{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *(Yang, 2020)*: Dynamical system analysis for RNN\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/recurrent_networks/Yang_2020_RNN_Analysis.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/examples/blob/main/recurrent_networks/Yang_2020_RNN_Analysis.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of the paper:\n",
    "\n",
    "- Yang G R, Wang X J. Artificial neural networks for neuroscientists: A primer[J]. Neuron, 2020, 107(6): 1048-1070.\n",
    "\n",
    "The original implementation is based on PyTorch: https://github.com/gyyang/nn-brain/blob/master/RNN%2BDynamicalSystemAnalysis.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:44.819433800Z",
     "start_time": "2023-07-22T10:48:44.772536600Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "import brainpy_datasets as bd\n",
    "\n",
    "bp.math.set_platform('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:44.872101900Z",
     "start_time": "2023-07-22T10:48:44.787584100Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.4.3'"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bp.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:44.873659300Z",
     "start_time": "2023-07-22T10:48:44.803836200Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0.0.6'"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:44.888349800Z",
     "start_time": "2023-07-22T10:48:44.819433800Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will use supervised learning to train a recurrent neural network on a simple perceptual decision making task, and analyze the trained network using dynamical system analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a cognitive task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:44.932902Z",
     "start_time": "2023-07-22T10:48:44.835824500Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = bd.cognitive.RatePerceptualDecisionMaking()\n",
    "task = bd.cognitive.TaskLoader(dataset, batch_size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a vanilla continuous-time recurrent network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will define a continuous-time neural network but discretize it in time using the Euler method.\n",
    "\n",
    "\\begin{align}\n",
    "    \\tau \\frac{d\\mathbf{r}}{dt} = -\\mathbf{r}(t) + f(W_r \\mathbf{r}(t) + W_x \\mathbf{x}(t) + \\mathbf{b}_r).\n",
    "\\end{align}\n",
    "\n",
    "This continuous-time system can then be discretized using the Euler method with a time step of $\\Delta t$, \n",
    "\n",
    "\\begin{align}\n",
    "    \\mathbf{r}(t+\\Delta t) = \\mathbf{r}(t) + \\Delta \\mathbf{r} = \\mathbf{r}(t) + \\frac{\\Delta t}{\\tau}[-\\mathbf{r}(t) + f(W_r \\mathbf{r}(t) + W_x \\mathbf{x}(t) + \\mathbf{b}_r)].\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:44.933935600Z",
     "start_time": "2023-07-22T10:48:44.856431Z"
    }
   },
   "outputs": [],
   "source": [
    "class RNN(bp.DynamicalSystem):\n",
    "  def __init__(self, \n",
    "               num_input, \n",
    "               num_hidden, \n",
    "               num_output, \n",
    "               num_batch, \n",
    "               dt=None, seed=None,\n",
    "               w_ir=bp.init.KaimingNormal(scale=1.),\n",
    "               w_rr=bp.init.KaimingNormal(scale=1.),\n",
    "               w_ro=bp.init.KaimingNormal(scale=1.)):\n",
    "    super(RNN, self).__init__()\n",
    "\n",
    "    # parameters\n",
    "    self.tau = 100\n",
    "    self.num_batch = num_batch\n",
    "    self.num_input = num_input\n",
    "    self.num_hidden = num_hidden\n",
    "    self.num_output = num_output\n",
    "    if dt is None:\n",
    "      self.alpha = 1\n",
    "    else:\n",
    "      self.alpha = dt / self.tau\n",
    "    self.rng = bm.random.RandomState(seed=seed)\n",
    "\n",
    "    # input weight\n",
    "    self.w_ir = bm.TrainVar(bp.init.parameter(w_ir, (num_input, num_hidden)))\n",
    "\n",
    "    # recurrent weight\n",
    "    bound = 1 / num_hidden ** 0.5\n",
    "    self.w_rr = bm.TrainVar(bp.init.parameter(w_rr, (num_hidden, num_hidden)))\n",
    "    self.b_rr = bm.TrainVar(self.rng.uniform(-bound, bound, num_hidden))\n",
    "\n",
    "    # readout weight\n",
    "    self.w_ro = bm.TrainVar(bp.init.parameter(w_ro, (num_hidden, num_output)))\n",
    "    self.b_ro = bm.TrainVar(self.rng.uniform(-bound, bound, num_output))\n",
    "\n",
    "    self.reset_state(self.mode)\n",
    "\n",
    "  def reset_state(self, batch_size):\n",
    "    self.h = bp.init.variable_(bm.zeros, self.num_hidden, batch_size)\n",
    "    self.o = bp.init.variable_(bm.zeros, self.num_output, batch_size)\n",
    "\n",
    "  def cell(self, x, h):\n",
    "    ins = x @ self.w_ir + h @ self.w_rr + self.b_rr\n",
    "    state = h * (1 - self.alpha) + ins * self.alpha\n",
    "    return bm.relu(state)\n",
    "\n",
    "  def readout(self, h):\n",
    "    return h @ self.w_ro + self.b_ro\n",
    "\n",
    "  def update(self, x):\n",
    "    self.h.value = self.cell(x, self.h)\n",
    "    self.o.value = self.readout(self.h)\n",
    "    return self.h.value, self.o.value\n",
    "\n",
    "  def predict(self, xs):\n",
    "    self.h[:] = 0.\n",
    "    return bm.for_loop(self.update, xs)\n",
    "\n",
    "  def loss(self, xs, ys):\n",
    "    hs, os = self.predict(xs)\n",
    "    os = os.reshape((-1, os.shape[-1]))\n",
    "    loss = bp.losses.cross_entropy_loss(os, ys.flatten())\n",
    "    return loss, os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the recurrent network on the decision-making task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:44.933935600Z",
     "start_time": "2023-07-22T10:48:44.873659300Z"
    }
   },
   "outputs": [],
   "source": [
    "# Instantiate the network and print information\n",
    "hidden_size = 64\n",
    "with bm.environment(mode=bm.TrainingMode(batch_size=16)):\n",
    "    net = RNN(num_input=dataset.num_inputs,\n",
    "              num_hidden=hidden_size,\n",
    "              num_output=dataset.num_outputs,\n",
    "              num_batch=task.batch_size,\n",
    "              dt=dataset.dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:44.959876300Z",
     "start_time": "2023-07-22T10:48:44.891350100Z"
    }
   },
   "outputs": [],
   "source": [
    "# Adam optimizer\n",
    "opt = bp.optim.Adam(lr=0.001, train_vars=net.train_vars().unique())\n",
    "\n",
    "# gradient function\n",
    "grad_f = bm.grad(net.loss,\n",
    "                 grad_vars=net.train_vars().unique(),\n",
    "                 return_value=True,\n",
    "                 has_aux=True)\n",
    "\n",
    "# training function\n",
    "@bm.jit\n",
    "def train(xs, ys):\n",
    "  grads, l, os = grad_f(xs, ys)\n",
    "  opt.update(grads)\n",
    "  return l, os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:47.166302Z",
     "start_time": "2023-07-22T10:48:44.933935600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch 1, Loss 0.2494, Acc 0.164\n",
      "Batch 2, Loss 0.0526, Acc 0.663\n",
      "Batch 3, Loss 0.0375, Acc 0.766\n",
      "Batch 4, Loss 0.0314, Acc 0.775\n",
      "Batch 5, Loss 0.0294, Acc 0.780\n",
      "Batch 6, Loss 0.0291, Acc 0.796\n",
      "Batch 7, Loss 0.0249, Acc 0.830\n",
      "Batch 8, Loss 0.0251, Acc 0.812\n",
      "Batch 9, Loss 0.0223, Acc 0.827\n",
      "Batch 10, Loss 0.0209, Acc 0.848\n",
      "Batch 11, Loss 0.0218, Acc 0.817\n",
      "Batch 12, Loss 0.0220, Acc 0.822\n",
      "Batch 13, Loss 0.0191, Acc 0.853\n",
      "Batch 14, Loss 0.0176, Acc 0.861\n",
      "Batch 15, Loss 0.0216, Acc 0.832\n",
      "Batch 16, Loss 0.0177, Acc 0.882\n",
      "Batch 17, Loss 0.0180, Acc 0.864\n",
      "Batch 18, Loss 0.0166, Acc 0.869\n",
      "Batch 19, Loss 0.0162, Acc 0.861\n",
      "Batch 20, Loss 0.0160, Acc 0.871\n"
     ]
    }
   ],
   "source": [
    "running_acc = []\n",
    "running_loss = []\n",
    "for i_batch in range(20):\n",
    "    for X, Y in task:\n",
    "        # training\n",
    "        loss, outputs = train(bm.asarray(X), bm.asarray(Y))\n",
    "        # Compute performance\n",
    "        output_np = np.asarray(bm.argmax(outputs, axis=-1)).flatten()\n",
    "        labels_np = np.asarray(Y).flatten()\n",
    "        ind = labels_np > 0 # 0: fixation, 1: choice 1, 2: choice 2\n",
    "        running_loss.append(loss)\n",
    "        running_acc.append(np.mean(labels_np[ind] == output_np[ind]))\n",
    "    print(f'Batch {i_batch + 1}, Loss {np.mean(running_loss):0.4f}, Acc {np.mean(running_acc):0.3f}')\n",
    "    running_loss = []\n",
    "    running_acc = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize neural activity for in sample trials\n",
    "\n",
    "We will run the network for 100 sample trials, then visual the neural activity trajectories in a PCA space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:47.227288900Z",
     "start_time": "2023-07-22T10:48:47.166302Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the neural activity: (Time points, Neurons):  (2200, 64)\n"
     ]
    }
   ],
   "source": [
    "num_trial = 100\n",
    "task = bd.cognitive.TaskLoader(dataset, batch_size=num_trial)\n",
    "inputs, trial_infos = task.get_batch()\n",
    "\n",
    "# reset the network state to match the required batch size\n",
    "net.reset_state(num_trial)\n",
    "\n",
    "# get the RNN activity\n",
    "rnn_activity, _ = net.predict(inputs)\n",
    "rnn_activity = np.asarray(rnn_activity)\n",
    "trial_infos = np.asarray(trial_infos)\n",
    "\n",
    "# Concatenate activity for PCA\n",
    "activity = rnn_activity.reshape(-1, hidden_size)\n",
    "print('Shape of the neural activity: (Time points, Neurons): ', activity.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:47.243974400Z",
     "start_time": "2023-07-22T10:48:47.227288900Z"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca.fit(activity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform individual trials and Visualize in PC space based on ground-truth color. We see that the neural activity is organized by stimulus ground-truth in PC1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:47.298709Z",
     "start_time": "2023-07-22T10:48:47.243974400Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22, 100), (22, 100, 3))"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial_infos.shape, inputs.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:47.423046100Z",
     "start_time": "2023-07-22T10:48:47.259007500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, sharex=True, figsize=(12, 5))\n",
    "for i in range(num_trial):\n",
    "    activity_pc = pca.transform(rnn_activity[:, i])\n",
    "    color = 'red' if trial_infos[-1, i] == 1 else 'blue'\n",
    "    _ = ax1.plot(activity_pc[:, 0], activity_pc[:, 1], 'o-', color=color)\n",
    "    if i < 5:\n",
    "        _ = ax2.plot(activity_pc[:, 0], activity_pc[:, 1], 'o-', color=color)\n",
    "\n",
    "ax1.set_xlabel('PC 1')\n",
    "ax1.set_ylabel('PC 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search for approximate fixed points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we search for approximate fixed points and visualize them in the same PC space. In a generic dynamical system,\n",
    "\\begin{align}\n",
    "    \\frac{d\\mathbf{x}}{dt} = F(\\mathbf{x}),\n",
    "\\end{align}\n",
    "We can search for fixed points by doing the optimization\n",
    "\\begin{align}\n",
    "    \\mathrm{argmin}_{\\mathbf{x}} |F(\\mathbf{x})|^2.\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:47.489821600Z",
     "start_time": "2023-07-22T10:48:47.423046100Z"
    }
   },
   "outputs": [],
   "source": [
    "f_cell = lambda h: net.cell(bm.asarray([1, 0.5, 0.5]), h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:47.489821600Z",
     "start_time": "2023-07-22T10:48:47.438712Z"
    }
   },
   "outputs": [],
   "source": [
    "fp_candidates = bm.asarray(activity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:47.489821600Z",
     "start_time": "2023-07-22T10:48:47.458556600Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 64)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_candidates.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:56.309345100Z",
     "start_time": "2023-07-22T10:48:47.458556600Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizing with Adam(lr=Constant(lr=0.001, last_epoch=-1), beta1=0.9, beta2=0.999, eps=1e-08) to find fixed points:\n",
      "    Batches 1-200 in 0.16 sec, Training loss 0.0802390501\n",
      "    Batches 201-400 in 0.14 sec, Training loss 0.0552543662\n",
      "    Batches 401-600 in 0.14 sec, Training loss 0.0439432897\n",
      "    Batches 601-800 in 0.15 sec, Training loss 0.0374152139\n",
      "    Batches 801-1000 in 0.14 sec, Training loss 0.0330325253\n",
      "    Batches 1001-1200 in 0.14 sec, Training loss 0.0297763441\n",
      "    Batches 1201-1400 in 0.14 sec, Training loss 0.0271856301\n",
      "    Batches 1401-1600 in 0.13 sec, Training loss 0.0250399429\n",
      "    Batches 1601-1800 in 0.14 sec, Training loss 0.0232114382\n",
      "    Batches 1801-2000 in 0.14 sec, Training loss 0.0216213744\n",
      "    Batches 2001-2200 in 0.15 sec, Training loss 0.0202192534\n",
      "    Batches 2201-2400 in 0.13 sec, Training loss 0.0189735591\n",
      "    Batches 2401-2600 in 0.13 sec, Training loss 0.0178660452\n",
      "    Batches 2601-2800 in 0.14 sec, Training loss 0.0168767571\n",
      "    Batches 2801-3000 in 0.14 sec, Training loss 0.0159866065\n",
      "    Batches 3001-3200 in 0.13 sec, Training loss 0.0151826618\n",
      "    Batches 3201-3400 in 0.14 sec, Training loss 0.0144545259\n",
      "    Batches 3401-3600 in 0.14 sec, Training loss 0.0137864584\n",
      "    Batches 3601-3800 in 0.14 sec, Training loss 0.0131686088\n",
      "    Batches 3801-4000 in 0.15 sec, Training loss 0.0125987381\n",
      "    Batches 4001-4200 in 0.13 sec, Training loss 0.0120685510\n",
      "    Batches 4201-4400 in 0.14 sec, Training loss 0.0115718376\n",
      "    Batches 4401-4600 in 0.12 sec, Training loss 0.0111064734\n",
      "    Batches 4601-4800 in 0.29 sec, Training loss 0.0106634693\n",
      "    Batches 4801-5000 in 0.13 sec, Training loss 0.0102427835\n",
      "    Batches 5001-5200 in 0.13 sec, Training loss 0.0098414142\n",
      "    Batches 5201-5400 in 0.15 sec, Training loss 0.0094555821\n",
      "    Batches 5401-5600 in 0.14 sec, Training loss 0.0090830158\n",
      "    Batches 5601-5800 in 0.13 sec, Training loss 0.0087208683\n",
      "    Batches 5801-6000 in 0.13 sec, Training loss 0.0083650518\n",
      "    Batches 6001-6200 in 0.14 sec, Training loss 0.0080158152\n",
      "    Batches 6201-6400 in 0.15 sec, Training loss 0.0076781083\n",
      "    Batches 6401-6600 in 0.14 sec, Training loss 0.0073548621\n",
      "    Batches 6601-6800 in 0.14 sec, Training loss 0.0070449994\n",
      "    Batches 6801-7000 in 0.13 sec, Training loss 0.0067473040\n",
      "    Batches 7001-7200 in 0.15 sec, Training loss 0.0064625386\n",
      "    Batches 7201-7400 in 0.14 sec, Training loss 0.0061870413\n",
      "    Batches 7401-7600 in 0.14 sec, Training loss 0.0059184977\n",
      "    Batches 7601-7800 in 0.14 sec, Training loss 0.0056603295\n",
      "    Batches 7801-8000 in 0.13 sec, Training loss 0.0054131425\n",
      "    Batches 8001-8200 in 0.12 sec, Training loss 0.0051786788\n",
      "    Batches 8201-8400 in 0.14 sec, Training loss 0.0049580932\n",
      "    Batches 8401-8600 in 0.14 sec, Training loss 0.0047499253\n",
      "    Batches 8601-8800 in 0.15 sec, Training loss 0.0045528994\n",
      "    Batches 8801-9000 in 0.14 sec, Training loss 0.0043659979\n",
      "    Batches 9001-9200 in 0.14 sec, Training loss 0.0041924547\n",
      "    Batches 9201-9400 in 0.14 sec, Training loss 0.0040282174\n",
      "    Batches 9401-9600 in 0.13 sec, Training loss 0.0038750202\n",
      "    Batches 9601-9800 in 0.16 sec, Training loss 0.0037310245\n",
      "    Batches 9801-10000 in 0.14 sec, Training loss 0.0035944246\n",
      "Excluding fixed points with squared speed above tolerance 0.0001:\n",
      "    Kept 157/2200 fixed points with tolerance under 0.0001.\n",
      "Excluding non-unique fixed points:\n",
      "    Kept 69/157 unique fixed points with uniqueness tolerance 0.03.\n",
      "Excluding outliers:\n",
      "    Kept 66/69 fixed points with within outlier tolerance 0.1.\n"
     ]
    }
   ],
   "source": [
    "finder = bp.analysis.SlowPointFinder(f_cell=f_cell, f_type='discrete')\n",
    "finder.find_fps_with_gd_method(\n",
    "    candidates=fp_candidates,\n",
    "    tolerance=1e-4,\n",
    "    num_batch=200,\n",
    "    optimizer=bp.optim.Adam(lr=1e-3),\n",
    ")\n",
    "finder.filter_loss(tolerance=1e-4)\n",
    "finder.keep_unique(tolerance=0.03)\n",
    "finder.exclude_outliers(0.1)\n",
    "fixed_points = finder.fixed_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the found approximate fixed points.\n",
    "\n",
    "We see that they found an approximate line attrator, corresponding to our PC1, along which evidence is integrated during the stimulus period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:56.326085600Z",
     "start_time": "2023-07-22T10:48:56.309345100Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 64)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixed_points.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:56.404854900Z",
     "start_time": "2023-07-22T10:48:56.326085600Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot in the same space as activity\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i in range(10):\n",
    "    activity_pc = pca.transform(rnn_activity[:, i])\n",
    "    color = 'red' if trial_infos[-1, i] == 0 else 'blue'\n",
    "    plt.plot(activity_pc[:, 0], activity_pc[:, 1], 'o-', color=color, alpha=0.1)\n",
    "\n",
    "# Fixed points are shown in cross\n",
    "fixedpoints_pc = pca.transform(fixed_points)\n",
    "plt.plot(fixedpoints_pc[:, 0], fixedpoints_pc[:, 1], 'x', label='fixed points')\n",
    "\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing the Jacobian and finding the line attractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:56.451564100Z",
     "start_time": "2023-07-22T10:48:56.406947900Z"
    }
   },
   "outputs": [],
   "source": [
    "from jax import jacobian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:56.451564100Z",
     "start_time": "2023-07-22T10:48:56.421143100Z"
    }
   },
   "outputs": [],
   "source": [
    "dFdh = jacobian(f_cell)(fixed_points[2])\n",
    "\n",
    "eigval, eigvec = np.linalg.eig(bm.as_numpy(dFdh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-22T10:48:56.514034300Z",
     "start_time": "2023-07-22T10:48:56.436357400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8/UyKa1jL02EAgEtwGQ5wM4o1AoBvI1kC3KwsxRor+hwnAPAFXIYD3MzZYo2VZY4TAHg7RpYAN3OmWGP+HKdrR6Ty5zit3Z/q0hgBAEVjZAlws/xijWmWq4XOWzIp79b6/GKNxc1xMilvjlP35hHcYQagQgsJ8NeWp2+3PfYURpYANyttsUYWpAWAPH5+JkXVrKqomlXl58E/DkmWgHJQmmKNLEiLyo4bG+BtuAwHlJOSFmtkQVpUZtzYgD/KysnV/607JEl6Kr6JggI8M8ZDsgSXseYaVG0uRkmKNZZ2jhNQUVS24q0oXk5urt7cfFSSNK5bYwV56IIYyRJcgr8GXSd/jtOo9/bIJNl9cbAgLSoqbmyAN2POEsqM29xdjwVpUdlwYwO8GSNLKBP+GnQfFqRFZcKNDfBmJEsok9L8NcjCqqXHgrSoLLixAd6My3AoE/4aBOAK+Tc2FDVualLePEhubIAnkCyhTPhrEIArlLZ4K1CeSJZQJvw1CMBVuLEB1woJ8Ne68bdo3fhbPLrcCXOWUCbc5g7AlbixAX/k52fSTeHVPR2GTIZhUEe+jDIyMmQ2m2WxWBQaGurpcDyCOksAAF9T0u9vRpbgEvw1CABwtaycXL2+4WdJ0uO3N2K5E/g+bnMHALhSTm6uXvnqsCTp0Vtv9NhyJ0zwBgAAcIBkCQAAwAGfS5YWLFigBg0aKCQkRDExMdqyZUuRbT/66CN1795df/rTnxQaGqq4uDh9+eWXdm2WLFkik8lUYLt6lSKKAADAx5Kl5cuXa9y4cZoyZYpSUlLUtWtX9erVSydOnCi0/ebNm9W9e3etWbNGu3fv1u23364+ffooJSXFrl1oaKhSU1PttpAQiigClZU111DykXP6ZO9pJR85J2suNw0DlZlPTfCeO3euhg8frocffliSNG/ePH355ZdauHChZs2aVaD9vHnz7H5+4YUX9Mknn2j16tVq166dbb/JZFJERIRbYwfgGyiDAeBaPjOylJWVpd27dys+Pt5uf3x8vLZt21aic+Tm5urixYuqWdO+mvSlS5cUHR2tevXqqXfv3gVGnq6VmZmpjIwMuw2A71u7P1Wj3ttTYHHoNMtVjXpvj9buT/VQZAA8yWeSpbNnz8pqtSo8PNxuf3h4uNLS0kp0jpdeekn/+c9/NGDAANu+pk2basmSJfr000+1bNkyhYSEqEuXLjp8+HCR55k1a5bMZrNti4qKcq5TALyGNdfQjNUHVNgFt/x9M1Yf4JIcUI6CA/z1yeNd9MnjXRTsweVOfCZZymcy2Rc5NAyjwL7CLFu2TNOnT9fy5ctVu3Zt2/7Y2FgNGTJEbdq0UdeuXfXBBx/opptu0muvvVbkuSZPniyLxWLbTp486XyHAHiFncd+LzCi9EeGpFTLVe089nv5BQVUcv5+JrWJqqE2UTU8WuTYZ+YshYWFyd/fv8AoUnp6eoHRpmstX75cw4cP14cffqhu3bo5bOvn56ebb77Z4chScHCwgoODSx48KgxrrkGV8goq/WLJ7oAtaTsAFYfPJEtBQUGKiYlRUlKS+vXrZ9uflJSke+65p8jjli1bpoceekjLli3T3XffXezvMQxDe/fuVatWrVwSNyoOJv5WbLWrl+wO2JK2A1B2WTm5euebY5KkB7s0YLmTkpgwYYISExPVoUMHxcXF6c0339SJEyc0cuRISXmXx06fPq13331XUl6i9MADD+iVV15RbGysbVSqSpUqMpvNkqQZM2YoNjZWjRs3VkZGhl599VXt3btXr7/+umc6Ca+UP/H32tkq+RN/Fw5pT8Lk4zo2qKlIc4jSLFcLnbdkkhRhzhtNBFA+cnJzNeuLHyVJiXHRLHdSEgkJCZo3b55mzpyptm3bavPmzVqzZo2io6MlSampqXY1l9544w3l5OTo8ccfV2RkpG0bO3asrc2FCxf0yCOPqFmzZoqPj9fp06e1efNmdezYsdz7B+/ExF/n+VK9In8/k6b1aS4pLzH6o/yfp/VpzmVXoBIyGYbhvZ9ePiIjI0Nms1kWi0WhoaGeDgculnzknO5ftL3YdstGxLKQ8B/46mVLX40bqIguZ+Wo+XN5K28cmNlDVYNce0GspN/fPnUZDvAEJv6Wni9ftuzZMlLdm0cwkR+ADckSUAwm/pZOcZctTcq7bNm9eYTXJiD+fiZGCQHY+NScJcAT8if+FvW1blLeZRom/uahXhGAioZkCSgGE39Lh8uWACoap5KlYcOGafPmza6OBfBaPVtGauGQ9oow219qizCHePX8G0/gsiUAVwkO8NeyEbFaNiLWo8udODVn6eLFi4qPj1dUVJQefPBBDR06VHXr1nV1bIBXYeJvyVCvCICreMv8QadGllauXKnTp09r9OjR+vDDD1W/fn316tVLK1asUHZ2tqtjBLxG/n/ce9rWVVzDWiRKheCypfv5Uv0qoCJwSZ2llJQUvf3223rrrbd03XXXaciQIXrsscfUuHFjV8To9aizBBREvSL34N8VlUm2NVfLduYVm76/4w0K9HftVOtyq7OUmpqqdevWad26dfL399ddd92lH374Qc2bN9ecOXM0fvz4sv4KAD6Iy5au58v1qwBnZFtz9dwnP0iS/hJTz+XJUkk5lSxlZ2fr008/1TvvvKN169apdevWGj9+vAYPHqzq1atLkt5//32NGjWKZAmoxLxlvkFFUBHqVwG+yqlkKTIyUrm5ubr//vu1c+dOtW3btkCbHj16qEaNGmUMDwAgla5+FQkq4FpOJUtz587VgAEDFBJS9K2/119/vY4dO+Z0YACA/6J+FeA5pb74l5OTo4ceekg///yzO+IBABSC+lWA55Q6WQoICFB0dLSsVqs74gEAFIJldwDPcWpa+bPPPqvJkyfr999Z2wkAygP1qwDPcarOUrt27fTzzz8rOztb0dHRqlatmt3ze/bscVmAvoA6SwDKC3WWUJnkWHO1+fBvkqRbGv9JAb5UZ+nee+91Ni4AQBlQvwqVSYC/n+5oGu7pMFxTwbuyY2QJAADfU24VvAEAANwh25qrj1NOS5LubVfXtyp4W61Wvfzyy/rggw904sQJZWVl2T3PxG8AAFBW2dZc/XXFd5Kku1tHeixZcuq3zpgxw1aY0mKxaMKECerfv7/8/Pw0ffp0F4cId2MFcwAAiubUyNLSpUu1aNEi3X333ZoxY4buv/9+NWzYUK1bt9b27ds1ZswYV8cJN+HOGgAAHHNqZCktLU2tWrWSJF133XWyWCySpN69e+vzzz93XXRwq/wVzK9dbyp/BfO1+1M9FBkAAN7DqWSpXr16Sk3N+yJt1KiR1q1bJ0n69ttvFRwc7Lro4DbFrWAu5a1gziU5AEBl51Sy1K9fP3311VeSpLFjx2rq1Klq3LixHnjgAT300EMuDRDuUZoVzAEAqMycmrP04osv2h7/5S9/Ub169bRt2zY1atRIffv2dVlwcB9WMAcAoGRcUmcpNjZWsbGxrjgVygkrmAMAvF2Qv59eH9Te9thTnE6WfvrpJ23cuFHp6enKzc21e+65554rc2Bwr/wVzNMsVwudt2SSFMEK5gAADwrw99PdrT1/Z7ZTydKiRYs0atQohYWFKSIiQibTf9ckMplMJEs+IH8F81Hv7ZFJskuYWMEcAID/cmptuOjoaD322GOaOHGiO2LyOb68Nhx1lgAA3irHmqsvfzgjSerRIlwBLr4U59a14c6fP6/77rvP6eBQPGuuUS6rirOCufcrr/eCs5ZsOKTpX/5s+3l6j0YadnuTUp3D2/sI9+M9UPE58xpnWXP1+L/3SJIOzOzh8mSppJxKlu677z6tW7dOI0eOdHU8xVqwYIH+/ve/KzU1VS1atNC8efPUtWvXIttv2rRJEyZM0A8//KA6dero6aefLhD3ypUrNXXqVB05ckQNGzbU3/72N/Xr18/dXSlSeY/2+PuZFNewlsvPi7Lz9pG/+pMKFqGd/uXPmv7lzzr+4t0lOoe39xHux3ug4vP119ipFK1Ro0aaOnWqhg0bppdeekmvvvqq3eYuy5cv17hx4zRlyhSlpKSoa9eu6tWrl06cOFFo+2PHjumuu+5S165dlZKSomeeeUZjxozRypUrbW2Sk5OVkJCgxMRE7du3T4mJiRowYIB27Njhtn44QlVt5PP290JhiVJpnpe8v49wP94DFV9FeI2dmrPUoEGDok9oMuno0aNlCqoonTp1Uvv27bVw4ULbvmbNmunee+/VrFmzCrSfOHGiPv30Ux08eNC2b+TIkdq3b5+Sk5MlSQkJCcrIyNAXX3xha9OzZ09df/31WrZsWYnictWcJWuuof+Z/XWRxSLz71DbOvEOhqcrOG9/L1x76a0oji7JeXsf4X68Byq+sr7Gl7Ny1Py5LyXlXYarGuSSikc2Jf3+dmpk6dixY0Vu7kqUsrKytHv3bsXHx9vtj4+P17Zt2wo9Jjk5uUD7Hj16aNeuXcrOznbYpqhzSlJmZqYyMjLsNlegqjbyeft7oSSJUnHtvL2PcD/eAxVfRXmNPVfhqZTOnj0rq9Wq8PBwu/3h4eFKS0sr9Ji0tLRC2+fk5Ojs2bMO2xR1TkmaNWuWzGazbYuKinKmSwVQVRv5KsN7oTL0EY7xHqj4KsprXOLxrAkTJuj5559XtWrVNGHCBIdt586dW+bAivLHmk6SZBhGgX3Ftb92f2nPOXnyZLt/g4yMDJckTFTVRr7K8F6oDH2EY7wHKr6K8hqXOFlKSUmxXbpKSUkpsp2jJKMswsLC5O/vX2DEJz09vcDIUL6IiIhC2wcEBKhWrVoO2xR1TkkKDg5WcHCwM91wiKrayOft74XpPRqVeM5SUby9j3A/3gMVX1lf40B/P/39L61tjz2lxL95w4YNqlGjhu1xUdvXX3/tlkCDgoIUExOjpKQku/1JSUnq3LlzocfExcUVaL9u3Tp16NBBgYGBDtsUdU53yq+qLf23inY+qmpXLt7+XihpHSVH7by9j3A/3gMVX1lf40B/P93XIUr3dYjyjWTJG0yYMEFvvfWW3n77bR08eFDjx4/XiRMnbHWTJk+erAceeMDWfuTIkfrll180YcIEHTx4UG+//bYWL16sp556ytZm7NixWrdunWbPnq0ff/xRs2fP1vr16zVu3Ljy7p6kvCKRC4e0V4TZfkgywhyihUPa+0Q9CriGt78XiqujVJI6S97eR7gf74GKryK8xk6VDujXr1+hl9tMJpNCQkLUqFEjDRo0SE2alK6Kb0ksWLBAc+bMUWpqqlq2bKmXX35Zt9xyiyRp2LBhOn78uDZu3Ghrv2nTJo0fP95WlHLixIkFilKuWLFCzz77rI4ePWorStm/f/8Sx+SO5U7Ks5otlXO9m7e/PlTwhivwHqj4nHmNc6y52nz4N0nSLY3/5LHlTpxKloYNG6aPP/5YNWrUUExMjAzDUEpKii5cuKD4+Hjt27dPx48f11dffaUuXbqUqSO+gLXhAABwPZ+usxQREaFBgwbp6NGjWrlypT766CMdOXJEQ4YMUcOGDXXw4EENHTqUhXa9XEWoqgoAgLs5lSwtXrxY48aNk5/ffw/38/PTE088oTfffFMmk0mjR4/W/v37XRYoXMuaa2jG6gOF3p2Qv2/G6gOy5pZ64BEAgArFqWQpJydHP/74Y4H9P/74o6xWqyQpJCTEbWUEUHYVpaoqAADu5tTFv8TERA0fPlzPPPOMbr75ZplMJu3cuVMvvPCC7W60TZs2qUWLFi4NFq5TUaqqAgDgbk4lSy+//LLCw8M1Z84cnTlzRlLeEiHjx4+3zVOKj49Xz549XRcpXKqiVFUFAMDdnEqW/P39NWXKFE2ZMsW2iOy1s8hvuOGGskcHt6FyLgAAJVPme/B87VZ55MmvqjrqvT0ySXYJE5VzAQDeINDfTzPvaWF77ClO1VmS8go5fvDBBzpx4oSysrLsntuzZ49LgvMV1FkCAMD3lPT726mRpVdffVVTpkzR0KFD9cknn+jBBx/UkSNH9O233+rxxx93OmiUv54tI9W9eQSVcwEAKIJTI0tNmzbVtGnTdP/996t69erat2+fbrzxRj333HP6/fffNX/+fHfE6rV8eWQJAABvlb9EiiS3/CHv1greJ06cUOfOnSVJVapU0cWLFyXllRRYtmyZM6cEAACwk5lj1f2Ltuv+RduVmWP1WBxOL3dy7tw5SVJ0dLS2b98uSTp27JicnAIFACgha66h5CPn9Mne00o+co5K+4CbOTVn6Y477tDq1avVvn17DR8+XOPHj9eKFSu0a9cu9e/f39UxAgD+P27KAMqfU8nSm2++qdzcXEnSyJEjVbNmTW3dulV9+vTRyJEjXRogACBP/uLX144j5S9+vXBIexImwA2cSpb8/PzsFtEdMGCABgwY4LKgAAD2ilv82qS8xa+7N4/gblbAxZwuSnn16lV99913Sk9Pt40y5evbt2+ZAwMA/FdpFr+Oa1ir/AIDKgGnkqW1a9fqgQce0NmzZws8ZzKZZLV6bsY6AFRELH4NeI5Td8ONHj1a9913n1JTU5Wbm2u3kSgBgOux+DUqowA/P03u1VSTezVVgJ/nljtxamQpPT1dEyZMUHh4uKvjAQAUgsWvURkFBfjp0VsbejoM50aW/vKXv2jjxo0uDgUAUJT8xa+l/y52nY/FrwH3cmq5k8uXL+u+++7Tn/70J7Vq1UqBgYF2z48ZM8ZlAfoCljsBCpe/VAHrDroOdZZQmVhzDe0/bZEktaxr9thyJ04lS2+99ZZGjhypKlWqqFatWjKZ/hu8yWTS0aNHnYvaR5EsAQXxpe4+JKGoLC5n5aj5c19Kkg7M7KGqQU7fxF+okn5/O/Vbn332Wc2cOVOTJk2yq7cEABLFE93N389EeQCgHDmV6WRlZSkhIYFECZUOa3IVr7jiiVJe8UT+7QD4CqdGloYOHarly5frmWeecXU8gNfislLJUDwRQEXjVLJktVo1Z84cffnll2rdunWBCd5z5851SXCAt+CyUslRPBFAReNUsvT999+rXbt2kqT9+/e7NCDA27AmV+lQPBFAReNUsrRhwwZXxwF4LS4rlQ7FEwFUNKVKlvr3719sG5PJpJUrVzodEOBtuKxUOvnFE0e9t0cmyS5h8pXiidyaD3iHAD8/jb2zse2xx+IoTWOz2eyuOACvxWWl0uvZMlILh7QvMCE+wgcmxDORH/AeQQF+Gt/9Jk+H4VxRStijKGXFZs019D+zvy72stLWiXcw+nANXxuhKWoif37ETOQHKpaSfn/7TKGk8+fPKzExUWazWWazWYmJibpw4UKR7bOzszVx4kS1atVK1apVU506dfTAAw/o119/tWt32223yWQy2W0DBw50c2/gS1iTy3n5xRPvaVtXcQ1refW/EfWhAO+Tm2vopzMX9dOZi8r14P89n0mWBg0apL1792rt2rVau3at9u7dq8TExCLbX758WXv27NHUqVO1Z88effTRR/rpp5/Ut2/fAm1HjBih1NRU2/bGG2+4syvwQfmXlSLM9pfaIswhjDZUEKWZyA+gfFzNsSr+5c2Kf3mzruZYPRaHaxdZcZODBw9q7dq12r59uzp16iRJWrRokeLi4nTo0CE1adKkwDFms1lJSUl2+1577TV17NhRJ06c0A033GDbX7VqVUVERLi3E/B5PVtGqnvzCJ+6rISSYyI/gKL4xMhScnKyzGazLVGSpNjYWJnNZm3btq3E57FYLDKZTKpRo4bd/qVLlyosLEwtWrTQU089pYsXLzo8T2ZmpjIyMuw2VA6+dFkJpcNEfgBF8YmRpbS0NNWuXbvA/tq1aystLa1E57h69aomTZqkQYMG2U3iGjx4sBo0aKCIiAjt379fkydP1r59+wqMSv3RrFmzNGPGjNJ3BIDXoj4UgKJ4dGRp+vTpBSZXX7vt2rVLUl79pmsZhlHo/mtlZ2dr4MCBys3N1YIFC+yeGzFihLp166aWLVtq4MCBWrFihdavX689e/YUeb7JkyfLYrHYtpMnT5ay5wC8DRP5ARTFoyNLo0ePLvbOs/r16+u7777TmTNnCjz322+/KTw83OHx2dnZGjBggI4dO6avv/662Fv727dvr8DAQB0+fFjt27cvtE1wcLCCg4MdngeA7/Hl+lAA3MejyVJYWJjCwsKKbRcXFyeLxaKdO3eqY8eOkqQdO3bIYrGoc+fORR6XnygdPnxYGzZsUK1axS9F8cMPPyg7O1uRkXwoApURE/kBXMtnilL26tVLv/76q+22/kceeUTR0dFavXq1rU3Tpk01a9Ys9evXTzk5Ofrzn/+sPXv26LPPPrMbgapZs6aCgoJ05MgRLV26VHfddZfCwsJ04MABPfnkk6pSpYq+/fZb+fv7lyg2ilICAOB6WTm5+r91hyRJT8U3UVCAa2cPlfT72ycmeEt5d6yNGTNG8fHxkqS+fftq/vz5dm0OHToki8UiSTp16pQ+/fRTSVLbtm3t2m3YsEG33XabgoKC9NVXX+mVV17RpUuXFBUVpbvvvlvTpk0rcaIEAADcIyjAT8/c1czTYfjOyJI3Y2QJAADfU+FGlgAAQOWSm2vo9IUrkqS6NarIz0NzB0mW4DK+tmgqAMC7Xc2xquucDZKkAzN7qGqQZ9IWkiW4xNr9qQVut47kdmsAQAXgE8udwLut3Z+qUe/tKbAIaZrlqka9t0dr96d6KDIAvsiaayj5yDl9sve0ko+ck9WDq80DEiNLKCNrrqEZqw8UujyEobzKxzNWH1D35hFckgNQLEap4Y0YWUKZ7Dz2e4ERpT8yJKVarmrnsd/LLygAPolRangrkiWUSfrFohMlZ9oBqJyKG6WW8kapuSQHTyBZQpnUrh7i0nYAKidGqeHNmLOEMunYoKYizSFKs1wt9C9Ck/IWIe3YoGZ5h1YhUI4BlQWj1CiMv59JibHRtseeQrKEMvH3M2lan+Ya9d4emSS7hCn/bT2tT3O+4J3ARFdUJoxSozDBAf56/t6Wng6Dy3Aou54tI7VwSHtFmO0/xCLMIVo4pD1f7E5goisqm/xR6qL+rDIp748FRqnhCYwswSV6toxU9+YRXDJyAcoxoDJilBqFMQxDv/8nS5JUs1qQTCbPvP6MLMFl/P1MimtYS/e0rau4hrX4UCtESYrtMdEVlRWj1LjWlWyrYv53vWL+d72uZFs9FgcjS0A5KekcJCa6ojJjlBreiGQJKAf5c5CuHUfKn4P0x7+ameiKyi5/lBrwFlyGA9ystMX2mOgKAN6FZAlws9LOQcqf6CqpQMLERFcAKH8kS4CbOTMHiYmuAOA9mLMEuJmzc5CY6AoA3oFkCXCzsiwJw0RXAJWZv59Jf25fz/bYU0iWADej2B4AOCc4wF8vDWjj6TCYswSUh8o2B6kkxTcBwFcwsgSUk8oyB4kFgAG4imEYtsrdVQL9PbbcickwDP7kK6OMjAyZzWZZLBaFhoZ6OhzAY4oqvpn/8VYRR9EAuM/lrBw1f+5LSdKBmT1UNci1Yzwl/f7mMhwAlyht8U0A8BUkSwBcggWAAVRUJEsAXIIFgAFUVCRLAFyCBYABVFQkSwBcggWAAVRUJEsAXIIFgAFUVCRLAFymshXfBOBefiaT7moVobtaRcjPQzWWJB9Kls6fP6/ExESZzWaZzWYlJibqwoULDo8ZNmyYTCaT3RYbG2vXJjMzU0888YTCwsJUrVo19e3bV6dOnXJjT4CKrWfLSG2deIeWjYjVKwPbatmIWG2deAeJEoBSCwn014LBMVowOEYhgf4ei8NnkqVBgwZp7969Wrt2rdauXau9e/cqMTGx2ON69uyp1NRU27ZmzRq758eNG6dVq1bp/fff19atW3Xp0iX17t1bVqvVXV0BKrz8BYDvaVtXcQ1rcekNgE/zieVODh48qLVr12r79u3q1KmTJGnRokWKi4vToUOH1KRJkyKPDQ4OVkRERKHPWSwWLV68WP/617/UrVs3SdJ7772nqKgorV+/Xj169HB9ZwAAgE/xiZGl5ORkmc1mW6IkSbGxsTKbzdq2bZvDYzdu3KjatWvrpptu0ogRI5Senm57bvfu3crOzlZ8fLxtX506ddSyZUuH583MzFRGRobdBgAAXOtyVo7qT/pc9Sd9rstZOR6LwyeSpbS0NNWuXbvA/tq1aystLa3I43r16qWlS5fq66+/1ksvvaRvv/1Wd9xxhzIzM23nDQoK0vXXX293XHh4uMPzzpo1yzZ3ymw2KyoqysmeAQAAb+fRZGn69OkFJmBfu+3atUuSCl1p2DAMhysQJyQk6O6771bLli3Vp08fffHFF/rpp5/0+eefO4yruPNOnjxZFovFtp08ebKEPQYAAL7Go3OWRo8erYEDBzpsU79+fX333Xc6c+ZMged+++03hYeHl/j3RUZGKjo6WocPH5YkRUREKCsrS+fPn7cbXUpPT1fnzp2LPE9wcLCCg4NL/HsBAIDv8miyFBYWprCwsGLbxcXFyWKxaOfOnerYsaMkaceOHbJYLA6TmmudO3dOJ0+eVGRk3i3MMTExCgwMVFJSkgYMGCBJSk1N1f79+zVnzhwnegQAACoan5iz1KxZM/Xs2VMjRozQ9u3btX37do0YMUK9e/e2uxOuadOmWrVqlSTp0qVLeuqpp5ScnKzjx49r48aN6tOnj8LCwtSvXz9Jktls1vDhw/Xkk0/qq6++UkpKioYMGaJWrVrZ7o4DAACVm0+UDpCkpUuXasyYMbY71/r27av58+fbtTl06JAsFoskyd/fX99//73effddXbhwQZGRkbr99tu1fPlyVa9e3XbMyy+/rICAAA0YMEBXrlzRnXfeqSVLlsjf33PFrwAAgPcwGYZheDoIX5eRkSGz2SyLxaLQ0FBPhwMAQIVwNduqUe/tliQtHOL6Kt4l/f72mZElAABQuYQE+uudBzt6OgzfmLMEAADgKSRLAAAADpAsAQAAr3Q5K0fNpq5Vs6lrPbrcCXOWAACA17qSbfV0CIwsAQAAOEKyBAAA4ADJEgAAgAMkSwAAAA6QLAEAADjA3XAAAMAr+ZlM6tSgpu2xp5AsAQAArxQS6K/lj8Z5OgwuwwEAADhCsgQAAOAAyRIAAPBKl7Ny1P75JLV/PonlTgAAAArz+3+yPB0CI0sAAACOkCwBAAA4QLIEAADgAMkSAACAAyRLAAAADnA3HAAA8Ep+JpNa1zPbHnsKyRIAAPBKIYH++nT0/3g6DC7DAQAAOEKyBAAA4ADJEgAA8EpXsqzq8uLX6vLi17qSZfVYHMxZAgAAXsmQodMXrtgeewojSwAAAA6QLAEAADhAsgQAAOAAyRIAAIADPpMsnT9/XomJiTKbzTKbzUpMTNSFCxccHmMymQrd/v73v9va3HbbbQWeHzhwoJt7AwAAfIXP3A03aNAgnTp1SmvXrpUkPfLII0pMTNTq1auLPCY1NdXu5y+++ELDhw/Xn//8Z7v9I0aM0MyZM20/V6lSxYWRAwAAZ5hkUuPa19kee4pPJEsHDx7U2rVrtX37dnXq1EmStGjRIsXFxenQoUNq0qRJocdFRETY/fzJJ5/o9ttv14033mi3v2rVqgXaAgAAz6oS5K+kCbd6OgzfuAyXnJwss9lsS5QkKTY2VmazWdu2bSvROc6cOaPPP/9cw4cPL/Dc0qVLFRYWphYtWuipp57SxYsXHZ4rMzNTGRkZdhsAAKiYfGJkKS0tTbVr1y6wv3bt2kpLSyvROf75z3+qevXq6t+/v93+wYMHq0GDBoqIiND+/fs1efJk7du3T0lJSUWea9asWZoxY0bpOgEAAHySR0eWpk+fXuQk7Pxt165dkvIma1/LMIxC9xfm7bff1uDBgxUSEmK3f8SIEerWrZtatmypgQMHasWKFVq/fr327NlT5LkmT54si8Vi206ePFmKXgMAgJK4kmVV97mb1H3upsq73Mno0aOLvfOsfv36+u6773TmzJkCz/32228KDw8v9vds2bJFhw4d0vLly4tt2759ewUGBurw4cNq3759oW2Cg4MVHBxc7LkAAIDzDBk6nH7J9thTPJoshYWFKSwsrNh2cXFxslgs2rlzpzp27ChJ2rFjhywWizp37lzs8YsXL1ZMTIzatGlTbNsffvhB2dnZioyMLL4DAACgwvOJCd7NmjVTz549NWLECG3fvl3bt2/XiBEj1Lt3b7s74Zo2bapVq1bZHZuRkaEPP/xQDz/8cIHzHjlyRDNnztSuXbt0/PhxrVmzRvfdd5/atWunLl26uL1fAADA+/lEsiTl3bHWqlUrxcfHKz4+Xq1bt9a//vUvuzaHDh2SxWKx2/f+++/LMAzdf//9Bc4ZFBSkr776Sj169FCTJk00ZswYxcfHa/369fL393drfwAAgG8wGYbhuYuAFURGRobMZrMsFotCQ0M9HQ4AABXC5awcNX/uS0nSgZk9VDXItbOHSvr97TMjSwAAAJ7gE3WWAABA5WOSSXVrVLE99hSSJQAA4JWqBPnrm0l3eDoMLsMBAAA4QrIEAADgAMkSAADwSlezreo7f6v6zt+qq9mVdLkTAACAouQahr47ZbE99hRGlgAAABwgWQIAAHCAZAkAAMABkiUAAAAHSJYAAAAc4G44AADgtWpWC/J0CCRLAADAO1UNCtCeqd09HQaX4QAAABwhWQIAAHCAZAkAAHilq9lWJbyRrIQ3klnuBAAA4Fq5hqEdx363PfYURpYAAAAcIFkCAABwgGQJAADAAZIlAAAAB0iWAAAAHOBuOAAA4LWqBPp7OgSSJQAA4J2qBgXo4PM9PR0Gl+EAAAAcIVkCAABwgGQJAAB4pavZVj34zk49+M5OljsBAAC4Vq5haMOh32yPPYWRJQAAAAdIlgAAABzwmWTpb3/7mzp37qyqVauqRo0aJTrGMAxNnz5dderUUZUqVXTbbbfphx9+sGuTmZmpJ554QmFhYapWrZr69u2rU6dOuaEHAADAF/lMspSVlaX77rtPo0aNKvExc+bM0dy5czV//nx9++23ioiIUPfu3XXx4kVbm3HjxmnVqlV6//33tXXrVl26dEm9e/eW1eq5iWQAAMB7+MwE7xkzZkiSlixZUqL2hmFo3rx5mjJlivr37y9J+uc//6nw8HD9+9//1qOPPiqLxaLFixfrX//6l7p16yZJeu+99xQVFaX169erR48ebukLAADwHT4zslRax44dU1pamuLj4237goODdeutt2rbtm2SpN27dys7O9uuTZ06ddSyZUtbm8JkZmYqIyPDbgMAABWTz4wslVZaWpokKTw83G5/eHi4fvnlF1uboKAgXX/99QXa5B9fmFmzZtlGugAAgHtUDQrQ8Rfv9nQYnh1Zmj59ukwmk8Nt165dZfodJpPJ7mfDMArsu1ZxbSZPniyLxWLbTp48WaYYAQCA9/LoyNLo0aM1cOBAh23q16/v1LkjIiIk5Y0eRUZG2vanp6fbRpsiIiKUlZWl8+fP240upaenq3PnzkWeOzg4WMHBwU7FBQAAfItHk6WwsDCFhYW55dwNGjRQRESEkpKS1K5dO0l5d9Rt2rRJs2fPliTFxMQoMDBQSUlJGjBggCQpNTVV+/fv15w5c9wSFwAA8C0+M2fpxIkT+v3333XixAlZrVbt3btXktSoUSNdd911kqSmTZtq1qxZ6tevn0wmk8aNG6cXXnhBjRs3VuPGjfXCCy+oatWqGjRokCTJbDZr+PDhevLJJ1WrVi3VrFlTTz31lFq1amW7Ow4AAFRuPpMsPffcc/rnP/9p+zl/tGjDhg267bbbJEmHDh2SxWKxtXn66ad15coVPfbYYzp//rw6deqkdevWqXr16rY2L7/8sgICAjRgwABduXJFd955p5YsWSJ/f//y6RgAAPBqJsPw4Mp0FURGRobMZrMsFotCQ0M9HQ4AACiBkn5/V9g6SwAAAK5AsgQAAOAAyRIAAIADJEsAAAAOkCwBAAA4QLIEAADgAMkSAACAAyRLAAAADpAsAQAAOOAzy514s/wi6BkZGR6OBAAAlFT+93Zxi5mQLLnAxYsXJUlRUVEejgQAAJTWxYsXZTabi3yeteFcIDc3V7/++quqV68uk8nk8vNnZGQoKipKJ0+erJBrz1X0/kkVv4/0z7fRP99G/5xnGIYuXryoOnXqyM+v6JlJjCy5gJ+fn+rVq+f23xMaGloh/yPkq+j9kyp+H+mfb6N/vo3+OcfRiFI+JngDAAA4QLIEAADgAMmSDwgODta0adMUHBzs6VDcoqL3T6r4faR/vo3++Tb6535M8AYAAHCAkSUAAAAHSJYAAAAcIFkCAABwgGQJAADAAZIlL3X+/HklJibKbDbLbDYrMTFRFy5cKPa4gwcPqm/fvjKbzapevbpiY2N14sQJ9wdcSs72L9+jjz4qk8mkefPmuS3Gsiht/7KzszVx4kS1atVK1apVU506dfTAAw/o119/Lb+gi7FgwQI1aNBAISEhiomJ0ZYtWxy237Rpk2JiYhQSEqIbb7xR//jHP8opUueUpn8fffSRunfvrj/96U8KDQ1VXFycvvzyy3KMtvRK+/rl++abbxQQEKC2bdu6N8AyKm3/MjMzNWXKFEVHRys4OFgNGzbU22+/XU7Rll5p+7d06VK1adNGVatWVWRkpB588EGdO3eunKItnc2bN6tPnz6qU6eOTCaTPv7442KPKffPFwNeqWfPnkbLli2Nbdu2Gdu2bTNatmxp9O7d2+ExP//8s1GzZk3jr3/9q7Fnzx7jyJEjxmeffWacOXOmnKIuOWf6l2/VqlVGmzZtjDp16hgvv/yyewN1Umn7d+HCBaNbt27G8uXLjR9//NFITk42OnXqZMTExJRj1EV7//33jcDAQGPRokXGgQMHjLFjxxrVqlUzfvnll0LbHz161KhataoxduxY48CBA8aiRYuMwMBAY8WKFeUcecmUtn9jx441Zs+ebezcudP46aefjMmTJxuBgYHGnj17yjnykilt//JduHDBuPHGG434+HijTZs25ROsE5zpX9++fY1OnToZSUlJxrFjx4wdO3YY33zzTTlGXXKl7d+WLVsMPz8/45VXXjGOHj1qbNmyxWjRooVx7733lnPkJbNmzRpjypQpxsqVKw1JxqpVqxy298TnC8mSFzpw4IAhydi+fbttX3JysiHJ+PHHH4s8LiEhwRgyZEh5hFgmzvbPMAzj1KlTRt26dY39+/cb0dHRXpkslaV/f7Rz505DUrFfaOWhY8eOxsiRI+32NW3a1Jg0aVKh7Z9++mmjadOmdvseffRRIzY21m0xlkVp+1eY5s2bGzNmzHB1aC7hbP8SEhKMZ5991pg2bZpXJ0ul7d8XX3xhmM1m49y5c+URXpmVtn9///vfjRtvvNFu36uvvmrUq1fPbTG6SkmSJU98vnAZzgslJyfLbDarU6dOtn2xsbEym83atm1bocfk5ubq888/10033aQePXqodu3a6tSpU4mGM8ubM/2T8vqYmJiov/71r2rRokV5hOoUZ/t3LYvFIpPJpBo1arghypLLysrS7t27FR8fb7c/Pj6+yP4kJycXaN+jRw/t2rVL2dnZbovVGc7071q5ubm6ePGiatas6Y4Qy8TZ/r3zzjs6cuSIpk2b5u4Qy8SZ/n366afq0KGD5syZo7p16+qmm27SU089pStXrpRHyKXiTP86d+6sU6dOac2aNTIMQ2fOnNGKFSt09913l0fIbueJzxeSJS+Ulpam2rVrF9hfu3ZtpaWlFXpMenq6Ll26pBdffFE9e/bUunXr1K9fP/Xv31+bNm1yd8il4kz/JGn27NkKCAjQmDFj3BlemTnbvz+6evWqJk2apEGDBnl8YcyzZ8/KarUqPDzcbn94eHiR/UlLSyu0fU5Ojs6ePeu2WJ3hTP+u9dJLL+k///mPBgwY4I4Qy8SZ/h0+fFiTJk3S0qVLFRDg3eutO9O/o0ePauvWrdq/f79WrVqlefPmacWKFXr88cfLI+RScaZ/nTt31tKlS5WQkKCgoCBFRESoRo0aeu2118ojZLfzxOcLyVI5mj59ukwmk8Nt165dkiSTyVTgeMMwCt0v5f1lK0n33HOPxo8fr7Zt22rSpEnq3bt3uU2sdWf/du/erVdeeUVLliwpso27ubN/f5Sdna2BAwcqNzdXCxYscHk/nHVt7MX1p7D2he33FqXtX75ly5Zp+vTpWr58eaFJsrcoaf+sVqsGDRqkGTNm6Kabbiqv8MqsNK9fbm6uTCaTli5dqo4dO+quu+7S3LlztWTJEq8cXZJK178DBw5ozJgxeu6557R7926tXbtWx44d08iRI8sj1HJR3p8v3v0nQwUzevRoDRw40GGb+vXr67vvvtOZM2cKPPfbb78VyKbzhYWFKSAgQM2bN7fb36xZM23dutX5oEvBnf3bsmWL0tPTdcMNN9j2Wa1WPfnkk5o3b56OHz9epthLwp39y5edna0BAwbo2LFj+vrrrz0+qiTlvbf8/f0L/BWbnp5eZH8iIiIKbR8QEKBatWq5LVZnONO/fMuXL9fw4cP14Ycfqlu3bu4M02ml7d/Fixe1a9cupaSkaPTo0ZLykgvDMBQQEKB169bpjjvuKJfYS8KZ1y8yMlJ169aV2Wy27WvWrJkMw9CpU6fUuHFjt8ZcGs70b9asWerSpYv++te/SpJat26tatWqqWvXrvrf//1fRUZGuj1ud/LE5wvJUjkKCwtTWFhYse3i4uJksVi0c+dOdezYUZK0Y8cOWSwWde7cudBjgoKCdPPNN+vQoUN2+3/66SdFR0eXPfgScGf/EhMTC3wZ9ejRQ4mJiXrwwQfLHnwJuLN/0n8TpcOHD2vDhg1ek1QEBQUpJiZGSUlJ6tevn21/UlKS7rnnnkKPiYuL0+rVq+32rVu3Th06dFBgYKBb4y0tZ/on5Y0oPfTQQ1q2bJlXzwUpbf9CQ0P1/fff2+1bsGCBvv76a61YsUINGjRwe8yl4czr16VLF3344Ye6dOmSrrvuOkl5n5V+fn6qV69eucRdUs707/LlywUun/r7+0v67wiML/PI54vbpo6jTHr27Gm0bt3aSE5ONpKTk41WrVoVuPW8SZMmxkcffWT7+aOPPjICAwONN9980zh8+LDx2muvGf7+/saWLVvKO/xiOdO/a3nr3XCGUfr+ZWdnG3379jXq1atn7N2710hNTbVtmZmZnuiCnfxblxcvXmwcOHDAGDdunFGtWjXj+PHjhmEYxqRJk4zExERb+/xbe8ePH28cOHDAWLx4sU+UDihp//79738bAQEBxuuvv273Wl24cMFTXXCotP27lrffDVfa/l28eNGoV6+e8Ze//MX44YcfjE2bNhmNGzc2Hn74YU91waHS9u+dd94xAgICjAULFhhHjhwxtm7danTo0MHo2LGjp7rg0MWLF42UlBQjJSXFkGTMnTvXSElJsd0J7A2fLyRLXurcuXPG4MGDjerVqxvVq1c3Bg8ebJw/f96ujSTjnXfesdu3ePFio1GjRkZISIjRpk0b4+OPPy6/oEvB2f79kTcnS6Xt37FjxwxJhW4bNmwo9/gL8/rrrxvR0dFGUFCQ0b59e2PTpk2254YOHWrceuutdu03btxotGvXzggKCjLq169vLFy4sJwjLp3S9O/WW28t9LUaOnRo+QdeQqV9/f7I25Mlwyh9/w4ePGh069bNqFKlilGvXj1jwoQJxuXLl8s56pIrbf9effVVo3nz5kaVKlWMyMhIY/DgwcapU6fKOeqS2bBhg8P/T97w+WIyjAowJgcAAOAm3A0HAADgAMkSAACAAyRLAAAADpAsAQAAOECyBAAA4ADJEgAAgAMkSwAAAA6QLAEAADhAsgQALjR9+nS1bdvW02EAcCGSJQCVxrBhw2QymWQymRQQEKAbbrhBo0aN0vnz5z0dGgAvRrIEoFLp2bOnUlNTdfz4cb311ltavXq1HnvsMU+HBcCLkSwBqFSCg4MVERGhevXqKT4+XgkJCVq3bp3t+XfeeUfNmjVTSEiImjZtqgULFtgdP3HiRN10002qWrWqbrzxRk2dOlXZ2dnl3Q0A5SjA0wEAgKccPXpUa9euVWBgoCRp0aJFmjZtmubPn6927dopJSVFI0aMULVq1TR06FBJUvXq1bVkyRLVqVNH33//vUaMGKHq1avr6aef9mRXALgRyRKASuWzzz7TddddJ6vVqqtXr0qS5s6dK0l6/vnn9dJLL6l///6SpAYNGujAgQN64403bMnSs88+aztX/fr19eSTT2r58uUkS0AFRrIEoFK5/fbbtXDhQl2+fFlvvfWWfvrpJz3xxBP67bffdPLkSQ0fPlwjRoywtc/JyZHZbLb9vGLFCs2bN08///yzLl26pJycHIWGhnqiKwDKCXOWAFQq1apVU6NGjdS6dWu9+uqryszM1IwZM5Sbmysp71Lc3r17bdv+/fu1fft2SdL27ds1cOBA9erVS5999plSUlI0ZcoUZWVlebJLANyMkSUAldq0adPUq1cvjRo1SnXr1tXRo0c1ePDgQtt+8803io6O1pQpU2z7fvnll/IKFYCHkCwBqNRuu+02tWjRQi+88IKmT5+uMWPGKDQ0VL169VJmZqZ27dql8+fPa8KECWrUqJFOnDih999/XzfffLM+//xzrVq1ytNdAOBmXIYDUOlNmDBBixYtUo8ePfTWW29pyZIlatWqlW699VYtWbJEDRo0kCTdc889Gj9+vEaPHq22bdtq27Ztmjp1qoejB+BuJsMwDE8HAQAA4K0YWQIAAHCAZAkAAMABkiUAAAAHSJYAAAAcIFkCAABwgGQJAADAAZIlAAAAB0iWAAAAHCBZAgAAcIBkCQAAwAGSJQAAAAf+H2dBUwClUKb1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of eigenvalues in a 2-d real-imaginary plot\n",
    "plt.figure()\n",
    "plt.scatter(np.real(eigval), np.imag(eigval))\n",
    "plt.plot([1, 1], [-1, 1], '--')\n",
    "plt.xlabel('Real')\n",
    "plt.ylabel('Imaginary')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "brainpy",
   "language": "python",
   "name": "brainpy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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  "latex_envs": {
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   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
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   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
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   "number_sections": false,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "279.273px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
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}