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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PynamiX example notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pynamix import io, data, measure, color, plotting, exposure\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load example data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ims,logfile = data.pendulum() # load some example data\n",
    "# im, logfile = io.load_seq('path_to_data') # load your own data\n",
    "# save_as_tiffs('tt',ims,tmin=1000,tmax=1050,tstep=10) # save some of the SEQ file as tiffs in a folder `tt`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply an ROI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ims, logfile = exposure.apply_ROI(ims, logfile, left=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find min and max threshold values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = plotting.hist_GUI(ims)\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "masked_ims = exposure.clamp(ims,w.kwargs['vmin'],w.kwargs['vmax'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate size and orientation fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "virino = color.virino()\n",
    "X,Y,orient, dzeta = measure.orientation_map(masked_ims,logfile,tmin=1,tmax=2)\n",
    "x,y,size = measure.average_size_map(masked_ims,logfile,tmin=1,tmax=2)\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.imshow(masked_ims[0])\n",
    "plt.subplot(132)\n",
    "plt.pcolormesh(X,Y,orient[0],cmap=virino)\n",
    "plt.colorbar()\n",
    "plt.subplot(133)\n",
    "plt.pcolormesh(x,y,size[0])\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load some PIVLab data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y,u,v = io.load_PIVLab_txtfiles('/Volumes/LTS/DynamiX/PerpetualAvalanche/PerpetualAvalanche-3mm-4mm-80-20/PIV/',start=1000,end=1020,tstep=5)\n",
    "plt.quiver(x,y,u[0,:,:],v[0,:,:])\n",
    "plt.show()"
   ]
  }
 ],
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