diff --git a/brainpy/connect/random_conn.py b/brainpy/connect/random_conn.py index 5c3227629..9e93c0295 100644 --- a/brainpy/connect/random_conn.py +++ b/brainpy/connect/random_conn.py @@ -68,7 +68,7 @@ def build_conn(self): if posts is not None: ind.append(posts) count[i] = len(posts) - ind = np.concatenate(ind) + ind = np.concatenate(ind) if len(ind) > 0 else np.asarray([], dtype=IDX_DTYPE) indptr = np.concatenate(([0], count)).cumsum() return 'csr', (ind, indptr) @@ -143,7 +143,7 @@ def build_conn(self): for i in range(self.post_num): pres = self._connect(num_need=num, num_total=self.pre_num, i=i) pre_ids.append(pres) - pre_ids = np.concatenate(pre_ids) + pre_ids = np.concatenate(pre_ids) if len(pre_ids) > 0 else np.asarray([], dtype=IDX_DTYPE) post_ids = np.repeat(np.arange(self.post_num), num) return 'ij', (pre_ids, post_ids) diff --git a/brainpy/dyn/base.py b/brainpy/dyn/base.py index 1e0cee9c9..048712f05 100644 --- a/brainpy/dyn/base.py +++ b/brainpy/dyn/base.py @@ -269,9 +269,13 @@ def update_local_delays(self, nodes: Union[Sequence, Dict] = None): """ # update delays if nodes is None: - nodes = self.nodes(level=1, include_self=False).subset(DynamicalSystem).unique().values() + nodes = tuple(self.nodes(level=1, include_self=False).subset(DynamicalSystem).unique().values()) + elif isinstance(nodes, DynamicalSystem): + nodes = (nodes, ) elif isinstance(nodes, dict): - nodes = nodes.values() + nodes = tuple(nodes.values()) + if not isinstance(nodes, (tuple, list)): + raise ValueError('Please provide nodes as a list/tuple/dict of DynamicalSystem.') for node in nodes: for name in node.local_delay_vars: delay = self.global_delay_data[name][0] @@ -554,7 +558,8 @@ def update(self, *args, **kwargs): nodes = nodes.unique() neuron_groups = nodes.subset(NeuGroup) synapse_groups = nodes.subset(SynConn) - other_nodes = nodes - neuron_groups - synapse_groups + ds_views = nodes.subset(DSView) + other_nodes = nodes - neuron_groups - synapse_groups - ds_views # shared arguments shared = args[0] @@ -636,11 +641,11 @@ def __init__( if len(size) <= 0: raise ModelBuildError(f'size must be int, or a tuple/list of int. ' f'But we got {type(size)}') - if not isinstance(size[0], int): + if not isinstance(size[0], (int, np.integer)): raise ModelBuildError('size must be int, or a tuple/list of int.' f'But we got {type(size)}') size = tuple(size) - elif isinstance(size, int): + elif isinstance(size, (int, np.integer)): size = (size,) else: raise ModelBuildError('size must be int, or a tuple/list of int.' @@ -1318,7 +1323,7 @@ def __setattr__(self, key, value): super(DSView, self).__setattr__(key, value) def update(self, *args, **kwargs): - pass + raise NoImplementationError(f'DSView {self} cannot be updated. Please update its parent {self.target}') def reset_state(self, batch_size=None): pass diff --git a/brainpy/math/delayvars.py b/brainpy/math/delayvars.py index 0926cf97e..c37744c24 100644 --- a/brainpy/math/delayvars.py +++ b/brainpy/math/delayvars.py @@ -299,6 +299,9 @@ def __init__( # initialization self.reset(delay_target, delay_len, initial_delay_data, batch_axis) + def __repr__(self): + return f'{self.__class__.__name__}(num_delay_step={self.num_delay_step}, delay_target_shape={self.data.shape[1:]})' + def reset( self, delay_target, diff --git a/brainpy/tools/others/others.py b/brainpy/tools/others/others.py index 79ccdecf2..45306a30f 100644 --- a/brainpy/tools/others/others.py +++ b/brainpy/tools/others/others.py @@ -39,7 +39,7 @@ def feedback(self): def size2num(size): - if isinstance(size, int): + if isinstance(size, (int, np.integer)): return size elif isinstance(size, (tuple, list)): a = 1 @@ -53,7 +53,7 @@ def size2num(size): def to_size(x) -> Optional[Tuple[int]]: if isinstance(x, (tuple, list)): return tuple(x) - if isinstance(x, int): + if isinstance(x, (int, np.integer)): return (x, ) if x is None: return x diff --git a/changelog.rst b/changelog.rst index a31ed8f26..1827bc619 100644 --- a/changelog.rst +++ b/changelog.rst @@ -10,6 +10,111 @@ tackling the shortcomings of brainpy 2.1.x generation, effectively bringing it to research needs and standards. + + +Version 2.2.1 (2022.09.09) +========================== + +This release fixes bugs found in the codebase and improves the usability and functions of BrainPy. + +Bug fixes +~~~~~~~~~~~~~~ + + +#. Fix the bug of operator customization in ``brainpy.math.XLACustomOp`` and ``brainpy.math.register_op``. Now, it supports operator customization by using NumPy and Numba interface. For instance, + +.. code-block:: python + + import brainpy.math as bm + + def abs_eval(events, indices, indptr, post_val, values): + return post_val + + def con_compute(outs, ins): + post_val = outs + events, indices, indptr, _, values = ins + for i in range(events.size): + if events[i]: + for j in range(indptr[i], indptr[i + 1]): + index = indices[j] + old_value = post_val[index] + post_val[index] = values + old_value + + event_sum = bm.XLACustomOp(eval_shape=abs_eval, con_compute=con_compute) + + +#. Fix the bug of ``brainpy.tools.DotDict``. Now, it is compatible with the transformations of JAX. For instance, + +.. code-block:: python + + import brainpy as bp + from jax import vmap + + @vmap + def multiple_run(I): + hh = bp.neurons.HH(1) + runner = bp.dyn.DSRunner(hh, inputs=('input', I), numpy_mon_after_run=False) + runner.run(100.) + return runner.mon + + mon = multiple_run(bp.math.arange(2, 10, 2)) + +New features +~~~~~~~~~~~~~~ + + +#. Add numpy operators ``brainpy.math.mat``\ , ``brainpy.math.matrix``\ , ``brainpy.math.asmatrix``. +#. Improve translation rules of brainpylib operators, improve its running speeds. +#. Support ``DSView`` of ``DynamicalSystem`` instance. Now, it supports defining models with a slice view of a DS instance. For example, + +.. code-block:: python + + import brainpy as bp + import brainpy.math as bm + + + class EINet_V2(bp.dyn.Network): + def __init__(self, scale=1.0, method='exp_auto'): + super(EINet_V2, self).__init__() + + # network size + num_exc = int(3200 * scale) + num_inh = int(800 * scale) + + # neurons + self.N = bp.neurons.LIF(num_exc + num_inh, + V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5., + method=method, V_initializer=bp.initialize.Normal(-55., 2.)) + + # synapses + we = 0.6 / scale # excitatory synaptic weight (voltage) + wi = 6.7 / scale # inhibitory synaptic weight + self.Esyn = bp.synapses.Exponential(pre=self.N[:num_exc], post=self.N, + conn=bp.connect.FixedProb(0.02), + g_max=we, tau=5., + output=bp.synouts.COBA(E=0.), + method=method) + self.Isyn = bp.synapses.Exponential(pre=self.N[num_exc:], post=self.N, + conn=bp.connect.FixedProb(0.02), + g_max=wi, tau=10., + output=bp.synouts.COBA(E=-80.), + method=method) + + net = EINet_V2(scale=1., method='exp_auto') + # simulation + runner = bp.dyn.DSRunner( + net, + monitors={'spikes': net.N.spike}, + inputs=[(net.N.input, 20.)] + ) + runner.run(100.) + + # visualization + bp.visualize.raster_plot(runner.mon.ts, runner.mon['spikes'], show=True) + + + + Version 2.2.0 (2022.08.12) ========================== diff --git a/docs/index.rst b/docs/index.rst index dcdb10d30..042734235 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -72,6 +72,7 @@ The code of BrainPy is open-sourced at GitHub: tutorial_toolbox/synaptic_weights tutorial_toolbox/optimizers tutorial_toolbox/saving_and_loading + tutorial_toolbox/inputs .. toctree:: diff --git a/docs/tutorial_building/build_conductance_neurons.ipynb b/docs/tutorial_building/build_conductance_neurons.ipynb index a31aad49b..232416c49 100644 --- a/docs/tutorial_building/build_conductance_neurons.ipynb +++ b/docs/tutorial_building/build_conductance_neurons.ipynb @@ -13,7 +13,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) " ] @@ -32,32 +36,42 @@ ] }, { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "cell_type": "code", + "execution_count": null, + "outputs": [], "source": [ "On the other hand, simplified models do not care about the physiological features of neurons but mainly focus on how to reproduce the exact spike timing. Therefore, they are more simplified and maybe not biologically explicable.\n", "\n", "BrainPy provides a large volume of [predefined neuron models](../apis/auto/dyn/neurons.rst) including conductance-based and simplified models for ease of use. In this section, we will only talk about how to build conductance-based models by ion channels. Users please refer to [Customizing Your Neuron Models](customize_neuron_models.ipynb) for more information." - ] - }, - { - "cell_type": "markdown", + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%%\n" } - }, + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], "source": [ "## Building an ion channel" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "As we have known, ion channels are crucial for conductance-based neuron models. So how do we model an ion channel? Let's take a look at the potassium channel for instance.\n", "\n", @@ -66,7 +80,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "The diagram above shows how a potassium channel is changed to an electric circuit. By this, we have the differential equation:\n", "\n", @@ -102,7 +120,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Now we have learned the mathematical expression of the potassium channel. Next, we try to build this channel in BrainPy." ] @@ -110,7 +132,11 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "import brainpy as bp\n", @@ -144,21 +170,33 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Note that besides the initialzation and update function, **another function named ``current()`` that computes the current flow through this channel must be implemented**. Then this potassium channel model can be used as a building block for assembling a conductance-based neuron model." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Building a conductance-based neuron model with ion channels" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Instead of building a conductance-based model from scratch, we can utilize ion channel models as building blocks to assemble a neuron model in a modular and convenient way. Now let's try to construct a **Hodgkin-Hoxley (HH) model** (jump to [here](customize_neuron_models.ipynb) for the complete mathematical expression of the HH model).\n", "\n" @@ -166,7 +204,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "The HH neuron models the cuurent flows of potassium, sodium, and leaky channels. Besides the potassium channel that we implemented, we can import the other channel models from ``brainpy.dyn.channels``:" ] @@ -174,7 +216,11 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "from brainpy.dyn.channels import INa_HH1952, IL\n", @@ -183,7 +229,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Then we wrap these three channels into a single neuron model:" ] @@ -191,7 +241,11 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "class HH(bp.dyn.CondNeuGroup):\n", @@ -204,7 +258,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Here the `HH` class should inherit the superclass **`bp.dyn.CondNeuGroup`**, which will automatically integrate the current flows by calling the `current()` function of each channel model to compute the neuronal activity when running a simulation.\n", "\n", @@ -213,7 +271,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Now let's run a simulation of this HH model to examine the changes of the inner variables.\n", "\n", @@ -223,7 +285,11 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "neu = HH(1)" @@ -231,7 +297,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Then we wrap the neuron group into a dynamical-system runner `DSRunner` for running a simulation:" ] @@ -239,7 +309,11 @@ { "cell_type": "code", "execution_count": 28, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "runner = bp.dyn.DSRunner(\n", @@ -251,7 +325,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Then we run the simulation and visualize the result:" ] @@ -259,7 +337,11 @@ { "cell_type": "code", "execution_count": 29, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -302,7 +384,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We can also visualize the changes of the gating variables of sodium and potassium channels:" ] @@ -310,7 +396,11 @@ { "cell_type": "code", "execution_count": 30, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -338,7 +428,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "By combining different ion channels, we can get different types of conductance-based neuron models easily and straightforwardly. To see all predifined channel models in BrainPy, please click [here](../apis/dyn.rst)." ] @@ -346,7 +440,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [] } @@ -372,4 +470,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/docs/tutorial_building/build_network_models.ipynb b/docs/tutorial_building/build_network_models.ipynb index 66545a958..bb59eda28 100644 --- a/docs/tutorial_building/build_network_models.ipynb +++ b/docs/tutorial_building/build_network_models.ipynb @@ -87,7 +87,7 @@ } }, "source": [ - "The E-I balanced network was first proposed to explain the irregular firing patterns of cortical neurons and confirmed by experimental data. The network [1] we are going to implement consists of excitatory (E) neurons and inhibitory (I) neurons, the ratio of which is about 4 : 1. The biggest difference between excitatory and inhibitory neurons is the reversal potential - the reversal potential of inhibitory neurons is much lower than that of excitatory neurons. Besides, the membrane time constant of inhibitory neurons is longer than that of excitatory neurons, which indicates that inhibitory neurons have slower dynamics." + "The E-I balanced network was first proposed to explain the irregular firing patterns of cortical neurons and comfirmed by experimental data. The network [1] we are going to implement consists of excitatory (E) neurons and inhibitory (I) neurons, the ratio of which is about 4 : 1. The biggest difference between excitatory and inhibitory neurons is the reversal potential - the reversal potential of inhibitory neurons is much lower than that of excitatory neurons. Besides, the membrane time constant of inhibitory neurons is longer than that of excitatory neurons, which indicates that inhibitory neurons have slower dynamics." ] }, { @@ -230,7 +230,7 @@ "outputs": [ { "data": { - "text/plain": "{'EINet0': EINet(),\n 'Exponential0': Exponential(name=Exponential0, mode=NormalMode),\n 'Exponential1': Exponential(name=Exponential1, mode=NormalMode),\n 'Exponential2': Exponential(name=Exponential2, mode=NormalMode),\n 'Exponential3': Exponential(name=Exponential3, mode=NormalMode),\n 'LIF0': LIF(name=LIF0, mode=NormalMode),\n 'LIF1': LIF(name=LIF1, mode=NormalMode),\n 'COBA2': COBA,\n 'NullSynSTP1': NullSynSTP,\n 'NullSynLTP0': NullSynLTP,\n 'COBA4': COBA,\n 'NullSynSTP2': NullSynSTP,\n 'NullSynLTP1': NullSynLTP,\n 'COBA3': COBA,\n 'NullSynSTP3': NullSynSTP,\n 'NullSynLTP2': NullSynLTP,\n 'COBA5': COBA,\n 'NullSynSTP4': NullSynSTP,\n 'NullSynLTP3': NullSynLTP}" + "text/plain": "{'EINet0': EINet(),\n 'Exponential0': Exponential(name=Exponential0, mode=NormalMode, pre=LIF(name=LIF0, mode=NormalMode, size=(8,)), post=LIF(name=LIF0, mode=NormalMode, size=(8,))),\n 'Exponential1': Exponential(name=Exponential1, mode=NormalMode, pre=LIF(name=LIF0, mode=NormalMode, size=(8,)), post=LIF(name=LIF1, mode=NormalMode, size=(2,))),\n 'Exponential2': Exponential(name=Exponential2, mode=NormalMode, pre=LIF(name=LIF1, mode=NormalMode, size=(2,)), post=LIF(name=LIF0, mode=NormalMode, size=(8,))),\n 'Exponential3': Exponential(name=Exponential3, mode=NormalMode, pre=LIF(name=LIF1, mode=NormalMode, size=(2,)), post=LIF(name=LIF1, mode=NormalMode, size=(2,))),\n 'LIF0': LIF(name=LIF0, mode=NormalMode, size=(8,)),\n 'LIF1': LIF(name=LIF1, mode=NormalMode, size=(2,)),\n 'COBA2': COBA,\n 'NullSynSTP1': NullSynSTP,\n 'NullSynLTP0': NullSynLTP,\n 'COBA4': COBA,\n 'NullSynSTP2': NullSynSTP,\n 'NullSynLTP1': NullSynLTP,\n 'COBA3': COBA,\n 'NullSynSTP3': NullSynSTP,\n 'NullSynLTP2': NullSynLTP,\n 'COBA5': COBA,\n 'NullSynSTP4': NullSynSTP,\n 'NullSynLTP3': NullSynLTP}" }, "execution_count": 4, "metadata": {}, @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "a74c5b2e", "metadata": { "pycharm": { @@ -281,7 +281,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2f27dcd6a3a440a3b6da61a2999ea260" + "model_id": "b2b14ebc29a54f8ab5e24b5b29f11dff" } }, "metadata": {}, @@ -297,7 +297,7 @@ { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" 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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -357,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "14e659ca", "metadata": { "pycharm": { @@ -395,12 +395,12 @@ } }, "source": [ - "All elements are passed as ``**kwargs`` argument can be accessed by the provided keys. This will affect the following dynamics simulation and will be discussed in greater detail in tutorial of [Runners](../tutorial_toolbox/runners.ipynb)." + "All elements are passed as ``**kwargs`` argument can be accessed by the provided keys. This will affect the following dynamics simulation." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "36f54a4f", "metadata": { "pycharm": { @@ -410,9 +410,9 @@ "outputs": [ { "data": { - "text/plain": "LIF(name=LIF4, mode=NormalMode)" + "text/plain": "LIF(name=LIF4, mode=NormalMode, size=(3200,))" }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -423,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "ad57ec70", "metadata": { "pycharm": { @@ -433,9 +433,9 @@ "outputs": [ { "data": { - "text/plain": "LIF(name=LIF5, mode=NormalMode)" + "text/plain": "LIF(name=LIF5, mode=NormalMode, size=(800,))" }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "29ebd650", "metadata": { "pycharm": { @@ -472,7 +472,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "68c84417212541578975034aa74e81be" + "model_id": "16a2811f76a54584a4fc2a169dbb0961" } }, "metadata": {}, @@ -488,7 +488,7 @@ { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -522,15 +522,130 @@ }, { "cell_type": "markdown", - "id": "ee0ef0f9", + "source": [ + "## Customizing update function" + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%% md\n" } - }, + } + }, + { + "cell_type": "markdown", "source": [ - "Above are some simulation examples showing the possible application of network models. The detailed description of dynamics simulation is covered in the toolboxes, where the use of [runners](../tutorial_toolbox/runners.ipynb), [monitors](../tutorial_toolbox/monitors.ipynb), and [inputs](../tutorial_toolbox/inputs.ipynb) will be expatiated." - ] + "If you want to control your updating logic in a network, you can overwrite the updating function ``update(tdi)`` and customize it by yourself." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "For the above E/I balanced network model, we can define its update function as:" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "class EINetV2(bp.dyn.Network):\n", + " def __init__(self, num_exc, num_inh, method='exp_auto', **kwargs):\n", + " super(EINetV2, self).__init__(**kwargs)\n", + "\n", + " # neurons\n", + " self.N = LIF(num_exc + num_inh,\n", + " V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,\n", + " method=method, V_initializer=bp.init.Normal(-55., 2.))\n", + "\n", + " # synapses\n", + " self.Esyn = bp.synapses.Exponential(self.N[:num_exc], self.N,\n", + " bp.conn.FixedProb(prob=0.02),\n", + " output=bp.synouts.COBA(E=0.),\n", + " g_max=0.6, tau=5.,\n", + " method=method)\n", + " self.Isyn = bp.synapses.Exponential(self.N[num_exc:], self.N,\n", + " bp.conn.FixedProb(prob=0.02),\n", + " output=bp.synouts.COBA(E=-80.),\n", + " g_max=6.7, tau=10.,\n", + " method=method)\n", + "\n", + " def update(self, tdi):\n", + " self.Esyn(tdi)\n", + " self.Isyn(tdi)\n", + " self.N(tdi)\n", + " self.update_local_delays() # IMPORTANT" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "In the above, we define one population, and create E (excitatory) and I (inhibitory) projections within this population. Then, we first update synapse models by calling `self.Esyn(tdi)` and `self.Isyn(tdi)`. This operation can ensure that all synapse inputs can be gathered onto neuron models before we update neurons. After updating synapses, we update the state of neurons by calling ``self.N(tdi)``. Finally, it's worthy to note that we need to update all delays used in this network through ``self.update_local_delays()``. This is because delay variables relying on neurons. Once upon neuronal states have been updated, we need to update delays according to these new values of neuronal states." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/1000 [00:00", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "net = EINetV2(3200, 800)\n", + "runner = bp.dyn.DSRunner(net, monitors={'spikes': net.N.spike}, inputs=[(net.N.input, 20.)])\n", + "runner.run(100.)\n", + "bp.visualize.raster_plot(runner.mon.ts, runner.mon['spikes'], show=True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } } ], "metadata": { diff --git a/docs/tutorial_toolbox/inputs.ipynb b/docs/tutorial_toolbox/inputs.ipynb index 759668b8d..3f1c914a0 100644 --- a/docs/tutorial_toolbox/inputs.ipynb +++ b/docs/tutorial_toolbox/inputs.ipynb @@ -3,15 +3,23 @@ { "cell_type": "markdown", "id": "9f5ef59c", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ - "# Inputs" + "# Inputs Construction" ] }, { "cell_type": "markdown", "id": "95e252ca", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "@[Chaoming Wang](https://github.com/chaoming0625)\n", "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn)" @@ -23,7 +31,10 @@ "In this section, we are going to talk about stimulus inputs." ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -44,7 +55,11 @@ { "cell_type": "markdown", "id": "3451b77b", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Input construction functions " ] @@ -52,7 +67,11 @@ { "cell_type": "markdown", "id": "e377d41a", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Like electrophysiological experiments, model simulation also needs various kind of inputs. BrainPy provide several convenient input functions to help users construct input currents. " ] @@ -60,7 +79,11 @@ { "cell_type": "markdown", "id": "844fcb78", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "### 1\\. ``brainpy.inputs.section_input()``\n", "\n", @@ -97,7 +120,11 @@ { "cell_type": "markdown", "id": "64f9a99c", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Where `values` receive a list/arrray of the current values in each section and `durations` receives a list/array of the duration of each section. The function returns a tensor as the current, the length of which is `duration`$/\\mathrm{d}t$ (if not specified, $\\mathrm{d}t=0.1 \\mathrm{ms}$). We can visualize the current input by:" ] @@ -106,7 +133,11 @@ "cell_type": "code", "execution_count": 3, "id": "078fbd0d", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -175,7 +206,11 @@ { "cell_type": "markdown", "id": "26708359", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Where each tuple in the list contains the value and duration of the input in this section." ] @@ -184,7 +219,11 @@ "cell_type": "code", "execution_count": 5, "id": "8ea6dea6", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -202,7 +241,11 @@ { "cell_type": "markdown", "id": "6cc74d90", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "### 3\\. ``brainpy.inputs.spike_input()``" ] @@ -228,7 +271,11 @@ { "cell_type": "markdown", "id": "067aae19", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "For example, if you want to generate a spike train at 10 ms, 20 ms, 30 ms, 200 ms, 300 ms, where each spike lasts 1 ms and the average value for each spike is 0.5, then you can define the current by:" ] @@ -265,7 +312,11 @@ { "cell_type": "markdown", "id": "146ebc19", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "### 4\\. ``brainpy.inputs.ramp_input()``" ] @@ -294,7 +345,11 @@ { "cell_type": "markdown", "id": "68262531", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "In the first example, we increase the current size from 0. to 1. between the start time (0 ms) and the end time (500 ms). " ] @@ -329,7 +384,11 @@ { "cell_type": "markdown", "id": "7f765623", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "In the second example, we increase the current size from 0. to 1. from the 100 ms to 400 ms." ] @@ -338,7 +397,11 @@ "cell_type": "code", "execution_count": 8, "id": "d0caf6ea", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -363,7 +426,10 @@ "### 5\\. ``brainpy.inputs.wiener_process``" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -372,7 +438,10 @@ "[brainpy.inputs.wiener_process()](../apis/inputs/generated/brainpy.inputs.wiener_process.rst) is used to generate the basic Wiener process $dW$, i.e. random numbers drawn from $N(0, \\sqrt{dt})$." ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { @@ -554,13 +623,20 @@ "### More complex inputs" ], "metadata": { - "collapsed": false + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } } }, { "cell_type": "markdown", "id": "5ec7e24c", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Because the current input is stored as a tensor, a complex input can be realized by the combination of several simple currents." ] @@ -569,7 +645,11 @@ "cell_type": "code", "execution_count": 13, "id": "64ac8ffa", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { @@ -587,7 +667,11 @@ { "cell_type": "markdown", "id": "307b4eb1", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## General properties of input functions" ] @@ -595,7 +679,11 @@ { "cell_type": "markdown", "id": "4601f294", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "**1\\. Every input function receives a ``dt`` specification.**\n", "\n", @@ -606,7 +694,11 @@ "cell_type": "code", "execution_count": 14, "id": "bf9084a9", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -627,7 +719,11 @@ { "cell_type": "markdown", "id": "3b0ac63a", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "**2\\. All input functions can automatically broadcast the current shapes if they are heterogenous among different periods.**\n", "\n", diff --git a/docs/tutorial_toolbox/monitors.ipynb b/docs/tutorial_toolbox/monitors.ipynb deleted file mode 100644 index 1ba542475..000000000 --- a/docs/tutorial_toolbox/monitors.ipynb +++ /dev/null @@ -1,816 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f753f3ab", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Monitors" - ] - }, - { - "cell_type": "markdown", - "id": "904397dd", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "@[Chaoming Wang](https://github.com/chaoming0625)\n", - "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn)" - ] - }, - { - "cell_type": "markdown", - "id": "7717e918", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "BrainPy has a [systematic naming system](../tutorial_math/base.ipynb). Any model in BrainPy have a unique name. Thus, nodes, integrators, and variables can be easily accessed in a huge network. Based on this naming system, BrainPy provides a set of convenient monitoring supports. In this section, we are going to talk about this. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "19ba3a79", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import brainpy as bp\n", - "import brainpy.math as bm\n", - "\n", - "bp.math.set_platform('cpu')\n", - "bp.math.set_dt(0.02)" - ] - }, - { - "cell_type": "markdown", - "id": "596a3c54", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Initializing Monitors in a Runner" - ] - }, - { - "cell_type": "markdown", - "id": "1143dc69", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "In BrainPy, any instance of ``brainpy.Runner`` has a built-in monitor. Users can set up a monitor when initializing a runner. \n", - "\n", - "For example, if we want to simulate a Hodgkin-Hoxley (HH) model and monitor its membrane potential $V$ and the spikes it generates:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "243db637", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "HH = bp.dyn.HH\n", - "model = HH(1)" - ] - }, - { - "cell_type": "markdown", - "id": "cedf74d0", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "After defining a HH neuron, we can add monitors while setting up the runner. When specifying the `monitors` parameter, a monitor, which is an instance of ``brainpy.Monitor``, will be initialized. The first method to initialize a monitor is through a list/tuple of strings:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "db284f81", - "metadata": { - "scrolled": true, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": "brainpy.running.monitor.Monitor" - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set up a monitor using a list of str\n", - "runner1 = bp.dyn.DSRunner(model,\n", - " monitors=['V', 'spike'], \n", - " inputs=('input', 10))\n", - "\n", - "type(runner1.mon)" - ] - }, - { - "cell_type": "markdown", - "id": "44336645", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "where the string `'V'` and `'spike'` corresponds to the name of the variables in the HH model:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "74426fa9", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": "(Variable([-68.89985], dtype=float32), Variable([False], dtype=bool))" - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.V, model.spike" - ] - }, - { - "cell_type": "markdown", - "id": "b42f65b5", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Besides using a list/tuple of strings, users can also directly use the ``Monitor`` class to initialize a monitor:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "85524b4f", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# set up a monitor using brainpy.Monitor\n", - "runner2 = bp.StructRunner(model, monitors=bp.Monitor(variables=['V', 'spike']))" - ] - }, - { - "cell_type": "markdown", - "id": "fff55d35", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Once we call the runner with a given time duration, the monitor will automatically record the variable evolutions in the corresponding models. Afterwards, users can access these variable trajectories by using ``.mon.[variable_name]``. The default history times ``.mon.ts`` will also be generated after the model finishes its running. Let's see an example. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "43451236", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": " 0%| | 0/5000 [00:00", - "image/png": 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gn4icfNMSEXK8Fs6ZaLCO10jDf2gIRCPe2CuilUzorwdTCKGMSqajMT2ja5T3rWdttjTqzngtyjXRqAfj+ByvUTglLAzEh+R9ljxE4whAx5el29yQUL0olcbJa890LJRxTumXa3J+azOle9zmShZ70E3yXnYueJq83nEQFmJF8n6Wb45A6r1XRAvyYYmKt5IFTT4iDrfCyVARaKsxRWvTiwDRWlDNCdTk9fatt6MD8qeE6W1rWAid5EXkQhFZJiIrReTmUK9FT7kmEnHyvsmt2ZIvFiiLSugcPYq/abbgck5Pa1PzgTcFJ3FUnNpOcY65hpTetoaFUEleRJLAj4GLgOOAy0TkuDCu5T80BKIj13hWhvb2epZlMgLRQAVrM+/vaEiKaodbD2tT+YE3ucI4iMZin8tXI4Vo+OnCQNiW/BnASmPMKmNMN/AQcEkYFypRa9RXSnR80SrgOYV0Tmwonl7kxp7rtuD8sdyQ17kVT26/tVmQwpQuSn65piGZIJUQ3fPMp8m3NOo/7zcMhE3yU4B1vufr868VICLXiUiriLS2tbUN+kL+UsOgf9UuJaIW5SGfRcerfgvO7xwE1z+j2d+R65GwozvHw++AB/3OzFK5Rmu/homwSV4CXuvhUTLG3G6MmWGMmTFp0qRBX8h/aAjoT2EuJaIW5eFdXnp4FEojO75dB0RhwS/u6LRb8v4CZaA/Z8IpWUDTWaewGxkuCJvk1wOH+J4fDGwM40Kuvl18rl/jdh97WkR62+vVEAf9zkz/rgP0j4VSaxM0W/LuY8JPnIr7NucUa1ppX0DDQtgkPweYLiLTRKQRuBSYHcaFjL/WMO5k6VA6UaBnBinoJ6KcQy/dWGt7c6bE2mxM0ql0QYKeUVZRW0CbG3Tr3N6ZDeA7Q1cxL4SBVJhfbozJisi/AU8CSeAuY8zikK7WU5NXLtc4AUSkdWKDG13j7ZSKoXM6+9f0kmt0x0fnHF8EiPJidb19SdqlsN47UK3GSVgIleQBjDF/BP4Y/nV6Z7xmcoZMzqEhqS/ny9ON/ZmOmuP6c0GSgtLJkguQa7bv665jiyrDCVhA1fatrxopoL4iqXvWb4lco7Rvw4I+9hsk/FUoQf8P6q8BAm7NEq0TG/IRICUkr1WLLXUOak8081ub6sdtns+j6HhtUS6FhYX4kLzpGV3T3BgRa9NXBjXruDsPjTCmp7MN9E4WL3FLJBpEFKVdUlFmdJ+rDxhwepaMAL19GxbiQ/KUseSVZg6Wxhtr12JLU+9B72QpdbxGITy1V8ar0gU0F7CAai4VkPNlwmuPXAoL8SH5AE0e9BJRaSx3FIjTI00v01ErcfqLqUEEnIP+c0i1O7WDkvhU921AopnSvg0LMSL5nvXkRyjfmgVtewG1ddpL+1ezBOL4iqlBscSF1sqOfmuzMZkgIXpJPhdgnGgOocwFavJ62xsG4kPyJc+1b81KoxS07zy80r0eNKezB2Vlqq7s6LM2vYxi7ePWiwbS3FYoqQs0TOPkY0PylNauUa5xm1JJoVF7zZIiaYLuTMde/o4I+Gd69q3eXVKpXNOU1+S17pKCSkZo7duwEBuSd6tQ9qwnD6jNei1G17jPm1O6FyXHiU4pZ6ckckl7VEXO0EsKU7tLKo0Ky+vcWp2vPQ7ytiQfbQTVkwe9P2ivan7qiai3tal1QfJUmdLYc619654QVnzerDhDN+cE75L07kCLck1TKoEIqpMOw0B8SJ6e0TXNjbo96aacpKC0ve62t6dco9badIKd2lqJyG9tgu6SHMXCeu5jIeRTqfM1F+Dv6FK66wgL8SH5gHryoHfVDopSAL3x0Y4vTh5068blchA0t7fHAqr4MO/eTm3dvqScLwcB9DuKw0B8SJ7eIX6ge2JD0dpULylESq7pnQwFmndJPUlesyVf1qmt1DgxvXagehfQsBAfki9JhmpIJmhI6j2arFwIpdbJ7U+9B+UkH3AgCyi2Np2g6BqdpFlK8k1RkGtK/B1aOSEsxIfkodc5VJqP+yoNoWxSno3Xy9pUrMl7yVC9wlPV9i09JAXVC2hJgTL9smiJXNOou9prGIgNyWN6hlCCm/WqdrKUhFBq9/z3jpPXm+kYlAwFmkm+p79Dc/37cpnaWseCv8In6A79DQuxIXlTcmgI6P5BS7e9IuLWlFfq+c85pUSkd5eUi1jkUlA2sfZx668nD9CpNNEsWArT2bdhIT4kb3qfGq75CECnJN4YdNc9d0qiFLxMR42HIpfWPNeezt5LUlAs1wSdDAV6F1CnJNFMs3ESFuJD8tDbklcs1xQqJZaGd6ltb6njVW+mY65UUkjplmuML/UeihmvGksFlA391SzX+FiupVF3aeQwEB+SLzk0BHSv2sXomuJrTYq12FJJQbMFV7pLSiSEppTeqIqgbGLQuYAW+jZKiWbWko8HAi15xZZxaaEn0L5NL05s0G3BlTpeIW/BKY4GKo1cAp3E2esAesU7Osj3rdXk44FATb5R7w9aGssNuhel3kSk14IrdbyCbgsuKNEMdMpLpX3bmHSjwrT2bc5Ex6kdFmJD8kAvU76lQW9MrKfJ904wUmoR9SIivee8OgGWvObJnQvIeAWlUljJuC3Ug1HYVugdMNDSkKQ76xR2e8MBsSD5wsHNJa9rt4yht8NNr0XUM0qhKQLWZlRI3nF6LvZNih3F5catRtkOevetlxindVEKA1WRvIh8V0SWishrIvIbERnre+8WEVkpIstE5F1Vt7QCvCCEoOgajRMFemuboDwaqCQ9XLPjtbQcLuhPMCqNAAGdu6RAf0dDUm+cfGnfKjZOwkK1lvxTwAnGmJOA5cAtACJyHHApcDxwIfATEUlWea2y8DZeQdE1XRmlsdxBck1KOxFFJLqmzAKqdpdUxt+huW975kzoHbdBiWag14cQBqoieWPMn4wx2fzTl4CD839fAjxkjEkbY94EVgJnVHOtPtoBBFvyoNPzHxRCqXnnkXN6VvnUXL639NAQUC7dmdIIEL2SQmmmNuiNCiuGe0bD3xEWaqnJXwM8nv97CrDO9976/Gu9ICLXiUiriLS2tbUN6sJFS74nikcAZtEGE2RtKiYiY3qTJui0iEpjuUG3Jl8uB0Fje6O0gJZbkECnFBYW+iR5EXlaRBYF/LvE95kvAVngAe+lgK8K1EyMMbcbY2YYY2ZMmjRpMPdQPK0m0VuuAaWTJWAANmnOdDQmOE5ecd+WLqCaI62CQig1EpETsGvWugMtNw5AJyeEhVRfHzDGXFDpfRG5EngPcL4pstN64BDfxw4GNg62kX3BKUOKzYq3ZuUcWODKS95E14JecfKq5ZqARDOlRAS9D0nXHCdfLjy1rT1dryaVhVNSggF0R4WFhWqjay4EvgC8zxjT4XtrNnCpiDSJyDRgOvBKNdfqX3t6Pi9KCvosIo+IUj1IXrcW22Nip/RWHwxyDmqVFKB3wk5BN1a48yjUXIqAJl9awwj8nKBPwg0LfVryfeB/gSbgqbxT7iVjzA3GmMUi8jDwOq6Mc6MxJrRRUAihDIiuAZ2rdjYXbBGB296x9WhUBZQm7KSSCRqTOuvBBFnyzb5Iq1JZr94ot4BqJs7S85Q1j4PgRDN9xklYqIrkjTFHVnjvG8A3qvn+freDctE1ek8EyhYs+aKZobkkbi5neuw6wI0CUUlElaIqsjlGNFZr29QWjtMz0Syl+OhK70D30kgrjWPWVHC8auzbsBCTjFf3MaiePOjcmuXygmHKl2HkZTpqtDIyjunRVtA7uQPj5BU7M0sTdsAtj6yxrRnHIVXSWK3lOMoVqgOdhlRYiAfJ5x9LLXnPYtO4ameDNHnlzsxkQPSSzra6j0E6t7b2GmPIOabHjg70FtcL2tG1NCTpzjlkc7qIvlwRQNA3DsJEPEi+ULumjCav0DlYKbpGowSSzTm9iUgpyRcdr8XXtMb1By324EphaYV9m3UCSN6rB6Ms6dAJWOwbkkIyIerGQZiIB8nnH8tlvGpMhgrS5DVnOgZNbq0HpQeGUGol+bwDvlQC0bpLygbINVr7NpPrLYl6VTM19m1YiAfJl8kd0mwZV7LkNQ7ArGN6T26lmnxFLVZZ32Y930xkpLAgB7zOeeYZUg3J3u3V2LdhIRYk75nyUmLKN6YSpBKi8jDvggUXFEKpsr1OZIjIMW5ykUhA3yprb9GS79m3TUrLTmeCNHmtC6hnySdKjZOEyhyEsBALki+EUAa8p5WIco6DCL2OJgON2qbplXoPei2iTM7QUDqxlS6gBdkuQALRNg4gb8mXtHWE0oiVTC7YktfKCWEhHiRfpp486JUUMoEOLJ0hn16UQtBk0WgRZXNOYLgnaJQUguWaEY06+zYTsKNTu0tyyljyluSjh3JVKEFvzZKgkMRipqMuC66YnRugySvs23JOYlBIRAGyHbhE1JHRtdiDZ8lHI7M8U0YK02r4hYV4kHwh1bo3zbc0JNVq8qWSgtZSAZ5FFJVtbybn0JjqHe4JqBsLRbmmZMFv1HnaUiZnAhd70Fdrx9PkGyISuRQW4kHy+cdyco22LTq4mnwy2bvBzQ0JdVZGULQK9KwHowmZgJh+rZFWZZ2DWot+OU7gYg/6LPlyOQjWko8gypU1AHebrs16g2BJAXQuSplykoLSk7eyud6SgtYkmHJhfp61qe1sgWyZzGfQR/LFOPloJPGFhXiQPOU9ry1KQ9GCNHnQuZXMVYgAAYWT2zG9tuhak2Aq+TtyjikssFoQJDM2K42uyVaIrtFmSIWJWJA8FSz5lsaUuokNniXfu/ubFS5KnkUUFQsum+stKYBOCy4TUKgONId8OmXHgTbirBhdo6xfw0QsSL6iJq9Q4waXiAI4nhEKI1Zy5TIHlVpwQZo86AxLDDo8BhQnGAVE1zQk3aRDbW0tGyffqFMKCwvxIPkyh4aAW4lSY+2aTM7QWFpfFp1OoaxTRlJQasFlcibQktcYaZWp4HgFhSQfkPEKnnWszDfjlNfkHQPdyqpmhoV4kHyZQ0NAb63rdNahMdX7HFeNurFHRA1RkWsCimiBzvK9ZXdJauWa3iGU4PWtLmOqbMCA0r4NC/Eg+T6iazTWuu4OiOUGnbpxdz56prS9hZO3lE2W8pa8whyEgGMgQa9c053N0RQwbjXq3EXHa5kyDMr6NizEg+Tzj8GavM4ftDuboylIrlFYKsALkWwq2XloTWfP5JxeExt0RlWks257SvtWaz2YdNYpT/LK+jZbzqmttG/DQjxIvsyhIaD3B+3OBlvyGksFlLXklWryZXVjhf6OwgLaEA1Nvjvr9GoreHKNrt1ywfEacNgN6OvbsBATks//ESVLvoxco9EiKlqbwens2ogzkwvW5Fsa9IXTFndJ0SCidNYJDhho0Fe+NxtwaAjoNU7CQixI3kM5TR701SzpLjNZNJYK6MuS10hEHkn60dKoL5y2nBSmtR6Ma8lHI2AgXdaXpPdY0DAQC5IvlhoOSIBR6mRJV5BrvPe1oC9rU9sC2pWp4BzUNg7y7YnCAmqMIV3O8apQZuzK5EgmJLBAGejq2zARD5KvcGjICKXhUmU1eYUDsJwl35RKIKJv29uVyRXOy/VDYz2Ycgtoi8IFNJs/PKbcDlTbHOvKOIXy3X5olcLCQk1IXkQ+KyJGRCb6XrtFRFaKyDIReVctrlMOfR0aAtEjeU0JXOUiQESEEQondzrr0ByQg9DcmMSYqOyS8uGpioiou4yTGHRGLnVlc2VkO52H84SFVLVfICKHAO8E1vpeOw64FDgeOAh4WkSOMsaEMgoqhVAWNHllA7CsJq/wBKNy2ibo26YbY/KWfLBuDJR9vx7wFvtSqdErqKZxHJQu9qBTCutrHGgzTsJCLSz5/wY+T5FrAS4BHjLGpI0xbwIrgTNqcK1AVAqhLJybquwHTeeCQ9GK8pJ+axP0JW9lcq6kECTXaEyCKadxg76Qz3KyHeisB5POlJljhXGgZ46FiapIXkTeB2wwxiwoeWsKsM73fH3+taDvuE5EWkWkta2tbVDtqGzJu5sVTfKHMcaNUihTuwZ0EVFhckcgwagrLy0FWXAaHcXlkotAX62dcqG04PatNimsK5MLlO289muaY2GiT7lGRJ4GDgh460vAF4F/DPpvAa8FLvHGmNuB2wFmzJgxKDOgr+P/QNeq7U0ET5rxQ6NTKJ11S/cmIpBg5C045cL8QNc2PZ1xAuUPcHcjmhbQirKdQinM1eR7t1WjFBYm+iR5Y8wFQa+LyInANGBBnlwPBuaJyBm4lvshvo8fDGysurVl25hvU8B7BQeWIkves85GNvbufo1E1NmdLeyISqFNrklnyktLLQr9HV19yTWK2uqN2xEBxol/Bzp2KBtVAV2Z4HwJ0GechIlByzXGmIXGmMnGmKnGmKm4xH6aMWYzMBu4VESaRGQaMB14pSYtDmpL/jFIrtF4ItC+tLvgVJosmohobzrHqKZgknf7Vs8uyeu3ig43RX27L51lZJm+HdGQUkVE3rgNMk401tpJl4muAZ2O4rBQdXRNEIwxi0XkYeB1IAvcGFZkjXs99zHI8QoucWrSNguWfMDk1khEHd3ZwAUJ3PZu3t01xC0qD6/fWipo8pqIyCX5MnJNY5I9nZkhblF5FEg+YNxqlBk7u3OB4wDcHb6mcRAmakbyeWve//wbwDdq9f0Vr12hnjzoW7X3dVew5BUS0d4K1qY2SaG9y+3b0c0VrE1F7d2bzjFlbHPgey0NCbbs1tNWb9xWMk407UDbu7KB4wD0jdswEY+M1wqaPOjT3zrS5S355kZ9nv996WxZuUabJt/e5Vq+QZNbY2Jcpb7VZpzsLYzbSglGeqS7PV2Z8iSvMIkvLMSL5MuwvLZzUytZ8o3JBAnRRUQd3bmKco2mHIQ9eUt+v+aGXu9plMIqafLarM1Kmry2vs3kHLoyDqMDxgHoM07CRDxIngq1hnF/UE2afKXJotFRvLeStdmo67SlSnKNRt24Yt82pFQtoPvSWUSCjRNtfVtpHED+UHclbQ0b8SD5fljymn7QXR2upDCmJdjK0GbBVdQ2G5JkHVM4B7be2Juf3EHEWSiopoQ4MzmHdNapYMnrW0BHNaaC81GUlUYuynZl5pgyQypMxILkPZTV5JVZ8rs6ukkI7FeG5JsVSSDZnMPuzgzjRjYGvq/PgsswojEZeGiItl3Szn3dAGX71ltAu5Vkke7Y1834UeXbCprGQWVLXpufLkzEguQr1ZMHfan3Ozq6GdPS0OvwZg+aiGhXPoRvfDkiUmbBVXK2gS7/zI4Ol+QnRGQB3dnRzbgR0SB5L/S03FjQpsn/6JkVPNK6ru8PDgLxIPkK9eTBTXHvUpSws7MjU3aygC4iKlibEZnc2/Z2M3FUU9n3Nflnduzto2+VJcbt2NdddrEv1INR0rfb8uN2UpmxoC265qE56/j7qu2hfHc8SL4PTV5bDZBdHd1lt+ig6wCG7fsqW5vaSL6tPV2R5DXt6gp924cEomVR2rmvvCWfSIibYKSkb7e1pwHKjgVNviRjDG1702UXpGoRD5LPP5YneT0TG2D73vKTBXQ5Xnf0oRs3K4s937Y3zaTRFUhekRa7o5+7JA1j1xjD9n3dZRck0GUdt+1N05CUisENoMM4aU9n6c46Ffu2GsSC5Pffr4kb334Eh00YGfi+t2pnFazaABt3dXJQmSxHgOaUnkVp465OAA4cUy4rU89kcRzTJ8lr0mI37e6iISl9avIaxsLOjgzprMMB+5Uft5p8Sd6OLqhyKug6Z8IrC3LAmJZQvj+U2jVDjQPHtPC5dx1T9n2vEmVX1mFUQNTFUKK9K8OeriwHjS3/g7ryko4Faf3OTkY2JstbRIqIaNu+NJmcYf9KlnxDkl15h2e9sWFXJweNbSlLRN6BFxrGwvqdHQBMGVdh3CragW7a3cnkCguSphIXhb6twAnVIBaWfF/QZBFtyq/alUlejyW/YVcnU8a1lI1cGqEonX3tdneyHDYxeEcHniZf/7aCO7kPrkSa3rjN1n8sbNjp7ugqEZGm7Oc12zs4bPyIsu9r2oGuz/ftIRXGQjUYHiSf0qMbF1ftCnKNIpJfv7Oz4sTWFOa32iP5CpO7qSGhgjQB1u3oo29TeiQFj4gqLUpa5JrurMPGXZ0cNqH8ONB0Stj6nZ00phIVAwaqwbAgeW/bm1YwuZdv2QvAkZNGl/2MS0T1tzazOYc32vYyff/ybdXkwFqzfR8JgYPHVZjcSvwdO/d1s21vmiMmjSr7mUIIpYpx287EUY2MjUDAwPqdHTgGDu1jsQcdu/sVW9o5fOLIsrJdtRgWJF/UjetPnMs3t3PAfs2MGRGscYNLRN1ZB8ep76HIq7fvozvrcHQlklfkwFq8cQ9HTh4VeDydBy3+jqWb2wE45sD9yn6mWZEmv3RzO8ccUL6toCf0d/HGPQAcW7Fv3XGbVtC3Sza1V2xrtRgWJK9Jk1+yuZ2jDihPmuAbgHW25pdscono6Art1STXLNywmxOmjKn4GS1S2JJNeSKq1LcpHeM2k3NYvqW94jgAPXLNog27aUwlOKqCcaKlb3fu62bzni6O6aNvq8EwI/n6kuaergzLNu/h1EPGVvxcs5Kt5Ctv7mBEY7Li5E4mhMZk/ZNgNu3upK09zYl9kHxTQ5J01ikc/l4vzFm9gyljW/oM94T6j9uFG3aTzjqcdui4ip9rbkiosIznr9vFsQeM7nNHB/WXwuas3gHAyX1wQjUYJiSv4yCOuat34hg4c9r4ip/TYh2/tGo7M6eOp6GPsNMmBRnFf16xDYCzj5hQ8XPNBf9M/cjIcQwvrdrOWYdPKBu1BMVSAfXu27+/4abbn3V43+O23qS5L51l3tqdnHV4X+NAxwL6tze209yQ4NRDx4Z2jWFC8jq2Zs8t20pTKsGp/bCIoL7tXb+zgxVb9/KWPkgTPAmkvpPlhWVt7L9fU0X/AejYpr+2YTc7OzJ99m0iITSm6r+AvrCsjWMOGM2EPqI/NEhhf3tjO5mc4W1HTar4OQ2cYIzhxeVtzJw6nqZU8KE8tcCwIHkNCTs5x/D4os28/ejJhaiJcigSUf2I83cLNgHw7hMP7POzTakE6Tr2bXtXhmeXbuX8Y/evaBmDDgtu9vyNNCYTXHDc/n1+tt61djbu6uSV1Tu4uJ/joCtTXynsdws2MqalgdOn9teQqt84WLRhD6u27etX31aDYUHyTb6M13rhLyu30dae5uKT+v5B650EY4zh0XnrOfXQsRxSIQzNQ3Ne564X/vDaJjozOT54+sF9frbeu6R0NsfsBRs57+hJZbOI/ah3NNBvXt0AwHtPPqjPz3rjtrtO5UN2d2R4YvFm3n/KQX1axp4hVc+w6l/PW09DUrjoBEvyVaMYLlW/H/Tuv77JpNFN/OPxfVtv9d5KPr+sjRVb9/LRsw7r1+frWeXTcQx3/3U1R+0/ilP64byqd+TSb1/dwLa9aS7vd9/WT+fuyuS452+rOXf6RKZWyCL2UPQh1KdvH3hlDd1Zhw/NPKTPzybyAQP1auvOfd083LqO9550UMVw6lpgeJB8nTNeF23YzfPL2rj8zMP6pb0VnIN1GIDGGH707AoOGtPcL+sN8glGdSKiJxZvZtmWdm58+5F9SjVQX0u+O+vw0xdWcfxB+3Hu9In9+j/1TN56pHUdbe1pbnjbEf36fD2Nqb3pLD9/cRVvP3oSxx9UOcLKQz0DBu7+65t0dOe4vp99Ww2qJnkR+XcRWSYii0XkO77XbxGRlfn33lXtdapBQ1JISH3kD2MMX//D64wf2cjVb53ar/9TT0t+9oKNzFu7i0+dP73PqBoPTXWSFLoyOb7zxFKOmDSS95zUvwWpqY6O11l/W82b2/bxmQuO6teCBPWTa3Z1dPP9p5Zz5rTx/XK+Q339HT9+biU7OzLcdMFR/f4/rsw49ONg3Y4OfvbiKi4+6cA+cw9qgaqqUIrI24FLgJOMMWkRmZx//TjgUuB44CDgaRE5yhhTl2XTO9uzHoPv969t4qVVO/jaJcezX5lDhUtRL01+d2eGb/1xKSdOGcMHZ/S95fXQnEoWDicfSvz4uZWs3t7BAx87s+xRiqVorpN/ZvPuLn7wzArefvQkzj92cr//X1OdHK/feXIZuzsz3Pq+4/u9IHlyzVAT57LN7fz8xVX8y+kH90uy81CPBdQYw9d+/zoJEb707mOH5JrVWvKfAG4zxqQBjDFb869fAjxkjEkbY94EVgJnVHmtqlCP8K6t7V3852OLOOWQsXzkjEP7/f/q5fn/z98uom1vmq+//4R+kybUp28XrNvFT194g386dQrnHNk/6QPqY8k7juH/PDyfnGP4ynv7T5pQn+iaZ5du4cGX13LtW6cNKN2+HpZ8Opvjs48sYHRzii8OkDTrIYU90rqep17fwqcvmF6xEm0tUS3JHwWcKyIvi8gLIjIz//oUwH8q7fr8a70gIteJSKuItLa1tVXZnPIY6lhuxzHc/OuFdHbn+K8PnUxqAHXs6xHL/dtXNzB7wUY+ff70AWffNTUkhtSRuacrw7/9Yh6TRzfzlfceN6D/Ww8p7PY/r+Jvb2znK+89rl8OTD+G2tpsa0/z+V+9xjEHjOaz7zp6QP+3Hlmktz2+lIUbdnPbP59U9vzZchhq4+TNbfu49XeLOfvwCXz83MOH7Lp9yjUi8jRwQMBbX8r//3HAWcBM4GEROZzgM7UDg2eNMbcDtwPMmDEjtADboXay/OT5lTy7dCtffd/xFSsNBmGoLaKlm/fwxd8sZMZh4/jk248c8P9vSg3dAmqM4eZfv8bGXV08fP3ZFasiBmGondovrdrO955cxoXHH8CH+xH1UYqhjK7pzjrc+OA82ruyPPjxswacoDPUC+iTizdz919Xc/U5U3nX8UEUVRleXP9QYF86yyfun0tDMsH3P3xyaBUng9AnyRtjLij3noh8AnjUuNkPr4iIA0zEtdz9I/pgYGOVba0KQ7k1e27ZVv7rqeW8/5SDuOLs/oXK+TGU6ew79nXzsVmtjGpK8b8fOW1AMo0Ht2bJ0PTtT55/gz8u3MzNFx3D6YdVTngJwlD6O9Zu7+AT98/l0Akj+Pa/nDQgmcbDUI7br/5uMa+8uYMfXHpKxeJe5VDQ5IeAOJdtbuc/Hl7ASQeP4eaLyp8KVwnNDUn2dWdr3LLecBzDZ345n+Vb2rnrqpkcGNIxf+VQrVzzW+AdACJyFNAIbANmA5eKSJOITAOmA69Uea2q0NI4NBbRmu37uOkXr3LMAfvxrQ8MbmIX0tlDbm8m5/CJ++eytT3N7VfM4IAy57j2haGyNp9YtInvPrmM9518ENf/w+C2u0NlbbZ3ZfjYvXNwDNx55cx+JT4FYajkmvteWsMDL6/lE+cdwSWnBCqrfWKoFtBte9NcO2sOIxqT/Oyjpw+6JMBQ9e33n1rOn17fwpcvPo7zju6/071WqPaM17uAu0RkEdANXJm36heLyMPA60AWuLFekTUehuIH3ZvOcv19cxERfnb56X2WL6iE5lT4Ff1unb2Yl9/cwf98+JQBRSWUojmVJJMz5BwzqJ1Af7Bw/W4+/cv5nHLIWL4zSKsY3H6FcKWwnGP49EPzeaNtH7OuPoNpA9Th/RgK3fjvb2znq7MX845jJvPZfxyYDu/HUJTjSGdz3HDfXNra0zx8/dlVWcVNDcnQd6CPzd/A/z63kktnHsLV50wN9VrlUBXJG2O6gcvLvPcN4BvVfH8tEXaYX84xfOoXr7Ji617uuXomh1Y4eqw/CHtye5bbDW87gvefOjjLzYP/5K0RjbU/G37z7i4+du8cxo9o5PYrTi9YjINBKpkglZBQ+/Y7Ty7lmaVb+dolx/PWfiY9lYNXGtlxTCg67rodHXzygblMnTiSH1x6SlWLdHNDuCGUxhhu+fVCWtfs5McfOa3q8rxhS2Hz1+3ic796jTOmjedrl5wwaMOkWgyLjFfIn1oT4g/69T+8XnC0nju9cgW8/iBMkv/bym0Fy+1zA4ygCEKY1nFnd46P39tKe1eWO6+ayeTRg5OU/Agz0urXc9fzsxdW8a9nHtrvshCV0BJiGYY9XRmunTWHnGP4+RUzGN3PPI5yaArZkv/J82/w6Ksb+D/vPKpfNaD6QnOIx2xu2t3Jx+9tZfLoJn56+ekVa9uHjWFD8k0hHmhw30truPuvq7nmnGn9rknSF8KSl1Zu3csN989lWg0sNw9h6dxefPmijbv54aWn1uyItKaQ/B1z1+zklkcXcvbhEwaURFQJYZVhyOQcbnxgHqva9vHTy0+vSlLyEOa5qZ4/5pJTDuLf3zHwCLAghGVIdXbnuO7euXSks9x55cwBh3bWGsOG5MP6QV9c3satsxdz/jGT+dLFtctgC2PnsX1vmqvveYXGVIK7rppZteXmISyS//5Ty3l80WZuueiYfpXl7S/CGAvrdnRw3b2tHDi2mZ/862n9LgnRF8JwZhpj+Mrsxfx5xTa++YETecsAkskqoSmVQKT2tWsWbdjNZ365gFMPHcu3/3nw/phSeIX1alka2W+Y/ODSU4ekbEFfGDYkH0bm4Iot7dz4wDymTx7FDy47taZOx1oTUVcmx3X3zWXrnjQ/v2JGv0oI9xdhVB/8zavr+d/nVvLhGYfUPHGk1ru63R0Zrrr7FbKO4a6rZjKuhpZbGNnPd/z5TR58eS2fPO8IPjSA8hV9QUTcswVqKIFs3t3FtbPmMH5kI7d/dEZV/phSNKeSOAYyudqR/LefWMrjizbzpXcfW1PDpBoMG5L39Ldardrb96a5ZtYcmhqS3HnVTEY11dbh6IYl1mayOI7hs48sYO6anfz3h0/p82SqgaJYvrc2i9LcNTv4wq8Wcua08fx/76+9w6qWDrfurMMnHpjL2h0d/Oyjpw848a0v1Dr7+cnFm/nm40u4+MQDq4qkKYemGvat54/Z25XlzqtmVDwPdzCo9S7pgZfX8LMXV/HRsw7j2rdOq8l31gLDh+RTSXKOqcmqnc7muD5vFd9x5QymhFCDormGpy19/6nl/P61TXzhwmP6ddLTQNFUQ2vzzW37+NgsV/YIy2HlLvjV960xhi/9ZiF/e2M73/7nk/o8V3QwqKUU9tr6Xdz00KucfPBY/utD4WRd1sqXlHMM//6LV1m8cTc/vOxUjjmgNv4YP2qZ/fzC8jb+72OLOe/oSXzlvcfVLZImCMOH5Gu0ajuO4XOPvEZr3iquJr68Emol1zzSuq4ge9zwtnDqZdSqb7ftTXPV3a8gItxz9Rk1lT38qFV0zU+ef4NH5q7npvOn84HT+j6VajCo1aHuG3Z1cu2sViaOauLnV9RW9vCjFolxxhhunb2Yp5ds4db3Hc/5x4YjezTVaAFdsmkPNz4wj6P2H83/fuS0AdWpGgroak2IqFWUwjf/uITZCzaGZhV7qIVF9OcVbXzxNws558gJfP2fwovTLaazD75vO7tzXDurlc27u7jjyhk1ifYoh1rUEX9s/ga+++Qy/unUKXz6guk1allv1MLa3NXRzVV3vUJXd467r5pZc9nDj+ZUsmrL+GcvruK+l9Zw/T8czhVnT61NwwJQC5lxy54urr1nDiObktx11Yyay7a1gL4WhYTiqTWDH4B3/HkVd/zlTa56y9TQrGIP1VpEC9bt4vr75nLEpFH85F9Pr1m0RxCqLaiWcwyfeuhVXlu/i59efjqn1dhnUIpqF9AXlrfx2UcWcOa08dz2zyeGujWvVq7xFs812zu455qZTB9ETZqBoFop7LH5G7jt8aW856QD+cKFg6tJ019Um9+xq6ObK+58hd2dGX5ZZfZtmBh2JD/YyfLY/A18/Q9LePeJB/Cf7wlfc6tGrnmjbS9X3f0KE0Y1cu81Zwy6bkp/UY1F5DiGLz66kKde38JX33f8oKoJDhTVOF7nrtnJDffNZfrk0fz8yhmDrpvSX1QjhWVyblXJeWt38pOPnMZbjqhNqGQlVON4fX7ZVj77yALOmDae730w/EqN1XBCR3eWa+6Zw5vb9nH31TM5YUr/jhysB4aRXDN4bfOJRZv5j4ddy+37H6pNAlFfaM6XQR1oNND6nR1ccecrJBPCfdecyeT9qs8Q7QuDtYiMMXz1d4v5Zes6PvWOI7nyLVNDaF1vNA1Sk1+6eQ/X3DOH/fdrYtY1Z/T7pK9qMNgQypxj+MKvXuPZpVv5+vtP4KIQpUU/BnsU5N/f2M71983lqP1H8/Mah0qWw2BDf7uzDjfcP4/563bxw8tOGdChNfXAMCL5wf2gT72+hX97cB4nHTyGO64cmsEHRafQQGKO1+3o4NLbX6K9K8M9V58x4AMqBovBWETGGG57Yimz/r6Gj587jc+8s/9nc1aLwZRGfn3jHj7y85dpbkhw37Vnhqpr+zGYEMqcY/jcIwt49NUNfO5dR/OvZ9YmC7s/aM7X2hkI5q7ZybWz5nDo+BHcd+2ZjBkR/uIJgxu3XZkcN9w/lxeXt/GtD5zIhScMzeJZDaxcUwFPLNrMv/9iHsdPGcM915xRswzR/sDvQ+jPwrJ2eweX/fwl9qazPPCxs4Z0++hZRP3dJTmOa8HP+vsaLj/rUL747mOHNOSsKTUwf8fC9bu5/M6XGdGY5MGPn1XTRLK+4FUy7W/fZnMO//HIAh6bv5HP/uNR3DiIQ2CqQUtDko4B1Gj/84o2rr9vLpNHN/HAx84c0hIAA5XCOrtzXHdfK39esY1v/NMJfHhm/4/0rCeGDcm3DJDk7/37ar4yezGnHjKWu68emq25H/6j1MZQ+doL1u3i2lmtZB2HBz525pDrg6lkgtHNqX5V+ezOOnz2kQXMXrCRj587jVsuGlqCB7dvMzlDNuf0Ge724vI2bnxgHmNGNPCLISZ4GJiksDed5cYH5vHC8jY+f+HRfPK8oSV4gHEjGvpd7fWPCzdx00OvcsSkUdx7zRlDIi364XFCZ3ffnLB9b5rr7pvLvLU7+e6/nDSgg+7rjWFD8kXSrDxZMjmHbz++lDv+8iYXHLs/P7rs1Krqwg8W3gDs6GMAPrFoE5/+5XwmjW7i7qvO5MjJ9amVMX5kIzv2dVf8zNb2Lm58YB5zVu/kCxceww1vO7wuSSPj8kcG7urMMHFUednlvpfWcOvsxUyfPIq7rpo5ZAcv+1EoFdCHcbJhVycfm9XK8i3t3PaBE7l0AAfH1xLjRjayuzNTcQF1HMOPnl3Jfz+9nBmHjXMPVRkiicaPsSPda/a1KK3Y0s41s+awdU+aH3/ktFBDp8PAsCH5QhnUCqS5aXcn//7gq7Su2clVb5nKly8+tm6JDR75tLWnA2PG09kc3/rjUu7522pOOWQsd1w5oyJhhY1xIxrZ2VGe5F9etZ1PPfQquzsz/ODSUwZ9+lAt4OnpW/ekA/usvSvD/31sMb95dQPnHzOZH1x2al3jn8eOaKi4gD65eDOf/9Vr5PK1c952VPWlrgcLT24pt4Bu35vmC79+jaeXbOUDp07hmx84ccj8XKUY3ZSiKZWgbW868H1jDI+0rufW3y1mRGOKX15/dmjJj2Fi2JB8JW3TcQy/mLOW2x5fSs4x/PCyU3nfyQcNdRN74MD8UXyb93T1eq919Q6+/NtFLN3czjXnTOMLFx0deihfXxg/spEtAW3dl87y3SeXcc/fVnPYhBHcfdUZHHdQ7VPUB4LJeZIPmtwvLG/jy79dyMZdXXzmgqP4t3ccOSTRVJVw4JgWNu7u7PX69r1pvvX4Un41dz0nThnDjy47dcic7eXg7ZJ27uvuQfLGGP64cDP/97FF7OnKcOt7j+PKt0yta/q/iDBpdBPb2nuPg427Ovna717nicWbOfvwCXz/wyerjYPvC8OG5MeNaKQxmWDjruJkMcbwl5Xb+N6flrNg3S7OPnwC3/zAiaFmW/YX+3sk75vcq7ft44fPruDReRs4aEwzd145I7SU74HigDHNzF2zE2MMIkIm5/DLOev4wTMraGtPc9VbpvL5C48O5eSogcKz5LfsLi5KSzfv4XtPLufpJVuYNnEkD19/9qAOCg8DU8a2sGTTnsLzju4sD768lh89u5J96SyfPO8Ibrpget0XeigaJ2t3dBQSr15du5PbHl/Ky2/u4MQpY3jwg2epKMEL7ljY5BsHuzsz3PPX1fz0hTdwjOHmi47h4+ceXveFvhrUf8YNEZIJ4bAJI1ixdS+d3Tn+sHAT97+0hvnrdnHQmGa+98GT+efTpqgpLDS6KcXk0U38/Y3tHHPAfjzcuo4/LtxEQzLBDW87gk+df6QKwvRw3IH78eDLa3lu2VZe37iHB15ey6bdXcycOo6fXn66GsIEOHjcCEY3pXhxRRtjRzTw0Jx1PLt0KyMbk9x80TFcc860up7kU4ojJo3kicWbeWnVdv66chsPvLyWHfu6eeuRE/nKe48LPYt1IPDI+8XlbXR057j/pTW8/OYOJo5q5GuXHM9HzjhUVW2X4w7cj8fmb2Tumh08vnAzD81Zx950lotOOIAvXXwsB48bWkd7GJBaFsyvFjNmzDCtra2hff+tsxdzz99W05hK0J11OHziSK46ZyofnnmICiuoFF/93WLu/utqAEY3p7jsjEP52LnTanIEXq2xaXcn5333+UKM9LnTJ3LNOdM47+hJahZOPz73yAIembsegAkjG7nyLVO54uzDGDuivqf4BGHJpj2850d/IecYEgLnHT2ZT553BDOmjq930wJx9d2v8NyyNgAOHtfCR886jMvPOoyRCuu6/HXlNv71jpcB1xC86IQDuOFtR6jOYA2CiMw1xswIfG84kfzOfd38z9PLaUgmeOdx+3PGtPEqCchDOpvj9ws2sV9LA+dOn1g3B1V/8frGPSzauJszp43nsAn1l7wqoSuT4/FFm5gwsomzj5gQam2fWuDVtTtZ1baPc46cyAFj9C3yfuzpyvDkos0cMn4EM6eOVy91/P2N7Wxt7+Lc6ZPqflTfYGFJ3sLCwiLGqETyus0XCwsLC4uqYEnewsLCIsaoiuRF5BQReUlE5otIq4ic4XvvFhFZKSLLRORd1TfVwsLCwmKgqNbd/R3gq8aYx0Xk3fnn54nIccClwPHAQcDTInKUMaY2h5ZaWFhYWPQL1co1BvDSF8cAG/N/XwI8ZIxJG2PeBFYCZwT8fwsLCwuLEFGtJf9p4EkR+R7ugvGW/OtTgJd8n1uff60XROQ64DqAQw+NRulOCwsLi6igT5IXkaeBoDPZvgScD3zGGPNrEfkQcCdwARAUGBsYq2mMuR24HdwQyn6228LCwsKiH+iT5I0xF5R7T0TuBW7KP30EuCP/93rAX3D5YIpSjoWFhYXFEKFauWYj8DbgeeAdwIr867OBB0Xk+7iO1+nAK3192dy5c7eJyJpBtmUisG2Q/zeqsPc8PGDveXigmnsue8ZjtST/ceAHIpICushr68aYxSLyMPA6kAVu7E9kjTFm0IWwRaS1XMZXXGHveXjA3vPwQFj3XBXJG2P+Apxe5r1vAN+o5vstLCwsLKqDzXi1sLCwiDHiRPK317sBdYC95+EBe8/DA6Hcs6oqlBYWFhYWtUWcLHkLCwsLixJYkrewsLCIMWJB8iJyYb7a5UoRubne7QkDInKIiDwnIktEZLGI3JR/fbyIPCUiK/KPeg5TrQFEJCkir4rI7/PPY32/ACIyVkR+JSJL87/32XG+bxH5TH5MLxKRX4hIc9zuV0TuEpGtIrLI91rZe6xlFd/Ik7yIJIEfAxcBxwGX5atgxg1Z4D+MMccCZwE35u/zZuAZY8x04Jn88zjhJmCJ73nc7xfgB8ATxphjgJNx7z+W9y0iU4BPATOMMScASdwKtnG733uAC0teC7zHkiq+FwI/yfPcoBB5ksetbrnSGLPKGNMNPIRbBTNWMMZsMsbMy//djjvxp+De66z8x2YB769LA0OAiBwMXEyxXAbE+H4BRGQ/4B9w60BhjOk2xuwi3vedAlrySZUjcDPpY3W/xpgXgR0lL5e7x5pW8Y0DyU8B1vmel614GReIyFTgVOBlYH9jzCZwFwJgch2bVmv8D/B5wPG9Fuf7BTgcaAPuzstUd4jISGJ638aYDcD3gLXAJmC3MeZPxPR+S1DuHmvKaXEg+X5XvIwDRGQU8Gvg08aYPfVuT1gQkfcAW40xc+vdliFGCjgN+P+NMacC+4i+VFEWeR36EmAabp2rkSJyeX1bVXfUlNPiQPLDpuKliDTgEvwDxphH8y9vEZED8+8fCGytV/tqjHOA94nIalwJ7h0icj/xvV8P64H1xpiX889/hUv6cb3vC4A3jTFtxpgM8CjuuRRxvV8/yt1jTTktDiQ/B5guItNEpBHXYTG7zm2qOUREcHXaJcaY7/vemg1cmf/7SuCxoW5bGDDG3GKMOdgYMxX3N33WGHM5Mb1fD8aYzcA6ETk6/9L5uIX+4nrfa4GzRGREfoyfj+tviuv9+lHuHmcDl4pIk4hMo59VfMvCGBP5f8C7geXAG8CX6t2ekO7xrbhbtteA+fl/7wYm4HrmV+Qfx9e7rSHc+3nA7/N/D4f7PQVozf/WvwXGxfm+ga8CS4FFwH1AU9zuF/gFrs8hg2upX1vpHnEPZXoDWAZcVM21bVkDCwsLixgjDnKNhYWFhUUZWJK3sLCwiDEsyVtYWFjEGJbkLSwsLGIMS/IWFhYWMYYleYtYQEQmiMj8/L/NIrIh//deEflJSNf8tIhcUYPveUhEpteiTRYWpbAhlBaxg4jcCuw1xnwvxGukgHnAacaYbJXf9TbgcmPMx2vSOAsLH6wlbxFriMh5vlr0t4rILBH5k4isFpEPiMh3RGShiDyRLxuBiJwuIi+IyFwRedJLPS/BO4B5HsGLyPMi8t8i8mK+BvxMEXk0Xyv86/nPjBSRP4jIgnzt9A/nv+vPwAX5hcPCoqawJG8x3HAEbvniS4D7geeMMScCncDFeaL/EfAvxpjTgbuAbwR8zzlAafG0bmPMPwA/xU1RvxE4AbhKRCbg1gbfaIw52bi1058AMMY4uOVkT67pnVpYYEneYvjhceMWwlqIe0DFE/nXFwJTgaNxifkpEZkPfBm3QFQpDsQtCeyHVzNpIbDYuGcApIFVuAWnFuJa7N8WkXONMbt9/3crbhVGC4uawm4PLYYb0uBazyKSMUWnlIM7HwSXoM/u43s6geag785/V9r3ugOkjDHLReR03JpD3xKRPxljvpb/THP+Oy0sagpryVtY9MQyYJKInA1ueWcROT7gc0uAIwfyxSJyENBhjLkf96CM03xvHwUsHlyTLSzKw1ryFhY+GGO6ReRfgB+KyBjcOfI/9Cbgx3ErJg4EJwLfFREHtxrhJwBEZH+g0+RPCbKwqCVsCKWFxSAhIr8BPm+MWVHl93wG2GOMubM2LbOwKMLKNRYWg8fNuA7YarGL4oHOFhY1hbXkLSwsLGIMa8lbWFhYxBiW5C0sLCxiDEvyFhYWFjGGJXkLCwuLGMOSvIWFhUWM8f8AedsRBHr/DXwAAAAASUVORK5CYII=\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "runner1(100.)\n", - "\n", - "bp.visualize.line_plot(runner1.mon.ts, runner1.mon.V, show=True)" - ] - }, - { - "cell_type": "markdown", - "id": "8389cad4", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "The monitor in ``runner1`` has recorded the evolution of `V`. Therefore, it can be accessed by ``runner1.mon.V`` or equivalently ``runner1.mon['V']``. Similarly, the recorded trajectory of variable `spike` can also be obtained through ``runner1.mon.spike``. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4d08caa4", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": "array([[False],\n [False],\n [False],\n ...,\n [False],\n [False],\n [False]])" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "runner1.mon.spike" - ] - }, - { - "cell_type": "markdown", - "id": "935cfe6d", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Where ``True`` indicates a spike is generated at this time step." - ] - }, - { - "cell_type": "markdown", - "id": "8e46f299", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## The Mechanism of ``monitors``" - ] - }, - { - "cell_type": "markdown", - "id": "f4cd5f06", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "No matter we use a list/tuple or instantiate a `Monitor` class to generate a monitor, we specify the target variables by strings of their names. How does ``brainpy.Monitor`` find the target variables through these strings?" - ] - }, - { - "cell_type": "markdown", - "id": "2a95ada6", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Actually, BrainPy first tries to find the target variables in the simulated model by [the relative path](../tutorial_math/base.ipynb). If the variables are not found, BrainPy checks whether they can be accessed by [the absolute path](../tutorial_math/base.ipynb). If they not found again, an error will be raised. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1f2acef5", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": ".func(_t, _dt)>" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net = bp.Network(HH(size=10, name='X'), \n", - " HH(size=20, name='Y'), \n", - " HH(size=30))\n", - "\n", - "# it's ok\n", - "bp.StructRunner(net, monitors=['X.V', 'Y.spike']).build_monitors() " - ] - }, - { - "cell_type": "markdown", - "id": "1a290ec8", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "In the above ``net``, there are ``HH`` instances named as \"X\" and \"Y\". Therefore, trying to monitor \"X.V\" and \"Y.spike\" is successful. " - ] - }, - { - "cell_type": "markdown", - "id": "a143e5ab", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "However, in the following example, the node named with \"Z\" is not accessible in the generated ``net``, and the monitoring setup fails. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2730ace5", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RunningError : Cannot find target Z.V in monitor of Network(HH2=HH(name=HH2), HH3=HH(name=HH3)), please check.\n" - ] - } - ], - "source": [ - "z = HH(size=30, name='Z')\n", - "net = bp.Network(HH(size=10), HH(size=20))\n", - "\n", - "# node \"Z\" can not be accessed in the simulation target 'net'\n", - "try:\n", - " bp.StructRunner(net, monitors=['Z.V']).build_monitors() \n", - "except Exception as e:\n", - " print(type(e).__name__, \":\", e)" - ] - }, - { - "cell_type": "markdown", - "id": "50ee199f", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "BrainPy only supports to monitor [Variables](../tutorial_math/variables.ipynb). Monitoring [Variables](../tutorial_math/variables.ipynb)' trajectory is meaningful for they are dynamically changed. What is not marked as Variable will be compiled as constants. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8bacf930", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RunningError : \"gNa\" in HH(name=HH4) is not a dynamically changed Variable, its value will not change, we think there is no need to monitor its trajectory.\n" - ] - } - ], - "source": [ - "try:\n", - " bp.StructRunner(HH(size=1), monitors=['gNa']).build_monitors() \n", - "except Exception as e:\n", - " print(type(e).__name__, \":\", e)" - ] - }, - { - "cell_type": "markdown", - "id": "a6732c5b", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "The monitors in BrainPy only record the flattened tensor values. This means if the target variable is a matrix with the shape of ``(N, M)``, the resulting trajectory value in the monitor after running ``T`` times will be a tensor with the shape of ``(T, N x M)``." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "78583b04", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": " 0%| | 0/500 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'], show=True)" - ] - }, - { - "cell_type": "markdown", - "id": "b8b45777", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Note that if the parameter ``jit`` is set to ``True``, then all the variables will be JIT compiled and thus the system cannot be debugged by Python debugging tools. For debugging, users can set ``jit=False``." - ] - }, - { - "cell_type": "markdown", - "id": "3d9e82a9", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## ``brainpy.ReportRunner``" - ] - }, - { - "cell_type": "markdown", - "id": "eaab18b7", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "``brainpy.ReportRunner`` aims to provide a Pythonic interface for model debugging. Users can use the standard Python debugging tools when simulating the model with ``ReportRunner``." - ] - }, - { - "cell_type": "markdown", - "id": "cb659ddd", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "The drawback of the ``brainpy.ReportRunner`` is that it is relatively slow. It iterates the loop along times during the simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a6c62e4b", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dd49b2b05a7a4d77b3f4557c68c8ffb4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1000 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'], show=True)" - ] - }, - { - "cell_type": "markdown", - "id": "3551f214", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Runners for Neural Network Training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26d3e6e1", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file