{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c12ae952ffe6d41e",
   "metadata": {},
   "source": [
    "# Simulating whole-brain Drosophila spiking model with ``brainstate``\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/chaobrain/brain-modeling-ecosystem/blob/main/docs/brainstate_drosophila_wholebrain_simulation.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/chaobrain/brain-modeling-ecosystem/blob/main/docs/brainstate_drosophila_wholebrain_simulation.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ccc0b04a12c826",
   "metadata": {},
   "source": [
    "Reproducing the simulation in the paper of:\n",
    "\n",
    "- Shiu, P.K., Sterne, G.R., Spiller, N. et al. A Drosophila computational brain model reveals sensorimotor processing. Nature 634, 210–219 (2024). [https://doi.org/10.1038/s41586-024-07763-9](https://doi.org/10.1038/s41586-024-07763-9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "explain_overview_1",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "This notebook builds a whole-brain spiking network for Drosophila using `brainstate`, driven by the FlyWire-derived connectome and stimulus protocols that mirror the paper.\n",
    "\n",
    "What you will find here:\n",
    "- Connectome ingestion (FlyWire 630 mirror) and neuron indexing.\n",
    "- A leaky integrate-and-fire (LIF) population model with event-driven synapses, delays, and Poisson drive.\n",
    "- Convenience helpers to run experiments, silence subsets, and aggregate activity.\n",
    "- Code that reproduces/illustrates figure-style experiments (e.g., GRN/JON activation and MN/DN responses).\n",
    "\n",
    "Prerequisites and runtime notes:\n",
    "- JAX runs on CPU by default; a GPU accelerates long runs but is not required.\n",
    "- The first build of sparse connectivity can take time proportional to the size of the edge list. Subsequent runs are faster due to caching.\n",
    "\n",
    "Navigation:\n",
    "- Start with the Connectome and Model sections, then use the Example Trial to sanity-check the setup.\n",
    "- The later sections script specific figure-like experiments (sugar, water, bitter GRN; JON subtypes; MN9/DN readouts).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:00.392573Z",
     "start_time": "2025-09-16T12:43:00.387413Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import os\n",
    "import pickle\n",
    "from pathlib import Path\n",
    "from typing import Callable, Union, Sequence, Dict, Optional\n",
    "\n",
    "import brainpy.state\n",
    "import brainevent\n",
    "import brainstate\n",
    "import braintools\n",
    "import brainunit as u\n",
    "import jax\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313d6748cf67c961",
   "metadata": {},
   "source": [
    "## Connectome dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3cf84b26b3d456",
   "metadata": {},
   "source": [
    "This section fetches the FlyWire 630 connectome mirror via `kagglehub` and prepares the neuron index set used throughout.\n",
    "\n",
    "Data files used by this notebook:\n",
    "- `2023_03_23_completeness_630_final.csv`: canonical list of FlyWire neuron IDs (these define the row/column ordering of the network).\n",
    "- `2023_03_23_connectivity_630_final.parquet`: long-format edge list with at least the columns `Presynaptic_Index`, `Postsynaptic_Index`, and `Excitatory x Connectivity`.\n",
    "\n",
    "Implementation details:\n",
    "- We construct a CSR sparse matrix from the edge list, sorted by presynaptic index for efficient iteration.\n",
    "- Only the excitatory connectivity term is used to build the weight matrix; the scalar `w_syn` later rescales these values into mV.\n",
    "- The returned `data_path` points to the locally cached dataset so repeated runs avoid re-downloading.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fcf168e7701804d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import kagglehub\n",
    "\n",
    "# Download latest version of Flywire 630 dataset from Kaggle\n",
    "data_path = kagglehub.dataset_download(\"oujago/drosophila-630-data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c703c128ee0d3829",
   "metadata": {},
   "source": [
    "## Whole-brain Spiking Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "explain_model_1",
   "metadata": {},
   "source": [
    "We model each neuron as a single-compartment LIF unit receiving event-based synaptic input and optional external Poisson drive. State variables:\n",
    "- `v` membrane potential (mV), relaxing to `v_rest` with time constant `tau_m`.\n",
    "- `g` a lumped synaptic drive (mV), relaxing with time constant `tau_syn` and incremented by inputs.\n",
    "- `t_ref` tracks refractory timing; within `tau_ref`, updates are held constant.\n",
    "\n",
    "Numerics and spikes:\n",
    "- Dynamics are integrated with an exponential Euler step (`brainstate.nn.exp_euler_step`) for stability on reasonable `dt`.\n",
    "- Spiking uses a smooth surrogate (`braintools.surrogate.ReluGrad`), which keeps code differentiable; we only simulate (no training) here.\n",
    "- On a spike, `v` is reset to `v_reset`, `g` is cleared for that step, and `t_ref` is stamped.\n",
    "\n",
    "Network wiring:\n",
    "- Connectivity comes from a CSR matrix over the FlyWire index set and is applied via `brainstate.nn.SparseLinear`.\n",
    "- A fixed synaptic delay (default 1.8 ms) is modeled using `brainstate.nn.Delay` and `brainevent` event arrays.\n",
    "- External drive is applied to selected neurons by `brainstate.nn.poisson_input` with a per-event weight scaled by `w_syn * 250` to balance drive.\n",
    "- Sets of neurons can be silenced through a boolean mask; silencing gates their outgoing spikes before they propagate.\n",
    "\n",
    "Outputs and units:\n",
    "- We record spike counts and convert to firing rates (Hz) by dividing by the simulated duration in seconds.\n",
    "- All quantities carry physical units via `brainunit` (e.g., `ms`, `mV`, `Hz`) to help prevent unit mismatches.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d5075bcd2dd005a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:00.449236Z",
     "start_time": "2025-09-16T12:43:00.440365Z"
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   "outputs": [],
   "source": [
    "\n",
    "class Population(brainpy.state.Neuron):\n",
    "    \"\"\"\n",
    "    A population of neurons with leaky integrate-and-fire dynamics.\n",
    "\n",
    "    The dynamics of the neurons are given by the following equations::\n",
    "\n",
    "       dv/dt = (v_0 - v + g) / t_mbr : volt (unless refractory)\n",
    "       dg/dt = -g / tau               : volt (unless refractory)\n",
    "\n",
    "\n",
    "    Args:\n",
    "      size: The number of neurons in the population.\n",
    "      v_rest: The resting potential of the neurons.\n",
    "      v_reset: The reset potential of the neurons after a spike.\n",
    "      v_th: The threshold potential of the neurons for spiking.\n",
    "      tau_m: The membrane time constant of the neurons.\n",
    "      tau_syn: The synaptic time constant of the neurons.\n",
    "      tau_ref: The refractory period of the neurons.\n",
    "      spk_fun: The spike function of the neurons.\n",
    "      name: The name of the population.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        size: brainstate.typing.Size,\n",
    "        v_rest: u.Quantity = -52 * u.mV,  # resting potential\n",
    "        v_reset: u.Quantity = -52 * u.mV,  # reset potential after spike\n",
    "        v_th: u.Quantity = -45 * u.mV,  # potential threshold for spiking\n",
    "        tau_m: u.Quantity = 20 * u.ms,  # membrane time constant\n",
    "        # Jürgensen et al https://doi.org/10.1088/2634-4386/ac3ba6\n",
    "        tau_syn: u.Quantity = 5 * u.ms,  # synaptic time constant\n",
    "        # Lazar et al https://doi.org/10.7554/eLife.62362\n",
    "        tau_ref: u.Quantity = 2.2 * u.ms,  # refractory period\n",
    "        spk_fun: Callable = braintools.surrogate.ReluGrad(),  # spike function\n",
    "        name: str = None,\n",
    "    ):\n",
    "        super().__init__(size, name=name)\n",
    "\n",
    "        self.v_rest = v_rest\n",
    "        self.v_reset = v_reset\n",
    "        self.v_th = v_th\n",
    "        self.tau_m = tau_m\n",
    "        self.tau_syn = tau_syn\n",
    "        self.tau_ref = u.math.full(self.varshape, tau_ref)\n",
    "        self.spk_fun = spk_fun\n",
    "\n",
    "    def init_state(self, batch_size=None):\n",
    "        self.v = brainstate.ShortTermState(\n",
    "            braintools.init.param(braintools.init.Constant(self.v_rest), self.varshape, batch_size))\n",
    "        self.g = brainstate.ShortTermState(\n",
    "            braintools.init.param(braintools.init.Constant(0.0 * u.mV), self.varshape, batch_size))\n",
    "        self.t_ref = brainstate.ShortTermState(\n",
    "            braintools.init.param(braintools.init.Constant(-1e7 * u.ms), self.varshape, batch_size))\n",
    "        self.spike_count = brainstate.ShortTermState(\n",
    "            braintools.init.param(braintools.init.Constant(0.), self.varshape, batch_size))\n",
    "\n",
    "    def reset_state(self, batch_size=None):\n",
    "        self.v.value = u.math.ones(self.varshape) * self.v_rest\n",
    "        self.g.value = u.math.zeros(self.varshape) * u.mV\n",
    "        self.t_ref.value = u.math.full(self.varshape, -1e7 * u.ms)\n",
    "\n",
    "    def get_spike(self, v=None):\n",
    "        v = self.v.value if v is None else v\n",
    "        return self.spk_fun((v - self.v_th) / (20. * u.mV))\n",
    "\n",
    "    def update(self, x):\n",
    "        t = brainstate.environ.get('t')\n",
    "\n",
    "        # numerical integration\n",
    "        dv = lambda v, t, g: (self.v_rest - v + g) / self.tau_m\n",
    "        dg = lambda g, t: -g / self.tau_syn\n",
    "        v = brainstate.nn.exp_euler_step(dv, self.v.value, t, self.g.value)\n",
    "        g = brainstate.nn.exp_euler_step(dg, self.g.value, t)\n",
    "        g += x  # external input current\n",
    "        v = self.sum_delta_inputs(v)\n",
    "\n",
    "        # refractory period\n",
    "        ref = (t - self.t_ref.value) <= self.tau_ref\n",
    "        v = u.math.where(ref, self.v.value, v)\n",
    "        g = u.math.where(ref, self.g.value, g)\n",
    "\n",
    "        # spikes\n",
    "        spk = self.get_spike(v)\n",
    "        self.spike_count.value += spk\n",
    "\n",
    "        # update states\n",
    "        self.v.value = spk * (self.v_reset - v) + v\n",
    "        self.g.value = g - spk * g\n",
    "        self.t_ref.value = u.math.where(spk, t, self.t_ref.value)\n",
    "        return spk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d2f94459e6837b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:00.472802Z",
     "start_time": "2025-09-16T12:43:00.460975Z"
    }
   },
   "outputs": [],
   "source": [
    "class Network(brainstate.nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        path_neu: Union[Path, str],\n",
    "        path_syn: Union[Path, str],\n",
    "        neuron_to_excite: Sequence[Dict] = (),\n",
    "        neuron_to_inhibit: Sequence[Dict] = (),\n",
    "        w_syn: u.Quantity = 0.275 * u.mV,\n",
    "        neuron_params: Dict = None,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        self.path_neu = Path(path_neu)\n",
    "        self.path_syn = Path(path_syn)\n",
    "\n",
    "        # neuron ids\n",
    "        flywire_ids = pd.read_csv(self.path_neu, index_col=0)\n",
    "        self.n_neuron = len(flywire_ids)\n",
    "        self._flyid2i = {f: i for i, f in enumerate(flywire_ids.index)}\n",
    "        self._i2flyid = {i: f for i, f in enumerate(flywire_ids.index)}\n",
    "\n",
    "        # neurons ids to excite\n",
    "        self.neuron_to_excite = tuple([\n",
    "            {\n",
    "                'indices': (\n",
    "                    u.math.asarray(exc['indices'])\n",
    "                    if 'indices' in exc else\n",
    "                    np.asarray([self._flyid2i[id_] for id_ in exc['ids']])\n",
    "                ),  # neuron ids to excite\n",
    "                'rate': exc['rate'],  # Poisson rate\n",
    "                'w_syn': w_syn.clone() * 250,  # synaptic weight, 250 is the scaling factor for Poisson synapse\n",
    "            }\n",
    "            for exc in neuron_to_excite\n",
    "        ])\n",
    "        # neuron ids to inhibit\n",
    "        neuron_to_inhibit = [\n",
    "            (\n",
    "                inh['indices']\n",
    "                if 'indices' in inh else\n",
    "                [self._flyid2i[i] for i in inh['ids']]\n",
    "            )\n",
    "            for inh in neuron_to_inhibit\n",
    "        ]\n",
    "        self.neuron_mask = jax.numpy.ones(self.n_neuron, dtype=bool)\n",
    "        if len(neuron_to_inhibit) > 0:\n",
    "            for indices in neuron_to_inhibit:\n",
    "                self.neuron_mask = self.neuron_mask.at[indices].set(False)\n",
    "\n",
    "        # neuronal and synaptic dynamics\n",
    "        neuron_params = neuron_params or {}\n",
    "        self.pop = Population(size=self.n_neuron, **neuron_params)\n",
    "\n",
    "        # delay for changes in post-synaptic neuron\n",
    "        # Paul et al 2015 doi: https://doi.org/10.3389/fncel.2015.00029\n",
    "        self.delay = brainstate.nn.Delay(\n",
    "            jax.ShapeDtypeStruct(self.pop.varshape, brainstate.environ.dftype()),\n",
    "            entries={'D': 1.8 * u.ms}\n",
    "        )\n",
    "\n",
    "        # synapses: CSR connectivity matrix\n",
    "        flywire_conns = pd.read_parquet(self.path_syn)\n",
    "        i_pre = flywire_conns.loc[:, 'Presynaptic_Index'].values\n",
    "        i_post = flywire_conns.loc[:, 'Postsynaptic_Index'].values\n",
    "        weight = flywire_conns.loc[:, 'Excitatory x Connectivity'].values\n",
    "        sort_indices = np.argsort(i_pre)\n",
    "        i_pre = i_pre[sort_indices]\n",
    "        i_post = i_post[sort_indices]\n",
    "        weight = weight[sort_indices]\n",
    "\n",
    "        values, counts = np.unique(i_pre, return_counts=True)\n",
    "        indptr = np.zeros(self.n_neuron + 1, dtype=int)\n",
    "        indptr[values + 1] = counts\n",
    "        indptr = np.cumsum(indptr)\n",
    "        indices = i_post\n",
    "\n",
    "        self.conn = brainstate.nn.SparseLinear(\n",
    "            brainevent.CSR(\n",
    "                (weight * w_syn, indices, indptr),\n",
    "                shape=(self.n_neuron, self.n_neuron)\n",
    "            ),\n",
    "            b_init=None,\n",
    "        )\n",
    "\n",
    "        # Poisson input\n",
    "        for exc in self.neuron_to_excite:\n",
    "            self.pop.tau_ref[exc['indices']] = 0. * u.ms  # set refractory period to 0.5 ms\n",
    "\n",
    "    def update(self, *args, **kwargs):\n",
    "        # excite neurons\n",
    "        for exc in self.neuron_to_excite:\n",
    "            brainpy.state.poisson_input(\n",
    "                freq=exc['rate'],\n",
    "                num_input=1,\n",
    "                weight=exc['w_syn'],\n",
    "                target=self.pop.v,\n",
    "                indices=exc['indices'],\n",
    "            )\n",
    "\n",
    "        # delayed spikes\n",
    "        pre_spk = self.delay.at('D')\n",
    "\n",
    "        # inhibit neurons\n",
    "        if len(self.neuron_mask):\n",
    "            pre_spk = pre_spk * self.neuron_mask\n",
    "\n",
    "        # compute recurrent connections and update neurons\n",
    "        spk = self.pop(self.conn(brainevent.EventArray(pre_spk)))\n",
    "\n",
    "        # update delay spikes\n",
    "        self.delay.update(spk)\n",
    "        return spk\n",
    "\n",
    "    def step_run(self, i, ret_val: str = 'none'):\n",
    "        with brainstate.environ.context(t=i * brainstate.environ.get_dt(), i=i):\n",
    "            spk = self.update()\n",
    "            if ret_val == 'spike':\n",
    "                return spk\n",
    "            elif ret_val == 'voltage':\n",
    "                return self.pop.v.value\n",
    "            elif ret_val == 'conductance':\n",
    "                return self.pop.g.value\n",
    "            elif ret_val == 'spike_count':\n",
    "                return self.pop.spike_count.value\n",
    "            elif ret_val == 'none':\n",
    "                return None\n",
    "            else:\n",
    "                raise ValueError('ret_val must be \"spike\", \"voltage\", \"conductance\", or \"spike_count\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "369baa205c65a3f6",
   "metadata": {},
   "source": [
    "## Utility Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "explain_utils_1",
   "metadata": {},
   "source": [
    "This section provides helpers used across experiments:\n",
    "- `run_one_exp(...)`: build the network with specified excitation/inhibition, run for a duration, and return per-neuron firing rates.\n",
    "- `flywire_ids(path)`: load the canonical neuron ID ordering.\n",
    "- `ndarray_to_dataframe(...)`: reshape multi-dimensional arrays into tidy Pandas DataFrames for plotting/analysis.\n",
    "\n",
    "Later sections also define targeted wrappers (e.g., `which_*`, `*_activate_*`) that bundle stimulus configurations for the figure-style analyses.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91c61a3f8998c8be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:00.489808Z",
     "start_time": "2025-09-16T12:43:00.484815Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_one_exp(\n",
    "    neurons_to_excite: Sequence[dict],\n",
    "    neuron_to_inhibit: Sequence = (),\n",
    "    duration: brainstate.typing.ArrayLike = 1000 * u.ms,\n",
    "    dt: brainstate.typing.ArrayLike = 0.1 * u.ms,\n",
    "    pbar: int = 1000,\n",
    "):\n",
    "    with brainstate.environ.context(dt=dt):\n",
    "        indices = np.arange(int(duration / brainstate.environ.get_dt()))\n",
    "\n",
    "        assert isinstance(neurons_to_excite, (tuple, list)), 'neurons_to_excite must be a list or tuple'\n",
    "        for exc in neurons_to_excite:\n",
    "            assert isinstance(exc, dict), 'neurons_to_excite must be a list of dictionaries'\n",
    "            assert 'ids' in exc or 'indices' in exc, 'neurons_to_excite must have a key \"ids\" or \"indices\"'\n",
    "            assert 'rate' in exc, 'neurons_to_excite must have a key \"rate\"'\n",
    "        assert isinstance(neuron_to_inhibit, (tuple, list)), 'neuron_to_inhibit must be a list or tuple'\n",
    "\n",
    "        net = Network(\n",
    "            path_neu=os.path.join(data_path, './2023_03_23_completeness_630_final.csv'),\n",
    "            path_syn=os.path.join(data_path, './2023_03_23_connectivity_630_final.parquet'),\n",
    "            neuron_to_excite=neurons_to_excite,\n",
    "            neuron_to_inhibit=neuron_to_inhibit,\n",
    "        )\n",
    "        brainstate.nn.init_all_states(net)\n",
    "        brainstate.transform.for_loop(\n",
    "            net.step_run,\n",
    "            indices,\n",
    "            pbar=pbar\n",
    "        )\n",
    "        return net.pop.spike_count.value / duration.to_decimal(u.second)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "823fcc5d185b9194",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:00.511792Z",
     "start_time": "2025-09-16T12:43:00.507820Z"
    }
   },
   "outputs": [],
   "source": [
    "def flywire_ids(neu_path: str | Path):\n",
    "    df = pd.read_csv(neu_path, index_col=0)\n",
    "    return np.asarray(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1cde690f63b9f4b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:00.551610Z",
     "start_time": "2025-09-16T12:43:00.540581Z"
    }
   },
   "outputs": [],
   "source": [
    "def ndarray_to_dataframe(\n",
    "    arr,\n",
    "    dim_names: Sequence,\n",
    "    axis_names: Optional[Sequence[str]] = None,\n",
    "    column_axis: Optional[int] = None,\n",
    "    row_axis: Optional[int] = None\n",
    "):\n",
    "    \"\"\"\n",
    "    Convert a multidimensional NumPy array to a two-dimensional Pandas DataFrame.\n",
    "\n",
    "    Parameters:\n",
    "    - arr (numpy.ndarray): Input multi-dimensional array.\n",
    "    - dim_names (list of list of str): List of names for each dimension.\n",
    "    - column_axis (int): Specifies which dimension to use as DataFrame columns.\n",
    "    - row_axis (int): Specifies which dimension to use as DataFrame rows, the remaining\n",
    "        dimensions will be flattened into columns.\n",
    "\n",
    "    Returns:\n",
    "    - pd.DataFrame: Converted two-dimensional DataFrame.\n",
    "    \"\"\"\n",
    "\n",
    "    # Validate input\n",
    "    if not isinstance(arr, (np.ndarray, jax.Array)):\n",
    "        raise TypeError(\"Input must be a NumPy array.\")\n",
    "\n",
    "    if not isinstance(dim_names, list) or len(dim_names) != arr.ndim:\n",
    "        raise ValueError(\"dim_names must be a list with the same length \"\n",
    "                         \"as the number of dimensions of the array.\")\n",
    "    for i, names in enumerate(dim_names):\n",
    "        if len(names) != arr.shape[i]:\n",
    "            raise ValueError(f\"Length of dim_names[{i}] must be equal to \"\n",
    "                             f\"the size of the corresponding dimension.\")\n",
    "\n",
    "    # Get indices for all dimensions\n",
    "    axes = list(range(arr.ndim))\n",
    "\n",
    "    if column_axis is None:\n",
    "        assert row_axis is not None, 'row_axis must be specified if column_axis is None'\n",
    "\n",
    "        if not isinstance(row_axis, int):\n",
    "            if row_axis < 0:\n",
    "                row_axis = row_axis + arr.ndim\n",
    "            if not (0 <= row_axis < arr.ndim):\n",
    "                raise ValueError(\"row_axis must be a valid dimension index.\")\n",
    "\n",
    "        # Separate row dimension and column dimensions\n",
    "        row_dim = row_axis\n",
    "        column_dims = axes[:row_dim] + axes[row_dim + 1:]\n",
    "\n",
    "        # Get row labels\n",
    "        row_labels = dim_names[row_dim]\n",
    "\n",
    "        # Get labels for each column dimension\n",
    "        column_dim_names = [dim_names[dim] for dim in column_dims]\n",
    "\n",
    "        # Generate all combinations of column labels\n",
    "        if column_dim_names:\n",
    "            column_tuples = list(itertools.product(*column_dim_names))\n",
    "            # Convert tuples to string labels, e.g., ('Feature_X', 'Measurement_I') -> 'Feature_X_Measurement_I'\n",
    "            column_labels = ['_'.join(col) for col in column_tuples]\n",
    "        else:\n",
    "            # If there is only one dimension as rows, there are no columns\n",
    "            column_labels = []\n",
    "\n",
    "        # Rearrange array axes to move row axis to the first position,\n",
    "        # keeping the order of the remaining dimensions\n",
    "        reordered_axes = [row_dim] + column_dims\n",
    "        arr_reordered = np.transpose(arr, axes=reordered_axes)\n",
    "\n",
    "        # Get new shape\n",
    "        new_shape = arr_reordered.shape\n",
    "        num_rows = new_shape[0]\n",
    "        num_cols = np.prod(new_shape[1:]).astype(int) if len(new_shape) > 1 else 1\n",
    "\n",
    "        # Flatten all dimensions except the row dimension into columns\n",
    "        arr_flat = arr_reordered.reshape(num_rows, num_cols)\n",
    "\n",
    "        # If there are multiple column dimensions, generate combined\n",
    "        # column labels; otherwise, use a single column label\n",
    "        if len(column_dims) > 0:\n",
    "            if len(column_dims) == 1:\n",
    "                # Only one column dimension, directly use its labels\n",
    "                column_labels = dim_names[column_dims[0]]\n",
    "            else:\n",
    "                # Multiple column dimensions, combine labels\n",
    "                column_labels = [\n",
    "                    '_'.join([dim_names[dim][i] for dim, i in zip(column_dims, idx)])\n",
    "                    for idx in itertools.product(*[range(len(dim_names[dim])) for dim in column_dims])\n",
    "                ]\n",
    "\n",
    "        # Create row index\n",
    "        index = pd.Index(row_labels, name=dim_names[row_dim][0] if len(dim_names[row_dim]) > 0 else f\"dim_{row_dim}\")\n",
    "\n",
    "        # Create column index\n",
    "        if column_labels:\n",
    "            columns = column_labels\n",
    "        else:\n",
    "            # If there are no column labels, create a default single column\n",
    "            columns = ['Value']\n",
    "            arr_flat = arr_flat.flatten()\n",
    "\n",
    "    else:\n",
    "        assert row_axis is None, 'row_axis must be None if column_axis is specified'\n",
    "\n",
    "        if not isinstance(column_axis, int):\n",
    "            if column_axis < 0:\n",
    "                column_axis = column_axis + arr.ndim\n",
    "            if not (0 <= column_axis < arr.ndim):\n",
    "                raise ValueError(\"column_axis must be a valid dimension index.\")\n",
    "\n",
    "        # Separate column dimension and row dimensions\n",
    "        column_dim = column_axis\n",
    "        row_dims = axes[:column_dim] + axes[column_dim + 1:]\n",
    "\n",
    "        # Get column labels\n",
    "        columns = dim_names[column_dim]\n",
    "\n",
    "        # Get labels for each row dimension\n",
    "        row_dim_names = [dim_names[dim] for dim in row_dims]\n",
    "\n",
    "        # Generate all combinations of row labels\n",
    "        row_tuples = list(itertools.product(*row_dim_names))\n",
    "\n",
    "        # Rearrange array axes to move column axis to the last position\n",
    "        reordered_axes = row_dims + [column_dim]\n",
    "        arr_reordered = arr.transpose(reordered_axes)\n",
    "\n",
    "        # Calculate number of rows and columns\n",
    "        num_rows = arr_reordered.shape[:-1]\n",
    "        num_cols = arr_reordered.shape[-1]\n",
    "\n",
    "        # Flatten all dimensions except the column dimension\n",
    "        arr_flat = arr_reordered.reshape(-1, num_cols)\n",
    "\n",
    "        # Create row index, use MultiIndex if there are multiple row dimensions\n",
    "        if len(row_dims) > 1:\n",
    "            if axis_names is None:\n",
    "                names = [f\"dim_{dim}\" for dim in row_dims]\n",
    "            else:\n",
    "                assert len(axis_names) == arr.ndim, 'axis_names must have the same length as the number of dimensions'\n",
    "                names = [axis_names[dim] for dim in range(arr.ndim)]\n",
    "            index = pd.MultiIndex.from_tuples(row_tuples, names=names)\n",
    "        else:\n",
    "            index = pd.Index(row_tuples, )\n",
    "\n",
    "    # Create DataFrame\n",
    "    df = pd.DataFrame(arr_flat, index=index, columns=columns)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6571b9d6c46da301",
   "metadata": {},
   "source": [
    "## Model settings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "explain_settings_1",
   "metadata": {},
   "source": [
    "Global parameters used by the examples that follow:\n",
    "- `n_trial`: how many repeated runs to average over for robustness.\n",
    "- `duration`: simulation length per run (with `dt` typically set in context, e.g., 0.1 ms).\n",
    "- `all_neuron_ids` and `flywire_id_to_index`: mappings between FlyWire IDs and zero-based indices into the state vectors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39bb7644a9d11358",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:02.143779Z",
     "start_time": "2025-09-16T12:43:02.095257Z"
    }
   },
   "outputs": [],
   "source": [
    "n_trial = 5\n",
    "duration = 100. * u.ms\n",
    "\n",
    "all_neuron_ids = flywire_ids(os.path.join(data_path, './2023_03_23_completeness_630_final.csv'))\n",
    "flywire_id_to_index = {f: i for i, f in enumerate(all_neuron_ids)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e6c80133d59619a",
   "metadata": {},
   "source": [
    "## Example Trial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "explain_example_1",
   "metadata": {},
   "source": [
    "A minimal end-to-end run that drives a subset of sugar-sensing GRNs (right hemisphere) with a Poisson process and visualizes the resulting spikes across the whole network.\n",
    "\n",
    "How to adapt:\n",
    "- Replace `neu_sugar` with another neuron list (e.g., water or bitter GRNs, JON subtypes, or arbitrary IDs).\n",
    "- Tweak the input `rate` (in Hz), the `duration`, or neuron parameters (e.g., `tau_m`, `tau_syn`, `w_syn`) to explore sensitivity.\n",
    "- Use `ret_val='spike'|'voltage'|'conductance'` in `step_run` to capture different observables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ebe225d98c656078",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:02.152518Z",
     "start_time": "2025-09-16T12:43:02.149296Z"
    }
   },
   "outputs": [],
   "source": [
    "# define inputs\n",
    "# list of the labellar sugar-sensing gustatory receptor neurons on right hemisphere\n",
    "neu_sugar = [\n",
    "    720575940624963786, 720575940630233916, 720575940637568838, 720575940638202345, 720575940617000768,\n",
    "    720575940630797113, 720575940632889389, 720575940621754367, 720575940621502051, 720575940640649691,\n",
    "    720575940639332736, 720575940616885538, 720575940639198653, 720575940620900446, 720575940617937543,\n",
    "    720575940632425919, 720575940633143833, 720575940612670570, 720575940628853239, 720575940629176663,\n",
    "    720575940611875570,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "39ca5883e4a284e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:02.166694Z",
     "start_time": "2025-09-16T12:43:02.161658Z"
    }
   },
   "outputs": [],
   "source": [
    "def example_to_run(duration=1000 * u.ms):\n",
    "    with brainstate.environ.context(dt=0.1 * u.ms):\n",
    "        net = Network(\n",
    "            path_neu=os.path.join(data_path, './2023_03_23_completeness_630_final.csv'),\n",
    "            path_syn=os.path.join(data_path, './2023_03_23_connectivity_630_final.parquet'),\n",
    "            neuron_to_excite=[\n",
    "                # sugar neuron\n",
    "                {\n",
    "                    'ids': neu_sugar,\n",
    "                    'rate': 150 * u.Hz\n",
    "                },\n",
    "                # other neuron groups\n",
    "            ],\n",
    "        )\n",
    "        brainstate.nn.init_all_states(net)\n",
    "\n",
    "        indices = np.arange(int(duration / brainstate.environ.get_dt()))\n",
    "        times = indices * brainstate.environ.get_dt()\n",
    "        spks = brainstate.transform.for_loop(lambda i: net.step_run(i, ret_val='spike'), indices, pbar=1000)\n",
    "\n",
    "    fig, gs = braintools.visualize.get_figure(1, 1, 5, 5)\n",
    "    ax = fig.add_subplot(gs[0, 0])\n",
    "    i_times, i_indices = np.where(spks)\n",
    "    plt.scatter(times[i_times], i_indices, s=1)\n",
    "    plt.xlabel('Time [ms]')\n",
    "    plt.ylabel('Neuron index')\n",
    "    plt.ylim(0, net.n_neuron)\n",
    "    plt.xlim(0 * u.ms, duration)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f2ca29ecfe6e783",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:25.870035Z",
     "start_time": "2025-09-16T12:43:02.174193Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7e9ad7af59a47ca9d8b5a426b402c4a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_to_run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf96f19a188962a9",
   "metadata": {},
   "source": [
    "## Figure 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "explain_fig1_1",
   "metadata": {},
   "source": [
    "The following cells script experiments that mirror Figure 1 panels in the paper. For each panel we:\n",
    "- Define the stimulated neuron set(s) and their Poisson drive rates.\n",
    "- Optionally silence subsets (to assess causal contribution).\n",
    "- Run the simulation and aggregate whole-brain activity or specific readouts (e.g., MN9).\n",
    "\n",
    "Note: Exact numerical replication may differ due to model simplifications (single-compartment LIF, weight rescaling) and environment details (JAX device, random seeds). The qualitative patterns and causal trends should match.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "337a6c395ba78c67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:25.913204Z",
     "start_time": "2025-09-16T12:43:25.908517Z"
    }
   },
   "outputs": [],
   "source": [
    "fig1_path_res = Path('./figure_1')\n",
    "os.makedirs(fig1_path_res, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e3c187b53319f8",
   "metadata": {},
   "source": [
    "### Figure 1 D: Whole Brain Activity when Activating Sugar GRN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b3d1b918001aba2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:25.921659Z",
     "start_time": "2025-09-16T12:43:25.918806Z"
    }
   },
   "outputs": [],
   "source": [
    "# frequencies\n",
    "freqs = np.arange(10, 201, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9dd7ebaf277d4c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:43:25.942393Z",
     "start_time": "2025-09-16T12:43:25.935303Z"
    }
   },
   "outputs": [],
   "source": [
    "def whole_brain_activation():\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_freq(freq_):\n",
    "        return run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_sugar, 'rate': freq_ * u.Hz}],\n",
    "            duration=duration, pbar=10,\n",
    "        )\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def run_simulations():\n",
    "        return run_with_freq(freqs)\n",
    "\n",
    "    if not os.path.exists(fig1_path_res / 'firing_rate.csv'):\n",
    "        rates = run_simulations()\n",
    "\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "                all_neuron_ids\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(fig1_path_res / 'firing_rate.csv')\n",
    "\n",
    "        df_rate = df.loc['190 Hz'].mean(axis=1)\n",
    "        df_rate_std = df.loc['190 Hz'].std(axis=1)\n",
    "\n",
    "        # save 200 neurons for next simulation\n",
    "        id_top200 = df_rate.sort_values(ascending=False).index[:200]\n",
    "\n",
    "        print(df_rate.sort_values(ascending=False))\n",
    "\n",
    "        with open(fig1_path_res / 'fig_1d_id_top200.pickle', 'wb') as f:\n",
    "            pickle.dump(id_top200, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64a0e5fc1567281d",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-09-16T12:43:25.960887Z"
    },
    "jupyter": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "whole_brain_activation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd57d6ba1424cc5",
   "metadata": {},
   "source": [
    "### Figure 1 E:  Which neurons activate MN9?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "444335a9ef93d1e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:29.989330600Z",
     "start_time": "2025-07-08T09:24:30.506404Z"
    }
   },
   "outputs": [],
   "source": [
    "# define inputs\n",
    "# flywire ID for MN9\n",
    "id_mn9 = 720575940660219265\n",
    "\n",
    "index_id_mn9 = flywire_id_to_index[id_mn9]\n",
    "\n",
    "# identify the top 200 neurons that respond due to 200 Hz sugar firing (Figure 1D)\n",
    "with open(fig1_path_res / 'fig_1d_id_top200.pickle', 'rb') as f:\n",
    "    id_top200 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d81ccf4d48166e21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.062348800Z",
     "start_time": "2025-07-08T09:24:31.030718Z"
    }
   },
   "outputs": [],
   "source": [
    "# neuron indices\n",
    "indices = np.asarray([flywire_id_to_index[id_] for id_ in id_top200])\n",
    "indices = np.expand_dims(indices, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4bf303e51912a1a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.062959300Z",
     "start_time": "2025-07-08T09:24:31.593891Z"
    }
   },
   "outputs": [],
   "source": [
    "def which_neuron_activate_MN9(freqs):\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_freq(freq_, indices_):\n",
    "        assert indices_.ndim == 1 and indices_.size == 1\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'indices': indices_, 'rate': freq_ * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_id_mn9]\n",
    "\n",
    "    def run_with_indices(indices_):\n",
    "        return run_with_freq(freqs, indices_)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        # due to the limited memory of the device, we choose using \"brainstate.transform.map\" instead of \"brainstate.transform.vmap2\"\n",
    "\n",
    "        # return bst.transform.vmap2(run_with_indices)(bst.random.split_key(len(indices)), indices)\n",
    "        return brainstate.transform.map(run_with_indices, indices, batch_size=20)\n",
    "\n",
    "    path = fig1_path_res / f'which_neuron_activate_NM9_firing_rate.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                id_top200,\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6bc3992d4bb62b5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.064994Z",
     "start_time": "2025-07-08T09:24:32.028994Z"
    }
   },
   "outputs": [],
   "source": [
    "which_neuron_activate_MN9(\n",
    "    np.asarray([25, 50, 75, 100, 125, 150, 175, 200])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c37feb21e8cc369c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.064994Z",
     "start_time": "2025-07-08T09:30:38.151154Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(fig1_path_res / f'which_neuron_activate_NM9_firing_rate.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "pivot_df.sort_index(ascending=False, inplace=True)\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "pivot_df = pivot_df.reindex([f'{f} Hz' for f in [25, 50, 75, 100, 125, 150, 175, 200]], axis=1)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef25e80708d3dab6",
   "metadata": {},
   "source": [
    "### Figure 1F: Silencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9ae90b8bc08e621f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.064994Z",
     "start_time": "2025-07-08T09:39:14.062350Z"
    }
   },
   "outputs": [],
   "source": [
    "# neuron indices\n",
    "silencing_indices = np.asarray([flywire_id_to_index[id_] for id_ in id_top200])\n",
    "silencing_indices = np.expand_dims(silencing_indices, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2c0105a45c300b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.121511200Z",
     "start_time": "2025-07-08T09:39:14.149327Z"
    }
   },
   "outputs": [],
   "source": [
    "def slice_neuron_and_activate_MN9(freqs):\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_freq(freq_, indices_):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_sugar, 'rate': freq_ * u.Hz}],\n",
    "            neuron_to_inhibit=[{'indices': indices_}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_id_mn9]\n",
    "\n",
    "    def run_with_indices(indices_):\n",
    "        return run_with_freq(freqs, indices_)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        # Due to the limited memory of the device, we choose using \"brainstate.transform.map\" instead of \"brainstate.transform.vmap2\"\n",
    "        # Decrease \"batch_size\" when out of memory\n",
    "\n",
    "        # return bst.transform.vmap2(run_with_indices)(bst.random.split_key(len(silencing_indices)), silencing_indices)\n",
    "        return brainstate.transform.map(run_with_indices, silencing_indices, batch_size=20)\n",
    "\n",
    "    path = fig1_path_res / f'sugarR-silencing_which_neuron_firing_rate.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                id_top200,\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f4e39f9bc624e3cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.131043200Z",
     "start_time": "2025-07-08T09:39:14.748817Z"
    }
   },
   "outputs": [],
   "source": [
    "slice_neuron_and_activate_MN9(\n",
    "    np.asarray([50, 60, 70, 80, 90, 100, 110, 120])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "afcd65435e0f4bb6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.166565300Z",
     "start_time": "2025-07-08T09:43:44.959659Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(fig1_path_res / f'sugarR-silencing_which_neuron_firing_rate.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "pivot_df = pivot_df.reindex([f'{f} Hz' for f in [50, 60, 70, 80, 90, 100, 110, 120]], axis=1)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e90f5a9cc246a5e",
   "metadata": {},
   "source": [
    "## Figure 2: Which Cell Type Activates MN9?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d11d8355655242d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.214011800Z",
     "start_time": "2025-07-08T11:14:00.027696Z"
    }
   },
   "outputs": [],
   "source": [
    "# output path\n",
    "fig2_path_res = Path('./figure_2')\n",
    "os.makedirs(fig2_path_res, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "268ea31697223317",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.221009500Z",
     "start_time": "2025-07-08T11:14:03.040079Z"
    }
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "# define input\n",
    "ids_two_mn9 = [720575940660219265, 720575940645521262]  # left and right\n",
    "\n",
    "index_id_mn9 = np.asarray(\n",
    "    [flywire_id_to_index[ids_two_mn9[0]],\n",
    "     flywire_id_to_index[ids_two_mn9[1]]]\n",
    ")\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/chaobrain/drosophila_whole_brain_snn_simulation/main/sez_neurons.pickle'\n",
    "types_and_ids = pickle.loads(requests.get(url).content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fa00f5381eaaa7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.248551900Z",
     "start_time": "2025-07-08T11:14:03.111684Z"
    }
   },
   "outputs": [],
   "source": [
    "def which_cell_type_activates_MN9(cell_ids, type_name, freqs):\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_freq(freq_):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': cell_ids, 'rate': freq_ * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_id_mn9]\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        return run_with_freq(freqs)\n",
    "\n",
    "    path = fig2_path_res / f'cell_type={type_name}.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "                ids_two_mn9\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "34b3f7483396b6a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.249549100Z",
     "start_time": "2025-07-08T11:14:03.234874Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activating MN9 with cell type aDT6\n",
      "Activating MN9 with cell type amulet\n",
      "Activating MN9 with cell type aSG1\n",
      "Activating MN9 with cell type aSG7\n",
      "Activating MN9 with cell type aster\n",
      "Activating MN9 with cell type Asteroid\n",
      "Activating MN9 with cell type bamboo\n",
      "Activating MN9 with cell type basket\n",
      "Activating MN9 with cell type bluebell\n",
      "Activating MN9 with cell type bobber\n",
      "Activating MN9 with cell type box\n",
      "Activating MN9 with cell type bract\n",
      "Activating MN9 with cell type bridle\n",
      "Activating MN9 with cell type brontosaraus\n",
      "Activating MN9 with cell type broom\n",
      "Activating MN9 with cell type buddy\n",
      "Activating MN9 with cell type clavicle\n",
      "Activating MN9 with cell type coy\n",
      "Activating MN9 with cell type crab\n",
      "Activating MN9 with cell type cradle\n",
      "Activating MN9 with cell type damsel\n",
      "Activating MN9 with cell type diatom\n",
      "Activating MN9 with cell type doublescoop\n",
      "Activating MN9 with cell type earmuff\n",
      "Activating MN9 with cell type eiffel\n",
      "Activating MN9 with cell type Fdg\n",
      "Activating MN9 with cell type fluff\n",
      "Activating MN9 with cell type FMIn\n",
      "Activating MN9 with cell type foxglove\n",
      "Activating MN9 with cell type fudog\n",
      "Activating MN9 with cell type G2N_1\n",
      "Activating MN9 with cell type gallinule\n",
      "Activating MN9 with cell type gazebo\n",
      "Activating MN9 with cell type genie\n",
      "Activating MN9 with cell type gnome\n",
      "Activating MN9 with cell type good_dog\n",
      "Activating MN9 with cell type gumdrop\n",
      "Activating MN9 with cell type handle\n",
      "Activating MN9 with cell type handup\n",
      "Activating MN9 with cell type haystack\n",
      "Activating MN9 with cell type horntail\n",
      "Activating MN9 with cell type horseshoe\n",
      "Activating MN9 with cell type horn\n",
      "Activating MN9 with cell type hound\n",
      "Activating MN9 with cell type hyacinth\n",
      "Activating MN9 with cell type justice\n",
      "Activating MN9 with cell type kelp\n",
      "Activating MN9 with cell type kitty\n",
      "Activating MN9 with cell type knees\n",
      "Activating MN9 with cell type kokopelli\n",
      "Activating MN9 with cell type landslide\n",
      "Activating MN9 with cell type lion\n",
      "Activating MN9 with cell type mandala\n",
      "Activating MN9 with cell type marge\n",
      "Activating MN9 with cell type meteor\n",
      "Activating MN9 with cell type mime\n",
      "Activating MN9 with cell type mist\n",
      "Activating MN9 with cell type moor\n",
      "Activating MN9 with cell type mothership\n",
      "Activating MN9 with cell type mute\n",
      "Activating MN9 with cell type nagini\n",
      "Activating MN9 with cell type nori\n",
      "Activating MN9 with cell type oink\n",
      "Activating MN9 with cell type OinkT\n",
      "Activating MN9 with cell type oval\n",
      "Activating MN9 with cell type peacock\n",
      "Activating MN9 with cell type peahen\n",
      "Activating MN9 with cell type peafowl\n",
      "Activating MN9 with cell type phantom\n",
      "Activating MN9 with cell type planter\n",
      "Activating MN9 with cell type pleco\n",
      "Activating MN9 with cell type pringle\n",
      "Activating MN9 with cell type pSG1\n",
      "Activating MN9 with cell type puddle\n",
      "Activating MN9 with cell type rattle\n",
      "Activating MN9 with cell type rocket\n",
      "Activating MN9 with cell type rose\n",
      "Activating MN9 with cell type roundup\n",
      "Activating MN9 with cell type ruby\n",
      "Activating MN9 with cell type Salivary_MN13\n",
      "Activating MN9 with cell type seagull\n",
      "Activating MN9 with cell type shark\n",
      "Activating MN9 with cell type sink_sync\n",
      "Activating MN9 with cell type snake\n",
      "Activating MN9 with cell type spray\n",
      "Activating MN9 with cell type spirit\n",
      "Activating MN9 with cell type sullivan\n",
      "Activating MN9 with cell type sundrop\n",
      "Activating MN9 with cell type tentacular\n",
      "Activating MN9 with cell type TH_VUM\n",
      "Activating MN9 with cell type tinctoria\n",
      "Activating MN9 with cell type tophat\n",
      "Activating MN9 with cell type TPN4\n",
      "Activating MN9 with cell type trident\n",
      "Activating MN9 with cell type trogon\n",
      "Activating MN9 with cell type trumpet\n",
      "Activating MN9 with cell type tulip\n",
      "Activating MN9 with cell type tundra\n",
      "Activating MN9 with cell type turner\n",
      "Activating MN9 with cell type twirl\n",
      "Activating MN9 with cell type usnea\n",
      "Activating MN9 with cell type vice\n",
      "Activating MN9 with cell type wafflecone\n",
      "Activating MN9 with cell type weaver\n",
      "Activating MN9 with cell type web\n",
      "Activating MN9 with cell type whisker\n"
     ]
    }
   ],
   "source": [
    "for cell_type_name, neurons in types_and_ids.items():\n",
    "    print('Activating MN9 with cell type', cell_type_name)\n",
    "    which_cell_type_activates_MN9(\n",
    "        neurons,\n",
    "        cell_type_name,\n",
    "        np.asarray([50, 100, 150, 200])\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bce338d4fbad5a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T13:01:27.978478Z",
     "start_time": "2025-09-16T13:01:27.973901Z"
    }
   },
   "outputs": [],
   "source": [
    "# dfs = [\n",
    "#     pd.read_csv(fig2_path_res / f'cell_type={cell_type_name}.csv').mean(axis=1)\n",
    "#     for cell_type_name in types_and_ids.keys()\n",
    "# ]\n",
    "#\n",
    "# # process results\n",
    "# for freq in freqs:\n",
    "#     plt.figure(figsize=(12, 6))\n",
    "#     x_labels = list(types_and_ids.keys())\n",
    "#     y_values = [df.loc[ids_two_mn9].mean() for df in dfs]\n",
    "#\n",
    "#     plt.bar(x_labels, y_values, color='skyblue')\n",
    "#     plt.xlabel('Cell Type')\n",
    "#     plt.ylabel('Firing of MN9')\n",
    "#     plt.title(f'Relationship Between Cell Type and MN9 Firing (Frequency = {freq} Hz)')\n",
    "#     plt.xticks(rotation=45, ha='right')\n",
    "#     plt.tight_layout()\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e44a56cdbb6fa33",
   "metadata": {},
   "source": [
    "## Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e68a956a9201d749",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.250556500Z",
     "start_time": "2025-07-08T12:01:18.844552Z"
    }
   },
   "outputs": [],
   "source": [
    "fig3_path_res = Path('./figure_3')\n",
    "os.makedirs(fig3_path_res, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b681b05f9cfd90",
   "metadata": {},
   "source": [
    "### Figure 3 A: Combination of Bitter and Sugar GRN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f556cfd5d645c631",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.251558200Z",
     "start_time": "2025-07-08T12:01:22.027448Z"
    }
   },
   "outputs": [],
   "source": [
    "# define inputs\n",
    "# lists of all the labelled sugar-sensing gustatory receptor neurons on right hemisphere\n",
    "neu_bitter = [\n",
    "    720575940621778381, 720575940602353632, 720575940617094208, 720575940619197093, 720575940626287336,\n",
    "    720575940618600651, 720575940627692048, 720575940630195909, 720575940646212996, 720575940610483162,\n",
    "    720575940645743412, 720575940627578156, 720575940622298631, 720575940621008895, 720575940629146711,\n",
    "    720575940610259370, 720575940610481370, 720575940619028208, 720575940614281266, 720575940613061118,\n",
    "    720575940604027168\n",
    "]\n",
    "\n",
    "neu_ir94e = [\n",
    "    720575940614211295, 720575940638218173, 720575940628832256, 720575940626016017, 720575940621375231,\n",
    "    720575940612920386, 720575940614273292, 720575940628198503, 720575940626241636, 720575940619387814,\n",
    "    720575940624604560, 720575940615274425, 720575940610683315, 720575940627265265, 720575940624079544,\n",
    "    720575940629211607, 720575940615089369, 720575940631082124\n",
    "]\n",
    "\n",
    "id_mn9 = 720575940660219265  # left\n",
    "index_mn9 = flywire_id_to_index[id_mn9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bd43c1368d5cfa66",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.252556400Z",
     "start_time": "2025-07-08T12:01:22.080607Z"
    }
   },
   "outputs": [],
   "source": [
    "def sugar_bitter_activate_MN9(freqs):\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_sugar_freq(sugar_freq, bitter_freq):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_sugar, 'rate': sugar_freq * u.Hz},\n",
    "                               {'ids': neu_bitter, 'rate': bitter_freq * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10,\n",
    "        )\n",
    "        return rate[index_mn9]\n",
    "\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_bitter_freq(bitter_freq):\n",
    "        return run_with_sugar_freq(freqs, bitter_freq)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        return run_with_bitter_freq(freqs)\n",
    "\n",
    "    path = fig3_path_res / f'sugar_bitter_activate_MN9.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'bitter {freq} Hz' for freq in freqs],\n",
    "                [f'sugar {freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "23b658b659ac1f39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.252556400Z",
     "start_time": "2025-07-08T12:01:22.140855Z"
    }
   },
   "outputs": [],
   "source": [
    "sugar_bitter_activate_MN9(\n",
    "    np.asarray([0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ca469f9530d2cab2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.255555800Z",
     "start_time": "2025-07-08T12:02:07.488422Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(fig3_path_res / f'sugar_bitter_activate_MN9.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "pivot_df = pivot_df.reindex([f'sugar {f} Hz' for f in [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200]], axis=1)\n",
    "pivot_df = pivot_df.reindex([f'bitter {f} Hz' for f in [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200]], axis=0)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c540662cd2313adc",
   "metadata": {},
   "source": [
    "### Figure 3 B: Combination of Sugar and IR94e GRN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9337e3ac8de10ec3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.255555800Z",
     "start_time": "2025-07-08T12:02:07.698267Z"
    }
   },
   "outputs": [],
   "source": [
    "def sugar_ir94e_activates_MN9(freqs):\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_sugar_freq(sugar_freq, ir94e_freq):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_sugar, 'rate': sugar_freq * u.Hz},\n",
    "                               {'ids': neu_ir94e, 'rate': ir94e_freq * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10,\n",
    "        )\n",
    "        return rate[index_mn9]\n",
    "\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_ir94e_freq(ir94e_freq):\n",
    "        return run_with_sugar_freq(freqs, ir94e_freq)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        return run_with_ir94e_freq(freqs)\n",
    "\n",
    "    path = fig3_path_res / f'sugar_ir94e_activates_MN9.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'ir94e {freq} Hz' for freq in freqs],\n",
    "                [f'sugar {freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b4b55de25a483b90",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.256557600Z",
     "start_time": "2025-07-08T12:02:07.731038Z"
    }
   },
   "outputs": [],
   "source": [
    "sugar_ir94e_activates_MN9(\n",
    "    np.asarray([0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "12f64c42c3bb8f34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.256557600Z",
     "start_time": "2025-07-08T12:02:42.225963Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(fig3_path_res / f'sugar_ir94e_activates_MN9.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "pivot_df = pivot_df.reindex([f'sugar {f} Hz' for f in [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200]], axis=1)\n",
    "pivot_df = pivot_df.reindex([f'ir94e {f} Hz' for f in [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200]], axis=0)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d834eb7c5ed91fe",
   "metadata": {},
   "source": [
    "## Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "14576243b6b0f349",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.256557600Z",
     "start_time": "2025-07-08T12:02:42.378274Z"
    }
   },
   "outputs": [],
   "source": [
    "fig4_path_res = Path('./figure_4')\n",
    "os.makedirs(fig4_path_res, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e80ff4099f4e76",
   "metadata": {},
   "source": [
    "### Figure 4a: Whole Brain Activity when Activating Water GRN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4c9c7e326129c97e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.257554200Z",
     "start_time": "2025-07-08T12:02:42.394016Z"
    }
   },
   "outputs": [],
   "source": [
    "# define inputs\n",
    "# list of the labelled water-sensing gustatory receptor neurons on right hemisphere\n",
    "neu_water = [\n",
    "    720575940612950568, 720575940631898285, 720575940606002609, 720575940612579053, 720575940622902535,\n",
    "    720575940616177458, 720575940660292225, 720575940622486922, 720575940613786774, 720575940629852866,\n",
    "    720575940625861168, 720575940613996959, 720575940617857694, 720575940644965399, 720575940625203504,\n",
    "    720575940630553415, 720575940635172191, 720575940634796536\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "393ac9a5c8535eb6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.257554200Z",
     "start_time": "2025-07-08T12:02:42.428638Z"
    }
   },
   "outputs": [],
   "source": [
    "def water_GRN_whole_brain_activation(freqs):\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_freq(freq_):\n",
    "        return run_one_exp(\n",
    "            neurons_to_excite=[\n",
    "                {\n",
    "                    'ids': neu_water,\n",
    "                    'rate': freq_ * u.Hz\n",
    "                }\n",
    "            ],\n",
    "            duration=duration,\n",
    "            pbar=10,\n",
    "        )\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def run_simulations():\n",
    "        return run_with_freq(freqs)\n",
    "\n",
    "    path = fig4_path_res / 'water_GRN_whole_brain_firing_rate.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = run_simulations()\n",
    "\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "                all_neuron_ids\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)\n",
    "\n",
    "        df_rate = df.loc['260 Hz'].mean(axis=1)\n",
    "        df_rate_std = df.loc['260 Hz'].std(axis=1)\n",
    "\n",
    "        # save 200 neurons for next simulation\n",
    "        id_top200 = df_rate.sort_values(ascending=False).index[:200]\n",
    "\n",
    "        print(df_rate.sort_values(ascending=False))\n",
    "\n",
    "        with open(fig4_path_res / 'id_top200_water.pickle', 'wb') as f:\n",
    "            pickle.dump(id_top200, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e585cbfe777d181a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.257554200Z",
     "start_time": "2025-07-08T12:02:42.459825Z"
    }
   },
   "outputs": [],
   "source": [
    "water_GRN_whole_brain_activation(\n",
    "    np.asarray([20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db4b5bef9216735",
   "metadata": {},
   "source": [
    "### Figure 4b: Which Water GRN activates MN9?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ebac236478565d22",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.258556600Z",
     "start_time": "2025-07-08T12:02:56.550186Z"
    }
   },
   "outputs": [],
   "source": [
    "# define inputs\n",
    "# flywire ID for MN9\n",
    "id_mn9 = 720575940660219265\n",
    "\n",
    "index_id_mn9 = flywire_id_to_index[id_mn9]\n",
    "\n",
    "# identify the top 200 neurons that respond due to 200 Hz water firing (Figure 1D)\n",
    "with open(fig4_path_res / 'id_top200_water.pickle', 'rb') as f:\n",
    "    id_top200_water = pickle.load(f)\n",
    "\n",
    "# neuron indices\n",
    "water_top200_indices = np.asarray([flywire_id_to_index[id_] for id_ in id_top200_water])\n",
    "water_top200_indices = np.expand_dims(water_top200_indices, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9bcf7e9127d439d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.258556600Z",
     "start_time": "2025-07-08T12:13:38.240199Z"
    }
   },
   "outputs": [],
   "source": [
    "def which_water_neuron_activate_MN9(freqs):\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_freq(freq_, indices_):\n",
    "        assert indices_.ndim == 1 and indices_.size == 1\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'indices': indices_, 'rate': freq_ * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_id_mn9]\n",
    "\n",
    "    def run_with_indices(indices_):\n",
    "        return run_with_freq(freqs, indices_)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        # due to the limited memory of the device, we choose using \"brainstate.transform.map\" instead of \"brainstate.transform.vmap2\"\n",
    "\n",
    "        # return bst.transform.vmap2(run_with_indices)(bst.random.split_key(len(water_top200_indices)), water_top200_indices)\n",
    "        return brainstate.transform.map(run_with_indices,\n",
    "                                        water_top200_indices,\n",
    "                                        batch_size=10)\n",
    "\n",
    "    path = fig4_path_res / f'which_water_neuron_activate_NM9_firing_rate.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                id_top200_water,\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b2a24e24915dc3ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.259555300Z",
     "start_time": "2025-07-08T12:13:38.430541Z"
    }
   },
   "outputs": [],
   "source": [
    "which_water_neuron_activate_MN9(\n",
    "    np.asarray([25, 50, 75, 100, 125, 150, 175, 200])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dbed6d18f9fa6675",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.259555300Z",
     "start_time": "2025-07-08T12:19:59.158690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(fig4_path_res / f'which_water_neuron_activate_NM9_firing_rate.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.title('Which Water GRN activates NM9?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261cbfeedd19ae9a",
   "metadata": {},
   "source": [
    "### Figure 4c: Silencing Most Active Neurons while Activating Water GRN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "38ca3816636b0e52",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.259555300Z",
     "start_time": "2025-07-08T12:19:59.491040Z"
    }
   },
   "outputs": [],
   "source": [
    "# neuron indices\n",
    "water_silencing_indices = np.asarray([flywire_id_to_index[id_] for id_ in id_top200])\n",
    "water_silencing_indices = np.expand_dims(water_silencing_indices, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "899a507b9e6e4f97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.260557300Z",
     "start_time": "2025-07-08T12:19:59.541667Z"
    }
   },
   "outputs": [],
   "source": [
    "def slice_water_neuron_and_activate_MN9(freqs):\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_freq(freq_, indices_):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_water, 'rate': freq_ * u.Hz}],\n",
    "            neuron_to_inhibit=[{'indices': indices_}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_id_mn9]\n",
    "\n",
    "    def run_with_indices(indices_):\n",
    "        return run_with_freq(freqs, indices_)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        # due to the limited memory of the device, we choose using \"brainstate.transform.map\" instead of \"brainstate.transform.vmap2\"\n",
    "\n",
    "        # return bst.transform.vmap2(run_with_indices)(bst.random.split_key(len(water_silencing_indices)), water_silencing_indices)\n",
    "        return brainstate.transform.map(run_with_indices, water_silencing_indices, batch_size=20)\n",
    "\n",
    "    path = fig4_path_res / f'water_neuron_silencing_firing_rate.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                id_top200,\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dbbade4cc4350cc9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.260557300Z",
     "start_time": "2025-07-08T12:19:59.574479Z"
    },
    "jupyter": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "slice_water_neuron_and_activate_MN9(\n",
    "    np.asarray([160, 170, 180, 190, 200, 210, 220])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d30b15759590ce56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(fig4_path_res / f'water_neuron_silencing_firing_rate.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb32aedc36d28c4e",
   "metadata": {},
   "source": [
    "### Figure 4e: Combination of sugar and water GRN activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "158cc4665665b6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sugar_freqs = np.asarray([0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200])\n",
    "water_freqs = np.asarray([0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1d8c2cfffc497bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sugar_water_activates_MN9():\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_sugar_freq(sugar_freq, water_freq):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_sugar, 'rate': sugar_freq * u.Hz},\n",
    "                               {'ids': neu_water, 'rate': water_freq * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10,\n",
    "        )\n",
    "        return rate[index_mn9]\n",
    "\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_water_freq(water_freq):\n",
    "        return run_with_sugar_freq(sugar_freqs, water_freq)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        return run_with_water_freq(water_freqs)\n",
    "\n",
    "    path = fig4_path_res / f'sugar_water_activates_MN9.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'water {freq} Hz' for freq in water_freqs],\n",
    "                [f'sugar {freq} Hz' for freq in sugar_freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "63240e2bbbcd06ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "sugar_water_activates_MN9()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1ed0146393ff6221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(fig4_path_res / f'sugar_water_activates_MN9.csv')\n",
    "# sns.heatmap(df)\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb71e845e541c36",
   "metadata": {},
   "source": [
    "## Figure 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "42f45ccad01527bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-16T12:45:30.268078300Z",
     "start_time": "2025-07-08T12:09:00.806474Z"
    }
   },
   "outputs": [],
   "source": [
    "fig5_path_res = Path('./figure_5/data')\n",
    "os.makedirs(fig5_path_res, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc760c09a7dfd28",
   "metadata": {},
   "source": [
    "### Figure 5b: Whole Brain Activity when Activating JON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ab9d278f13a6347f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define inputs\n",
    "# list of the  JONs\n",
    "neu_JON_CE = [\n",
    "    720575940619341105, 720575940630122015, 720575940611061526, 720575940615848788, 720575940628444667,\n",
    "    720575940627941431, 720575940632449619, 720575940650244342,\n",
    "    720575940631866508, 720575940638681845, 720575940628978450, 720575940609522461, 720575940621442224,\n",
    "    720575940602506208, 720575940629022149, 720575940627109991,\n",
    "    720575940630020111, 720575940615986459, 720575940618684481, 720575940620382889, 720575940630080071,\n",
    "    720575940626565455, 720575940630319671, 720575940602720940,\n",
    "    720575940630564179, 720575940637632419, 720575940615809349, 720575940626042149, 720575940637054835,\n",
    "    720575940602132509, 720575940614188149, 720575940616951124,\n",
    "    720575940628101126, 720575940629055721, 720575940616589878, 720575940622449388, 720575940614427195,\n",
    "    720575940625797617, 720575940638664437, 720575940618467195,\n",
    "    720575940621729757, 720575940613971485, 720575940627585688, 720575940629650997, 720575940630059847,\n",
    "    720575940608742409, 720575940614351477, 720575940633153375,\n",
    "    720575940622937528, 720575940604753437, 720575940611783464, 720575940618599872, 720575940609541917,\n",
    "    720575940637410869, 720575940630070343, 720575940621397417,\n",
    "    720575940614035485, 720575940610018266, 720575940626307902, 720575940634634606, 720575940614060829,\n",
    "    720575940624799290, 720575940641921421, 720575940623298559,\n",
    "    720575940625559358, 720575940629138959, 720575940621625597, 720575940625962568, 720575940632767383,\n",
    "    720575940624915230]\n",
    "\n",
    "neu_JON_F = [\n",
    "    720575940606239243, 720575940626956777, 720575940604973746, 720575940622222856, 720575940642517284,\n",
    "    720575940629719404, 720575940616613022, 720575940604299454,\n",
    "    720575940615473186, 720575940622217992, 720575940606800341, 720575940629267498, 720575940637366335,\n",
    "    720575940624224408, 720575940609543197, 720575940633364179,\n",
    "    720575940629502009, 720575940606431189, 720575940625733960, 720575940638529525, 720575940617524053,\n",
    "    720575940628935564, 720575940624308355, 720575940631170346,\n",
    "    720575940627704375, 720575940625885512, 720575940614929245, 720575940647493241, 720575940618888368,\n",
    "    720575940625087546, 720575940606657493, 720575940617273560,\n",
    "    720575940640591861, 720575940639410035, 720575940621532413, 720575940627523584, 720575940621521917,\n",
    "    720575940621097398, 720575940625915338, 720575940606222428,\n",
    "    720575940627868471, 720575940622179497, 720575940608297774, 720575940614026269, 720575940613012959,\n",
    "    720575940628100614, 720575940606611401, 720575940628649465,\n",
    "    720575940610008217, 720575940623791152, 720575940625571240, 720575940634923621, 720575940609530653,\n",
    "    720575940635968745, 720575940625703434, 720575940613105311,\n",
    "    720575940629386819, 720575940623077389, 720575940625763015, 720575940628359017\n",
    "]\n",
    "\n",
    "neu_JON_D_m = [\n",
    "    720575940630834171, 720575940622892988, 720575940621289537, 720575940641395163, 720575940616064546,\n",
    "    720575940628978409, 720575940652566177, 720575940627493096,\n",
    "    720575940619085397, 720575940635545310, 720575940645728803, 720575940629141775, 720575940626557995,\n",
    "    720575940631098338, 720575940639904475, 720575940635067034]\n",
    "\n",
    "neu_JON_all = neu_JON_CE + neu_JON_F + neu_JON_D_m\n",
    "\n",
    "# frequencies\n",
    "freqs = np.asarray([20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f870e9db87f5361a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def JON_GRN_whole_brain_activation():\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_freq(freq_):\n",
    "        return run_one_exp(\n",
    "            neurons_to_excite=[\n",
    "                {\n",
    "                    'ids': neu_JON_all,\n",
    "                    'rate': freq_ * u.Hz\n",
    "                }\n",
    "            ],\n",
    "            duration=duration,\n",
    "            pbar=10,\n",
    "        )\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def run_simulations():\n",
    "        return run_with_freq(freqs)\n",
    "\n",
    "    path = fig5_path_res / 'JON_GRN_whole_brain_firing_rate.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = run_simulations()\n",
    "\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "                all_neuron_ids\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)\n",
    "\n",
    "        df_rate = df.loc['220 Hz'].mean(axis=1)\n",
    "        df_rate_std = df.loc['220 Hz'].std(axis=1)\n",
    "\n",
    "        # save 200 neurons for next simulation\n",
    "        id_top300 = df_rate.sort_values(ascending=False).index[:300]\n",
    "\n",
    "        print(df_rate.sort_values(ascending=False))\n",
    "\n",
    "        with open(fig5_path_res / 'id_top300_water.pickle', 'wb') as f:\n",
    "            pickle.dump(id_top300, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a5f83904e8895b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "JON_GRN_whole_brain_activation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e2baad7f12a285",
   "metadata": {},
   "source": [
    "### Figure 5c: Which JON activates DN?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "90e4b3a957c9b9c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax.numpy as jnp\n",
    "# flywire ID for MN9\n",
    "id_DN1_1 = 720575940616185531\n",
    "id_DN2_l = 720575940629806974\n",
    "\n",
    "# index_DN = [flywire_id_to_index[id_DN1_1], flywire_id_to_index[id_DN2_l]]\n",
    "index_DN = [flywire_id_to_index[id_DN1_1], flywire_id_to_index[id_DN2_l]]\n",
    "\n",
    "# identify the top 200 neurons that respond due to 220 Hz JON firing (Figure 5A)\n",
    "with open(fig5_path_res / 'id_top300_water.pickle', 'rb') as f:\n",
    "    id_top300_JON = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5d32a85b3a6a28a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# frequencies\n",
    "freqs = np.asarray([25, 50, 75, 100, 125, 150, 175, 200])\n",
    "\n",
    "# neuron indices\n",
    "JON_top300_indices = np.asarray([flywire_id_to_index[id_] for id_ in id_top300_JON])\n",
    "JON_top300_indices = np.expand_dims(JON_top300_indices, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "959e513c3789a099",
   "metadata": {},
   "outputs": [],
   "source": [
    "def which_JON_neuron_activate_DN():\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_freq(freq_, indices_):\n",
    "        assert indices_.ndim == 1 and indices_.size == 1\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'indices': indices_, 'rate': freq_ * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_DN]\n",
    "\n",
    "    def run_with_indices(indices_):\n",
    "        return run_with_freq(freqs, indices_)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        # due to the limited memory of the device, we choose using \"brainstate.transform.map\" instead of \"brainstate.transform.vmap2\"\n",
    "\n",
    "        # return bst.transform.vmap2(run_with_indices)(bst.random.split_key(len(JON_top300_indices)), JON_top300_indices)\n",
    "        return brainstate.transform.map(run_with_indices,\n",
    "                                        JON_top300_indices,\n",
    "                                        batch_size=10)\n",
    "\n",
    "    path = fig5_path_res / f'which_JON_neuron_activate_DN.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                id_top300_JON,\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "                [id_DN1_1, id_DN2_l]\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "44b86add3e3c2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "which_JON_neuron_activate_DN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d5eaeedea651f498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(fig5_path_res / f'which_JON_neuron_activate_DN.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "# pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "# pivot_df = df.groupby(['dim_1', 'dim_2'])['mean'].first().unstack()\n",
    "pivot_df = df.pivot_table(index='dim_1', columns='dim_2', values='mean', aggfunc='mean')\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.title('Which JON GRN activates NM9?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16807b85bc3c55dc",
   "metadata": {},
   "source": [
    "### Figure 5d: Silencing Most Active Neurons while Activating JON GRN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1f2c8d38b79170c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# frequencies\n",
    "freqs = np.asarray([140, 150, 160, 170, 180])\n",
    "\n",
    "# neuron indices\n",
    "JON_silencing_indices = np.asarray([flywire_id_to_index[id_] for id_ in id_top300_JON])\n",
    "JON_silencing_indices = np.expand_dims(JON_silencing_indices, axis=1)\n",
    "\n",
    "brainstate.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2286789d71182007",
   "metadata": {},
   "outputs": [],
   "source": [
    "def slice_water_neuron_and_activate_MN9():\n",
    "    @brainstate.transform.vmap2(in_axes=(0, None))\n",
    "    def run_with_freq(freq_, indices_):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_water, 'rate': freq_ * u.Hz}],\n",
    "            neuron_to_inhibit=[{'indices': indices_}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_DN]\n",
    "        \n",
    "    \n",
    "\n",
    "    def run_with_indices(indices_):\n",
    "        return run_with_freq(freqs, indices_)\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        # due to the limited memory of the device, we choose using \"brainstate.transform.map\" instead of \"brainstate.transform.vmap2\"\n",
    "\n",
    "        # return bst.transform.vmap2(run_with_indices)(bst.random.split_key(len(JON_silencing_indices)), JON_silencing_indices)\n",
    "        return brainstate.transform.map(run_with_indices,\n",
    "                                        JON_silencing_indices,\n",
    "                                        batch_size=20)\n",
    "\n",
    "    path = fig4_path_res / f'JON_neuron_silencing_firing_rate.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                id_top200,\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "                [id_DN1_1, id_DN2_l]\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e720ed62629a10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "slice_water_neuron_and_activate_MN9()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64a3dd92c16b13f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(fig4_path_res / f'JON_neuron_silencing_firing_rate.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', columns='dim_2', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)\n",
    "\n",
    "sns.heatmap(pivot_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9907740a8cd70ae7",
   "metadata": {},
   "source": [
    "## Figure 5g + S5: Which JON Type activates aBN?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44d1c4f36428b9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_aBN1 = 720575940630907434\n",
    "\n",
    "index_aBN1 = flywire_id_to_index[id_aBN1]\n",
    "\n",
    "# frequencies\n",
    "freqs = np.asarray([20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e6969e21ad8f865",
   "metadata": {},
   "source": [
    "### JON-CE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f63a28b9288340a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def which_JONCE_neuron_activate_aBN():\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_freq(freq_):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_JON_CE, 'rate': freq_ * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_aBN1]\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        return run_with_freq(freqs)\n",
    "\n",
    "    path = fig5_path_res / f'JONCE_neuron_activate_aBN.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77df11b203270b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "which_JONCE_neuron_activate_aBN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22982ae7965346a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(fig5_path_res / f'JONCE_neuron_activate_aBN.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed3add84d73edcb",
   "metadata": {},
   "source": [
    "### JON-F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12dc2cd3642e5fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def which_JONF_neuron_activate_aBN():\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_freq(freq_):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_JON_F, 'rate': freq_ * u.Hz}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_aBN1]\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        return run_with_freq(freqs)\n",
    "\n",
    "    path = fig5_path_res / f'JONF_neuron_activate_aBN.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bfb94c3bbc92d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(fig5_path_res / f'JONF_neuron_activate_aBN.csv')\n",
    "\n",
    "trial_columns = [f'Trial {i}' for i in range(n_trial)]\n",
    "df['mean'] = df[trial_columns].mean(axis=1)\n",
    "pivot_df = df.pivot(index='dim_1', values='mean')\n",
    "pivot_df = pivot_df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15b29a8ae5ca20a",
   "metadata": {},
   "source": [
    "### JON-F + silencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "311db3320502bbbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "three_inhibitory = [720575940636066222, 720575940609957315, 720575940624986407]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2d7b4620b6c99ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def silence_which_JONF_neuron_activate_aBN():\n",
    "    @brainstate.transform.vmap2\n",
    "    def run_with_freq(freq_):\n",
    "        rate = run_one_exp(\n",
    "            neurons_to_excite=[{'ids': neu_JON_F, 'rate': freq_ * u.Hz}],\n",
    "            neuron_to_inhibit=[{'ids': three_inhibitory}],\n",
    "            duration=duration,\n",
    "            pbar=10\n",
    "        )\n",
    "        return rate[index_aBN1]\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    @brainstate.transform.vmap2(axis_size=n_trial)\n",
    "    def simulating():\n",
    "        return run_with_freq(freqs)\n",
    "\n",
    "    path = fig5_path_res / f'silence_JONF_neuron_activate_aBN.csv'\n",
    "    if not os.path.exists(path):\n",
    "        rates = simulating()\n",
    "        df = ndarray_to_dataframe(\n",
    "            rates,\n",
    "            [\n",
    "                [f'Trial {i}' for i in range(n_trial)],\n",
    "                [f'{freq} Hz' for freq in freqs],\n",
    "            ],\n",
    "            column_axis=0\n",
    "        )\n",
    "        df.to_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "536dbd6e1643cb61",
   "metadata": {},
   "outputs": [],
   "source": [
    "silence_which_JONF_neuron_activate_aBN()"
   ]
  }
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