def setUp(self, fn="testdata/bias_nobias"):
TestGeneric.setUp(self, fn)
self.seqlen = 1e6
self.num_samples = 8
self.pop = populationmodels.Population(sequence_length=self.seqlen,
scrmpath=self.scrmpath,
num_samples=self.num_samples,
num_populations=1,
change_points=[0, 0.5, 1],
population_sizes=[[1.2], [0.8],
[1.0]])
# set default inference parameters
self.missing_leaves = range(self.num_samples)
self.smcsmc_initial_pop_sizes = self.pop.population_sizes
self.lag = 2
self.popt = None
self.em = 0
self.np = 100
self.seed = (8, )
self.bias_heights = None
self.bias_strengths = None
# set targets
self.targets = [{
'type': "Recomb",
'min': 9.9e-9,
'max': 1.01e-8,
'truth': 1e-8,
'ess': self.np - 1
}, {
'type': "Coal",
'pop': 0,
'epoch': 0,
'min': 11800,
'max': 12200,
'truth': 12000,
'ess': self.np - 1
}, {
'type': "Coal",
'pop': 0,
'epoch': 1,
'min': 7800,
'max': 8200,
'truth': 8000,
'ess': self.np - 1
}, {
'type': "Coal",
'pop': 0,
'epoch': 2,
'min': 9800,
'max': 10200,
'truth': 10000,
'ess': self.np - 1
}]
self.max_out_of_range = 0
def setUp(self, name="testdata/constpopsize_3epochs"):
# base class for a number of experiments.
TestGeneric.setUp(self, name)
self.seqlen = 10000 # dummy length, for test
self.pop = populationmodels.Population(
sequence_length=self.seqlen,
scrmpath=self.scrmpath,
change_points=[0, 1, 2], # three epochs
num_populations=1,
population_sizes=[[1], [1], [1]])
self.em = 5
self.np = 100
self.bias_heights = None
self.bias_strenghts = None
self.alpha = 0
self.debug = True
# no tests here
self.targets = []
self.max_out_of_range = 0
def setUp(self, fn="testdata/twopops"):
TestGeneric.setUp(self, fn)
self.debug = True
self.seqlen = 1e6
self.num_samples = 8
self.pop = populationmodels.Population( sequence_length = self.seqlen,
scrmpath = self.scrmpath,
num_samples = self.num_samples,
num_populations = 2,
change_points = [0, 0.5, 1],
population_sizes = [ [1,1], [1,1], [1,1] ],
sample_populations = [1,1,1,1,2,2,2,2] )
# set default inference parameters
self.smcsmc_initial_pop_sizes = self.pop.population_sizes
self.lag = 1
self.popt = None
self.em = 4
self.np = 100
self.seed = (8,)
self.bias_heights = [400]
self.bias_strengths = [10,1]
# set targets
self.targets = [{'type':"Recomb",
'min':9.9e-9, 'max':1.01e-8, 'truth':1e-8, 'ess':self.np-1},
{'type':"Coal", 'pop':0, 'epoch':0,
'min':9800, 'max':10200, 'truth':10000, 'ess':self.np-1},
{'type':"Coal", 'pop':0, 'epoch':1,
'min':9800, 'max':10200, 'truth': 10000, 'ess':self.np-1},
{'type':"Coal", 'pop':0, 'epoch':2,
'min':9800, 'max':10200, 'truth':10000, 'ess':self.np-1}]
self.max_out_of_range = 0
def setUp(self, name="testdata/runtime"):
TestGeneric.setUp(self, name)
self.seqlen = 1e6
self.pop = populationmodels.Population(sequence_length=self.seqlen,
scrmpath=self.scrmpath,
change_points=[0],
num_populations=1,
num_samples=8,
population_sizes=[[1]])
# use smcsmc front-end
self.smcsmcpath = "../smcsmc"
# use pattern for inference (default)
#self.popt = None
self.em = 0
self.np = 1000
self.bias_heights = [800]
self.bias_strengths = [1, 1]
self.seed = (1, )
self.debug = True
self.targets = []
self.max_out_of_range = -1
def setUp(self):
TestGeneric.setUp(self, "testdata/constpopsize_migration")
self.seqlen = 1e6
self.pop = populationmodels.Population(
sequence_length=self.seqlen,
scrmpath=self.scrmpath,
change_points=[0, 0.01, 0.25, 0.5, 1, 1.5],
num_populations=2,
num_samples=8,
sample_populations=[1, 1, 1, 1, 2, 2, 2, 2],
population_sizes=[[1, 1]] * 6,
migration_rates=[
[[0, 0], [1, 0]], # -em 0 2 1 1
[[0, 0], [1, 0]], # em 0.01 2 1 1
[[0, 0], [1, 0]],
[[0, 0], [1, 0]],
[[0, 0], [1, 0]],
[[0, 0], [1, 0]]
])
# use python front-end
self.smcsmcpath = "../python/smcsmc.py"
# set default inference parameters
self.popt = None
self.smcsmc_initial_pop_sizes = self.pop.population_sizes
self.lag = 2
self.em = 0
self.np = 1000
self.debug = True
self.bias_heights = [800]
self.bias_strengths = [2, 1]
self.tmax = 4
self.seed = (1, )
# set targets
scaling = 1.0 / (4.0 * self.pop.N0)
self.targets = [{
'type': "Coal",
'pop': 0,
'epoch': 0,
'min': 0,
'max': 1.97e8,
'truth': 10000
}, {
'type': "Coal",
'pop': 1,
'epoch': 0,
'min': 0,
'max': 2.14e8,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 1,
'min': 9211,
'max': 11671,
'truth': 10000
}, {
'type': "Coal",
'pop': 1,
'epoch': 1,
'min': 9833,
'max': 12253,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 2,
'min': 8920,
'max': 11205,
'truth': 10000
}, {
'type': "Coal",
'pop': 1,
'epoch': 2,
'min': 8783,
'max': 12353,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 3,
'min': 9130,
'max': 10790,
'truth': 10000
}, {
'type': "Coal",
'pop': 1,
'epoch': 3,
'min': 8695,
'max': 11327,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 4,
'min': 9084,
'max': 10583,
'truth': 10000
}, {
'type': "Coal",
'pop': 1,
'epoch': 4,
'min': 8128,
'max': 12132,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 5,
'min': 9460,
'max': 10360,
'truth': 10000
}, {
'type': "Coal",
'pop': 1,
'epoch': 5,
'min': 8913,
'max': 11451,
'truth': 10000
}, {
'type': "Recomb",
'min': 9.89e-9,
'max': 1.06e-8,
'truth': 1e-8
}, {
'type': "Migr",
'from_pop': 0,
'to_pop': 1,
'epoch': 0,
'min': 0,
'max': 0
}, {
'type': "Migr",
'from_pop': 1,
'to_pop': 0,
'epoch': 0,
'min': 0,
'max': 1.47e-4
}, {
'type': "Migr",
'from_pop': 0,
'to_pop': 1,
'epoch': 1,
'min': 0,
'max': 0
}, {
'type': "Migr",
'from_pop': 1,
'to_pop': 0,
'epoch': 1,
'min': 1.66e-5,
'max': 3.21e-5
}, {
'type': "Migr",
'from_pop': 0,
'to_pop': 1,
'epoch': 2,
'min': 0,
'max': 0
}, {
'type': "Migr",
'from_pop': 1,
'to_pop': 0,
'epoch': 2,
'min': 2.00e-5,
'max': 3.36e-5
}, {
'type': "Migr",
'from_pop': 0,
'to_pop': 1,
'epoch': 3,
'min': 0,
'max': 0
}, {
'type': "Migr",
'from_pop': 1,
'to_pop': 0,
'epoch': 3,
'min': 2.24e-5,
'max': 3.28e-5
}, {
'type': "Migr",
'from_pop': 0,
'to_pop': 1,
'epoch': 4,
'min': 0,
'max': 0
}, {
'type': "Migr",
'from_pop': 1,
'to_pop': 0,
'epoch': 4,
'min': 2.32e-5,
'max': 3.42e-5
}, {
'type': "Migr",
'from_pop': 0,
'to_pop': 1,
'epoch': 5,
'min': 0,
'max': 0
}, {
'type': "Migr",
'from_pop': 1,
'to_pop': 0,
'epoch': 5,
'min': 2.39e-5,
'max': 3.00e-5
}]
for t in self.targets:
if t['type'] == "Migr":
t['truth'] = scaling * self.pop.migration_rates[t['epoch']][
t['from_pop']][t['to_pop']]
self.max_out_of_range = 0
def setUp(self, name="testdata/constpopsize"):
TestGeneric.setUp(self, name)
self.seqlen = 1e7
self.pop = populationmodels.Population(
sequence_length=self.seqlen,
scrmpath=self.scrmpath,
change_points=[0, 0.01, 0.25, 0.5, 1, 1.5],
num_populations=1,
population_sizes=[[1], [1], [1], [1], [1], [1]])
# use python front-end
self.smcsmcpath = "../python/smcsmc.py"
# set default inference parameters
self.popt = None
self.smcsmc_initial_pop_sizes = self.pop.population_sizes
self.lag = 2
self.em = 0
self.np = 1000
self.debug = True
self.bias_heights = [400]
self.bias_strengths = [3, 1]
self.tmax = 4
self.seed = (1, )
#self.guided_recomb_alpha = 0.5 #TEST
# set targets
self.targets = [{
'type': "Coal",
'pop': 0,
'epoch': 0,
'min': 0,
'max': 124573,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 1,
'min': 10202,
'max': 10471,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 2,
'min': 9927,
'max': 10072,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 3,
'min': 9963,
'max': 10066,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 4,
'min': 9934,
'max': 10086,
'truth': 10000
}, {
'type': "Coal",
'pop': 0,
'epoch': 5,
'min': 9862,
'max': 10098,
'truth': 10000
}, {
'type': "Recomb",
'min': 9.77e-9,
'max': 9.89e-9,
'truth': 1e-8
}]
self.max_out_of_range = 0
def setUp(self):
TestGeneric.setUp(self, "testdata/constpopsize_migration")
self.seqlen = 1e6
self.pop = populationmodels.Population(
sequence_length=self.seqlen,
scrmpath=self.scrmpath,
change_points=[0, 0.01, 0.25, 0.5, 1, 1.5],
num_populations=2,
num_samples=8,
sample_populations=[1, 1, 1, 1, 2, 2, 2, 2],
population_sizes=[[1, 1]] * 6,
migration_rates=[
[[0, 0], [1, 0]], # -em 0 2 1 1
[[0, 0], [1, 0]], # em 0.01 2 1 1
[[0, 0], [1, 0]],
[[0, 0], [1, 0]],
[[0, 0], [1, 0]],
[[0, 0], [1, 0]]
])
# use python front-end
self.smcsmcpath = "../python/smcsmc.py"
# set default inference parameters
self.popt = None
self.smcsmc_initial_pop_sizes = self.pop.population_sizes
self.lag = 2
self.em = 1
self.np = 1000
self.debug = True
self.bias_heights = [800]
self.bias_strengths = [2, 1]
self.tmax = 4
self.seed = (1, )
# set targets
self.targets = [{
'type': "Recomb",
'min': 9.6e-9,
'max': 1.04e-8,
'truth': 1e-8,
'ess': 1.5
}]
for idx, pop in itertools.product(range(6), range(2)):
self.targets.append({
'type':
"Coal",
'pop':
pop,
'epoch':
idx,
'min':
9300 if idx > 1 else 6000,
'max': [15000, 11500, 10600, 10600, 10600, 11500][idx],
'truth':
10000,
'ess': [1, 1, 1.3, 1.5, 2, 3][idx]
})
self.targets += [{
'type': 'Migr',
'from_pop': fp,
'to_pop': tp,
'epoch': ep,
'min': mi,
'max': ma,
'truth': tr
} for (fp, tp, ep, mi, ma,
tr) in [(0, 1, 0, 0.0, 1e-5,
0), (0, 1, 1, 0.0, 1e-5,
0), (0, 1, 2, 0.0, 1e-5,
0), (0, 1, 3, 0.0, 1e-5,
0), (0, 1, 4, 0.0, 1e-5,
0), (0, 1, 5, 0.0, 1e-5, 0),
(1, 0, 0, 0.0, 3e-5,
2.5e-5), (1, 0, 1, 0.0, 3e-5,
2.5e-5), (1, 0, 2, 0.0, 3e-5, 2.5e-5),
(1, 0, 3, 0.0, 3e-5,
2.5e-5), (1, 0, 4, 0.0, 3e-5,
2.5e-5), (1, 0, 5, 0.0, 3e-5, 2.5e-5)]]
self.max_out_of_range = -1