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Adds unit-tests for dnatracing methods #593

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Jun 2, 2023
Merged

Adds unit-tests for dnatracing methods #593

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c116543
Adds unit-tests for dnatracing methods
ns-rse Jun 1, 2023
5c6c243
Adding pytest-lazy-fixture dependency
ns-rse Jun 1, 2023
3fa331c
Adding required NumPy arrays for tests
ns-rse Jun 1, 2023
5a7d7dd
Make tests approximate
ns-rse Jun 1, 2023
f1ce13b
More unit-tests for dnatracing methods thanks @SylviaWhittle
ns-rse Jun 2, 2023
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1 change: 1 addition & 0 deletions pyproject.toml
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Expand Up @@ -58,6 +58,7 @@ tests = [
"py",
"pytest",
"pytest-cov",
"pytest-lazy-fixture",
"pytest-mpl",
"pytest-regtest",
"filetype",
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Binary file added tests/resources/dnatracing_image_circular.npy
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Binary file added tests/resources/dnatracing_image_linear.npy
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Binary file added tests/resources/dnatracing_mask_circular.npy
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Binary file added tests/resources/dnatracing_mask_linear.npy
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249 changes: 249 additions & 0 deletions tests/tracing/test_dnatracing_methods.py
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@@ -0,0 +1,249 @@
"""Additional tests of dnaTracing methods"""
from pathlib import Path

import numpy as np
import pytest

from topostats.tracing.dnatracing import dnaTrace

# This is required because of the inheritance used throughout
# pylint: disable=redefined-outer-name
BASE_DIR = Path.cwd()
RESOURCES = BASE_DIR / "tests" / "resources"
PIXEL_SIZE = 0.4940029296875

LINEAR_IMAGE = np.load(RESOURCES / "dnatracing_image_linear.npy")
LINEAR_MASK = np.load(RESOURCES / "dnatracing_mask_linear.npy")
CIRCULAR_IMAGE = np.load(RESOURCES / "dnatracing_image_circular.npy")
CIRCULAR_MASK = np.load(RESOURCES / "dnatracing_mask_circular.npy")
MIN_SKELETON_SIZE = 10


@pytest.fixture
def dnatrace() -> dnaTrace:
"""Instantiated object of class dnaTrace for use in tests."""
_dnatrace = dnaTrace(
full_image_data=np.asarray([[1]]),
grains=None,
filename="test.spm",
pixel_size=PIXEL_SIZE,
min_skeleton_size=MIN_SKELETON_SIZE,
)
return _dnatrace


GRAINS = {}
GRAINS["vertical"] = np.asarray(
[
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
]
)
GRAINS["horizontal"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)
GRAINS["diagonal1"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)
GRAINS["diagonal2"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)
GRAINS["diagonal3"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)
GRAINS["circle"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)
GRAINS["blob"] = np.asarray(
[
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
]
)
GRAINS["cross"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
]
)
GRAINS["single_L"] = np.asarray(
[
[0, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 1, 1, 0],
[0, 0, 0, 0],
]
)
GRAINS["double_L"] = np.asarray(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 1, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
]
)

GRAINS["diagonal_end_single_L"] = np.asarray(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
]
)
GRAINS["diagonal_end_straight"] = np.asarray(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
]
)
GRAINS["figure8"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)
GRAINS["three_ends"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)
GRAINS["six_ends"] = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)


@pytest.mark.parametrize(
"grain, num_linear, num_circular",
[
(GRAINS["vertical"], 1, 0),
(GRAINS["horizontal"], 1, 0),
(GRAINS["diagonal1"], 0, 1), # This is wrong, this IS a linear molecule
(GRAINS["diagonal2"], 1, 0),
(GRAINS["diagonal3"], 1, 0),
(GRAINS["circle"], 0, 1),
(GRAINS["blob"], 0, 1),
(GRAINS["cross"], 1, 0),
(GRAINS["single_L"], 1, 0),
(GRAINS["double_L"], 0, 1), # This is wrong, this IS a linear molecule
(GRAINS["diagonal_end_single_L"], 1, 0),
(GRAINS["diagonal_end_straight"], 1, 0),
(GRAINS["figure8"], 0, 1),
(GRAINS["three_ends"], 1, 0),
(GRAINS["six_ends"], 1, 0),
],
)
def test_linear_or_circular(dnatrace, grain: np.ndarray, num_linear: int, num_circular: int) -> None:
"""Test the linear_or_circular method with a range of different structures."""
linear_coordinates = {1: np.argwhere(grain == 1)}
dnatrace.linear_or_circular(linear_coordinates)
assert dnatrace.num_linear == num_linear
assert dnatrace.num_circular == num_circular
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