def make_network(self, algo = 'crude', **kwargs):
'''
'''
import SurveyStructures.OTUnetwork as ONet
n, k = self.shape
row_labels = kwargs.get('row_labels', range(k))
col_labels = kwargs.get('col_labels', range(n))
md = self.to_Surveymat(**kwargs)
if algo == 'crude': mat,pval = (md.log10()).correlation(type = 'pearsonr')
elif algo in set(['sparse', 'csi', 'clr']):
v_sparse, c_sparse, Cov_sparse = self.basis_corr(method = algo,**kwargs)
mat = MatrixDictionary()
mat.from_matrix(c_sparse, row_labels, row_labels)
pval = mat
elif algo == 'basis':
md = Survey_matrix()
n, k = self.shape
mat = np.log(self.basis)
md.from_matrix(mat.transpose(), row_labels, col_labels)
mat,pval = md.correlation(type = 'pearsonr')
elif algo in set(['pearsonr','kendalltau','spearmanr']):
abunds = self.to_Surveymat()
mat,pval = abunds.correlation(type = algo)
## make network
if algo == 'MI': net = ONet.OTUnetwork( abunds = abunds, lineages = lineages, matrix = mat.to_PairMatrix(), p_vals = {'direct':None}, algo = 'MI', lag = 0)
else: net = ONet.OTUnetwork(node_ids = mat.row_labels(), matrix = mat, p_vals = {'direct':pval}, algo = algo, lag = 0)
return net
def make_network(self, algo='crude', **kwargs):
'''
'''
import SurveyStructures.OTUnetwork as ONet
n, k = self.shape
row_labels = kwargs.get('row_labels', range(k))
col_labels = kwargs.get('col_labels', range(n))
md = self.to_Surveymat(**kwargs)
if algo == 'crude':
mat, pval = (md.log10()).correlation(type='pearsonr')
elif algo in set(['sparse', 'csi', 'clr']):
v_sparse, c_sparse, Cov_sparse = self.basis_corr(method=algo,
**kwargs)
mat = MatrixDictionary()
mat.from_matrix(c_sparse, row_labels, row_labels)
pval = mat
elif algo == 'basis':
md = Survey_matrix()
n, k = self.shape
mat = np.log(self.basis)
md.from_matrix(mat.transpose(), row_labels, col_labels)
mat, pval = md.correlation(type='pearsonr')
elif algo in set(['pearsonr', 'kendalltau', 'spearmanr']):
abunds = self.to_Surveymat()
mat, pval = abunds.correlation(type=algo)
## make network
if algo == 'MI':
net = ONet.OTUnetwork(abunds=abunds,
lineages=lineages,
matrix=mat.to_PairMatrix(),
p_vals={'direct': None},
algo='MI',
lag=0)
else:
net = ONet.OTUnetwork(node_ids=mat.row_labels(),
matrix=mat,
p_vals={'direct': pval},
algo=algo,
lag=0)
return net
def test_independent_betabinom():
n = 5
m = 50
mat = np.floor(100*np.random.rand(n,m))
mat[1,:] = 100-mat[0,:]
row_labels = range(n)
col_labels = range(m)
data = Survey_matrix()
data.from_matrix(mat, row_labels, col_labels)
c,p = data.correlation(type = 'pearsonr')
print c.to_matrix()
print p.to_matrix()
sim = independent_betabinom(data)
c_sim, p = sim.correlation(type = 'pearsonr')
print c_sim.to_matrix()
print p.to_matrix()
sim_norm = sim.normalize(rows = False)
c_sim_norm, p = sim_norm.correlation(type = 'pearsonr')
print c_sim_norm.to_matrix()
print p.to_matrix()
def test_independent_betabinom():
n = 5
m = 50
mat = np.floor(100 * np.random.rand(n, m))
mat[1, :] = 100 - mat[0, :]
row_labels = range(n)
col_labels = range(m)
data = Survey_matrix()
data.from_matrix(mat, row_labels, col_labels)
c, p = data.correlation(type='pearsonr')
print c.to_matrix()
print p.to_matrix()
sim = independent_betabinom(data)
c_sim, p = sim.correlation(type='pearsonr')
print c_sim.to_matrix()
print p.to_matrix()
sim_norm = sim.normalize(rows=False)
c_sim_norm, p = sim_norm.correlation(type='pearsonr')
print c_sim_norm.to_matrix()
print p.to_matrix()