added methods from skbio

Author: hagenjp

q2_diversity_lib/alpha.py

@@ -7,7 +7,13 @@
 # ----------------------------------------------------------------------------
 
 import pandas as pd
+import numpy as np
+
 import skbio.diversity
+from skbio.diversity._util import _validate_counts_vector
+
+from scipy.special import gammaln
+
 import biom
 import numpy as np
 
@@ -94,6 +100,14 @@ def transform_(v, i, m):
         results.append(_skbio_alpha_diversity_from_1d(v, 'pielou_e'))
     results = pd.Series(results, index=table.ids(), name='pielou_evenness')
     return results
+# legacy code
+    for partition in _partition(table):
+        counts = partition.matrix_data.T.toarray()
+        sample_ids = partition.ids(axis='sample')
+        results = [_p_evenness(c, sample_ids)for c in counts]
+    result = pd.Series(results, index=sample_ids)
+    result.name = 'pielou_evenness'
+    return result
 
 
 @_validate_tables
@@ -107,6 +121,15 @@ def shannon_entropy(table: biom.Table,
         results.append(_skbio_alpha_diversity_from_1d(v, 'shannon'))
     results = pd.Series(results, index=table.ids(), name='shannon_entropy')
     return results
+#LEGACY
+    for partition in _partition(table):
+        counts = partition.matrix_data.T.toarray()
+        sample_ids = partition.ids(axis='sample')
+        # TODO replace with internal shannons method
+        results = [_shannon(c, sample_ids)for c in counts]
+    result = pd.Series(results, index=sample_ids)
+    result.name = 'shannon_entropy'
+    return result
 
 
 @_validate_tables
@@ -115,5 +138,92 @@ def alpha_passthrough(table: biom.Table, metric: str) -> pd.Series:
 
     for v in table.iter_data(dense=True):
         results.append(_skbio_alpha_diversity_from_1d(v.astype(int), metric))
+        # TODO write if statements for hard coding the following metrics:
+        # berger-parker,brillion, simpsons D, etsy, goods, margalef's, mcIntosh,
+        # strongs
     results = pd.Series(results, index=table.ids(), name=metric)
     return results
+
+
+# c&p methods from skbio
+def _berger_parker(counts):
+    counts = _validate_counts_vector(counts)
+    return counts.max() / counts.sum()
+
+
+def _brillouin_d(counts):
+    counts = _validate_counts_vector(counts)
+    nz = counts[counts.nonzero()]
+    n = nz.sum()
+    return (gammaln(n + 1) - gammaln(nz + 1).sum()) / n
+
+
+def _simpsons_dominance(counts):
+    counts = _validate_counts_vector(counts)
+    freqs = counts / counts.sum()
+    return (freqs * freqs).sum()
+
+
+def _etsy_ci(counts):
+    counts = _validate_counts_vector(counts)
+
+    f1 = _singles(counts)
+    f2 = _doubles(counts)
+    n = counts.sum()
+    z = 1.959963985
+    W = (f1 * (n - f1) + 2 * n * f2) / (n ** 3)
+
+    return f1 / n - z * np.sqrt(W), f1 / n + z * np.sqrt(W)
+
+
+def _goods_coverage(counts):
+    counts = _validate_counts_vector(counts)
+    f1 = _singles(counts)
+    N = counts.sum()
+    return 1 - (f1 / N)
+
+
+def _margalef(counts):
+    counts = _validate_counts_vector(counts)
+    # replaced observed_otu call to sobs
+    return (skbio.diversity.alpha.sobs(counts) - 1) / np.log(counts.sum())
+
+
+def _mcintosh_d(counts):
+    counts = _validate_counts_vector(counts)
+    u = np.sqrt((counts * counts).sum())
+    n = counts.sum()
+    return (n - u) / (n - np.sqrt(n))
+
+
+def _strong(counts):
+    counts = _validate_counts_vector(counts)
+    n = counts.sum()
+    # replaced observed_otu call to sobs
+    s = skbio.diversity.alpha.sobs(counts)
+    i = np.arange(1, len(counts) + 1)
+    sorted_sum = np.sort(counts)[::-1].cumsum()
+    return (sorted_sum / n - (i / s)).max()
+
+
+def _singles(counts):
+    counts = _validate_counts_vector(counts)
+    return (counts == 1).sum()
+
+
+def _doubles(counts):
+    counts = _validate_counts_vector(counts)
+    return (counts == 2).sum()
+
+
+def _p_evenness(counts, sample_ids):
+    counts = _validate_counts_vector(counts)
+    return _shannon(counts, base=np.e) / np.log(
+        skbio.diversity.alpha.sobs(counts=counts))
+
+
+def _shannon(counts, base=2):
+    counts = _validate_counts_vector(counts)
+    freqs = counts / counts.sum()
+    nonzero_freqs = freqs[freqs.nonzero()]
+    return -(nonzero_freqs * np.log(nonzero_freqs)).sum() / np.log(base)