{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Sinaps` includes a convenient way to directly plot a neuron geometry and simulation results, through the .plot accesor. This is based on the [holoview](http://holoviews.org/) library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate the plotting tool, we first create a simulation of voltage propagation and calcium electrodiffusion, in a simple neuron. Calcium dynamics includes buffering and extrusion.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sinaps as sn\n",
    "\n",
    "# Defining the nueron\n",
    "nrn = sn.Neuron([(0,1),(1,2),(1,3),(3,4)])\n",
    "\n",
    "# Setting up channels\n",
    "nrn[0].clear_channels()#Reset all channels, useful if you re-run this cell (does nothing the first time)\n",
    "nrn[0].add_channel(sn.channels.PulseCurrent(200,2,3),0)\n",
    "nrn[0].add_channel(sn.channels.PulseCurrent(200,22,23),0)\n",
    "nrn[0].add_channel(sn.channels.Hodgkin_Huxley())\n",
    "nrn[0].add_channel(sn.channels.Hodgkin_Huxley_Ca(gCa=14.5,V_Ca=115)) \n",
    "\n",
    "# Defining and running the simulation\n",
    "sim = sn.Simulation(nrn,dx=10)\n",
    "sim.run((0,60))\n",
    "\n",
    "#Setting initial concentrations\n",
    "for s in nrn.sections:\n",
    "    s.C0[sn.Species.Ca]=10**(-4)\n",
    "    s.C0[sn.Species.Buffer]=2*10**(-3)\n",
    "    s.C0[sn.Species.Buffer_Ca]=0\n",
    "\n",
    "# Adding chemical reactions: claicum binding to a buffer, and calcium extrusion\n",
    "nrn.add_reaction(\n",
    "    {sn.Species.Ca:1,\n",
    "     sn.Species.Buffer:1},\n",
    "    {sn.Species.Buffer_Ca:1},\n",
    "    k1=0.2,\n",
    "    k2=0.1)\n",
    "\n",
    "nrn.add_reaction(\n",
    "    {sn.Species.Ca:1},\n",
    "    {},\n",
    "    k1=0.05,k2=0)\n",
    "\n",
    "# Running electro-diffusion simulations\n",
    "sim.run_diff(max_step=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neuronal geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hvplot\n",
    "hvplot.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot the neuron geometry:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrn.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Potential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sim.plot.V` plots the voltage dynamics at nodes evenly distributed on the neuron. Default number of curves is 10, change number with `max_plot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot.V(max_plot=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specific sections can be accesed through their names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim['Section0000'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Holoview or matplotlib can be used directly through the [pandas](https://pandas.pydata.org/) Dataframe structure of the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.V.loc[:,'Section0000'][25].hvplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is equivalent to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim['Section0000'].V.loc[:,25].hvplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The name of the sections can be accessed through the columns function. See the [pandas](https://pandas.pydata.org/) doc for more features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.V.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.V[('Section0000', 25.0)].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Field plots are also acccessible through the API. The time can be zoomed through the command `time`. See the [holoview](https://holoviews.org/) doc for more features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot.V_field(time=(0,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To help navigating in the field plot, the neuronal geometry is plotted on the left, with a colorbar legend mapping the linear position on the field to the geometry. In a live notebook, the view is interactive. Passing the mouse on the field plot will enhance the corresponding section on the neuronal geometry. \n",
    "\n",
    "Setting the parameter `neuron` to False displays only the field plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the currents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The currents computed by the simulation environment are stored in a [pandas](https://pandas.pydata.org/) Dataframe. They are positive when they go toward inside.\n",
    "\n",
    "`sim.plot.I(ChannelClass)` plots the currents for the channels of type ChannelClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot.I(sn.channels.Hodgkin_Huxley)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The field plots are also accessible:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.plot.I_field(sn.channels.Hodgkin_Huxley)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If several channels of the same type are defined on a section, sim.plot.I will plot the sum of all the currents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim['Section0000'].plot.I(sn.channels.PulseCurrent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Holoview or matplotlib can be used directly through the [pandas](https://pandas.pydata.org/) Dataframe structure of the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.current(sn.channels.Hodgkin_Huxley) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.current(sn.channels.PulseCurrent)['Section0000'].hvplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the species dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The species concentration dynamics computed by the simulation environment are stored in a [pandas](https://pandas.pydata.org/) Dataframe.\n",
    "\n",
    "`sim.plot.C(Species)` plots the concentration of Class Species:"
   ]
  },
  {
   "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['Section0000'].plot.C(sn.Species.Ca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The species dynamics are stored in a [pandas](https://pandas.pydata.org/) Dataframe, and are accessible via their class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.C[sn.Species.Ca]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.C[sn.Species.Ca].loc[:,'Section0000'][25].hvplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is equivalent to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim['Section0000'].C(sn.Species.Ca).loc[:,25].hvplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim['Section0000'].C(sn.Species.Buffer_Ca).loc[:,5].hvplot()"
   ]
  }
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