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  {
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   "metadata": {},
   "source": [
    "# Overview\n",
    "This is a short introduction to sinaps for new users.\n",
    "\n",
    "The primary data structures of sinaps are:\n",
    "\n",
    "* `Section` wich aims to represent a segment of a neuron with uniform physicals values\n",
    "* `Neuron` wich aims to represent a complete neuron is a directed graph whose edges are of `Section` type.\n",
    "* `Channels` wich aim to represent ionic channels with various dynamics. Several channels are already implemented, such as Hodgkin-Huxley channels, Pulse currents or AMPA receptors. <!-- Exhaustive list can be found in section XXX -->\n",
    "\n",
    "The outputs of a simulation are:\n",
    "\n",
    "* Electric potential, at each position in the neuron, for each time point\n",
    "* Electric currents computed from channels defined in the neuron, each postion, each time point\n",
    "* Concentration of species, at each position in the neuron, for each time point\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The package can be imported as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sinaps as sn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object creation\n",
    "Creating an empty `Neuron`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrn = sn.Neuron()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a `Section`, letting sinaps setting default attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec = sn.Section()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding `HodgkinHuxley` [channels](https://en.wikipedia.org/wiki/Hodgkin%E2%80%93Huxley_model) (sodium, potassium and leak) to the newly created section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec.add_channel(sn.channels.Hodgkin_Huxley())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding a Hodgkin-Huxley type calcium channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec.add_channel(sn.channels.Hodgkin_Huxley_Ca())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Adding a `PulseCurrent` channel with a current of `200` pA, between t =`2` and t=`5` ms, at the beginning of the section (position `0`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec.add_channel(sn.channels.PulseCurrent(200,2,5),0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the section `s` to the neuron `nrn` as an edge between nodes `0` and `1` :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrn.add_section(sec,0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding two new sections\n",
    "* `sec2` with a radius of `2` μm\n",
    "* `sec3` with a length of `200` μm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec2 = sn.Section(a = 2)\n",
    "sec3 = sn.Section(L = 200)\n",
    "nrn.add_sections_from_dict({\n",
    "    sec2:(1,2),\n",
    "    sec3:(1,3)\n",
    "})\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the neuron structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrn.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding calcium ions in the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrn.add_species(sn.Species.Ca,C0=2E-4, D=100) # For the sake of the example, we increase the calcium diffusion coefficient to speed-up its dynamics, in order to observe variations within 50 ms. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a `Simulation` of neuron `nrn` with spatial resolution `10` μm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = sn.Simulation(nrn,dx=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the simulation for timespan `0` to `50` ms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.run((0,50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results of the simulation are stored as [pandas](https://pandas.pydata.org/) Dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting up to 10 curves distributed evenly on the neuron:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the Hodgkin-Huxley currents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot.I(sn.channels.Hodgkin_Huxley)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting a field view of the potential:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot.V_field()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the electrodiffusion part:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.run_diff(max_step=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the calcium concentration dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot.C(sn.Species.Ca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot.C_field(sn.Species.Ca)"
   ]
  }
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