Add person graphs endpoint for workouts per week & PRs per week, consumed via dashboard, person overview and notes

utils.py

@@ -260,134 +260,6 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
         'max_date': max_date
     }
 
-
-def get_workout_counts(workouts, period='week'):
-    df = pd.DataFrame(workouts)
-    
-    # Convert 'StartDate' to datetime and set period
-    df['StartDate'] = pd.to_datetime(df['StartDate'])
-    df['Period'] = df['StartDate'].dt.to_period('W' if period == 'week' else 'M')
-
-    # Group by PersonId, Period and count unique workouts
-    workout_counts = df.groupby(['PersonId', 'Period'])['WorkoutId'].nunique().reset_index()
-
-    # Convert 'Period' to timestamp using the start date of the period
-    workout_counts['Period'] = workout_counts['Period'].apply(lambda x: x.start_time)
-
-    # Pivot the result to get periods as columns
-    workout_counts_pivot = workout_counts.pivot(index='PersonId', columns='Period', values='WorkoutId').fillna(0)
-
-    # Include person names
-    names = df[['PersonId', 'PersonName']].drop_duplicates().set_index('PersonId')
-    workout_counts_final = names.join(workout_counts_pivot, how='left').fillna(0)
-
-    # Convert DataFrame to dictionary
-    result = workout_counts_final.reset_index().to_dict('records')
-
-    # Reformat the dictionary to desired structure
-    formatted_result = {}
-    for record in result:
-        person_id = record.pop('PersonId')
-        person_name = record.pop('PersonName')
-        pr_counts = {k: v for k, v in record.items()}
-        formatted_result[person_id] = {'PersonName': person_name, 'PRCounts': pr_counts}
-
-    return formatted_result
-
-def count_prs_over_time(workouts, period='week'):
-    df = pd.DataFrame(workouts)
-    
-    # Convert 'StartDate' to datetime
-    df['StartDate'] = pd.to_datetime(df['StartDate'])
-
-    # Set period as week or month
-    df['Period'] = df['StartDate'].dt.to_period('W' if period == 'week' else 'M')
-
-    # Group by Person, Exercise, and Period to find max Estimated1RM in each period
-    period_max = df.groupby(['PersonId', 'ExerciseId', 'Period'])['Estimated1RM'].max().reset_index()
-
-    # Determine all-time max Estimated1RM up to the start of each period
-    period_max['AllTimeMax'] = period_max.groupby(['PersonId', 'ExerciseId'])['Estimated1RM'].cummax().shift(1)
-
-    # Identify PRs as entries where the period's max Estimated1RM exceeds the all-time max
-    period_max['IsPR'] = period_max['Estimated1RM'] > period_max['AllTimeMax']
-
-    # Count PRs in each period for each person
-    pr_counts = period_max.groupby(['PersonId', 'Period'])['IsPR'].sum().reset_index()
-
-    # Convert 'Period' to timestamp using the start date of the period
-    pr_counts['Period'] = pr_counts['Period'].apply(lambda x: x.start_time)
-
-    # Pivot table to get the desired output format
-    output = pr_counts.pivot(index='PersonId', columns='Period', values='IsPR').fillna(0)
-
-    # Convert only the PR count columns to integers
-    for col in output.columns:
-        output[col] = output[col].astype(int)
-
-    # Merge with names and convert to desired format
-    names = df[['PersonId', 'PersonName']].drop_duplicates().set_index('PersonId')
-    output = names.join(output, how='left').fillna(0)
-
-    # Reset the index to bring 'PersonId' back as a column
-    output.reset_index(inplace=True)
-
-    # Convert to the final dictionary format with PRCounts nested
-    result = {}
-    for index, row in output.iterrows():
-        person_id = row['PersonId']
-        person_name = row['PersonName']
-        pr_counts = row.drop(['PersonId', 'PersonName']).to_dict()
-        result[person_id] = {"PersonName": person_name, "PRCounts": pr_counts}
-
-    return result
-
-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_value = max(max(user_data["PRCounts"].values()) for user_data in weekly_pr_data.values()) or 1
-    min_value = 0
-    value_range = max_value - min_value
-    vb_width = 200
-    vb_height= 75
-
-    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()
-
-        values_scaled = [((value - min_value) / value_range) * vb_height for value in values]
-        plot_points = list(zip(values_scaled, relative_positions))
-        messages = [f'{value} for {person_name} at {date.strftime("%d %b %y")}' for value, date in zip(values, pr_counts.keys())]
-        plot_labels = zip(values_scaled, relative_positions, messages)
-
-        # Create a plot for each user
-        plot = {
-            'label': person_name,  # Use PersonName instead of User ID
-            'color': colors[count],
-            'points': plot_points,
-            'plot_labels': plot_labels
-        }
-        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):