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Minor bug fixes #158

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Minor bug fixes #158

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5f9b9a3
fix: Handle non-integer x, y in pyvale
rhysgt Oct 15, 2026
2eed82e
fix: cost column in csv has different name
rhysgt Oct 15, 2026
5e064dc
fix: Relax pattern image size check in HRDIC Map
rhysgt Oct 16, 2026
f2faa0d
fix: Revert change to file_reader
rhysgt Oct 21, 2026
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10 changes: 5 additions & 5 deletions defdap/file_readers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -984,12 +984,12 @@ def load(self, file_name: pathlib.Path) -> None:
"""
if not file_name.is_file():
raise FileNotFoundError(f"Cannot open file {file_name}")

int_type = 'int32'
double_type = 'double'
data_format = np.dtype([
('x', int_type),
('y', int_type),
('x', double_type),
('y', double_type),
('u', double_type),
('v', double_type),
('displacement_mag', double_type),
Expand Down Expand Up @@ -1026,12 +1026,12 @@ def load(self, file_name: pathlib.Path) -> None:
yx_max = yx_array.max(axis=0)
shape = yx_max - yx_min
assert np.allclose(shape % binning, 0.)
shape = np.rint(shape).astype(int)
shape = tuple((shape // binning + 1).tolist())
self.loaded_metadata['shape'] = shape

self.checkMetadata()

index_array = (yx_array - yx_min) // binning
index_array = np.rint((yx_array - yx_min) / binning).astype(int)
disp_dense = np.zeros(shape + (2,))
disp_dense[index_array[:, 0], index_array[:, 1]] = (
structured_to_unstructured(data[['u', 'v']]))
Expand Down
21 changes: 18 additions & 3 deletions defdap/hrdic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -534,12 +534,27 @@ def load_pattern(self):
raise FileNotFoundError("First set path to pattern image.")

img = imread(path)
exp_shape = tuple(v * binning for v in self.original_shape)
if img.shape != exp_shape:

bounds = {
'DaVis': (-1, 0), # Davis rounds up to nearest subwindow
'PyVale': (0, 1), # PyVale rounds down to nearest subwindow
'OpenPIV': (0, 1) # OpenPIV rounds down to nearest subwindow
}
lower, upper = bounds[self.format]

img_shape = np.array(img.shape)
dic_shape = np.array(self.original_shape)

min_shape = np.array((dic_shape + lower) * binning, dtype=int)
max_shape = np.array((dic_shape + upper) * binning, dtype=int)

if not np.all((min_shape <= img_shape) & (img_shape <= max_shape)):
raise ValueError(
f'Incorrect size of pattern image. For binning of {binning} '
f'expected size {exp_shape[::-1]} but got {img.shape[::-1]}'
f'expected size {min_shape[::-1]} to {max_shape[::-1]} '
f'but got {img_shape[::-1]}'
)

return img

def plot_grain_av_max_shear(self, **kwargs):
Expand Down
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