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glucose-dashboard/tests/test_data_processing.py
T

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Python

import pandas as pd
import pytest
from data_processing import classify_treatments, glucose_status, summarize_time_in_range
@pytest.fixture
def sample_glucose():
return pd.DataFrame(
{
"timestamp": pd.to_datetime(
["2026-07-25 08:00", "2026-07-25 09:00", "2026-07-25 10:00", "2026-07-25 11:00"]
),
"mmol": [3.5, 6.0, 11.0, 7.0], # laag, in bereik, hoog, in bereik
}
)
@pytest.fixture
def sample_treatments():
return pd.DataFrame(
{
"timestamp": pd.to_datetime(
[
"2026-07-25 08:00",
"2026-07-25 12:00",
"2026-07-24 09:00",
"2026-07-20 09:00",
]
),
"eventType": ["Meal Bolus", "Correction Bolus", "Site Change", "Sensor Change"],
"insulin": [4.0, 1.5, None, None],
"carbs": [45.0, None, None, None],
}
)
def test_glucose_status_classifies_correctly():
assert glucose_status(3.5) == "low"
assert glucose_status(6.0) == "in_range"
assert glucose_status(11.0) == "high"
def test_summarize_time_in_range(sample_glucose):
result = summarize_time_in_range(sample_glucose)
assert result == {"low": 25.0, "in_range": 50.0, "high": 25.0}
def test_summarize_time_in_range_handles_empty_input():
empty = pd.DataFrame(columns=["timestamp", "mmol"])
result = summarize_time_in_range(empty)
assert result == {"low": 0.0, "in_range": 0.0, "high": 0.0}
def test_classify_treatments_splits_into_expected_subsets(sample_treatments):
events = classify_treatments(sample_treatments)
assert len(events["insulin"]) == 2
assert set(events["insulin"]["insulin"]) == {4.0, 1.5}
assert len(events["carbs"]) == 1
assert events["carbs"].iloc[0]["carbs"] == 45.0
assert len(events["site_changes"]) == 1
assert events["site_changes"].iloc[0]["eventType"] == "Site Change"
assert len(events["sensor_changes"]) == 1
assert events["sensor_changes"].iloc[0]["eventType"] == "Sensor Change"
def test_classify_treatments_handles_empty_input():
empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
events = classify_treatments(empty)
assert all(df.empty for df in events.values())