def setUp(self): GN = fwdpy11.GaussianNoise self.w = fwdpy11.Additive(2.0) self.t = fwdpy11.Additive( 2.0, fwdpy11.GSS(0.0, 1.0)) self.tn = fwdpy11.Additive(1.0, fwdpy11.GSS( 0.0, 1.0), GN(mean=0.1, sd=2.0))
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 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): 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): 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 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 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 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): """ 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.Region(0, 1, 1)], 'rates': (0.0, 0.005, 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) 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), len(pop.fixations)) # 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 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)
import fwdpy11 print(fwdpy11.__file__) import numpy as np import FixedCrossoverInterval N = 1000 pdict = { 'demography': fwdpy11.DiscreteDemography(), 'simlen': 100, 'nregions': [], 'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)], 'recregions': [FixedCrossoverInterval.FixedCrossoverInterval(0, 1, 2)], 'rates': (0., 5e-3, None), 'gvalue': fwdpy11.Additive(2., fwdpy11.GSS(VS=1, opt=1)), 'prune_selected': False } params = fwdpy11.ModelParams(**pdict) print(params.recregions) pop = fwdpy11.DiploidPopulation(N, 1.0) rng = fwdpy11.GSLrng(42) fwdpy11.evolvets(rng, pop, params, 100)
def setUp(self): self.a = fwdpy11.Additive( 2.0, fwdpy11.GSS(0.0, 1.0)) self.b = fwdpy11.Additive( 2.0, fwdpy11.GSSmo([(0, 0.0, 1.0)])) self.pop = fwdpy11.DiploidPopulation(1000)