{% for plot in plots %}
diff --git a/utils.py b/utils.py
index aa3d728..51fde2f 100644
--- a/utils.py
+++ b/utils.py
@@ -9,6 +9,14 @@ from decimal import Decimal
E1RM_BASE = 101.3
E1RM_REP_FACTOR = 2.67123
+
+
+def _format_kg(value):
+ """Format a load without noisy trailing zeroes."""
+ numeric_value = float(value)
+ if numeric_value.is_integer():
+ return str(int(numeric_value))
+ return f"{numeric_value:.1f}".rstrip('0').rstrip('.')
def convert_str_to_date(date_str, format='%Y-%m-%d'):
try:
@@ -162,6 +170,22 @@ def _round_weight(value, increment, direction='nearest'):
return round(rounded, 2)
+def infer_rep_targets(repetitions, buffer=5):
+ """Return an expanded rep range based on the currently visible history."""
+ numeric_reps = sorted({int(round(float(value))) for value in repetitions if value is not None})
+ if not numeric_reps:
+ return [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 5, None, None
+
+ observed_min = min(numeric_reps)
+ observed_max = max(numeric_reps)
+ target_min = max(1, observed_min - buffer)
+ target_max = min(20, observed_max + buffer)
+ target_reps = list(range(target_min, target_max + 1))
+ centered_reps = int(round(float(np.median(numeric_reps))))
+ centered_reps = max(target_min, min(target_max, centered_reps))
+ return target_reps, centered_reps, observed_min, observed_max
+
+
def _top_set_estimates(e1rm, lower_e1rm, upper_e1rm, target_reps, increment):
estimates = []
for repetitions in target_reps:
@@ -171,29 +195,33 @@ def _top_set_estimates(e1rm, lower_e1rm, upper_e1rm, target_reps, increment):
expected_weight = e1rm * conversion
low_weight = lower_e1rm * conversion
high_weight = upper_e1rm * conversion
+ expected = _round_weight(expected_weight, increment)
+ lower = _round_weight(low_weight, increment, 'floor')
+ upper = _round_weight(high_weight, increment, 'ceil')
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')
+ 'weight': expected,
+ 'weight_display': _format_kg(expected),
+ 'lower_weight': lower,
+ 'lower_weight_display': _format_kg(lower),
+ 'upper_weight': upper,
+ 'upper_weight_display': _format_kg(upper),
+ 'e1rm_display': _format_kg(e1rm)
})
return estimates
-def add_top_set_forecast(forecast, historical_weights, target_reps=5):
+def add_top_set_forecast(forecast, historical_weights, historical_repetitions, target_reps=None):
"""Attach projected reps x weight combinations to each forecast point."""
if not forecast.get('available'):
return forecast
+ target_reps_list, centered_reps, observed_rep_min, observed_rep_max = infer_rep_targets(historical_repetitions)
try:
- selected_reps = max(1, min(int(target_reps), 12))
+ selected_reps = centered_reps if target_reps is None else int(target_reps)
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()
-
+ selected_reps = centered_reps
+ selected_reps = max(target_reps_list[0], min(target_reps_list[-1], selected_reps))
increment = infer_weight_increment(historical_weights)
for point in forecast['points']:
point['top_sets'] = _top_set_estimates(
@@ -202,6 +230,10 @@ def add_top_set_forecast(forecast, historical_weights, target_reps=5):
forecast['target_reps'] = target_reps_list
forecast['target_weight_increment'] = increment
+ forecast['target_weight_increment_display'] = _format_kg(increment)
+ forecast['observed_rep_min'] = observed_rep_min
+ forecast['observed_rep_max'] = observed_rep_max
+ forecast['rep_buffer'] = 5
forecast['top_sets'] = forecast['points'][-1]['top_sets']
forecast['selected_reps'] = selected_reps
forecast['selected_top_set'] = next(
@@ -211,12 +243,12 @@ def add_top_set_forecast(forecast, historical_weights, target_reps=5):
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):
+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=None):
# 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)
+ forecast = add_top_set_forecast(forecast, weight, repetitions, 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
@@ -294,11 +326,24 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
# Prepare plot labels
plot_labels = list(zip(relative_positions.tolist(), messages))
+ plot_details = []
+ for position, point_date, point_e1rm, point_reps, point_weight in zip(
+ relative_positions.tolist(), start_dates, estimated_1rm, repetitions, weight
+ ):
+ plot_details.append({
+ 'position': position,
+ 'date': point_date.strftime('%d %b %y'),
+ 'kind': 'Recorded top set',
+ 'e1rm': f"{_format_kg(point_e1rm)}kg",
+ 'top_set': f"{_format_kg(point_weight)}kg × {int(point_reps)} reps",
+ 'range': 'Recorded value'
+ })
forecast_points = []
forecast_band_lower = []
forecast_band_upper = []
forecast_labels = []
+ forecast_details = []
forecast_start_position = None
if forecast.get('available'):
forecast_start_position = round((observed_max_date - observed_min_date).days / total_span, 3)
@@ -314,9 +359,18 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
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)"
+ f" · Est. {selected_top_set['weight_display']}kg × {selected_top_set['repetitions']} "
+ f"(range {selected_top_set['lower_weight_display']}–{selected_top_set['upper_weight_display']}kg)"
)
+ forecast_details.append({
+ 'position': position,
+ 'date': point['date'].strftime('%d %b %y'),
+ 'kind': 'Projected top set',
+ 'e1rm': f"{_format_kg(point['value'])}kg",
+ 'top_set': f"{selected_top_set['weight_display']}kg × {selected_top_set['repetitions']} reps",
+ 'range': f"{selected_top_set['lower_weight_display']}–{selected_top_set['upper_weight_display']}kg",
+ 'confidence': forecast['confidence']
+ })
forecast_labels.append((
position,
f"{point['date'].strftime('%d %b %y')} · Projected E1RM: {point['value']}kg "
@@ -331,7 +385,8 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
'plots': [repetitions_data, weight_data, estimated_1rm_data],
'best_fit_points': best_fit_points,
'best_fit_formula': best_fit_formula,
- 'plot_labels': plot_labels,
+ 'plot_labels': plot_labels,
+ 'plot_details': plot_details,
'epochs': ['Custom', '1M', '3M', '6M', 'All'],
'selected_epoch': epoch,
'person_id': person_id,
@@ -342,11 +397,12 @@ 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_target_reps': forecast.get('selected_reps', forecast_target_reps or 5),
'forecast_points': forecast_points,
'forecast_band_lower': forecast_band_lower,
'forecast_band_upper': forecast_band_upper,
'forecast_labels': forecast_labels,
+ 'forecast_details': forecast_details,
'forecast_start_position': forecast_start_position
}