format

jaxpm/distributed.py

@@ -133,7 +133,6 @@ def get_local_shape(mesh_shape):
         ]
 
 
-
 def normal_field(mesh_shape, seed=None):
     """Generate a Gaussian random field with the given power spectrum."""
     if distributed and not (mesh_lib.thread_resources.env.physical_mesh.empty):

jaxpm/pm.py

@@ -38,6 +38,7 @@ def pm_forces(positions, mesh_shape=None, delta=None, r_split=0, halo_size=0):
 
     return forces
 
+
 def lpt(cosmo, initial_conditions, a, halo_size=0):
     """
     Computes first order LPT displacement

scripts/distributed_pm.py

@@ -1,6 +1,8 @@
 import os
+
 os.environ["EQX_ON_ERROR"] = "nan"  # avoid an allgather caused by diffrax
 import jax
+
 jax.distributed.initialize()
 
 rank = jax.process_index()
@@ -8,18 +10,15 @@ size = jax.process_count()
 
 import jax.numpy as jnp
 import jax_cosmo as jc
-
-from jax.experimental.ode import odeint
-
-from jaxpm.painting import cic_paint, cic_read , cic_paint_dx , cic_read_dx
-from jaxpm.pm import linear_field, lpt, make_ode_fn
-from diffrax import diffeqsolve, ODETerm, Dopri5, PIDController, SaveAt
 import numpy as np
+from diffrax import Dopri5, ODETerm, PIDController, SaveAt, diffeqsolve
 from jax.experimental import mesh_utils
-from jax.sharding import Mesh, PartitionSpec as P , NamedSharding
+from jax.sharding import Mesh, NamedSharding
-from jaxpm.distributed import normal_field
+from jax.sharding import PartitionSpec as P
-from jaxpm.kernels import interpolate_power_spectrum
 
+from jaxpm.kernels import interpolate_power_spectrum
+from jaxpm.painting import cic_paint_dx
+from jaxpm.pm import linear_field, lpt, make_ode_fn
 
 size = 256
 mesh_shape = [size] * 3
@@ -38,15 +37,19 @@ if jax.device_count() > 1 :
 def run_simulation(omega_c, sigma8):
     # Create a small function to generate the matter power spectrum
     k = jnp.logspace(-4, 1, 128)
-    pk = jc.power.linear_matter_power(jc.Planck15(Omega_c=omega_c, sigma8=sigma8), k)
+    pk = jc.power.linear_matter_power(
+        jc.Planck15(Omega_c=omega_c, sigma8=sigma8), k)
 
     pk_fn = lambda x: interpolate_power_spectrum(x, k, pk)
     # Create initial conditions
-    initial_conditions = linear_field(mesh_shape, box_size, pk_fn, seed=jax.random.PRNGKey(0))
+    initial_conditions = linear_field(mesh_shape,
-
+                                      box_size,
+                                      pk_fn,
+                                      seed=jax.random.PRNGKey(0))
 
     # Create particles
-    particles = jnp.stack(jnp.meshgrid(*[jnp.arange(s) for s in mesh_shape]),axis=-1).reshape([-1,3])
+    particles = jnp.stack(jnp.meshgrid(*[jnp.arange(s) for s in mesh_shape]),
+                          axis=-1).reshape([-1, 3])
 
     cosmo = jc.Planck15(Omega_c=omega_c, sigma8=sigma8)
 
@@ -55,11 +58,17 @@ def run_simulation(omega_c, sigma8):
 
     # Evolve the simulation forward
     ode_fn = make_ode_fn(mesh_shape, halo_size=halo_size)
-    term = ODETerm(lambda t, state, args: jnp.stack(ode_fn(state, t, args), axis=0))
+    term = ODETerm(
+        lambda t, state, args: jnp.stack(ode_fn(state, t, args), axis=0))
     solver = Dopri5()
 
     stepsize_controller = PIDController(rtol=1e-4, atol=1e-4)
-    res = diffeqsolve(term, solver, t0=0.1, t1=1., dt0=0.01, y0=jnp.stack([dx, p],axis=0), 
+    res = diffeqsolve(term,
+                      solver,
+                      t0=0.1,
+                      t1=1.,
+                      dt0=0.01,
+                      y0=jnp.stack([dx, p], axis=0),
                       args=cosmo,
                       saveat=SaveAt(ts=snapshots),
                       stepsize_controller=stepsize_controller)
@@ -67,7 +76,9 @@ def run_simulation(omega_c, sigma8):
     # Return the simulation volume at requested
     states = res.ys
     field = cic_paint_dx(dx, halo_size=halo_size)
-    final_fields = [cic_paint_dx(state[0] , halo_size = halo_size) for state in states]
+    final_fields = [
+        cic_paint_dx(state[0], halo_size=halo_size) for state in states
+    ]
 
     return initial_conditions, field, final_fields, res.stats
 
@@ -89,7 +100,6 @@ np.save(f'field_{rank}.npy', field.addressable_data(0))
 
 if final_fields is not None:
     for i, final_field in enumerate(final_fields):
-            np.save(f'final_field_{i}_{rank}.npy',
+        np.save(f'final_field_{i}_{rank}.npy', final_field.addressable_data(0))
-                    final_field.addressable_data(0))
 
 print(f"Finished!!")