def test_model1_otu(self):
"""Tests that otus are created correctly according to given params."""
seed(0) #seed for reproducibility
exp = lognorm.rvs(2, 0, size=10)
seed(0)
obs = model1_otu([lognorm, 2, 0], 10)
assert_array_almost_equal(exp, obs)
def test_model1_otu(self):
"""Tests that otus are created correctly according to given params."""
seed(0) #seed for reproducibility
exp = lognorm.rvs(2,0,size=10)
seed(0)
obs = model1_otu([lognorm, 2, 0], 10)
assert_array_almost_equal(exp, obs)
def _generate_data(features, samples):
'''Make featuresXsamples data matrix.'''
return lognorm.rvs(3, 0, 1, size=features*samples).round(0).reshape(
features, samples)
# ecological table #
##################################################
# seed at 0 for reproduccibility
seed(0)
######################
# Amensalism 1d
#####################
# choose 60 otus and relate them via o1^o2-> decrease to o2.
# note, odd otus will be affected by the last even otu. o0 will decrease 01,
# o2 will decrease 03 etc.
D = 30
strength = .5
os = lognorm.rvs(3, 0, size=(60, 50))
amensally_related_1d_st_5 = []
truth_amensally_related_1d_st_5 = np.zeros(2 * D, 2 * D)
for i in range(D):
ind_i, ind_j = 2 * i, 2 * i + 1
truth_amensally_related_1d_st_5[ind_i, ind_j] = 1
am_otu = amensal_1d(os[ind_i], os[ind_j], strength)
amensally_related_1d_st_5.extend([os[ind_i], am_otu])
strength = .3
os = lognorm.rvs(3, 0, size=(60, 50))
amensally_related_1d_st_3 = []
truth_amensally_related_1d_st_3 = np.zeros(2 * D, 2 * D)
for i in range(D):
ind_i, ind_j = 2 * i, 2 * i + 1
truth_amensally_related_1d_st_3[ind_i, ind_j] = 1
pass
def rvs(self, loc, scale, size):
pass
# test 2/25/2013
# create 200 otu X 50 sample table. use 4 otu's per rule
# generate background otus which will be used to induce
w = lognorm.rvs(2,1,size=10000).astype(int).reshape(200,50)
# generate model 1 rules
model1_rules = []
for i in range(10):
rule_vals = uniform.rvs(-100,400,size=8)
# sort rules so that left is lb <= ub
rule_vals = where(rule_vals > 0, rule_vals, 0).reshape(4,2)
rule_vals.sort()
model1_rules.append(map(list, rule_vals))
# define weights and new otu distribution function
weights = array([1.0, 1.0, 1.0, 1.0, .2])
df_and_params = [lognorm, 2, 1]
# create new otus
table_index = 0
new_otus = []
table_map = []
def init_data(params, num_groups=30, num_samps=50):
"""
Fills in the contingency tables and correlation matrices
for each interaction group
Parameters
----------
params : dict of dicts:
First set of keys - type of group interaction and strength
Second set of keys - attributes for group
strength : float
strength of interaction
func : function
generator function
dim : int
number of individual per interaction
data : np.array
count table
truth : np.array
adjancency matrix
name : str
name of the interaction
num_groups : int
number of groups of interactions
num_samps : int
number of samples
Returns
-------
dict of dicts:
First set of keys - type of group interaction and strength
Second set of keys - attributes for group
strength : float
strength of interaction
func : function
generator function
dim : int
number of individual per interaction
data : np.array
count table
truth : np.array
adjancency matrix
name : str
name of the interaction
"""
D, S = num_groups, num_samps
# Start filling in some tables
for k in params.keys():
x = params[k]['dim']+1
os = lognorm.rvs(3, 0, size=(x*D, S))
strength = params[k]['strength']
params[k]['data'] = np.zeros((S, x*D))
params[k]['truth'] = np.zeros((x*D, x*D))
func = params[k]['func']
for i in range(D):
idx = range(x*i, x*i+x)
if params[k]['name'] == 'partial_obligate_syntroph':
if '1d' in k:
obs_otu = func(*list(os[idx]))
else:
obs_otu = func(os[idx])
else:
if '1d' in k:
obs_otu = func(*(list(os[idx])+[strength]))
else:
obs_otu = func(os[idx], strength)
params[k]['data'][:, idx] = np.vstack(obs_otu).T
if params[k]['name'] in {'commensal', 'mutual',
'obligate_syntroph',
'partial_obligate_syntroph'}:
params[k]['truth'][idx[:-1], idx[-1]] = 1
if params[k]['name'] in {'amensal', 'parasite', 'competition'}:
params[k]['truth'][idx[:-1], idx[-1]] = -1
return params
# ecological table #
##################################################
# seed at 0 for reproduccibility
seed(0)
######################
# Amensalism 1d
#####################
# choose 60 otus and relate them via o1^o2-> decrease to o2.
# note, odd otus will be affected by the last even otu. o0 will decrease 01,
# o2 will decrease 03 etc.
D = 30
strength = .5
os = lognorm.rvs(3, 0, size=(60, 50))
amensally_related_1d_st_5 = []
truth_amensally_related_1d_st_5 = np.zeros(2*D, 2*D)
for i in range(D):
ind_i, ind_j = 2*i, 2*i+1
truth_amensally_related_1d_st_5[ind_i, ind_j] = 1
am_otu = amensal_1d(os[ind_i], os[ind_j], strength)
amensally_related_1d_st_5.extend([os[ind_i], am_otu])
strength = .3
os = lognorm.rvs(3,0,size=(60,50))
amensally_related_1d_st_3 = []
truth_amensally_related_1d_st_3 = np.zeros(2*D, 2*D)
for i in range(D):
ind_i, ind_j = 2*i, 2*i+1
truth_amensally_related_1d_st_3[ind_i, ind_j] = 1
