def setUp(self): import fwdpy11 import numpy as np self.N = 1000 self.demography = np.array([self.N] * self.N, dtype=np.uint32) self.rho = 1. self.theta = 100. self.nreps = 500 self.mu = self.theta / (4 * self.N) self.r = self.rho / (4 * self.N) self.GSS = fwdpy11.GSS(VS=1, opt=0) a = fwdpy11.Additive(2.0, self.GSS) self.p = { 'nregions': [], 'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 0.025, self.r), 'gvalue': a, 'prune_selected': False, 'demography': self.demography } self.params = fwdpy11.ModelParams(**self.p) self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210) self.pop = fwdpy11.DiploidPopulation(self.N, 1.0) fwdpy11.evolvets(self.rng, self.pop, self.params, 100)
def runsim(args): rng = fwdpy11.GSLrng(args.seed) pdict = {'gvalue': fwdpy11.Multiplicative(FITNESS_SCALING), 'rates': (0., args.mu, None), 'nregions': [], 'sregions': [fwdpy11.GammaS(0., GENOME_LENGTH, 1.0-args.proportion, args.mean, args.shape, FITNESS_SCALING/2.0, scaling=2*args.popsize, label=1), fwdpy11.ConstantS(0, GENOME_LENGTH, args.proportion, TWONS, FITNESS_SCALING/2.0, label=2, scaling=2*args.popsize)], 'recregions': [fwdpy11.PoissonInterval(0, GENOME_LENGTH, args.recrate)], 'demography': np.array([args.popsize]*SIMLEN*args.popsize, dtype=np.uint32), # This could easily be True for these sims: 'prune_selected': False } params = fwdpy11.ModelParams(**pdict) pop = fwdpy11.DiploidPopulation(args.popsize, GENOME_LENGTH) sampler = fwdpy11.RandomAncientSamples( args.seed, args.popsize, [i for i in range(10*pop.N, SIMLEN*pop.N)]) # With a lot of ancient samples: # 1. RAM use already skyrockets # 2. Simplification slows down # So, we should do it a little less often: fwdpy11.evolvets(rng, pop, params, 1000, sampler, suppress_table_indexing=True) return pop
def runsim(args): popsizes = np.array([args.popsize] * 20 * args.popsize, dtype=np.int32) pop = fwdpy11.DiploidPopulation(args.popsize, 1.0) sregions = [fwdpy11.GaussianS(0, 1, 1, args.sigma)] recregions = [fwdpy11.PoissonInterval(0, 1, args.recrate)] optima = fwdpy11.GSSmo([(0, 0, args.VS), (10 * args.popsize, args.opt, args.VS)]) p = { 'nregions': [], # No neutral mutations -- add them later! 'gvalue': fwdpy11.Additive(2.0, optima), 'sregions': sregions, 'recregions': recregions, 'rates': (0.0, args.mu, None), # Keep mutations at frequency 1 in the pop if they affect fitness. 'prune_selected': False, 'demography': np.array(popsizes, dtype=np.uint32) } params = fwdpy11.ModelParams(**p) rng = fwdpy11.GSLrng(args.seed) s = Recorder(args.popsize) fwdpy11.evolvets(rng, pop, params, 100, s, suppress_table_indexing=True, track_mutation_counts=True) return pop
def runsim(args): popsizes = np.array([args.popsize] * 20 * args.popsize, dtype=np.int32) locus_boundaries = [(i, i + 11) for i in range(0, 10 * 11, 11)] sregions = [fwdpy11.GaussianS(i[0] + 5, i[0] + 6, 1, args.sigma) for i in locus_boundaries] recregions = [fwdpy11.PoissonInterval( *i, args.rho / (4 * args.popsize)) for i in locus_boundaries] recregions.extend([fwdpy11.BinomialPoint(i[1], 0.5) for i in locus_boundaries[:-1]]) pop = fwdpy11.DiploidPopulation(args.popsize, locus_boundaries[-1][1]) optima = fwdpy11.GSSmo( [(0, 0, args.VS), (10 * args.popsize, args.opt, args.VS)]) p = {'nregions': [], # No neutral mutations -- add them later! 'gvalue': fwdpy11.Additive(2.0, optima), 'sregions': sregions, 'recregions': recregions, 'rates': (0.0, args.mu, None), # Keep mutations at frequency 1 in the pop if they affect fitness. 'prune_selected': False, 'demography': np.array(popsizes, dtype=np.uint32) } params = fwdpy11.ModelParams(**p) rng = fwdpy11.GSLrng(args.seed) s = Recorder(args.popsize) fwdpy11.evolvets(rng, pop, params, 100, s, suppress_table_indexing=True, track_mutation_counts=True) return pop
def setUp(self): self.N = 1000 self.demography = np.array([self.N] * 100, dtype=np.uint32) self.rho = 1. self.theta = 100. self.nreps = 500 self.mu = self.theta / (4 * self.N) self.r = self.rho / (4 * self.N) self.GSS = fwdpy11.GSS(VS=1, opt=0) a = fwdpy11.Additive(2.0, self.GSS) self.p = { 'nregions': [], 'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 0.025, self.r), 'gvalue': a, 'prune_selected': False, 'demography': self.demography } self.params = fwdpy11.ModelParams(**self.p) self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210) self.pop = fwdpy11.DiploidPopulation(self.N, 1.0) self.recorder = fwdpy11.RandomAncientSamples( seed=42, samplesize=10, timepoints=[i for i in range(1, 101)]) fwdpy11.evolvets(self.rng, self.pop, self.params, 100, self.recorder)
def set_up_quant_trait_model(): # TODO add neutral variants N = 1000 demography = np.array([N] * 10 * N, dtype=np.uint32) rho = 1. # theta = 100. # nreps = 500 # mu = theta/(4*N) r = rho / (4 * N) GSSmo = fwdpy11.GSSmo([(0, 0, 1), (N, 1, 1)]) a = fwdpy11.Additive(2.0, GSSmo) p = { 'nregions': [], 'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 0.025, r), 'gvalue': a, 'prune_selected': False, 'demography': demography } params = fwdpy11.ModelParams(**p) rng = fwdpy11.GSLrng(101 * 45 * 110 * 210) pop = fwdpy11.DiploidPopulation(N, 1.0) return params, rng, pop
def setUpClass(self): # TODO add neutral variants self.N = 1000 self.demography = np.array([self.N] * self.N, dtype=np.uint32) self.rho = 1. self.theta = 100. self.nreps = 500 self.mu = self.theta / (4 * self.N) self.r = self.rho / (4 * self.N) self.GSS = fwdpy11.GSS(VS=1, opt=0) a = fwdpy11.Additive(2.0, self.GSS) self.p = { 'nregions': [], 'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 0.025, self.r), 'gvalue': a, 'prune_selected': False, 'demography': self.demography } self.params = fwdpy11.ModelParams(**self.p) self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210) self.pop = fwdpy11.DiploidPopulation(self.N, 1.0) self.all_samples = [i for i in range(2 * self.N)] fwdpy11.evolvets(self.rng, self.pop, self.params, 100) self.dm = fwdpy11.data_matrix_from_tables(self.pop.tables, self.all_samples, True, True) self.neutral = np.array(self.dm.neutral) self.npos = np.array(self.dm.neutral.positions) self.selected = np.array(self.dm.selected) self.spos = np.array(self.dm.selected.positions)
def testPopGenSim(self): N = 1000 demography = np.array([N] * 10 * N, dtype=np.uint32) rho = 1. r = rho / (4 * N) a = fwdpy11.Multiplicative(2.0) p = { 'nregions': [], 'sregions': [fwdpy11.ExpS(0, 1, 1, 0.01)], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 0.00005, r), 'gvalue': a, 'prune_selected': True, 'demography': demography } params = fwdpy11.ModelParams(**p) rng = fwdpy11.GSLrng(101 * 45 * 110 * 210) pop = fwdpy11.DiploidPopulation(N, 1.0) fwdpy11.evolvets(rng, pop, params, 100, track_mutation_counts=True) mc = fwdpy11.count_mutations(pop.tables, pop.mutations, [i for i in range(2 * pop.N)]) assert len(pop.fixations) > 0, "Test is meaningless without fixations" fixations = np.where(mc == 2 * pop.N)[0] self.assertEqual(len(fixations), 0) # Brute-force calculation of fixations brute_force = np.zeros(len(pop.mutations), dtype=np.int32) for g in pop.haploid_genomes: if g.n > 0: for k in g.smutations: brute_force[k] += g.n self.assertTrue(np.array_equal(brute_force, mc)) self.assertTrue(np.array_equal(brute_force, pop.mcounts))
def evolve_snowdrift(args): """ We write the function taking a tuple out of habit, simplifying later integration with multiprocessing or concurrent.futures. """ N, seed = args # Construct as single-deme object # with N diploids pop = fp11.DiploidPopulation(N) # Initialize a random number generator rng = fp11.GSLrng(seed) p = { 'sregions': [fp11.ExpS(0, 1, 1, -0.1, 1.0)], 'recregions': [fp11.Region(0, 1, 1)], 'nregions': [], 'gvalue': snowdrift.DiploidSnowdrift(0.2, -0.2, 1, -2), # evolve for 100 generations so that unit tests are # fast 'demography': np.array([N] * 100, dtype=np.uint32), 'rates': (0.0, 0.0025, 0.001), 'prune_selected': False } params = fwdpy11.ModelParams(**p) sampler = SamplePhenotypes(params.gvalue) fp11.evolve_genomes(rng, pop, params, sampler) # return our pop return pop
def set_up_quant_trait_model(): N = 1000 demography = np.array([N] * 10 * N, dtype=np.uint32) rho = 1000. r = rho / (4 * N) timepoints = np.array([0], dtype=np.uint32) ntraits = 4 optima = np.array(np.zeros(len(timepoints) * ntraits)) optima = optima.reshape((len(timepoints), ntraits)) GSSmo = fwdpy11.MultivariateGSSmo(timepoints, optima, 1) cmat = np.identity(ntraits) np.fill_diagonal(cmat, 0.1) a = fwdpy11.StrictAdditiveMultivariateEffects(ntraits, 0, GSSmo) p = { 'nregions': [], 'sregions': [fwdpy11.MultivariateGaussianEffects(0, 1, 1, cmat)], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 0.001, r), 'gvalue': a, 'prune_selected': False, 'demography': demography } params = fwdpy11.ModelParams(**p) rng = fwdpy11.GSLrng(101 * 45 * 110 * 210) pop = fwdpy11.DiploidPopulation(N, 1.0) return params, rng, pop, ntraits
def evolve_and_return(args): """ This function runs our simulation. The input arguments come in a tuple, which is required by many of Python's functions for execution in separate processes. For this function, the arguments are the population size and a random number seed. """ from fwdpy11 import Multiplicative N, seed = args # Construct as single-deme object # with N diploids pop = fp11.DiploidPopulation(N) theta = 100.0 # Initialize a random number generator rng = fp11.GSLrng(seed) p = fp11ez.mslike(pop, simlen=100, rates=(theta / float(4 * pop.N), 1e-3, theta / float(4 * pop.N))) p['gvalue'] = Multiplicative(2.) params = fp11.ModelParams(**p) fp11.evolve_genomes(rng, pop, params) # The population is picklable, and so # we can return it from another process return pop
def setUpClass(self): class CountSamplesPerTimePoint(object): def __init__(self): self.sample_timepoints = [] self.sample_sizes = [] self.timepoint_seen = {} def __call__(self, pop): assert len(pop.tables.preserved_nodes)//2 == \ len(pop.ancient_sample_metadata) # Get the most recent ancient samples # and record their number. We do this # by a "brute-force" approach for t, n, m in pop.sample_timepoints(False): if t not in self.timepoint_seen: self.timepoint_seen[t] = 1 else: self.timepoint_seen[t] += 1 if t not in self.sample_timepoints: self.sample_timepoints.append(t) self.sample_sizes.append(len(n) // 2) # simplify to each time point tables, idmap = fwdpy11.simplify_tables(pop.tables, n) for ni in n: assert idmap[ni] != fwdpy11.NULL_NODE assert tables.nodes[idmap[ni]].time == t self.N = 1000 self.demography = np.array([self.N] * 101, dtype=np.uint32) self.rho = 1. self.r = self.rho / (4 * self.N) self.GSS = fwdpy11.GSS(VS=1, opt=0) a = fwdpy11.Additive(2.0, self.GSS) self.p = { 'nregions': [], 'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 0.025, self.r), 'gvalue': a, 'prune_selected': False, 'demography': self.demography } self.params = fwdpy11.ModelParams(**self.p) self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210) self.pop = fwdpy11.DiploidPopulation(self.N, 1.0) self.all_samples = [i for i in range(2 * self.N)] self.ancient_sample_recorder = \ fwdpy11.RandomAncientSamples(seed=42, samplesize=10, timepoints=[i for i in range(1, 101)]) self.resetter = CountSamplesPerTimePoint() fwdpy11.evolvets(self.rng, self.pop, self.params, 5, recorder=self.ancient_sample_recorder, post_simplification_recorder=self.resetter)
def testMutationLookupTable(self): params = fwdpy11.ModelParams(**self.pdict) fwdpy11.evolve_genomes(self.rng, self.pop, params) lookup = self.pop.mut_lookup for i in range(len(self.pop.mcounts)): if self.pop.mcounts[i] > 0: self.assertTrue(self.pop.mutations[i].pos in lookup) self.assertTrue(i in lookup[self.pop.mutations[i].pos])
def setUp(self): self.pop = fwdpy11.DiploidPopulation(1000) self.pdict = fwdpy11.ezparams.mslike(self.pop, dfe=fwdpy11.ExpS(0, 1, 1, -0.05), pneutral=0.95, simlen=10) self.pdict['gvalue'] = ca.additive() self.rng = fwdpy11.GSLrng(42) self.params = fwdpy11.ModelParams(**self.pdict)
def testParentalData(self): params = fwdpy11.ModelParams(**self.pdict) fwdpy11.evolve_genomes(self.rng, self.pop, params) parents = [i.parents for i in self.pop.diploid_metadata] for i in parents: self.assertTrue(i is not None) self.assertTrue(len(i) == 2) self.assertTrue(i[0] < self.pop.N) self.assertTrue(i[1] < self.pop.N)
def testMutationIndices(self): params = fwdpy11.ModelParams(**self.pdict) fwdpy11.evolve_genomes(self.rng, self.pop, params) lookup = self.pop.mut_lookup for key, val in lookup.items(): indexes = self.pop.mutation_indexes(key) self.assertTrue(indexes is not None) for i in indexes: self.assertTrue(i in val)
def testPopGenSimWithPruning(self): import fwdpy11 import numpy as np self.p['prune_selected'] = True params = fwdpy11.ModelParams(**self.p) fwdpy11.evolve_genomes(self.rng, self.pop, params) assert len( self.pop.fixations) > 0, "Test is meaningless without fixations" mc = np.array(self.pop.mcounts) self.assertEqual(len(np.where(mc == 2 * self.pop.N)[0]), 0)
def setUp(self): self.pop = fwdpy11.DiploidPopulation(1000) self.pdict = fwdpy11.ezparams.mslike(self.pop, dfe=fwdpy11.ConstantS( 0, 1, 1, -0.05, 0.05), pneutral=0.95, simlen=10) self.pdict['gvalue'] = general.GeneralW() self.rng = fwdpy11.GSLrng(42) self.params = fwdpy11.ModelParams(**self.pdict)
def runsim(argtuple): seed = argtuple rng = fwdpy11.GSLrng(seed) pdict = { 'gvalue': fwdpy11.Multiplicative(2.), 'rates': (0., U / 2., R), # The U/2. is from their eqn. 2. 'nregions': [], 'sregions': [ fwdpy11.ConstantS(0, 1. / 3., 1, -0.02, 1.), fwdpy11.ConstantS(2. / 3., 1., 1, -0.02, 1.) ], 'recregions': [fwdpy11.Region(0, 1. / 3., 1), fwdpy11.Region(2. / 3., 1., 1)], 'demography': np.array([N] * 20 * N, dtype=np.uint32) } params = fwdpy11.ModelParams(**pdict) pop = fwdpy11.DiploidPopulation(N, GENOME_LENGTH) fwdpy11.evolvets(rng, pop, params, 100, suppress_table_indexing=True) rdips = np.random.choice(N, NSAM, replace=False) md = np.array(pop.diploid_metadata, copy=False) rdip_nodes = md['nodes'][rdips].flatten() nodes = np.array(pop.tables.nodes, copy=False) # Only visit trees spanning the # mutation-free segment of the genome tv = fwdpy11.TreeIterator(pop.tables, rdip_nodes, begin=1. / 3., end=2. / 3.) plist = np.zeros(len(nodes), dtype=np.int8) sum_pairwise_tmrca = 0 for t in tv: for i in range(len(rdip_nodes) - 1): u = rdip_nodes[i] while u != fwdpy11.NULL_NODE: plist[u] = 1 u = t.parent(u) for j in range(i + 1, len(rdip_nodes)): u = rdip_nodes[j] while u != fwdpy11.NULL_NODE: if plist[u] == 1: sum_pairwise_tmrca += 2 * \ (pop.generation-nodes['time'][u]) u = fwdpy11.NULL_NODE else: u = t.parent(u) plist.fill(0) return 2 * sum_pairwise_tmrca / (len(rdip_nodes) * (len(rdip_nodes) - 1))
def setUp(self): self.pop = fwdpy11.DiploidPopulation(1000, 1.0) p = {'nregions': [], # No neutral mutations -- add them later! 'gvalue': fwdpy11.Additive(2.0), 'sregions': [fwdpy11.ExpS(0, 1, 1, -0.1)], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 1e-3, 1e-3), # Keep mutations at frequency 1 in the pop if they affect fitness. 'prune_selected': False, 'demography': np.array([1000]*10000, dtype=np.uint32) } self.params = fwdpy11.ModelParams(**p)
def runsim(args, simseed, npseed): dg = demes.load(args.yaml) final_demes = get_final_demes(dg) demog = fwdpy11.discrete_demography.from_demes(dg, burnin=args.burnin) final_deme_ids = sorted([ i for i in demog.metadata["deme_labels"] if demog.metadata["deme_labels"][i] in final_demes ]) initial_sizes = [ demog.metadata["initial_sizes"][i] for i in sorted(demog.metadata["initial_sizes"].keys()) ] recrate = RHO / (4.0 * initial_sizes[0]) pdict = { "nregions": [], "sregions": [], "recregions": [fwdpy11.PoissonInterval(0, 1, recrate)], "gvalue": fwdpy11.Multiplicative(2.0), "rates": (0.0, 0.0, None), "simlen": demog.metadata["total_simulation_length"], "demography": demog, } params = fwdpy11.ModelParams(**pdict) pop = fwdpy11.DiploidPopulation(initial_sizes, 1.0) # FIXME: need seed as input argument to this fxn rng = fwdpy11.GSLrng(simseed) np.random.seed(npseed) fwdpy11.evolvets(rng, pop, params, 100) nmuts = fwdpy11.infinite_sites(rng, pop, THETA / (4.0 * initial_sizes[0])) md = np.array(pop.diploid_metadata, copy=False) sample_nodes = [] for i in final_deme_ids: w = np.where(md["deme"] == i) s = np.random.choice(w[0], args.nsam, replace=False) sample_nodes.append(md["nodes"][s].flatten()) fs = pop.tables.fs(sample_nodes) return fs
def set_up_standard_pop_gen_model(): """ For this sort of model, when mutations fix, they are removed from the simulation, INCLUDING THE TREE SEQUENCES. The fact of their existence gets recorded in pop.fixations and pop.fixation_times """ # TODO add neutral variants N = 1000 demography = np.array([N] * 10 * N, dtype=np.uint32) rho = 1. # theta = 100. # nreps = 500 # mu = theta/(4*N) r = rho / (4 * N) a = fwdpy11.Multiplicative(2.0) pselected = 1e-3 p = { 'nregions': [], 'sregions': [ fwdpy11.GammaS(0, 1, 1. - pselected, mean=-5, shape=1, scaling=2 * N), fwdpy11.ConstantS(0, 1, pselected, 1000, scaling=2 * N) ], 'recregions': [fwdpy11.Region(0, 1, 1)], 'rates': (0.0, 0.001, r), 'gvalue': a, 'prune_selected': True, 'demography': demography } params = fwdpy11.ModelParams(**p) rng = fwdpy11.GSLrng(666**2) pop = fwdpy11.DiploidPopulation(N, 1.0) return params, rng, pop
def runsim(model, num_subsamples, nsam, seed): rng = fwdpy11.GSLrng(seed) pop = fwdpy11.DiploidPopulation(model["Nref"], model["genome_length"]) fwdpy11.evolvets(rng, pop, fwdpy11.ModelParams(**model["pdict"]), 100) if model["mutations_are_neutral"] is True: fwdpy11.infinite_sites(rng, pop, model["theta"] / 4 / model["Nref"]) mean_fst = 0.0 deme_zero_fs = np.zeros(2 * args.nsam - 1) deme_one_fs = np.zeros(2 * args.nsam - 1) for _ in range(num_subsamples): fs = testutils.analysis_tools.tskit_fs(pop, nsam) fs = moments.Spectrum(fs) mean_fst += fs.Fst() deme_zero_fs += fs.marginalize([1]).data[1:-1] deme_one_fs += fs.marginalize([0]).data[1:-1] mean_fst /= num_subsamples deme_zero_fs /= num_subsamples deme_one_fs /= num_subsamples return mean_fst, deme_zero_fs, deme_one_fs
def runsim(args): """ Run the simulation and deliver output to files. """ pop = fwdpy11.DiploidPopulation(args.popsize, GENOME_LENGTH) rng = fwdpy11.GSLrng(args.seed) GSSmo = fwdpy11.GSSmo([(0, 0, args.VS), (10 * args.popsize, args.opt, args.VS)]) popsizes = [args.popsize ] * (10 * args.popsize + int(args.time * float(args.popsize))) p = { 'nregions': [], # No neutral mutations -- add them later! 'gvalue': fwdpy11.Additive(2.0, GSSmo), 'sregions': [fwdpy11.GaussianS(0, GENOME_LENGTH, 1, args.sigma)], 'recregions': [fwdpy11.Region(0, GENOME_LENGTH, 1)], 'rates': (0.0, args.mu, args.rho / float(4 * args.popsize)), # Keep mutations at frequency 1 in the pop if they affect fitness. 'prune_selected': False, 'demography': np.array(popsizes, dtype=np.uint32) } params = fwdpy11.ModelParams(**p) r = Recorder(args.record, args.preserve, args.num_ind) fwdpy11.evolvets(rng, pop, params, 100, r, suppress_table_indexing=True) with lzma.open(args.filename, 'wb') as f: pickle.dump(pop, f) if args.statfile is not None: stats = pd.DataFrame(r.data, columns=DATA._fields) # Write the statistics to an sqlite3 database, # which can be processed in R via dplyr. conn = sqlite3.connect(args.statfile) stats.to_sql('data', conn)
def testQtraitSim(self): N = 1000 demography = np.array([N] * 10 * N, dtype=np.uint32) rho = 1. r = rho / (4 * N) GSS = fwdpy11.GSS(VS=1, opt=1) a = fwdpy11.Additive(2.0, GSS) p = { 'nregions': [], 'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)], 'recregions': [fwdpy11.PoissonInterval(0, 1, r)], 'rates': (0.0, 0.005, None), 'gvalue': a, 'prune_selected': False, 'demography': demography } params = fwdpy11.ModelParams(**p) rng = fwdpy11.GSLrng(101 * 45 * 110 * 210) pop = fwdpy11.DiploidPopulation(N, 1.0) class Recorder(object): """ Records entire pop every 100 generations """ def __call__(self, pop, recorder): if pop.generation % 100 == 0.0: recorder.assign(np.arange(pop.N, dtype=np.int32)) r = Recorder() fwdpy11.evolvets(rng, pop, params, 100, r) ancient_sample_metadata = np.array(pop.ancient_sample_metadata, copy=False) alive_sample_metadata = np.array(pop.diploid_metadata, copy=False) metadata = np.hstack((ancient_sample_metadata, alive_sample_metadata)) nodes = np.array(pop.tables.nodes, copy=False) metadata_nodes = metadata['nodes'].flatten() metadata_node_times = nodes['time'][metadata_nodes] metadata_record_times = nodes['time'][metadata['nodes'][:, 0]] genetic_trait_values_from_sim = [] genetic_values_from_ts = [] for u in np.unique(metadata_node_times): samples_at_time_u = metadata_nodes[np.where( metadata_node_times == u)] vi = fwdpy11.VariantIterator(pop.tables, samples_at_time_u) sum_esizes = np.zeros(len(samples_at_time_u)) for variant in vi: g = variant.genotypes r = variant.records[0] mutant = np.where(g == 1)[0] sum_esizes[mutant] += pop.mutations[r.key].s ind = int(len(samples_at_time_u) / 2) temp_gvalues = np.zeros(ind) temp_gvalues += sum_esizes[0::2] temp_gvalues += sum_esizes[1::2] genetic_values_from_ts.extend(temp_gvalues.tolist()) genetic_trait_values_from_sim.extend(metadata['g'][np.where( metadata_record_times == u)[0]].tolist()) for i, j in zip(genetic_trait_values_from_sim, genetic_values_from_ts): self.assertAlmostEqual(i, j)
with concurrent.futures.ProcessPoolExecutor( max_workers=args.nworkers) as e: futures = { e.submit(runsim, model, args.num_subsamples, args.nsam, i) for i in seeds } for fut in concurrent.futures.as_completed(futures): fst, d0, d1 = fut.result() fsta[idx] = fst idx += 1 mean_deme_zero_fs += d0 mean_deme_one_fs += d1 mean_deme_zero_fs /= args.nreps mean_deme_one_fs /= args.nreps with open(args.outdir + "/caption.rst", "w") as f: f.write(f"The initial_seed was {initial_seed}.\n") f.write("The model details are:\n\n::\n\n") mp = fwdpy11.ModelParams(**model["pdict"]) for i in mp.asblack().split("\n"): f.write(f"\t{i}\n") f.write("\n") with open(args.outdir + "/fst.np", "wb") as f: fsta.tofile(f) with open(args.outdir + "/deme0.np", "wb") as f: mean_deme_zero_fs.tofile(f) with open(args.outdir + "/deme1.np", "wb") as f: mean_deme_one_fs.tofile(f)
import sys import fwdpy11 N = int(sys.argv[1]) seed = int(sys.argv[2]) pdict = { "nregions": [], "sregions": [], "recregions": [], "rates": [0, 0, 0], "gvalue": fwdpy11.Multiplicative(2.0), "simlen": 5 * N, } mp = fwdpy11.ModelParams(**pdict) pop = fwdpy11.DiploidPopulation(N, 1.0) rng = fwdpy11.GSLrng(seed) fwdpy11.evolvets(rng, pop, mp, 100, suppress_table_indexing=True) ts = pop.dump_tables_to_tskit() # Copies all data from C++ to Python ts.dump("fwdpy11.trees")
def test_nodes_after_evolution(self): params = fwdpy11.ModelParams(**self.pdict) fwdpy11.evolve_genomes(self.rng, self.pop, params) for i in self.pop.diploid_metadata: self.assertEqual(i.nodes[0], fwdpy11.NULL_NODE) self.assertEqual(i.nodes[1], fwdpy11.NULL_NODE)