def test_dynamic_glucose_effect_absorption_never_fully_observed(self):
input_ice = self.load_ice_input_fixture("ice_slow_absorption")
(carb_starts,
carb_values,
carb_absorptions
) = self.load_carb_entry_fixture()
carb_ratio_tuple = self.load_schedules()
default_absorption_times = self.DEFAULT_ABSORPTION_TIMES
carb_entry_starts = [carb_starts[1]]
carb_entry_quantities = [carb_values[1]]
carb_entry_absorptions = [carb_absorptions[1]]
(expected_dates,
expected_values
) = self.load_cob_output_fixture(
"dynamic_glucose_effect_never_fully_observed_output"
)
(absorptions,
timelines,
entries, # pylint: disable=W0612
) = map_(
carb_entry_starts,
carb_entry_quantities,
carb_entry_absorptions,
*input_ice,
*carb_ratio_tuple,
self.INSULIN_SENSITIVITY_START_DATES,
self.INSULIN_SENSITIVITY_END_DATES,
self.INSULIN_SENSITIVITY_VALUES,
default_absorption_times[1] / default_absorption_times[0],
default_absorption_times[1],
0
)
self.assertEqual(len(absorptions), 1)
self.assertIsNotNone(absorptions[0])
self.assertAlmostEqual(absorptions[0][6], 10488/60, 2)
(effect_dates,
effect_values
) = dynamic_glucose_effects(
carb_entry_starts,
carb_entry_quantities,
carb_entry_absorptions,
absorptions,
timelines,
*carb_ratio_tuple,
self.INSULIN_SENSITIVITY_START_DATES,
self.INSULIN_SENSITIVITY_END_DATES,
self.INSULIN_SENSITIVITY_VALUES,
default_absorption_times[1],
delay=10,
delta=5,
start=input_ice[0][0],
end=(
input_ice[0][0]
+ timedelta(hours=18)
)
)
assert len(expected_dates) == len(effect_dates)
for i in range(0, len(expected_dates)):
self.assertEqual(
expected_dates[i], effect_dates[i]
)
self.assertAlmostEqual(
expected_values[i], effect_values[i], 2
)
def get_carb_glucose_effects(carb_dates,
carb_values,
absorption_times,
at_date,
effect_starts,
effect_ends,
effect_values,
carb_ratio_starts,
carb_ratios,
sensitivity_starts,
sensitivity_ends,
sensitivity_values,
default_absorption_times,
absorption_time_overrun=1.5,
delay=10,
delta=5,
end_date=None):
""" Retrieve a timeline of effect on blood glucose from carbohydrates
Arguments:
carb_dates -- list of times of carb entry (datetime objects)
carb_values -- list of grams of carbs eaten
absorption_times -- list of lengths of absorption times (mins)
at_date -- the time to calculate the effect at (datetime object)
effect_starts -- list of start times of carb effect (datetime objects)
effect_ends -- list of end times of carb effect (datetime objects)
effect_values -- list of glucose velocities (mg/dL)
carb_ratio_starts -- list of start times of carb ratios (time objects)
carb_ratios -- list of carb ratios (g/U)
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)
default_absorption_time -- absorption time to use for unspecified
carb entries
absorption_time_overrun -- multiplier to determine absorption time
from the specified absorption time
delay -- the time to delay the carb effect
delta -- time interval between glucose values
end_date -- date to end calculation of glucose effects
Output:
An array of effects in chronological order
"""
assert len(carb_dates) == len(carb_values) == len(absorption_times),\
"expected input shapes to match"
assert len(effect_starts) == len(effect_ends) == len(effect_values),\
"expected input shapes to match"
if not carb_dates:
return ([], [])
maximum_absorption_time_interval = default_absorption_times[2] * 2
# To know glucose effects at the requested start date, we need to fetch
# samples that might still be absorbing
food_start = at_date - timedelta(minutes=maximum_absorption_time_interval)
filtered_carbs = filter_date_range_for_carbs(carb_dates, carb_values,
absorption_times, food_start,
end_date)
# if we have counteraction effects, generate our carb glucose effects
# with a dynamic model
if effect_starts and effect_starts[0]:
(absorption_results,
timelines) = map_(*filtered_carbs, effect_starts, effect_ends,
effect_values, carb_ratio_starts, carb_ratios,
sensitivity_starts, sensitivity_ends,
sensitivity_values, absorption_time_overrun,
default_absorption_times[1], delay, delta)[0:2]
effects = dynamic_glucose_effects(*filtered_carbs,
absorption_results,
timelines,
carb_ratio_starts,
carb_ratios,
sensitivity_starts,
sensitivity_ends,
sensitivity_values,
default_absorption_times[1],
delay,
delta,
start=at_date,
end=end_date,
scaler=1.7)
# otherwise, use a static model
else:
effects = carb_glucose_effects(*filtered_carbs, carb_ratio_starts,
carb_ratios, sensitivity_starts,
sensitivity_ends, sensitivity_values,
default_absorption_times[1], delay,
delta, at_date, end_date)
assert len(effects[0]) == len(effects[1]), "expected output shape to match"
return effects