def test_ace():
assert_almost_equal(ace(np.array([2, 0])), 1.0)
assert_almost_equal(ace(np.array([12, 0, 9])), 2.0)
assert_almost_equal(ace(np.array([12, 2, 8])), 3.0)
assert_almost_equal(ace(np.array([12, 2, 1])), 4.0)
assert_almost_equal(ace(np.array([12, 1, 2, 1])), 7.0)
assert_almost_equal(ace(np.array([12, 3, 2, 1])), 4.6)
assert_almost_equal(ace(np.array([12, 3, 6, 1, 10])), 5.62749672)
# Just returns the number of OTUs when all are abundant.
assert_almost_equal(ace(np.array([12, 12, 13, 14])), 4.0)
# Border case: only singletons and 10-tons, no abundant OTUs.
assert_almost_equal(ace([0, 1, 1, 0, 0, 10, 10, 1, 0, 0]), 9.35681818182)
def test_ace():
assert_almost_equal(ace(np.array([2, 0])), 1.0)
assert_almost_equal(ace(np.array([12, 0, 9])), 2.0)
assert_almost_equal(ace(np.array([12, 2, 8])), 3.0)
assert_almost_equal(ace(np.array([12, 2, 1])), 4.0)
assert_almost_equal(ace(np.array([12, 1, 2, 1])), 7.0)
assert_almost_equal(ace(np.array([12, 3, 2, 1])), 4.6)
assert_almost_equal(ace(np.array([12, 3, 6, 1, 10])), 5.62749672)
# Just returns the number of OTUs when all are abundant.
assert_almost_equal(ace(np.array([12, 12, 13, 14])), 4.0)
# Border case: only singletons and 10-tons, no abundant OTUs.
assert_almost_equal(ace([0, 1, 1, 0, 0, 10, 10, 1, 0, 0]), 9.35681818182)
def mercat_compute_alpha_beta_diversity(counts,bif):
abm = dict()
abm['shannon'] = skbio_alpha.shannon(counts)
abm['simpson'] = skbio_alpha.simpson(counts)
abm['simpson_e'] = skbio_alpha.simpson_e(counts)
abm['goods_coverage'] = skbio_alpha.goods_coverage(counts)
abm['fisher_alpha'] = skbio_alpha.fisher_alpha(counts)
abm['dominance'] = skbio_alpha.dominance(counts)
abm['chao1'] = skbio_alpha.chao1(counts)
abm['chao1_ci'] = skbio_alpha.chao1_ci(counts)
abm['ace'] = skbio_alpha.ace(counts)
with open(bif + "_diversity_metrics.txt", 'w') as dmptr:
for abmetric in abm:
dmptr.write(abmetric + " = " + str(abm[abmetric]) + "\n")
def test_ace_only_rare_singletons():
with assert_raises(ValueError):
ace([0, 0, 43, 0, 1, 0, 1, 42, 1, 43])
def alpha_diversity(args):
"""
Our counts data in the biomfile is per OTU NOT per sample as needed.
So it must be transformed
"""
try:
json_data = open(args.in_file, 'r')
except:
print("NO FILE FOUND ERROR")
sys.exit()
data = json.load(json_data)
json_data.close()
#size = len(data['rows'])*len(data['columns'])
#A = np.arange(size).reshape((len(data['rows']),len(data['columns'])))
A = np.zeros(shape=(len(data['rows']), len(data['columns'])))
#A.astype(int)
#print A
for i, counts in enumerate(data['data']):
#print 'OTU:',data['rows'][i]['id'], counts
#print alpha.chao1(counts)
A[i] = counts
#pass
X = A.astype(int) # insure int
#print X
Y = np.transpose(X)
txt = "Dataset\tobserved richness\tACE\tchao1\tShannon\tSimpson"
print(txt)
for i, row in enumerate(Y):
ds = data['columns'][i]['id']
row = row.tolist()
try:
ace = alpha.ace(row)
except:
ace = 'error'
try:
chao1 = alpha.chao1(row)
except:
chao1 = 'error'
try:
osd = alpha.osd(row)
except:
osd = ['error']
try:
simpson = alpha.simpson(row)
except:
simpson = 'error'
try:
shannon = alpha.shannon(row)
except:
shannon = 'error'
txt = ds + "\t" + str(osd[0]) + "\t" + str(ace) + "\t" + str(
chao1) + "\t" + str(shannon) + "\t" + str(simpson)
print(txt)
def test_ace_only_rare_singletons():
with assert_raises(ValueError):
ace([0, 0, 43, 0, 1, 0, 1, 42, 1, 43])
def test_ace_only_rare_singletons(self):
with self.assertRaises(ValueError):
ace([0, 0, 43, 0, 1, 0, 1, 42, 1, 43])
def alpha_diversity(args):
"""
Our counts data in the biomfile is per OTU NOT per sample as needed.
So it must be transformed
"""
try:
json_data = open(args.in_file, 'r')
except:
print("NO FILE FOUND ERROR")
sys.exit()
data = json.load(json_data)
json_data.close()
#size = len(data['rows'])*len(data['columns'])
#A = np.arange(size).reshape((len(data['rows']),len(data['columns'])))
A = np.zeros(shape=(len(data['rows']),len(data['columns'])))
#A.astype(int)
#print A
for i,counts in enumerate(data['data']):
#print 'OTU:',data['rows'][i]['id'], counts
#print alpha.chao1(counts)
A[i] = counts
#pass
X = A.astype(int) # insure int
#print X
Y = np.transpose(X)
txt = "Dataset\tobserved richness\tACE\tchao1\tShannon\tSimpson"
print(txt)
for i,row in enumerate(Y):
ds = data['columns'][i]['id']
row = row.tolist()
try:
ace = alpha.ace(row)
except:
ace = 'error'
try:
chao1 = alpha.chao1(row)
except:
chao1 = 'error'
try:
osd = alpha.osd(row)
except:
osd = ['error']
try:
simpson = alpha.simpson(row)
except:
simpson = 'error'
try:
shannon = alpha.shannon(row)
except:
shannon = 'error'
txt = ds+"\t"+str(osd[0])+"\t"+str(ace)+"\t"+str(chao1)+"\t"+str(shannon)+"\t"+str(simpson)
print(txt)
