Delete brainpy.state examples and docs (#806)

* Refactor example scripts and update package dependencies

* Remove /docs/ from .gitignore to allow documentation files to be tracked

* Add new documentation sections for various modules and functionalities

* Remove unused code for document cleanup and version handling in conf.py

* Add brainpy_state to dependencies in pyproject.toml and requirements.txt

* Bump version to 2.7.4 and update environment handling in brainstate integration

* Remove brainpy_state from dependencies in pyproject.toml and requirements.txt

* Refactor dt handling in currents.py to use brainpy.math.get_dt()

* Update CI configuration to trigger on main branch only

* Fix spike generation logic to use updated spike value attribute

Author: chaoming0625

.github/workflows/CI.yml

@@ -3,13 +3,12 @@
 
 name: Continuous Integration
 
+
 on:
   push:
     branches:
-      - '**'        # matches every branch
+      - main
   pull_request:
-    branches:
-      - '**'        # matches every branch
 
 
 permissions:

.gitignore

@@ -236,5 +236,4 @@ cython_debug/
 /docs_state/_build/
 /docs_state/_static/logos/
 /docs_state/changelog.md
-/examples_classic/dynamics_training/data/
-/docs/
+/examples/dynamics_training/data/

brainpy/__init__.py

@@ -14,8 +14,8 @@
 # limitations under the License.
 # ==============================================================================
 
-__version__ = "2.7.3"
-__version_info__ = (2, 7, 3)
+__version__ = "2.7.4"
+__version_info__ = tuple(map(int, __version__.split(".")))
 
 
 from brainpy import _errors as errors
@@ -142,7 +142,6 @@
     ArrayCollector as ArrayCollector,
     Collector as Collector,
 )
-from brainpy import state
 
 from brainpy.deprecations import deprecation_getattr
 
@@ -151,7 +150,6 @@
 
 
 if __name__ == '__main__':
-    state
     connect
     initialize,  # weight initialization
     optim,  # gradient descent optimizers

brainpy/context.py

@@ -21,6 +21,7 @@
 from typing import Any, Union
 
 import brainstate
+from brainpy.math.defaults import env
 from brainpy.tools.dicts import DotDict
 
 __all__ = [
@@ -40,14 +41,14 @@ def __init__(self):
 
     @property
     def dt(self):
-        return brainstate.environ.get_dt()
+        return brainstate.environ.get_dt(env=env)
 
     @dt.setter
     def dt(self, dt):
         self.set_dt(dt)
 
     def set_dt(self, dt: Union[int, float]):
-        brainstate.environ.set(dt=dt)
+        brainstate.environ.set(dt=dt, env=env)
 
     def load(self, key, value: Any = None, desc: str = None):
         """Load the shared data by the ``key``.
@@ -57,16 +58,16 @@ def load(self, key, value: Any = None, desc: str = None):
           value (Any): the default value when ``key`` is not defined in the shared.
           desc: (str): the description of the key.
         """
-        return brainstate.environ.get(key, value, desc)
+        return brainstate.environ.get(key, value, desc, env=env)
 
     def save(self, *args, **kwargs) -> None:
         """Save shared arguments in the global context."""
         assert len(args) % 2 == 0
         for i in range(0, len(args), 2):
             identifier = args[i]
             data = args[i + 1]
-            brainstate.environ.set(**{identifier: data})
-        brainstate.environ.set(**kwargs)
+            brainstate.environ.set(**{identifier: data}, env=env)
+        brainstate.environ.set(**kwargs, env=env)
 
     def __setitem__(self, key, value):
         """Enable setting the shared item by ``bp.share[key] = value``."""
@@ -78,7 +79,7 @@ def __getitem__(self, item):
 
     def get_shargs(self) -> DotDict:
         """Get all shared arguments in the global context."""
-        return DotDict(brainstate.environ.all())
+        return DotDict(brainstate.environ.all(env=env))
 
 
 share = _ShareContext()

brainpy/dyn/others/input.py

@@ -228,7 +228,7 @@ 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.value) <= (self.freqs * share['dt'] / 1000.)
         spikes = bm.asarray(spikes, dtype=self.spk_type)
         # import jax
         # jax.debug.print('PoissonGroup: freqs = {f}, spikes = {s}', f=self.freqs, s=spikes)

brainpy/inputs/currents.py

@@ -18,6 +18,7 @@
 import braintools
 
 import brainstate
+import brainpy.math
 
 __all__ = [
     'section_input',
@@ -59,7 +60,7 @@ def section_input(values, durations, dt=None, return_length=False):
 
     current_and_duration
     """
-    with brainstate.environ.context(dt=brainstate.environ.get_dt() if dt is None else dt):
+    with brainstate.environ.context(dt=brainpy.math.get_dt() if dt is None else dt):
         return braintools.input.section(values, durations, return_length=return_length)
 
 
@@ -88,7 +89,7 @@ def constant_input(I_and_duration, dt=None):
     current_and_duration : tuple
         (The formatted current, total duration)
     """
-    with brainstate.environ.context(dt=brainstate.environ.get_dt() if dt is None else dt):
+    with brainstate.environ.context(dt=brainpy.math.get_dt() if dt is None else dt):
         return braintools.input.constant(I_and_duration)
 
 
@@ -136,7 +137,7 @@ def spike_input(sp_times, sp_lens, sp_sizes, duration, dt=None):
     current : bm.ndarray
         The formatted input current.
     """
-    with brainstate.environ.context(dt=brainstate.environ.get_dt() if dt is None else dt):
+    with brainstate.environ.context(dt=brainpy.math.get_dt() if dt is None else dt):
         return braintools.input.spike(sp_times, sp_lens, sp_sizes, duration)
 
 
@@ -175,7 +176,7 @@ def ramp_input(c_start, c_end, duration, t_start=0, t_end=None, dt=None):
     current : bm.ndarray
       The formatted current
     """
-    with brainstate.environ.context(dt=brainstate.environ.get_dt() if dt is None else dt):
+    with brainstate.environ.context(dt=brainpy.math.get_dt() if dt is None else dt):
         return braintools.input.ramp(c_start, c_end, duration, t_start, t_end)
 
 
@@ -210,7 +211,7 @@ def wiener_process(duration, dt=None, n=1, t_start=0., t_end=None, seed=None):
     seed: int
       The noise seed.
     """
-    with brainstate.environ.context(dt=brainstate.environ.get_dt() if dt is None else dt):
+    with brainstate.environ.context(dt=brainpy.math.get_dt() if dt is None else dt):
         return braintools.input.wiener_process(duration, sigma=1.0, n=n, t_start=t_start, t_end=t_end, seed=seed)
 
 
@@ -242,7 +243,7 @@ def ou_process(mean, sigma, tau, duration, dt=None, n=1, t_start=0., t_end=None,
     seed: optional, int
       The random seed.
     """
-    with brainstate.environ.context(dt=brainstate.environ.get_dt() if dt is None else dt):
+    with brainstate.environ.context(dt=brainpy.math.get_dt() if dt is None else dt):
         return braintools.input.ou_process(mean, sigma, tau, duration, n=n, t_start=t_start, t_end=t_end, seed=seed)
 
 
@@ -267,7 +268,7 @@ def sinusoidal_input(amplitude, frequency, duration, dt=None, t_start=0., t_end=
       Whether the sinusoid oscillates around 0 (False), or
       has a positive DC bias, thus non-negative (True).
     """
-    with brainstate.environ.context(dt=brainstate.environ.get_dt() if dt is None else dt):
+    with brainstate.environ.context(dt=brainpy.math.get_dt() if dt is None else dt):
         return braintools.input.sinusoidal(amplitude, frequency, duration, t_start=t_start, t_end=t_end, bias=bias)
 
 
@@ -292,5 +293,5 @@ def square_input(amplitude, frequency, duration, dt=None, bias=False, t_start=0.
       Whether the sinusoid oscillates around 0 (False), or
       has a positive DC bias, thus non-negative (True).
     """
-    with brainstate.environ.context(dt=brainstate.environ.get_dt() if dt is None else dt):
+    with brainstate.environ.context(dt=brainpy.math.get_dt() if dt is None else dt):
         return braintools.input.square(amplitude, frequency, duration, t_start=t_start, t_end=t_end, duty_cycle=0.5, bias=bias)

brainpy/math/defaults.py

@@ -19,6 +19,8 @@
 from .modes import NonBatchingMode
 from .scales import IdScaling
 
+env = brainstate.environ.EnvironmentState()
+
 
 class setting:
     def __init__(self):
@@ -42,79 +44,80 @@ def __init__(self):
             # default return array type
             # numpy_func_return='jax_array',  # 'bp_array','jax_array'
             numpy_func_return='bp_array',  # 'bp_array','jax_array'
+            env=env,
         )
 
     @property
     def mode(self):
-        return brainstate.environ.get('mode')
+        return brainstate.environ.get('mode', env=env)
 
     @property
     def membrane_scaling(self):
-        return brainstate.environ.get('membrane_scaling')
+        return brainstate.environ.get('membrane_scaling', env=env)
 
     @property
     def dt(self):
-        return brainstate.environ.get('dt')
+        return brainstate.environ.get('dt', env=env)
 
     @property
     def bool_(self):
-        return brainstate.environ.get('bool_')
+        return brainstate.environ.get('bool_', env=env)
 
     @property
     def int_(self):
-        return brainstate.environ.get('int_')
+        return brainstate.environ.get('int_', env=env)
 
     @property
     def float_(self):
-        return brainstate.environ.get('float_')
+        return brainstate.environ.get('float_', env=env)
 
     @property
     def complex_(self):
-        return brainstate.environ.get('complex_')
+        return brainstate.environ.get('complex_', env=env)
 
     @property
     def bp_object_as_pytree(self):
-        return brainstate.environ.get('bp_object_as_pytree')
+        return brainstate.environ.get('bp_object_as_pytree', env=env)
 
     @property
     def numpy_func_return(self):
-        return brainstate.environ.get('numpy_func_return')
+        return brainstate.environ.get('numpy_func_return', env=env)
 
     @mode.setter
     def mode(self, value):
-        brainstate.environ.set(mode=value)
+        brainstate.environ.set(mode=value, env=env)
 
     @membrane_scaling.setter
     def membrane_scaling(self, value):
-        brainstate.environ.set(membrane_scaling=value)
+        brainstate.environ.set(membrane_scaling=value, env=env)
 
     @dt.setter
     def dt(self, value):
-        brainstate.environ.set(dt=value)
+        brainstate.environ.set(dt=value, env=env)
 
     @bool_.setter
     def bool_(self, value):
-        brainstate.environ.set(bool_=value)
+        brainstate.environ.set(bool_=value, env=env)
 
     @int_.setter
     def int_(self, value):
-        brainstate.environ.set(int_=value)
+        brainstate.environ.set(int_=value, env=env)
 
     @float_.setter
     def float_(self, value):
-        brainstate.environ.set(float_=value)
+        brainstate.environ.set(float_=value, env=env)
 
     @complex_.setter
     def complex_(self, value):
-        brainstate.environ.set(complex_=value)
+        brainstate.environ.set(complex_=value, env=env)
 
     @bp_object_as_pytree.setter
     def bp_object_as_pytree(self, value):
-        brainstate.environ.set(bp_object_as_pytree=value)
+        brainstate.environ.set(bp_object_as_pytree=value, env=env)
 
     @numpy_func_return.setter
     def numpy_func_return(self, value):
-        brainstate.environ.set(numpy_func_return=value)
+        brainstate.environ.set(numpy_func_return=value, env=env)
 
 
 defaults = setting()