Add projected top set estimates

app.py

@@ -244,6 +244,8 @@ def get_exercise_progress_for_user(person_id, exercise_id):
     forecast_enabled = request.args.get('forecast', '').lower() in ('1', 'true', 'yes', 'on')
     forecast_weeks = request.args.get('forecast_weeks', type=int, default=8)
     forecast_weeks = max(4, min(forecast_weeks, 26))
+    forecast_target_reps = request.args.get('forecast_reps', type=int, default=5)
+    forecast_target_reps = max(1, min(forecast_target_reps, 12))
 
     if epoch == 'Custom' and (min_date is None or max_date is None):
         (min_date, max_date) = db.get_exercise_earliest_and_latest_dates(person_id, exercise_id)
@@ -256,7 +258,8 @@ def get_exercise_progress_for_user(person_id, exercise_id):
         epoch,
         degree=degree,
         forecast_enabled=forecast_enabled,
-        forecast_weeks=forecast_weeks
+        forecast_weeks=forecast_weeks,
+        forecast_target_reps=forecast_target_reps
     )
 
     if not exercise_progress:

db.py

@@ -405,7 +405,7 @@ class DataBase():
     def get_all_exercises(self):
         return self.exercises.get("")
     
-    def get_exercise_progress_for_user(self, person_id, exercise_id, min_date=None, max_date=None, epoch='all', degree=1, forecast_enabled=False, forecast_weeks=8):
+    def get_exercise_progress_for_user(self, person_id, exercise_id, min_date=None, max_date=None, epoch='all', degree=1, forecast_enabled=False, forecast_weeks=8, forecast_target_reps=5):
         today = datetime.now()
         if epoch == '1M':
             min_date = today - relativedelta(months=1)
@@ -467,7 +467,8 @@ class DataBase():
             max_date,
             degree,
             forecast_enabled,
-            forecast_weeks)
+            forecast_weeks,
+            forecast_target_reps)
         
         exercise_progress['latest_topset_id'] = latest_topset_id
         exercise_progress['latest_workout_id'] = latest_workout_id

templates/partials/sparkline.html

@@ -87,7 +87,7 @@ M{% for value, position in lower_points %}{{ (((position * vb_width) + margin) |
             <button
                 class="inline-flex items-center gap-1 px-2 py-1 text-xs font-medium rounded-lg border transition-colors {% if forecast_enabled %}text-emerald-700 bg-emerald-50 border-emerald-200 hover:bg-emerald-100{% else %}text-gray-600 bg-white border-gray-300 hover:bg-gray-50{% endif %}"
                 title="{% if forecast_enabled %}Hide forecast{% else %}Show forecast{% endif %}"
-                hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id, epoch=selected_epoch, forecast='false' if forecast_enabled else 'true', forecast_weeks=forecast_weeks) }}"
+                hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id, epoch=selected_epoch, forecast='false' if forecast_enabled else 'true', forecast_weeks=forecast_weeks, forecast_reps=forecast_target_reps) }}"
                 hx-include="#svg-plot-{{ unique_id }} [name='min_date'], #svg-plot-{{ unique_id }} [name='max_date'], #svg-plot-{{ unique_id }} [name='degree']"
                 hx-target="#svg-plot-{{ unique_id }}" hx-swap="outerHTML">
                 <svg class="w-3.5 h-3.5" fill="none" viewBox="0 0 24 24" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M13 7h8m0 0v8m0-8l-8 8-4-4-6 6" /></svg>
@@ -106,7 +106,7 @@ M{% for value, position in lower_points %}{{ (((position * vb_width) + margin) |
             class="px-4 py-2 text-sm font-medium text-blue-700 bg-white border border-gray-200 rounded-s-lg hover:bg-gray-100 focus:z-10 focus:ring-2 focus:ring-blue-700 focus:text-blue-700 dark:bg-gray-800 dark:border-gray-700 dark:text-white dark:hover:text-white dark:hover:bg-gray-700 dark:focus:ring-blue-500 dark:focus:text-white"
             {% else %}
             class="px-4 py-2 text-sm font-medium text-gray-900 bg-white border border-gray-200 rounded-e-lg hover:bg-gray-100 hover:text-blue-700 focus:z-10 focus:ring-2 focus:ring-blue-700 focus:text-blue-700 dark:bg-gray-800 dark:border-gray-700 dark:text-white dark:hover:text-white dark:hover:bg-gray-700 dark:focus:ring-blue-500 dark:focus:text-white cursor-pointer"
-            hx-get='{{ url_for("get_exercise_progress_for_user", person_id=person_id,  exercise_id=exercise_id, epoch=epoch) }}'
+            hx-get='{{ url_for("get_exercise_progress_for_user", person_id=person_id,  exercise_id=exercise_id, epoch=epoch, forecast="true" if forecast_enabled else "false", forecast_weeks=forecast_weeks, forecast_reps=forecast_target_reps) }}'
             hx-target="#svg-plot-{{ unique_id }}" hx-swap="outerHTML" hx-trigger="click" {% endif %}>
             {% if epoch == 'Custom' %}
             <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor"
@@ -133,11 +133,35 @@ M{% for value, position in lower_points %}{{ (((position * vb_width) + margin) |
                 <div class="text-xs text-gray-500">{{ forecast.confidence }} confidence · {{ forecast.observed_points }} sessions</div>
             </div>
         </div>
+        {% if forecast.selected_top_set %}
+        <div class="mt-3 rounded-lg border border-emerald-100 bg-white/80 px-3 py-2.5">
+            <div class="flex items-center justify-between gap-2">
+                <div>
+                    <div class="text-xs font-semibold uppercase tracking-wide text-gray-500">Estimated top set</div>
+                    <div class="text-lg font-bold text-gray-800">{{ forecast.selected_top_set.weight }}kg × {{ forecast.selected_top_set.repetitions }}</div>
+                </div>
+                <div class="text-right text-xs text-gray-500">
+                    <div>Likely range</div>
+                    <div class="font-semibold text-emerald-700">{{ forecast.selected_top_set.lower_weight }}–{{ forecast.selected_top_set.upper_weight }}kg</div>
+                </div>
+            </div>
+            <div class="flex flex-wrap items-center gap-1.5 mt-2">
+                <span class="text-xs text-gray-400 mr-1">Target reps</span>
+                {% for reps in forecast.target_reps %}
+                <button class="px-2 py-0.5 rounded text-xs {% if forecast.selected_reps == reps %}bg-emerald-700 text-white{% else %}bg-emerald-50 text-emerald-700 hover:bg-emerald-100{% endif %}"
+                    hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id, epoch=selected_epoch, forecast='true', forecast_weeks=forecast_weeks, forecast_reps=reps) }}"
+                    hx-include="#svg-plot-{{ unique_id }} [name='min_date'], #svg-plot-{{ unique_id }} [name='max_date'], #svg-plot-{{ unique_id }} [name='degree']"
+                    hx-target="#svg-plot-{{ unique_id }}" hx-swap="outerHTML">{{ reps }}</button>
+                {% endfor %}
+            </div>
+            <div class="text-[11px] text-gray-400 mt-2">Rounded to the nearest {{ forecast.target_weight_increment }}kg based on your recorded loads.</div>
+        </div>
+        {% endif %}
         <div class="flex flex-wrap items-center gap-2 mt-3">
             <span class="text-xs text-gray-500 mr-1">Horizon</span>
             {% for weeks in [4, 8, 12, 26] %}
             <button class="px-2.5 py-1 rounded-md text-xs font-medium {% if forecast_weeks == weeks %}bg-emerald-700 text-white{% else %}bg-white text-gray-600 border border-emerald-200 hover:bg-emerald-100{% endif %}"
-                hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id, epoch=selected_epoch, forecast='true', forecast_weeks=weeks) }}"
+                hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id, epoch=selected_epoch, forecast='true', forecast_weeks=weeks, forecast_reps=forecast_target_reps) }}"
                 hx-include="#svg-plot-{{ unique_id }} [name='min_date'], #svg-plot-{{ unique_id }} [name='max_date'], #svg-plot-{{ unique_id }} [name='degree']"
                 hx-target="#svg-plot-{{ unique_id }}" hx-swap="outerHTML">{{ weeks }}w</button>
             {% endfor %}
@@ -169,7 +193,7 @@ M{% for value, position in lower_points %}{{ (((position * vb_width) + margin) |
                 <input type="date" class="bg-gray-50 border border-gray-300 text-gray-900 
                     text-sm rounded-lg focus:ring-blue-500 
                     focus:border-blue-500 block w-full pl-10 p-2.5" name="min_date" value="{{ min_date }}"
-                    hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id) }}"
+                    hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id, forecast='true' if forecast_enabled else 'false', forecast_weeks=forecast_weeks, forecast_reps=forecast_target_reps) }}"
                     hx-include="#svg-plot-{{ unique_id }} [name='max_date'], #svg-plot-{{ unique_id }} [name='degree']"
                     hx-vals='{"epoch": "Custom"}' hx-target="#svg-plot-{{ unique_id }}" hx-swap="outerHTML"
                     hx-trigger="change">
@@ -197,7 +221,7 @@ M{% for value, position in lower_points %}{{ (((position * vb_width) + margin) |
                         text-gray-900 text-sm rounded-lg 
                         focus:ring-blue-500 focus:border-blue-500 
                         block w-full pl-10 p-2.5" name="max_date" value="{{ max_date }}"
-                    hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id) }}"
+                    hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id, forecast='true' if forecast_enabled else 'false', forecast_weeks=forecast_weeks, forecast_reps=forecast_target_reps) }}"
                     hx-include="#svg-plot-{{ unique_id }} [name='min_date'], #svg-plot-{{ unique_id }} [name='degree']"
                     hx-vals='{"epoch": "Custom"}' hx-target="#svg-plot-{{ unique_id }}" hx-swap="outerHTML"
                     hx-trigger="change">
@@ -222,7 +246,7 @@ M{% for value, position in lower_points %}{{ (((position * vb_width) + margin) |
                 <input type="number"
                     class="bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full pl-10 p-2.5  w-full"
                     name="degree" value="{{ degree }}" min="1" step="1"
-                    hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id) }}"
+                    hx-get="{{ url_for('get_exercise_progress_for_user', person_id=person_id, exercise_id=exercise_id, forecast='true' if forecast_enabled else 'false', forecast_weeks=forecast_weeks, forecast_reps=forecast_target_reps) }}"
                     hx-include="#svg-plot-{{ unique_id }} [name='min_date'], #svg-plot-{{ unique_id }} [name='max_date']"
                     hx-vals='{"epoch": "Custom"}' hx-target="#svg-plot-{{ unique_id }}" hx-swap="outerHTML"
                     hx-trigger="change">

utils.py

@@ -7,6 +7,9 @@ import numpy as np
 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:
         return datetime.strptime(date_str, format).date()
@@ -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,
@@ -275,7 +368,7 @@ 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)