Refactor STDP and event ops for new brainevent binary backend (#817)

chore: update version to 2.7.7, enhance README and requirements, and refactor event handling

Author: chaoming0625

brainpy/__init__.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 
-__version__ = "2.7.6"
+__version__ = "2.7.7"
 __version_info__ = tuple(map(int, __version__.split(".")))
 
 from brainpy import _errors as errors
@@ -133,20 +133,14 @@
     synouts,  # synaptic output
     synplast,  # synaptic plasticity
 )
-from brainpy.math.object_transform.base import (
-    Base as Base,
-)
+from brainpy.math.object_transform.base import Base as Base
 
 from brainpy.math.object_transform.collectors import (
     ArrayCollector as ArrayCollector,
     Collector as Collector,
 )
 
-from brainpy.deprecations import deprecation_getattr
-
 optimizers = optim
 
-
 # New package
 from brainpy import state
-

brainpy/dnn/linear.py

@@ -19,8 +19,12 @@
 import jax
 import jax.numpy as jnp
 import numpy as np
-from brainevent import csr_on_pre, csr2csc_on_post
-from brainevent import dense_on_pre, dense_on_post
+from brainevent import (
+    update_csr_on_binary_pre,
+    update_csr_on_binary_post,
+    update_dense_on_binary_pre,
+    update_dense_on_binary_post,
+)
 
 from brainpy import connect, initialize as init
 from brainpy import math as bm
@@ -226,11 +230,11 @@ def stdp_update(
         if on_pre is not None:
             spike = on_pre['spike']
             trace = on_pre['trace']
-            self.W.value = dense_on_pre(self.W.value, spike, trace, w_min, w_max)
+            self.W.value = update_dense_on_binary_pre(self.W.value, spike, trace, w_min, w_max)
         if on_post is not None:
             spike = on_post['spike']
             trace = on_post['trace']
-            self.W.value = dense_on_post(self.W.value, trace, spike, w_min, w_max)
+            self.W.value = update_dense_on_binary_post(self.W.value, trace, spike, w_min, w_max)
 
 
 Linear = Dense
@@ -321,11 +325,11 @@ def stdp_update(
         if on_pre is not None:
             spike = on_pre['spike']
             trace = on_pre['trace']
-            self.weight.value = dense_on_pre(self.weight.value, spike, trace, w_min, w_max)
+            self.weight.value = update_dense_on_binary_pre(self.weight.value, spike, trace, w_min, w_max)
         if on_post is not None:
             spike = on_post['spike']
             trace = on_post['trace']
-            self.weight.value = dense_on_post(self.weight.value, trace, spike, w_min, w_max)
+            self.weight.value = update_dense_on_binary_post(self.weight.value, trace, spike, w_min, w_max)
 
 
 class OneToOne(Layer, SupportSTDP):
@@ -449,11 +453,11 @@ def stdp_update(
         if on_pre is not None:
             spike = on_pre['spike']
             trace = on_pre['trace']
-            self.weight.value = dense_on_pre(self.weight.value, spike, trace, w_min, w_max)
+            self.weight.value = update_dense_on_binary_pre(self.weight.value, spike, trace, w_min, w_max)
         if on_post is not None:
             spike = on_post['spike']
             trace = on_post['trace']
-            self.weight.value = dense_on_post(self.weight.value, trace, spike, w_min, w_max)
+            self.weight.value = update_dense_on_binary_post(self.weight.value, trace, spike, w_min, w_max)
 
 
 class _CSRLayer(Layer, SupportSTDP):
@@ -500,7 +504,7 @@ def stdp_update(
         if on_pre is not None:  # update on presynaptic spike
             spike = on_pre['spike']
             trace = on_pre['trace']
-            self.weight.value = csr_on_pre(
+            self.weight.value = update_csr_on_binary_pre(
                 self.weight.value, self.indices, self.indptr, spike, trace, w_min, w_max,
                 shape=(spike.shape[0], trace.shape[0]),
             )
@@ -512,7 +516,7 @@ def stdp_update(
                     )
             spike = on_post['spike']
             trace = on_post['trace']
-            self.weight.value = csr2csc_on_post(
+            self.weight.value = update_csr_on_binary_post(
                 self.weight.value, self._pre_ids, self._post_indptr,
                 self.w_indices, trace, spike, w_min, w_max,
                 shape=(trace.shape[0], spike.shape[0]),

brainpy/initialize/base.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 # Copyright 2025 BrainX Ecosystem Limited. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,6 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+
+# -*- coding: utf-8 -*-
+
 import abc
 
 __all__ = [

brainpy/math/compat_numpy.py

@@ -36,7 +36,7 @@
     'lcm', 'gcd', 'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan',
     'arctan2', 'arctanh', 'cos', 'cosh', 'sin', 'sinc', 'sinh', 'tan',
     'tanh', 'deg2rad', 'hypot', 'rad2deg', 'degrees', 'radians', 'round',
-    'around', 'round_', 'rint', 'floor', 'ceil', 'trunc', 'fix', 'prod',
+    'around', 'round_', 'rint', 'floor', 'ceil', 'trunc', 'prod',
     'sum', 'diff', 'median', 'nancumprod', 'nancumsum', 'nanprod', 'nansum',
     'cumprod', 'cumsum', 'ediff1d', 'cross', 'isfinite', 'isinf',
     'isnan', 'signbit', 'copysign', 'nextafter', 'ldexp', 'frexp', 'convolve',
@@ -397,7 +397,6 @@ def msort(a):
 floor = _compatible_with_brainpy_array(jnp.floor)
 ceil = _compatible_with_brainpy_array(jnp.ceil)
 trunc = _compatible_with_brainpy_array(jnp.trunc)
-fix = _compatible_with_brainpy_array(jnp.fix)
 prod = _compatible_with_brainpy_array(jnp.prod)
 
 sum = _compatible_with_brainpy_array(jnp.sum)

brainpy/math/event/csr_matmat.py

@@ -59,7 +59,7 @@ def csrmm(
     if isinstance(matrix, Array):
         matrix = matrix.value
 
-    matrix = brainevent.EventArray(matrix)
+    matrix = brainevent.BinaryArray(matrix)
     csr = brainevent.CSR((data, indices, indptr), shape=shape)
     if transpose:
         return matrix @ csr

brainpy/math/event/csr_matvec.py

@@ -84,7 +84,7 @@ def csrmv(
     if isinstance(events, Array):
         events = events.value
 
-    events = brainevent.EventArray(events)
+    events = brainevent.BinaryArray(events)
     csr = brainevent.CSR((data, indices, indptr), shape=shape)
     if transpose:
         return events @ csr

brainpy/math/jitconn/event_matvec.py

@@ -49,8 +49,8 @@ def event_mv_prob_homo(
     if isinstance(weight, Array):
         weight = weight.value
 
-    events = brainevent.EventArray(events)
-    csr = brainevent.JITCHomoR((weight, conn_prob, seed), shape=shape, corder=outdim_parallel)
+    events = brainevent.BinaryArray(events)
+    csr = brainevent.JITCScalarR((weight, conn_prob, seed), shape=shape, corder=outdim_parallel)
     if transpose:
         return events @ csr
     else:
@@ -75,7 +75,7 @@ def event_mv_prob_uniform(
         seed = np.random.randint(0, 1000000000)
     if isinstance(events, Array):
         events = events.value
-    events = brainevent.EventArray(events)
+    events = brainevent.BinaryArray(events)
     if isinstance(w_low, Array):
         w_low = w_low.value
     if isinstance(w_high, Array):
@@ -106,7 +106,7 @@ def event_mv_prob_normal(
         seed = np.random.randint(0, 1000000000)
     if isinstance(events, Array):
         events = events.value
-    events = brainevent.EventArray(events)
+    events = brainevent.BinaryArray(events)
     if isinstance(w_mu, Array):
         w_mu = w_mu.value
     if isinstance(w_sigma, Array):

brainpy/math/jitconn/matvec.py

@@ -96,7 +96,7 @@ def mv_prob_homo(
     if isinstance(weight, Array):
         weight = weight.value
 
-    csr = brainevent.JITCHomoR((weight, conn_prob, seed), shape=shape, corder=outdim_parallel)
+    csr = brainevent.JITCScalarR((weight, conn_prob, seed), shape=shape, corder=outdim_parallel)
     if transpose:
         return vector @ csr
     else:
@@ -290,7 +290,7 @@ def get_homo_weight_matrix(
     """
     if seed is None:
         seed = np.random.randint(0, 1000000000)
-    csr = brainevent.JITCHomoR((weight, conn_prob, seed), shape=shape, corder=outdim_parallel)
+    csr = brainevent.JITCScalarR((weight, conn_prob, seed), shape=shape, corder=outdim_parallel)
     if transpose:
         csr = csr.T
     return csr.todense()

brainpy/state/README.md

@@ -1,4 +1,4 @@
-# `brainpy.state` - State-based Brain Dynamics Programming
+# `brainpy.state`
 
 ## Overview
 
@@ -37,7 +37,7 @@ pip install brainpy -U
 For development or to install the state module separately:
 
 ```bash
-pip install brainpy_state -U
+pip install brainpy.state -U
 ```
 
 ## Usage

brainpy/state/__init__.py

@@ -16,5 +16,6 @@
 from brainpy_state import *
 from brainpy_state import __all__
 
-
-
+if __name__ == '__main__':
+    print(LIF)
+    print(__all__)