{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting routines in `touchsim`\n",
    "The `touchsim` package uses `holoviews` for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import touchsim as ts\n",
    "from touchsim.plotting import plot, figsave\n",
    "import numpy as np\n",
    "import holoviews as hv\n",
    "hv.notebook_extension()\n",
    "%output holomap='scrubber' # animate holomaps\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hand model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot() # short for plot(ts.hand_surface)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regions labels and the coordinate system can be overlaid on the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%output size=250\n",
    "plot(tags=True,coord=10) # coord sets the lengths of the coordinate axes in mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising `AfferentPopulation` objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = ts.affpop_hand(region='D2d')\n",
    "plot(a,size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plots can be overlaid using the `*` operator.\n",
    "\n",
    "Plots are `holoviews` objects and can be indexed to only show, say, a specific afferent population. The `*` operator overlays different plots (e.g. the hand outline and the afferent locations, as shown below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(region='D2d') * plot(a,size=10)['PC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New subpanels can be added using the `+` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(plot(region='D2d') * plot(a,size=10)['SA1']) + (plot(region='D2d') * plot(a,size=10)['RA']) + (plot(region='D2d') * plot(a,size=10)['PC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first index of an `AfferentPopulation` plot object is the afferent type, while the next two indices are pixel coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zoom into fingertip\n",
    "plot(a,size=10)[:,120:140,450:475]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising `Stimulus` objects\n",
    "Plotting a `Stimulus` object shows the trace of all pins by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = ts.stim_ramp(len=0.25,amp=.1,ramp_len=0.05)\n",
    "plot(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s += ts.stim_sine(freq=25.,len=.25,loc=[1.,1.])\n",
    "plot(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pin traces can also be shown in a grid view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(s,grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pin positions can also be shown spatially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = ts.stim_indent_shape(ts.shape_circle(hdiff=0.5,pins_per_mm=2,radius=3),ts.stim_ramp(len=0.1))\n",
    "plot(s,spatial=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spatial view can be animated with pin depths indicated by color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(region='D2d') * plot(s,spatial=True,bin=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising `Response` objects\n",
    "Plotting a `Response` object shows the spike trains of all included neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = ts.affpop_hand(region='D2')\n",
    "s = ts.stim_sine(freq=50.,amp=0.1,len=0.5)\n",
    "r = a.response(s)\n",
    "plot(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second index is the time index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(r)[:,0:0.2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Responses can also be plotted spatially, in which case the size of each dot scaled with the neuron's firing rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%output size=150 # increase size of plot by 150%\n",
    "plot(region='D2') * plot(r,spatial=True,scaling_factor=.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, firing rate can be indicated by color instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(region='D2') * plot(r,spatial=True,scale=False)[:,'RA'] + plot(region='D2') * plot(r,spatial=True,scale=False)[:,'PC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Responses in spatial view can also be shown animated if a bin size (in ms) is given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(region='D2') * plot(r,spatial=True,bin=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advanced example (might be slow to compute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contact_locs = np.zeros((2,2))\n",
    "contact_locs[0] = np.array([0.,0.])\n",
    "contact_locs[1] = np.array([150.,0])\n",
    "    \n",
    "a = ts.affpop_hand(noisy=False)\n",
    "s = ts.stim_indent_shape(contact_locs,ts.stim_ramp(amp=0.75,len=.2,ramp_len=0.05,ramp_type='lin',pin_radius=5.,pad_len=0.025))\n",
    "r = a.response(s)\n",
    "plot(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot() * plot(r,spatial=True,bin=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving figures and animations\n",
    "Figures can be saved using the `figsave` function; figure size and resolution can be controlled using the `size` and `dpi` parameters, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "figsave(plot(),'test_fig',size=150,dpi=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the `gif` format is selected, animations will be saved as animated gifs; their framerate can be controlled with the `fps` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin = 50\n",
    "fig = plot(s,bin=bin) + plot()*plot(r,spatial=True,bin=bin)\n",
    "figsave(fig,'test_gif',size=150,fps=5,fmt='gif')"
   ]
  }
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