites+1.0, self.tree[i], i) - - child_or_parent = [1 for _ in range(2 * self.sCounter - 1)] - for i in range(2 * self.sCounter - 2): - child_or_parent[self.tree[i]] = 0 - - for i in range(2 * self.sCounter - 1): - tc.nodes.add_row(child_or_parent[i], self.times[0] - self.times[i], self.tree_pop[i]) - - for i in range(self.sites): - tc.sites.add_row(i+1, 'A') - - allele = ['A', 'T', 'C', 'G'] - for i in range(self.mut.nodeId.size()): - mut_nodeId, mut_DS, mut_AS, mut_site, mut_time = self.mut.get_mutation(i) - tc.mutations.add_row(site=mut_site, node=mut_nodeId, derived_state=allele[mut_DS], time=self.times[0] - mut_time) - - tc.sort() - return tc.tree_sequence() - - def get_tree(self): - if self.tree.shape[0] == 1: - print('Genealogy was not simulated. Use VGsim.genealogy() method to simulate it.') - sys.exit(1) - return self.tree, self.times - - def get_data_infectious(self, pop, hap, step_num): - time_points = [i*self.currentTime/step_num for i in range(step_num+1)] - Data = np.zeros(step_num+1) - Sample = np.zeros(step_num+1) - Data[0] = self.initial_infectious[pop, hap] - - point = 0 - for i in range(self.events.ptr): - while point != step_num and time_points[point] < self.events.times[i]: - Data[point+1] = Data[point] - Sample[point+1] = Sample[point] - point += 1 - - if self.events.types[i] == BIRTH and self.events.populations[i] == pop and self.events.haplotypes[i] == hap: - Data[point] += 1 - elif self.events.types[i] == DEATH or self.events.types[i] == SAMPLING or self.events.types[i] == MUTATION and self.events.populations[i] == pop and self.events.haplotypes[i] == hap: - Data[point] -= 1 - if self.events.types[i] == SAMPLING: - Sample[point] += 1 - elif self.events.types[i] == MUTATION and self.events.newHaplotypes[i] == hap and self.events.populations[i] == pop: - Data[point] += 1 - elif self.events.types[i] == MIGRATION and self.events.newPopulations[i] == pop and self.events.haplotypes[i] == hap: - Data[point] += 1 - elif self.events.types[i] == MULTITYPE: - for j in range(self.events.haplotypes[i], self.events.populations[i]): - if self.multievents.types[j] == BIRTH and self.multievents.haplotypes[j] == hap and self.multievents.populations[j] == pop: - Data[point] += self.multievents.num[j] - elif self.multievents.types[j] == DEATH or self.multievents.types[j] == SAMPLING or self.multievents.types[j] == MUTATION and self.multievents.haplotypes[j] == hap and self.multievents.populations[j] == pop: - Data[point] -= self.multievents.num[j] - if self.multievents.types[j] == SAMPLING: - Sample[point] += self.multievents.num[j] - elif self.multievents.types[j] == MUTATION and self.multievents.newHaplotypes[j] == hap and self.multievents.populations[j] == pop: - Data[point] += self.multievents.num[j] - elif self.multievents.types[j] == MIGRATION and self.multievents.newPopulations[j] == pop and self.multievents.haplotypes[j] == hap: - Data[point] += self.multievents.num[j] - - Lockdowns = [] - for i in range(self.loc.times.size()): - if self.loc.populationsId[i] == pop: - Lockdowns.append([self.loc.states[i], self.loc.times[i]]) - - return Data, Sample, time_points, Lockdowns - - def get_data_susceptible(self, pop, sus, step_num): - time_points = [i*self.currentTime/step_num for i in range(step_num+1)] - Data = np.zeros(step_num+1) - Data[0] = self.initial_susceptible[pop, sus] - - point = 0 - for i in range(self.events.ptr): - while point != step_num and time_points[point] < self.events.times[i]: - Data[point+1] = Data[point] - point += 1 - - if self.events.types[i] == BIRTH and self.events.populations[i] == pop and self.events.newHaplotypes[i] == sus: - Data[point] -= 1 - elif (self.events.types[i] == DEATH or self.events.types[i] == SAMPLING or self.events.types[i] == SUSCCHANGE) and self.events.populations[i] == pop and self.events.newHaplotypes[i] == sus: - Data[point] += 1 - elif self.events.types[i] == SUSCCHANGE and self.events.haplotypes[i] == sus and self.events.populations[i] == pop: - Data[point] -= 1 - elif self.events.types[i] == MIGRATION and self.events.newPopulations[i] == pop and self.events.newHaplotypes[i] == sus: - Data[point] -= 1 - elif self.events.types[i] == MULTITYPE: - for j in range(self.events.haplotypes[i], self.events.populations[i]): - if self.multievents.types[j] == BIRTH and self.multievents.newHaplotypes[j] == sus and self.multievents.populations[j] == pop: - Data[point] -= self.multievents.num[j] - elif (self.multievents.types[j] == DEATH or self.multievents.types[j] == SAMPLING or self.multievents.types[j] == SUSCCHANGE) and self.multievents.newHaplotypes[j] == sus and self.multievents.populations[j] == pop: - Data[point] += self.multievents.num[j] - elif self.multievents.types[j] == SUSCCHANGE and self.multievents.haplotypes[j] == sus and self.multievents.populations[j] == pop: - Data[point] -= self.multievents.num[j] - elif self.multievents.types[j] == MIGRATION and self.multievents.newPopulations[j] == pop and self.multievents.haplotypes[j] == sus: - Data[point] -= self.multievents.num[j] - - Lockdowns = [] - for i in range(self.loc.times.size()): - if self.loc.populationsId[i] == pop: - Lockdowns.append([self.loc.states[i], self.loc.times[i]]) - - return Data, time_points, Lockdowns - - - def Stats(self, time_simulation): - print("Number of samples:", self.sCounter) - print("Total number of iterations:", self.events.ptr) - print('Success number:', self.good_attempt) - print("Epidemic time:", self.currentTime) - print('Simulation time:', time_simulation) - print('Number of infections:', self.bCounter) - print('Number of recoveries:', self.dCounter) - if self.sites >= 1: - print('Number of mutations:', self.mCounter) - if self.popNum >= 2: - print('Number of accepted migrations:', self.migPlus) - print('Number of rejected migrations:', self.migNonPlus) - check = False - for sn in range(self.susNum): - if self.suscepCumulTransition[sn] != 0.0: - check = True - - if check: - print('Number of immunity transitions:', self.iCounter) - print('----------------------------------') - - def Debug(self): - print("Parameters") - print('first_simulation(mutable): ', self.first_simulation) - print("sampling_probability(const): ", self.sampling_probability) - print("memory_optimization(const): ", self.memory_optimization) - print() - print("sites(const): ", self.sites) - print("hapNum(const): ", self.hapNum) - print("currentHapNum(mutable): ", self.currentHapNum) - print("maxHapNum(mutable): ", self.maxHapNum) - print("popNum(const): ", self.popNum) - print("susNum(const): ", self.susNum) - print("bCounter(mutable): ", self.bCounter) - print("dCounter(mutable): ", self.dCounter) - print("sCounter(mutable): ", self.sCounter) - print("mCounter(mutable): ", self.mCounter) - print("iCounter(mutable):", self.iCounter) - print("swapLockdown(mutable): ", self.swapLockdown) - print("migPlus(mutable): ", self.migPlus) - print("migNonPlus(mutable): ", self.migNonPlus) - print("globalInfectious(mutable): ", self.globalInfectious) - print() - print("currentTime(mutable): ", self.currentTime) - print("totalRate(mutable): ", self.totalRate) - print("totalMigrationTate(mutable): ", self.totalMigrationRate) - print("rn(mutable): ", self.rn) - print() - print("suscType(const): ", sep=" ", end="") - for hn in range(self.hapNum): - print(self.suscType[hn], end=" ") - print() - print() - print("hapToNum(mutable): ", sep="", end="") - for hn in range(self.hapNum): - print(self.hapToNum[hn], end=" ") - print() - print("numToHap(mutable): ", sep="", end="") - for hn in range(self.maxHapNum): - print(self.numToHap[hn], end=" ") - print() - print("sizes(const): ", end="") - for pn in range(self.popNum): - print(self.sizes[pn], end=" ") - print() - print("totalSusceptible(mutable): ", end="") - for pn in range(self.popNum): - print(self.totalSusceptible[pn], end=" ") - print() - print("totalInfectious(mutable): ", end="") - for pn in range(self.popNum): - print(self.totalInfectious[pn], end=" ") - print() - print("lockdownON(mutable): ", end="") - for pn in range(self.popNum): - print(self.lockdownON[pn], end=" ") - print() - print() - print("susceptible(mutable)----") - for pn in range(self.popNum): - for sn in range(self.susNum): - print(self.susceptible[pn, sn], end=" ") - print() - print() - print("infectious(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - print(self.infectious[pn, hn], end=" ") - print() - print() - print() - print("Birth rate(const): ", sep="", end="") - for hn in range(self.hapNum): - print(self.bRate[hn], end=" ") - print() - print("Death rate(const): ", sep="", end="") - for hn in range(self.hapNum): - print(self.dRate[hn], end=" ") - print() - print("Sampling rate(const): ", sep="", end="") - for hn in range(self.hapNum): - print(self.sRate[hn], end=" ") - print() - print("tmRate(const): ", sep="", end="") - for hn in range(self.currentHapNum): - print(self.tmRate[hn], end=" ") - print() - print("maxEffectiveBirthMigration(const): ", sep="", end="") - for pn in range(self.popNum): - print(self.maxEffectiveBirthMigration[pn], end=" ") - print() - print("suscepCumulTransition(const): ", sep="", end="") - for sn in range(self.susNum): - print(self.suscepCumulTransition[sn], end=" ") - print() - print("immunePopRate(mutable): ", sep="", end="") - for pn in range(self.popNum): - print(self.immunePopRate[pn], end=" ") - print() - print("infectPopRate(mutable): ", sep="", end="") - for pn in range(self.popNum): - print(self.infectPopRate[pn], end=" ") - print() - print("popRate(mutable): ", sep="", end="") - for pn in range(self.popNum): - print(self.popRate[pn], end=" ") - print() - print("migPopRate(mutable): ", sep="", end="") - for pn in range(self.popNum): - print(self.migPopRate[pn], end=" ") - print() - print("actualSizes(const): ", end=" ") - for pn in range(self.popNum): - print(self.actualSizes[pn], end=" ") - print() - print("contactDensity(const): ", end=" ") - for pn in range(self.popNum): - print(self.contactDensity[pn], end=" ") - print() - print("contactDensityBeforeLockdown(const): ", end=" ") - for pn in range(self.popNum): - print(self.contactDensityBeforeLockdown[pn], end=" ") - print() - print("contactDensityBeforeLockdown(const): ", end=" ") - for pn in range(self.popNum): - print(self.contactDensityAfterLockdown[pn], end=" ") - print() - print("startLD(const): ", end=" ") - for pn in range(self.popNum): - print(self.startLD[pn], end=" ") - print() - print("endLD(const): ", end=" ") - for pn in range(self.popNum): - print(self.endLD[pn], end=" ") - print() - print("samplingMultiplier(const): ", end=" ") - for pn in range(self.popNum): - print(self.samplingMultiplier[pn], end=" ") - print() - print() - print("mRate(const)----") - for hn in range(self.hapNum): - for s in range(self.sites): - print(self.mRate[hn, s], end=" ") - print() - print() - print("susceptibility(const)----") - for hn in range(self.currentHapNum): - for sn in range(self.susNum): - print(self.susceptibility[hn, sn], end=" ") - print() - print() - print("tEventHapPopRate(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - print(self.tEventHapPopRate[pn, hn], end=" ") - print() - print() - print("suscepTransition(const)----") - for sn1 in range(self.susNum): - for sn2 in range(self.susNum): - print(self.suscepTransition[sn1, sn2], end=" ") - print() - print() - print("immuneSourcePopRate(mutable)----") - for pn in range(self.popNum): - for sn in range(self.susNum): - print(self.immuneSourcePopRate[pn, sn], end=" ") - print() - print() - print("hapPopRate(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - print(self.hapPopRate[pn, hn], end=" ") - print() - print() - print("migrationRates(const)----") - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - print(self.migrationRates[pn1, pn2], end=" ") - print() - print() - print("effectiveMigration(const)----") - for pn1 in range(self.popNum): - for pn2 in range(self.popNum): - print(self.effectiveMigration[pn1, pn2], end=" ") - print() - print() - print() - print("hapMutType(const)----") - for hn in range(self.currentHapNum): - for s in range(self.sites): - for i in range(3): - print(self.hapMutType[hn, s, i], end=" ") - print() - print() - print() - print("eventHapPopRate(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - for i in range(4): - print(self.eventHapPopRate[pn, hn, i], end=" ") - print() - print() - print() - print("susceptHapPopRate(mutable)----") - for pn in range(self.popNum): - for hn in range(self.currentHapNum): - for sn in range(self.susNum): - print(self.susceptHapPopRate[pn, hn, sn], end=" ") - print() - print() - print() - - - def get_proportion(self): - return self.migNonPlus / (self.events.ptr-1) - - ############################# - ### TAU LEAPING ALGORITHM ### - ############################# - - @cython.boundscheck(False) - @cython.wraparound(False) - cpdef void SimulatePopulation_tau(self, Py_ssize_t iterations, Py_ssize_t sample_size, float time, Py_ssize_t attempts): - cdef: - Py_ssize_t i, pn, propNum - bint success - - self.PrepareParameters(iterations) - self.CheckSizes() - - propNum = self.popNum*((self.popNum-1)*self.hapNum*self.susNum+self.susNum*(self.susNum-1)+self.hapNum*(2+self.sites*3+self.susNum)) - if self.globalInfectious == 0: - self.FirstInfection() - #TODO less memory for multievents - self.multievents.CreateEvents(iterations*propNum) - self.events.CreateEvents(iterations) - self.UpdateAllRates() - - for i in range(attempts): - self.seed = RndmWrapper(seed=(self.user_seed, i)) - if self.totalRate+self.totalMigrationRate != 0.0 and self.globalInfectious != 0: - while ((self.events.ptr 100: - self.Restart() - else: - self.good_attempt = i+1 - break - - if self.totalRate == 0.0 or self.globalInfectious == 0: - print('Simulation finished because no infections individuals remain!') - if self.events.ptr>=self.events.size: - print("Achieved maximal number of iterations.") - if self.sCounter>sample_size and sample_size!=-1: - print("Achieved sample size.") - if self.currentTime>time and time != -1: - print("Achieved internal time limit.") - if self.sCounter <= 1: - print('\033[41m{}\033[0m'.format('WARNING!'), 'Simulated less 2 samples, so genealogy will not work!') - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void Propensities(self): - cdef Py_ssize_t pn, spn, tpn, hn, sn, ssn, tsn, s, i - - for pn in range(self.popNum): - for hn in range(self.hapNum): - self.infectiousAuxTau[pn, hn] = 0.0 - for sn in range(self.susNum): - self.susceptibleAuxTau[pn, sn] = 0.0 - - for spn in range(self.popNum): - for tpn in range(self.popNum): - if spn == tpn: - continue - for sn in range(self.susNum): - for hn in range(self.hapNum): - for cn in range(self.conditionNum): - self.PropensitiesMigr[spn, tpn, sn, hn] = self.effectiveMigration[tpn, spn]*self.susceptible[tpn, sn]*\ - self.infectious[spn, hn]*self.bRate[hn, cn]*self.susceptibility[hn, sn]*self.migrationRates[spn, spn] - - self.infectiousAuxTau[tpn, hn] += self.PropensitiesMigr[spn, tpn, sn, hn] - self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesMigr[spn, tpn, sn, hn] - - for pn in range(self.popNum): - #Susceptibility transition - for ssn in range(self.susNum): - for tsn in range(self.susNum): - if ssn == tsn: - continue - self.PropensitiesSuscep[pn, ssn, tsn] = self.suscepTransition[ssn, tsn]*self.susceptible[pn, ssn] - - self.susceptibleAuxTau[pn, tsn] += self.PropensitiesSuscep[pn, ssn, tsn] - self.susceptibleAuxTau[pn, ssn] -= self.PropensitiesSuscep[pn, ssn, tsn] - - #Infectious-realted event - for hn in range(self.hapNum): - #Recovery - for cn in range(self.conditionNum): - self.PropensitiesRecovery[pn, hn] = self.dRate[hn, cn]*self.infectious[pn, hn] - - self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesRecovery[pn, hn] - self.infectiousAuxTau[pn, hn] -= self.PropensitiesRecovery[pn, hn] - - #Sampling - for cn in range(self.conditionNum): - self.PropensitiesSampling[pn, hn] = self.sRate[hn, cn]*self.infectious[pn, hn]*self.samplingMultiplier[pn] - - self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesSampling[pn, hn] - self.infectiousAuxTau[pn, hn] -= self.PropensitiesSampling[pn, hn] - - #Mutation - for s in range(self.sites): - for cn in range(self.conditionNum): - for i in range(3): - self.PropensitiesMutatations[pn, hn, s, i] = self.mRate[hn, cn, s]*self.hapMutType[hn, s, i]/\ - (self.hapMutType[hn, s, 0] + self.hapMutType[hn, s, 1] + self.hapMutType[hn, s, 2])*self.infectious[pn, hn] - - self.infectiousAuxTau[pn, self.Mutate(hn, s, i)] += self.PropensitiesMutatations[pn, hn, s, i] - self.infectiousAuxTau[pn, hn] -= self.PropensitiesMutatations[pn, hn, s, i] - - #Transmission - for tpn in range(self.popNum): - for hn in range(self.hapNum): - for cn in range(self.conditionNum): - for sn in range(self.susNum): - self.PropensitiesTransmission[tpn, hn, sn] = 0.0 - for spn in range(self.popNum): - self.PropensitiesTransmission[tpn, hn, sn] += self.bRate[hn, cn]*self.susceptibility[hn, sn]*self.migrationRates[tpn, spn]*\ - self.migrationRates[tpn, spn]*self.contactDensity[spn]*self.susceptible[tpn, sn]*self.infectious[tpn, hn]/\ - self.actualSizes[spn] - - self.infectiousAuxTau[tpn, hn] += self.PropensitiesTransmission[tpn, hn, sn] - self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesTransmission[tpn, hn, sn] - - @cython.cdivision(True) - cdef Py_ssize_t Mutate(self, Py_ssize_t hi, Py_ssize_t s, Py_ssize_t DS): - cdef Py_ssize_t digit4, AS - - digit4 = 4**(self.sites-s-1) - AS = int(floor(hi/digit4) % 4) - if DS >= AS: - DS += 1 - return hi + (DS-AS)*digit4 - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void ChooseTau(self, float epsilon=0.03): - cdef: - Py_ssize_t pn, hn, sn - double tmp - - self.tau_l = 1.0 - for pn in range(self.popNum): - for hn in range(self.hapNum): - if abs(self.infectiousAuxTau[pn, hn]) < 1e-8: - continue - tmp = max(epsilon*self.infectious[pn,hn]/2.0,1.0)/abs(self.infectiousAuxTau[pn, hn]) - if tmp < self.tau_l: - self.tau_l = tmp - for sn in range(self.susNum): - if abs(self.susceptibleAuxTau[pn, sn]) < 1e-8: - continue - tmp = max(epsilon*self.susceptible[pn,sn]/2.0,1.0)/abs(self.susceptibleAuxTau[pn, sn]) - if tmp < self.tau_l: - self.tau_l = tmp - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef bint GenerateEvents_tau(self): - cdef Py_ssize_t event_num, pn, spn, tpn, hn, sn, ssn, tsn, s, i - - for pn in range(self.popNum): - for sn in range(self.susNum): - self.susceptibleDelta[pn, sn] = 0 - for hn in range(self.hapNum): - self.infectiousDelta[pn, hn] = 0 - - #Migrations - for spn in range(self.popNum): - for tpn in range(self.popNum): - if spn == tpn: - continue - for sn in range(self.susNum): - for hn in range(self.hapNum): - event_num = self.DrawEventsNum(self.PropensitiesMigr[spn, tpn, sn, hn]*self.tau_l) - self.eventsMigr[spn, tpn, sn, hn] = event_num - - self.infectiousDelta[spn, hn] += event_num - self.susceptibleDelta[tpn, sn] -= event_num - - - for pn in range(self.popNum): - #Susceptibility transition - for ssn in range(self.susNum): - for tsn in range(self.susNum): - if ssn == tsn: - continue - event_num = self.DrawEventsNum(self.PropensitiesSuscep[pn, ssn, tsn]*self.tau_l) - self.eventsSuscep[pn, ssn, tsn] = event_num - - self.susceptibleDelta[pn, tsn] += event_num - self.susceptibleDelta[pn, ssn] -= event_num - - #Infectious-realted event - for hn in range(self.hapNum): - #Recovery - event_num = self.DrawEventsNum(self.PropensitiesRecovery[pn, hn]*self.tau_l) - self.eventsRecovery[pn, hn] = event_num - - self.susceptibleDelta[pn, self.suscType[hn]] += event_num - self.infectiousDelta[pn, hn] -= event_num - - #Sampling - event_num = self.DrawEventsNum(self.PropensitiesSampling[pn, hn]*self.tau_l) - self.eventsSampling[pn, hn] = event_num - - self.susceptibleDelta[pn, self.suscType[hn]] += event_num - self.infectiousDelta[pn, hn] -= event_num - - #Mutation - for s in range(self.sites): - for i in range(3): - event_num = self.DrawEventsNum(self.PropensitiesMutatations[pn, hn, s, i]*self.tau_l) - self.eventsMutatations[pn, hn, s, i] = event_num - - self.infectiousDelta[pn, self.Mutate(hn, s, i)] += event_num - self.infectiousDelta[pn, hn] -= event_num - - #Transmission - for sn in range(self.susNum): - event_num = self.DrawEventsNum(self.PropensitiesTransmission[pn, hn, sn]*self.tau_l) - self.eventsTransmission[pn, hn, sn] = event_num - - self.infectiousDelta[pn, hn] += event_num - self.susceptibleDelta[pn, sn] -= event_num - - for pn in range(self.popNum): - for sn in range(self.susNum): - if self.susceptibleDelta[pn, sn] + self.susceptible[pn, sn] < 0 or self.susceptibleDelta[pn, sn] + self.susceptible[pn, sn] > self.sizes[pn]: - return False - for hn in range(self.hapNum): - if self.infectiousDelta[pn, hn] + self.infectious[pn, hn] < 0 or self.infectiousDelta[pn, hn] + self.infectious[pn, hn] > self.sizes[pn]: - return False - return True - - cdef inline Py_ssize_t DrawEventsNum(self, double tau): - return random_poisson(self.seed.rng, lam=tau) - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void UpdateCompartmentCounts_tau(self): - #TODO not write events with 0 num - cdef Py_ssize_t event_num, pn, spn, tpn, hn, sn, ssn, tsn, s, i - - #Migrations - for spn in range(self.popNum): - for tpn in range(self.popNum): - if spn == tpn: - continue - for sn in range(self.susNum): - for hn in range(self.hapNum): - event_num = self.eventsMigr[spn, tpn, sn, hn] - self.NewInfections(tpn, sn, hn, event_num) - self.multievents.AddEvents(event_num, self.currentTime, MIGRATION, hn, spn, sn, tpn) - self.migPlus += event_num - - for pn in range(self.popNum): - #Susceptibility transition - for ssn in range(self.susNum): - for tsn in range(self.susNum): - if ssn == tsn: - continue - event_num = self.eventsSuscep[pn, ssn, tsn] - self.susceptible[pn, tsn] += event_num - self.susceptible[pn, ssn] -= event_num - self.multievents.AddEvents(event_num, self.currentTime, SUSCCHANGE, ssn, pn, tsn, 0) - self.iCounter += event_num - - #Infectious-realted event - for hn in range(self.hapNum): - #Recovery - event_num = self.eventsRecovery[pn, hn] - self.NewRecoveries(pn, self.suscType[hn], hn, event_num) - self.multievents.AddEvents(event_num, self.currentTime, DEATH, hn, pn, self.suscType[hn], 0) - self.dCounter += event_num - - #Sampling - event_num = self.eventsSampling[pn, hn] - self.NewRecoveries(pn, self.suscType[hn], hn, event_num) - self.multievents.AddEvents(event_num, self.currentTime, SAMPLING, hn, pn, self.suscType[hn], 0) - self.sCounter += event_num - - #Mutation - for s in range(self.sites): - for i in range(3): - nhn = self.Mutate(hn, s, i) - event_num = self.eventsMutatations[pn, hn, s, i] - self.infectious[pn, nhn] += event_num - self.infectious[pn, hn] -= event_num - self.multievents.AddEvents(event_num, self.currentTime, MUTATION, hn, pn, nhn, 0) - self.mCounter += event_num - - #Transmission - for sn in range(self.susNum): - event_num = self.eventsTransmission[pn, hn, sn] - self.NewInfections(pn, sn, hn, event_num) - self.multievents.AddEvents(event_num, self.currentTime, BIRTH, hn, pn, sn, 0) - self.bCounter += event_num - - @cython.boundscheck(False) - @cython.wraparound(False) - def Get_MultiEvents(self, id = None): - if id is None: - for i in range(self.multievents.ptr): - ev = self.multievents.GetEvent(i) - ev.PrintEvent() - else: - ev = self.multievents.GetEvent(id) - ev.PrintEvent() - - - def PrintCounters(self): - print("Birth counter(mutable): ", self.bCounter) - print("Death counter(mutable): ", self.dCounter) - print("Sampling counter(mutable): ", self.sCounter) - print("Mutation counter(mutable): ", self.mCounter) - print("Immunity transition counter(mutable):", self.iCounter) - print("Migration counter(mutable):", self.migPlus) - - def PrintPropensities(self): - #self.FirstInfection() - self.UpdateAllRates() - self.Propensities() - print("Migrations") - for s in range(self.popNum): - for r in range(self.popNum): - if s == r: - continue - for i in range(self.susNum): - for h in range(self.hapNum): - print (s, r, i, h, self.PropensitiesMigr[s, r, i, h]) - - - for s in range(self.popNum): - print("Susceptibility transition") - for i in range(self.susNum): - for j in range(self.susNum): - if i == j: - continue - print(s, i, j, self.PropensitiesSuscep[s, i, j]) - - #Infectious-realted event - for h in range(self.hapNum): - print("Recovery ", s, h, self.suscType[h], self.PropensitiesRecovery[s, h]) - print("Sampling ", s, h, self.suscType[h], self.PropensitiesSampling[s, h]) - - for site in range(self.sites): - for i in range(3): - #ht = self.Mutate(h, site, i) - print("Mutation", s, h, site, i, self.PropensitiesMutatations[s, h, site, i]) - #Transmission - for i in range(self.susNum): - print("Transmission", s, h, i, self.PropensitiesTransmission[s, h, i]) - #print("migr=", self.migrationRates[s, s], " prop", prop) diff --git a/VGsim/src/__init__.py b/VGsim/src/__init__.py deleted file mode 100644 index 4824c80..0000000 --- a/VGsim/src/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from ._interface import Simulator - -__version__ = "0.2" diff --git a/VGsim/src/_interface.py b/VGsim/src/_interface.py deleted file mode 100644 index ca2eb3b..0000000 --- a/VGsim/src/_interface.py +++ /dev/null @@ -1,295 +0,0 @@ -from ._BirthDeath import BirthDeathModel -from .IO import writeGenomeNewick, writeMutations -from random import randrange -import sys -import matplotlib.pyplot as plt -import numpy as np -import time - -class Simulator: - def __init__(self, number_of_sites=0, populations_number=1, number_of_susceptible_groups=1, seed=None, sampling_probability=False, memory_optimization=False, conditionNum=1): - self.fig = None - if seed == None: - seed = int(randrange(sys.maxsize)) - print('User seed:', seed) - - self.simulation = BirthDeathModel(number_of_sites=number_of_sites, populations_number=populations_number, \ - number_of_susceptible_groups=number_of_susceptible_groups, seed=seed, sampling_probability=sampling_probability, \ - memory_optimization=memory_optimization, conditionNum=conditionNum) - - - def print_basic_parameters(self): - self.simulation.print_basic_parameters() - - def print_populations(self): - self.simulation.print_populations() - - def print_immunity_model(self): - self.simulation.print_immunity_model() - - def print_all(self, basic_parameters=True, populations=True, immunity_model=True): - if basic_parameters: - self.simulation.print_basic_parameters() - if populations: - self.simulation.print_populations() - if immunity_model: - self.simulation.print_immunity_model() - - - def set_initial_haplotype(self, amount): - self.simulation.set_initial_haplotype(amount) - - def set_step_haplotype(self, amount): - self.simulation.set_step_haplotype(amount) - - - def set_transmission_rate(self, rate, haplotype=None, condition=None): - self.simulation.set_transmission_rate(rate, haplotype, condition) - - def set_recovery_rate(self, rate, haplotype=None, condition=None): - self.simulation.set_recovery_rate(rate, haplotype, condition) - - def set_sampling_rate(self, rate, haplotype=None, condition=None): - self.simulation.set_sampling_rate(rate, haplotype, condition) - - def set_mutation_rate(self, rate=None, probabilities=None, haplotype=None, mutation=None, condition=None): - self.simulation.set_mutation_rate(rate, probabilities, haplotype, mutation, condition) - - @property - def condition_rate(self): - return self.simulation.condition_rate - - def set_condition_rate(self, rate, c1=0, c2=0): - self.simulation.set_condition_rate(rate, c1, c2) - - - def set_susceptibility_type(self, susceptibility_type, haplotype=None): - self.simulation.set_susceptibility_type(susceptibility_type, haplotype) - - def set_susceptibility(self, rate, haplotype=None, susceptibility_type=None): - self.simulation.set_susceptibility(rate, haplotype, susceptibility_type) - - def set_immunity_transition(self, rate, source=None, target=None): - self.simulation.set_immunity_transition(rate, source, target) - - - def set_population_size(self, size, population=None): - self.simulation.set_population_size(size, population) - - def set_contact_density(self, value, population=None): - self.simulation.set_contact_density(value, population) - - def set_npi(self, parameters, population=None): - self.simulation.set_npi(parameters, population) - - def set_sampling_multiplier(self, multiplier, population=None): - self.simulation.set_sampling_multiplier(multiplier, population) - - def set_migration_probability(self, probability=None, total_probability=None, source=None, target=None): - self.simulation.set_migration_probability(probability, total_probability, source, target) - - def set_susceptible(self, amount, source_type, target_type, population=None): - self.simulation.set_susceptible(amount, source_type, target_type, population) - - def set_infectious(self, amount, source_type, target_haplotype, population=None): - self.simulation.set_infectious(amount, source_type, target_haplotype, population) - - - def set_chain_events(self, file_name): - self.simulation.set_chain_events(file_name) - - def set_settings(self, file_template): - self.simulation.set_settings(file_template) - - def set_state(self, file_template): - self.set_chain_events(file_template) - self.set_settings(file_template) - - - def output_newick(self, file_template=None, file_path = None): - pruferSeq, times, mut, populations = self.simulation.output_tree_mutations() - writeGenomeNewick(pruferSeq, times, populations, file_template, file_path) - - def output_mutations(self, file_template=None, file_path = None): - pruferSeq, times, mut, populations = self.simulation.output_tree_mutations() - writeMutations(mut, len(pruferSeq), file_template, file_path) - - def output_migrations(self, file_template=None, file_path = None): - self.simulation.output_migrations(file_template, file_path) - - def output_sample_data(self, output_print=False): - time, pop, hap = self.simulation.output_sample_data() - if output_print: - return time, pop, hap - else: - print(time) - print(pop) - print(hap) - - def output_epidemiology_timelines(self, step=1000, output_file=False): - if output_file: - self.simulation.output_epidemiology_timelines(step, output_file) - else: - return self.simulation.output_epidemiology_timelines(step, output_file) - - def output_chain_events(self, file_name="chain_events"): - self.simulation.output_chain_events(file_name) - - def output_settings(self, file_template="parameters"): - self.simulation.output_settings(file_template) - - def output_state(self): - self.output_chain_events() - self.output_settings() - - def get_ts_object(self): - return self.simulation.get_ts_object() - - def get_tree(self): - return self.simulation.get_tree() - - def get_data_susceptible(self, population, susceptibility_type, - step_num): # returns susceptible, time_points, lockdowns - return self.simulation.get_data_susceptible(population, susceptibility_type, step_num) - - def get_data_infectious(self, population, haplotype, - step_num): # returns infections, sample, time_points, lockdowns - return self.simulation.get_data_infectious(population, haplotype, step_num) - - def add_plot_infectious(self, population, haplotype, step_num=100, label_infectious=None, label_samples=None): - if self.fig == None: - self.fig, self.ax = plt.subplots(figsize=(8, 6)) - self.ax.set_ylabel('Number of samples') - self.ax.set_xlabel('Time') - self.ax_2 = self.ax.twinx() - self.ax_2.set_ylabel('Number of individuals') - - if isinstance(haplotype, int) == True: - self.plot_infectious(population, haplotype, step_num, label_infectious, label_samples) - elif isinstance(haplotype, str) == True: - haplotypes = self.simulation.create_list_haplotypes(haplotype) - for hi in haplotypes: - self.plot_infectious(population, hi, step_num, label_infectious, label_samples) - else: - print("#TODO") - - def plot_infectious(self, population, haplotype, step_num, label_infectious, label_samples): - infections, sample, time_points, lockdowns = self.simulation.get_data_infectious(population, haplotype, step_num) - if label_infectious == None: - self.ax_2.plot(time_points, infections, label='Infectious pop:' + str(population) + ' hap:' + self.simulation.calculate_string(haplotype)) - elif isinstance(label_infectious, str) == True: - self.ax_2.plot(time_points, infections, label=label_infectious) - else: - print("#TODO") - - if label_samples == None: - self.ax.plot(time_points, sample, "--", label='Samples pop:' + str(population) + ' hap:' + self.simulation.calculate_string(haplotype)) - elif isinstance(label_samples, str) == True: - self.ax.plot(time_points, sample, "--", label=label_samples) - else: - print("#TODO") - - if len(lockdowns) != 0: - point = 0 - pointEnd = 0 - for ld in range(0, len(lockdowns), 2): - if ld+1 == len(lockdowns): - while time_points[point] < lockdowns[ld][1]: - point += 1 - plt.fill_between(time_points[point:], infections[point:], alpha=0.2) - else: - while time_points[point] < lockdowns[ld][1]: - point += 1 - while time_points[pointEnd] < lockdowns[ld+1][1]: - pointEnd += 1 - if pointEnd == point: - continue - plt.fill_between(time_points[point:pointEnd+1], infections[point:pointEnd+1], alpha=0.2) - - def add_plot_susceptible(self, population, susceptibility_type, step_num=100, label_susceptible=None): - if self.fig == None: - self.fig, self.ax = plt.subplots(figsize=(8, 6)) - self.ax.set_ylabel('Number of samples') - self.ax.set_xlabel('Time') - self.ax_2 = self.ax.twinx() - self.ax_2.set_ylabel('Number of individuals') - - susceptible, time_points, lockdowns = self.simulation.get_data_susceptible(population, susceptibility_type, step_num) - if label_susceptible == None: - self.ax_2.plot(time_points, susceptible, label='Susceptible pop:' + str(population) + ' sus:' + str(susceptibility_type)) - elif isinstance(label_susceptible, str) == True: - self.ax_2.plot(time_points, susceptible, label=label_susceptible) - else: - print("#TODO") - - if len(lockdowns) != 0: - point = 0 - pointEnd = 0 - for ld in range(0, len(lockdowns), 2): - if ld+1 == len(lockdowns): - while time_points[point] < lockdowns[ld][1]: - point += 1 - plt.fill_between(time_points[point:], susceptible[point:], alpha=0.2) - else: - while time_points[point] < lockdowns[ld][1]: - point += 1 - while time_points[pointEnd] < lockdowns[ld+1][1]: - pointEnd += 1 - if pointEnd == point: - continue - plt.fill_between(time_points[point:pointEnd+1], susceptible[point:pointEnd+1], alpha=0.2) - - def add_legend(self): - lines_1, labels_1 = self.ax.get_legend_handles_labels() - lines_2, labels_2 = self.ax_2.get_legend_handles_labels() - lines = lines_1 + lines_2 - labels = labels_1 + labels_2 - self.ax.legend(lines, labels, loc=0) - - def add_title(self, name="Plot"): - self.ax.set_title(name) - - def plot(self, name_file=None): - if name_file: - plt.savefig(name_file) - else: - plt.show() - self.fig = None - - - def simulate(self, iterations=1000, sample_size=None, epidemic_time=-1, method='direct', attempts=200): - if sample_size is None: - sample_size = iterations - if epidemic_time is None: - epidemic_time = -1 - - start_time = time.time() - if method == 'direct': - self.simulation.SimulatePopulation(iterations, sample_size, epidemic_time, attempts) - self.simulation.Stats(time.time() - start_time) - elif method == 'tau': - self.simulation.SimulatePopulation_tau(iterations, sample_size, epidemic_time, attempts) - self.simulation.Stats(time.time() - start_time) - else: - print("Unknown method. Choose between 'direct' and 'tau'.") - - def genealogy(self, seed=None): - self.simulation.GetGenealogy(seed) - - - def citation(self): - print("VGsim: scalable viral genealogy simulator for global pandemic") - print("Vladimir Shchur, Vadim Spirin, Dmitry Sirotkin, EvgeniBurovski, Nicola De Maio, Russell Corbett-Detig") - print("medRxiv 2021.04.21.21255891; doi: https://doi.org/10.1101/2021.04.21.21255891") - - def debug(self): - self.simulation.Debug() - - def get_proportion(self): - return self.simulation.get_proportion() - - def print_counters(self): - self.simulation.PrintCounters() - - def print_propensities(self): - self.simulation.PrintPropensities() diff --git a/VGsim/src/events.pxi b/VGsim/src/events.pxi deleted file mode 100644 index 015b0f5..0000000 --- a/VGsim/src/events.pxi +++ /dev/null @@ -1,155 +0,0 @@ -# use named constants for event types -DEF BIRTH = 0 -DEF DEATH = 1 -DEF SAMPLING = 2 -DEF MUTATION = 3 -DEF SUSCCHANGE = 4 -DEF MIGRATION = 5 -DEF MULTITYPE = 6 -DEF CONDITION = 7 - -cdef class Event: - cdef: - Py_ssize_t type_, haplotype, population, newHaplotype, newPopulation - double time - - def __init__(self, double time, Py_ssize_t type_, Py_ssize_t haplotype, Py_ssize_t population, Py_ssize_t newHaplotype, Py_ssize_t newPopulation): - self.time = time - self.type_ = type_ - self.haplotype = haplotype - self.population = population - self.newHaplotype = newHaplotype - self.newPopulation = newPopulation - - -cdef class Events: - cdef: - Py_ssize_t size, ptr - - Py_ssize_t[::1] types, haplotypes, populations, newHaplotypes, newPopulations - double[::1] times - - def __init__(self): - self.size = 0 - self.ptr = 0#pointer to the first empty cell - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void AddEvent(self, double time_, Py_ssize_t type_, Py_ssize_t haplotype, Py_ssize_t population, Py_ssize_t newHaplotype, Py_ssize_t newPopulation): - self.times[ self.ptr ] = time_ - self.types[ self.ptr ] = type_ - self.haplotypes[ self.ptr ] = haplotype - self.populations[ self.ptr ] = population - self.newHaplotypes[ self.ptr ] = newHaplotype - self.newPopulations[ self.ptr ] = newPopulation - self.ptr += 1 - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef Event GetEvent(self, Py_ssize_t e_id): - ev = Event( self.times[ e_id ], self.types[ e_id ], self.haplotypes[ e_id ], self.populations[ e_id ], self.newHaplotypes[ e_id ], self.newPopulations[ e_id ]) - return( ev ) - - cdef void CreateEvents(self, Py_ssize_t iterations): - if self.ptr == 0: - self.size += iterations - self.times = np.zeros(self.size, dtype=float) - self.types = np.zeros(self.size, dtype=np.int64) - self.haplotypes = np.zeros(self.size, dtype=np.int64) - self.populations = np.zeros(self.size, dtype=np.int64) - self.newHaplotypes = np.zeros(self.size, dtype=np.int64) - self.newPopulations = np.zeros(self.size, dtype=np.int64) - elif iterations + self.ptr - self.size > 0: - self.times = np.concatenate((self.times, np.zeros(iterations + self.ptr - self.size, dtype=float))) - self.types = np.concatenate((self.types, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.haplotypes = np.concatenate((self.haplotypes, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.populations = np.concatenate((self.populations, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.newHaplotypes = np.concatenate((self.newHaplotypes, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.newPopulations = np.concatenate((self.newPopulations, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.size = iterations + self.ptr - -cdef class multiEvent: - cdef: - Py_ssize_t num, type_, haplotype, population, newHaplotype, newPopulation - double time - - def __init__(self, Py_ssize_t num, double time, Py_ssize_t type_, Py_ssize_t haplotype, Py_ssize_t population, Py_ssize_t newHaplotype, Py_ssize_t newPopulation): - self.num = num - self.time = time - self.type_ = type_ - self.haplotype = haplotype - self.population = population - self.newHaplotype = newHaplotype - self.newPopulation = newPopulation - - def PrintEvent(self): - if self.type_ == BIRTH: - tn = "B" - elif self.type_ == DEATH: - tn = "D" - elif self.type_ == SAMPLING: - tn = "S" - elif self.type_ == MUTATION: - tn = "MUT" - elif self.type_ == SUSCCHANGE: - tn = "SUS" - elif self.type_ == MIGRATION: - tn = "MIG" - elif self.type_ == CONDITION: - tn = "CON" - print("num=", self.num, - " time=", self.time, - " type=", tn, - " hap=", self.haplotype, - " pop=", self.population, - " dest1=", self.newHaplotype, - " dest2=", self.newPopulation) - -cdef class multiEvents: - cdef: - Py_ssize_t size, ptr - - Py_ssize_t[::1] num, types, haplotypes, populations, newHaplotypes, newPopulations - double[::1] times - - def __init__(self): - self.size = 0 - self.ptr = 0#pointer to the first empty cell - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void AddEvents(self, Py_ssize_t num, double time_, Py_ssize_t type_, Py_ssize_t haplotype, Py_ssize_t population, Py_ssize_t newHaplotype, Py_ssize_t newPopulation): - self.num[ self.ptr ] = num - self.times[ self.ptr ] = time_ - self.types[ self.ptr ] = type_ - self.haplotypes[ self.ptr ] = haplotype - self.populations[ self.ptr ] = population - self.newHaplotypes[ self.ptr ] = newHaplotype - self.newPopulations[ self.ptr ] = newPopulation - self.ptr += 1 - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef multiEvent GetEvent(self, Py_ssize_t e_id): - ev = multiEvent( self.num[ e_id ], self.times[ e_id ], self.types[ e_id ], self.haplotypes[ e_id ], self.populations[ e_id ], self.newHaplotypes[ e_id ], self.newPopulations[ e_id ]) - return( ev ) - - cdef void CreateEvents(self, Py_ssize_t iterations): - if self.ptr == 0: - self.size = iterations - self.num = np.zeros(self.size, dtype=np.int64) - self.times = np.zeros(self.size, dtype=float) - self.types = np.zeros(self.size, dtype=np.int64) - self.haplotypes = np.zeros(self.size, dtype=np.int64) - self.populations = np.zeros(self.size, dtype=np.int64) - self.newHaplotypes = np.zeros(self.size, dtype=np.int64) - self.newPopulations = np.zeros(self.size, dtype=np.int64) - else: - self.num = np.concatenate((self.num, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.times = np.concatenate((self.times, np.zeros(iterations + self.ptr - self.size, dtype=float))) - self.types = np.concatenate((self.types, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.haplotypes = np.concatenate((self.haplotypes, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.populations = np.concatenate((self.populations, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.newHaplotypes = np.concatenate((self.newHaplotypes, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.newPopulations = np.concatenate((self.newPopulations, np.zeros(iterations + self.ptr - self.size, dtype=np.int64))) - self.size = iterations + self.ptr diff --git a/VGsim/src/fast_choose.pxi b/VGsim/src/fast_choose.pxi deleted file mode 100644 index 309b53c..0000000 --- a/VGsim/src/fast_choose.pxi +++ /dev/null @@ -1,110 +0,0 @@ -def print_err(*args, **kwargs): - print(*args, file=sys.stderr, **kwargs) - -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.cdivision(True) -cdef inline (Py_ssize_t, double) fastChoose1(double[::1] w, double tw, double rn): - cdef: - Py_ssize_t i - double total - - rn = tw*rn - i = 0 - total = w[0] - while total < rn and i < w.shape[0] - 1: - i += 1 - total += w[i] - if w[i] == 0.0: - print_err("fastChoose1() alert: 0-weight sampled") - for i in range(w.shape[0]): - print_err(w[i], end=", ") - print_err() - print_err(tw) - print_err(rn) - sys.exit(1) - return [ i, ( rn-(total-w[i]) )/w[i] ] - -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.cdivision(True) -cdef inline (Py_ssize_t, double) fastChoose2(npy_int64[::1] w, npy_int64 tw, double rn): - cdef: - Py_ssize_t i - double total - - rn = tw*rn - i = 0 - total = w[0] - while total < rn and i < w.shape[0] - 1: - i += 1 - total += w[i] - if w[i] == 0.0: - print_err("fastChoose2() alert: 0-weight sampled") - for i in range(w.shape[0]): - print_err(w[i], end=", ") - print_err() - print_err(tw) - print_err(rn) - sys.exit(1) - return [ i, ( rn-(total-w[i]) )/w[i] ] - -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.cdivision(True) -cdef inline (Py_ssize_t, double) fastChoose1_skip(double[::1] w, double tw, double rn, Py_ssize_t skip): - cdef: - Py_ssize_t i - double total - - rn = tw*rn - if skip > 0: - i = 0 - total = w[0] - else: - i = 1 - total = w[1] - while total < rn and i < w.shape[0] - 1: - i += 1 - if i == skip: - i += 1 - total += w[i] - if w[i] == 0.0: - print_err("fastChoose1_skip() alert: 0-weight sampled") - for i in range(w.shape[0]): - print_err(w[i], end=", ") - print_err() - print_err(tw) - print_err(rn) - sys.exit(1) - return [ i, ( rn-(total-w[i]) )/w[i] ] - -@cython.boundscheck(False) -@cython.wraparound(False) -@cython.cdivision(True) -cdef inline (Py_ssize_t, double) fastChoose2_skip(npy_int64[::1] w, npy_int64 tw, double rn, Py_ssize_t skip): - cdef: - Py_ssize_t i - double total - - rn = tw*rn - if skip > 0: - i = 0 - total = w[0] - else: - i = 1 - total = w[1] - while total < rn and i < w.shape[0] - 1: - i += 1 - if i == skip: - i += 1 - total += w[i] - if w[i] == 0.0: - print_err("fastChoose2_skip() alert: 0-weight sampled") - for i in range(w.shape[0]): - print_err(w[i], end=", ") - print_err() - print_err(tw) - print_err(rn) - sys.exit(1) - return [ i, ( rn-(total-w[i]) )/w[i] ] diff --git a/VGsim/src/meson.build b/VGsim/src/meson.build deleted file mode 100644 index 23ec3cb..0000000 --- a/VGsim/src/meson.build +++ /dev/null @@ -1,40 +0,0 @@ -### Build the cython extension ### - -# compile .cpp for _BirthDeath.pyx -_birth_death = custom_target('_BirthDeath', - output : '_BirthDeath.cxx', - input : '_BirthDeath.pyx', - depend_files: ['models.pxi', 'fast_choose.pxi'], - command : [cython, '--cplus', '-3', '--fast-fail', '@INPUT@', '-o', '@OUTPUT@'] -) - -npymath_path = incdir_numpy / '..' / 'lib' -npymath_lib = meson.get_compiler('cpp').find_library('npymath', dirs: npymath_path) -npyrandom_path = incdir_numpy / '..' / '..' / 'random' / 'lib' -npyrandom_lib = meson.get_compiler('cpp').find_library('npyrandom', dirs: npyrandom_path) - -# generate .so for rndm -py3.extension_module( - '_BirthDeath', _birth_death, - include_directories: [inc_np, npymath_path, npyrandom_path], - dependencies : [py3_dep, npymath_lib, npyrandom_lib], - install: true, - subdir: 'VGsim' -) - - -### Install stuff: python files + the extension just built ### - -#create a list of files which will be installed as source -python_sources = [ - '__init__.py', - 'IO.py', - '_interface.py', -] - -# install source files in current dir -py3.install_sources( - python_sources, - pure: false, - subdir: 'VGsim' -) diff --git a/VGsim/src/models.pxi b/VGsim/src/models.pxi deleted file mode 100644 index c21c893..0000000 --- a/VGsim/src/models.pxi +++ /dev/null @@ -1,65 +0,0 @@ -cdef class Mutations: - cdef: - vector[Py_ssize_t] nodeId, AS, DS, site - vector[double] time - - def __init__(self):#AS = ancestral state, DS = derived state - pass - - @cython.boundscheck(False) - @cython.wraparound(False) - @cython.cdivision(True) - cdef void AddMutation(self, Py_ssize_t nodeId, Py_ssize_t haplotype, Py_ssize_t newHaplotype, double time): - cdef: - Py_ssize_t ASDSdigit4, site, digit4 - - ASDSdigit4 = int(abs(newHaplotype - haplotype)) - site = 0 - while ASDSdigit4 >= 4: - ASDSdigit4 = ASDSdigit4 / 4 - site += 1 - digit4 = int(4**site) - self.nodeId.push_back(nodeId) - self.DS.push_back(int(floor(newHaplotype/digit4) % 4)) - self.AS.push_back(int(floor(haplotype/digit4) % 4)) - self.site.push_back(int(site)) - self.time.push_back(time) - - def get_mutation(self, id_mut): - return self.nodeId[id_mut], self.DS[id_mut], self.AS[id_mut], self.site[id_mut], self.time[id_mut] - - -cdef class Migrations: - cdef: - vector[Py_ssize_t] nodeId, oldPop, newPop - vector[double] time - - def __init__(self): - pass - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void AddMigration(self, Py_ssize_t nodeId, double time, Py_ssize_t oldPop, Py_ssize_t newPop): - self.nodeId.push_back(nodeId) - self.time.push_back(time) - self.oldPop.push_back(oldPop) - self.newPop.push_back(newPop) - - def get_migration(self, id_mig): - return self.nodeId[id_mig], self.time[id_mig], self.oldPop[id_mig], self.newPop[id_mig] - -cdef class Lockdowns: - cdef: - vector[bint] states - vector[Py_ssize_t] populationsId - vector[double] times - - def __init__(self): - pass - - @cython.boundscheck(False) - @cython.wraparound(False) - cdef void AddLockdown(self, bint state, Py_ssize_t populationId, double time): - self.states.push_back(state) - self.populationsId.push_back(populationId) - self.times.push_back(time) diff --git a/VGsim/testing/check_simulator.py b/VGsim/testing/check_simulator.py deleted file mode 100644 index 9492949..0000000 --- a/VGsim/testing/check_simulator.py +++ /dev/null @@ -1,179 +0,0 @@ -import VGsim -import sys -import os -import numpy as np - -def check_data(num): - ref = np.load("reference_" + str(num) + ".npy") - test = np.load("test_" + str(num) + ".npy") - - for i in range(ref.shape[1]): - if ref[0, i] != test[0, i] or ref[1, i] != test[1, i] or ref[2, i] != test[2, i] or ref[3, i] != test[3, i] or ref[4, i] != test[4, i] or ref[5, i] != test[5, i]: - print("Error!") - print(num) - print("--------------") - print('ref-time: ' + str(ref[0, i])) - print('ref-type: ' + str(ref[1, i])) - print('ref-haplotype: ' + str(ref[2, i])) - print('ref-population: ' + str(ref[3, i])) - print('ref-newHaplotype: ' + str(ref[4, i])) - print('ref-newPopulation: ' + str(ref[5, i])) - print("--------------") - print('test-time: ' + str(test[0, i])) - print('test-type: ' + str(test[1, i])) - print('test-haplotype: ' + str(test[2, i])) - print('test-population: ' + str(test[3, i])) - print('test-newHaplotype: ' + str(test[4, i])) - print('test-newPopulation: ' + str(test[5, i])) - print("--------------") - sys.exit(1) - - os.remove("test_" + str(num) + ".npy") - print('Correct data!') - -my_seed = 2020 -iterations = 100000 - -#1 model -simulator = VGsim.Simulator(seed=my_seed) - -name = 1 -simulator.set_transmission_rate(4.0) -simulator.set_recovery_rate(1.5) -simulator.set_sampling_rate(0.3) -simulator.simulate(iterations) -simulator.output_chain_events('test_' + str(name)) -check_data(name) - -#2 model -simulator = VGsim.Simulator(number_of_sites=1, seed=my_seed) - -name = 2 -simulator.set_transmission_rate(4, haplotype=3) -simulator.simulate(iterations) -simulator.output_chain_events('test_' + str(name)) -check_data(name) - -#3 model -simulator = VGsim.Simulator(number_of_susceptible_groups=2, seed=my_seed) - -name = 3 -simulator.set_susceptibility_type(1) -simulator.simulate(iterations) -simulator.output_chain_events('test_' + str(name)) -check_data(name) - -# #4 model -simulator = VGsim.Simulator(number_of_sites=1, number_of_susceptible_groups=3, seed=my_seed) - -name = 4 -simulator.set_mutation_rate(0.01) -simulator.set_susceptibility_type(1, haplotype=0) -simulator.set_susceptibility_type(2, haplotype=1) -simulator.set_susceptibility_type(2, haplotype=2) -simulator.set_susceptibility_type(2, haplotype=3) -simulator.set_immunity_transition(0.01, source=0, target=1) -simulator.set_immunity_transition(0.01, source=1, target=2) -simulator.set_immunity_transition(0.02, source=2, target=1) -simulator.simulate(iterations) -simulator.output_chain_events('test_' + str(name)) -check_data(name) - -#5 model -# simulator = VGsim.Simulator(populations_number=2, seed=my_seed) - -# name = 5 -# simulator.set_population_size(2000000) -# simulator.set_contact_density(1.3, population=0) -# simulator.set_contact_density(0.8, population=1) -# simulator.set_migration_probability(probability=0.01, source=0, target=1) -# simulator.set_migration_probability(probability=0.005, source=1, target=0) -# simulator.simulate(iterations) -# simulator.output_chain_events('test_' + str(name)) -# check_data(name) - -#6 model -simulator = VGsim.Simulator(populations_number=3, seed=my_seed) - -name = 6 -simulator.set_migration_probability(0.01, source=0, target=1) -simulator.set_migration_probability(0.005, source=2, target=1) -simulator.simulate(iterations) -simulator.output_chain_events('test_' + str(name)) -check_data(name) - -#7 model -simulator = VGsim.Simulator(populations_number=3, seed=my_seed) - -name = 7 -simulator.set_migration_probability(0.01, source=0, target=1) -simulator.set_migration_probability(0.005, source=2, target=1) -simulator.set_sampling_multiplier(2.5, population=1) -simulator.set_sampling_multiplier(2, population=2) -simulator.set_npi([0.5, 0.30, 0.15], population=0) -simulator.simulate(iterations) -simulator.output_chain_events('test_' + str(name)) -check_data(name) - -#8 model -simulator = VGsim.Simulator(number_of_sites=2, seed=my_seed) - -name = 8 -simulator.set_mutation_rate(0.01, probabilities=[1, 0, 0, 1]) -simulator.simulate(iterations) -simulator.output_chain_events('test_' + str(name)) -check_data(name) - -#9 model -simulator = VGsim.Simulator(number_of_sites=2, populations_number=3, number_of_susceptible_groups=3, seed=my_seed) - -name = 9 -simulator.set_transmission_rate(5.0, haplotype=12) -simulator.set_recovery_rate(1.5) -simulator.set_sampling_rate(0.3) -simulator.set_mutation_rate(0.01, probabilities=[1, 0, 0, 1]) -simulator.set_migration_probability(0.01, source=0, target=1) -simulator.set_migration_probability(0.005, source=2, target=1) -simulator.set_sampling_multiplier(2.5, population=1) -simulator.set_sampling_multiplier(2, population=2) -simulator.set_npi([0.5, 0.30, 0.15], population=1) -simulator.set_susceptibility_type(1, haplotype=0) -simulator.set_susceptibility_type(2, haplotype=1) -simulator.set_susceptibility_type(2, haplotype=2) -simulator.set_susceptibility_type(2, haplotype=3) -simulator.set_immunity_transition(0.000001, source=0, target=1) -simulator.set_immunity_transition(0.000001, source=1, target=2) -simulator.set_immunity_transition(0.000002, source=2, target=1) -simulator.simulate(iterations) -simulator.output_chain_events('test_' + str(name)) -check_data(name) - -# #10 model -# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=True) - -# name = 10 -# simulator.set_mutation_rate(0.01) -# simulator.simulate(iterations) -# simulator.output_chain_events('test_' + str(name)) -# check_data(name) - - -# #11 model -# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=20) - -# name = 11 -# simulator.set_mutation_rate(0.01) -# simulator.simulate(iterations) -# simulator.output_chain_events('test_' + str(name)) -# check_data(name) - - -# #12 model -# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=(20, 2)) - -# name = 12 -# simulator.set_mutation_rate(0.01) -# simulator.simulate(iterations) -# simulator.output_chain_events('test_' + str(name)) -# check_data(name) - diff --git a/VGsim/testing/cmd_example/example.mg b/VGsim/testing/cmd_example/example.mg deleted file mode 100644 index 4da7c07..0000000 --- a/VGsim/testing/cmd_example/example.mg +++ /dev/null @@ -1,4 +0,0 @@ -#Migration_format_version 0.0.1 -0.0 0.0136986301369863 0.0136986301369863 -0.0136986301369863 0.0 0.0136986301369863 -0.0136986301369863 0.0136986301369863 0.0 diff --git a/VGsim/testing/cmd_example/example.pp b/VGsim/testing/cmd_example/example.pp deleted file mode 100644 index bdc2c83..0000000 --- a/VGsim/testing/cmd_example/example.pp +++ /dev/null @@ -1,5 +0,0 @@ -#Population_format_version 0.0.1 -id size contactDensity conDenAfterLD startLD endLD samplingMulriplier -0 10000000 1.0 0.1,0.01,0.002 1.0 -1 5000000 1.0 0.1,0.01,0.002 3.0 -2 1000000 1.0 0.1,0.01,0.002 0.0 diff --git a/VGsim/testing/cmd_example/example.rt b/VGsim/testing/cmd_example/example.rt deleted file mode 100644 index 7b6dcb3..0000000 --- a/VGsim/testing/cmd_example/example.rt +++ /dev/null @@ -1,18 +0,0 @@ -#Rates_format_version 0.0.1 -H B D S M0 M1 -AA 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -AT 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -AC 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -AG 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,1.0 -TA 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -TT 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -TC 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -TG 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,1.0 -CA 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -CT 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -CC 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,2.0 -CG 0.25 0.149 0.001 3e-06,1.0,1.0,2.0 3e-06,1.0,1.0,1.0 -GA 0.25 0.149 0.001 3e-06,1.0,1.0,1.0 9e-06,1.0,1.0,2.0 -GT 0.25 0.149 0.001 3e-06,1.0,1.0,1.0 9e-06,1.0,1.0,2.0 -GC 0.25 0.149 0.001 3e-06,1.0,1.0,1.0 9e-06,1.0,1.0,2.0 -GG 0.5 0.149 0.001 3e-06,1.0,1.0,1.0 9e-06,1.0,1.0,1.0 diff --git a/VGsim/testing/cmd_example/example.st b/VGsim/testing/cmd_example/example.st deleted file mode 100644 index bb7c6d5..0000000 --- a/VGsim/testing/cmd_example/example.st +++ /dev/null @@ -1,4 +0,0 @@ -#Susceptibility_format_version 0.0.1 -0.0 0.0 0.0 -0.011111111111111112 0.0 0.0 -0.005555555555555556 0.0 0.0 diff --git a/VGsim/testing/cmd_example/example.su b/VGsim/testing/cmd_example/example.su deleted file mode 100644 index 4081a2d..0000000 --- a/VGsim/testing/cmd_example/example.su +++ /dev/null @@ -1,18 +0,0 @@ -#Susceptibility_format_version 0.0.1 -H T S0 S1 S2 -AA 1 1.0 0.1 0.0 -AT 1 1.0 0.1 0.0 -AC 1 1.0 0.1 0.0 -AG 1 1.0 0.1 0.0 -TA 1 1.0 0.1 0.0 -TT 1 1.0 0.1 0.0 -TC 1 1.0 0.1 0.0 -TG 1 1.0 0.1 0.0 -CA 1 1.0 0.1 0.0 -CT 1 1.0 0.1 0.0 -CC 1 1.0 0.1 0.0 -CG 1 1.0 0.1 0.0 -GA 2 1.0 0.5 0.0 -GT 2 1.0 0.5 0.0 -GC 2 1.0 0.5 0.0 -GG 2 1.0 0.5 0.0 diff --git a/VGsim/testing/example.ipynb b/VGsim/testing/example.ipynb deleted file mode 100644 index a4e3872..0000000 --- a/VGsim/testing/example.ipynb +++ /dev/null @@ -1,482 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "id": "7a8cbe89", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "User seed: 1234\n" - ] - } - ], - "source": [ - "#This is a notebook with the example from https://vg-sim.readthedocs.io/en/latest/Tutorials%20and%20examples.html\n", - "\n", - "import VGsim\n", - "number_of_sites = 2\n", - "populations_number = 3\n", - "number_of_susceptible_groups = 3\n", - "simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, seed=1234)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b65b865c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Actual sizes: 9808219.17808219 5013698.630136985 1178082.191780822 \n", - "\u001b[41mWARNING!\u001b[0m Actual population size in deme \n", - "2\n", - "is more than 10% different from the population size. The migration probabilities might be unrealistically high.\n", - "We recommend to check your model with print_populations() method before proceding to simulation.\n", - "Check the documentation file:https://vg-sim.readthedocs.io/en/latest/Migration.html for more details.\n", - "Achieved internal time limit.\n", - "Number of samples: 5426\n", - "Total number of iterations: 943958\n", - "Success number: 1\n", - "Epidemic time: 110.00008313493404\n", - "Number of infections: 478628\n", - "Number of recoveries: 353747\n", - "Number of mutations: 21\n", - "Number of accepted migrations: 25676\n", - "Number of rejected migrations: 84643\n", - "Number of immunity transitions: 80460\n", - "----------------------------------\n" - ] - } - ], - "source": [ - "simulator.set_transmission_rate(0.25)\n", - "simulator.set_recovery_rate(0.099)\n", - "simulator.set_sampling_rate(0.001)\n", - "simulator.set_transmission_rate(0.5, haplotype=\"GG\")\n", - "mutation_rate=0.000003\n", - "substitution_weights=[1,1,1,2]#ATCG\n", - "simulator.set_mutation_rate(mutation_rate, substitution_weights)\n", - "simulator.set_mutation_rate(3*mutation_rate, haplotype='G*', mutation=1)\n", - "\n", - "simulator.set_susceptibility_type(1)\n", - "simulator.set_susceptibility_type(2, haplotype='G*')\n", - "simulator.set_susceptibility(0.1, susceptibility_type=1)\n", - "simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype='G*')\n", - "simulator.set_immunity_transition(1/90, source=1, target=0)\n", - "simulator.set_susceptibility(0.0, susceptibility_type=2)\n", - "simulator.set_immunity_transition(1/180, source=2, target=0)\n", - "\n", - "simulator.set_population_size(10000000, population=0)\n", - "simulator.set_population_size(5000000, population=1)\n", - "simulator.set_population_size(1000000, population=2)\n", - "simulator.set_sampling_multiplier(3, population=1)\n", - "simulator.set_sampling_multiplier(0, population=2)\n", - "simulator.set_npi([0.1, 0.01, 0.002])\n", - "simulator.set_migration_probability(10/365/2)\n", - "\n", - "simulator.simulate(10000000, epidemic_time=110)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "89474358", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of iterations: 0\n", - "Simulation model time: 110.00008313493404\n", - "Number of samples: 5426\n", - "Total number of iterations: 943958\n", - "Success number: 1\n", - "Epidemic time: 110.00008313493404\n", - "Number of infections: 478628\n", - "Number of recoveries: 353747\n", - "Number of mutations: 21\n", - "Number of accepted migrations: 25676\n", - "Number of rejected migrations: 84643\n", - "Number of immunity transitions: 80460\n", - "----------------------------------\n" - ] - } - ], - "source": [ - "\n", - "simulator.set_immunity_transition(0.05, source=0, target=1)\n", - "simulator.set_immunity_transition(0.05, source=0, target=2)\n", - "simulator.set_contact_density(0.7, population=0)\n", - "simulator.set_contact_density(0.7, population=1)\n", - "simulator.set_migration_probability(2/365/2, source=0, target=2)\n", - "simulator.set_migration_probability(2/365/2, source=1, target=2)\n", - "\n", - "simulator.simulate(1000, method='tau', time=300)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5698f399", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "simulator.add_plot_infectious(population=0, haplotype='AA', step_num=100)\n", - "simulator.add_plot_infectious(population=1, haplotype='AA', step_num=100)\n", - "simulator.add_plot_infectious(population=2, haplotype='AA', step_num=100)\n", - "simulator.add_title(name=\"AA haplotype\")\n", - "simulator.add_legend()\n", - "simulator.plot()\n", - "\n", - "simulator.add_plot_susceptible(population=0, susceptibility_type=0, step_num=100)\n", - "simulator.add_plot_susceptible(population=0, susceptibility_type=1, step_num=100)\n", - "simulator.add_plot_susceptible(population=0, susceptibility_type=2, step_num=100)\n", - "simulator.add_title(name=\"Susceptible group sizes\")\n", - "simulator.add_legend()\n", - "simulator.plot()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7dd885ab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*****************\n", - "***Basic rates***\n", - "*****************\n", - "+----+------+-------+-------+----+-------+-------------+-------+-------------+\n", - "| H | TR | RR | SR | ST | M0 | MW0 | M1 | MW1 |\n", - "+----+------+-------+-------+----+-------+-------------+-------+-------------+\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mA\u001b[0mA->\u001b[31mT\u001b[0mA: 1.0 | | A\u001b[31mA\u001b[0m->A\u001b[31mT\u001b[0m: 1.0 |\n", - "| AA | | | | | 3e-06 | \u001b[31mA\u001b[0mA->\u001b[31mC\u001b[0mA: 1.0 | 3e-06 | A\u001b[31mA\u001b[0m->A\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mA\u001b[0mA->\u001b[31mG\u001b[0mA: 2.0 | | A\u001b[31mA\u001b[0m->A\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mA\u001b[0mT->\u001b[31mT\u001b[0mT: 1.0 | | A\u001b[31mT\u001b[0m->A\u001b[31mA\u001b[0m: 1.0 |\n", - "| AT | | | | | 3e-06 | \u001b[31mA\u001b[0mT->\u001b[31mC\u001b[0mT: 1.0 | 3e-06 | A\u001b[31mT\u001b[0m->A\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mA\u001b[0mT->\u001b[31mG\u001b[0mT: 2.0 | | A\u001b[31mT\u001b[0m->A\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mA\u001b[0mC->\u001b[31mT\u001b[0mC: 1.0 | | A\u001b[31mC\u001b[0m->A\u001b[31mA\u001b[0m: 1.0 |\n", - "| AC | | | | | 3e-06 | \u001b[31mA\u001b[0mC->\u001b[31mC\u001b[0mC: 1.0 | 3e-06 | A\u001b[31mC\u001b[0m->A\u001b[31mT\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mA\u001b[0mC->\u001b[31mG\u001b[0mC: 2.0 | | A\u001b[31mC\u001b[0m->A\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mA\u001b[0mG->\u001b[31mT\u001b[0mG: 1.0 | | A\u001b[31mG\u001b[0m->A\u001b[31mA\u001b[0m: 1.0 |\n", - "| AG | | | | | 3e-06 | \u001b[31mA\u001b[0mG->\u001b[31mC\u001b[0mG: 1.0 | 3e-06 | A\u001b[31mG\u001b[0m->A\u001b[31mT\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mA\u001b[0mG->\u001b[31mG\u001b[0mG: 2.0 | | A\u001b[31mG\u001b[0m->A\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mT\u001b[0mA->\u001b[31mA\u001b[0mA: 1.0 | | T\u001b[31mA\u001b[0m->T\u001b[31mT\u001b[0m: 1.0 |\n", - "| TA | | | | | 3e-06 | \u001b[31mT\u001b[0mA->\u001b[31mC\u001b[0mA: 1.0 | 3e-06 | T\u001b[31mA\u001b[0m->T\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mT\u001b[0mA->\u001b[31mG\u001b[0mA: 2.0 | | T\u001b[31mA\u001b[0m->T\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mT\u001b[0mT->\u001b[31mA\u001b[0mT: 1.0 | | T\u001b[31mT\u001b[0m->T\u001b[31mA\u001b[0m: 1.0 |\n", - "| TT | | | | | 3e-06 | \u001b[31mT\u001b[0mT->\u001b[31mC\u001b[0mT: 1.0 | 3e-06 | T\u001b[31mT\u001b[0m->T\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mT\u001b[0mT->\u001b[31mG\u001b[0mT: 2.0 | | T\u001b[31mT\u001b[0m->T\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mT\u001b[0mC->\u001b[31mA\u001b[0mC: 1.0 | | T\u001b[31mC\u001b[0m->T\u001b[31mA\u001b[0m: 1.0 |\n", - "| TC | | | | | 3e-06 | \u001b[31mT\u001b[0mC->\u001b[31mC\u001b[0mC: 1.0 | 3e-06 | T\u001b[31mC\u001b[0m->T\u001b[31mT\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mT\u001b[0mC->\u001b[31mG\u001b[0mC: 2.0 | | T\u001b[31mC\u001b[0m->T\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mT\u001b[0mG->\u001b[31mA\u001b[0mG: 1.0 | | T\u001b[31mG\u001b[0m->T\u001b[31mA\u001b[0m: 1.0 |\n", - "| TG | | | | | 3e-06 | \u001b[31mT\u001b[0mG->\u001b[31mC\u001b[0mG: 1.0 | 3e-06 | T\u001b[31mG\u001b[0m->T\u001b[31mT\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mT\u001b[0mG->\u001b[31mG\u001b[0mG: 2.0 | | T\u001b[31mG\u001b[0m->T\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mC\u001b[0mA->\u001b[31mA\u001b[0mA: 1.0 | | C\u001b[31mA\u001b[0m->C\u001b[31mT\u001b[0m: 1.0 |\n", - "| CA | | | | | 3e-06 | \u001b[31mC\u001b[0mA->\u001b[31mT\u001b[0mA: 1.0 | 3e-06 | C\u001b[31mA\u001b[0m->C\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mC\u001b[0mA->\u001b[31mG\u001b[0mA: 2.0 | | C\u001b[31mA\u001b[0m->C\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mC\u001b[0mT->\u001b[31mA\u001b[0mT: 1.0 | | C\u001b[31mT\u001b[0m->C\u001b[31mA\u001b[0m: 1.0 |\n", - "| CT | | | | | 3e-06 | \u001b[31mC\u001b[0mT->\u001b[31mT\u001b[0mT: 1.0 | 3e-06 | C\u001b[31mT\u001b[0m->C\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mC\u001b[0mT->\u001b[31mG\u001b[0mT: 2.0 | | C\u001b[31mT\u001b[0m->C\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mC\u001b[0mC->\u001b[31mA\u001b[0mC: 1.0 | | C\u001b[31mC\u001b[0m->C\u001b[31mA\u001b[0m: 1.0 |\n", - "| CC | | | | | 3e-06 | \u001b[31mC\u001b[0mC->\u001b[31mT\u001b[0mC: 1.0 | 3e-06 | C\u001b[31mC\u001b[0m->C\u001b[31mT\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mC\u001b[0mC->\u001b[31mG\u001b[0mC: 2.0 | | C\u001b[31mC\u001b[0m->C\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 1 | | \u001b[31mC\u001b[0mG->\u001b[31mA\u001b[0mG: 1.0 | | C\u001b[31mG\u001b[0m->C\u001b[31mA\u001b[0m: 1.0 |\n", - "| CG | | | | | 3e-06 | \u001b[31mC\u001b[0mG->\u001b[31mT\u001b[0mG: 1.0 | 3e-06 | C\u001b[31mG\u001b[0m->C\u001b[31mT\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mC\u001b[0mG->\u001b[31mG\u001b[0mG: 2.0 | | C\u001b[31mG\u001b[0m->C\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 2 | | \u001b[31mG\u001b[0mA->\u001b[31mA\u001b[0mA: 1.0 | | G\u001b[31mA\u001b[0m->G\u001b[31mT\u001b[0m: 1.0 |\n", - "| GA | | | | | 3e-06 | \u001b[31mG\u001b[0mA->\u001b[31mT\u001b[0mA: 1.0 | 9e-06 | G\u001b[31mA\u001b[0m->G\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mG\u001b[0mA->\u001b[31mC\u001b[0mA: 1.0 | | G\u001b[31mA\u001b[0m->G\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 2 | | \u001b[31mG\u001b[0mT->\u001b[31mA\u001b[0mT: 1.0 | | G\u001b[31mT\u001b[0m->G\u001b[31mA\u001b[0m: 1.0 |\n", - "| GT | | | | | 3e-06 | \u001b[31mG\u001b[0mT->\u001b[31mT\u001b[0mT: 1.0 | 9e-06 | G\u001b[31mT\u001b[0m->G\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mG\u001b[0mT->\u001b[31mC\u001b[0mT: 1.0 | | G\u001b[31mT\u001b[0m->G\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.25 | 0.099 | 0.001 | 2 | | \u001b[31mG\u001b[0mC->\u001b[31mA\u001b[0mC: 1.0 | | G\u001b[31mC\u001b[0m->G\u001b[31mA\u001b[0m: 1.0 |\n", - "| GC | | | | | 3e-06 | \u001b[31mG\u001b[0mC->\u001b[31mT\u001b[0mC: 1.0 | 9e-06 | G\u001b[31mC\u001b[0m->G\u001b[31mT\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mG\u001b[0mC->\u001b[31mC\u001b[0mC: 1.0 | | G\u001b[31mC\u001b[0m->G\u001b[31mG\u001b[0m: 2.0 |\n", - "| | | | | | | | | |\n", - "| | 0.5 | 0.099 | 0.001 | 2 | | \u001b[31mG\u001b[0mG->\u001b[31mA\u001b[0mG: 1.0 | | G\u001b[31mG\u001b[0m->G\u001b[31mA\u001b[0m: 1.0 |\n", - "| GG | | | | | 3e-06 | \u001b[31mG\u001b[0mG->\u001b[31mT\u001b[0mG: 1.0 | 9e-06 | G\u001b[31mG\u001b[0m->G\u001b[31mT\u001b[0m: 1.0 |\n", - "| | | | | | | \u001b[31mG\u001b[0mG->\u001b[31mC\u001b[0mG: 1.0 | | G\u001b[31mG\u001b[0m->G\u001b[31mC\u001b[0m: 1.0 |\n", - "| | | | | | | | | |\n", - "+----+------+-------+-------+----+-------+-------------+-------+-------------+\n", - "Legend:\n", - "H - haplotype\n", - "TR - transmission rate\n", - "RR - recovery rate\n", - "SR - sampling rate\n", - "ST - susceptibility type\n", - "M0 - 0 mutation rate\n", - "MW0 - 0 mutation weights\n", - "M1 - 1 mutation rate\n", - "MW1 - 1 mutation weights\n", - "\n", - "********************\n", - "***Immunity model***\n", - "********************\n", - "+------+-----+-----+-----+\n", - "| H\\ST | S0 | S1 | S2 |\n", - "+------+-----+-----+-----+\n", - "| AA | 1.0 | 0.1 | 0.0 |\n", - "| AT | 1.0 | 0.1 | 0.0 |\n", - "| AC | 1.0 | 0.1 | 0.0 |\n", - "| AG | 1.0 | 0.1 | 0.0 |\n", - "| TA | 1.0 | 0.1 | 0.0 |\n", - "| TT | 1.0 | 0.1 | 0.0 |\n", - "| TC | 1.0 | 0.1 | 0.0 |\n", - "| TG | 1.0 | 0.1 | 0.0 |\n", - "| CA | 1.0 | 0.1 | 0.0 |\n", - "| CT | 1.0 | 0.1 | 0.0 |\n", - "| CC | 1.0 | 0.1 | 0.0 |\n", - "| CG | 1.0 | 0.1 | 0.0 |\n", - "| GA | 1.0 | 0.5 | 0.0 |\n", - "| GT | 1.0 | 0.5 | 0.0 |\n", - "| GC | 1.0 | 0.5 | 0.0 |\n", - "| GG | 1.0 | 0.5 | 0.0 |\n", - "+------+-----+-----+-----+\n", - "Legend:\n", - "H - haplotype\n", - "ST - susceptibility type\n", - "\n", - "*******************************\n", - "***Immunity transition rates***\n", - "*******************************\n", - "+----+----------------------+------+------+\n", - "| ID | 0 | 1 | 2 |\n", - "+----+----------------------+------+------+\n", - "| 0 | 0.0 | 0.05 | 0.05 |\n", - "| 1 | 0.011111111111111112 | 0.0 | 0.0 |\n", - "| 2 | 0.005555555555555556 | 0.0 | 0.0 |\n", - "+----+----------------------+------+------+\n", - "Legend:\n", - "ID - ID susceptibility type\n", - "\n", - "*****************\n", - "***Populations***\n", - "*****************\n", - "+----+----------+--------------------+-----+-------+-------+------+-------+-----+\n", - "| ID | Size | Actual size | CD | CDBLC | CDALD | SLD | ELD | SM |\n", - "+----+----------+--------------------+-----+-------+-------+------+-------+-----+\n", - "| 0 | 10000000 | 9917808.219178082 | 0.7 | 0.7 | 0.1 | 0.01 | 0.002 | 1.0 |\n", - "| 1 | 5000000 | 5068493.150684931 | 0.7 | 0.7 | 0.1 | 0.01 | 0.002 | 3.0 |\n", - "| 2 | 1000000 | 1013698.6301369861 | 1.0 | 1.0 | 0.1 | 0.01 | 0.002 | 0.0 |\n", - "+----+----------+--------------------+-----+-------+-------+------+-------+-----+\n", - "Legend:\n", - "ID - number of population\n", - "Size - size of population\n", - "Actual size - actual size of population\n", - "CD - contact density\n", - "CDBLD - contact density without lockdown\n", - "CDALD - contact density at lockdown\n", - "SLD - start of lockdown\n", - "ELD - end of lockdown\n", - "SM - sampling multiplier\n", - "\n", - "*****************\n", - "***Susceptible***\n", - "*****************\n", - "+-------+---------+---------+--------+\n", - "| ST\\ID | 0 | 1 | 2 |\n", - "+-------+---------+---------+--------+\n", - "| 0 | 9720799 | 4881625 | 974187 |\n", - "| 1 | 179371 | 81927 | 16943 |\n", - "| 2 | 14 | 1 | 1 |\n", - "+-------+---------+---------+--------+\n", - "Legend:\n", - "ID - ID population\n", - "ST - susceptibility type\n", - "\n", - "****************\n", - "***Infectious***\n", - "****************\n", - "+------+-------+-------+------+\n", - "| H\\ID | 0 | 1 | 2 |\n", - "+------+-------+-------+------+\n", - "| AA | 99787 | 36444 | 8869 |\n", - "| AT | 0 | 0 | 0 |\n", - "| AC | 6 | 0 | 0 |\n", - "| AG | 3 | 0 | 0 |\n", - "| TA | 0 | 0 | 0 |\n", - "| TT | 0 | 0 | 0 |\n", - "| TC | 0 | 0 | 0 |\n", - "| TG | 0 | 0 | 0 |\n", - "| CA | 12 | 0 | 0 |\n", - "| CT | 0 | 0 | 0 |\n", - "| CC | 0 | 0 | 0 |\n", - "| CG | 0 | 0 | 0 |\n", - "| GA | 8 | 3 | 0 |\n", - "| GT | 0 | 0 | 0 |\n", - "| GC | 0 | 0 | 0 |\n", - "| GG | 0 | 0 | 0 |\n", - "+------+-------+-------+------+\n", - "Legend:\n", - "ID - ID population\n", - "H - haplotype\n", - "\n", - "**********************\n", - "***Migration matrix***\n", - "**********************\n", - "+-----+--------------------+--------------------+-----------------------+\n", - "| S\\T | 0 | 1 | 2 |\n", - "+-----+--------------------+--------------------+-----------------------+\n", - "| 0 | 0.9835616438356164 | 0.0136986301369863 | 0.0027397260273972603 |\n", - "| 1 | 0.0136986301369863 | 0.9835616438356164 | 0.0027397260273972603 |\n", - "| 2 | 0.0136986301369863 | 0.0136986301369863 | 0.9726027397260273 |\n", - "+-----+--------------------+--------------------+-----------------------+\n", - "Legend:\n", - "S - ID source population\n", - "T - ID target population\n", - "\n" - ] - } - ], - "source": [ - "simulator.genealogy()\n", - "file_name = \"example\"\n", - "simulator.output_newick(file_name)\n", - "simulator.output_mutations(file_name)\n", - "simulator.output_migrations(file_name)\n", - "\n", - "simulator.print_basic_parameters()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "921d9d31", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "********************\n", - "***Immunity model***\n", - "********************\n", - "+------+-----+-----+-----+\n", - "| H\\ST | S0 | S1 | S2 |\n", - "+------+-----+-----+-----+\n", - "| AA | 1.0 | 0.1 | 0.0 |\n", - "| AT | 1.0 | 0.1 | 0.0 |\n", - "| AC | 1.0 | 0.1 | 0.0 |\n", - "| AG | 1.0 | 0.1 | 0.0 |\n", - "| TA | 1.0 | 0.1 | 0.0 |\n", - "| TT | 1.0 | 0.1 | 0.0 |\n", - "| TC | 1.0 | 0.1 | 0.0 |\n", - "| TG | 1.0 | 0.1 | 0.0 |\n", - "| CA | 1.0 | 0.1 | 0.0 |\n", - "| CT | 1.0 | 0.1 | 0.0 |\n", - "| CC | 1.0 | 0.1 | 0.0 |\n", - "| CG | 1.0 | 0.1 | 0.0 |\n", - "| GA | 1.0 | 0.5 | 0.0 |\n", - "| GT | 1.0 | 0.5 | 0.0 |\n", - "| GC | 1.0 | 0.5 | 0.0 |\n", - "| GG | 1.0 | 0.5 | 0.0 |\n", - "+------+-----+-----+-----+\n", - "Legend:\n", - "H - haplotype\n", - "ST - susceptibility type\n", - "\n", - "*******************************\n", - "***Immunity transition rates***\n", - "*******************************\n", - "+----+----------------------+------+------+\n", - "| ID | 0 | 1 | 2 |\n", - "+----+----------------------+------+------+\n", - "| 0 | 0.0 | 0.05 | 0.05 |\n", - "| 1 | 0.011111111111111112 | 0.0 | 0.0 |\n", - "| 2 | 0.005555555555555556 | 0.0 | 0.0 |\n", - "+----+----------------------+------+------+\n", - "Legend:\n", - "ID - ID susceptibility type\n", - "\n" - ] - } - ], - "source": [ - "simulator.print_immunity_model()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35d66dd0", - "metadata": {}, - "outputs": [], - "source": [ - "simulator.print_populations()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/VGsim/testing/example.py b/VGsim/testing/example.py deleted file mode 100644 index bd3b2da..0000000 --- a/VGsim/testing/example.py +++ /dev/null @@ -1,82 +0,0 @@ -import VGsim -import os -import os.path - -number_of_sites = 2 -populations_number = 3 -number_of_susceptible_groups = 3 -simulator = VGsim.Simulator(number_of_sites, populations_number, number_of_susceptible_groups, seed=1234) - -#Set epidemiological parameters -simulator.set_transmission_rate(0.25) -simulator.set_transmission_rate(0.5, haplotype="GG") -simulator.set_recovery_rate(0.099) -simulator.set_sampling_rate(0.001) - -#Set mutation rates -mutation_rate=0.00003 -substitution_weights=[1,1,1,2]#ATCG -simulator.set_mutation_rate(mutation_rate, substitution_weights) -simulator.set_mutation_rate(3*mutation_rate, haplotype='G*', mutation=1) - -#Set host immunity types triggered by infection -simulator.set_susceptibility_type(1) -simulator.set_susceptibility_type(2, haplotype='G*') -simulator.set_susceptibility(0.1, susceptibility_type=1) -simulator.set_susceptibility(0.5, susceptibility_type=1, haplotype='G*') -simulator.set_susceptibility(0.0, susceptibility_type=2) - -#Set loss of immunity -simulator.set_immunity_transition(1/90, source=1, target=0) -simulator.set_immunity_transition(1/180, source=2, target=0) - -#Set host population structure with three populations and migration -simulator.set_population_size(10000000, population=0) -simulator.set_population_size(5000000, population=1) -simulator.set_population_size(1000000, population=2) -simulator.set_migration_probability(10/365/2) - -#Set specific sampling efforts in different populations -simulator.set_sampling_multiplier(3, population=1) -simulator.set_sampling_multiplier(0, population=2) - -#Set non-pharmasutical interventions (same for each population) -simulator.set_npi([0.1, 0.01, 0.002]) - -#Run simulation with the exact algorithm -simulator.simulate(10000000, epidemic_time=110) - -#Change some of parameters -##simulator.set_immunity_transition(0.05, source=0, target=1) -##simulator.set_immunity_transition(0.05, source=0, target=2) -##simulator.set_contact_density(0.7, population=0) -##simulator.set_contact_density(0.7, population=1) -##simulator.set_migration_probability(2/365/2, source=0, target=2) -##simulator.set_migration_probability(2/365/2, source=1, target=2) - -##print("\nSwitching to tau-leaping algorithm.\n") - -#Run simulation with the approximate tau-leaping algorithm -##simulator.simulate(1000, epidemic_time=210, method='tau') - -#Simulate genealogy of sampled individuals -simulator.genealogy() - -#Create directory to save results -os.chdir('testing') -if os.path.exists('example_output') == False: - os.mkdir('example_output') -os.chdir('example_output') - -#Generate a plot with epidemiological curves -simulator.add_plot_infectious(population=0, haplotype='AA', step_num=100) -simulator.add_plot_infectious(population=1, haplotype='AA', step_num=100) -simulator.add_plot_infectious(population=2, haplotype='AA', step_num=100) -simulator.add_title(name="AA haplotype") -simulator.add_legend() -simulator.plot('plot') - -#Save the genealogy with mutations and migrations -simulator.output_newick() -simulator.output_mutations('mutations') -simulator.output_migrations('migrations') diff --git a/VGsim/testing/getting_reference.py b/VGsim/testing/getting_reference.py deleted file mode 100644 index df2e1b2..0000000 --- a/VGsim/testing/getting_reference.py +++ /dev/null @@ -1,131 +0,0 @@ -import VGsim - -#1 model -simulator = VGsim.Simulator(seed=2020) - -name = 1 -simulator.set_transmission_rate(4.0) -simulator.set_recovery_rate(1.5) -simulator.set_sampling_rate(0.3) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#2 model -simulator = VGsim.Simulator(number_of_sites=1, seed=2020) - -name = 2 -simulator.set_transmission_rate(4, haplotype=3) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#3 model -simulator = VGsim.Simulator(number_of_susceptible_groups=2, seed=2020) - -name = 3 -simulator.set_susceptibility_type(1) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#4 model -simulator = VGsim.Simulator(number_of_sites=1, number_of_susceptible_groups=3, seed=2020) - -name = 4 -simulator.set_mutation_rate(0.01) -simulator.set_susceptibility_type(1, haplotype=0) -simulator.set_susceptibility_type(2, haplotype=1) -simulator.set_susceptibility_type(2, haplotype=2) -simulator.set_susceptibility_type(2, haplotype=3) -simulator.set_immunity_transition(0.01, source=0, target=1) -simulator.set_immunity_transition(0.01, source=1, target=2) -simulator.set_immunity_transition(0.02, source=2, target=1) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#5 model -simulator = VGsim.Simulator(populations_number=2, seed=2020) - -name = 5 -simulator.set_population_size(2000000) -simulator.set_contact_density(1.3, population=0) -simulator.set_contact_density(0.8, population=1) -simulator.set_migration_probability(0.01, source=0, target=1) -simulator.set_migration_probability(0.005, source=1, target=0) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#6 model -simulator = VGsim.Simulator(populations_number=3, seed=2020) - -name = 6 -simulator.set_migration_probability(0.01, source=0, target=1) -simulator.set_migration_probability(0.005, source=2, target=1) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#7 model -simulator = VGsim.Simulator(populations_number=3, seed=2020) - -name = 7 -simulator.set_migration_probability(0.01, source=0, target=1) -simulator.set_migration_probability(0.005, source=2, target=1) -simulator.set_sampling_multiplier(2.5, population=1) -simulator.set_sampling_multiplier(2, population=2) -simulator.set_npi([0.5, 0.30, 0.15], population=0) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#8 model -simulator = VGsim.Simulator(number_of_sites=2, seed=2020) - -name = 8 -simulator.set_mutation_rate(probabilities=[1, 0, 0, 1]) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#9 model -simulator = VGsim.Simulator(number_of_sites=2, populations_number=3, number_of_susceptible_groups=3, seed=2020) - -name = 9 -simulator.set_transmission_rate(5.0, haplotype=12) -simulator.set_recovery_rate(1.5) -simulator.set_sampling_rate(0.3) -simulator.set_mutation_rate(0.01, probabilities=[1, 0, 0, 1]) -simulator.set_migration_probability(0.01, source=0, target=1) -simulator.set_migration_probability(0.005, source=2, target=1) -simulator.set_sampling_multiplier(2.5, population=1) -simulator.set_sampling_multiplier(2, population=2) -simulator.set_npi([0.5, 0.30, 0.15], population=1) -simulator.set_susceptibility_type(1, haplotype=0) -simulator.set_susceptibility_type(2, haplotype=1) -simulator.set_susceptibility_type(2, haplotype=2) -simulator.set_susceptibility_type(2, haplotype=3) -simulator.set_immunity_transition(0.000001, source=0, target=1) -simulator.set_immunity_transition(0.000001, source=1, target=2) -simulator.set_immunity_transition(0.000002, source=2, target=1) -simulator.simulate(100000) -simulator.output_chain_events('reference_' + str(name)) - -#10 model -# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=True) - -# name = 10 -# simulator.set_mutation_rate(0.01) -# simulator.simulate(iterations) -# simulator.output_chain_events('reference_' + str(name)) - -# #11 model -# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=20) - -# name = 11 -# simulator.set_mutation_rate(0.01) -# simulator.simulate(iterations) -# simulator.output_chain_events('reference_' + str(name)) - -# #12 model -# simulator = VGsim.Simulator(number_of_sites=5, seed=my_seed, memory_optimization=(20, 2)) - -# name = 12 -# simulator.set_mutation_rate(0.01) -# simulator.simulate(iterations) -# simulator.output_chain_events('reference_' + str(name)) - diff --git a/VGsim/testing/reference_1.npy b/VGsim/testing/reference_1.npy deleted file mode 100644 index ced633d01a7b98ed7407fcd3890dbf58d0461d0c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4800128 zcmbT-_dnL%8vyWo5)Fiu>=9+9NHWebvr?3bXc(DMMoNhi6$)i!g(yUJ_7)|ZR3tO9 z_nzPD`4_(Z@_v1|ea^VXIeB_s=^C6mV@M?2CfpM;w{S9Z6gt5pB!B(5kSLGPbq7ah 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zfWXQE?#pXb_vt!5z1|vCvI_zP2oNAZfB*pk1PBlya7|#>&*8i7``T4v76AeT2oNAZ zU|fNxp6i}kXIyES5g1Rv>vFuB$cVt`0`425S4e*U5pbQl4hRq+Kww7!?|(a1-JW+5 z@cMqX(!8Jbw%WS_rR%+P{@v`YPJjRb0tDU_@ILvj|1PV$&UUG^eG(u*fB*pk1PBly zK!5-N0t5&UAV7e?N&>#$U#Xh*LVy5)aRj`-j8hAl5FkK+0D)BmJYTF*$v*pVee`h^ vbuIy~gSqM@TLJ_K>?YuSYLAMm^B#4!O9BK45FjwJfaj5st0Ol8slfjOKV`)M diff --git a/VGsim/testing/tests.py b/VGsim/testing/tests.py deleted file mode 100644 index 48c7453..0000000 --- a/VGsim/testing/tests.py +++ /dev/null @@ -1,302 +0,0 @@ -import VGsim - -# set_initial_haplotype -model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) -model.set_initial_haplotype(0) -model.set_initial_haplotype(1) -model.set_initial_haplotype(64) -model.set_initial_haplotype(65) - -try: - model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) - model.set_initial_haplotype(1/2) -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) - model.set_initial_haplotype(-1) -except ValueError: - pass - - -# set_step_haplotype -model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) -model.set_step_haplotype(0) -model.set_step_haplotype(1) -model.set_step_haplotype(64) -model.set_step_haplotype(65) - -try: - model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) - model.set_step_haplotype(1/2) -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=3, memory_optimization=True) - model.set_step_haplotype(-1) -except ValueError: - pass - - -# set_transmission_rate -model = VGsim.Simulator(number_of_sites=2) -model.set_transmission_rate(0) -model.set_transmission_rate(0.002, 'A*') -model.set_transmission_rate(0.003, 'AT') -model.set_transmission_rate(0.004, 0) -model.set_transmission_rate(0.005, 15) - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_transmission_rate('str') -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_transmission_rate(-1) -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_transmission_rate(0.01, haplotype=[1, 2]) -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_transmission_rate(0.01, haplotype=-1) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_transmission_rate(0.01, haplotype=16) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_transmission_rate(0.01, haplotype='str') -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_transmission_rate(0.01, haplotype='AAA') -except ValueError: - pass - - -# set_recovery_rate -model = VGsim.Simulator(number_of_sites=2) -model.set_recovery_rate(0) -model.set_recovery_rate(0.002, 'A*') -model.set_recovery_rate(0.003, 'AT') -model.set_recovery_rate(0.004, 0) -model.set_recovery_rate(0.005, 15) - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_recovery_rate('str') -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_recovery_rate(-1) -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_recovery_rate(0.01, haplotype=[1, 2]) -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_recovery_rate(0.01, haplotype=-1) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_recovery_rate(0.01, haplotype=16) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_recovery_rate(0.01, haplotype='str') -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_recovery_rate(0.01, haplotype='AAA') -except ValueError: - pass - - -# set_sampling_rate -model = VGsim.Simulator(number_of_sites=2) -model.set_sampling_rate(0) -model.set_sampling_rate(0.002, 'A*') -model.set_sampling_rate(0.003, 'AT') -model.set_sampling_rate(0.004, 0) -model.set_sampling_rate(0.005, 15) - -model = VGsim.Simulator(number_of_sites=2, sampling_probability=True) -model.set_sampling_rate(0) -model.set_sampling_rate(0.002, 'A*') -model.set_sampling_rate(0.003, 'AT') -model.set_sampling_rate(0.004, 0) -model.set_sampling_rate(0.005, 15) -model.set_sampling_rate(1) - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_sampling_rate('str') -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_sampling_rate(-1) -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2, sampling_probability=True) - model.set_sampling_rate('str') -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2, sampling_probability=True) - model.set_sampling_rate(-1) -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2, sampling_probability=True) - model.set_sampling_rate(2) -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_sampling_rate(0.01, haplotype=[1, 2]) -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_sampling_rate(0.01, haplotype=-1) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_sampling_rate(0.01, haplotype=16) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_sampling_rate(0.01, haplotype='str') -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_sampling_rate(0.01, haplotype='AAA') -except ValueError: - pass - - -model = VGsim.Simulator(number_of_sites=2) -model.set_mutation_rate(rate=0) -model.set_mutation_rate(rate=0.002, haplotype='A*') -model.set_mutation_rate(rate=0.003, haplotype='AT') -model.set_mutation_rate(rate=0.004, haplotype=0) -model.set_mutation_rate(rate=0.005, haplotype=15) -model.set_mutation_rate(rate=0.006, mutation=0) -model.set_mutation_rate(rate=0.007, mutation=1) -model.set_mutation_rate(probabilities=[5, 4, 8, 2], haplotype='A*') -model.set_mutation_rate(probabilities=[5, 4, 8, 2], haplotype='AT') -model.set_mutation_rate(probabilities=[5, 4, 8, 2], haplotype=0) -model.set_mutation_rate(probabilities=[5, 4, 8, 2], haplotype=15) -model.set_mutation_rate(probabilities=[5, 4, 8, 2], mutation=0) -model.set_mutation_rate(probabilities=[5, 4, 8, 2], mutation=1) - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(rate='str') -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(rate=-1) -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(probabilities=[5, -1, 8, 2]) -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(probabilities=[0, 0, 0, 0]) -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(probabilities=2) -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(rate=0.002, haplotype='R*') -except ValueError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(rate=0.002, haplotype=-1) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(rate=0.002, haplotype=[1, 2]) -except TypeError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(rate=0.002, haplotype=16) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(rate=0.002, mutation=-1) -except IndexError: - pass - -try: - model = VGsim.Simulator(number_of_sites=2) - model.set_mutation_rate(rate=0.002, mutation=2) -except IndexError: - pass - From 923fb278cf8aeb8ad219ba705af75d386641ca92 Mon Sep 17 00:00:00 2001 From: tutuhi <65420132+IvanTurHi@users.noreply.github.com> Date: Sun, 19 Mar 2023 15:36:18 +0300 Subject: [PATCH 13/13] Add files via upload --- src/_BirthDeath.pyx | 110 ++++++++++++++++++++++++-------------------- 1 file changed, 61 insertions(+), 49 deletions(-) diff --git a/src/_BirthDeath.pyx b/src/_BirthDeath.pyx index cf72c03..f4590ef 100644 --- a/src/_BirthDeath.pyx +++ b/src/_BirthDeath.pyx @@ -42,14 +42,14 @@ cdef class BirthDeathModel: Lockdowns loc npy_int64[::1] suscType, sizes, totalSusceptible, totalInfectious, lockdownON, hapToNum, numToHap, tree, tree_pop - npy_int64[:,::1] susceptible, initial_susceptible, infectious + npy_int64[:,::1] susceptible, initial_susceptible, infectious, npy_int64[:,:,::1] infectByCondition, initial_infectious double[::1] maxEffectiveBirthMigration, suscepCumulTransition, immunePopRate, infectPopRate, popRate, migPopRate, actualSizes, contactDensity, contactDensityBeforeLockdown, contactDensityAfterLockdown, startLD, endLD, samplingMultiplier, times - double[::1] cumuConditionRate, conditionPopRate, bRate, dRate, sRate, tmRate + double[::1] conditionPopRate, cumuConditionRate double[:,::1] susceptibility, suscepTransition, immuneSourcePopRate, hapPopRate, migrationRates, effectiveMigration - double[:,::1] conditionRate, massivForThree, conditionPopHapRate, mRate - double[:,:,::1] hapMutType, susceptHapPopRate, tEventHapPopRate, + double[:,::1] conditionRate,massivForThree, conditionPopHapRate, bRate, dRate, sRate, tmRate + double[:,:,::1] hapMutType, susceptHapPopRate, tEventHapPopRate, mRate double[:,:,::1] conditionPopHapConRate, tEventConHapPopRate double[:,:,:,::1] eventHapPopRate @@ -65,7 +65,7 @@ cdef class BirthDeathModel: npy_int64[:,::1] infectiousDelta, susceptibleDelta - def __init__(self, number_of_sites, populations_number, number_of_susceptible_groups, seed, sampling_probability, memory_optimizationm, conditionNum): + def __init__(self, number_of_sites, populations_number, number_of_susceptible_groups, seed, sampling_probability, memory_optimization, conditionNum): self.user_seed = seed self.seed = RndmWrapper(seed=(self.user_seed, 0)) @@ -159,9 +159,9 @@ cdef class BirthDeathModel: for cn1 in range(self.conditionNum): for cn2 in range(self.conditionNum): if cn1 != cn2: - conditionRate[cn1, cn2] = 2 + self.conditionRate[cn1, cn2] = 2 else: - conditionRate[cn1, cn2] = 0 + self.conditionRate[cn1, cn2] = 0 for cn1 in range(self.conditionNum): self.cumuConditionRate[cn1] = 0 @@ -322,7 +322,7 @@ cdef class BirthDeathModel: for i in range(4): self.tEventHapPopRate[pn, hn, cn] += self.eventHapPopRate[pn, hn, cn, i] self.tEventConHapPopRate[pn, hn, cn] = self.tEventHapPopRate[pn, hn, cn] * self.infectByCondition[pn, hn, cn] - self.hapPopRate[pn, hn] += tEventConHapPopRate[pn, hn, cn] + self.hapPopRate[pn, hn] += self.tEventConHapPopRate[pn, hn, cn] self.infectPopRate[pn] += self.hapPopRate[pn, hn] for sn in range(self.susNum): self.immuneSourcePopRate[pn, sn] = self.suscepCumulTransition[sn]*self.susceptible[pn, sn] @@ -401,7 +401,12 @@ cdef class BirthDeathModel: for pn in range(self.popNum): ps += self.susceptHapPopRate[pi, hi, sn]*self.migrationRates[pi, pn]*self.migrationRates[pi, pn]*self.contactDensity[pn]/self.actualSizes[pn] - return self.bRate[self.numToHap[hi]]*ps + for cn in range(self.conditionNum): + self.bRate[self.numToHap[hi], cn] = self.bRate[self.numToHap[hi], cn] * ps + cdef double hi_ps = 0.0 + for cn in range(self.conditionNum): + hi_ps += self.bRate[self.numToHap[hi], cn] + return hi_ps @cython.boundscheck(False) @cython.wraparound(False) @@ -497,7 +502,7 @@ cdef class BirthDeathModel: self.rn = choose / self.totalRate pi, self.rn = fastChoose1(self.popRate, self.totalRate, self.rn) #choose = self.rn * self.popRate[pi] - ei, self.rn = fastChoose1(self.massivForThree[pn], self.totalRate, self.rn) + ei, self.rn = fastChoose1(self.massivForThree[pi], self.totalRate, self.rn) if ei == 1: #self.rn = choose / self.immunePopRate[pi] self.ImmunityTransition(pi) @@ -507,7 +512,7 @@ cdef class BirthDeathModel: self.rn = (choose - self.immunePopRate[pi]) / self.infectPopRate[pi] hi, self.rn = fastChoose1(self.hapPopRate[pi], self.infectPopRate[pi], self.rn) # hi - program number ci, self.rn = fastChoose1(self.tEventConHapPopRate[pi, hi], self.hapPopRate[pi, hi], self.rn) - ei, self.rn = fastChoose1(self.tEventHapPopRate[pi, hi, ci], self.tEvenContHapPopRate[pi, hi, ci], self.rn) + ei, self.rn = fastChoose1(self.tEventHapPopRate[pi, hi], self.tEvenContHapPopRate[pi, hi, ci], self.rn) if ei == BIRTH: self.Birth(pi, hi, ci) elif ei == DEATH: @@ -515,7 +520,7 @@ cdef class BirthDeathModel: elif ei == SAMPLING: self.Sampling(pi, hi, ci) else: - self.Mutation(pi, hi) + self.Mutation(pi, hi, ci) else: pi = self.GenerateMigration() return pi @@ -528,14 +533,14 @@ cdef class BirthDeathModel: self.infectPopRate[pi] = 0.0 for hn in range(self.currentHapNum): for cn in range(self.conditionNum): - self.eventHapPopRate[pi, hn, cn, 0] = self.BirthRate(pi, hn) + self.eventHapPopRate[pi, hn, cn, 0] = self.BirthRate(pi, hn, cn) tmp = (self.eventHapPopRate[pi, hn, cn, 0] + self.eventHapPopRate[pi, hn, cn, 1] + self.eventHapPopRate[pi, hn, cn, 2] + self.eventHapPopRate[pi, hn, cn, 3] ) self.tEventHapPopRate[pi, hn, cn] = tmp - self.tEventConHapPopRate[pn, hn, cn] = self.tEventHapPopRate[pn, hn, cn] * self.infectious[pn, hn, cn] - self.hapPopRate[pn, hn] += tEventConHapPopRate[pn, hn, cn] + self.tEventConHapPopRate[pi, hn, cn] = self.tEventHapPopRate[pi, hn, cn] * self.infectByCondition[pi, hn, cn] + self.hapPopRate[pi, hn] += self.tEventConHapPopRate[pi, hn, cn] self.infectPopRate[pi] += self.hapPopRate[pi, hn] if immune: @@ -545,10 +550,10 @@ cdef class BirthDeathModel: if infect or immune: self.popRate[pi] = 0 - self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi]+ self.conditionPopRate[pn] - self.massivForThree[0] = self.infectPopRate[pn] - self.massivForThree[1] = self.immunePopRate[pn] - self.massivForThree[2] = self.conditionPopRate[pn] + self.popRate[pi] = self.infectPopRate[pi] + self.immunePopRate[pi]+ self.conditionPopRate[pi] + self.massivForThree[0] = self.infectPopRate[pi] + self.massivForThree[1] = self.immunePopRate[pi] + self.massivForThree[2] = self.conditionPopRate[pi] self.totalRate = 0.0 for pn in range(self.popNum): self.totalRate += self.popRate[pn] @@ -584,8 +589,8 @@ cdef class BirthDeathModel: Py_ssize_t ssi, tsi, hsi hsi, self.rn = fastChoose1(self.conditionPopHapRate[pi], self.conditionPopRate[pi], self.rn) - ssi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self,conditionPopHapRate[pi, hsi], self.rn) - tsi, self.rn = fastChoose2_skip(self.conditionRate[ssi], self.cumuConditionRate[ssi], self.rn) + ssi, self.rn = fastChoose1(self.conditionPopHapConRate[pi, hsi], self.conditionPopHapRate[pi, hsi], self.rn) + tsi, self.rn = fastChoose1_skip(self.conditionRate[ssi], self.cumuConditionRate[ssi], self.rn, ssi) self.infectByCondition[pi, hsi, ssi] -= 1 self.infectByCondition[pi, hsi, tsi] += 1 @@ -674,12 +679,12 @@ cdef class BirthDeathModel: tpi, self.rn = fastChoose1(self.migPopRate, self.totalMigrationRate, self.rn) spi, self.rn = fastChoose2_skip(self.totalInfectious, self.globalInfectious-self.totalInfectious[tpi], self.rn, skip=tpi) hi, self.rn = fastChoose2(self.infectious[spi], self.totalInfectious[spi], self.rn) # hi - program number - ci, self.rn = fastChoose1(self.conditionRate[ssi], self.cumuConditionRate[ssi], self.rn) + ci, self.rn = fastChoose1(self.conditionRate[spi], self.cumuConditionRate[spi], self.rn) si, self.rn = fastChoose2(self.susceptible[tpi], self.totalSusceptible[tpi], self.rn) p_accept = self.effectiveMigration[spi, tpi]*self.bRate[self.numToHap[hi], ci]*self.susceptibility[self.numToHap[hi], si]/self.maxEffectiveBirthMigration[tpi] if self.rn < p_accept: - self.NewInfections(tpi, si, hi) + self.NewInfections(tpi, si, hi, ci) self.UpdateRates(tpi, True, True, True) self.migPlus += 1 @@ -726,9 +731,10 @@ cdef class BirthDeathModel: self.susceptible[pn, sn] = self.initial_susceptible[pn, sn] self.totalSusceptible[pn] += self.initial_susceptible[pn, sn] for hn in range(self.hapNum): - self.infectious[pn, hn] = self.initial_infectious[pn, hn] - self.totalInfectious[pn] += self.initial_infectious[pn, hn] - self.globalInfectious += self.initial_infectious[pn, hn] + for cn in range(self.conditionNum): + self.infectious[pn, hn] += self.initial_infectious[pn, hn, cn] + self.totalInfectious[pn] += self.initial_infectious[pn, hn, cn] + self.globalInfectious += self.initial_infectious[pn, hn, cn] for pn in range(self.popNum): self.CheckLockdown(pn) self.UpdateAllRates() @@ -1577,7 +1583,7 @@ cdef class BirthDeathModel: self.initial_susceptible[pn, source_type] -= amount self.initial_susceptible[pn, target_type] += amount - def set_infectious(self, amount, source_type, target_haplotype, population): + def set_infectious(self, amount, source_type, target_haplotype, population, condition): if self.first_simulation: raise ValueError('This function is available only before first simulation!') self.check_amount(amount) @@ -1589,13 +1595,14 @@ cdef class BirthDeathModel: self.check_amount_inf(amount, source_type, target_haplotype, population) self.initial_susceptible[population, source_type] -= amount - self.initial_infectious[population, target_haplotype] += amount + + self.initial_infectious[population, target_haplotype, condition] += amount elif population == None: for pn in range(self.popNum): self.check_amount_inf(amount, source_type, target_haplotype, pn) self.initial_susceptible[pn, source_type] -= amount - self.initial_infectious[pn, target_haplotype] += amount + self.initial_infectious[pn, target_haplotype, condition] += amount def set_contact_density(self, value, population): self.check_rate_1(value, 'contact density') @@ -2315,11 +2322,12 @@ cdef class BirthDeathModel: continue for sn in range(self.susNum): for hn in range(self.hapNum): - self.PropensitiesMigr[spn, tpn, sn, hn] = self.effectiveMigration[tpn, spn]*self.susceptible[tpn, sn]*\ - self.infectious[spn, hn]*self.bRate[hn]*self.susceptibility[hn, sn]*self.migrationRates[spn, spn] + for cn in range(self.conditionNum): + self.PropensitiesMigr[spn, tpn, sn, hn] = self.effectiveMigration[tpn, spn]*self.susceptible[tpn, sn]*\ + self.infectious[spn, hn]*self.bRate[hn, cn]*self.susceptibility[hn, sn]*self.migrationRates[spn, spn] - self.infectiousAuxTau[tpn, hn] += self.PropensitiesMigr[spn, tpn, sn, hn] - self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesMigr[spn, tpn, sn, hn] + self.infectiousAuxTau[tpn, hn] += self.PropensitiesMigr[spn, tpn, sn, hn] + self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesMigr[spn, tpn, sn, hn] for pn in range(self.popNum): #Susceptibility transition @@ -2335,38 +2343,42 @@ cdef class BirthDeathModel: #Infectious-realted event for hn in range(self.hapNum): #Recovery - self.PropensitiesRecovery[pn, hn] = self.dRate[hn]*self.infectious[pn, hn] + for cn in range(self.conditionNum): + self.PropensitiesRecovery[pn, hn] = self.dRate[hn, cn]*self.infectious[pn, hn] self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesRecovery[pn, hn] self.infectiousAuxTau[pn, hn] -= self.PropensitiesRecovery[pn, hn] #Sampling - self.PropensitiesSampling[pn, hn] = self.sRate[hn]*self.infectious[pn, hn]*self.samplingMultiplier[pn] + for cn in range(self.conditionNum): + self.PropensitiesSampling[pn, hn] = self.sRate[hn, cn]*self.infectious[pn, hn]*self.samplingMultiplier[pn] self.susceptibleAuxTau[pn, self.suscType[hn]] += self.PropensitiesSampling[pn, hn] self.infectiousAuxTau[pn, hn] -= self.PropensitiesSampling[pn, hn] #Mutation for s in range(self.sites): - for i in range(3): - self.PropensitiesMutatations[pn, hn, s, i] = self.mRate[hn, s]*self.hapMutType[hn, s, i]/\ - (self.hapMutType[hn, s, 0] + self.hapMutType[hn, s, 1] + self.hapMutType[hn, s, 2])*self.infectious[pn, hn] + for cn in range(self.conditionNum): + for i in range(3): + self.PropensitiesMutatations[pn, hn, s, i] = self.mRate[hn, cn, s]*self.hapMutType[hn, s, i]/\ + (self.hapMutType[hn, s, 0] + self.hapMutType[hn, s, 1] + self.hapMutType[hn, s, 2])*self.infectious[pn, hn] - self.infectiousAuxTau[pn, self.Mutate(hn, s, i)] += self.PropensitiesMutatations[pn, hn, s, i] - self.infectiousAuxTau[pn, hn] -= self.PropensitiesMutatations[pn, hn, s, i] + self.infectiousAuxTau[pn, self.Mutate(hn, s, i)] += self.PropensitiesMutatations[pn, hn, s, i] + self.infectiousAuxTau[pn, hn] -= self.PropensitiesMutatations[pn, hn, s, i] #Transmission for tpn in range(self.popNum): for hn in range(self.hapNum): - for sn in range(self.susNum): - self.PropensitiesTransmission[tpn, hn, sn] = 0.0 - for spn in range(self.popNum): - self.PropensitiesTransmission[tpn, hn, sn] += self.bRate[hn]*self.susceptibility[hn, sn]*self.migrationRates[tpn, spn]*\ - self.migrationRates[tpn, spn]*self.contactDensity[spn]*self.susceptible[tpn, sn]*self.infectious[tpn, hn]/\ - self.actualSizes[spn] - - self.infectiousAuxTau[tpn, hn] += self.PropensitiesTransmission[tpn, hn, sn] - self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesTransmission[tpn, hn, sn] + for cn in range(self.conditionNum): + for sn in range(self.susNum): + self.PropensitiesTransmission[tpn, hn, sn] = 0.0 + for spn in range(self.popNum): + self.PropensitiesTransmission[tpn, hn, sn] += self.bRate[hn, cn]*self.susceptibility[hn, sn]*self.migrationRates[tpn, spn]*\ + self.migrationRates[tpn, spn]*self.contactDensity[spn]*self.susceptible[tpn, sn]*self.infectious[tpn, hn]/\ + self.actualSizes[spn] + + self.infectiousAuxTau[tpn, hn] += self.PropensitiesTransmission[tpn, hn, sn] + self.susceptibleAuxTau[tpn, sn] -= self.PropensitiesTransmission[tpn, hn, sn] @cython.cdivision(True) cdef Py_ssize_t Mutate(self, Py_ssize_t hi, Py_ssize_t s, Py_ssize_t DS):

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