Code Refactor and optimizations

.gitignore

@@ -1,7 +1,8 @@
 # Output files
 /api_interfaces/python/input_data/
 /api_interfaces/python/out/
-/out/
+/out/*
+!/out/.gitkeep
 /in/*
 !/in/.gitkeep
 !/in/example_data/

cellphonedb/src/core/exceptions/NoInteractionsFound.py

@@ -0,0 +1,5 @@
+class NoInteractionsFound(Exception):
+    def __init__(self, description: str = None, hint: str = None):
+        super(NoInteractionsFound, self).__init__('No CellPhoneDB interacions found in this input.')
+        self.description = description
+        self.hint = hint

cellphonedb/src/core/methods/cpdb_analysis_helper.py

@@ -7,8 +7,7 @@ def percent_analysis(clusters: dict,
                      cluster_interactions: list,
                      base_result: pd.DataFrame,
                      separator: str,
-                     suffixes: tuple = ('_1', '_2'),
-                     counts_data: str = 'ensembl') -> pd.DataFrame:
+                     ) -> pd.DataFrame:
     result = base_result.copy()
     percents = {}
     for cluster_name in clusters['names']:
@@ -20,8 +19,7 @@ def percent_analysis(clusters: dict,
         for cluster_interaction in cluster_interactions:
             cluster_interaction_string = '{}{}{}'.format(cluster_interaction[0], separator, cluster_interaction[1])
 
-            interaction_percent = cluster_interaction_percent(cluster_interaction, interaction, percents, suffixes,
-                                                              counts_data=counts_data)
+            interaction_percent = cluster_interaction_percent(cluster_interaction, interaction, percents)
 
             result.at[interaction_index, cluster_interaction_string] = interaction_percent
 
@@ -42,14 +40,14 @@ def counts_percent(counts: pd.Series,
 def cluster_interaction_percent(cluster_interaction: tuple,
                                 interaction: pd.Series,
                                 clusters_percents: dict,
-                                suffixes: tuple = ('_1', '_2'),
-                                counts_data: str = 'ensembl'
                                 ) -> int:
     percent_cluster_receptors = clusters_percents[cluster_interaction[0]]
     percent_cluster_ligands = clusters_percents[cluster_interaction[1]]
 
-    percent_receptor = percent_cluster_receptors[interaction['{}{}'.format(counts_data, suffixes[0])]]
-    percent_ligand = percent_cluster_ligands[interaction['{}{}'.format(counts_data, suffixes[1])]]
+    receptor = interaction['multidata_1_id']
+    percent_receptor = percent_cluster_receptors.loc[receptor]
+    ligand = interaction['multidata_2_id']
+    percent_ligand = percent_cluster_ligands.loc[ligand]
 
     if percent_receptor and percent_ligand:
         interaction_percent = 1

cellphonedb/src/core/methods/cpdb_analysis_method.py

@@ -1,8 +1,6 @@
 import pandas as pd
 
-from cellphonedb.src.core.exceptions.EmptyResultException import EmptyResultException
-from cellphonedb.src.core.exceptions.NoComplexException import NoComplexException
-from cellphonedb.src.core.methods import cpdb_analysis_simple_method, cpdb_analysis_complex_method
+from cellphonedb.src.core.methods import cpdb_analysis_complex_method
 
 
 def call(meta: pd.DataFrame,
@@ -15,41 +13,17 @@ def call(meta: pd.DataFrame,
          separator: str,
          threshold: float = 0.1,
          result_precision: int = 3) -> (pd.DataFrame, pd.DataFrame, pd.DataFrame):
-    means_simple, significant_means_simple, deconvoluted_simple = \
-        cpdb_analysis_simple_method.call(meta.copy(),
-                                         counts.copy(),
-                                         counts_data,
-                                         interactions.copy(),
-                                         separator,
-                                         threshold,
-                                         result_precision)
-
-    try:
-        means_complex, significant_means_complex, deconvoluted_complex = \
-            cpdb_analysis_complex_method.call(meta.copy(),
-                                              counts.copy(),
-                                              counts_data,
-                                              interactions.copy(),
-                                              genes,
-                                              complexes,
-                                              complex_compositions,
-                                              separator,
-                                              threshold,
-                                              result_precision)
-        means = means_simple.append(means_complex, sort=False)
-        significant_means = significant_means_simple.append(significant_means_complex, sort=False)
-        deconvoluted = deconvoluted_simple.append(deconvoluted_complex, sort=False)
-
-    except NoComplexException:
-        means = means_simple
-        significant_means = significant_means_simple
-        deconvoluted = deconvoluted_simple
-
-    deconvoluted.drop_duplicates(inplace=True)
-
-    if means.empty:
-        raise EmptyResultException
-
+    means, significant_means, deconvoluted = \
+        cpdb_analysis_complex_method.call(meta.copy(),
+                                          counts.copy(),
+                                          counts_data,
+                                          interactions.copy(),
+                                          genes,
+                                          complexes,
+                                          complex_compositions,
+                                          separator,
+                                          threshold,
+                                          result_precision)
     max_rank = significant_means['rank'].max()
     significant_means['rank'] = significant_means['rank'].apply(lambda rank: rank if rank != 0 else (1 + max_rank))
     significant_means.sort_values('rank', inplace=True)