Merge pull request #425 from brainpy/doc

rewrite old synapses with decomposed components

Author: chaoming0625

brainpy/_src/dyn/channels/calcium.py

@@ -122,7 +122,7 @@ def current(self, V, C, E):
   def reset_state(self, V, C, E, batch_size=None):
     self.p.value = self.f_p_inf(V)
     self.q.value = self.f_q_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 
@@ -217,7 +217,7 @@ def reset_state(self, V, C, E, batch_size=None):
     self.p.value = alpha / (alpha + beta)
     alpha, beta = self.f_q_alpha(V), self.f_q_beta(V)
     self.q.value = alpha / (alpha + beta)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 
@@ -316,7 +316,7 @@ def current(self, V, C, E):
 
   def reset_state(self, V, C, E, batch_size=None):
     self.p.value = 1.0 / (1 + bm.exp(-(V + 43.) / 5.2))
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
 
 

brainpy/_src/dyn/channels/hyperpolarization_activated.py

@@ -89,7 +89,7 @@ def derivative(self, p, t, V):
 
   def reset_state(self, V, batch_size=None):
     self.p.value = self.f_p_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
 
   def update(self, V):
@@ -237,7 +237,7 @@ def reset_state(self, V, C_Ca, E_Ca, batch_size=None):
     beta = (1 - inf) / tau
     self.O.value = alpha / (alpha + alpha * self.k3 * self.P1 / self.k4 + beta)
     self.OL.value = self.k3 * self.P1 * self.O / self.k4
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.P1.shape[0] == batch_size
       assert self.O.shape[0] == batch_size
       assert self.OL.shape[0] == batch_size

brainpy/_src/dyn/channels/potassium.py

@@ -120,7 +120,7 @@ def reset_state(self, V, C, E, batch_size=None):
     alpha = self.f_p_alpha(V)
     beta = self.f_p_beta(V)
     self.p.value = alpha / (alpha + beta)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
 
   def f_p_alpha(self, V):
@@ -434,7 +434,7 @@ def current(self, V, C, E):
   def reset_state(self, V, C, E, batch_size=None):
     self.p.value = self.f_p_inf(V)
     self.q.value = self.f_q_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 
@@ -722,7 +722,7 @@ def current(self, V, C, E):
   def reset_state(self, V, C, E, batch_size=None):
     self.p.value = self.f_p_inf(V)
     self.q.value = self.f_q_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 
@@ -1001,7 +1001,7 @@ def current(self, V, C, E):
 
   def reset_state(self, V, C, E, batch_size=None):
     self.p.value = self.f_p_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
 
   def f_p_inf(self, V):
@@ -1087,7 +1087,7 @@ def reset_state(self, V, batch_size=None):
     alpha = self.f_p_alpha(V)
     beta = self.f_p_beta(V)
     self.p.value = alpha / (alpha + beta)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
 
   def f_p_alpha(self, V):
@@ -1410,7 +1410,7 @@ def current(self, V):
   def reset_state(self, V, batch_size=None):
     self.p.value = self.f_p_inf(V)
     self.q.value = self.f_q_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 
@@ -1705,7 +1705,7 @@ def current(self, V):
   def reset_state(self, V, batch_size=None):
     self.p.value = self.f_p_inf(V)
     self.q.value = self.f_q_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 
@@ -1991,7 +1991,7 @@ def current(self, V):
 
   def reset_state(self, V, batch_size=None):
     self.p.value = self.f_p_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
 
   def f_p_inf(self, V):

brainpy/_src/dyn/channels/potassium_calcium_compatible.py

@@ -119,9 +119,17 @@ def current(self, V, C_Ca, E_Ca):
   def reset_state(self, V, C_Ca, E_Ca, batch_size=None):
     C2 = self.alpha * bm.power(C_Ca, self.n)
     C3 = C2 + self.beta
-    if batch_size is None:
-      self.p.value = bm.broadcast_to(C2 / C3, self.varshape)
+    self.p[:] = C2 / C3
+    if isinstance(batch_size, int):
+      batch_size = batch_size
+      size = (batch_size,) + self.varshape
+    elif isinstance(batch_size, bm.Mode):
+      if isinstance(batch_size, bm.BatchingMode):
+        size = (batch_size.batch_size,) + self.varshape
+      else:
+        batch_size = None
+        size = self.varshape
     else:
-      self.p.value = bm.broadcast_to(C2 / C3, (batch_size,) + self.varshape)
-      assert self.p.shape[0] == batch_size
+      size = self.varshape
+    self.p.value = bm.broadcast_to(C2 / C3, size)
 

brainpy/_src/dyn/channels/potassium_compatible.py

@@ -103,7 +103,7 @@ def reset_state(self, V, batch_size=None):
     alpha = self.f_p_alpha(V)
     beta = self.f_p_beta(V)
     self.p.value = alpha / (alpha + beta)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
 
   def f_p_alpha(self, V):
@@ -426,7 +426,7 @@ def current(self, V):
   def reset_state(self, V, batch_size=None):
     self.p.value = self.f_p_inf(V)
     self.q.value = self.f_q_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 
@@ -721,7 +721,7 @@ def current(self, V):
   def reset_state(self, V, batch_size=None):
     self.p.value = self.f_p_inf(V)
     self.q.value = self.f_q_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 
@@ -1007,7 +1007,7 @@ def current(self, V):
 
   def reset_state(self, V, batch_size=None):
     self.p.value = self.f_p_inf(V)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
 
   def f_p_inf(self, V):

brainpy/_src/dyn/channels/sodium.py

@@ -104,7 +104,7 @@ def reset_state(self, V, C, E, batch_size=None):
     alpha = self.f_q_alpha(V)
     beta = self.f_q_beta(V)
     self.q.value = alpha / (alpha + beta)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 

brainpy/_src/dyn/channels/sodium_compatible.py

@@ -88,7 +88,7 @@ def reset_state(self, V, batch_size=None):
     alpha = self.f_q_alpha(V)
     beta = self.f_q_beta(V)
     self.q.value = alpha / (alpha + beta)
-    if batch_size is not None:
+    if isinstance(batch_size, int):
       assert self.p.shape[0] == batch_size
       assert self.q.shape[0] == batch_size
 

brainpy/_src/dyn/neurons/lif.py

@@ -365,12 +365,6 @@ def __init__(
     self.ref_var = ref_var
     self.tau_ref = self.init_param(tau_ref)
 
-    # initializers
-    self._V_initializer = is_initializer(V_initializer)
-
-    # integral
-    self.integral = odeint(method=method, f=self.derivative)
-
     # variables
     if init_var:
       self.reset_state(self.mode)

brainpy/_src/dyn/others/input.py

@@ -219,7 +219,7 @@ def __init__(
     self.reset_state(self.mode)
 
   def update(self):
-    spikes = bm.random.rand_like(self.spike) <= (self.freqs * share.dt / 1000.)
+    spikes = bm.random.rand_like(self.spike) <= (self.freqs * share['dt'] / 1000.)
     spikes = bm.asarray(spikes, dtype=self.spk_type)
     # spikes = bm.sharding.partition(spikes, self.spike.sharding)
     self.spike.value = spikes

brainpy/_src/dyn/outs/base.py

@@ -9,7 +9,10 @@
 
 
 class SynOut(DynamicalSystem, ParamDesc, BindCondData):
-  """Base class for synaptic outputs."""
+  """Base class for synaptic outputs.
+
+  :py:class:`~.SynOut` is also subclass of :py:class:`~.ParamDesc` and :pu:class:`~.BindCondData`.
+  """
   def __init__(self, name: Optional[str] = None):
     super().__init__(name=name)