def test_end_to_end(self) -> None:
config["od_config.photodiode_channel"]["1"] = "90"
config["od_config.photodiode_channel"]["2"] = "135"
unit = get_unit_name()
experiment = get_latest_experiment_name()
interval = 0.1
config["od_config"]["samples_per_second"] = "0.2"
od = start_od_reading(
"135",
"90",
sampling_rate=interval,
unit=unit,
experiment=experiment,
fake_data=True,
)
st = start_stirring(target_rpm=500, unit=unit, experiment=experiment)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
time.sleep(35)
assert calc.ekf.state_[-2] != 1.0
calc.set_state(calc.DISCONNECTED)
st.set_state(st.DISCONNECTED)
od.set_state(od.DISCONNECTED)
def test_od_blank_being_non_zero(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
with local_persistant_storage("od_blank") as cache:
cache[experiment] = json.dumps({"1": 0.25, "2": 0.4})
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({"1": 0.5, "2": 0.8})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({"1": 1e-6, "2": 1e-4})
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
pause()
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"], [0.50, 0.80], ["90", "135"], timestamp="2010-01-01 12:02:00"
),
retain=True,
)
pause()
pause()
assert calc.od_normalization_factors == {"2": 0.8, "1": 0.5}
assert calc.od_blank == {"2": 0.4, "1": 0.25}
results = calc.scale_raw_observations({"2": 1.0, "1": 0.6})
print(results)
assert abs(results["2"] - 1.5) < 0.00001
assert abs(results["1"] - 1.4) < 0.00001
calc.set_state(calc.DISCONNECTED)
def test_single_observation(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({1: 1})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({1: 1})
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1"], [1.153], ["90"], timestamp="2010-01-01 12:00:30"
),
retain=True,
)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1"], [1.155], ["90"], timestamp="2010-01-01 12:00:35"
),
)
pause()
assert True
calc.set_state(calc.DISCONNECTED)
def test_restart():
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/median",
'{"135/0": 1, "135/1": 1, "90/0": 1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/variance",
'{"135/0": 1, "135/1": 1, "90/0": 1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.778586260567034, "135/1": 0.20944389172032837, "90/0": 0.1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/median",
'{"135/0": 0.778586260567034, "135/1": 0.20944389172032837, "90/0": 0.1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/variance",
'{"135/0": 1, "135/1": 1, "90/0": 1}',
retain=True,
)
publish(f"pioreactor/{unit}/{experiment}/growth_rate", None, retain=True)
pause()
calc1 = GrowthRateCalculator(unit=unit, experiment=experiment)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 1.808586260567034, "135/1": 1.21944389172032837, "90/0": 1.2}',
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 2.808586260567034, "135/1": 2.21944389172032837, "90/0": 2.2}',
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 3.808586260567034, "135/1": 3.21944389172032837, "90/0": 3.2}',
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 4.808586260567034, "135/1": 4.21944389172032837, "90/0": 4.2}',
)
pause()
assert calc1.state_[-1] != 0
calc2 = GrowthRateCalculator(unit=unit, experiment=experiment)
pause()
assert calc2.initial_growth_rate != 0
def test_mis_shapen_data(monkeypatch):
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/median",
'{"135/0": 1, "90/0": 1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/variance",
'{"135/0": 1, "90/0": 1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.778586260567034, "90/0": 0.1}',
retain=True,
)
GrowthRateCalculator(unit=unit, experiment=experiment)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.778586260567034, "90/0": 0.1}',
)
pause()
publish(f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.808586260567034}')
pause()
def test_same_angles(monkeypatch):
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/median",
'{"135/0": 1, "135/1": 1, "90/0": 1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/variance",
'{"135/0": 1, "135/1":1, "90/0": 1}',
retain=True,
)
publish(f"pioreactor/{unit}/{experiment}/growth_rate", None, retain=True)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.778586260567034, "135/1": 0.20944389172032837, "90/0": 0.1}',
retain=True,
)
GrowthRateCalculator(unit=unit, experiment=experiment)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.778586260567034, "135/1": 0.20944389172032837, "90/0": 0.1}',
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.808586260567034, "135/1": 0.21944389172032837, "90/0": 0.2}',
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.808586260567034, "135/1": 0.21944389172032837, "90/0": 0.2}',
)
def test_single_observation():
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/median",
'{"135/0": 1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/variance",
'{"135/0": 1}',
retain=True,
)
publish(f"pioreactor/{unit}/{experiment}/growth_rate", None, retain=True)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.20944389172032837}',
retain=True,
)
GrowthRateCalculator(unit=unit, experiment=experiment)
publish(f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.20944389172032837}')
pause()
assert True
def test_skip_180():
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/median",
'{"135/0": 1, "180/2": 1, "90/1": 1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/variance",
'{"135/0": 1, "180/2": 1, "90/1": 1}',
retain=True,
)
publish(f"pioreactor/{unit}/{experiment}/growth_rate", None, retain=True)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"180/2": 0.778586260567034, "135/0": 0.20944389172032837, "90/1": 0.1}',
retain=True,
)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"180/2": 0.778586260567034, "135/0": 0.20944389172032837, "90/1": 0.1}',
)
pause()
assert "180/2" not in calc.angles
def test_od_blank_being_higher_than_observations(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
with local_persistant_storage("od_blank") as cache:
cache[experiment] = json.dumps({"1": 0.25, "2": 0.4})
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({"1": 0.5, "2": 0.8})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({"1": 1e-6, "2": 1e-4})
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
pause()
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"], [0.50, 0.80], ["90", "135"], timestamp="2010-01-01 12:02:00"
),
retain=True,
)
pause()
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"], [0.1, 0.1], ["90", "135"], timestamp="2010-01-01 12:02:05"
),
retain=True,
)
pause()
pause()
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"], [0.1, 0.1], ["90", "135"], timestamp="2010-01-01 12:02:10"
),
retain=True,
)
pause()
pause()
calc.set_state(calc.DISCONNECTED)
def test_subscribing(monkeypatch):
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/median",
'{"135/0": 1, "90/0": 1}',
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/variance",
'{"135/0": 1, "90/0": 1}',
retain=True,
)
publish(f"pioreactor/{unit}/{experiment}/growth_rate", None, retain=True)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.778586260567034, "90/0": 0.20944389172032837}',
retain=True,
)
publish(f"pioreactor/{unit}/{experiment}/growth_rate", 1.0, retain=True)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
pause()
assert calc.initial_growth_rate == 1.0
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.778586260567034, "90/0": 0.20944389172032837}',
)
pause()
assert calc.ekf is not None
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.778586260567034, "90/0": 0.20944389172032837}',
)
publish(
f"pioreactor/{unit}/{experiment}/dosing_events",
'{"volume_change": "1.5", "event": "add_media"}',
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 1.778586260567034, "90/0": 1.20944389172032837}',
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 1.778586260567034, "90/0": 1.20944389172032837}',
)
pause()
assert calc.state_ is not None
def test_scaling_works(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({"1": 0.5, "2": 0.8})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({"1": 1e-6, "2": 1e-4})
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"], [0.5, 0.8], ["90", "135"], timestamp="2010-01-01 12:00:35"
),
retain=True,
)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
pause()
assert calc.od_normalization_factors == {"2": 0.8, "1": 0.5}
calc.set_state(calc.DISCONNECTED)
def test_zero_blank_and_zero_od_coming_in(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({"1": 0})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({"1": 0})
with local_persistant_storage("od_blank") as cache:
cache[experiment] = json.dumps({"1": 0})
with pytest.raises(ZeroDivisionError):
with GrowthRateCalculator(unit=unit, experiment=experiment):
...
def test_observation_order_is_preserved_in_job(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({"1": 2, "2": 1})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({"1": 1, "2": 1})
with local_persistant_storage("growth_rate") as cache:
cache[experiment] = str(1.0)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["2", "1"], [0.9, 1.1], ["135", "90"], timestamp="2010-01-01 12:00:00"
),
retain=True,
)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
assert calc.scale_raw_observations({"2": 2, "1": 0.5}) == {"2": 2.0, "1": 0.25}
calc.set_state(calc.DISCONNECTED)
def test_90_angle(self) -> None:
import json
import numpy as np
from pioreactor.utils.timing import RepeatedTimer
unit = get_unit_name()
experiment = get_latest_experiment_name()
samples_per_second = 0.2
config["od_config"]["samples_per_second"] = str(samples_per_second)
config["od_config.photodiode_channel"]["1"] = "90"
config["od_config.photodiode_channel"]["2"] = None
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({"1": 0.1})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({"1": 8.2e-02})
class Mock180ODReadings:
growth_rate = 0.1
od_reading = 1.0
def __call__(self):
self.od_reading *= np.exp(self.growth_rate / 60 / 60 / samples_per_second)
voltage = 0.1 * self.od_reading
payload = {
"od_raw": {"1": {"voltage": voltage, "angle": "90"}},
"timestamp": "2021-06-06T15:08:12.081153",
}
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
json.dumps(payload),
)
thread = RepeatedTimer(0.025, Mock180ODReadings()).start()
with GrowthRateCalculator(unit=unit, experiment=experiment) as calc:
time.sleep(35)
assert calc.ekf.state_[1] > 0
thread.cancel()
def test_scaling_works():
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/median",
json.dumps({
"135/0": 0.5,
"90/1": 0.8
}),
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_normalization/variance",
json.dumps({
"135/0": 1e-6,
"90/1": 1e-4
}),
retain=True,
)
publish(
f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.5, "90/1": 0.8}',
retain=True,
)
publish(f"pioreactor/{unit}/{experiment}/growth_rate", "", retain=True)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
publish(f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.51, "90/1": 0.82}')
publish(f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.51, "90/1": 0.83}')
publish(f"pioreactor/{unit}/{experiment}/od_raw_batched",
'{"135/0": 0.51, "90/1": 0.84}')
pause()
assert calc.od_normalization_factors == {"90/1": 0.8, "135/0": 0.5}
assert ((calc.ekf.observation_noise_covariance -
30 * np.array([[1e-4 / 0.8**2, 0], [0, 1e-6 / 0.5**2]])) <
1e-7).all()
def test_restart(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
config["od_config.photodiode_channel"]["1"] = "90"
config["od_config.photodiode_channel"]["2"] = "135"
config["od_config.photodiode_channel"]["3"] = "90"
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({1: 1, 2: 1, 2: 1})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({1: 1, 2: 1, 2: 1})
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2", "3"],
[1.15, 0.93, 1.0],
["90", "135", "90"],
timestamp="2010-01-01 12:00:15",
),
retain=True,
)
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = '{"1": 1.15, "2": 0.93, "3": 1.0}'
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = '{"1": 1, "2": 1, "3": 1}'
pause()
calc1 = GrowthRateCalculator(unit=unit, experiment=experiment)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2", "3"],
[1.151, 0.931, 1.1],
["90", "135", "90"],
timestamp="2010-01-01 12:00:20",
),
)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2", "3"],
[1.152, 0.932, 1.2],
["90", "135", "90"],
timestamp="2010-01-01 12:00:25",
),
)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2", "3"],
[1.153, 0.933, 1.3],
["90", "135", "90"],
timestamp="2010-01-01 12:00:30",
),
)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2", "3"],
[1.154, 0.934, 1.4],
["90", "135", "90"],
timestamp="2010-01-01 12:00:35",
),
)
pause()
assert calc1.ekf.state_[-1] != 0
calc1.set_state(calc1.DISCONNECTED)
calc2 = GrowthRateCalculator(unit=unit, experiment=experiment)
pause()
assert calc2.initial_growth_rate != 0
calc2.set_state(calc2.DISCONNECTED)
def test_kalman_filter_entries() -> None:
config["storage"]["database"] = "test.sqlite"
config["od_config"]["samples_per_second"] = "0.2"
unit = "unit"
exp = "exp"
def parse_kalman_filter_outputs(topic, payload) -> dict:
metadata, _ = m2db.produce_metadata(topic)
payload = json.loads(payload)
return {
"experiment": metadata.experiment,
"pioreactor_unit": metadata.pioreactor_unit,
"timestamp": current_utc_time(),
"state": json.dumps(payload["state"]),
"covariance_matrix": json.dumps(payload["covariance_matrix"]),
}
# init the database
connection = sqlite3.connect(config["storage"]["database"])
cursor = connection.cursor()
cursor.executescript("""
CREATE TABLE IF NOT EXISTS kalman_filter_outputs (
timestamp TEXT NOT NULL,
pioreactor_unit TEXT NOT NULL,
experiment TEXT NOT NULL,
state TEXT NOT NULL,
covariance_matrix TEXT NOT NULL
);
""")
connection.commit()
# turn on data collection
interval = 0.5
od = start_od_reading(
od_angle_channel1="135",
od_angle_channel2="90",
sampling_rate=interval,
fake_data=True,
unit=unit,
experiment=exp,
)
with local_persistant_storage("od_normalization_mean") as cache:
cache[exp] = json.dumps({"1": 0.5, "2": 0.8})
with local_persistant_storage("od_normalization_variance") as cache:
cache[exp] = json.dumps({"1": 1e-6, "2": 1e-4})
gr = GrowthRateCalculator(unit=unit, experiment=exp)
# turn on our mqtt to db
parsers = [
m2db.TopicToParserToTable(
"pioreactor/+/+/growth_rate_calculating/kalman_filter_outputs",
parse_kalman_filter_outputs,
"kalman_filter_outputs",
)
]
m = m2db.MqttToDBStreamer(parsers, unit=unit, experiment=exp)
# let data collect
time.sleep(10)
cursor.execute("SELECT * FROM kalman_filter_outputs WHERE experiment = ?",
(exp, ))
results = cursor.fetchall()
assert len(results) > 0
cursor.execute(
'SELECT json_array_length("state"), json_array_length("covariance_matrix"), json("covariance_matrix") FROM kalman_filter_outputs WHERE experiment = ? ORDER BY timestamp DESC LIMIT 1',
(exp, ),
)
results = cursor.fetchone()
assert (
results[0] == 3
) # why 3? growth rate, od filtered, and acceleration are the three hidden states
assert results[1] == 3
assert np.array(json.loads(results[2])).shape == (3, 3)
od.set_state(od.DISCONNECTED)
gr.set_state(gr.DISCONNECTED)
m.set_state(m.DISCONNECTED)
def test_shock_from_dosing_works(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({"1": 0.5, "2": 0.8})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({"1": 8.2e-07, "2": 8.2e-07})
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"],
[0.5, 0.8],
["90,90", "135,45"],
timestamp="2010-01-01 12:00:35",
),
retain=True,
)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"],
[0.51, 0.82],
["90,90", "135,45"],
timestamp="2010-01-01 12:00:40",
),
)
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"],
[0.51, 0.82],
["90,90", "135,45"],
timestamp="2010-01-01 12:00:45",
),
)
pause()
previous_covariance_matrix = calc.ekf.covariance_.copy()
# trigger dosing events, which change the "regime"
publish(
f"pioreactor/{unit}/{experiment}/dosing_events",
json.dumps(
{"source_of_event": "algo", "event": "add_media", "volume_change": 1.0}
),
)
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"],
[0.49, 0.80],
["90,90", "135,45"],
timestamp="2010-01-01 12:00:50",
),
)
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"],
[0.48, 0.80],
["90,90", "135,45"],
timestamp="2010-01-01 12:00:55",
),
)
pause()
assert not np.array_equal(previous_covariance_matrix, calc.ekf.covariance_)
publish(
f"pioreactor/{unit}/{experiment}/dosing_events",
json.dumps(
{"source_of_event": "algo", "event": "add_media", "volume_change": 1.0}
),
)
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"],
[0.40, 0.70],
["90,90", "135,45"],
timestamp="2010-01-01 12:02:00",
),
)
pause()
time.sleep(8)
assert calc.ekf._currently_scaling_covariance
assert not np.array_equal(previous_covariance_matrix, calc.ekf.covariance_)
time.sleep(10)
pause()
# should revert back
assert not calc.ekf._currently_scaling_covariance
assert_array_equal(calc.ekf.covariance_, previous_covariance_matrix)
calc.set_state(calc.DISCONNECTED)
def test_subscribing(self) -> None:
unit = get_unit_name()
experiment = get_latest_experiment_name()
with local_persistant_storage("od_normalization_mean") as cache:
cache[experiment] = json.dumps({1: 1, 2: 1})
with local_persistant_storage("od_normalization_variance") as cache:
cache[experiment] = json.dumps({1: 1, 2: 1})
with local_persistant_storage("growth_rate") as cache:
cache[experiment] = str(1.0)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["2", "1"], [0.9, 1.1], ["135", "90"], timestamp="2010-01-01 12:00:00"
),
retain=True,
)
calc = GrowthRateCalculator(unit=unit, experiment=experiment)
pause()
assert calc.initial_growth_rate == 1.0
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"], [1.12, 0.88], ["90", "135"], timestamp="2010-01-01 12:00:05"
),
)
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["2", "1"], [0.87, 1.14], ["135", "90"], timestamp="2010-01-01 12:00:05"
),
)
pause()
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["2", "1"], [0.85, 1.16], ["135", "90"], timestamp="2010-01-01 12:00:05"
),
)
pause()
assert calc.ekf is not None
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"], [1.14, 0.92], ["90", "135"], timestamp="2010-01-01 12:00:10"
),
)
publish(
f"pioreactor/{unit}/{experiment}/dosing_events",
'{"volume_change": "1.5", "event": "add_media", "source_of_event": "test"}',
)
publish(
f"pioreactor/{unit}/{experiment}/od_reading/od_raw_batched",
create_od_raw_batched_json(
["1", "2"], [1.15, 0.93], ["90", "135"], timestamp="2010-01-01 12:00:15"
),
)
pause()
assert calc.ekf.state_ is not None
calc.set_state(calc.DISCONNECTED)
None,
retain=True,
)
publish(f"pioreactor/{unit}/{exp}/od_normalization/mean", None, retain=True)
publish(f"pioreactor/{unit}/{exp}/od_normalization/variance", None, retain=True)
start_time = time.time()
od = ODReader(
channel_label_map={"A0": "90/0", "A1": "90/1"},
sampling_rate=interval_for_testing,
unit=unit,
experiment=exp,
fake_data=True,
stop_IR_led_between_ADC_readings=False,
)
calc = GrowthRateCalculator(unit=unit, experiment=exp)
actual_grs = []
estimated_grs = []
def append_actual_growth_rates(msg):
actual_grs.append(float(msg.payload))
def append_estimated_growth_rates(msg):
actual_grs.append(od.adc_reader.analog_in[0][1].gr)
estimated_grs.append(float(msg.payload))
c1 = subscribe_and_callback(
append_actual_growth_rates, "pioreactor/mock/0/actual_gr"
)
c2 = subscribe_and_callback(