def test_in_range_and_rising_bolus(self):
glucose = self.load_glucose_value_fixture(
"recommend_temp_basal_in_range_and_rising")
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, glucose[0][0],
self.SUSPEND_THRESHOLD, *self.SENSITIVITY,
self.WALSH_MODEL, 0, self.MAX_BOLUS, 0.025)
self.assertEqual(0.325, dose[0])
# Less existing temp
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, glucose[0][0],
self.SUSPEND_THRESHOLD, *self.SENSITIVITY,
self.WALSH_MODEL, 0.8, self.MAX_BOLUS, 0.025)
self.assertEqual(0, dose[0])
def test_no_input_glucose_bolus(self):
glucose = ([], [])
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, datetime.now(),
self.SUSPEND_THRESHOLD, *self.SENSITIVITY,
self.WALSH_MODEL, 0, self.MAX_BOLUS, 0.025)
self.assertEqual(0, dose[0])
def test_rise_after_dia_bolus(self):
glucose = self.load_glucose_value_fixture(
"far_future_high_bg_forecast")
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, glucose[0][0],
self.SUSPEND_THRESHOLD, *self.SENSITIVITY,
self.WALSH_MODEL, 0, self.MAX_BOLUS, 0.025)
self.assertEqual(0, dose[0])
def test_start_low_and_end_just_above_range_bolus(self):
glucose = self.load_glucose_value_fixture(
"recommended_temp_start_low_end_just_above_range")
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, glucose[0][0],
self.SUSPEND_THRESHOLD, *self.SENSITIVITY,
self.MODEL, 0, self.MAX_BOLUS, 0.025)
self.assertEqual(0.275, dose[0])
def test_flat_and_high_bolus(self):
glucose = self.load_glucose_value_fixture(
"recommend_temp_basal_flat_and_high")
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, glucose[0][0],
self.SUSPEND_THRESHOLD, *self.SENSITIVITY,
self.WALSH_MODEL, 0, self.MAX_BOLUS, 0.025)
self.assertEqual(1.575, dose[0])
def test_dropping_below_range_then_rising_bolus(self):
glucose = self.load_glucose_value_fixture(
"recommend_temp_basal_dropping_then_rising")
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, glucose[0][0],
self.SUSPEND_THRESHOLD, *self.SENSITIVITY,
self.WALSH_MODEL, 0, self.MAX_BOLUS, 0.025)
self.assertEqual(1.4, dose[0])
self.assertEqual("predictedGlucoseBelowTarget", dose[2][0])
def test_start_below_suspend_threshold_end_high_bolus(self):
glucose = self.load_glucose_value_fixture(
"recommend_temp_basal_start_low_end_high")
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, glucose[0][0],
70, *self.SENSITIVITY, self.WALSH_MODEL, 0,
self.MAX_BOLUS, 0.025)
self.assertEqual(0, dose[0])
self.assertEqual("glucoseBelowSuspendThreshold", dose[2][0])
self.assertEqual(60, dose[2][1])
def test_start_low_end_high_bolus(self):
glucose = self.load_glucose_value_fixture(
"recommend_temp_basal_start_low_end_high")
dose = recommended_bolus(*glucose, *self.TARGET_RANGE, glucose[0][0],
self.SUSPEND_THRESHOLD, *self.SENSITIVITY,
self.WALSH_MODEL, 0, self.MAX_BOLUS, 0.025)
self.assertEqual(1.575, dose[0])
self.assertEqual("predictedGlucoseBelowTarget", dose[2][0])
self.assertEqual(60, dose[2][1])
def update_predicted_glucose_and_recommended_basal_and_bolus(
at_date,
glucose_dates, glucose_values,
momentum_dates, momentum_values,
carb_effect_dates, carb_effect_values,
insulin_effect_dates, insulin_effect_values,
retrospective_effect_dates, retrospective_effect_values,
target_starts, target_ends, target_mins, target_maxes,
suspend_threshold,
sensitivity_starts, sensitivity_ends, sensitivity_values,
model,
basal_starts, basal_rates, basal_minutes,
max_basal_rate, max_bolus,
last_temp_basal,
duration=30,
continuation_interval=11,
rate_rounder=None
):
""" Generate glucose predictions, then use the predicted glucose along
with settings and dose data to recommend a temporary basal rate and
a bolus
Arguments:
at_date -- date to calculate the temp basal and bolus recommendations
glucose_dates -- dates of glucose values (datetime)
glucose_values -- glucose values (in mg/dL)
momentum_dates -- times of calculated momentums (datetime)
momentum_values -- values (mg/dL) of momentums
carb_effect_dates -- times of carb effects (datetime)
carb_effect -- values (mg/dL) of effects from carbs
insulin_effect_dates -- times of insulin effects (datetime)
insulin_effect -- values (mg/dL) of effects from insulin
correction_effect_dates -- times of retrospective effects (datetime)
correction_effect -- values (mg/dL) retrospective glucose effects
target_starts -- start times for given target ranges (datetime)
target_ends -- stop times for given target ranges (datetime)
target_mins -- the lower bounds of target ranges (mg/dL)
target_maxes -- the upper bounds of target ranges (mg/dL)
suspend_threshold -- value at which to suspend all insulin delivery (mg/dL)
sensitivity_starts -- list of time objects of start times of
given insulin sensitivity values
sensitivity_ends -- list of time objects of start times of
given insulin sensitivity values
sensitivity_values -- list of sensitivities (mg/dL/U)
model -- list of insulin model parameters in format [DIA, peak_time] if
exponential model, or [DIA] if Walsh model
basal_starts -- list of times the basal rates start at
basal_rates -- list of basal rates(U/hr)
basal_minutes -- list of basal lengths (in mins)
max_basal_rate -- max basal rate that Loop can give (U/hr)
max_bolus -- max bolus that Loop can give (U)
last_temp_basal -- list of last temporary basal information in format
[type, start time, end time, basal rate]
duration -- length of the temp basal (mins)
continuation_interval -- length of time before an ongoing temp basal
should be continued with a new command (mins)
rate_rounder -- the smallest fraction of a unit supported in basal
delivery; if None, no rounding is performed
Output:
The predicted glucose values, recommended temporary basal, and
recommended bolus in the format [
(predicted glucose times, predicted glucose values),
temporary basal recommendation,
bolus recommendation
]
"""
assert glucose_dates, "expected to receive glucose data"
assert target_starts and sensitivity_starts and basal_starts and model,\
"expected to receive complete settings data"
if (not momentum_dates
and not carb_effect_dates
and not insulin_effect_dates
):
warnings.warn("Warning: expected to receive effect data")
return (None, None, None)
predicted_glucoses = predict_glucose(
glucose_dates[-1], glucose_values[-1],
momentum_dates, momentum_values,
carb_effect_dates, carb_effect_values,
insulin_effect_dates, insulin_effect_values,
retrospective_effect_dates, retrospective_effect_values
)
# Dosing requires prediction entries at least as long as the insulin
# model duration. If our prediction is shorter than that, extend it here.
if len(model) == 1: # Walsh model
final_date = glucose_dates[-1] + timedelta(hours=model[0])
else:
final_date = glucose_dates[-1] + timedelta(minutes=model[0])
if predicted_glucoses[0][-1] < final_date:
predicted_glucoses[0].append(final_date)
predicted_glucoses[1].append(predicted_glucoses[1][-1])
pending_insulin = get_pending_insulin(
at_date,
basal_starts, basal_rates, basal_minutes,
last_temp_basal
)
temp_basal = recommended_temp_basal(
*predicted_glucoses,
target_starts, target_ends, target_mins, target_maxes,
at_date,
suspend_threshold,
sensitivity_starts, sensitivity_ends, sensitivity_values,
model,
basal_starts, basal_rates, basal_minutes,
max_basal_rate,
last_temp_basal,
duration,
continuation_interval,
rate_rounder
)
bolus = recommended_bolus(
*predicted_glucoses,
target_starts, target_ends, target_mins, target_maxes,
at_date,
suspend_threshold,
sensitivity_starts, sensitivity_ends, sensitivity_values,
model,
pending_insulin,
max_bolus,
rate_rounder
)
return {
"predicted_glucose_dates": predicted_glucoses[0],
"predicted_glucose_values": predicted_glucoses[1],
"recommended_temp_basal": temp_basal,
"recommended_bolus": bolus
}