def test_fetch_program_from_model():
model_path = os.path.join(os.path.dirname(__file__), "models",
"ogasawara_HL.sav")
model = Model(model_path)
program = fetch_program_from_model(model)
assert program is not None
assert len(program) == 18
# Assertions for program rows structure
for _day, sets in program.items():
for p_set in sets:
assert isinstance(p_set.exercise, int)
assert isinstance(p_set.percent_1rm, float)
assert isinstance(p_set.repetitions, int)
# Assert all sets in same day actual is done on the same
# day.
previous_set_date = None
for p_set in sets:
if previous_set_date is None:
previous_set_date = p_set.datetime.date()
continue
today = p_set.datetime.date()
assert previous_set_date == today
def pbar(age, weight, performance, sex, hideprogram):
"""
Makes recommendation based on performance before the training program.
"""
if sex == 'MAN':
converted_sex = 0
elif sex == 'WOMAN':
converted_sex = 1
else:
converted_sex = 2
data = np.array([age, weight, converted_sex, performance]).reshape(1, -1)
recengine = RecommendationEngine("pbar")
best_pred, _ = recengine.recommend_training(data)
click.secho("\nTraining program: " + best_pred["model"].name, fg="green")
click.secho("Predicted performance: " +
str(best_pred["predicted_performance"]) + "\n",
fg="green")
hide_program_output = hideprogram
if hide_program_output is False:
program = fetch_program_from_model(best_pred["model"])
click.secho("Program structure: ", fg="green")
for day, sets in program.items():
click.secho("Day: " + str(day), fg="green")
for i, p_set in enumerate(sets):
calculated_weight = (p_set.percent_1rm / 100 * performance)
click.secho(" (Set " + str(i) + ") Weight: " +
str(calculated_weight) + " Reps: " +
str(p_set.repetitions))
def print_training_program_from_model(model, performance):
"""
Prints the training program related to the given model.
:param model: Model to find program from.
:param performance: Current performance of the individual.
"""
program = fetch_program_from_model(model)
click.secho("Program structure: ", fg="green")
for day in sorted(program.keys()):
sets = program[day]
click.secho("Day: " + str(day), fg="green")
for i, p_set in enumerate(sets):
calculated_weight = (p_set.percent_1rm / 100 * performance)
click.secho(" (Set " + str(i) + ") Weight: " +
str(calculated_weight) + ", Reps: " +
str(p_set.repetitions) + ", Rest: " + str(p_set.rest) +
" minutes.")
def formttr():
name = request.form.get("fname")
file = request.files["ffile"]
stream = io.StringIO(file.stream.read().decode("UTF8"), newline=None)
# TODO Let user input format.
ttrdata = ttrdata_from_csv_bytes(stream, "%m/%d/%Y %H:%M")
four_weeks_ttrdata = ttrdata[-8:]
data = np.array(four_weeks_ttrdata).reshape(1, -1)
recengine = RecommendationEngine("ttr")
best_pred, _ = recengine.recommend_training(data)
program = fetch_program_from_model(best_pred["model"])
return render_template(
"ttr.html",
best_pred=best_pred["model"].name,
predicted_performance=best_pred["predicted_performance"],
program=program,
performance=four_weeks_ttrdata[-1],
name=name)
def formpbar():
recengine = RecommendationEngine("pbar")
name = request.form.get("fname")
age = int(request.form.get("fage"))
sex = int(request.form.get("fsex"))
weight = float(request.form.get("fweight"))
performance = float(request.form.get("fperformance"))
data = np.array([age, weight, sex, performance]).reshape(1, -1)
best_pred, _ = recengine.recommend_training(data)
program = fetch_program_from_model(best_pred["model"])
return render_template(
"index.html",
age=age,
sex=sex,
weight=weight,
performance=performance,
best_pred=best_pred["model"].name,
predicted_performance=best_pred["predicted_performance"],
program=program,
name=name)
def ttr():
sets = request.json.get('sets', [])
time_format = request.json.get('timeformat', '')
weeks = split_into_weeks(sets, time_format)
if len(weeks.keys()) < 4:
return jsonify(error=400,
text="Sets spanning atleast four weeks is required.")
ttr_d = calculate_ttrdata_from_week_dict(weeks)
four_weeks_ttrdata = ttr_d[-8:]
data = np.array(four_weeks_ttrdata).reshape(1, -1)
recengine = RecommendationEngine("ttr")
best_pred, _ = recengine.recommend_training(data)
program = fetch_program_from_model(best_pred["model"])
res = {
"predicted_performance": best_pred["predicted_performance"],
"training_program": best_pred["model"].name,
"program_structure": program,
"calculated_current_1rm": four_weeks_ttrdata[-1]
}
return jsonify(res)
def pbar():
recengine = RecommendationEngine("pbar")
age = int(request.json.get('age', ''))
weight = float(request.json.get('weight', ''))
sex = request.json.get('sex', '')
performance = float(request.json.get('performance', ''))
if sex == 'MAN':
converted_sex = 0
elif sex == 'WOMAN':
converted_sex = 1
else:
converted_sex = 2
data = np.array([age, weight, converted_sex, performance]).reshape(1, -1)
best_pred, all_predictions = recengine.recommend_training(data)
program = fetch_program_from_model(best_pred["model"])
res = {
"predicted_performance": best_pred["predicted_performance"],
"training_program": best_pred["model"].name,
"program_structure": program
}
return jsonify(res)
def test_fetch_program_bad_input():
model_path = os.path.join(os.path.dirname(__file__), "models",
"programdoesnotexist.sav")
model = Model(model_path)
program = fetch_program_from_model(model)
assert program == {}
