def test_two_deme_growth_without_hard_reset(self):
N0 = [90, 10]
md = np.array(self.pop.diploid_metadata, copy=False)
md['deme'][N0[0]:] = 1
t = [14, 23] # generations of growth in each deme
N1 = [5361, 616]
G0 = np.exp((np.log(N1[0]) - np.log(N0[0])) / t[0])
G1 = np.exp((np.log(N1[1]) - np.log(N0[1])) / t[1])
g = []
g.append(fwdpy11.SetExponentialGrowth(7, 0, G0))
g.append(fwdpy11.SetExponentialGrowth(7 + t[0], 0, fwdpy11.NOGROWTH))
g.append(fwdpy11.SetExponentialGrowth(33, 1, G1))
g.append(fwdpy11.SetExponentialGrowth(33 + t[1], 1, fwdpy11.NOGROWTH))
# after X generations of growth, N[0] changes to 100
# and the growth rate is not reset.
p = [fwdpy11.SetDemeSize(11, 0, 100, False)]
d = fwdpy11.DiscreteDemography(set_growth_rates=g, set_deme_sizes=p)
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 100)
md = np.array(self.pop.diploid_metadata, copy=False)
deme_sizes = np.unique(md['deme'], return_counts=True)
N1[0] = np.round(100. * np.power(G0, 7 + t[0] - 11))
self.assertEqual(self.pop.N, sum(N1))
for i, j in zip(deme_sizes[1], N1):
self.assertEqual(i, j)
def test_two_deme_growth_with_hard_reset(self):
N0 = [90, 10]
md = np.array(self.pop.diploid_metadata, copy=False)
md['deme'][N0[0]:] = 1
t = [14, 23] # generations of growth in each deme
N1 = [5361, 616]
G0 = np.exp((np.log(N1[0]) - np.log(N0[0])) / t[0])
G1 = np.exp((np.log(N1[1]) - np.log(N0[1])) / t[1])
g = []
g.append(fwdpy11.SetExponentialGrowth(7, 0, G0))
g.append(fwdpy11.SetExponentialGrowth(33, 1, G1))
g.append(fwdpy11.SetExponentialGrowth(33 + t[1], 1, fwdpy11.NOGROWTH))
# Cut off the growth in deme 0 after a few generations,
# and manually set the new deme size to 100 w/no growth
p = [fwdpy11.SetDemeSize(11, 0, 100)]
d = fwdpy11.DiscreteDemography(set_deme_sizes=p, set_growth_rates=g)
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 100)
md = np.array(self.pop.diploid_metadata, copy=False)
deme_sizes = np.unique(md['deme'], return_counts=True)
N1 = [100, N1[1]]
self.assertEqual(self.pop.N, sum(N1))
for i, j in zip(deme_sizes[1], N1):
self.assertEqual(i, j)
def test_single_deme_growth(self):
N1 = 3412
t = 111
G = np.exp((np.log(N1) - np.log(self.pop.N)) / t)
g = [fwdpy11.SetExponentialGrowth(16, 0, G)]
d = fwdpy11.DiscreteDemography(set_growth_rates=g)
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 15 + t + 1)
self.assertEqual(self.pop.N, N1)
self.assertEqual(len(self.pop.diploid_metadata), N1)
def test_two_deme_growth(self):
N0 = [90, 10]
md = np.array(self.pop.diploid_metadata, copy=False)
md['deme'][N0[0]:] = 1
t = [14, 23] # generations of growth in each deme
N1 = [5361, 616]
G0 = np.exp((np.log(N1[0]) - np.log(N0[0])) / t[0])
G1 = np.exp((np.log(N1[1]) - np.log(N0[1])) / t[1])
g = []
g.append(fwdpy11.SetExponentialGrowth(7, 0, G0))
g.append(fwdpy11.SetExponentialGrowth(7 + t[0], 0, fwdpy11.NOGROWTH))
g.append(fwdpy11.SetExponentialGrowth(33, 1, G1))
g.append(fwdpy11.SetExponentialGrowth(33 + t[1], 1, fwdpy11.NOGROWTH))
d = fwdpy11.DiscreteDemography(set_growth_rates=g)
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 100)
md = np.array(self.pop.diploid_metadata, copy=False)
self.assertEqual(self.pop.N, sum(N1))
deme_sizes = np.unique(md['deme'], return_counts=True)
for i, j in zip(deme_sizes[1], N1):
self.assertEqual(i, j)
def test_mass_move_with_growth_no_reset(self):
"""
In generation 5, the mass movement from 0 to 1
"""
m = [fwdpy11.move_individuals(5, 0, 1, 0.5, False)]
g = [fwdpy11.SetExponentialGrowth(0, 0, 1.1)]
d = fwdpy11.DiscreteDemography(mass_migrations=m, set_growth_rates=g)
N0 = self.pop.N
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 10)
md = np.array(self.pop.diploid_metadata)
deme_counts = np.unique(md['deme'], return_counts=True)
N5 = np.round(N0 * np.power(g[0].G, 5))
N_after_mass_mig_0 = np.round((N5 // 2) * np.power(g[0].G, 5))
N = [N_after_mass_mig_0, N5 - N5 // 2]
for i, j in zip(N, deme_counts[1]):
self.assertEqual(i, j)
def test_mass_move_with_growth(self):
"""
In generation 5, the mass movement from 0 to 1
will reset the growth rate in deme 0 to
fwdpy11.NOGROWTH
This test is also handy b/c growth results in
an odd total N by the time the mass migration happens
"""
m = [fwdpy11.move_individuals(5, 0, 1, 0.5)]
g = [fwdpy11.SetExponentialGrowth(0, 0, 1.1)]
d = fwdpy11.DiscreteDemography(mass_migrations=m, set_growth_rates=g)
N0 = self.pop.N
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 10)
md = np.array(self.pop.diploid_metadata)
deme_counts = np.unique(md['deme'], return_counts=True)
N5 = np.round(N0 * np.power(g[0].G, 5))
N_after_mass_mig = [N5 // 2, N5 - N5 // 2]
for i, j in zip(deme_counts[1], N_after_mass_mig):
self.assertEqual(i, j)
def setUpClass(self):
self.seg = fwdpy11.SetExponentialGrowth(0, 1, 0.1)
def test_init_with_two_growth_changes_same_generation(self):
g = [fwdpy11.SetExponentialGrowth(0, 1, 0.3)] * 2
with self.assertRaises(ValueError):
fwdpy11.DiscreteDemography(set_growth_rates=g)
def test_setting_growth(self):
g = [fwdpy11.SetExponentialGrowth(0, 1, 0.3)]
fwdpy11.DiscreteDemography(set_growth_rates=g)
def test_invalid_growth_rate(self):
with self.assertRaises(ValueError):
self.seg = fwdpy11.SetExponentialGrowth(0, 1, np.nan)
def test_invalid_deme(self):
with self.assertRaises(ValueError):
self.seg = fwdpy11.SetExponentialGrowth(0, -1, 0.1)
