def test_randomization(self):
com = Community(sample_abundance)
com.fit_model('etienne')
etienne_model = com.get_model('etienne')
self.assertEqual(sum(etienne_model.random_community()), com.J)
self.assertEqual(sum(etienne_model.random_community(2500)), 2500)
def test_optimization(self):
com = Community(sample_abundance)
com.fit_model('lognormal')
model = com.get_model('lognormal')
print '* fitted to lognormal model:'
print model
self.assertEqual(round(2.14539 , 5), round(model.mu, 5))
self.assertEqual(round(1.28561 , 5), round(model.sd , 5))
def test_optimization(self):
com = Community(sample_abundance)
com.fit_model('etienne')
etienne_model = com.get_model('etienne')
print '* fitted to etienne model:'
print etienne_model
self.assertEqual(round(66.1478972798 , 5), round(etienne_model.theta, 5))
self.assertEqual(round(98.171564034001662, 5), round(etienne_model.lnL , 5))
self.assertEqual(round(134.10853894914644, 2), round(etienne_model.I , 2))
def test_optimization(self):
com = Community(sample_abundance)
com.fit_model('ewens')
ewens_model = com.get_model('ewens')
print '* fitted to ewens model:'
print ewens_model
self.assertEqual(round(29.257363187464055, 4), round(ewens_model.theta, 4))
self.assertEqual(round(107.69183285311374, 4), round(ewens_model.lnL, 4))
self.assertEqual(14.720795937459272, ewens_model.I)
def main():
"""
main function
kind = 'short'
infile = 'bci_short.txt'
"""
try:
kind = argv[1]
except IndexError:
kind = None
kinds = ['short', 'full']
if not kind in kinds or not kind:
exit('\nERROR: kind should be one of:%s\n' % \
((' "%s"' * len (kinds)) % tuple (kinds)))
if kind == 'short':
# reload abundance with shorter dataset
abd = Community ('../dataset_trial/bci_short.txt')
elif kind == 'full':
abd = Community ('../dataset_trial/bci_full.txt')
else:
exit()
test_failed = []
abd.fit_model(name='etienne')
abd.fit_model(name='ewens')
import matplotlib # These two lines is because you're in a cluster
matplotlib.use('Agg') # and there may be no "X".
from ecolopy_dev import Community
from ecolopy_dev.utils import draw_shannon_distrib
com = Community('Fulcaldea_stuessyi_log_abund.txt')
print com
com.fit_model('ewens')
com.set_current_model('ewens')
ewens_model = com.get_model('ewens')
print ewens_model
com.fit_model('lognormal')
com.set_current_model('lognormal')
lognormal_model = com.get_model('lognormal')
print lognormal_model
com.fit_model('etienne')
com.set_current_model('etienne')
etienne_model = com.get_model('etienne')
print etienne_model
tmp = {}
for met in ['fmin', 'slsqp', 'l_bfgs_b', 'tnc']:
print 'Optimizing with %s...' % met
try:
com.fit_model(name='etienne', method=met, verbose=False)
model = com.get_model('etienne')
tmp[met] = {}
tmp[met]['model'] = model
import matplotlib
matplotlib.use('Agg')
from ecolopy_dev import Community
from ecolopy_dev.utils import draw_shannon_distrib
abd = Community('dataset_trial/bci_short.txt')
abd.fit_model('etienne')
abd.set_current_model('etienne')
pval, neut_h = abd.test_neutrality (model='etienne',
gens=1000, full=True)
draw_shannon_distrib(neut_h, abd.shannon, outfile='lala.png')
abd2 = Community('test2.txt')
abd2.fit_model('lognormal')
abd2.set_current_model('lognormal')
pval, neut_h = abd2.test_neutrality (model='lognormal',
gens=1000, full=True)
draw_shannon_distrib(neut_h,abd2.shannon)
bci = Community('../dataset_trial/bci_full.txt')
bci.fit_model('lognormal')
bci.set_current_model('lognormal')
pval, bci_neut_h = bci.test_neutrality (model='lognormal',
gens=1000, full=True)
import matplotlib # These two lines is because you're in a cluster
matplotlib.use('Agg') # and there may be no "X".
from ecolopy_dev import Community
from ecolopy_dev.utils import draw_shannon_distrib
com = Community('Fulcaldea_stuessyi_1kfrac.txt')
print com
com.fit_model('ewens')
com.set_current_model('ewens')
ewens_model = com.get_model('ewens')
print ewens_model
com.fit_model('lognormal')
com.set_current_model('lognormal')
lognormal_model = com.get_model('lognormal')
print lognormal_model
com.fit_model('etienne')
com.set_current_model('etienne')
etienne_model = com.get_model('etienne')
print etienne_model
tmp = {}
for met in ['fmin', 'slsqp', 'l_bfgs_b', 'tnc']:
print 'Optimizing with %s...' % met
try:
com.fit_model(name='etienne', method=met, verbose=False)
model = com.get_model('etienne')
tmp[met] ={}
tmp[met]['model'] = model
import matplotlib
matplotlib.use('Agg')
from ecolopy_dev import Community
from ecolopy_dev.utils import draw_shannon_distrib
##test_abund.txt would be the genome abundance / the diploid number of chromosomes
## j_tot is obtained by taking the total number of individuals
com = Community('test_log_abund.txt', j_tot=2150924886)
print com
com.fit_model('ewens')
com.set_current_model('ewens')
ewens_model = com.get_model('ewens')
print ewens_model
com.fit_model('lognormal')
com.set_current_model('lognormal')
lognormal_model = com.get_model('lognormal')
print lognormal_model
com.fit_model('etienne')
com.set_current_model('etienne')
etienne_model = com.get_model('etienne')
print etienne_model
tmp = {}
likelihoods = []
for met in ['fmin', 'slsqp', 'l_bfgs_b', 'tnc']:
print 'Optimizing with %s...' % met
try:
com.fit_model(name='etienne', method=met, verbose=False)
import matplotlib
matplotlib.use('Agg')
from ecolopy_dev import Community
from ecolopy_dev.utils import draw_shannon_distrib
##test_abund.txt would be the genome abundance / the diploid number of chromosomes
## j_tot is obtained by taking the total number of individuals
com = Community('test_log_abund.txt', j_tot=2150924886)
print com
com.fit_model('ewens')
com.set_current_model('ewens')
ewens_model = com.get_model('ewens')
print ewens_model
com.fit_model('lognormal')
com.set_current_model('lognormal')
lognormal_model = com.get_model('lognormal')
print lognormal_model
com.fit_model('etienne')
com.set_current_model('etienne')
etienne_model = com.get_model('etienne')
print etienne_model
tmp = {}
likelihoods = []
for met in ['fmin', 'slsqp', 'l_bfgs_b', 'tnc']:
print 'Optimizing with %s...' % met
try:
com.fit_model(name='etienne', method=met, verbose=False)
def test_randomization(self):
com = Community(sample_abundance)
com.fit_model('lognormal')
model = com.get_model('lognormal')
self.assertTrue(sum(model.random_community())>1000)
... type 'enter' to continue
"""
raw_input()
print """
In the next step we are going to create an object 'Community' that will
represent the distribution of abundance of the well known/studied BCI dataset.
We are going to load this object under the name 'com':
>>> com = Community ('bci_full.txt')
"""
com = Community ('dataset_trial/bci_short.txt')
print """
In order to see quickly how does this abundance looks like, we can use the print
command:
>>> print com
"""
print com
print """
... type 'enter' to continue
"""
raw_input()