Added graphs to show workouts & PR's per week on dashboard. However there is no tooltip on hover and I duplicated the svg spark line template (May combine the two)

utils.py

@@ -1,7 +1,8 @@
+import colorsys
 from datetime import datetime, date, timedelta
+import random
 import numpy as np
-import json
-
+import pandas as pd
 
 def get_workouts(topsets):
     # Get all unique workout_ids (No duplicates)
@@ -305,4 +306,136 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
         'plots': [repetitions, weight, estimated_1rm],
         'best_fit_points': best_fit_points,
         'plot_labels': plot_labels
-    }
+    }
+
+def get_workout_counts(workouts, period='week'):
+    # Convert to DataFrame
+    df = pd.DataFrame(workouts)
+    
+    # Convert 'StartDate' to datetime
+    df['StartDate'] = pd.to_datetime(df['StartDate'])
+
+    # Determine the range of periods to cover
+    min_date = df['StartDate'].min()
+    max_date = pd.Timestamp(datetime.now())
+
+    # Generate a complete range of periods
+    freq = 'W-MON' if period == 'week' else 'MS'
+    period_range = pd.date_range(start=min_date, end=max_date, freq=freq)
+
+    # Initialize a dictionary to store workout counts and person names
+    workout_counts = {
+        person_id: {
+            "PersonName": person_name,
+            "PRCounts": {p: 0 for p in period_range}
+        } for person_id, person_name in df[['PersonId', 'PersonName']].drop_duplicates().values
+    }
+
+    # Process the workouts
+    for person_id, person_data in workout_counts.items():
+        person_df = df[df['PersonId'] == person_id]
+
+        for period_start in person_data["PRCounts"]:
+            period_end = period_start + pd.DateOffset(weeks=1) if period == 'week' else period_start + pd.DateOffset(months=1)
+            period_workouts = person_df[(person_df['StartDate'] >= period_start) & (person_df['StartDate'] < period_end)]
+            person_data["PRCounts"][period_start] = len(period_workouts)
+
+    return workout_counts
+
+def count_prs_over_time(workouts, period='week'):
+    # Convert to DataFrame
+    df = pd.DataFrame(workouts)
+    
+    # Convert 'StartDate' to datetime
+    df['StartDate'] = pd.to_datetime(df['StartDate'])
+
+    # Determine the range of periods to cover
+    min_date = df['StartDate'].min()
+    max_date = pd.Timestamp(datetime.now())
+
+    # Generate a complete range of periods
+    period_range = pd.date_range(start=min_date, end=max_date, freq='W-MON' if period == 'week' else 'MS')
+
+    # Initialize a dictionary to store PR counts and names
+    pr_counts = {
+        person_id: {
+            "PersonName": person_name,
+            "PRCounts": {p: 0 for p in period_range}
+        } for person_id, person_name in df[['PersonId', 'PersonName']].drop_duplicates().values
+    }
+
+    # Process the workouts
+    for person_id, person_data in pr_counts.items():
+        person_df = df[df['PersonId'] == person_id]
+
+        for period_start in person_data["PRCounts"]:
+            period_end = period_start + pd.DateOffset(weeks=1) if period == 'week' else period_start + pd.DateOffset(months=1)
+            period_workouts = person_df[(person_df['StartDate'] >= period_start) & (person_df['StartDate'] < period_end)]
+
+            for exercise_id in period_workouts['ExerciseId'].unique():
+                exercise_max = period_workouts[period_workouts['ExerciseId'] == exercise_id]['Estimated1RM'].max()
+
+                # Check if this is a PR
+                previous_max = person_df[(person_df['StartDate'] < period_start) & 
+                                         (person_df['ExerciseId'] == exercise_id)]['Estimated1RM'].max()
+                
+                if pd.isna(previous_max) or exercise_max > previous_max:
+                    person_data["PRCounts"][period_start] += 1
+
+    return pr_counts
+
+
+def get_weekly_pr_graph_model(title, weekly_pr_data):
+    # Assuming weekly_pr_data is in the format {1: {"PersonName": "Alice", "PRCounts": {Timestamp('2022-01-01', freq='W-MON'): 0, ...}}, 2: {...}, ...}
+
+    # Find the overall date range for all users
+    all_dates = [date for user_data in weekly_pr_data.values() for date in user_data["PRCounts"].keys()]
+    min_date, max_date = min(all_dates), max(all_dates)
+    total_span = (max_date - min_date).days or 1
+    relative_positions = [(date - min_date).days / total_span for date in all_dates]
+
+    # Calculate viewBox dimensions
+    max_pr_count = max(max(user_data["PRCounts"].values()) for user_data in weekly_pr_data.values()) or 1
+    vb_width, vb_height = total_span, max_pr_count
+    vb_width *= 200 / vb_width  # Scale to 200px width
+    vb_height *= 75 / vb_height  # Scale to 75px height
+
+    plots = []
+    colors = get_distinct_colors(len(weekly_pr_data.items()))
+    for count, (user_id, user_data) in enumerate(weekly_pr_data.items()):
+        pr_counts = user_data["PRCounts"]
+        person_name = user_data["PersonName"]
+
+        values = pr_counts.values()
+        min_value, max_value = min(values), max(values)
+        value_range = (max_value - min_value) or 1
+
+        values_scaled = [((value - min_value) / value_range) * vb_height for value in values]
+        plot_points = list(zip(values_scaled, relative_positions))
+
+        # Create a plot for each user
+        plot = {
+            'label': person_name,  # Use PersonName instead of User ID
+            'color': colors[count],
+            'points': plot_points
+        }
+        plots.append(plot)
+
+    # Return workout data with SVG dimensions and data points
+    return {
+        'title': title,
+        'vb_width': vb_width,
+        'vb_height': vb_height,
+        'plots': plots
+    }
+
+def get_distinct_colors(n):
+    colors = []
+    for i in range(n):
+        # Divide the color wheel into n parts
+        hue = i / n
+        # Convert HSL (Hue, Saturation, Lightness) to RGB and then to a Hex string
+        rgb = colorsys.hls_to_rgb(hue, 0.6, 0.4)  # Fixed lightness and saturation
+        hex_color = '#{:02x}{:02x}{:02x}'.format(int(rgb[0]*255), int(rgb[1]*255), int(rgb[2]*255))
+        colors.append(hex_color)
+    return colors