Add insertion of obsidian trainings with sqlalchemy

dbapi/tables.py

@@ -1,4 +1,4 @@
-from sqlalchemy import ForeignKey, String, Table, Column, Integer, MetaData, Date
+from sqlalchemy import Float, ForeignKey, String, Table, Column, Integer, MetaData, Date
 
 # NOTE: MetaData object - object where we place out tables
 # Essentialy - this is a facade around Python dicts, that stores series of Table objects keyed to their string name
@@ -8,21 +8,29 @@ metadata_obj = MetaData()
 training = Table(
     "training",
     metadata_obj,
-    Column("ID", Integer, primary_key=True),
+    Column("Id", Integer, primary_key=True),
-    Column("DATE", Date),
+    Column("Date", Date),
 )
 
 # Representation of exercise table
 exercise = Table(
     "exercise",
     metadata_obj,
-    Column("ID", Integer, primary_key=True),
+    Column("Id", Integer, primary_key=True),
-    Column("TRAINING", ForeignKey("training.ID"), nullable=False),
+    Column("Training", ForeignKey("training.Id"), nullable=False),
-    Column("NAME", String(256)),
+    Column("Name", String(256)),
+)
+
+approach = Table(
+    "approach",
+    metadata_obj,
+    Column("Id", Integer, primary_key=True),
+    Column("Exercise", ForeignKey("exercise.Id"), nullable=False),
+    Column("Weight", Float),
+    Column("Reps", Integer),
 )
 
 
 # NOTE: We can access table metadata with associative array `c`
 # print(training.c.keys())
 # print(training.primary_key)
-

elect

@@ -0,0 +1,84 @@
+  1 |        1 |     77 |   12
+  2 |        1 |     86 |   12
+  3 |        1 |     91 |   12
+  4 |        1 |     86 |   12
+  5 |        2 |     47 |   12
+  6 |        2 |     47 |   12
+  7 |        2 |     47 |   11
+  8 |        2 |     47 |   10
+  9 |        3 |     15 |   12
+ 10 |        3 |     25 |   12
+ 11 |        3 |   27.5 |   12
+ 12 |        3 |     30 |    8
+ 13 |        4 |      9 |   12
+ 14 |        4 |     11 |   12
+ 15 |        4 |     12 |   12
+ 16 |        5 |     33 |   12
+ 17 |        5 |     33 |   12
+ 18 |        5 |     33 |   12
+ 19 |        5 |     33 |   12
+ 20 |        6 |      0 |    9
+ 21 |        6 |      0 |    7
+ 22 |        6 |      0 |    5
+ 23 |        7 |     55 |   12
+ 24 |        7 |     59 |   12
+ 25 |        7 |     59 |   12
+ 26 |        7 |     59 |   10
+ 27 |        8 |     18 |   12
+ 28 |        8 |     20 |   12
+ 29 |        8 |     20 |   11
+ 30 |        9 |     47 |   12
+ 31 |        9 |     47 |   12
+ 32 |        9 |     47 |   12
+ 33 |       10 |     77 |   12
+ 34 |       10 |     77 |   12
+ 35 |       10 |     77 |   12
+ 36 |       11 |      0 |   11
+ 37 |       11 |      0 |    8
+ 38 |       11 |      0 |    6
+ 39 |       12 |     22 |   12
+ 40 |       12 |     28 |   10
+ 41 |       12 |     28 |   10
+ 42 |       12 |     28 |   10
+ 43 |       13 |     16 |   12
+ 44 |       13 |     28 |   10
+ 45 |       13 |     28 |    6
+ 46 |       13 |     28 |    7
+ 47 |       14 |     47 |   12
+ 48 |       14 |     47 |   12
+ 49 |       14 |     47 |   12
+ 50 |       15 |     65 |   12
+ 51 |       15 |     82 |   12
+ 52 |       15 |     91 |   12
+ 53 |       16 |    100 |   12
+ 54 |       16 |    150 |   10
+ 55 |       16 |    150 |   10
+ 56 |       16 |    150 |   10
+ 57 |       17 |     15 |   12
+ 58 |       17 |     15 |   12
+ 59 |       17 |     15 |   12
+ 60 |       18 |     12 |   12
+ 61 |       18 |     20 |   10
+ 62 |       18 |     20 |   10
+ 63 |       18 |     20 |   10
+ 64 |       19 |     47 |   11
+ 65 |       19 |     47 |   11
+ 66 |       19 |     47 |   12
+ 67 |       20 |     45 |   12
+ 68 |       20 |     54 |   12
+ 69 |       20 |     59 |   10
+ 70 |       20 |     59 |   10
+ 71 |       21 |     30 |   10
+ 72 |       21 |     32 |   10
+ 73 |       21 |     32 |   10
+ 74 |       22 |     50 |   12
+ 75 |       22 |     50 |   12
+ 76 |       22 |     50 |   12
+ 77 |       23 |     16 |   12
+ 78 |       23 |     24 |   10
+ 79 |       23 |     24 |   10
+ 80 |       23 |     24 |   10
+ 81 |       24 |     47 |   12
+ 82 |       24 |     47 |   12
+ 83 |       24 |     47 |   12
+

main.py

@@ -1,7 +1,10 @@
 import os
-from sqlalchemy import Table, create_engine, text
+from typing import List
+from sqlalchemy import Table, create_engine, text, insert
 from dotenv import load_dotenv
-from dbapi.tables import metadata_obj
+from dbapi.tables import metadata_obj, training, exercise, approach
+from obsidian.notes_parser import parse_training_data
+from obsidian.py_models import Training
 
 
 load_dotenv()
@@ -31,13 +34,40 @@ engine = create_engine(
 #         print(f"x: {row.x} -- y: {row.y}")
 
 # NOTE : Create all tables from metadata object
+# metadata_obj.create_all(engine)
 # TODO: Check how psycopg2 handles duplication of tables
 # TODO: Check how migrations are done
-metadata_obj.create_all(engine)
 
 # NOTE: Drop all Tables from database
-# metadata_obj.drop_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)
 # print(some_table.c)
+
+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()

obsidian/notes_parser.py

@@ -43,7 +43,6 @@ def serialize_exercise(reps: str, weight: str, name: str) -> Exercise:
     else:
         weight_pointer = 0
         for rep_index in range(0, len(reps)):
-            print(reps[rep_index])
             approach = Approach(weight=weight[weight_pointer], reps=reps[rep_index])
             if rep_index < len(weight) - 1:
                 weight_pointer += 1
@@ -51,7 +50,6 @@ def serialize_exercise(reps: str, weight: str, name: str) -> Exercise:
     exercise = Exercise(
         name=name, approaches=approaches, splitted_weight=weight_splitted
     )
-    print(exercise)
     return exercise
 
 
@@ -89,7 +87,7 @@ def filter_training_data(training_data: str):
 
 
 def parse_training_data():
-    training_data: str = filter_training_data(read_example_file("full.txt"))
+    training_data: str = filter_training_data(read_example_file("notes.txt"))
     lines = training_data.splitlines()
     current_training = None
     trains = []
@@ -108,9 +106,6 @@ def parse_training_data():
             current_training.exercises.append(exr)
         except ValueError:
             pass
-    for t in trains:
-        pprint(t)
-        print("\n")
     return trains
 
 pprint(parse_training_data()[1:])