Fix print clades only bug

metaphlan/strainphlan.py

@@ -440,42 +440,51 @@ def detect_clades(self):
         clade2markers = self.database_controller.get_clade2markers()
         sample2markers = {}
         clades_to_check = set()
+        all_markers = set()
         info('Processing samples...')
         consensus_markers = execute_pool(((ConsensusMarkers.from_file, sample_path) for sample_path in self.samples),
                                          nprocs=self.nprocs, return_generator=True, ordered=True)
         for sample_path, cm in zip(self.samples, consensus_markers):
-            markers = [marker.name for marker in cm.consensus_markers
-                       if (marker.name in markers2clade and marker.breadth >= self.breadth_thres)]
-            sample2markers[sample_path] = markers
-            clades_to_check.update((markers2clade[m] for m in markers))
+            sample_markers = [marker.name for marker in cm.consensus_markers
+                              if marker.name in markers2clade and marker.breadth >= self.breadth_thres]
+            sample2markers[sample_path] = sample_markers
+            all_markers.update(sample_markers)
+            clades_to_check.update((markers2clade[m] for m in sample_markers))
 
         info('Constructing the big marker matrix')
-        markers_matrix_big = [pd.Series({m: 1 for m in markers}, name=sample)
-                              for sample, markers in sample2markers.items()]
-        markers_matrix_big = pd.concat(markers_matrix_big, axis=1).fillna(0)
+        all_samples = list(sample2markers.keys())
+        all_markers = list(all_markers)
+        markers_matrix_big = np.zeros(shape=(len(all_samples), len(all_markers)), dtype=int)
+        for i, sample in enumerate(all_samples):
+            sample_markers = set(sample2markers[sample])
+            for j, marker in enumerate(all_markers):
+                if marker in sample_markers:
+                    markers_matrix_big[i, j] = 1
+
+        markers_matrix_big = pd.DataFrame(markers_matrix_big, index=all_samples, columns=all_markers)
 
         info(f'Checking {len(clades_to_check)} species')
         species2samples = {}
         for clade in clades_to_check:
-            markers_matrix = markers_matrix_big.reindex(clade2markers[clade]).fillna(0)
+            markers_for_clade = markers_matrix_big.columns.intersection(clade2markers[clade])
+            markers_matrix = markers_matrix_big.loc[:, markers_for_clade]
             markers_matrix_filtered = self.filter_markers_matrix(markers_matrix, messages=False)
-            n_samples, _ = markers_matrix_filtered.shape
-            if n_samples >= 4:
-                species2samples[clade] = n_samples
+            if len(markers_matrix_filtered) >= 4:
+                species2samples[clade] = markers_matrix_filtered.index
         info('Done.')
         return species2samples
 
 
     def print_clades(self):
         """Prints the clades detected in the reconstructed markers"""
         species2samples = self.detect_clades()
-        info('Detected clades: ')
-        sorted_species2samples = OrderedDict(sorted(species2samples.items(), key=lambda kv: kv[1], reverse=True))
-        with open(os.path.join(self.output_dir, 'print_clades_only.tsv'), 'w') as wf:
-            wf.write('Clade\tNumber_of_samples\n')
-            for species in sorted_species2samples:
-                info('\t{}: in {} samples.'.format(species, sorted_species2samples[species]))
-                wf.write('{}\t{}\n'.format(species, sorted_species2samples[species]))
+        info(f'Detected {len(species2samples)} clades: ')
+        with open(os.path.join(self.output_dir, 'print_clades_only.tsv'), 'w') as f:
+            f.write('Clade\tNumber_of_samples\tSamples\n')
+            for species, samples in sorted(species2samples.items(), key=lambda kv: len(kv[1]), reverse=True):
+                info('\t{}: in {} samples.'.format(species, len(samples)))
+                f.write('{}\t{}\t{}\n'.format(species, len(samples),
+                                              ','.join(map(Strainphlan.sample_path_to_name, samples))))
         info('Done.')