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Add projected top set estimates

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Peter Stockings
2026-09-06 14:12:48 +10:00
parent cada28b54b
commit 499ecdc3c2
4 changed files with 136 additions and 15 deletions
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utils.py
+99 -6
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@@ -4,8 +4,11 @@ from flask import request
import numpy as np
import math
from decimal import Decimal
import math
from decimal import Decimal
E1RM_BASE = 101.3
E1RM_REP_FACTOR = 2.67123
def convert_str_to_date(date_str, format='%Y-%m-%d'):
try:
@@ -130,11 +133,90 @@ def build_forecast(values, dates, horizon_weeks=8, metric='E1RM'):
}
def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_dates, messages, epoch, person_id, exercise_id, min_date=None, max_date=None, degree=1, forecast_enabled=False, forecast_weeks=8):
def infer_weight_increment(weights):
"""Infer a sensible display increment from the exercise's recorded weights."""
numeric_weights = sorted({round(float(weight), 4) for weight in weights if weight is not None})
differences = [
round(current - previous, 4)
for previous, current in zip(numeric_weights, numeric_weights[1:])
if current > previous
]
if not differences:
return 2.5
common_increments = [0.5, 1.0, 1.25, 2.0, 2.5, 5.0, 10.0]
smallest_difference = min(differences)
return min(common_increments, key=lambda increment: abs(increment - smallest_difference))
def _round_weight(value, increment, direction='nearest'):
if value is None:
return None
scaled = max(float(value), 0) / increment
if direction == 'floor':
rounded = math.floor(scaled) * increment
elif direction == 'ceil':
rounded = math.ceil(scaled) * increment
else:
rounded = round(scaled) * increment
return round(rounded, 2)
def _top_set_estimates(e1rm, lower_e1rm, upper_e1rm, target_reps, increment):
estimates = []
for repetitions in target_reps:
conversion = (E1RM_BASE - E1RM_REP_FACTOR * repetitions) / 100
if conversion <= 0:
continue
expected_weight = e1rm * conversion
low_weight = lower_e1rm * conversion
high_weight = upper_e1rm * conversion
estimates.append({
'repetitions': repetitions,
'weight': _round_weight(expected_weight, increment),
'lower_weight': _round_weight(low_weight, increment, 'floor'),
'upper_weight': _round_weight(high_weight, increment, 'ceil')
})
return estimates
def add_top_set_forecast(forecast, historical_weights, target_reps=5):
"""Attach projected reps x weight combinations to each forecast point."""
if not forecast.get('available'):
return forecast
try:
selected_reps = max(1, min(int(target_reps), 12))
except (TypeError, ValueError):
selected_reps = 5
target_reps_list = [3, 5, 8, 10]
if selected_reps not in target_reps_list:
target_reps_list.append(selected_reps)
target_reps_list.sort()
increment = infer_weight_increment(historical_weights)
for point in forecast['points']:
point['top_sets'] = _top_set_estimates(
point['value'], point['lower'], point['upper'], target_reps_list, increment
)
forecast['target_reps'] = target_reps_list
forecast['target_weight_increment'] = increment
forecast['top_sets'] = forecast['points'][-1]['top_sets']
forecast['selected_reps'] = selected_reps
forecast['selected_top_set'] = next(
(top_set for top_set in forecast['top_sets'] if top_set['repetitions'] == selected_reps),
None
)
return forecast
def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_dates, messages, epoch, person_id, exercise_id, min_date=None, max_date=None, degree=1, forecast_enabled=False, forecast_weeks=8, forecast_target_reps=5):
# Precompute ranges. Forecast values are included so the projected line and
# confidence band have room inside the same SVG coordinate system.
observed_min_date, observed_max_date = min(start_dates), max(start_dates)
forecast = build_forecast(estimated_1rm, start_dates, forecast_weeks, metric='E1RM') if forecast_enabled else {'available': False}
forecast = add_top_set_forecast(forecast, weight, forecast_target_reps)
chart_max_date = forecast.get('end_date', observed_max_date) if forecast.get('available') else observed_max_date
total_span = (chart_max_date - observed_min_date).days or 1
@@ -225,10 +307,20 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
forecast_points.append((round(((point['value'] - min_e1rm) / e1rm_range) * vb_height, 1), position))
forecast_band_lower.append((round(((point['lower'] - min_e1rm) / e1rm_range) * vb_height, 1), position))
forecast_band_upper.append((round(((point['upper'] - min_e1rm) / e1rm_range) * vb_height, 1), position))
selected_top_set = next(
(top_set for top_set in point['top_sets'] if top_set['repetitions'] == forecast['selected_reps']),
None
)
top_set_message = ''
if selected_top_set:
top_set_message = (
f" · Est. {selected_top_set['weight']}kg × {selected_top_set['repetitions']} "
f"(range {selected_top_set['lower_weight']}–{selected_top_set['upper_weight']}kg)"
)
forecast_labels.append((
position,
f"{point['date'].strftime('%d %b %y')} · Projected E1RM: {point['value']}kg "
f"(range {point['lower']}–{point['upper']}kg)"
f"(range {point['lower']}–{point['upper']}kg){top_set_message}"
))
# Return exercise data with SVG dimensions and data points
@@ -250,6 +342,7 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
'forecast': forecast,
'forecast_enabled': forecast_enabled,
'forecast_weeks': forecast_weeks,
'forecast_target_reps': forecast_target_reps,
'forecast_points': forecast_points,
'forecast_band_lower': forecast_band_lower,
'forecast_band_upper': forecast_band_upper,
@@ -271,11 +364,11 @@ def get_distinct_colors(n):
def calculate_estimated_1rm(weight, repetitions):
def calculate_estimated_1rm(weight, repetitions):
# Ensure the inputs are numeric
if repetitions == 0: # Avoid division by zero
return 0
estimated_1rm = round((100 * int(weight)) / (101.3 - 2.67123 * repetitions), 0)
estimated_1rm = round((100 * int(weight)) / (E1RM_BASE - E1RM_REP_FACTOR * repetitions), 0)
return int(estimated_1rm)