Skip to content

Couple of numpy-related fixes #140

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
tom-andersson merged 3 commits into from
Jan 15, 2025
Merged

Couple of numpy-related fixes #140

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
cfdc10e
fix to be compatible with numpy > 1.16
davidwyld Dec 13, 2024
f169427
ensure correct type for model saving
davidwyld Dec 13, 2024
d432a79
add comment explaining types
davidwyld Jan 2, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 3 additions & 1 deletion deepsensor/data/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,9 @@ def compute_xarray_data_resolution(ds: Union[xr.DataArray, xr.Dataset]) -> float
"""
x1_res = np.abs(np.mean(np.diff(ds["x1"])))
x2_res = np.abs(np.mean(np.diff(ds["x2"])))
data_resolution = np.min([x1_res, x2_res])

# ensure float type, since numpy 2, np.mean returns a numpy float32
data_resolution = float(np.min([x1_res, x2_res]))
return data_resolution


Expand Down
4 changes: 2 additions & 2 deletions tests/test_model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -156,7 +156,7 @@ def test_prediction_shapes_lowlevel(self, n_target_sets):
):
task = tl("2020-01-01", context_sampling, target_sampling)

n_targets = np.product(expected_obs_shape)
n_targets = np.prod(expected_obs_shape)

# Tensors
mean = model.mean(task)
Expand Down Expand Up @@ -192,7 +192,7 @@ def test_prediction_shapes_lowlevel(self, n_target_sets):
)

if likelihood in ["cnp", "gnp"]:
n_target_dims = np.product(tl.target_dims)
n_target_dims = np.prod(tl.target_dims)
assert_shape(
model.covariance(task),
(
Expand Down
Loading