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
23
app.py
23
app.py
@@ -2,12 +2,11 @@ from datetime import datetime, date, timedelta
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from dateutil.relativedelta import relativedelta
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import os
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from flask import Flask, abort, render_template, redirect, request, url_for
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from jinja2 import Environment, FileSystemLoader, select_autoescape
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import jinja_partials
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from jinja2_fragments import render_block
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from decorators import validate_person, validate_topset, validate_workout
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from db import DataBase
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from utils import count_prs_over_time, get_people_and_exercise_rep_maxes, convert_str_to_date, get_earliest_and_latest_workout_date, filter_workout_topsets, first_and_last_visible_days_in_month, get_weekly_pr_graph_model, get_workout_counts, generate_plot
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from utils import get_people_and_exercise_rep_maxes, convert_str_to_date, get_earliest_and_latest_workout_date, filter_workout_topsets, first_and_last_visible_days_in_month, generate_plot
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from flask_htmx import HTMX
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import minify_html
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from urllib.parse import quote
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@@ -66,13 +65,9 @@ def dashboard():
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people_and_exercise_rep_maxes = get_people_and_exercise_rep_maxes(
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all_topsets, selected_person_ids, selected_exercise_ids, min_date, max_date)
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weekly_counts = get_workout_counts(all_topsets, 'week')
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weekly_pr_counts = count_prs_over_time(all_topsets, 'week')
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dashboard_graphs = [get_weekly_pr_graph_model('Workouts per week', weekly_counts), get_weekly_pr_graph_model('PRs per week', weekly_pr_counts)]
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if htmx:
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return render_block(app.jinja_env, 'dashboard.html', 'content', model=people_and_exercise_rep_maxes, people=people, exercises=exercises, min_date=min_date, max_date=max_date, selected_person_ids=selected_person_ids, selected_exercise_ids=selected_exercise_ids, tags=tags, dashboard_graphs=dashboard_graphs)
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return render_template('dashboard.html', model=people_and_exercise_rep_maxes, people=people, exercises=exercises, min_date=min_date, max_date=max_date, selected_person_ids=selected_person_ids, selected_exercise_ids=selected_exercise_ids, tags=tags, dashboard_graphs=dashboard_graphs)
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return render_block(app.jinja_env, 'dashboard.html', 'content', model=people_and_exercise_rep_maxes, people=people, exercises=exercises, min_date=min_date, max_date=max_date, selected_person_ids=selected_person_ids, selected_exercise_ids=selected_exercise_ids, tags=tags)
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return render_template('dashboard.html', model=people_and_exercise_rep_maxes, people=people, exercises=exercises, min_date=min_date, max_date=max_date, selected_person_ids=selected_person_ids, selected_exercise_ids=selected_exercise_ids, tags=tags)
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@ app.route("/person/list", methods=['GET'])
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@@ -457,6 +452,18 @@ def get_stats():
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stats = db.stats.fetch_stats(selected_people_ids, min_date, max_date, selected_exercise_ids)
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return render_template('partials/stats.html', stats=stats, refresh_url=request.full_path)
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@app.route("/graphs", methods=['GET'])
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def get_people_graphs():
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selected_people_ids = request.args.getlist('person_id', type=int)
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min_date = request.args.get('min_date', type=convert_str_to_date)
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max_date = request.args.get('max_date', type=convert_str_to_date)
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selected_exercise_ids = request.args.getlist('exercise_id', type=int)
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graphs = db.people_graphs.get(selected_people_ids, min_date, max_date, selected_exercise_ids)
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return render_template('partials/people_graphs.html', graphs=graphs, refresh_url=request.full_path)
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@ app.route("/person/<int:person_id>/workout/<int:workout_id>", methods=['GET'])
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def show_workout(person_id, workout_id):
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view_model = db.workout.get(person_id, workout_id)
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12
db.py
12
db.py
@@ -1,6 +1,5 @@
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import os
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import psycopg2
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import numpy as np
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from psycopg2.extras import RealDictCursor
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from datetime import datetime
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from dateutil.relativedelta import relativedelta
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@@ -9,11 +8,12 @@ from flask import g
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import pandas as pd
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from features.calendar import Calendar
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from features.exercises import Exercises
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from features.people_graphs import PeopleGraphs
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from features.person_overview import PersonOverview
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from features.stats import Stats
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from features.workout import Workout
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from features.sql_explorer import SQLExplorer
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from utils import count_prs_over_time, get_all_exercises_from_topsets, get_exercise_graph_model, get_topsets_for_person, get_weekly_pr_graph_model, get_workout_counts, get_workouts
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from utils import get_all_exercises_from_topsets, get_exercise_graph_model, get_topsets_for_person, get_workouts
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class DataBase():
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@@ -24,6 +24,7 @@ class DataBase():
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self.exercises = Exercises(self.execute)
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self.sql_explorer = SQLExplorer(self.execute)
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self.person_overview = PersonOverview(self.execute)
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self.people_graphs = PeopleGraphs(self.execute)
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db_url = urlparse(os.environ['DATABASE_URL'])
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# if db_url is null then throw error
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if not db_url:
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@@ -207,17 +208,12 @@ class DataBase():
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LEFT JOIN Exercise E ON T.exercise_id=E.exercise_id
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WHERE P.person_id=%s""", [person_id])
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weekly_counts = get_workout_counts(topsets, 'week')
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weekly_pr_counts = count_prs_over_time(topsets, 'week')
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person_graphs = [get_weekly_pr_graph_model('Workouts per week', weekly_counts), get_weekly_pr_graph_model('PRs per week', weekly_pr_counts)]
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return {
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'PersonId': next((t['PersonId'] for t in topsets), -1),
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'PersonName': next((t['PersonName'] for t in topsets), 'Unknown'),
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'Exercises': get_all_exercises_from_topsets(topsets),
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'Workouts': get_workouts(topsets),
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'ExerciseProgressGraphs': get_topsets_for_person(topsets),
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'PersonGraphs': person_graphs
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'ExerciseProgressGraphs': get_topsets_for_person(topsets)
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}
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def get_workout(self, person_id, workout_id):
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184
features/people_graphs.py
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184
features/people_graphs.py
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@@ -0,0 +1,184 @@
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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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@@ -2,12 +2,9 @@
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{% block content %}
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<div class="w-full mb-4 grid grid-cols-1 xl:grid-cols-2 gap-4">
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{% for graph in dashboard_graphs %}
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<div class="bg-white shadow rounded-lg p-4 sm:p-6 xl:p-8">
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{{ render_partial('partials/svg_line_graph.html', **graph) }}
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</div>
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{% endfor %}
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<div class="hidden" hx-get="{{ url_for('get_people_graphs') }}"
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hx-include="[name='exercise_id'],[name='min_date'],[name='max_date'],[name='person_id']" hx-trigger="load"
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hx-target="this" hx-swap="outerHTML">
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</div>
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<div class="bg-white shadow rounded-lg pt-4 p-3 md:p-4 w-full mb-4">
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@@ -3,6 +3,10 @@
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{% block content %}
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<div class="flex flex-grow flex-col bg-white sm:rounded shadow overflow-hidden">
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<div class="hidden" hx-get="{{ url_for('get_people_graphs') }}" hx-vals='{"person_id": "{{ person_id }}"}'
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hx-trigger="load" hx-target="this" hx-swap="outerHTML">
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</div>
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<div class="flex items-center justify-between pt-2 pb-2">
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<div class="flex">
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<h2 class="ml-2 text-xl font-bold leading-none">Notes</h2>
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8
templates/partials/people_graphs.html
Normal file
8
templates/partials/people_graphs.html
Normal file
@@ -0,0 +1,8 @@
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<div class="w-full mb-4 grid grid-cols-1 xl:grid-cols-2 gap-4" hx-get="{{ refresh_url }}" hx-target="this"
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hx-swap="outerHTML" hx-trigger="refreshStats from:body">
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{% for graph in graphs %}
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<div class="bg-white shadow rounded-lg p-4 sm:p-6 xl:p-8">
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{{ render_partial('partials/svg_line_graph.html', **graph) }}
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</div>
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{% endfor %}
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</div>
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@@ -65,7 +65,7 @@
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{% endfor %}
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</svg>
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{% if plots|length > 1 %}
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<div class="flex justify-center pt-2">
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{% for plot in plots %}
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<div class="flex items-center px-2 select-none cursor-pointer"
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@@ -76,6 +76,5 @@
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</div>
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{% endfor %}
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</div>
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{% endif %}
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</div>
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@@ -5,6 +5,11 @@
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<div class="flex max-w-full overflow-x-hidden">
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<div class="bg-white shadow rounded-lg pt-2 pb-2 sm:w-full xl:p-8 md:w-full">
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<div class="hidden" hx-get="{{ url_for('get_people_graphs') }}"
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hx-include="[name='exercise_id'],[name='min_date'],[name='max_date']"
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hx-vals='{"person_id": "{{ person_id }}"}' hx-trigger="load" hx-target="this" hx-swap="outerHTML">
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</div>
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<div class="mb-4 flex items-center justify-between px-2 md:px-3">
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<div>
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<h3 class="text-xl font-bold text-gray-900 mb-2">{{ person_name }}</h3>
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128
utils.py
128
utils.py
@@ -260,134 +260,6 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
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'max_date': max_date
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}
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def get_workout_counts(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
|
||||
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):
|
||||
|
||||
Reference in New Issue
Block a user