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Fix CI test failures #393

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Merged
merged 5 commits into from
Mar 22, 2025
Merged

Fix CI test failures #393

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2ca8fd6
Use np.ptp function instead of method
m-albert Feb 24, 2025
571c4d0
Replace deprecated tifffile.TiffWriter.save by tifffile.TiffWriter.write
m-albert Feb 24, 2025
4c67935
Avoid converting dataframes to string type in find_objects
m-albert Feb 24, 2025
e26a9c6
Stop using deprecated mambaforge in CI
m-albert Feb 24, 2025
043e9c1
ndinterp.rotate: convert list to np array
m-albert Feb 25, 2025
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2 changes: 0 additions & 2 deletions .github/workflows/test_and_deploy.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,6 @@ jobs:
- name: Setup Conda Environment
uses: conda-incubator/setup-miniconda@v3
with:
miniforge-variant: Mambaforge
use-mamba: true
python-version: ${{ matrix.python-version }}
environment-file: continuous_integration/environment-${{ matrix.python-version }}.yml
activate-environment: dask-image-testenv
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7 changes: 4 additions & 3 deletions dask_image/ndinterp/__init__.py
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Expand Up @@ -367,10 +367,11 @@ def rotate(
if reshape:
# Compute transformed input bounds
iy, ix = in_plane_shape
out_bounds = rot_matrix @ [[0, 0, iy, iy],
[0, ix, 0, ix]]
in_bounds = np.array([[0, 0, iy, iy],
[0, ix, 0, ix]])
out_bounds = rot_matrix @ in_bounds
# Compute the shape of the transformed input plane
out_plane_shape = (out_bounds.ptp(axis=1) + 0.5).astype(int)
out_plane_shape = (np.ptp(out_bounds, axis=1) + 0.5).astype(int)
else:
out_plane_shape = img_shape[axes]

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5 changes: 4 additions & 1 deletion dask_image/ndmeasure/__init__.py
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Expand Up @@ -5,6 +5,7 @@
import operator
import warnings
from dask import compute, delayed
import dask.config as dask_config

import dask.array as da
import dask.bag as db
Expand Down Expand Up @@ -244,7 +245,9 @@ def find_objects(label_image):
result = bag.fold(functools.partial(_find_objects, label_image.ndim), split_every=2).to_delayed()
meta = dd.utils.make_meta([(i, object) for i in range(label_image.ndim)])
result = delayed(compute)(result)[0] # avoid the user having to call compute twice on result
result = dd.from_delayed(result, meta=meta, prefix="find-objects-", verify_meta=False)

with dask_config.set({'dataframe.convert-string': False}):
result = dd.from_delayed(result, meta=meta, prefix="find-objects-", verify_meta=False)

return result

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7 changes: 5 additions & 2 deletions dask_image/ndmeasure/_utils/_find_objects.py
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Expand Up @@ -2,6 +2,7 @@
import pandas as pd
from dask.delayed import Delayed
import dask.dataframe as dd
import dask.config as dask_config


def _array_chunk_location(block_id, chunks):
Expand Down Expand Up @@ -67,9 +68,11 @@ def _find_objects(ndim, df1, df2):
"""Main utility function for find_objects."""
meta = dd.utils.make_meta([(i, object) for i in range(ndim)])
if isinstance(df1, Delayed):
df1 = dd.from_delayed(df1, meta=meta)
with dask_config.set({'dataframe.convert-string': False}):
df1 = dd.from_delayed(df1, meta=meta)
if isinstance(df2, Delayed):
df2 = dd.from_delayed(df2, meta=meta)
with dask_config.set({'dataframe.convert-string': False}):
df2 = dd.from_delayed(df2, meta=meta)
Comment on lines 70 to +75
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Is this enough given compute happens later?

Do we have a way to test this?

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Is this enough given compute happens later?

Yes, consider this code snippet:

import dask.dataframe as dd
import dask.array as da
from dask import delayed
import pandas as pd
import dask.config as dask_config

x = dd.from_delayed(delayed(pd.Series([slice(2)])))

with dask_config.set({'dataframe.convert-string': False}):
    x_cm = dd.from_delayed(delayed(pd.Series([slice(2)])))

print('x', x)
print('x_cm', x_cm)

print('x computed', x.compute())
print('x_cm computed', x_cm.compute())

outputs

x Dask Series Structure:
npartitions=1
    string
       ...
Dask Name: to_string_dtype, 3 expressions
Expr=ArrowStringConversion(frame=FromDelayed(5621810))
x_cm Dask Series Structure:
npartitions=1
    object
       ...
Dask Name: fromdelayed, 2 expressions
Expr=FromDelayed(79a507f)
x computed 0    slice(None, 2, None)
dtype: string
x_cm computed 0    slice(None, 2, None)
dtype: object

Also he tests do perform a compute and fail without the context manager in place, so it should be covered:

dask-image/tests/test_dask_image/test_ndmeasure/test_find_objects.py

Lines 61 to 67 in cb1360a

computed_result = result.compute()
assert isinstance(computed_result, pd.DataFrame)
expected = pd.DataFrame.from_dict(
{0: {111: slice(1, 3), 222: slice(3, 4), 333: slice(0, 2)},
1: {111: slice(0, 2), 222: slice(3, 8), 333: slice(7, 10)}}
)
assert computed_result.equals(expected)

ddf = dd.merge(df1, df2, how="outer", left_index=True, right_index=True)
result = ddf.apply(_merge_bounding_boxes, ndim=ndim, axis=1, meta=meta)
return result
3 changes: 2 additions & 1 deletion tests/test_dask_image/test_imread/test_core.py
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Expand Up @@ -76,7 +76,8 @@ def test_tiff_imread(tmpdir, seed, nframes, shape, dtype, is_pathlib_Path): # n
fn = str(dirpth.join("test.tiff"))
with tifffile.TiffWriter(fn) as fh:
for i in range(len(a)):
fh.save(a[i], contiguous=True)
fh.write(a[i], contiguous=True)

if is_pathlib_Path:
fn = pathlib.Path(fn)
d = dask_image.imread.imread(fn, nframes=nframes)
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