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Add exercise progress forecasting

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Peter Stockings
2026-09-06 13:53:23 +10:00
parent d1100ece80
commit e2c4196792
4 changed files with 251 additions and 36 deletions
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app.py
+13 -1
View File
@@ -241,11 +241,23 @@ def get_exercise_progress_for_user(person_id, exercise_id):
'max_date'), '%Y-%m-%d')
epoch = request.args.get('epoch', default='All')
degree = request.args.get('degree', type=int, default=1)
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))
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)
exercise_progress = db.get_exercise_progress_for_user(person_id, exercise_id, min_date, max_date, epoch, degree=degree)
exercise_progress = db.get_exercise_progress_for_user(
person_id,
exercise_id,
min_date,
max_date,
epoch,
degree=degree,
forecast_enabled=forecast_enabled,
forecast_weeks=forecast_weeks
)
if not exercise_progress:
abort(404)
db.py
+6 -4
View File
@@ -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):
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):
today = datetime.now()
if epoch == '1M':
min_date = today - relativedelta(months=1)
@@ -463,9 +463,11 @@ class DataBase():
epoch,
person_id,
exercise_id,
min_date,
max_date,
degree)
min_date,
max_date,
degree,
forecast_enabled,
forecast_weeks)
exercise_progress['latest_topset_id'] = latest_topset_id
exercise_progress['latest_workout_id'] = latest_workout_id
templates/partials/sparkline.html
+73 -15
View File
@@ -9,13 +9,20 @@
{% endfor %}
{% endmacro %}
{% macro path_best_fit(best_fit_points, vb_height) %}
{% macro path_best_fit(best_fit_points, vb_height) %}
{% for value, position in best_fit_points %}
{% set x = (position * vb_width)+margin %}
{% set y = (vb_height - value)+margin %}
{% if loop.first %}M{{ x | int }} {{ y | int }}{% else %} L{{ x | int }} {{ y | int }}{% endif %}
{% endfor %}
{% endmacro %}
{% endmacro %}
{% macro path_band(lower_points, upper_points, vb_height) %}
{% if lower_points and upper_points %}
M{% for value, position in lower_points %}{{ (position * vb_width)+margin | int }} {{ (vb_height - value)+margin | int }}{% if not loop.last %} L{% endif %}{% endfor %}
{% for value, position in upper_points|reverse %} L{{ (position * vb_width)+margin | int }} {{ (vb_height - value)+margin | int }}{% endfor %} Z
{% endif %}
{% endmacro %}
{% macro circles(data_points, color) %}
{% for value, position in data_points %}
@@ -46,14 +53,14 @@
<div id="popover-{{ unique_id }}" class="absolute t-0 r-0 hidden bg-white border border-gray-300 p-2 z-10">
<!-- Popover content will be dynamically inserted here -->
</div>
<div class="flex items-center justify-between sm:justify-center relative mb-1">
<div class="flex items-center justify-between sm:justify-center relative mb-1">
<div class="flex flex-col sm:flex-row items-center justify-center w-full gap-x-2">
<h4 class="text-lg font-semibold text-blue-400">{{ title }}</h4>
<div hx-get="{{ url_for('workout.get_topset_achievements', person_id=person_id, workout_id=latest_workout_id, topset_id=latest_topset_id) }}"
hx-trigger="load" hx-target="this" hx-swap="innerHTML" class="flex items-center">
</div>
</div>
<div class="absolute left-0 z-10">
<div class="absolute left-0 z-10">
<button
class="inline-flex justify-center p-1 text-gray-500 rounded cursor-pointer hover:text-gray-900 hover:bg-gray-100 dark:text-gray-400 dark:hover:text-white dark:hover:bg-gray-600"
title="Show History"
@@ -65,15 +72,26 @@
d="M4 6h16M4 10h16M4 14h16M4 18h16"></path>
</svg>
<span class="sr-only">Show History</span>
</button>
</div>
</button>
</div>
<div class="absolute right-0 z-10">
<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-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>
{% if forecast_enabled %}Forecast on{% else %}Forecast{% endif %}
</button>
</div>
</div>
<h2 class="text-xs font-semibold text-blue-200 mb-1 text-center">
{% if best_fit_formula %}
{{ best_fit_formula.kg_per_week }} kg/week, {{ best_fit_formula.kg_per_month }} kg/month
{% endif %}
</h2>
<div class="inline-flex rounded-md shadow-sm w-full items-center justify-center mb-1">
<div class="inline-flex rounded-md shadow-sm w-full items-center justify-center mb-1">
{% for epoch in epochs %}
<div {% if selected_epoch==epoch %}
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"
@@ -92,8 +110,36 @@
{% endif %}
</div>
{% endfor %}
</div>
{% if selected_epoch == 'Custom' %}
</div>
{% if forecast_enabled %}
<div class="mx-2 mb-3 rounded-xl border {% if forecast.available %}border-emerald-100 bg-emerald-50/70{% else %}border-amber-100 bg-amber-50/70{% endif %} p-3 text-left">
{% if forecast.available %}
<div class="flex flex-col sm:flex-row sm:items-center sm:justify-between gap-2">
<div>
<div class="text-xs font-semibold uppercase tracking-wide text-emerald-800">E1RM projection</div>
<div class="text-sm text-gray-700 mt-0.5">{{ forecast.last_value }}kg now <span class="text-gray-400">→</span> <span class="font-bold text-emerald-800">{{ forecast.projected_value }}kg</span> in {{ forecast.horizon_weeks }} weeks</div>
</div>
<div class="text-left sm:text-right">
<div class="text-sm font-semibold {% if forecast.weekly_change >= 0 %}text-emerald-700{% else %}text-rose-700{% endif %}">{% if forecast.weekly_change >= 0 %}+{% endif %}{{ forecast.weekly_change }}kg/week</div>
<div class="text-xs text-gray-500">{{ forecast.confidence }} confidence · {{ forecast.observed_points }} sessions</div>
</div>
</div>
<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-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 %}
</div>
{% else %}
<div class="text-sm font-medium text-amber-800">Forecast unavailable</div>
<div class="text-xs text-amber-700 mt-1">{{ forecast.reason }}</div>
{% endif %}
</div>
{% endif %}
{% if selected_epoch == 'Custom' %}
<div class="flex flex-col md:flex-row justify-center pb-4">
<!-- Min Date -->
<div class="w-full md:w-1/3 px-2 md:px-3 mb-6 md:mb-0">
@@ -176,8 +222,14 @@
</div>
{% endif %}
<svg viewBox="0 0 {{ (vb_width + 2*margin) | int }} {{ (vb_height + 2*margin) | int }}" preserveAspectRatio="none">
{% for plot in plots %}
<svg viewBox="0 0 {{ (vb_width + 2*margin) | int }} {{ (vb_height + 2*margin) | int }}" preserveAspectRatio="none">
{% if forecast_enabled and forecast.available %}
<path d="{{ path_band(forecast_band_lower, forecast_band_upper, vb_height) }}" fill="#10b981" fill-opacity="0.14" stroke="none"></path>
<path d="{{ path(forecast_points, vb_height) }}" stroke="#059669" stroke-width="1.5" stroke-dasharray="4,3" fill="none"></path>
{{ circles(forecast_points, '#059669') }}
<line x1="{{ (forecast_start_position * vb_width)+margin | int }}" y1="{{ margin }}" x2="{{ (forecast_start_position * vb_width)+margin | int }}" y2="{{ vb_height + margin }}" stroke="#059669" stroke-width="0.8" stroke-dasharray="2,2" stroke-opacity="0.7"></line>
{% endif %}
{% for plot in plots %}
<g class="{{ plot.label }}" style="fill: {{ plot.color }}; stroke: {{ plot.color }};">
{{ plot_line(plot.points, plot.color) }}
</g>
@@ -198,13 +250,19 @@
stroke-opacity="60%" />
</svg>
<div class="flex justify-center pt-2">
{% for plot in plots %}
{% for plot in plots %}
<div class="flex items-center px-2 select-none cursor-pointer" _="on load put document.querySelector('#svg-plot-{{ unique_id }} g.{{plot.label}}') into my.plot_line
on click toggle .hidden on my.plot_line then toggle .line-through on me">
<div class="w-3 h-3 mr-1" style="background-color: {{ plot.color }};"></div>
<div class="text-xs">{{ plot.label }}</div>
</div>
{% endfor %}
</div>
{% endfor %}
{% if forecast_enabled and forecast.available %}
<div class="flex items-center px-2 select-none">
<div class="w-3 h-0.5 mr-1 border-t-2 border-dashed border-emerald-600"></div>
<div class="text-xs">Projected E1RM</div>
</div>
{% endif %}
</div>
</div>
</div>
utils.py
+159 -16
View File
@@ -15,12 +15,131 @@ def convert_str_to_date(date_str, format='%Y-%m-%d'):
except TypeError:
return None
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):
# Precompute ranges
min_date, max_date = min(start_dates), max(start_dates)
total_span = (max_date - min_date).days or 1
min_e1rm, max_e1rm = min(estimated_1rm), max(estimated_1rm)
def build_forecast(values, dates, horizon_weeks=8, metric='E1RM'):
"""Build a conservative linear forecast from dated exercise observations."""
try:
horizon_weeks = max(4, min(int(horizon_weeks), 26))
except (TypeError, ValueError):
horizon_weeks = 8
observations = []
for value, point_date in zip(values, dates):
if value is None or point_date is None:
continue
try:
numeric_value = float(value)
except (TypeError, ValueError):
continue
if isinstance(point_date, datetime):
point_date = point_date.date()
observations.append((point_date, numeric_value))
# Multiple sets on one date should not make the forecast look more certain.
# Use the best observed value for that date, which matches the E1RM use case.
best_by_date = {}
for point_date, numeric_value in observations:
best_by_date[point_date] = max(best_by_date.get(point_date, numeric_value), numeric_value)
observations = sorted(best_by_date.items())
if len(observations) < 4:
return {
'available': False,
'reason': 'Add at least 4 sessions to generate a forecast.',
'metric': metric,
'horizon_weeks': horizon_weeks,
'observed_points': len(observations)
}
observed_dates = [point_date for point_date, _ in observations]
observed_values = np.array([value for _, value in observations], dtype=float)
first_date = observed_dates[0]
last_date = observed_dates[-1]
x = np.array([(point_date - first_date).days for point_date in observed_dates], dtype=float)
if len(np.unique(x)) < 3 or x[-1] <= 0:
return {
'available': False,
'reason': 'Use at least 3 different workout dates to generate a forecast.',
'metric': metric,
'horizon_weeks': horizon_weeks,
'observed_points': len(observations)
}
try:
slope, intercept = np.polyfit(x, observed_values, 1)
except (np.linalg.LinAlgError, ValueError, TypeError):
return {
'available': False,
'reason': 'There is not enough consistent data to generate a forecast.',
'metric': metric,
'horizon_weeks': horizon_weeks,
'observed_points': len(observations)
}
fitted = intercept + slope * x
residuals = observed_values - fitted
degrees_of_freedom = max(len(observed_values) - 2, 1)
residual_std = float(np.sqrt(np.sum(residuals ** 2) / degrees_of_freedom))
mean_x = float(np.mean(x))
sum_squared_x = float(np.sum((x - mean_x) ** 2)) or 1.0
horizon_days = horizon_weeks * 7
future_x = np.arange(0, horizon_days + 1, 7, dtype=float) + x[-1]
future_dates = [last_date + timedelta(days=int(day - x[-1])) for day in future_x]
future_values = intercept + slope * future_x
# A 95% prediction band, widened for points further from the observations.
prediction_error = 1.96 * residual_std * np.sqrt(
1 + (1 / len(observed_values)) + ((future_x - mean_x) ** 2 / sum_squared_x)
)
lower_values = np.maximum(future_values - prediction_error, 0)
upper_values = future_values + prediction_error
total_variation = float(np.sum((observed_values - np.mean(observed_values)) ** 2))
residual_variation = float(np.sum(residuals ** 2))
r_squared = None if total_variation == 0 else max(0.0, 1 - residual_variation / total_variation)
weekly_change = float(slope * 7)
confidence = 'High' if len(observed_values) >= 8 and (r_squared is None or r_squared >= 0.6) else 'Moderate'
if len(observed_values) < 6 or (r_squared is not None and r_squared < 0.25):
confidence = 'Low'
points = []
for point_date, predicted, lower, upper in zip(future_dates, future_values, lower_values, upper_values):
points.append({
'date': point_date,
'value': round(float(predicted), 1),
'lower': round(float(lower), 1),
'upper': round(float(upper), 1)
})
return {
'available': True,
'metric': metric,
'horizon_weeks': horizon_weeks,
'observed_points': len(observations),
'last_date': last_date,
'last_value': round(float(observed_values[-1]), 1),
'projected_value': round(float(future_values[-1]), 1),
'weekly_change': round(weekly_change, 1),
'r_squared': None if r_squared is None else round(r_squared, 2),
'confidence': confidence,
'end_date': future_dates[-1],
'points': points
}
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):
# 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}
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
e1rm_values_for_scale = list(estimated_1rm)
if forecast.get('available'):
e1rm_values_for_scale.extend([point['lower'] for point in forecast['points']])
e1rm_values_for_scale.extend([point['upper'] for point in forecast['points']])
min_e1rm, max_e1rm = min(e1rm_values_for_scale), max(e1rm_values_for_scale)
min_reps, max_reps = min(repetitions), max(repetitions)
min_weight, max_weight = min(weight), max(weight)
@@ -34,17 +153,22 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
vb_height *= 75 / vb_height # Scale to 75px height
# Use NumPy arrays for efficient scaling
relative_positions = np.round(np.array([(date - min_date).days / total_span for date in start_dates]), 1)
relative_positions = np.round(np.array([(date - observed_min_date).days / total_span for date in start_dates]), 3)
estimated_1rm_scaled = np.round(((np.array(estimated_1rm) - min_e1rm) / e1rm_range) * vb_height, 1)
repetitions_scaled = np.round(((np.array(repetitions) - min_reps) / reps_range) * vb_height, 1)
weight_scaled = np.round(((np.array(weight) - min_weight) / weight_range) * vb_height, 1)
# Calculate slope and line of best fit
slope_kg_per_day = e1rm_range / total_span
best_fit_formula = {
'kg_per_week': round(slope_kg_per_day * 7, 1),
'kg_per_month': round(slope_kg_per_day * 30, 1)
}
best_fit_formula = {
'kg_per_week': round(slope_kg_per_day * 7, 1),
'kg_per_month': round(slope_kg_per_day * 30, 1)
}
if forecast.get('available'):
best_fit_formula = {
'kg_per_week': forecast['weekly_change'],
'kg_per_month': round(forecast['weekly_change'] * 4.345, 1)
}
best_fit_points = []
try:
@@ -83,7 +207,19 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
}
# Prepare plot labels
plot_labels = list(zip(relative_positions.tolist(), messages))
plot_labels = list(zip(relative_positions.tolist(), messages))
forecast_points = []
forecast_band_lower = []
forecast_band_upper = []
forecast_start_position = None
if forecast.get('available'):
forecast_start_position = round((observed_max_date - observed_min_date).days / total_span, 3)
for point in forecast['points']:
position = round((point['date'] - observed_min_date).days / total_span, 3)
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))
# Return exercise data with SVG dimensions and data points
return {
@@ -98,10 +234,17 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
'selected_epoch': epoch,
'person_id': person_id,
'exercise_id': exercise_id,
'min_date': min_date,
'max_date': max_date,
'degree': degree
}
'min_date': observed_min_date,
'max_date': observed_max_date,
'degree': degree,
'forecast': forecast,
'forecast_enabled': forecast_enabled,
'forecast_weeks': forecast_weeks,
'forecast_points': forecast_points,
'forecast_band_lower': forecast_band_lower,
'forecast_band_upper': forecast_band_upper,
'forecast_start_position': forecast_start_position
}
def get_distinct_colors(n):
colors = []