From 916fc47d26876fdc78e5621b623e53dea8c35d95 Mon Sep 17 00:00:00 2001
From: Peter Stockings <pstockings@jadeworld.com>
Date: Sun, 7 Apr 2024 22:09:18 +1000
Subject: [PATCH] Fix issue where sets werent displayed in order on month
 overview

---
 utils.py | 76 ++++++++++++++++++++++++++++++++------------------------
 1 file changed, 43 insertions(+), 33 deletions(-)

diff --git a/utils.py b/utils.py
index b11ee63..698bf21 100644
--- a/utils.py
+++ b/utils.py
@@ -5,36 +5,46 @@ import numpy as np
 import pandas as pd
 
 def get_workouts(topsets):
-    # Get all unique workout_ids (No duplicates)
-    workout_ids = list(set([t['WorkoutId']
-                            for t in topsets if t['WorkoutId'] is not None]))
+    # Ensure all entries have 'WorkoutId' and 'TopSetId', then sort by 'WorkoutId' and 'TopSetId'
+    filtered_topsets = sorted(
+        [t for t in topsets if t['WorkoutId'] is not None and t['TopSetId'] is not None],
+        key=lambda x: (x['WorkoutId'], x['TopSetId'])
+    )
 
-    # Group topsets into workouts
-    workouts = []
-    for workout_id in reversed(workout_ids):
-        topsets_in_workout = [
-            t for t in topsets if t['WorkoutId'] == workout_id]
-        workouts.append({
-            'WorkoutId': workout_id,
-            'StartDate': topsets_in_workout[0]['StartDate'],
-            'TopSets': [{"TopSetId": t['TopSetId'], "ExerciseId": t['ExerciseId'], "ExerciseName": t['ExerciseName'], "Weight": t['Weight'], "Repetitions": t['Repetitions'], "Estimated1RM": t['Estimated1RM']} for t in topsets_in_workout if t['TopSetId'] is not None]
+    workouts = {}
+    for t in filtered_topsets:
+        workout_id = t['WorkoutId']
+        if workout_id not in workouts:
+            workouts[workout_id] = {
+                'WorkoutId': workout_id,
+                'StartDate': t['StartDate'],
+                'TopSets': []
+            }
+        workouts[workout_id]['TopSets'].append({
+            'TopSetId': t['TopSetId'],
+            'ExerciseId': t['ExerciseId'],
+            'ExerciseName': t['ExerciseName'],
+            'Weight': t['Weight'],
+            'Repetitions': t['Repetitions'],
+            'Estimated1RM': t['Estimated1RM']
         })
 
-    workouts.sort(key=lambda x: x['StartDate'], reverse=True)
+    # Convert the workouts dictionary back to a list and sort by 'StartDate'
+    sorted_workouts = sorted(workouts.values(), key=lambda x: x['StartDate'], reverse=True)
 
-    return workouts
+    return sorted_workouts
 
 
 def get_all_exercises_from_topsets(topsets):
-    exercise_ids = set([t['ExerciseId']
-                       for t in topsets if t['ExerciseId'] is not None])
-    exercises = []
-    for exercise_id in exercise_ids:
-        exercises.append({
-            'ExerciseId': exercise_id,
-            'ExerciseName': next((t['ExerciseName'] for t in topsets if t['ExerciseId'] == exercise_id), 'Unknown')
-        })
-    return exercises
+    exercises_dict = {}
+    for t in topsets:
+        exercise_id = t.get('ExerciseId')
+        if exercise_id and exercise_id not in exercises_dict:
+            exercises_dict[exercise_id] = {
+                'ExerciseId': exercise_id,
+                'ExerciseName': t.get('ExerciseName', 'Unknown')
+            }
+    return list(exercises_dict.values())
 
 def get_topsets_for_person(person_topsets):
     person_exercises = get_all_exercises_from_topsets(person_topsets)
@@ -262,10 +272,17 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
     
     relative_positions = [(date - min_date).days / total_span for date in start_dates]
 
-    best_fit_points = []
-    best_fit_formula = {}
+    # Convert the slope from scaled units per day to kg per day
+    slope_kg_per_day = (max_e1rm - min_e1rm) / total_span
 
-    # trry catch LinAlgError
+    best_fit_formula = {
+        'kg_per_week': round(slope_kg_per_day * 7, 1),  # Convert to kg/week
+        'kg_per_month': round(slope_kg_per_day * 30, 1)  # Convert to kg/month
+    }
+
+    best_fit_points = []
+
+    # Catch LinAlgError
     try:
         # Convert relative positions and scaled estimated 1RM values to numpy arrays
         x = np.array(relative_positions)
@@ -278,13 +295,6 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
         y_best_fit = [m * xi + b for xi in x]
         best_fit_points = list(zip(y_best_fit, relative_positions))
 
-        # Convert the slope from scaled units per day to kg per day
-        slope_kg_per_day = (max_e1rm - min_e1rm) / total_span
-
-        best_fit_formula = {
-            'kg_per_week': round(slope_kg_per_day * 7, 1),  # Convert to kg/week
-            'kg_per_month': round(slope_kg_per_day * 30, 1)  # Convert to kg/month
-        }
     except:
         pass