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Fixed warning for complex perm #110

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Merged
liubenyuan merged 5 commits into from
Dec 12, 2025
Merged

Fixed warning for complex perm #110

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b288001
fixed ComplexWarning
JacobTh98 Dec 3, 2025
dbc5724
Fix NaN representation
JacobTh98 Dec 10, 2025
3b22a57
Replace NaN with nan in test_merit.py
JacobTh98 Dec 10, 2025
1bf51e7
np.nan instead of np.NaN
JacobTh98 Dec 10, 2025
349d74c
Fix mask_value to use np.nan instead of np.NAN
JacobTh98 Dec 10, 2025
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2 changes: 1 addition & 1 deletion pyeit/eit/fem.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,7 @@ def solve_vectorized(self, ex_mat: np.ndarray) -> np.ndarray:
# using natural boundary conditions
b = np.zeros((ex_mat.shape[0], self.mesh.n_nodes))
b[np.arange(b.shape[0])[:, None], self.mesh.el_pos[ex_mat]] = [1, -1]
result = np.empty((ex_mat.shape[0], self.kg.shape[0]))
result = np.empty((ex_mat.shape[0], self.kg.shape[0]), dtype=complex)

# TODO Need to inspect this deeper
for i in range(result.shape[0]):
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2 changes: 1 addition & 1 deletion pyeit/eit/render.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,7 +208,7 @@ def map_image(image, values):
"""
vals = values[image.astype(int)]
mask = image == -1
vals[mask] = np.NaN
vals[mask] = np.nan

return vals

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4 changes: 2 additions & 2 deletions pyeit/quality/merit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -600,7 +600,7 @@ def lambda_max(
return arr.flatten()[idxs]


def get_image_bounds(image, background=np.NaN):
def get_image_bounds(image, background=np.nan):
"""
Get the bounds of an image.

Expand All @@ -618,7 +618,7 @@ def get_image_bounds(image, background=np.NaN):
"""
if not np.isnan(background):
image = image.astype(float)
image[np.where(image == background)] = np.NaN
image[np.where(image == background)] = np.nan

rowmin = np.argmax(np.any(~np.isnan(image), axis=0))
rowmax = image.shape[0] - np.argmax(np.any(~np.isnan(image[::-1]), axis=0))
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2 changes: 1 addition & 1 deletion tests/test_eit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,7 @@ def test_greit(self):
eit = pyeit.eit.greit.GREIT(self.mesh_obj, self.protocol_obj)
eit.setup(p=0.50, lamb=0.01, perm=1, jac_normalized=True)
ds = eit.solve(self.v1, self.v0, normalize=True)
x, y, ds = eit.mask_value(ds, mask_value=np.NAN)
x, y, ds = eit.mask_value(ds, mask_value=np.nan)

# evaluate GREIT
loc = np.where(np.abs(ds) == np.nanmax(np.abs(ds)))
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224 changes: 112 additions & 112 deletions tests/test_merit.py
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Expand Up @@ -19,57 +19,57 @@
patches as mpatches,
axes as mpl_axes,
)
from numpy import NaN
from numpy import nan

parent_dir = str(Path(__file__).parent)

test_image = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 2, 2, 2, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 2, 2, 2, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

test_image_2 = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

test_image_3 = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, -1, -1, -1, 0, NaN],
[NaN, 0, 0.1, 0.1, 0.1, 0, NaN],
[NaN, 0, 0.1, 0.1, 0.1, 0, NaN],
[NaN, 0, 0.75, 0.75, 0.75, 0, NaN],
[NaN, 0, 2, 2, 2, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, -1, -1, -1, 0, nan],
[nan, 0, 0.1, 0.1, 0.1, 0, nan],
[nan, 0, 0.1, 0.1, 0.1, 0, nan],
[nan, 0, 0.75, 0.75, 0.75, 0, nan],
[nan, 0, 2, 2, 2, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)


def test_calc_circle():
square = imread(parent_dir + "/data/square_image.bmp", pilmode="RGB")

fractional_image = np.full(np.shape(square)[0:2], NaN)
fractional_image = np.full(np.shape(square)[0:2], nan)
fractional_image[
np.where(
(square[:, :, 0] == 255)
Expand Down Expand Up @@ -101,9 +101,9 @@ def test_calc_circle():
#
# img = axs[1, 0].imshow(fractional_image)
# axs[1, 0].set_title("Fractional Image")
# colors = [img.cmap(img.norm(value)) for value in [NaN, 0, 1]]
# colors = [img.cmap(img.norm(value)) for value in [nan, 0, 1]]
# patches = [
# mpatches.Patch(color=colors[0], label="NAN"),
# mpatches.Patch(color=colors[0], label="nan"),
# mpatches.Patch(color=colors[1], label="0"),
# mpatches.Patch(color=colors[2], label="1")
# ]
Expand Down Expand Up @@ -134,43 +134,43 @@ def test_calc_amplitude():
def test_calc_position_error():
test_image_p1 = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

test_image_p2 = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

test_image_p2_flipped = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

Expand Down Expand Up @@ -223,43 +223,43 @@ def test_calc_fractional_amplitude_set():

correct_fractional_amplitude_set = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

correct_fractional_amplitude_set_range = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

correct_fractional_amplitude_set_negative_target = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

Expand Down Expand Up @@ -350,43 +350,43 @@ def test_classify_target_and_background():
def test_calc_ringing():
test_image_ringing = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, 1, 1, 1, 0, NaN],
[NaN, 0, -1, -1, -1, 0, NaN],
[NaN, 0, 0, 0, 0, 0, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, 1, 1, 1, 0, nan],
[nan, 0, -1, -1, -1, 0, nan],
[nan, 0, 0, 0, 0, 0, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

test_image_target_non_conductive = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, 3, 3, 3, 3, 3, NaN],
[NaN, 3, 3, 3, 3, 3, NaN],
[NaN, 3, 3, 3, 3, 3, NaN],
[NaN, 3, 3, 3, 3, 3, NaN],
[NaN, 3, 3, 3, 3, 3, NaN],
[NaN, 3, 2, 2, 2, 3, NaN],
[NaN, 3, 3, 3, 3, 3, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, 3, 3, 3, 3, 3, nan],
[nan, 3, 3, 3, 3, 3, nan],
[nan, 3, 3, 3, 3, 3, nan],
[nan, 3, 3, 3, 3, 3, nan],
[nan, 3, 3, 3, 3, 3, nan],
[nan, 3, 2, 2, 2, 3, nan],
[nan, 3, 3, 3, 3, 3, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

test_image_recon_non_conductive = np.array(
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, -0.4, -0.4, -0.4, -0.4, -0.4, NaN],
[NaN, -0.4, -0.4, -0.4, -0.4, -0.4, NaN],
[NaN, -0.4, -0.4, -0.4, -0.4, -0.4, NaN],
[NaN, -0.4, -0.4, -0.4, -0.4, -0.4, NaN],
[NaN, -0.4, 1, 1, 1, -0.4, NaN],
[NaN, -0.4, -2, -2, -2, -0.4, NaN],
[NaN, -0.4, -0.4, -0.4, -0.4, -0.4, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[nan, nan, nan, nan, nan, nan, nan],
[nan, -0.4, -0.4, -0.4, -0.4, -0.4, nan],
[nan, -0.4, -0.4, -0.4, -0.4, -0.4, nan],
[nan, -0.4, -0.4, -0.4, -0.4, -0.4, nan],
[nan, -0.4, -0.4, -0.4, -0.4, -0.4, nan],
[nan, -0.4, 1, 1, 1, -0.4, nan],
[nan, -0.4, -2, -2, -2, -0.4, nan],
[nan, -0.4, -0.4, -0.4, -0.4, -0.4, nan],
[nan, nan, nan, nan, nan, nan, nan],
]
)

Expand Down