Add person graphs endpoint for workouts per week & PRs per week, consumed via dashboard, person overview and notes
This commit is contained in:
Peter Stockings
184
features/people_graphs.py
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184
features/people_graphs.py
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import pandas as pd
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from utils import get_distinct_colors
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class PeopleGraphs:
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def __init__(self, db_connection_method):
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self.execute = db_connection_method
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def get(self, selected_people_ids=None, min_date=None, max_date=None, selected_exercise_ids=None):
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# Base query
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query = """
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SELECT
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P.person_id AS "PersonId",
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P.name AS "PersonName",
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W.workout_id AS "WorkoutId",
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W.start_date AS "StartDate",
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T.topset_id AS "TopSetId",
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E.exercise_id AS "ExerciseId",
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E.name AS "ExerciseName",
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T.repetitions AS "Repetitions",
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T.weight AS "Weight",
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round((100 * T.Weight::numeric::integer)/(101.3-2.67123 * T.Repetitions),0)::numeric::integer AS "Estimated1RM"
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FROM Person P
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LEFT JOIN Workout W ON P.person_id = W.person_id
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LEFT JOIN TopSet T ON W.workout_id = T.workout_id
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LEFT JOIN Exercise E ON T.exercise_id = E.exercise_id
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WHERE TRUE
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"""
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# Parameters for the query
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params = []
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# Add optional filters
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if selected_people_ids:
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placeholders = ", ".join(["%s"] * len(selected_people_ids))
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query += f" AND P.person_id IN ({placeholders})"
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params.extend(selected_people_ids)
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if min_date:
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query += " AND W.start_date >= %s"
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params.append(min_date)
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if max_date:
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query += " AND W.start_date <= %s"
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params.append(max_date)
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if selected_exercise_ids:
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placeholders = ", ".join(["%s"] * len(selected_exercise_ids))
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query += f" AND E.exercise_id IN ({placeholders})"
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params.extend(selected_exercise_ids)
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# Execute the query
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topsets = self.execute(query, params)
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# Generate graphs
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weekly_counts = self.get_workout_counts(topsets, 'week')
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weekly_pr_counts = self.count_prs_over_time(topsets, 'week')
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graphs = [self.get_weekly_pr_graph_model('Workouts per week', weekly_counts), self.get_weekly_pr_graph_model('PRs per week', weekly_pr_counts)]
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return graphs
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def get_weekly_pr_graph_model(self, title, weekly_pr_data):
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# Assuming weekly_pr_data is in the format {1: {"PersonName": "Alice", "PRCounts": {Timestamp('2022-01-01', freq='W-MON'): 0, ...}}, 2: {...}, ...}
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# Find the overall date range for all users
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all_dates = [date for user_data in weekly_pr_data.values() for date in user_data["PRCounts"].keys()]
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min_date, max_date = min(all_dates), max(all_dates)
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total_span = (max_date - min_date).days or 1
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relative_positions = [(date - min_date).days / total_span for date in all_dates]
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# Calculate viewBox dimensions
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max_value = max(max(user_data["PRCounts"].values()) for user_data in weekly_pr_data.values()) or 1
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min_value = 0
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value_range = max_value - min_value
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vb_width = 200
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vb_height= 75
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plots = []
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colors = get_distinct_colors(len(weekly_pr_data.items()))
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for count, (user_id, user_data) in enumerate(weekly_pr_data.items()):
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pr_counts = user_data["PRCounts"]
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person_name = user_data["PersonName"]
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values = pr_counts.values()
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values_scaled = [((value - min_value) / value_range) * vb_height for value in values]
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plot_points = list(zip(values_scaled, relative_positions))
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messages = [f'{value} for {person_name} at {date.strftime("%d %b %y")}' for value, date in zip(values, pr_counts.keys())]
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plot_labels = zip(values_scaled, relative_positions, messages)
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# Create a plot for each user
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plot = {
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'label': person_name, # Use PersonName instead of User ID
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'color': colors[count],
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'points': plot_points,
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'plot_labels': plot_labels
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}
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plots.append(plot)
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# Return workout data with SVG dimensions and data points
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return {
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'title': title,
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'vb_width': vb_width,
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'vb_height': vb_height,
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'plots': plots
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}
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def get_workout_counts(self, workouts, period='week'):
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df = pd.DataFrame(workouts)
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# Convert 'StartDate' to datetime and set period
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df['StartDate'] = pd.to_datetime(df['StartDate'])
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df['Period'] = df['StartDate'].dt.to_period('W' if period == 'week' else 'M')
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# Group by PersonId, Period and count unique workouts
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workout_counts = df.groupby(['PersonId', 'Period'])['WorkoutId'].nunique().reset_index()
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# Convert 'Period' to timestamp using the start date of the period
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workout_counts['Period'] = workout_counts['Period'].apply(lambda x: x.start_time)
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# Pivot the result to get periods as columns
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workout_counts_pivot = workout_counts.pivot(index='PersonId', columns='Period', values='WorkoutId').fillna(0)
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# Include person names
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names = df[['PersonId', 'PersonName']].drop_duplicates().set_index('PersonId')
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workout_counts_final = names.join(workout_counts_pivot, how='left').fillna(0)
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# Convert DataFrame to dictionary
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result = workout_counts_final.reset_index().to_dict('records')
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# Reformat the dictionary to desired structure
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formatted_result = {}
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for record in result:
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person_id = record.pop('PersonId')
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person_name = record.pop('PersonName')
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pr_counts = {k: v for k, v in record.items()}
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formatted_result[person_id] = {'PersonName': person_name, 'PRCounts': pr_counts}
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return formatted_result
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def count_prs_over_time(self, workouts, period='week'):
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df = pd.DataFrame(workouts)
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# Convert 'StartDate' to datetime
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df['StartDate'] = pd.to_datetime(df['StartDate'])
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# Set period as week or month
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df['Period'] = df['StartDate'].dt.to_period('W' if period == 'week' else 'M')
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# Group by Person, Exercise, and Period to find max Estimated1RM in each period
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period_max = df.groupby(['PersonId', 'ExerciseId', 'Period'])['Estimated1RM'].max().reset_index()
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# Determine all-time max Estimated1RM up to the start of each period
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period_max['AllTimeMax'] = period_max.groupby(['PersonId', 'ExerciseId'])['Estimated1RM'].cummax().shift(1)
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# Identify PRs as entries where the period's max Estimated1RM exceeds the all-time max
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period_max['IsPR'] = period_max['Estimated1RM'] > period_max['AllTimeMax']
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# Count PRs in each period for each person
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pr_counts = period_max.groupby(['PersonId', 'Period'])['IsPR'].sum().reset_index()
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# Convert 'Period' to timestamp using the start date of the period
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pr_counts['Period'] = pr_counts['Period'].apply(lambda x: x.start_time)
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# Pivot table to get the desired output format
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output = pr_counts.pivot(index='PersonId', columns='Period', values='IsPR').fillna(0)
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# Convert only the PR count columns to integers
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for col in output.columns:
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output[col] = output[col].astype(int)
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# Merge with names and convert to desired format
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names = df[['PersonId', 'PersonName']].drop_duplicates().set_index('PersonId')
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output = names.join(output, how='left').fillna(0)
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# Reset the index to bring 'PersonId' back as a column
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output.reset_index(inplace=True)
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# Convert to the final dictionary format with PRCounts nested
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result = {}
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for index, row in output.iterrows():
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person_id = row['PersonId']
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person_name = row['PersonName']
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pr_counts = row.drop(['PersonId', 'PersonName']).to_dict()
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result[person_id] = {"PersonName": person_name, "PRCounts": pr_counts}
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return result
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