def estimate_diversity(taxon_table_t):
""" Function to estimate common diversity matrics from a taxon table
with samples as rows and observation/otus/pathogens as column
the output of this function is a dataframe of the diversity matrices per sample
"""
# Import the modules for diversity calculations and manipulating dataframes
# Get the sample names which are the indices of the transposed taxon table
table_samples = taxon_table_t.index.tolist()
# Get the number of observed otus / pathogens per sample
observed_pathogens = [] #initialize an empty list
# Calculate the observed species per sample
for sample in table_samples:
observed_pathogens.append(
observed_otus(table=taxon_table_t, sample_id=sample))
# Estimate diversity
shannon = ep.diversity(x=taxon_table_t, method='shannon')
simpson = ep.diversity(x=taxon_table_t, method='simpson')
species_richness = ep.diversity(x=taxon_table_t, method='spRich')
eveness = ep.diversity(x=taxon_table_t, method='even')
# Convert the estimates to Pandas series
shannon = pd.Series(data=shannon, index=table_samples)
simpson = pd.Series(data=simpson, index=table_samples)
observed_pathogens = pd.Series(data=observed_pathogens,
index=table_samples)
species_richness = pd.Series(data=species_richness, index=table_samples)
eveness = pd.Series(data=eveness, index=table_samples)
diversity_dict = {
'observed_species': observed_pathogens,
'species_richness': species_richness,
'eveness': eveness,
'shannon': shannon,
'simpson': simpson
}
diversity_table = pd.DataFrame(data=diversity_dict)
return (diversity_table)
def diversity(otu_table):
methods = ['shannon' , 'gini-simpson', 'simpson' , 'dominance', 'spRich', 'even']
index = list(otu_table.index)
methods_res = {}
for m in methods:
div = ecopy.diversity(otu_table, method=m, breakNA=True)
# methods_res.append(list(div))
methods_res.update({m:list(div)})
div_pd = pd.DataFrame(methods_res, columns=methods)
div_pd.index = otu_table.index
return div_pd
def test_diversity(self):
sp = np.array([0, 1, 2, 3, 0]).reshape(1, 5)
div = np.round(diversity(sp))
self.assertEqual(div, 1)
def test_diversity(self): sp = np.array([0, 1, 2, 3, 0]).reshape(1,5) div = np.round(diversity(sp)) self.assertEqual(div, 1)