0.3.0 -> 0.3.1 ; several changes

- New .csv (V. Clichet)
- Completely renamed epitopes in eplets (functions, variables, docstrings, etc ...)
- Fixed unit tests (AbV)
- Optimised is_eplet_to_be_added function
- Fixed Zenodo stuff
- Updated CITATION.cff
- Better README.md
- Fixed ghost alleles

CITATION.cff

@@ -1,19 +1,22 @@
 cff-version: 1.2.0
 message: "If you use this software, please cite it as below.
-          Lhotte, R., Usureau, C. & Taupin, J. (2022). Python Epitope Charge Calculator (PECC) package
-          (Version 0.3.0) [Computer software]. https://doi.org/10.5281/zenodo.7254809"
+          Lhotte, R., Clichet, V., Usureau, C. & Taupin, J. (2022). Python Eplet Load Calculator (PELC) package
+          (Version 0.3.1) [Computer software]. https://doi.org/10.5281/zenodo.7254809"
 authors:
   - family-names: Lhotte
     given-names: Romain
     orcid: https://orcid.org/0000-0002-6131-5415
+  - family-names: Clichet
+    given-names: Valentin
+    orcid:
   - family-names: Usureau
     given-names: Cédric
     orcid:
   - family-names: Taupin
     given-names: Jean-Luc
     orcid: https://orcid.org/0000-0002-5766-046X
-title: Python Epitope Charge Calculator
-version: 0.3.0
+title: Python Eplet Load Calculator
+version: 0.3.1
 doi: doi.org/10.5281/zenodo.7254809
 link: https://github.com/MICS-Lab/pecc
 date-released: 2022-10-26

README.md

@@ -5,7 +5,8 @@
 ### Overview
 PELC is a Python package designed to calculate efficiently the HLA Eplet Load (based on the
 [EpRegistry database](https://www.epregistry.com.br/)) between donors and recipients by loading in a pandas.DataFrame
-in `epitope_comparison.compute_epitopic_charge`.  See minimal reproducible example for more details.
+in `eplet_comparison.compute_epletic_load` the recipients' and donors' typings.  See minimal reproducible example for
+more details.
 
 
 ### Getting started
@@ -14,12 +15,13 @@ To use `pelc`, run `pip install pelc` in your terminal.
 
 
 #### Usage
-Here is a minimal example with the file [Template.xlsx](https://github.com/MICS-Lab/pelc/raw/main/Template.xlsx) (click to download):
+Here is a minimal example with the file [Template.xlsx](https://github.com/MICS-Lab/pelc/raw/main/Template.xlsx)
+(click to download):
+
 ```py
 import pandas as pd
 
-from pelc import epitope_comparison, epitope_comparison_aux, output_type
-
+from pelc import eplet_comparison, eplet_comparison_aux, output_type
 
 if __name__ == "__main__":
     input_path: str = "Template.xlsx"
@@ -31,13 +33,18 @@ if __name__ == "__main__":
 
     donordf: pd.DataFrame
     recipientdf: pd.DataFrame
-    donordf, recipientdf = epitope_comparison_aux.split_dataframe(input_df)
+    donordf, recipientdf = eplet_comparison_aux.split_dataframe(input_df)
 
-    epitope_comparison.compute_epitopic_charge(
+    eplet_comparison.compute_epletic_load(
         donordf,
         recipientdf,
         output_path,
-        output_type.OutputType.DETAILS_AND_COUNT
+        output_type.OutputType.DETAILS_AND_COUNT,
+        class_i = True,  # Compute class I eplets comparison?
+        class_ii = True,  # Compute class II eplets comparison?
+        verifiedonly = False,  # How should the epletic charge be computed? Verified eplets only? Or all eplets?
+        exclude = None,  # list of indices to exclude
+        interlocus2 = True  # whether or not to take into account interlocus eplets for HLA of class II
     )
 ```
 
@@ -115,7 +122,7 @@ If you use this software, please cite it as below.
 
 - APA:
 ```
-Lhotte, R., Usureau, C., & Taupin, J. (2022). Python Epitope Charge Calculator (Version 0.3.0) [Computer software].
+Lhotte, R., Usureau, C., & Taupin, J. (2022). Python Epitope Charge Calculator (Version 0.3.1) [Computer software].
 https://doi.org/10.5281/zenodo.7254809
 ```
 
@@ -126,7 +133,7 @@ https://doi.org/10.5281/zenodo.7254809
     doi = {doi.org/10.5281/zenodo.7254809},
     month = {10},
     title = {{Python Eplet Load Calculator}},
-    version = {0.3.0},
+    version = {0.3.1},
     year = {2022}
 }
 ```

pelc/__init__.py

@@ -2,4 +2,4 @@
 
 __version__: str = importlib.metadata.version("pelc")
 
-from . import epitope_comparison, epitope_comparison_aux, output_type
+from . import eplet_comparison, eplet_comparison_aux, output_type

pelc/_open_epregistry_databases.py

@@ -0,0 +1,22 @@
+import pandas as pd
+
+
+def _open_epregistry_database(path_to_csv: str, ghost_allele: str, no_eplets: bool = False) -> pd.DataFrame:
+    """
+    :param path_to_csv: path to the csv EpRegistry database file
+    :param ghost_allele: allele string to be used as a ghost allele (no eplets)
+
+    :return: pandas.DataFrame with the EpRegistry database and the ghost allele
+    """
+
+    df_db: pd.DataFrame
+    if no_eplets:
+        df_db = pd.read_csv(path_to_csv, sep=";", usecols=[0]).set_index("allele")
+        # add a row to an empty dataframe with concat
+        df_db = pd.concat([df_db, pd.DataFrame(columns=[], index=[ghost_allele])])
+    else:
+        df_db = pd.read_csv(path_to_csv, sep=";").set_index("allele")
+        # add row with nan values
+        df_db.loc[ghost_allele] = float("nan")
+
+    return df_db

pelc/_unexpected_alleles.py

@@ -2,6 +2,10 @@
 
 
 def read_available_alleles(df_ref: pd.DataFrame) -> list[str]:
+    """
+    :param df_ref: reference dataframe
+    :return: all known alleles in the reference dataframe (df_a, df_b, df_c, df_dr, df_dq, or df_dp)
+    """
     list_ref: list[str] = list(df_ref.index.values)
 
     return list_ref