def generate_covariance(size, scale_metod='beta', return_tree=False):
tree = generate_phylogeny(size)
cov = make_covariance(tree)
s = calc_s(scale_metod)
if return_tree:
return cov * s, scale_tree(tree, s)
return cov * s
def create_treemix_csv_output(tree, add, m_scale, outfile):
if m_scale is not None:
tree = scale_tree(tree, 1.0 / m_scale)
add = add / m_scale
with open(outfile, 'w') as f:
f.write('tree,add' + '\n')
f.write(unique_identifier_and_branch_lengths(tree) + ',' + str(add))
def get_two_estimates(no_leaves, no_snps, no_individuals_per_population,
rho=0.0,
no_admixtures=None,
no_bootstrap_samples=100,
scale_factor=0.02,
blocksize=100,
reps=1,
collapsing=['nothing', 'Areduce','Rreduce'],
summaries=[np.mean, np.std],
sim_method=['normal','ms']):
lik_based=[]
var_based=[]
sim_method=initor(sim_method)
for _ in range(reps):
if sim_method=='normal':
Sigma=generate_covariance(no_leaves, scale_metod='None')*scale_factor
Ps=simulate_ps(no_snps, Sigma, rho)
#print Ps
#print Ps
Ps, p0s=decentralize(Ps, no_individuals_per_population)
elif sim_method=='ms':
Sigma, tree=generate_covariance(no_leaves, scale_metod='None', return_tree=True)
tree=scale_tree(tree, scale_factor)
Ps,p0s=simulate_P_from_tree(tree, no_individuals_per_population, no_snps)
print Ps, p0s
#print Ps
#print Ps
bPs,b_p0s= draw_bootstrap_sample(Ps, no_bootstrap_samples, optional_p0=p0s, blocksize=blocksize)
print 'drew bootstrap samples'
#print bPs
covs=np.array(calc_covs(bPs, b_p0s, collapsing))
print 'calculated covs'
#print covs
#print covs
lik_based.append(likelihood_mean_based(covs))
print 'calculated likelihood based tickets'
print lik_based[-1]
var_based.append(variance_mean_based(covs))
print 'calculated variance based'
print var_based[-1]
lik_based=[lik for lik in lik_based if lik is not None]
lik_summa=[summary(lik_based) for summary in summaries]
var_summa=[summary(var_based) for summary in summaries]
return lik_summa, var_summa
def scale_tree_wrapper(tree, **kwargs):
if kwargs['time_adjust']:
tree=time_adjust_tree(tree)
return scale_tree(tree, kwargs['scale_tree_factor'])
def create_treemix_sfull_tree_csv_output(tree, m_scale, outfile):
if m_scale is not None:
tree = scale_tree(tree, 1.0 / m_scale)
with open(outfile, 'w') as f:
f.write('sfull_tree' + '\n')
f.write(unique_identifier_and_branch_lengths(tree))
if __name__=='__main__':
#print reduce_covariance(identity(10), 5)
#print file_to_emp_cov('out_stem.cov',4)
from sys import exit
exit()
from generate_prior_trees import generate_phylogeny
from Rcatalogue_of_trees import *
from Rtree_operations import create_trivial_tree, scale_tree
tree2=scale_tree(generate_phylogeny(5,1),0.05)
print pretty_string(tree2)
print pretty_string(identifier_to_tree_clean(unique_identifier_and_branch_lengths(tree2)))
print supplementary_text_ms_string()
tree_good=generate_phylogeny(7)
a=tree_to_ms_command(tree_good, 50,20)
#print call_ms_string(a, 'supp.txt')
b=time_adjusted_tree_to_ms_command(tree_good,50,20)
#print call_ms_string(b, 'supp2.txt')
#print call_ms_string(tree_to_ms_command(tree2, 50,20), 'tmp.txt')
#cov= ms_to_treemix2('supp.txt', 20, 20,400)
#cov= ms_to_treemix2('tmp.txt', 50, 5,20)
#cov2=calculate_covariance_matrix('tmp.txt', 50, 5,20)
#print cov
#print cov2
#print make_covariance(tree2)