Reorganise data tables location and storage management

.gitignore

@@ -3,8 +3,7 @@
 
 ### Added manually
 # Build files
-peptides/data/tables.py
-peptides/data/lut.py
+peptides/tables/__init__.py
 
 ### C ###
 # Prerequisites

MANIFEST.in

@@ -2,8 +2,7 @@ include           README.md
 include           CHANGELOG.md
 include           COPYING
 include           pyproject.toml
-recursive-include peptides/data             *.csv *.py
+recursive-include peptides/tables *.csv
+exclude           peptides/tables/__init__.py
 include           peptides/py.typed
-exclude           peptides/data/tables.py
-exclude           peptides/data/lut.py
 prune             tests

docs/conf.py

@@ -29,8 +29,7 @@ sys.path.insert(0, project_dir)
 # Mock dynamically generated Python files so that we can build the documentation
 # without having build these files first (e.g on ReadTheDocs)
 
-sys.modules['peptides.data.tables'] = types.ModuleType('peptides.data.tables')
-sys.modules['peptides.data.lut'] = types.ModuleType('peptides.data.lut')
+sys.modules['peptides.tables'] = types.ModuleType('peptides.tables')
 
 # -- Sphinx Setup ------------------------------------------------------------
 

peptides/__init__.py

@@ -8,7 +8,7 @@ import random
 import statistics
 import typing
 
-from .data import tables
+from . import tables
 
 __all__ = ["Peptide", "tables"]
 
@@ -1323,8 +1323,8 @@ class Peptide(typing.Sequence[str]):
             'zeta'
 
         To-Do:
-            Implement the structural class prediction method from
-            Chou & Zhang (1995), which uses the Mahalanobis distance.
+            - Implement the structural class prediction method from
+              Chou & Zhang (1995), which uses the Mahalanobis distance.
 
         References:
             - Chou, K-C., and C-T. Zhang.