def test_enspie(self):
# Totally even community should have ENS_pie = number of OTUs.
self.assertAlmostEqual(enspie(np.array([1, 1, 1, 1, 1, 1])), 6)
self.assertAlmostEqual(enspie(np.array([13, 13, 13, 13])), 4)
# Hand calculated.
arr = np.array([1, 41, 0, 0, 12, 13])
exp = 1 / ((arr / arr.sum()) ** 2).sum()
self.assertAlmostEqual(enspie(arr), exp)
# Using dominance.
exp = 1 / dominance(arr)
self.assertAlmostEqual(enspie(arr), exp)
arr = np.array([1, 0, 2, 5, 2])
exp = 1 / dominance(arr)
self.assertAlmostEqual(enspie(arr), exp)
def test_enspie(self):
# Totally even community should have ENS_pie = number of OTUs.
self.assertAlmostEqual(enspie(np.array([1, 1, 1, 1, 1, 1])), 6)
self.assertAlmostEqual(enspie(np.array([13, 13, 13, 13])), 4)
# Hand calculated.
arr = np.array([1, 41, 0, 0, 12, 13])
exp = 1 / ((arr / arr.sum())**2).sum()
self.assertAlmostEqual(enspie(arr), exp)
# Using dominance.
exp = 1 / dominance(arr)
self.assertAlmostEqual(enspie(arr), exp)
arr = np.array([1, 0, 2, 5, 2])
exp = 1 / dominance(arr)
self.assertAlmostEqual(enspie(arr), exp)
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_dominance(self):
self.assertEqual(dominance(np.array([5])), 1)
self.assertAlmostEqual(dominance(np.array([1, 0, 2, 5, 2])), 0.34)
# Dissolving
print('[INFO] - Dissolving results')
dissolved = joined.dissolve(by='id', aggfunc=lambda x: list(x))
# Getting output length
dis_len = len(dissolved)
# Counting language dominance, menhinick diversity and simpson index
print('[INFO] - Calculating variables..')
for i, row in dissolved.iterrows():
print("[INFO] - Calculating grid cell {}/{}...".format(i, dis_len))
lang_counts = list(Counter(
row[args['language']]).values()) # occurence counts
lang_counts = np.asarray(lang_counts) # cast as numpy array for skbio
dissolved.at[i, 'dominance'] = sk.dominance(lang_counts)
dissolved.at[i, 'menhinick'] = sk.menhinick(lang_counts)
dissolved.at[i, 'simpson'] = sk.simpson(lang_counts)
dissolved.at[i, 'berger'] = sk.berger_parker_d(lang_counts)
dissolved.at[i, 'singles'] = sk.singles(lang_counts)
dissolved.at[i, 'shannon'] = np.exp(sk.shannon(lang_counts, base=np.e))
dissolved.at[i, 'unique'] = sk.observed_otus(lang_counts)
# Select columns for output
cols = [
'geometry', 'dominance', 'menhinick', 'simpson', 'berger', 'singles',
'shannon', 'unique'
]
output = dissolved[cols]
# Save the output to pickle
joined = gpd.sjoin(grid, points, how='inner', op='contains')
# Dissolving
print('[INFO] - Dissolving results')
dissolved = joined.dissolve(by='id', aggfunc=lambda x: list(x))
# Getting output length
dis_len = len(dissolved)
# Counting topic dominance, menhinick diversity and simpson index
print('[INFO] - Calculating variables..')
for i, row in dissolved.iterrows():
print("[INFO] Processing row {}/{}...".format(i, dis_len))
topic_counts = list(Counter(
row[args['topic']]).values()) # occurence counts
topic_counts = np.asarray(topic_counts) # cast as numpy array for skbio
dissolved.at[i, 'dominance'] = sk.dominance(topic_counts)
dissolved.at[i, 'menhinick'] = sk.menhinick(topic_counts)
dissolved.at[i, 'simpson'] = sk.simpson(topic_counts)
# Select columns for output
cols = ['geometry', 'dominance', 'menhinick', 'simpson']
output = dissolved[cols]
# Save the output to pickle
print('[INFO] - Saving to shapefile')
output.to_file(args['output'], encoding='utf-8')
# Print status
print("[INFO] ... Done.")
def test_dominance(self):
self.assertEqual(dominance(np.array([5])), 1)
self.assertAlmostEqual(dominance(np.array([1, 0, 2, 5, 2])), 0.34)
# rename home detection column
data = data.rename(columns={'home_unique_weeks': 'home_country'})
# filter only users who most likely live in Finland
data = data[data['home_country'].str.contains('Finland')]
# count language use without singletons
print('[INFO] - Calculating language diversities...')
data['ulangs'] = data['langs'].apply(lambda x: langcount(x)[0])
data['counts'] = data['langs'].apply(lambda x: langcount(x)[1])
data = data[data['counts'].map(lambda d: len(d)) >
0] # drop empties if any exist
# calculate diversity metrics
data['dominance'] = data['counts'].apply(lambda x: sk.dominance(x))
data['berger'] = data['counts'].apply(lambda x: sk.berger_parker_d(x))
data['menhinick'] = data['counts'].apply(sk.menhinick)
data['simpson'] = data['counts'].apply(sk.simpson)
data['singles'] = data['counts'].apply(sk.singles)
data['shannon'] = data['counts'].apply(
lambda x: np.exp(sk.shannon(x, base=np.e)))
data['unique'] = data['counts'].apply(sk.observed_otus)
# language counts to dictionary
data['langdict'] = data.apply(lambda x: dict(zip(x['ulangs'], x['counts'])),
axis=1)
# calculate ellis et al diversity metrics
data['divs'] = data['langdict'].apply(lang_entropy)
areas.at[i, colname4] = (int(lposts) / int(lpostsum)) * 100
# get dominant language from selected columns
areas['propmax'] = areas[['fi_prop','en_prop','et_prop','ru_prop','sv_prop','es_prop','ja_prop','fr_prop','pt_prop','de_prop']].idxmax(axis=1)
areas['mean_propmax'] = areas[['fi_mean_prop','en_mean_prop','et_mean_prop','ru_mean_prop','sv_mean_prop','es_mean_prop','ja_mean_prop','fr_mean_prop','pt_mean_prop','de_mean_prop']].idxmax(axis=1)
areas['sum_propmax'] = areas[['fi_sum_prop','en_sum_prop','et_sum_prop','ru_sum_prop','sv_sum_prop','es_sum_prop','ja_sum_prop','fr_sum_prop','pt_sum_prop','de_sum_prop']].idxmax(axis=1)
# get all language column names
cols = list(areas[langlist].columns)
# loop over areas
print('[INFO] - Calculating diversity metrics per area..')
for i, row in areas.iterrows():
# get counts of languages
otus = list(row[cols])
# drop zeros
otus = [i for i in otus if i != 0]
# calculate diversity metrics
areas.at[i, 'dominance'] = sk.dominance(otus)
areas.at[i, 'berger'] = sk.berger_parker_d(otus)
areas.at[i, 'menhinick'] = sk.menhinick(otus)
areas.at[i, 'singletons'] = sk.singles(otus)
areas.at[i, 'shannon'] = np.exp(sk.shannon(otus, base=np.e))
areas.at[i, 'unique'] = sk.observed_otus(otus)
# save to file
print('[INFO] - Saving output geopackage...')
areas.to_file(args['output'], driver='GPKG')
print('[INFO] - ... done!')