{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Texture stimulus examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To be able to run the examples in this notebook, you need to have pymer insalled and set up.\n",
"\n",
"Also the conda environment (aka kernel) should be set to `pymer` (top right)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can test if everything works as intended by trying to import the `OMB` class."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pathadder\n",
"from omb import OMB"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by loading the OMB stimulus from the experiment from `20180710`. The OMB stimulus was the 8th in line."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"st = OMB('20180710', 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can call up a summary of the units in this experiment. Note that the terms _unit_ , _cluster_ and _cell_ are used interchangably throughout the code base."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 96 clusters in this experiment.\n",
"( 33.3%) 32 are rated 1.\n",
"( 65.6%) 63 are rated 2 or better.\n",
"( 86.5%) 83 are rated 3 or better.\n",
"(100.0%) 96 are rated 4 or better.\n"
]
}
],
"source": [
"st.clusterstats()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can easily load the spike times from a single cell. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1.64268000e+00, 1.65068000e+00, 2.10976000e+00, ...,\n",
" 1.97455104e+03, 1.97500244e+03, 1.97555540e+03])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"st.read_raster(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can visualize the spikes like so:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.eventplot(st.read_raster(0)[:50])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even more useful would be binned spikes, using the frame times information.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(96, 54000)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spikes = st.allspikes()\n",
"spikes.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 96 units in this experiment, and the stimulus was 54000 frames long."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Likewise, we can access the motion of the texture."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 54000)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bgsteps = st.bgsteps\n",
"bgsteps.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first row is the x coordinates, second is y. Values in each time bin corresponds to how much the texture moves in that time period."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(bgsteps.T, bins=30, label=['x', 'y'])\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get an idea about the trajectory, we can plot it. Note that the trajectory is simply the cumulative sum of the motion steps."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 54000)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bgtraj = st.bgtraj\n",
"bgtraj.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bgtraj[0, :20], bgtraj[1, :20], 'r-')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also visualize the texture that was used to generate the stimulus."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(st.texturebasic, cmap='Greys_r')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is also a way of playing the stimulus as an animation for visualization purposes but this needs to be done with an interactive backend."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib qt # To switch back to non-interactive backend, use %matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"st.playstimulus(0, 100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are a few more attributes to make plotting, saving etc easier. You can check the following scripts for some examples:\n",
"\n",
"```\n",
"ombtexturestas.py\n",
"cca/cca_withpyrcca.py\n",
"generalizedmodels/glm_omb_motion.py\n",
"```"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pymer",
"language": "python",
"name": "pymer"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}