Bugfixes, all tests pass. Need tests for new loci now.

Author: JasonMendoza2008

README.md

@@ -29,8 +29,8 @@ simple_comparison(
     interlocus2=True  # doesn't matter for class I alleles
 )
 ```
-In the `output.csv` file created in the current directory, you will find two rows: "In A\*68:02 but not in A\*68:01" and
-"In A*68:01 but not in A*68:02"
+In the `output.csv` file created in the current directory, you will find two rows: "In A*68:02 but not in 
+A*68:01" and "In A*68:01 but not in A*68:02".
 
 ##### b. Batch mode
 Here is a minimal example with the file [Template.xlsx](https://github.com/MICS-Lab/pelc/raw/main/Template.xlsx)

pelc/_input_sanity_check.py

@@ -8,9 +8,11 @@ def _equal_amount_of_unknown_alleles(
     :param input_df_donor: Input pandas.DataFrame with the donor alleles
     :param input_df_recipient: Input pandas.DataFrame with the recipient alleles
 
-    :return: True if the amount of unknown alleles is equal in both dataframes, False otherwise
+    :return: True if the amount of unknown* alleles is equal in both dataframes, False otherwise
 
-    Also tests if the unknown alleles are either for both the alleles of the locus or for none of them
+    Also tests if the unknown* alleles are either for both the alleles of the locus or for none of them.
+    * unknown alleles are the ones that were inputted as "A*", "B*", "C*", "DRB1*", "DQA1*", "DQB1*", "DPA1*" and/or
+    "DPB1*". Here we are not talking about the alleles that are unknown to the database.
     """
 
     unknown_alleles: list[str] = [

pelc/_open_epregistry_databases.py

@@ -4,7 +4,7 @@
 
 def _open_epregistry_database(
         path_to_csv: str,
-        ghost_alleles: str | list[str],
+        ghost_alleles: list[str],
         no_eplets: bool = False
 ) -> pd.DataFrame:
     """
@@ -17,7 +17,7 @@ def _open_epregistry_database(
     df_db: pd.DataFrame
 
     file_name_no_extension: str = path_to_csv.split('.csv')[0]
-    suffix: str = ghost_alleles if isinstance(ghost_alleles, str) else ghost_alleles[0]
+    suffix: str = ghost_alleles[0]
     pickle_file_name: str = f"{file_name_no_extension}_{no_eplets}_{suffix}.pickle"
 
     # replace * by _ in the pickle file name to avoid problems when * is in the file name

pelc/batch_eplet_comp.py

@@ -30,7 +30,8 @@ def compute_epletic_load(
     class_ii: bool = True,
     verifiedonly: bool = False,
     exclude: list[int | str] | None = None,
-    interlocus2: bool = True
+    interlocus2: bool = True,
+    simple_comparison: bool = False,
 ) -> None | pd.DataFrame | pd.Series | tuple[pd.DataFrame, pd.DataFrame]:
     """
     :param input_df_donor: Input Donors Typing (pandas.DataFrame)
@@ -42,6 +43,9 @@ def compute_epletic_load(
     :param verifiedonly: How should the epletic charge be computed? Verified eplets only? Or all eplets?
     :param exclude: list of indices to exclude
     :param interlocus2: whether or not to take into account interlocus eplets for HLA of class II
+    :param simple_comparison: whether or not it's a simple allele to allele comparison in which case, the function
+    _equal_amount_of_unknown_alleles is not called (checks are already made in simple_comparison.py and would not
+    pass here given the column names).
 
     :return: None (if output_type is not None, the result will be saved on disk as a csv), or pandas.DataFrame
              (OutputType.COUNT_AND_DETAILS) or pandas.Series (OutputType.COUNT, or OutputType.ONLY_DETAILS) or
@@ -62,12 +66,15 @@ def compute_epletic_load(
         )
         return None
 
-    if not _equal_amount_of_unknown_alleles(input_df_donor, input_df_recipient):
-        logging.error(
-            "Either the number of unknown alleles is different for one donor and recipient pair or one allele is "
-            "uknown whilst the other of the same locus isn't."
-        )
-        return None
+    if not simple_comparison:
+        if not _equal_amount_of_unknown_alleles(input_df_donor, input_df_recipient):
+            # unknown alleles are the ones that were inputted as "A*", "B*", "C*", "DRB1*", "DRB345*", "DQA1*", "DQB1*",
+            # "DPA1*" and/or "DPB1*". Here we are not talking about the alleles that are unknown to the database.
+            logging.error(
+                "Either the number of unknown alleles is different for one donor and recipient pair or one allele is "
+                "uknown whilst the other of the same locus isn't."
+            )
+            return None
 
 
     df_a: pd.DataFrame
@@ -83,17 +90,17 @@ def compute_epletic_load(
 
     this_file_directory_path: str = os.path.dirname(os.path.realpath(__file__))
     if class_i and class_ii:
-        df_a = _open_epregistry_database(f"{this_file_directory_path}/data/A.csv", "A*")
-        df_b = _open_epregistry_database(f"{this_file_directory_path}/data/B.csv", "B*")
-        df_c = _open_epregistry_database(f"{this_file_directory_path}/data/C.csv", "C*")
+        df_a = _open_epregistry_database(f"{this_file_directory_path}/data/A.csv", ["A*"])
+        df_b = _open_epregistry_database(f"{this_file_directory_path}/data/B.csv", ["B*"])
+        df_c = _open_epregistry_database(f"{this_file_directory_path}/data/C.csv", ["C*"])
         df_dr = _open_epregistry_database(f"{this_file_directory_path}/data/DR.csv", ["DRB1*", "DRB345*"])
         df_dq = _open_epregistry_database(f"{this_file_directory_path}/data/DQ.csv", ["DQB1*", "DQA1*"])
         df_dp = _open_epregistry_database(f"{this_file_directory_path}/data/DP.csv", ["DPB1*", "DPA1*"])
     else:
         if class_i:
-            df_a = _open_epregistry_database(f"{this_file_directory_path}/data/A.csv", "A*")
-            df_b = _open_epregistry_database(f"{this_file_directory_path}/data/B.csv", "B*")
-            df_c = _open_epregistry_database(f"{this_file_directory_path}/data/C.csv", "C*")
+            df_a = _open_epregistry_database(f"{this_file_directory_path}/data/A.csv", ["A*"])
+            df_b = _open_epregistry_database(f"{this_file_directory_path}/data/B.csv", ["B*"])
+            df_c = _open_epregistry_database(f"{this_file_directory_path}/data/C.csv", ["C*"])
             # we don't want to load .csv files if they are not needed
             df_dr = _open_epregistry_database(
                 f"{this_file_directory_path}/data/DR.csv", ["DRB1*", "DRB345*"], no_eplets=True
@@ -110,13 +117,13 @@ def compute_epletic_load(
             df_dp = _open_epregistry_database(f"{this_file_directory_path}/data/DP.csv", ["DPB1*", "DPA1*"])
             # we don't want to load .csv files if they are not needed
             df_a = _open_epregistry_database(
-                f"{this_file_directory_path}/data/A.csv", "A*", no_eplets=True
+                f"{this_file_directory_path}/data/A.csv", ["A*"], no_eplets=True
             )
             df_b = _open_epregistry_database(
-                f"{this_file_directory_path}/data/B.csv", "B*", no_eplets=True
+                f"{this_file_directory_path}/data/B.csv", ["B*"], no_eplets=True
             )
             df_c = _open_epregistry_database(
-                f"{this_file_directory_path}/data/C.csv", "C*", no_eplets=True
+                f"{this_file_directory_path}/data/C.csv", ["C*"], no_eplets=True
             )