Updates from review, mark tests as allowed to fail
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3 changed files with 85 additions and 68 deletions
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@ -722,9 +722,9 @@ class AutofocusThing(lt.Thing):
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# Manually test for monotomically increasing or decreasing sharpnesses, as
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# Manually test for monotomically increasing or decreasing sharpnesses, as
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# fitting can struggle with these and their behaviour is simpler to hardcode
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# fitting can struggle with these and their behaviour is simpler to hardcode
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if np.array_equal(sharpnesses, np.sort(sharpnesses)):
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if np.all(sharpnesses[:-1] <= sharpnesses[1:]):
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return "continue", capture_id
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return "continue", capture_id
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if np.array_equal(sharpnesses, np.sort(sharpnesses)[::-1]):
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if np.all(sharpnesses[:-1] >= sharpnesses[1:]):
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return "restart", capture_id
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return "restart", capture_id
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try:
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try:
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@ -34,10 +34,10 @@
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{"sharpnesses":[600000,700000,800000,900000,1100000,1300000,1500000,1700000,1800000],"label":"continue"},
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{"sharpnesses":[600000,700000,800000,900000,1100000,1300000,1500000,1700000,1800000],"label":"continue"},
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{"sharpnesses":[500000,700000,1000000,1400000,1800000,1700000,1400000,1000000,700000],"label":"success"},
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{"sharpnesses":[500000,700000,1000000,1400000,1800000,1700000,1400000,1000000,700000],"label":"success"},
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{"sharpnesses":[500000,600000,900000,1300000,1800000,1900000,1700000,1300000,900000],"label":"success"},
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{"sharpnesses":[500000,600000,900000,1300000,1800000,1900000,1700000,1300000,900000],"label":"success"},
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{"sharpnesses": [500000, 500000, 500000, 500000, 600000, 900000, 600000, 500000, 500000], "label": ["success"]},
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{"sharpnesses":[500000,500000,500000,500000,600000,900000,600000,500000,500000],"label":["success"],"allow_failure":true},
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{"sharpnesses":[500000,500000,500000,500000,500000,600000,900000,600000,500000],"label":["success","continue"]},
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{"sharpnesses":[500000,500000,500000,500000,500000,600000,900000,600000,500000],"label":["success","continue"]},
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{"sharpnesses":[500000,500000,500000,500000,500000,500000,600000,900000,600000],"label":"continue"},
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{"sharpnesses":[500000,500000,500000,500000,500000,500000,600000,900000,600000],"label":"continue"},
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{"sharpnesses": [100000, 100000, 100000, 100000, 200000, 500000, 200000, 100000, 100000], "label": ["success"]},
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{"sharpnesses":[100000,100000,100000,100000,200000,500000,200000,100000,100000],"label":["success"],"allow_failure":true},
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{"sharpnesses":[100000,100000,100000,100000,100000,200000,500000,200000,100000],"label":["success","continue"]},
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{"sharpnesses":[100000,100000,100000,100000,100000,200000,500000,200000,100000],"label":["success","continue"]},
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{"sharpnesses":[100000,100000,100000,100000,100000,100000,200000,500000,200000],"label":"continue"}
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{"sharpnesses":[100000,100000,100000,100000,100000,100000,200000,500000,200000],"label":"continue"}
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]
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]
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@ -11,6 +11,8 @@ objects to simulate real camera captures.
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import json
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import json
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import os
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import os
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import pytest
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from labthings_fastapi.testing import create_thing_without_server
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from labthings_fastapi.testing import create_thing_without_server
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from openflexure_microscope_server.things.autofocus import AutofocusThing
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from openflexure_microscope_server.things.autofocus import AutofocusThing
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@ -45,41 +47,56 @@ def make_captures(sharpness_list):
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return [MockCapture(s, i) for i, s in enumerate(sharpness_list)]
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return [MockCapture(s, i) for i, s in enumerate(sharpness_list)]
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def test_stack_labelling():
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def load_cases():
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"""Test stack classification accuracy against labelled sharpness cases.
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"""Load the sharpness data from the json.
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This test loads predefined sharpness profiles and their expected labels
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Includes the sharpnesses to test, manually written labels
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from a JSON file, converts them into mock capture objects, and evaluates
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on the required result, and whether the test is allowed to
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the classification returned by `check_stack_result`.
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fail. This ensures future tests will flag any regression on
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tests while allowing improvements.
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The test asserts that at least 90% of cases are correctly classified.
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Expected labels may be a single value or a list of acceptable values.
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"""
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"""
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autofocus_thing = create_thing_without_server(AutofocusThing, mock_all_slots=True)
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with open(DATA_PATH) as f:
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with open(DATA_PATH) as f:
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data = json.load(f)
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data = json.load(f)
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success = 0
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params = []
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total = len(data)
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for i, case in enumerate(data):
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for _i, case in enumerate(data):
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sharpnesses = case["sharpnesses"]
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expected = case["label"]
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expected = case["label"]
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# Allow multiple acceptable labels
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# Allow multiple acceptable labels
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if not isinstance(expected, list):
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if not isinstance(expected, list):
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expected = [expected]
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expected = [expected]
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# Convert to capture objects
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marks = []
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if case.get("allow_failure", False):
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marks.append(pytest.mark.xfail(reason="Known failing case"))
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params.append(
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pytest.param(
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case["sharpnesses"],
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expected,
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marks=marks,
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id=f"case_{i}",
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)
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)
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return params
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@pytest.mark.parametrize(("sharpnesses", "expected"), load_cases())
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def test_stack_labelling(sharpnesses, expected):
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"""Test stack classification accuracy against labelled sharpness cases.
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This test loads the test from load_cases and evaluates
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the classification returned by `check_stack_result`.
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The test ensures that only cases marked with "allow_failure: true" can fail.
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Expected labels may be a single value or a list of acceptable values.
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"""
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autofocus_thing = create_thing_without_server(AutofocusThing, mock_all_slots=True)
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captures = make_captures(sharpnesses)
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captures = make_captures(sharpnesses)
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# Call the method under test
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result, _ = autofocus_thing.check_stack_result(captures, check_turning_points=False)
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result, _ = autofocus_thing.check_stack_result(
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captures, check_turning_points=False
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)
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if result in expected:
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success += 1
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assert success > 0.8 * total
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assert result in expected
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