Add insertion of obsidian trainings with sqlalchemy
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4 changed files with 134 additions and 17 deletions
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from sqlalchemy import ForeignKey, String, Table, Column, Integer, MetaData, Date
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from sqlalchemy import Float, ForeignKey, String, Table, Column, Integer, MetaData, Date
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# NOTE: MetaData object - object where we place out tables
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# Essentialy - this is a facade around Python dicts, that stores series of Table objects keyed to their string name
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@ -8,21 +8,29 @@ metadata_obj = MetaData()
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training = Table(
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"training",
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metadata_obj,
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Column("ID", Integer, primary_key=True),
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Column("DATE", Date),
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Column("Id", Integer, primary_key=True),
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Column("Date", Date),
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)
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# Representation of exercise table
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exercise = Table(
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"exercise",
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metadata_obj,
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Column("ID", Integer, primary_key=True),
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Column("TRAINING", ForeignKey("training.ID"), nullable=False),
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Column("NAME", String(256)),
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Column("Id", Integer, primary_key=True),
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Column("Training", ForeignKey("training.Id"), nullable=False),
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Column("Name", String(256)),
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)
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approach = Table(
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"approach",
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metadata_obj,
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Column("Id", Integer, primary_key=True),
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Column("Exercise", ForeignKey("exercise.Id"), nullable=False),
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Column("Weight", Float),
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Column("Reps", Integer),
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)
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# NOTE: We can access table metadata with associative array `c`
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# print(training.c.keys())
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# print(training.primary_key)
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38
main.py
38
main.py
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@ -1,7 +1,10 @@
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import os
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from sqlalchemy import Table, create_engine, text
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from typing import List
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from sqlalchemy import Table, create_engine, text, insert
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from dotenv import load_dotenv
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from dbapi.tables import metadata_obj
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from dbapi.tables import metadata_obj, training, exercise, approach
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from obsidian.notes_parser import parse_training_data
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from obsidian.py_models import Training
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load_dotenv()
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@ -31,13 +34,40 @@ engine = create_engine(
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# print(f"x: {row.x} -- y: {row.y}")
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# NOTE : Create all tables from metadata object
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# metadata_obj.create_all(engine)
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# TODO: Check how psycopg2 handles duplication of tables
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# TODO: Check how migrations are done
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metadata_obj.create_all(engine)
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# NOTE: Drop all Tables from database
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# metadata_obj.drop_all(engine)
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metadata_obj.drop_all(engine)
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metadata_obj.create_all(engine)
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# NOTE: Table reflection - generating table object from existing tables (only tables, that are stored in metadata)
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# some_table = Table("some_table", metadata_obj, autoload_with=engine)
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# print(some_table.c)
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trainings: List[Training] = parse_training_data()
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for train in trainings:
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if not train:
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continue
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else:
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print("-------------------------\n" * 2)
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print(train)
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training_statement = insert(training).values(Date=train.date)
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# Create training
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with engine.connect() as conn:
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result = conn.execute(training_statement)
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train_pk = result.inserted_primary_key[0]
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for exr in train.exercises:
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approach_statements = []
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exercise_statement = insert(exercise).values(
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Training=train_pk, Name=exr.name
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)
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exr_insert = conn.execute(exercise_statement)
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exr_pk = exr_insert.inserted_primary_key[0]
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for appr in exr.approaches:
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appr_statement = insert(approach).values(
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Exercise=exr_pk, Weight=appr.weight, Reps=appr.reps
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)
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appr_insert = conn.execute(appr_statement)
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conn.commit()
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@ -43,7 +43,6 @@ def serialize_exercise(reps: str, weight: str, name: str) -> Exercise:
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else:
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weight_pointer = 0
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for rep_index in range(0, len(reps)):
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print(reps[rep_index])
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approach = Approach(weight=weight[weight_pointer], reps=reps[rep_index])
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if rep_index < len(weight) - 1:
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weight_pointer += 1
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@ -51,7 +50,6 @@ def serialize_exercise(reps: str, weight: str, name: str) -> Exercise:
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exercise = Exercise(
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name=name, approaches=approaches, splitted_weight=weight_splitted
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)
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print(exercise)
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return exercise
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@ -89,7 +87,7 @@ def filter_training_data(training_data: str):
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def parse_training_data():
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training_data: str = filter_training_data(read_example_file("full.txt"))
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training_data: str = filter_training_data(read_example_file("notes.txt"))
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lines = training_data.splitlines()
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current_training = None
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trains = []
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@ -108,9 +106,6 @@ def parse_training_data():
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current_training.exercises.append(exr)
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except ValueError:
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pass
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for t in trains:
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pprint(t)
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print("\n")
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return trains
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pprint(parse_training_data()[1:])
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