move import in local scope where possible

chiron/minimze.py

@@ -1,16 +1,16 @@
-import jax
-import jax.numpy as jnp
-from jaxopt import GradientDescent
-from loguru import logger as log
+from typing import Callable
+from jax import numpy as jnp
 
 
-def minimize_energy(coordinates, potential_fn, nbr_list=None, maxiter=1000):
+def minimize_energy(
+    coordinates: jnp.array, potential_fn: Callable, nbr_list=None, maxiter: int = 1000
+):
     """
     Minimize the potential energy of a system using JAXopt.
 
     Parameters
     ----------
-    coordinates : jnp.ndarray
+    coordinates : jnp.array
         The initial coordinates of the system.
     potential_fn : callable
         The potential energy function of the system, which takes coordinates as input.
@@ -24,6 +24,7 @@ def minimize_energy(coordinates, potential_fn, nbr_list=None, maxiter=1000):
     jnp.ndarray
         The optimized coordinates.
     """
+    from loguru import logger as log
 
     def objective_fn(x):
         if nbr_list is not None:
@@ -33,6 +34,9 @@ def objective_fn(x):
             log.debug("Using NO neighbor list")
             return potential_fn(x)
 
+    from jaxopt import GradientDescent
+    import jax
+
     optimizer = GradientDescent(
         fun=jax.value_and_grad(objective_fn), value_and_grad=True, maxiter=maxiter
     )

chiron/neighbors.py

@@ -3,9 +3,8 @@
 import jax
 import jax.numpy as jnp
 from functools import partial
-from typing import Tuple, Optional, Union
+from typing import Tuple, Union
 from .states import SamplerState
-from loguru import logger as log
 from openmm import unit
 
 
@@ -580,6 +579,7 @@ def build(
         )
 
         self.neighbor_list = self.neighbor_list.reshape(-1, self.n_max_neighbors)
+        from loguru import logger as log
 
         while jnp.any(self.n_neighbors == self.n_max_neighbors).block_until_ready():
             log.debug(

chiron/potential.py

@@ -1,13 +1,13 @@
 import jax
 import jax.numpy as jnp
-from loguru import logger as log
 from openmm import unit
 from openmm.app import Topology
-from typing import Optional
 
 
 class NeuralNetworkPotential:
     def __init__(self, model, **kwargs):
+        from loguru import logger as log
+
         if model is None:
             log.warning("No model provided, using default model")
         else:
@@ -166,6 +166,7 @@ def compute_energy(self, positions: jnp.array, nbr_list=None, debug_mode=False):
 
         """
         # Compute the pair distances and displacement vectors
+        from loguru import logger as log
 
         if nbr_list is None:
             log.debug(
@@ -319,11 +320,13 @@ def __init__(
                 f"U0 must be a unit.Quantity with units of energy, U0.unit = {U0.unit}"
             )
 
-        log.info("Initializing HarmonicOscillatorPotential")
-        log.info(f"k = {k}")
-        log.info(f"x0 = {x0}")
-        log.info(f"U0 = {U0}")
-        log.info("Energy is calculate: U(x) = (K/2) * ( (x-x0)^2 + y^2 + z^2 ) + U0")
+        from loguru import logger as log
+
+        log.debug("Initializing HarmonicOscillatorPotential")
+        log.debug(f"k = {k}")
+        log.debug(f"x0 = {x0}")
+        log.debug(f"U0 = {U0}")
+        log.debug("Energy is calculate: U(x) = (K/2) * ( (x-x0)^2 + y^2 + z^2 ) + U0")
         self.k = jnp.array(
             k.value_in_unit_system(unit.md_unit_system)
         )  # spring constant

chiron/states.py

@@ -1,7 +1,6 @@
 from openmm import unit
 from typing import List, Optional, Union
 from jax import numpy as jnp
-from loguru import logger as log
 from .potential import NeuralNetworkPotential
 
 
@@ -204,6 +203,7 @@ def check_variables(self) -> None:
     def _check_completness(self):
         # check which variables are set
         set_variables = self.check_variables()
+        from loguru import logger as log
 
         if len(set_variables) == 0:
             log.info("No variables are set.")
@@ -288,6 +288,7 @@ def calculate_reduced_potential_at_states(
 
     """
     import numpy as np
+    from loguru import logger as log
 
     reduced_potentials = np.zeros(len(thermodynamic_states))
     for state_idx, state in enumerate(thermodynamic_states):