Updates

brainpy/__init__.py

@@ -58,11 +58,11 @@
                      synapses,  # synaptic dynamics
                      synouts,  # synaptic output
                      synplast,  # synaptic plasticity
-                     experimental,  # experimental model
+                     syn,
                      )
-from brainpy._src.dyn.base import not_pass_shargs
-from brainpy._src.dyn.base import (DynamicalSystem as DynamicalSystem,
-                                   Module as Module,
+from brainpy._src.dyn.base import not_pass_sha
+from brainpy._src.dyn.base import (DynamicalSystem,
+                                   DynamicalSystemNS,
                                    Container as Container,
                                    Sequential as Sequential,
                                    Network as Network,
@@ -77,6 +77,8 @@
 from brainpy._src.dyn.transform import (NoSharedArg as NoSharedArg,  # transformations
                                         LoopOverTime as LoopOverTime,)
 from brainpy._src.dyn.runners import (DSRunner as DSRunner)  # runner
+from brainpy._src.dyn.context import share
+from brainpy._src.dyn.delay import Delay
 
 
 #  Part 4: Training  #
@@ -240,3 +242,7 @@
 dyn.__dict__['NMDA'] = compat.NMDA
 del compat
 
+
+from brainpy._src import checking
+tools.__dict__['checking'] = checking
+del checking

brainpy/_src/analysis/highdim/slow_points.py

@@ -752,7 +752,7 @@ def f_cell(h: Dict):
 
       # call update functions
       args = (shared,) + self.args
-      target.update(*args)
+      target(*args)
 
       # get new states
       new_h = {k: (v.value if (v.batch_axis is None) else jnp.squeeze(v.value, axis=v.batch_axis))

brainpy/_src/analysis/lowdim/lowdim_bifurcation.py

@@ -225,10 +225,10 @@ def plot_bifurcation(self, with_plot=True, show=False, with_return=False,
 
     if self._can_convert_to_one_eq():
       if self.convert_type() == C.x_by_y:
-        X = self.resolutions[self.y_var].value
+        X = bm.as_jax(self.resolutions[self.y_var])
       else:
-        X = self.resolutions[self.x_var].value
-      pars = tuple(self.resolutions[p].value for p in self.target_par_names)
+        X = bm.as_jax(self.resolutions[self.x_var])
+      pars = tuple(bm.as_jax(self.resolutions[p]) for p in self.target_par_names)
       mesh_values = jnp.meshgrid(*((X,) + pars))
       mesh_values = tuple(jnp.moveaxis(v, 0, 1).flatten() for v in mesh_values)
       candidates = mesh_values[0]

brainpy/_src/analysis/lowdim/lowdim_phase_plane.py

@@ -290,9 +290,9 @@ def plot_fixed_point(self, with_plot=True, with_return=False, show=False,
 
     if self._can_convert_to_one_eq():
       if self.convert_type() == C.x_by_y:
-        candidates = self.resolutions[self.y_var].value
+        candidates = bm.as_jax(self.resolutions[self.y_var])
       else:
-        candidates = self.resolutions[self.x_var].value
+        candidates = bm.as_jax(self.resolutions[self.x_var])
     else:
       if select_candidates == 'fx-nullcline':
         candidates = [self.analyzed_results[key][0] for key in self.analyzed_results.keys()

brainpy/_src/checkpoints/serialization.py

@@ -37,15 +37,13 @@
   get_tensorstore_spec = None
 
 from brainpy._src.math.ndarray import Array
-from brainpy._src.math.object_transform.base import Collector
 from brainpy.errors import (AlreadyExistsError,
                             MPACheckpointingRequiredError,
                             MPARestoreTargetRequiredError,
                             MPARestoreDataCorruptedError,
                             MPARestoreTypeNotMatchError,
                             InvalidCheckpointPath,
                             InvalidCheckpointError)
-from brainpy.tools import DotDict
 from brainpy.types import PyTree
 
 __all__ = [
@@ -154,17 +152,27 @@ def from_state_dict(target, state: Dict[str, Any], name: str = '.'):
     A copy of the object with the restored state.
   """
   ty = _NamedTuple if _is_namedtuple(target) else type(target)
-  if ty not in _STATE_DICT_REGISTRY:
+  for t in _STATE_DICT_REGISTRY.keys():
+    if issubclass(ty, t):
+      ty = t
+      break
+  else:
     return state
   ty_from_state_dict = _STATE_DICT_REGISTRY[ty][1]
   with _record_path(name):
     return ty_from_state_dict(target, state)
 
 
+
 def to_state_dict(target) -> Dict[str, Any]:
   """Returns a dictionary with the state of the given target."""
   ty = _NamedTuple if _is_namedtuple(target) else type(target)
-  if ty not in _STATE_DICT_REGISTRY:
+
+  for t in _STATE_DICT_REGISTRY.keys():
+    if issubclass(ty, t):
+      ty = t
+      break
+  else:
     return target
 
   ty_to_state_dict = _STATE_DICT_REGISTRY[ty][0]
@@ -269,8 +277,9 @@ def _restore_namedtuple(xs, state_dict: Dict[str, Any]):
 
 register_serialization_state(Array, _array_dict_state, _restore_array)
 register_serialization_state(dict, _dict_state_dict, _restore_dict)
-register_serialization_state(DotDict, _dict_state_dict, _restore_dict)
-register_serialization_state(Collector, _dict_state_dict, _restore_dict)
+# register_serialization_state(DotDict, _dict_state_dict, _restore_dict)
+# register_serialization_state(Collector, _dict_state_dict, _restore_dict)
+# register_serialization_state(ArrayCollector, _dict_state_dict, _restore_dict)
 register_serialization_state(list, _list_state_dict, _restore_list)
 register_serialization_state(tuple,
                              _list_state_dict,
@@ -1221,8 +1230,9 @@ def _save_main_ckpt_file2(target: bytes,
 def save_pytree(
     filename: str,
     target: PyTree,
-    overwrite: bool = False,
+    overwrite: bool = True,
     async_manager: Optional[AsyncManager] = None,
+    verbose: bool = True,
 ) -> None:
   """Save a checkpoint of the model. Suitable for single-host.
 
@@ -1250,12 +1260,16 @@ def save_pytree(
     if defined, the save will run without blocking the main
     thread. Only works for single host. Note that an ongoing save will still
     block subsequent saves, to make sure overwrite/keep logic works correctly.
+  verbose: bool
+    Whether output the print information.
 
   Returns
   -------
   out: str
     Filename of saved checkpoint.
   """
+  if verbose:
+    print(f'Saving checkpoint into {filename}')
   start_time = time.time()
   # Make sure all saves are finished before the logic of checking and removing
   # outdated checkpoints happens.
@@ -1284,6 +1298,7 @@ def save_main_ckpt_task():
                                           end_time - start_time)
 
 
+
 def multiprocess_save(
     ckpt_dir: Union[str, os.PathLike],
     target: PyTree,

brainpy/_src/dyn/__init__.py

@@ -8,7 +8,6 @@
   channels, neurons, rates,  # neuron related
   synapses, synouts, synplast,  # synapse related
   networks,
-  layers,  # ANN related
   runners,
   transform,
 )