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Fix sharding error (#37)
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* Use cosmo as arg for the ODE function

* Update examples

* format

* notebook update

* fix tests

* add correct annotations for weights in painting and warning for cic_paint in distributed pm

* update test_against_fpm

* update distributed tests and add jacfwd jacrev and vmap tests

* format

* add Caveats to notebook readme

* final touches

* update Growth.py to allow using FastPM solver

* fix 2D painting when input is (X , Y , 2) shape

* update cic read halo size and notebooks examples

* Allow env variable control of caching in growth

* Format

* update test jax version

* update notebooks/03-MultiGPU_PM_Halo.ipynb

* update numpy install in wf

* update tolerance :)

* reorganize install in test workflow

* update tests

* add mpi4py

* update tests.yml

* update tests

* update wf

* format

* make normal_field signature consistent with jax.random.normal

* update by default normal_field dtype to match JAX

* format

* debug test workflow

* format

* debug test workflow

* updating tests

* fix accuracy

* fixed tolerance

* adding caching

* Update conftest.py

* Update tolerance and precision settings in distributed PM tests

* revererting back changes to growth.py

---------

Co-authored-by: Francois Lanusse <fr.eiffel@gmail.com>
Co-authored-by: Francois Lanusse <EiffL@users.noreply.github.com>
This commit is contained in:
Wassim KABALAN 2025-06-28 23:07:31 +02:00 • committed by GitHub
parent cb2a7ab17f
commit 6693e5c725
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17 changed files with 675 additions and 298 deletions
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19
jaxpm/painting_utils.py
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@ -30,17 +30,14 @@ def enmesh(base_indices, displacements, cell_size, base_shape, offset,
"""Multilinear enmeshing."""
base_indices = jnp.asarray(base_indices)
displacements = jnp.asarray(displacements)
with jax.experimental.enable_x64():
cell_size = jnp.float64(
cell_size) if new_cell_size is not None else jnp.array(
cell_size, dtype=displacements.dtype)
if base_shape is not None:
base_shape = jnp.array(base_shape, dtype=base_indices.dtype)
offset = jnp.float64(offset)
if new_cell_size is not None:
new_cell_size = jnp.float64(new_cell_size)
if new_shape is not None:
new_shape = jnp.array(new_shape, dtype=base_indices.dtype)
cell_size = jnp.array(cell_size, dtype=displacements.dtype)
if base_shape is not None:
base_shape = jnp.array(base_shape, dtype=base_indices.dtype)
offset = offset.astype(base_indices.dtype)
if new_cell_size is not None:
new_cell_size = jnp.array(new_cell_size, dtype=displacements.dtype)
if new_shape is not None:
new_shape = jnp.array(new_shape, dtype=base_indices.dtype)
spatial_dim = base_indices.shape[1]
neighbor_offsets = (