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from path_analysis.analyse import *
import numpy as np
from math import pi
import xml.etree.ElementTree as ET
def test_get_paths_from_traces_file():
# Mock the XML traces file content
xml_content = '''<?xml version="1.0"?>
<root>
<path reallength="5.0">
<point x="1" y="2" z="3"/>
<point x="4" y="5" z="6"/>
</path>
<path reallength="10.0">
<point x="7" y="8" z="9"/>
<point x="10" y="11" z="12"/>
</path>
</root>
'''
# Create a temporary XML file
with open("temp_traces.xml", "w") as f:
f.write(xml_content)
all_paths, path_lengths = get_paths_from_traces_file("temp_traces.xml")
expected_paths = [[(1, 2, 3), (4, 5, 6)], [(7, 8, 9), (10, 11, 12)]]
expected_lengths = [5.0, 10.0]
assert all_paths == expected_paths, f"Expected paths {expected_paths}, but got {all_paths}"
assert path_lengths == expected_lengths, f"Expected lengths {expected_lengths}, but got {path_lengths}"
# Clean up temporary file
import os
os.remove("temp_traces.xml")
def test_measure_chrom2():
# Mock data
path = [(2, 3, 4), (4, 5, 6), (9, 9, 9)] # Sample ordered path points
hei10 = np.random.rand(10, 10, 10) # Random 3D fluorescence data
config = {
'z_res': 1,
'xy_res': 0.5,
'sphere_radius': 2.5
}
# Function call
_, measurements, measurements_max = measure_chrom2(path, hei10, config)
# Assertions
assert len(measurements) == len(path), "Measurements length should match path length"
assert len(measurements_max) == len(path), "Max measurements length should match path length"
assert all(0 <= val <= 1 for val in measurements), "All mean measurements should be between 0 and 1 for this mock data"
assert all(0 <= val <= 1 for val in measurements_max), "All max measurements should be between 0 and 1 for this mock data"
def test_measure_chrom2_z():
# Mock data
path = [(2, 3, 4), (4, 5, 6)] # Sample ordered path points
_,_,hei10 = np.meshgrid(np.arange(10), np.arange(10), np.arange(10)) # 3D fluorescence data - z dependent
config = {
'z_res': 1,
'xy_res': 0.5,
'sphere_radius': 2.5
}
# Function call
_, measurements, measurements_max = measure_chrom2(path, hei10, config)
# Assertions
assert len(measurements) == len(path), "Measurements length should match path length"
assert len(measurements_max) == len(path), "Max measurements length should match path length"
assert all(measurements == np.array([4,6]))
assert all(measurements_max == np.array([6,8]))
def test_measure_chrom2_z2():
# Mock data
path = [(0,0,0), (2, 3, 4), (4, 5, 6)] # Sample ordered path points
_,_,hei10 = np.meshgrid(np.arange(10), np.arange(10), np.arange(10)) # 3D fluorescence data - z dependent
config = {
'z_res': 0.25,
'xy_res': 0.5,
'sphere_radius': 2.5
}
# Function call
_, measurements, measurements_max = measure_chrom2(path, hei10, config)
# Assertions
assert len(measurements) == len(path), "Measurements length should match path length"
assert len(measurements_max) == len(path), "Max measurements length should match path length"
assert all(measurements_max == np.array([9,9,9]))
def test_measure_from_mask():
mask = np.array([
[0, 1, 0],
[1, 1, 1],
[0, 1, 0]
])
measure_stack = np.array([
[2, 4, 2],
[4, 8, 4],
[2, 4, 2]
])
result = measure_from_mask(mask, measure_stack)
assert result == 24 # Expected sum: 4+4+8+4+4
def test_max_from_mask():
mask = np.array([
[0, 1, 0],
[1, 1, 1],
[0, 1, 0]
])
measure_stack = np.array([
[2, 5, 2],
[4, 8, 3],
[2, 7, 2]
])
result = max_from_mask(mask, measure_stack)
assert result == 8 # Expected max: 8
def test_measure_at_point_mean():
measure_stack = np.array([
[[2, 2, 2, 0], [4, 4, 6, 0], [3, 3, 2, 0], [0, 0, 0, 0]],
[[4, 4, 4, 0], [8, 8, 8, 0], [4, 4, 4, 0], [0, 0, 0, 0]],
[[3, 3, 3, 0], [6, 6, 4, 0], [3, 2, 2, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
])
p = (1, 1, 1)
melem = np.ones((3, 3, 3))
result = measure_at_point(p, melem, measure_stack, op='mean')
assert result == 4, "Expected mean: 4"
def test_measure_at_point_mean_off1():
measure_stack = np.array([
[[2, 2, 2, 0], [4, 4, 6, 0], [5, 5, 2, 0], [0, 0, 0, 0]],
[[4, 4, 4, 0], [8, 8, 8, 0], [4, 4, 4, 0], [0, 0, 0, 0]],
[[3, 3, 3, 0], [6, 6, 4, 0], [3, 2, 2, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
])
p = (0, 0, 0)
melem = np.ones((3, 3, 3))
result = measure_at_point(p, melem, measure_stack, op='mean')
assert result == 4.5, "Expected mean: 4.5"
def test_measure_at_point_mean_off2():
measure_stack = np.array([
[[2, 2, 2, 0], [4, 4, 6, 0], [5, 5, 2, 0], [0, 0, 0, 0]],
[[4, 4, 4, 0], [8, 8, 8, 0], [4, 4, 4, 0], [0, 0, 0, 0]],
[[3, 3, 3, 0], [6, 6, 4, 0], [3, 2, 2, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
])
p = (3, 1, 1)
melem = np.ones((3, 3, 3))
print(measure_stack[p[0], p[1], p[2]])
result = measure_at_point(p, melem, measure_stack, op='mean')
assert result == 32/18 # Expected mean: 4.5
def test_measure_at_point_mean_off3():
measure_stack = np.array([
[[2, 2, 2, 0], [4, 4, 6, 0], [5, 5, 2, 0], [0, 0, 0, 0]],
[[4, 4, 4, 0], [8, 8, 8, 0], [4, 4, 4, 0], [0, 0, 0, 0]],
[[3, 3, 3, 0], [6, 6, 4, 0], [3, 2, 2, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
])
p = (3, 1, 1)
melem = np.ones((1, 1, 3))
print(measure_stack[p[0], p[1], p[2]])
result = measure_at_point(p, melem, measure_stack, op='mean')
assert result == 0, "Expected mean: 4.5"
def test_measure_at_point_mean_off3():
measure_stack = np.array([
[[2, 2, 2, 0], [4, 4, 6, 0], [5, 5, 2, 0], [0, 0, 0, 0]],
[[4, 4, 4, 0], [8, 8, 8, 0], [4, 4, 4, 0], [0, 0, 0, 0]],
[[3, 3, 3, 0], [6, 6, 4, 0], [3, 2, 2, 0], [0, 0, 0, 0]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
])
p = (3, 1, 1)
melem = np.ones((3, 1, 1))
print(measure_stack[p[0], p[1], p[2]])
result = measure_at_point(p, melem, measure_stack, op='mean')
assert result == 3, "Expected mean: 4.5"
def test_measure_at_point_max():
measure_stack = np.array([
[[2, 2, 2], [4, 4, 4], [2, 2, 2]],
[[4, 5, 4], [8, 7, 9], [4, 4, 4]],
[[2, 2, 2], [4, 4, 4], [2, 2, 2]]
])
p = (1, 1, 1)
melem = np.ones((3, 3, 3))
result = measure_at_point(p, melem, measure_stack, op='max')
assert result == 9, "Expected max: 9"
def test_make_sphere_equal():
R = 5
z_scale_ratio = 1.0
sphere = make_sphere(R, z_scale_ratio)
# Check the returned type
assert isinstance(sphere, np.ndarray), "Output should be a numpy ndarray"
# Check the shape
expected_shape = (2*R+1, 2*R+1, 2*R+1)
assert sphere.shape == expected_shape, f"Expected shape {expected_shape}, but got {sphere.shape}"
assert (sphere[:,:,::-1] == sphere).all(), f"Expected symmetrical mask"
assert (sphere[:,::-1,:] == sphere).all(), f"Expected symmetrical mask"
assert (sphere[::-1,:,:] == sphere).all(), f"Expected symmetrical mask"
assert abs(np.sum(sphere)-4/3*pi*R**3)<10, f"Expected approximate volume to be correct"
assert (sphere[R,R,0] == 1), f"Expected centre point on top plane to be within sphere"
assert (sphere[R+1,R,0] == 0), f"Expected point next to centre on top plane to be outside sphere"
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