Merge pull request #24 from caporaso-lab/release-prep

MAINT: prep for release

q2_boots/_beta.py

@@ -19,6 +19,13 @@
 
 from q2_boots._resample import resample
 
+_METRIC_MOD_DEFAULTS = {
+    'bypass_tips': False,
+    'pseudocount': 1,
+    'alpha': None,
+    'variance_adjusted': False
+}
+
 
 def beta_average(data: skbio.DistanceMatrix,
                  average_method: str) -> skbio.DistanceMatrix:
@@ -42,13 +49,19 @@ def beta_average(data: skbio.DistanceMatrix,
                          "mean, and medoid.")
 
 
-def beta_collection(ctx, table, metric, sampling_depth, n, replacement,
-                    phylogeny=None, bypass_tips=False, n_jobs=1,
-                    pseudocount=1, alpha=None, variance_adjusted=False):
+def beta_collection(
+        ctx, table, metric, sampling_depth, n, replacement,
+        phylogeny=None,
+        bypass_tips=_METRIC_MOD_DEFAULTS['bypass_tips'],
+        pseudocount=_METRIC_MOD_DEFAULTS['pseudocount'],
+        alpha=_METRIC_MOD_DEFAULTS['alpha'],
+        variance_adjusted=_METRIC_MOD_DEFAULTS['variance_adjusted']):
     _validate_beta_metric(metric, phylogeny)
 
     resample_function = resample
-    beta_metric_function = _get_beta_metric_function(ctx, metric, phylogeny)
+    beta_metric_function = _get_beta_metric_function(
+        ctx, metric, phylogeny, bypass_tips, pseudocount, alpha,
+        variance_adjusted)
 
     tables = resample_function(ctx=ctx,
                                table=table,
@@ -61,8 +74,11 @@ def beta_collection(ctx, table, metric, sampling_depth, n, replacement,
 
 
 def beta(ctx, table, metric, sampling_depth, n, replacement,
-         average_method='non-metric-median', phylogeny=None, bypass_tips=False,
-         n_jobs=1, pseudocount=1, alpha=None, variance_adjusted=False):
+         average_method='non-metric-median', phylogeny=None,
+         bypass_tips=_METRIC_MOD_DEFAULTS['bypass_tips'],
+         pseudocount=_METRIC_MOD_DEFAULTS['pseudocount'],
+         alpha=_METRIC_MOD_DEFAULTS['alpha'],
+         variance_adjusted=_METRIC_MOD_DEFAULTS['variance_adjusted']):
     beta_collection_function = beta_collection
     beta_average_action = ctx.get_action('boots', 'beta_average')
     dms = beta_collection_function(ctx=ctx,
@@ -73,7 +89,6 @@ def beta(ctx, table, metric, sampling_depth, n, replacement,
                                    n=n,
                                    pseudocount=pseudocount,
                                    replacement=replacement,
-                                   n_jobs=n_jobs,
                                    variance_adjusted=variance_adjusted,
                                    alpha=alpha,
                                    bypass_tips=bypass_tips)
@@ -114,18 +129,23 @@ def _validate_beta_metric(metric, phylogeny):
         raise ValueError(f'Metric {metric} requires a phylogenetic tree.')
 
 
-def _get_beta_metric_function(ctx, metric, phylogeny):
+def _get_beta_metric_function(
+        ctx, metric, phylogeny,
+        bypass_tips=_METRIC_MOD_DEFAULTS['bypass_tips'],
+        pseudocount=_METRIC_MOD_DEFAULTS['pseudocount'],
+        alpha=_METRIC_MOD_DEFAULTS['alpha'],
+        variance_adjusted=_METRIC_MOD_DEFAULTS['variance_adjusted']):
     if _is_phylogenetic_beta_metric(metric):
         beta_metric_function = q2div_beta_phylogenetic
-        beta_metric_function = functools.partial(beta_metric_function,
-                                                 ctx=ctx,
-                                                 phylogeny=phylogeny,
-                                                 metric=metric)
+        beta_metric_function = functools.partial(
+            beta_metric_function, ctx=ctx, phylogeny=phylogeny, metric=metric,
+            bypass_tips=bypass_tips, alpha=alpha,
+            variance_adjusted=variance_adjusted)
     else:
         beta_metric_function = q2div_beta
-        beta_metric_function = functools.partial(beta_metric_function,
-                                                 ctx=ctx,
-                                                 metric=metric)
+        beta_metric_function = functools.partial(
+            beta_metric_function, ctx=ctx, metric=metric,
+            pseudocount=pseudocount)
     return beta_metric_function
 
 

q2_boots/_core_metrics.py

@@ -16,7 +16,7 @@
 
 
 def core_metrics(ctx, table, sampling_depth, metadata, n, replacement,
-                 n_jobs=1, phylogeny=None, alpha_average_method='median',
+                 phylogeny=None, alpha_average_method='median',
                  beta_average_method='non-metric-median'):
 
     resample_function = resample

q2_boots/plugin_setup.py

@@ -6,9 +6,8 @@
 # The full license is in the file LICENSE, distributed with this software.
 # ----------------------------------------------------------------------------
 
-from qiime2.plugin import (Plugin, Int, Range, Collection,
-                           Citations, Str, Choices, Bool, Float, Threads,
-                           Metadata, Visualization)
+from qiime2.plugin import (Plugin, Int, Range, Collection, Str, Choices, Bool,
+                           Float, Metadata, Visualization)
 
 from q2_types.feature_table import (
     FeatureTable, Frequency, RelativeFrequency, PresenceAbsence
@@ -33,8 +32,6 @@
                                 _core_metrics_rarefaction_example)
 
 
-Citations = Citations.load('citations.bib', package='q2_boots')
-
 plugin = Plugin(
     name='boots',
     version=q2_boots.__version__,
@@ -48,7 +45,12 @@
 )
 
 
-_feature_table_description = 'The feature table to be resampled.'
+_feature_table_description = 'The input feature table.'
+_phylogeny_description = (
+    'The phylogenetic tree to use in phylogenetic diversity '
+    'calculations. All feature ids in `table` must be present in '
+    'this tree, but this tree can contain feature ids that are '
+    'not present in `table`.')
 _sampling_depth_description = (
     'The total number of observations that each sample in `table` should be '
     'resampled to. Samples where the total number of observations in `table` '
@@ -110,11 +112,8 @@
 }
 
 _diversity_input_descriptions = {
-    'table': 'The feature table to use in diversity computations.',
-    'phylogeny': ('The phylogenetic tree to use in phylogenetic diversity '
-                  'calculations. All feature ids in `table` must be present in '
-                  'this tree, but this tree can contain feature ids that are '
-                  'not present in `table`.')
+    'table': _feature_table_description,
+    'phylogeny': _phylogeny_description
 }
 
 _alpha_average_parameters = {
@@ -252,27 +251,18 @@
                                         beta_metrics['PHYLO']['UNIMPL']),
                 'pseudocount': Int % Range(1, None),
                 'replacement': Bool,
-                'n_jobs': Threads,
                 'n': Int % Range(1, None),
                 'sampling_depth': Int % Range(1, None),
                 'bypass_tips': Bool,
                 'variance_adjusted': Bool,
                 'alpha': Float % Range(0, 1, inclusive_end=True)
 }
 
-_n_jobs_description = (
-    'The number of CPU threads to use in performing this calculation. '
-    'May not exceed the number of available physical cores. If threads = '
-    '\'auto\', one thread will be created for each identified CPU core on the '
-    'host.'
-)
-
 _beta_collection_parameter_descriptions = {
     'metric': 'The beta diversity metric to be computed.',
     'pseudocount': ('A pseudocount to handle zeros for compositional '
                     'metrics.  This is ignored for other metrics.'),
     'replacement': _replacement_description,
-    'n_jobs': _n_jobs_description,
     'n': _n_description,
     'sampling_depth': _sampling_depth_description,
     'bypass_tips': ('Ignore tips of tree in phylogenetic diversity '
@@ -341,7 +331,6 @@
     inputs=_diversity_inputs,
     parameters={
         'metadata': Metadata,
-        'n_jobs': Threads,
         'n': Int % Range(1, None),
         'sampling_depth': Int % Range(1, None),
         'alpha_average_method': Str % Choices('mean', 'median'),
@@ -360,7 +349,6 @@
     input_descriptions=_diversity_input_descriptions,
     parameter_descriptions={
         'metadata': 'The sample metadata used in generating Emperor plots.',
-        'n_jobs': _n_jobs_description,
         'n': _n_description,
         'sampling_depth': _sampling_depth_description,
         'alpha_average_method': 'Method to use for averaging alpha diversity.',

q2_boots/tests/test_alpha.py

@@ -78,8 +78,7 @@ def test_alpha_collection_w_replacement(self):
                                     index=['S1', 'S2'],
                                     name='observed_features')
         for alpha_vector in observed.values():
-            observed_series = qiime2.Artifact.view(alpha_vector,
-                                                   view_type=pd.Series)
+            observed_series = alpha_vector.view(pd.Series)
             pdt.assert_series_equal(observed_series, expected_series)
 
         # at a sampling depth of 2, with table1 as input and sampling with
@@ -101,8 +100,7 @@ def test_alpha_collection_w_replacement(self):
         count_possible_series2_observed = 0
         other_series_observed = False
         for alpha_vector in observed.values():
-            observed_series = qiime2.Artifact.view(alpha_vector,
-                                                   view_type=pd.Series)
+            observed_series = alpha_vector.view(pd.Series)
             if observed_series.equals(possible_series1):
                 count_possible_series1_observed += 1
             elif observed_series.equals(possible_series2):
@@ -133,8 +131,7 @@ def test_alpha_collection_wo_replacement(self):
                                     index=['S1', 'S2'],
                                     name='observed_features')
         for alpha_vector in observed.values():
-            observed_series = qiime2.Artifact.view(alpha_vector,
-                                                   view_type=pd.Series)
+            observed_series = alpha_vector.view(pd.Series)
             pdt.assert_series_equal(observed_series, expected_series)
 
     def test_alpha_collection_phylogenetic(self):
@@ -160,8 +157,7 @@ def test_alpha_collection_phylogenetic(self):
                                     index=['S1', 'S2'],
                                     name='faith_pd')
         for alpha_vector in observed.values():
-            observed_series = qiime2.Artifact.view(alpha_vector,
-                                                   view_type=pd.Series)
+            observed_series = alpha_vector.view(pd.Series)
             pdt.assert_series_equal(observed_series, expected_series)
 
     def test_alpha_collection_invalid_input(self):
@@ -203,8 +199,7 @@ def test_alpha_w_replacement(self):
         observed, = self.alpha_pipeline(
             table=table1, sampling_depth=1, metric='observed_features', n=42,
             replacement=True)
-        observed_series = qiime2.Artifact.view(observed,
-                                               view_type=pd.Series)
+        observed_series = observed.view(pd.Series)
 
         expected_series = pd.Series([1., 1.],
                                     index=['S1', 'S2'],
@@ -219,8 +214,7 @@ def test_alpha_w_replacement(self):
         observed, = self.alpha_pipeline(
             table=table1, sampling_depth=2, metric='observed_features',
             n=99, replacement=True)
-        observed_series = qiime2.Artifact.view(observed,
-                                               view_type=pd.Series)
+        observed_series = observed.view(pd.Series)
         # note that because n is an odd number, the median for S1 will always
         # be one of the actual values that were observed (as opposed to possibly
         # being 1.5, if n was an even number).
@@ -236,8 +230,7 @@ def test_alpha_w_replacement(self):
         observed, = self.alpha_pipeline(
             table=table1, sampling_depth=2, metric='observed_features',
             n=100, replacement=True, average_method='mean')
-        observed_series = qiime2.Artifact.view(observed,
-                                               view_type=pd.Series)
+        observed_series = observed.view(pd.Series)
         # note that b/c these are <, not <=, we know that identical tables
         # were not always obtained from the resampling step
         self.assertTrue(1.0 < observed_series['S1'] < 2.0)
@@ -256,8 +249,7 @@ def test_alpha_wo_replacement(self):
         observed, = self.alpha_pipeline(
             table=table1, sampling_depth=1, metric='observed_features', n=42,
             replacement=False)
-        observed_series = qiime2.Artifact.view(observed,
-                                               view_type=pd.Series)
+        observed_series = observed.view(pd.Series)
 
         expected_series = pd.Series([1., 1.],
                                     index=['S1', 'S2'],
@@ -270,8 +262,7 @@ def test_alpha_wo_replacement(self):
         observed, = self.alpha_pipeline(
             table=table1, sampling_depth=2, metric='observed_features',
             n=100, replacement=False)
-        observed_series = qiime2.Artifact.view(observed,
-                                               view_type=pd.Series)
+        observed_series = observed.view(pd.Series)
 
         expected_series = pd.Series([2., 1.],
                                     index=['S1', 'S2'],
@@ -284,8 +275,7 @@ def test_alpha_wo_replacement(self):
         observed, = self.alpha_pipeline(
             table=table1, sampling_depth=2, metric='observed_features',
             n=100, replacement=False, average_method='mean')
-        observed_series = qiime2.Artifact.view(observed,
-                                               view_type=pd.Series)
+        observed_series = observed.view(pd.Series)
 
         expected_series = pd.Series([2., 1.],
                                     index=['S1', 'S2'],