Create repositories for tables

dbapi/connector.py

@@ -1,4 +0,0 @@
-from sqlalchemy import create_engine
-
-
-engine = create_engine("postgres+psycopg2:///:localhost:5433", echo=True)

dbapi/repositories.py

@@ -0,0 +1,76 @@
+from sqlalchemy import create_engine, insert
+from sqlalchemy.engine.base import Engine
+from datetime import date
+from dbapi.tables import training, exercise, approach
+
+
+class DatabaseInterfasesMixin:
+    """Mixin for interfaces, that works with database"""
+
+    def __init__(self, engine: Engine) -> None:
+        self.engine: Engine = engine
+
+
+class TrainingRepository(DatabaseInterfasesMixin):
+    """Training table repository"""
+
+    def create_training(self, date: date) -> int:
+        """Method for creating new instance of training
+        Args:
+            date: date of a training
+
+        Returns:
+            Primary key of created training entry
+        """
+        new_instance_statement = insert(training).values(Date=date)
+        with self.engine.connect() as conn:
+            result = conn.execute(new_instance_statement)
+            inerted_entry_pk: int = result.inserted_primary_key[0]
+            conn.commit()
+        return inerted_entry_pk
+
+
+class ExerciseRepository(DatabaseInterfasesMixin):
+    """Exercise table repository"""
+
+    def create_exercise(self, training_pk: int, exercise_name: str) -> int:
+        """Method for creating new instance of exercise table
+        Args:
+            training_pk: Primary key of associated training instance
+            exercise_name: Name of an exercise
+
+        Returns:
+            Primary key of created exercise entry
+        """
+        new_instance_statement = insert(exercise).values(
+            Training=training_pk, Name=exercise_name
+        )
+        with self.engine.connect() as conn:
+            result = conn.execute(new_instance_statement)
+            inserted_entry_pk: int = result.inserted_primary_key[0]
+            conn.commit()
+        return inserted_entry_pk
+
+
+class ApproachRepository(DatabaseInterfasesMixin):
+    """Approach table repository"""
+
+    def create_approach(self, exercise_pk: int, weight: float, reps: int) -> int:
+        """Method for creating new instance of approach table
+
+        Args:
+            exercise_pk: Primary key of an associated exercise
+            weight: Approach weight
+            reps: Amount of reps in approach
+
+        Returns:
+            Primary key of created exercise entry
+        """
+        new_instance_statement = insert(approach).values(
+            Exercise=exercise_pk, Weight=weight, Reps=reps
+        )
+        with self.engine.connect() as conn:
+            result = conn.execute(new_instance_statement)
+            inserted_entry_pk: int = result.inserted_primary_key[0]
+            conn.commit()
+        return inserted_entry_pk

dbapi/tables.py

@@ -34,3 +34,4 @@ approach = Table(
 # NOTE: We can access table metadata with associative array `c`
 # print(training.c.keys())
 # print(training.primary_key)
+print(training.name)

main.py

@@ -4,6 +4,7 @@ import os
 from typing import Dict, List
 from sqlalchemy import Table, create_engine, text, insert
 from dotenv import load_dotenv
+from dbapi.connector import ApproachRepository, ExerciseRepository, TrainingRepository
 from dbapi.tables import metadata_obj, training, exercise, approach
 from obsidian.notes_parser import parse_training_data, remap_unique_exercises
 from apple.notes_parser import parse_training_data as apple_parse_training_data
@@ -43,8 +44,8 @@ engine = create_engine(
 # TODO: Check how migrations are done
 
 # NOTE: Drop all Tables from database
-# metadata_obj.drop_all(engine)
-# metadata_obj.create_all(engine)
+metadata_obj.drop_all(engine)
+metadata_obj.create_all(engine)
 
 # NOTE: Table reflection - generating table object from existing tables (only tables, that are stored in metadata)
 # some_table = Table("some_table", metadata_obj, autoload_with=engine)
@@ -53,31 +54,24 @@ engine = create_engine(
 # -----
 
 # Inserting training values into database
-# trainings: List[Training] = parse_training_data()
-# for train in trainings:
-#     if not train:
-#         continue
-#     else:
-#         print("-------------------------\n" * 2)
-#         print(train)
-#         training_statement = insert(training).values(Date=train.date)
-#         # Create training
-#         with engine.connect() as conn:
-#             result = conn.execute(training_statement)
-#             train_pk = result.inserted_primary_key[0]
-#             for exr in train.exercises:
-#                 approach_statements = []
-#                 exercise_statement = insert(exercise).values(
-#                     Training=train_pk, Name=exr.name
-#                 )
-#                 exr_insert = conn.execute(exercise_statement)
-#                 exr_pk = exr_insert.inserted_primary_key[0]
-#                 for appr in exr.approaches:
-#                     appr_statement = insert(approach).values(
-#                         Exercise=exr_pk, Weight=appr.weight, Reps=appr.reps
-#                     )
-#                     appr_insert = conn.execute(appr_statement)
-#             conn.commit()
+
+trainings: List[Training] = parse_training_data()
+for train in trainings:
+    if not train:
+        continue
+    else:
+        print("-------------------------\n" * 2)
+        print(train)
+        new_training_pk: int = TrainingRepository(engine).create_training(train.date)
+        for exr in train.exercises:
+            approach_statements = []
+            new_exercise_pk: int = ExerciseRepository(engine).create_exercise(
+                training_pk=new_training_pk, exercise_name=exr.name
+            )
+            for appr in exr.approaches:
+                new_approach_pk: int = ApproachRepository(engine).create_approach(
+                    exercise_pk=new_exercise_pk, weight=appr.weight, reps=appr.reps
+                )
 
 # -----
 
@@ -155,21 +149,21 @@ engine = create_engine(
 # Combined trainings
 
 
-obsidian_trainings: List[Training] = parse_training_data()
-obsidian_parsed_trainings = remap_unique_exercises(obsidian_trainings)
-
-apple_trainings: List[Training] = apple_parse_training_data()
-apple_parsed_trainings = apple_remaper(apple_trainings)
-
-
-combined_trainings = obsidian_trainings + apple_trainings
-unique_exercise_parsed_names = defaultdict(int)
-for train in combined_trainings:
-    if not train:
-        continue
-    if train.exercises:
-        for exr in train.exercises:
-            if exr:
-                unique_exercise_parsed_names[exr.name] += 1
-pprint(unique_exercise_parsed_names)
-print(len(combined_trainings))
+# obsidian_trainings: List[Training] = parse_training_data()
+# obsidian_parsed_trainings = remap_unique_exercises(obsidian_trainings)
+#
+# apple_trainings: List[Training] = apple_parse_training_data()
+# apple_parsed_trainings = apple_remaper(apple_trainings)
+#
+#
+# combined_trainings = obsidian_trainings + apple_trainings
+# unique_exercise_parsed_names = defaultdict(int)
+# for train in combined_trainings:
+#     if not train:
+#         continue
+#     if train.exercises:
+#         for exr in train.exercises:
+#             if exr:
+#                 unique_exercise_parsed_names[exr.name] += 1
+# pprint(unique_exercise_parsed_names)
+# print(len(combined_trainings))