{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"# Effects of Light Intensity on Spike Rate\n",
"\n",
"Now we're going to load an even larger data set. The `ten_intensities` data set expands on the optogenetic spike train data we worked with in the previous chapter. Now we have data recorded from a neuron as it was stimulated with ten different intensities of the light that activates its receptors (550 nm green). We have 10 trials' worth of data at each intensity, so 100 trials in total. Each trial was 20 ms long, with optical stimulation starting at 4 ms and ending at 14 ms. Measuring the neuron's response across a range of intensities allows us to map out the response \"profile\" of the neuron in terms of the number of spikes elicited by differing intensities. \n",
"\n",
"[Watch a walk-through of this lesson on YouTube](https://youtu.be/SeE5omWkhnY)\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Objectives\n",
"- Understand spike data in sparse format\n",
"- Select data for a specific intensity level\n",
"- Plot raster plots and PSTHs for specific intensity levels\n",
"- Plot raster plots and PSTHs for all intensity levels\n",
"- Use Matplotlib subplots to visualize multivariate data\n",
"- Format a complex Matplotlib figure with appropriate labeling and scales\n",
"- Use a combination of raster and PSTH plots to make inferences about the relationship between an experimentally-manipulated variable and spike rate and timing. \n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import necessary packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define variables for known experimental parameters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"stim_on = 4 # time stimulus turned on\n",
"stim_off = 14 # time stimulus turned off\n",
"num_trials = 10 # number of trials per intensity level\n",
"num_tp = 21 # Number of time points in each trial (0-20 ms, inclusive)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read the data\n",
"\n",
"First we load the data. This data set is adapted from Nylen, E.L., and Wallisch, P.. (2017). [*Neural Data Science*](https://www.sciencedirect.com/book/9780128040430/neural-data-science). Academic Press."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"spikes = pd.read_csv('data/ten_intensities.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Figure out the structure of the data\n",
"\n",
"When first starting to work with a data set, you need to explore it and understand how it's structured. What variable type(s) are in it? How are different experimental conditions coded? Sometimes you know this, if you created the data, but often you only have a general idea of what's in a data file, and you need to inspect it to figure out the details. \n",
"\n",
"If we look at the `head` of the data, we can see that there are columns for intensitiy level, trial number, and spike time:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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},
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spikes.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is no data for Trial 2 shown, which means that no spikes occurred for Trial 2 at Intensity 0.\n",
"\n",
"It can also be helpful to view a random sample of trials to get a sense of the variability in the data:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spikes.sample(25)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can view the shape of the matrix to see how much data we have. Recall that `.shape` is a property of a DataFrame object, not a method, so we don't put parentheses after it."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(231, 3)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spikes.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data format\n",
"\n",
"This data set doesn't look like the previous spike trains we saw: rather than zeros and ones, we have a column of spike times. This is called a **sparse** representation of the data, and it is a much more efficient way of representing spike trains than the way we saw in the previous lesson. This is because spikes are relatively rare, occurring only a few times at most on each trial. So if we represent the data by having a vale for each time point on every trial, we have a lot of data points that are filled with zeros, and really tell us nothing. If our data only represent when the ones are, we can infer that the rest of the data points are zero, but we end up storing much less data. This makes working with the data faster, especially with very large data sets. \n",
"\n",
"In the `spikes` DataFrame, the first column encodes the intensity level:\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spikes['Intensity'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `Trial` column encodes trial number (nested in intensity levels):"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 3, 4, 5, 6, 0, 2, 8, 9, 7])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spikes['Trial'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And, as you might expect, `SpikeTimes` contains the times at which spike occurred. The order of trials is not sequential, because `.unique()` lists these values in the order it finds them in the DataFrame. As we saw above in the first few rows of the DataFrame, there were no spikes for Trial 2 at Intensity 0, so the first instance of `2` in the Trial column occurred further down.\n",
"\n",
"Since we'll want to plot trials in order (e.g., for raster plots), we'll create a new variable called `trials` to hold the sorted list of trial numbers:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"trials = sorted(spikes['Trial'].unique())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing the Data\n",
"\n",
"### Raster plot\n",
"We'd like to visualize this larger data set using raster plots and PSTHs, as we did for the previous data. To do this, we'll need to consider how to show the different responses for each each intensity level. There are a few ways we could do this, but here we'll do it by adding more subplots, expanding on how we drew the raster plot and PSTH in the same figure in the previous page.\n",
"\n",
"Here we'll build the code up in stages, first getting one plot working (a raster of one intensity level) and then adding the PSTH for that level, then plotting all the levels of intensity in different subplots, in a loop. We'll start with the highest intensity level (`9`) as our \"test case\" because we can expect that it will elicit the most intense responses. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# select just data for intensity level 9\n",
"dat = spikes[spikes['Intensity'] == 9]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code below is very similar to what we used in the previous lesson, except that before we had to convert the `1`s in the data to spike times, but here that data are already in that format, so we just need to select the data for a particular trial each time through the loop:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"# Shade time when stimulus was on\n",
"ax.axvspan(stim_on, stim_off, alpha=0.5, color='greenyellow')\n",
"\n",
"# Draw the raster one trial at a time\n",
"for trial in trials:\n",
" # get spike times for this trial\n",
" spike_times = dat[dat['Trial'] == trial]['SpikeTime']\n",
" # Draw the raster\n",
" ax.vlines(spike_times, \n",
" trial - 0.4, trial + 0.4)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see right away that there does seem to be a relationship between stimulation and spiking, since there are more ticks during the \"on\" (shaded) time than before it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PSTH \n",
"\n",
"Now we can draw a PSTH for this intensity level. We're going to draw this differently from before, because in the previous lesson we could sum the number of spikes in each time bin. Since our data here are represented as spike times, it's easiest to use the `ax.hist()` function to count the number of times each time point is found in the data, for this particular intensity level. For the PSTH, we want a bin for every time point, so we use a combination of two kwargs to force this (because otherwise, the histogram range will only include the minimum and maximum times at which spikes occur for this intensity level, which could potentially not include the earliest and/or latest time points in the trial. The `bins` kwarg is used to set the number of bins equal to the number of time points, and the `range` kwarg tell Matplotlib to space those bins evenly from 0 to the end of the trial."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAN1UlEQVR4nO3df6zd9V3H8ed7LcTBqrj1bDLgekUNCZoMmhvcRMmEuUC3gJrFQHSiztwsGQYSjdYsLjP+02lc/JFlpm44VGRzjCphMFl0ZFki1duusLKC/EjHunZtcXEFMWLx7R/ne+Hu9Jye770933PetM9HcnPPOd/POeeVz/fbV7/3e77feyMzkSTV9apZB5AknZhFLUnFWdSSVJxFLUnFWdSSVNz6Ll5048aNOT8/38VLn5L+m8Mkx2YdQ6eoYD2v5vWzjqExdu7c+Uxm9oYt66So5+fnWVpa6uKlT0lL/C5n8cZZx9Ap6nkOsMDvzzqGxoiIr41a5qEPSSrOopak4ixqSSrOopak4ixqSSrOopak4sYWdURcFBG7V3wdjYhbppBNkkSL86gz8zHgEoCIWAd8A9jebSxJ0rLVHvq4CngyM0eemC1JmqzVXpl4PXDHsAURsQgsAszNzZ1kLJ3qNm9Z+zZy79anJ5hEqq/1HnVEnAlcC3x62PLM3JaZC5m50OsNvVxdkrQGqzn0cQ2wKzMPdRVGknS81RT1DYw47CFJ6k6roo6Is4CfBu7qNo4kaVCrDxMz83ngdR1nkSQN4ZWJklScRS1JxVnUklScRS1JxVnUklScRS1JxVnUklScRS1JxVnUklScRS1JxVnUklScRS1JxVnUklScRS1JxVnUklScRS1JxVnUklScRS1JxVnUklScRS1JxbX9K+TnRMSdEfFoROyNiLd0HUyS1Nfqr5ADfwJ8LjPfFRFnAmd1mEmStMLYoo6I7wauAH4ZIDNfAF7oNpYkaVmbQx8XAkeAv4yIL0fExyLi7MFBEbEYEUsRsXTkyJGJB5Wk01Wbol4PbAI+mpmXAv8FbBkclJnbMnMhMxd6vd6EY0rS6atNUe8H9mfmjub+nfSLW5I0BWOLOjO/CXw9Ii5qHroK+GqnqSRJL2l71sevA7c3Z3w8BfxKd5EkSSu1KurM3A0sdBtFkjSMVyZKUnEWtSQVZ1FLUnEWtSQVZ1FLUnEWtSQVZ1FLUnEWtSQVZ1FLUnEWtSQVZ1FLUnEWtSQVZ1FLUnEWtSQVZ1FLUnEWtSQVZ1FLUnEWtSQVZ1FLUnEWtSQVZ1FLUnGt/gp5ROwDngVeBI5lpn+RXJKmpFVRN34qM5/pLIkkaSgPfUhScW2LOoH7I2JnRCwOGxARixGxFBFLR44cmVxCSTrNtS3qyzNzE3AN8L6IuGJwQGZuy8yFzFzo9XoTDSlJp7NWRZ2ZB5rvh4HtwGVdhpIkvWxsUUfE2RGxYfk28HZgT9fBJEl9bc76eAOwPSKWx/9tZn6u01SSpJeMLerMfAp40xSySJKG8PQ8SSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4loXdUSsi4gvR8Q9XQaSJH2n1exR3wzs7SqIJGm4VkUdEecD7wA+1m0cSdKg9S3H/THwW8CGUQMiYhFYBJibmzvpYNIom7esffu6d+vTM3nfk3UyufXKN3aPOiLeCRzOzJ0nGpeZ2zJzITMXer3exAJK0umuzaGPy4FrI2If8Engyoj4m05TSZJeMraoM/N3MvP8zJwHrgf+OTN/sfNkkiTA86glqby2HyYCkJkPAA90kkSSNJR71JJUnEUtScVZ1JJUnEUtScVZ1JJUnEUtScVZ1JJUnEUtScVZ1JJUnEUtScVZ1JJUnEUtScVZ1JJUnEUtScVZ1JJUnEUtScVZ1JJUnEUtScVZ1JJUnEUtScVZ1JJU3Niijojvioh/jYiHIuKRiPi9aQSTJPWtbzHmf4ArM/O5iDgD+FJE3JeZD3acTZJEi6LOzASea+6e0Xxll6EkSS9rs0dNRKwDdgI/BHwkM3cMGbMILALMzc2tOdD8ls+u+bn7tr5jzc+VVMvJdMHJqNgjrT5MzMwXM/MS4Hzgsoj40SFjtmXmQmYu9Hq9CceUpNPXqs76yMz/BB4Aru4ijCTpeG3O+uhFxDnN7VcDbwMe7TiXJKnR5hj1ucBtzXHqVwF/l5n3dBtLkrSszVkfDwOXTiGLJGkIr0yUpOIsakkqzqKWpOIsakkqzqKWpOIsakkqzqKWpOIsakkqzqKWpOIsakkqzqKWpOIsakkqzqKWpOIsakkqzqKWpOIsakkqzqKWpOIsakkqzqKWpOIsakkqbmxRR8QFEfGFiNgbEY9ExM3TCCZJ6hv7V8iBY8BvZOauiNgA7IyIz2fmVzvOJkmixR51Zh7MzF3N7WeBvcB5XQeTJPWt6hh1RMwDlwI7OkkjSTpOm0MfAETEa4DPALdk5tEhyxeBRYC5ubmJBVRNm7e8MtfxKzX3K9H8ls/OOsIpo9UedUScQb+kb8/Mu4aNycxtmbmQmQu9Xm+SGSXptNbmrI8APg7szcwPdx9JkrRSmz3qy4F3A1dGxO7ma3PHuSRJjbHHqDPzS0BMIYskaQivTJSk4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4ixqSSrOopak4sYWdUTcGhGHI2LPNAJJkr5Tmz3qTwBXd5xDkjTC2KLOzC8C35pCFknSEOsn9UIRsQgsAszNzU3qZTXG5i1rn+t7tz49wSTq0sms5zu3HphgEs3CxD5MzMxtmbmQmQu9Xm9SLytJpz3P+pCk4ixqSSquzel5dwD/AlwUEfsj4j3dx5IkLRv7YWJm3jCNIJKk4Tz0IUnFWdSSVJxFLUnFWdSSVJxFLUnFWdSSVJxFLUnFWdSSVJxFLUnFWdSSVJxFLUnFWdSSVJxFLUnFWdSSVJxFLUnFWdSSVJxFLUnFWdSSVJxFLUnFWdSSVJxFLUnFtSrqiLg6Ih6LiCciYkvXoSRJLxtb1BGxDvgIcA1wMXBDRFzcdTBJUl+bPerLgCcy86nMfAH4JHBdt7EkScsiM088IOJdwNWZ+WvN/XcDP5aZNw2MWwQWm7sXAY+tMdNG4Jk1PrdL5lodc62OuVbnVMz1/ZnZG7ZgfYsnx5DHjmv3zNwGbFtlsOPfLGIpMxdO9nUmzVyrY67VMdfqnG652hz62A9csOL++cCBSQeRJA3Xpqj/DfjhiPiBiDgTuB64u9tYkqRlYw99ZOaxiLgJ+EdgHXBrZj7SYaaTPnzSEXOtjrlWx1yrc1rlGvthoiRptrwyUZKKs6glqbiZFPW4S9Kj70+b5Q9HxKYp5bogIr4QEXsj4pGIuHnImLdGxLcjYnfz9YEpZdsXEV9p3nNpyPKpz1lEXLRiHnZHxNGIuGVgzFTmKyJujYjDEbFnxWOvjYjPR8TjzffvHfHczn5FwohcfxgRjzbraXtEnDPiuSdc5x3k+mBEfGPFuto84rnTnq9Prci0LyJ2j3hul/M1tBumto1l5lS/6H8g+SRwIXAm8BBw8cCYzcB99M/hfjOwY0rZzgU2Nbc3AP8+JNtbgXtmMG/7gI0nWD6TORtYr9+kf9L+1OcLuALYBOxZ8dgfAFua21uAD61le+wg19uB9c3tDw3L1Wadd5Drg8BvtljPU52vgeV/BHxgBvM1tBumtY3NYo+6zSXp1wF/lX0PAudExLldB8vMg5m5q7n9LLAXOK/r952QmczZClcBT2bm16b4ni/JzC8C3xp4+Drgtub2bcDPDHlqp78iYViuzLw/M481dx+kf23CVI2YrzamPl/LIiKAnwfumNT7tXWCbpjKNjaLoj4P+PqK+/s5vgzbjOlURMwDlwI7hix+S0Q8FBH3RcSPTClSAvdHxM7oX64/aNZzdj2j/wHNYr4A3pCZB6H/Dw14/ZAxs563X6X/k9Aw49Z5F25qDsncOuLH+FnO108ChzLz8RHLpzJfA90wlW1sFkXd5pL0VpetdyUiXgN8BrglM48OLN5F/8f7NwF/Bvz9lGJdnpmb6P8Ww/dFxBUDy2c2Z9G/EOpa4NNDFs9qvtqa5by9HzgG3D5iyLh1PmkfBX4QuAQ4SP8ww6BZ/tu8gRPvTXc+X2O6YeTThjy2qjmbRVG3uSR9ZpetR8QZ9FfE7Zl51+DyzDyamc81t+8FzoiIjV3nyswDzffDwHb6P06tNMtL/a8BdmXmocEFs5qvxqHlwz/N98NDxsxk3iLiRuCdwC9kcyBzUIt1PlGZeSgzX8zM/wP+YsT7zWq+1gM/B3xq1Jiu52tEN0xlG5tFUbe5JP1u4JeaMxneDHx7+ceLLjXHwD4O7M3MD48Y833NOCLiMvpz+B8d5zo7IjYs36b/YdSegWEzmbPGyD2dWczXCncDNza3bwT+YciYqf+KhIi4Gvht4NrMfH7EmDbrfNK5Vn6m8bMj3m9Wv1LibcCjmbl/2MKu5+sE3TCdbayLT0hbfIK6mf6npk8C728eey/w3uZ20P9jBU8CXwEWppTrJ+j/SPIwsLv52jyQ7SbgEfqf3D4I/PgUcl3YvN9DzXtXmrOz6Bfv96x4bOrzRf8/ioPA/9Lfg3kP8Drgn4DHm++vbca+Ebj3RNtjx7meoH/Mcnkb+/PBXKPWece5/rrZdh6mXyTnVpiv5vFPLG9TK8ZOc75GdcNUtjEvIZek4rwyUZKKs6glqTiLWpKKs6glqTiLWpKKs6glqTiLWpKK+39/NAh2fOqJGgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"# Shade time when stimulus was on\n",
"ax.axvspan(stim_on, stim_off, alpha=0.5, color='greenyellow')\n",
"\n",
"ax.hist(dat['SpikeTime'], bins=range(0, num_tp, 1))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Combine raster and PSTH\n",
"\n",
"Next we combined the raster and PSTH in one figure. Ultimately we want to show all intensity levels in one figure, to as a layout it will work better to have the raster and PSTH side-by-side in one row, rather than one above the other as in the previous lesson:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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MAbs/yVMu+PqqJA+sJg4AAMDBtUwB+1CSp1fVU6vqyiQ3J3n3amMBAAAcPEcutUB3P1RVr0ryW0muSPKm7r575ckAAAAOmEsWsCTp7tuT3L7iLAAAAAfaUm/EDAAAwN4pYAAAAANZ6hREDpdbTp4bO8IgprqeU83F5bvt5PWTvj8AYHiOgAEAAAxEAQMAABiIAgYAADAQBQwAAGAgChgAAMBAFDAAAICBKGAAsEtVdUNVfaqq7q2q02PnAWD6FDAA2IWquiLJ65K8KMkzkrysqp4xbioApk4BA4DduS7Jvd392e5+MMnbk9w0ciYAJq66e//vtOp8ks/t8W6ekOSr+xBnLPKPS/5xyT+uIfP/ue5eG+hnTUpV/ViSG7r7Hy6+/vEkf6W7X7VluRNJTiy+vCbJp/b4o+e2f84pr6yrMaesybzyyroa+5V12xl5ZB/u+BH2YxhX1dnuXt+PPGOQf1zyj0v+cc09/4zUNtc94q+a3X1rklv37YfO7PGdU15ZV2NOWZN55ZV1NVad1SmIALA79yd5ygVfX5XkgZGyADATChgA7M6Hkjy9qp5aVVcmuTnJu0fOBMDEreQUxH2yb6drjET+cck/LvnHNff8s9DdD1XVq5L8VpIrkrypu+8e4EfP7fGdU15ZV2NOWZN55ZV1NVaadSUvwgEAAMAjOQURAABgIAoYAADAQEYtYFV1Q1V9qqrurarT29xeVfXvFrd/rKquHSPnTqrqKVX1O1V1T1XdXVWv3maZH6mqr1XVXYuPnx8j606q6r6q+vgi29ltbp/sY1BV11ywXe+qqq9X1Wu2LDOp7V9Vb6qqc1X1iQuu+/6q+h9V9enF5+/b4Xsv+vsyhB3y/+uq+uRi/3hXVT1+h++96L42hB3y/2JVffGCfeTGHb53qtv/tguy31dVd+3wvaNvfy7PXGbkHGfhXGbf1Ofc3GbanGbYnObVnGbTTs9Xg++33T3KRzb/YfkzSZ6W5MokH03yjC3L3Jjkvdl8r5XnJLljrLw7rMMTk1y7uPy4JH+wzTr8SJL3jJ31IutwX5InXOT2ST8GW/anL2fzDe8mu/2TPC/JtUk+ccF1/yrJ6cXl00lu2WH9Lvr7MmL+H01yZHH5lu3yL7OvjZj/F5P80yX2r0lu/y23/9skPz/V7e/jsh7r2czIOc7COc6+Kc65uc20Oc2wOc2rOc2mnZ6vht5vxzwCdl2Se7v7s939YJK3J7lpyzI3JflPvemDSR5fVU8cOuhOuvtL3f3hxeVvJLknyZPHTbXvJv0YXOAFST7T3Z8bO8jFdPfvJvnDLVfflOQti8tvSfI3t/nWZX5fVm67/N39/u5+aPHlB7P5XkiTtMP2X8Zkt//DqqqS/J0kbxs0FKsymxl5QGfhJLbtFpObc3ObaXOaYXOaV3OaTRd5vhp0vx2zgD05yRcu+Pr+PPIJe5llJqGqrk7y7CR3bHPz9VX10ap6b1X95WGTXVIneX9V3VlVJ7a5fS6Pwc3Z+Zd7yts/SX6wu7+UbD4xJDm6zTJzeRz+QTb/arydS+1rY3rV4vSTN+1w2sEctv9fS/KV7v70DrdPefvzSLOckTOahXOcfXOZc3OeaXOYYXObV5OdTVuerwbdb8csYLXNdVtfE3+ZZUZXVd+T5DeSvKa7v77l5g9n83SBZyb590n+28DxLuW53X1tkhcleWVVPW/L7ZN/DGrzDVBfmuTXt7l56tt/WXN4HH4uyUNJ3rrDIpfa18by+iR/Psmzknwpm6dKbDX57Z/kZbn4Xxinuv3Z3uxm5Mxm4axm3wGcc5PavslsZtgc59UkZ9Mlnq92/LZtrtvVth2zgN2f5CkXfH1Vkgd2scyoqupR2XwA39rd79x6e3d/vbu/ubh8e5JHVdUTBo65o+5+YPH5XJJ3ZfPw6oUm/xhk85f2w939la03TH37L3zl4VNbFp/PbbPMpB+HqnpFkpck+bu9OFF6qyX2tVF091e6+9vd/adJ/mO2zzX17X8kyd9OcttOy0x1+7OjWc3Iuc3CGc6+Oc252c20ucywuc2rqc6mHZ6vBt1vxyxgH0ry9Kp66uIvOzcnefeWZd6d5O/Vpuck+drDhwenYHFe6xuT3NPdv7TDMn92sVyq6rpsbvP/M1zKnVXVY6vqcQ9fzuY/on5iy2KTfgwWdvzrypS3/wXeneQVi8uvSPLft1lmmd+XUVTVDUlOJXlpd39rh2WW2ddGseX/Ov5Wts812e2/8MIkn+zu+7e7ccrbnx3NZkbObRbOdPbNac7NaqbNaYbNcF5NbjZd5Plq2P22B3rVke0+svkqQ3+QzVcU+bnFdT+V5KcWlyvJ6xa3fzzJ+ph5t8n/V7N56PFjSe5afNy4ZR1eleTubL5SygeT/PDYuS/I/7RFro8uMs7xMXhMNgfNn7nguslu/2wO0C8l+ZNs/iXlJ5P8QJL/meTTi8/fv1j2SUluv+B7H/H7MpH892bznOiHfwf+w9b8O+1rE8n/nxf79sey+UT6xDlt/8X1b354n79g2cltfx+X/XjPYkZmZrNwp9+HKW7bRZbJzrkdnlMnO9N2yDvJGbZD1knOq+2yLq5/cyY2my7yfDXofluLOwMAAGDFRn0jZgAAgMNEAQMAABiIAgYAADAQBQwAAGAgChgAAMBAFDAAAICBKGAAAAAD+f+EN3Eti2iaKAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# here we hard-code the figure dimensions (x, y) to control the way the plot looks\n",
"fig, axs = plt.subplots(1, 2, figsize=[15, 4])\n",
"\n",
"# Shade time when stimulus was on\n",
"axs[0].axvspan(stim_on, stim_off, alpha=0.5, color='greenyellow')\n",
"\n",
"# select just data for intensity level 9\n",
"dat = spikes[spikes['Intensity'] == 9]\n",
"\n",
"# Draw the raster one trial at a time\n",
"for trial in trials:\n",
" # get spike times for this trial\n",
" spike_times = dat[dat['Trial'] == trial]['SpikeTime']\n",
" # Draw the raster\n",
" axs[0].vlines(spike_times, \n",
" trial - 0.4, trial + 0.4)\n",
"\n",
"# Shade time when stimulus was on\n",
"axs[1].axvspan(stim_on, stim_off, alpha=0.5, color='greenyellow')\n",
"axs[1].hist(dat['SpikeTime'], bins=range(0, num_tp, 1))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting all intensity levels\n",
"\n",
"Now we can embed the plot commands above into a loop through all intensity levels. \n",
"\n",
"First, for convenience we'll create a list of intensity levels, and sort them so that they plot in a logical order (lowest to highest intensity):"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"int_levels = sorted(spikes['Intensity'].unique())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll plot each raster and PSTH as a row, in a figure with 10 rows (intensity levels) and 2 columns of subplots. \n",
"\n",
"Note that we make use of the intensity levels not just for looping through the data, but also specify rows of subplots, and the intensity of the shading that shows when the stimulus was on. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Set the number of subplot rows based on number of intensity levels in data\n",
"# Also specify the figure dimensions since it will need to be a big figure\n",
"fig, axs = plt.subplots(len(int_levels), 2, figsize=[12, 12])\n",
"\n",
"for i in int_levels:\n",
" \n",
" ## Raster plot\n",
" # select just data for current intensity level\n",
" # this is convenient since we'll refer to this subset of data a few times below\n",
" dat = spikes[spikes['Intensity'] == i]\n",
"\n",
" # Draw the raster one trial at a time\n",
" for trial in trials:\n",
" # get spike times for this trial\n",
" spike_times = dat[dat['Trial'] == trial]['SpikeTime']\n",
" # Draw the raster\n",
" axs[i, 0].vlines(spike_times, \n",
" trial - 0.4, trial + 0.4)\n",
"\n",
" # Shade time when stimulus was on\n",
" axs[i, 0].axvspan(stim_on, stim_off, \n",
" alpha= i / 10 + .1, # Base intensity of shading (alpha) on intensity level\n",
" color='greenyellow')\n",
"\n",
" ## PSTH\n",
" # Shade time when stimulus was on\n",
" axs[i, 1].axvspan(stim_on, stim_off, \n",
" alpha= i / 10 + .1, \n",
" color='greenyellow')\n",
" \n",
" axs[i, 1].hist(dat['SpikeTime'], bins=range(0, num_tp, 1))\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scale the *x* axes\n",
"\n",
"You may note that the plot above looks wrong — the \"stimulus on\" shading doesn't line up for all the plots! This is because by default, Matplotlib scales each subplot's axes independently. For the low intensity levels, there are few spikes, and so if you look at the units on the *x* axes, they are quite variable. \n",
"\n",
"We can fix this by adding a `sharex=True` kwarg to the `plt.subplots()` call, which forces all the subplots to share the same *x* axis range. Since the histograms are already set to range from 0-21 ms, the raster plots will \"inherit\" the same range due to `sharex=True`. We add one further line, using `.set_xticks()` to specify the spacing of the tickmarks on the *x* axis to 2 ms:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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Xwp/ka/lWvjl1DLhkT8pluSLfM3UMdtHx48cf6e6VkWNW1dVJPpDkmUm+leRod79zqz9jrtp55ioWhblq+ZxvrpqswFpdXc3GxsZUp18KX8ov57vyjKljwCX7ev5D/kL+z1PHYBdV1Zd3YNiTSd7Y3XdV1dOSHK+qT3b358/3B8xVO89cxaIwVy2f881VtggCsBS6+6Huvmvz9qNJ7k1y5bSpAFg0k61gASyzA+vHkiS3Hrpu4iTj7KXXVFWrSV6Q5DPneO5gkoNJsn///t0NBjAjq4fvGDLO/UduGjLOXmEFC4ClUlVPTfKRJLd099fOfr67j3b3WnevrawM/RgYAEtAgQXA0qiqK3KquPpgd3906jwALB4FFgBLoaoqyfuS3Nvdb586DwCLSYEFwLK4Psmrkrykqu7e/Hrp1KEAWCyaXACwFLr700lq6hwALDYFFsAE9kKnvYu1iK8JAC6WLYIAAACDKLAAAAAGUWABAAAMsm2BVVXPPaPb0t1V9bWquuWsY36kqr56xjE/v2OJAQAAZmrbJhfd/cUkP5AkVXVZkj9Icts5Dv2t7n7Z0HQAAAB7yMVuEfzRJP+uu7+8E2EAAAD2sostsG5O8kvnee66qvqdqvq1qvq+cx1QVQeraqOqNk6cOHGRpwYAAJi3Cy6wqurJSX4yyb88x9N3JXl2dz8/yT9N8qvnGqO7j3b3WnevraysfAdxAQAA5utiVrBuTHJXd/+Hs5/o7q919x9t3r4zyRVVtW9QRgAAgD3hYgqsV+Y82wOr6plVVZu3r90c9w8vPR4AAMDesW0XwSSpqu9O8uNJDp3x2E8nSXe/J8krkvxMVZ1M8vUkN3d3j48LAAAwXxdUYHX3Y0n+3FmPveeM2+9K8q6x0QAAAPaWi+0iCAAAwHkosAAAAAZRYAEAAAyiwAIAABhEgQUAADCIAgsAAGAQBRYAAMAgCiwAAIBBLqjAqqr7q+pzVXV3VW2c4/mqqn9SVfdV1e9W1QvHRwX4zh1YP5YD68dmMw4AsJguv4hjX9zdj5znuRuTXLP59aIkv7j5HQAAYGmM2iL48iQf6FN+O8n3VtWzBo0NAENU1fur6uGq+r2pswCwmC60wOokn6iq41V18BzPX5nkK2fcf2DzsW9TVQeraqOqNk6cOHHxaQHg0vzzJDdMHQKAxXWhBdb13f3CnNoK+Lqq+uGznq9z/Jl+wgPdR7t7rbvXVlZWLjIqAFya7v5Ukv84dQ4AFtcFfQarux/c/P5wVd2W5NoknzrjkAeSXH3G/auSPDgqJADsls2dGgeTZP/+/ROnAZbJ6uE7po6wI+b2uu4/ctOOjr9tgVVVT0nypO5+dPP2TyT5hbMOuz3J66vqwznV3OKr3f3Q8LQA36FbD103q3GYr+4+muRokqytrT1hNwYAbOVCVrCekeS2qjp9/Ie6+2NV9dNJ0t3vSXJnkpcmuS/JY0n+1s7EBVgMp1u9K9gAYLFsW2B195eSPP8cj7/njNud5HVjowEAAOwto9q0A8DsVdUvJTmW5LlV9UBVvWbqTAAslou50DAA7Gnd/cqpMwCw2KxgAQAADKLAAgAAGMQWQYAJ6B4IAIvJChYAAMAgCiwAAIBBFFgAAACDbFtgVdXVVfUbVXVvVd1TVW84xzE/UlVfraq7N79+fmfiAgAAzNeFNLk4meSN3X1XVT0tyfGq+mR3f/6s436ru182PiIAAMDesO0KVnc/1N13bd5+NMm9Sa7c6WAAAAB7zUV9BquqVpO8IMlnzvH0dVX1O1X1a1X1fef58weraqOqNk6cOHHxaQEAAGbsggusqnpqko8kuaW7v3bW03cleXZ3Pz/JP03yq+cao7uPdvdad6+trKx8h5EBAADm6YIKrKq6IqeKqw9290fPfr67v9bdf7R5+84kV1TVvqFJAQAAZu5CughWkvclube7336eY565eVyq6trNcf9wZFAAAIC5u5AugtcneVWSz1XV3ZuP/WyS/UnS3e9J8ookP1NVJ5N8PcnN3d3j4wIAAMzXtgVWd386SW1zzLuSvGtUKAAAgL3ooroIAgAAcH4KLAAAgEEUWAB71IH1YzmwfmzqGADAGRRYAAAAgyiwAAAABlFgAQAADKLAAgAAGORCLjQMwAzdeui6qSPsOVV1Q5J3JrksyXu7+8jEkQBYMFawAFgKVXVZkncnuTHJ85K8sqqeN20qABaNAguAZXFtkvu6+0vd/Y0kH07y8okzAbBgJtsiePz48Ueq6ssDhtqX5JEB44wypzxzypLIs5055ZlTliTZlxyYWZ7ZvD9zypKMy/PsAWOc7cokXznj/gNJXnT2QVV1MMnBzbt/VFVfHHDuOf09zSlLIs9W5pQlkWc7c5qrZvjezCdPvW1n56rJCqzuXhkxTlVtdPfaiLFGmFOeOWVJ5NnOnPLMKUsiz1bmlCWZX56z1Dke6yc80H00ydGhJ57R+zKnLIk8W5lTlkSe7cwpz5yyJMuXxxZBAJbFA0muPuP+VUkenCgLAAtKgQXAsvhskmuq6jlV9eQkNye5feJMACyYRWjTPnQbxwBzyjOnLIk825lTnjllSeTZypyyJPPL87juPllVr0/y8Zxq0/7+7r5nl04/p/dlTlkSebYypyyJPNuZU545ZUmWLE91P2H7OQAAAN8BWwQBAAAGUWABAAAMosACAAAYRIEFAAAwiAILAABgEAUWAADAIAosAACAQRRYAAAAgyiwAAAABlFgAQAADKLAAgAAGESBBQAAMMjlU5143759vbq6OtXpl8If5z/mWzk5dQy4ZE/K5flT+bNTx2AXHT9+/JHuXhk5ZlVdneQDSZ6Z5FtJjnb3O7f6M+aqnWeuYlGYq5bP+eaqyQqs1dXVbGxsTHX6pXBP/mm+O8+aOgZcssfyUL4v/9XUMdhFVfXlHRj2ZJI3dvddVfW0JMer6pPd/fnz/QFz1c4zV7EozFXL53xzlS2CACyF7n6ou+/avP1oknuTXDltKgAWjQIL+Da3rD85t6w/eeoYC+/A+rEcWD82dYylVVWrSV6Q5DMTRwFgwUy2RRAAplBVT03ykSS3dPfXzvH8wSQHk2T//v27nA64UC8+/F1DxvmNI18fMs4iWj18x9QRdsT9R27a0fGtYAGwNKrqipwqrj7Y3R891zHdfbS717p7bWVlaJ8NAJaAAguApVBVleR9Se7t7rdPnQeAxaTAAmBZXJ/kVUleUlV3b369dOpQACwWn8ECYCl096eT1NQ5AFhsCizg27zj0DemjrAUbj103dQRAIAdYIsgAADAIAosAACAQRRYAAAAg2xbYFXVc8/otnR3VX2tqm4565gfqaqvnnHMz+9YYgAAgJnatslFd38xyQ8kSVVdluQPktx2jkN/q7tfNjQdAADAHnKxWwR/NMm/6+4v70QYAACAvexiC6ybk/zSeZ67rqp+p6p+raq+71wHVNXBqtqoqo0TJ05c5KkBAADm7YILrKp6cpKfTPIvz/H0XUme3d3PT/JPk/zqucbo7qPdvdbdaysrK99BXOB8bll/cm5Zf/LUMQAAltrFrGDdmOSu7v4PZz/R3V/r7j/avH1nkiuqat+gjAAAAHvCxRRYr8x5tgdW1TOrqjZvX7s57h9eejwAAIC9Y9sugklSVd+d5MeTHDrjsZ9Oku5+T5JXJPmZqjqZ5OtJbu7uHh8XAABgvi6owOrux5L8ubMee88Zt9+V5F1jowEAAOwtF9tFEAAAgPO4oBUsYP7ecegbU0cAAFh6VrAAAAAGUWABAAAMosACAAAYRIEFAAAwiAILAABgEAUWAADAIBdUYFXV/VX1uaq6u6o2zvF8VdU/qar7qup3q+qF46MCAADM28VcB+vF3f3IeZ67Mck1m18vSvKLm98BAACWxqgtgi9P8oE+5beTfG9VPWvQ2AAwRFW9v6oerqrfmzoLAIvpQgusTvKJqjpeVQfP8fyVSb5yxv0HNh8DgDn550lumDoEAIvrQrcIXt/dD1bV05N8sqq+0N2fOuP5Osef6bMf2CzODibJ/v37LzosAFyK7v5UVa1OnQOAxXVBBVZ3P7j5/eGqui3JtUnOLLAeSHL1GfevSvLgOcY5muRokqytrT2hAAOAqfnHQDi3Fx/+rqkjzNpNh/9CkjumjsEMbLtFsKqeUlVPO307yU8kOXvv+u1JXr3ZTfCHkny1ux8anhYAdlh3H+3ute5eW1lZmToOAHvMhaxgPSPJbVV1+vgPdffHquqnk6S735PkziQvTXJfkseS/K2diQsAADBf2xZY3f2lJM8/x+PvOeN2J3nd2GjAFG5Zf3KS5B2HvjFxklNuWX9yvpVn5f99aOokYx1YP5YkufXQdZOOAQCMNapNOwDMXlX9UpJjSZ5bVQ9U1WumzgTAYrmYCw0DwJ7W3a+cOgMAi80KFgAAwCAKLAAAgEFsEQS+zVyaW5z2jkPfyGNZvKs+jGhMobkFAMyPFSwAAIBBFFgAe9SB9WOPt2oHAOZBgQUAADDItgVWVV1dVb9RVfdW1T1V9YZzHPMjVfXVqrp78+vndyYuAADAfF1Ik4uTSd7Y3XdV1dOSHK+qT3b358867re6+2XjIwIAAOwN265gdfdD3X3X5u1Hk9yb5MqdDgYAALDXXFSb9qpaTfKCJJ85x9PXVdXvJHkwyX/d3fdcejwAzkebdgCYnwsusKrqqUk+kuSW7v7aWU/fleTZ3f1HVfXSJL+a5JpzjHEwycEk2b9//3eaGRbOLetPTjK/a1ABAHBxLqiLYFVdkVPF1Qe7+6NnP9/dX+vuP9q8fWeSK6pq3zmOO9rda929trKyconRAQAA5uVCughWkvclube7336eY565eVyq6trNcf9wZFAAAIC5u5AtgtcneVWSz1XV3ZuP/WyS/UnS3e9J8ookP1NVJ5N8PcnN3d3j4wIAAMzXtgVWd386SW1zzLuSvGtUKAAAgL3ogj6DBQAAwPYuqk07sDN0DwQAWAxWsIClcGD9WA6sH5vNOIvo+9/y8Xz/Wz4+dQwAmJQCCwAAYBAFFgAAwCAKLAAAgEEUWAAAAIMosABYGlV1Q1V9saruq6rDU+cBYPFo0w4shVsPXTercRbR597yV6eOsKWquizJu5P8eJIHkny2qm7v7s9PmwyARWIFC4BlcW2S+7r7S939jSQfTvLyiTMBsGAUWAAsiyuTfOWM+w9sPgYAw0y2RfD48eOPVNWXBwy1L8kjA8YZZU555pQlkWc7c8ozpyxJsi/5uzPLM5v3Z05ZknF5nj1gjLPVOR7rJxxUdTDJwc27f1RVXxxw7jn9Pc0pSyLPVuaUJVnQPH/hbQOSnDKn92dOWZKZ5am37excNVmB1d0rI8apqo3uXhsx1ghzyjOnLIk825lTnjllSeTZypyyJPPLc5YHklx9xv2rkjx49kHdfTTJ0ZEnntP7MqcsiTxbmVOWRJ7tzCnPnLIky5fHFkEAlsVnk1xTVc+pqicnuTnJ7RNnAmDB6CIIwFLo7pNV9fokH09yWZL3d/c9E8cCYMEsQoE1dBvHAHPKM6csiTzbmVOeOWVJ5NnKnLIk88vzbbr7ziR3TnDqOb0vc8qSyLOVOWVJ5NnOnPLMKUuyZHmq+wmf7wUAAOA74DNYAAAAgyiwAAAABlFgAQAADKLAAgAAGESBBQAAMIgCCwAAYBAFFgAAwCAKLAAAgEEUWAAAAIMosAAAAAZRYAEAAAyiwAIAABjk8qlOvG/fvl5dXZ3q9Evh63k4nZNTx4BLVrk835WnTx2DXXT8+PFHuntlJ8auqsuSbCT5g+5+2VbHmqt2nrmKRWGuWj7nm6smK7BWV1ezsbEx1emXwkbenO/On586Blyyx/Jg1vLWqWOwi6rqyzs4/BuS3Jvke7Y70Fy188xVLApz1fI531xliyAAS6OqrkpyU5L3Tp0FgMU02QoWsNjetH5qm8TbDj08cZJTDqwfS5Lceui6iZMwsXck+QdJnna+A6rqYJKDSbJ///7dSQXAtlYP3zFknPuP3DRknPOxggXAUqiqlyV5uLuPb3Vcdx/t7rXuXltZ2ZGPgQGwwBRYACyL65P8ZFXdn+TDSV5SVf9i2kgALBoFFgBLobv/YXdf1d2rSW5O8uvd/TcnjgXAglFgAQAADKLJBbAj5tLc4jTNLThTd/9mkt+cOAYAC8gKFgAAwCAKLIAld2D92ONt7AGAS6PAAgAAGESBBQAAMIgCCwAAYBAFFgAAwCDatAMsOS3sAWAcK1gAAACDKLAAGEK7dwBQYAEAAAyjwAIAABhEgQUAADCIAgsAAGAQbdoBGEK7dwAYXGBV1f1JHk3yzSQnu3tt5PjA3vGm9acnSd526OGJkwAA7J6dWMF6cXc/sgPjAgAAzJrPYAEAAAwyusDqJJ+oquNVdfDsJ6vqYFVtVNXGiRMnBp8aAABgWqMLrOu7+4VJbkzyuqr64TOf7O6j3b3W3WsrKyuDTw0AADCtoQVWdz+4+f3hJLcluXbk+AAAAHM2rMlFVT0lyZO6+9HN2z+R5BdGjQ/sLboHAgDLaGQXwWckua2qTo/7oe7+2MDxQetvmLED68eSuB4WAMttWIHV3V9K8vxR4wHASFV1dZIPJHlmkm8lOdrd75w2FQCLZieugwUAc3QyyRu7+66qelqS41X1ye7+/NTBAFgcroMFwFLo7oe6+67N248muTfJldOmAmDRWMECYOlU1WqSFyT5zDmeO5jkYJLs379/d4PBEnjp4TH/Xd155N8PGWdUnl858uCQcdj7rGABsFSq6qlJPpLklu7+2tnPu2YjAJfCChZ7iu6BMF97oXtgVV2RU8XVB7v7o1PnAWDxWMECYCnUqeuIvC/Jvd399qnzALCYFFgALIvrk7wqyUuq6u7Nr5dOHQqAxWKLIABLobs/naSmzgHAYrOCBQAAMIgCCwAAYBAFFgAAwCAKLAAAgEEUWLAg3rT+9Lxp/elTx+ACHVg/lgPrx6aOMdQiviYAuFgKLAAAgEEUWAAAAIMosAAAAAZRYAEAAAxy+dQBgDHedujhqSNwEW49dN3UEYZbxNcEABdLgQUATOYVh39oyDh3Hvn3Q8YZ5aWH91/yGIv4mtja6uE7hoxz/5Gbhowztzx7hS2CC+rA+rH8/Przpo4BAABLRYEFAAAwiAILAABgEAUWAADAIAosAACAQXQRXFC3HrouG7kzyZ+fOgoAACyNYStYVXVDVX2xqu6rqsOjxgUAANgrhhRYVXVZkncnuTHJ85K8sqr0CIcL9Kb1p+dN60+ffAwAAC7NqBWsa5Pc191f6u5vJPlwkpcPGhsAAGBPGFVgXZnkK2fcf2DzsW9TVQeraqOqNk6cODHo1AAAAPMwqsCqczzWT3ig+2h3r3X32srKyqBTAwAAzMOoAuuBJFefcf+qJA8OGhsAAGBPGNWm/bNJrqmq5yT5gyQ3J/mpQWPDwnvboYdnMQYsuqq6Ick7k1yW5L3dfWTiSAAsmCErWN19Msnrk3w8yb1Jfrm77xkxNgCMoOMtALth2IWGu/vOJHeOGg8ABnu8422SVNXpjrefnzQVAAulup/Qi2J3Tlx1IsmXBwy1L8kjA8YZZU555pQlkWc7c8ozpyyJPFuZU5ZkXJ5nd/fQbkhV9YokN3T3azfvvyrJi7r79WcddzDJwc27z03yxQGnn9Pf05yyJPJsZU5ZEnm2M6c8c8qSLG6ec85Vw1awLtaoibOqNrp7bcRYI8wpz5yyJPJsZ0555pQlkWcrc8qSzC/PWS64422So0NPPKP3ZU5ZEnm2MqcsiTzbmVOeOWVJli/PqC6CADB3Ot4CsOMUWAAsi8c73lbVk3Oq4+3tE2cCYMFMtkVwoKHbOAaYU545ZUnk2c6c8swpSyLPVuaUJZlfnsd198mqOt3x9rIk79/Fjrdzel/mlCWRZytzypLIs5055ZlTlmTJ8kzW5AIAAGDR2CIIAAAwiAILAABgEAUWAADAIAosAACAQRRYAAAAgyiwAAAABlFgAQAADKLAAgAAGESBBQAAMIgCCwAAYBAFFgAAwCAKLAAAgEEun+rE+/bt69XV1alOvxQey0P5Vv5k6hhwyZ6UK/LdedbUMdhFx48ff6S7V6bOYa7aeeYqFoW5avmcb66arMBaXV3NxsbGVKdfCp/O3/EfOgvhsTyUv5J/NnUMdlFVfXnqDIm5ajeYq1gU5qrlc765yhZBAACAQSZbwQK4EG9evybfzLPzsUOXNs6B9WNJklsPXTeLcQBYLH/z8E1J7rikMe4/ctOYMEzKChYAAMAgCiwAAIBBFFgAAACDKLAAAAAG0eQCmLW3Hvr9PJaHkrz6ksYZ1ZRCcwsAYCsKLGDWRnUR5Px0RgSAcWwRBAAAGESBBQAAMIgCCwAAYBAFFgBLpaouq6p/W1X/auosACweTS6AWRvVRZDzW8LmFm9Icm+S75k6CACLxwoWXII3r1+TN69fM5txWC7f/5aP5/vf8vGpY+wpVXVVkpuSvHfqLAAsJitYACyTdyT5B0medr4DqupgkoNJsn///t1JBbDAVg/fMWSc+4/cNGScnWYFC4ClUFUvS/Jwdx/f6rjuPtrda929trKyskvpAFgUCiwAlsX1SX6yqu5P8uEkL6mqfzFtJAAWjQILgKXQ3f+wu6/q7tUkNyf59e7+mxPHAmDB+AwWXIK3Hvr9WY3DcvncW/7q1BEAgLMosABYOt39m0l+c+IYACwgWwQBLsKB9WM5sH5s6hgAwEwpsAAAAAZRYAEAAAyiwAIAABhEgQUAADDI0C6CmxdvfDTJN5Oc7O61keMDTO3WQ9dNHQEAmLGdaNP+4u5+ZAfGBZbQm9evyTfz7Hzs0NRJ5ud0N0NFHwDMh+tgAQCwrb92+IVDxvnokbuGjMP5rR6+Y8g49x+5acg4y2b0Z7A6ySeq6nhVHTz7yao6WFUbVbVx4sSJwacGAACY1ugC6/rufmGSG5O8rqp++Mwnu/tod69199rKysrgUwMAAExraIHV3Q9ufn84yW1Jrh05PgAAwJwN+wxWVT0lyZO6+9HN2z+R5BdGjQ8sp7ce+v08loeSvHrqKLOjuQUAzM/IJhfPSHJbVZ0e90Pd/bGB4wO74M3r1yQ5VdjMwdy6COrcBwBsZViB1d1fSvL8UeMBAADsNaObXAAAACwtBRYAAMAgCiwAlkJVXV1Vv1FV91bVPVX1hqkzAbB4Rja5ABbAXJpbnDa3LoKaW+xpJ5O8sbvvqqqnJTleVZ/s7s9PHQyAxWEFC2bgzevXPN69D9gZ3f1Qd9+1efvRJPcmuXLaVAAsGitYACydqlpN8oIknznHcweTHEyS/fv3724wYKmtHr5j6ggMYAULgKVSVU9N8pEkt3T3185+vruPdvdad6+trKzsfkAA9jQFFgBLo6quyKni6oPd/dGp8wCweBRYACyFqqok70tyb3e/feo8ACwmn8GCGZhb5z5YUNcneVWSz1XV3ZuP/Wx33zldJAAWjQILZuB0B0GF1hO9ef2afDPPzscOTZ1kfg6sH0uidfyF6u5PJ6mpcwCw2GwRBAAAGESBBQAAMIgCCwAAYBAFFgAAwCCaXMAMaG5xfm899Pt5LA8lefXUUWZHcwsAmB8rWCylN69f83jnvkWxiK8JAGCvUWABAAAMYosgAMAM/bXDL5w6wo6Y2+v66JG7po7ABVo9fMeQce4/ctOQcc7HChYAAMAgCiwAAIBBbBFkKS1i175FfE0AAHvNsBWsqrqhqr5YVfdV1eFR4wKMcGD9WA6sH5s6BgCw4IYUWFV1WZJ3J7kxyfOSvLKqnjdibAAAgL1i1ArWtUnu6+4vdfc3knw4ycsHjQ0AALAnjCqwrkzylTPuP7D52LepqoNVtVFVGydOnBh0agAAgHkYVWDVOR7rJzzQfbS717p7bWVlZdCpAQAA5mFUF8EHklx9xv2rkjw4aGyAS3broeumjsAMVNUNSd6Z5LIk7+3uIxNHAmDBjFrB+mySa6rqOVX15CQ3J7l90NgAcMk0ZAJgNwwpsLr7ZJLXJ/l4knuT/HJ33zNibAAYREMmAHZcdT/ho1K7c+KqE0m+PGCofUkeGTDOKHPKM6csiTzbmVOeOWVJ5NnKnLIk4/I8u7uHfli3ql6R5Ibufu3m/VcleVF3v/6s4w4mObh597lJvjjg9HP6e5pTlkSercwpSyLPduaUZ05ZksXNc865atRnsC7aqImzqja6e23EWCPMKc+csiTybGdOeeaUJZFnK3PKkswvz1kuuCFTkqNDTzyj92VOWRJ5tjKnLIk825lTnjllSZYvz6jPYAHA3GnIBMCOU2ABsCw0ZAJgx022RXCgods4BphTnjllSeTZzpzyzClLIs9W5pQlmV+ex3X3yao63ZDpsiTv38WGTHN6X+aUJZFnK3PKksiznTnlmVOWZMnyTNbkAgAAYNHYIggAADCIAgsAAGAQBRYAAMAgCiwAAIBBFFgAAACDKLAAAAAGUWABAAAMosACAAAYRIEFAAAwiAILAABgEAUWAADAIAosAACAQRRYAAAAg1w+1Yn37dvXq6urU51+Kfwv+Uq+lT+ZOgZcsiflijwlV08dg110/PjxR7p7Zeoc5qqdZ65iUZirls/55qrJCqzV1dVsbGxMdfql8G9yc74rT586Blyyr+fh/Gg+PHUMdlFVfXnqDIm5ajeYq1gU5qrlc765yhZBAACAQSZbwQLGeuv6DyZJ3nzosxMnYa85sH4sSXLroesmTgKwd73m8KuS3HFJY9x/5KYxYRbU6uFLe39P2+n32QoWAADAIAosAACAQRRYAAAAg/gMFiwIn73iO+WzVwAwjhUsAACAQRRYM3Rg/djjXb1gr3rr+g8+3tlwDvx3tfO8xwCgwAIAABhGgQUAADCIAguApVJVl1XVv62qfzV1FgAWjwILgGXzhiT3Th0CgMWkTfsMaZnMIphb23j/Xe28vfAeV9VVSW5K8o+S/P2J4wCwgBRYbOl0F7i5/c8yXKzT3e32QhFwoUa9pkV8b7bwjiT/IMnTzndAVR1McjBJ9u/fvzupgMn81OG/OnWE2Vo9fMeQce4/ctOQcfYKWwQBWApV9bIkD3f38a2O6+6j3b3W3WsrKyu7lA6ARaHAAmBZXJ/kJ6vq/iQfTvKSqvoX00YCYNEosABYCt39D7v7qu5eTXJzkl/v7r85cSwAFowCCwAAYBBNLtiS5hYsikVs4DDqNS3ie7Od7v7NJL85cQwAFpACC9gRozpQvnX9B/OtfCM/emhEqks3quPe97/l40mSz71F9yoAWCS2CAIAAAyiwAIAABhEgQUAADCIAgsAAGCQoU0uNi/e+GiSbyY52d1rI8cH9o5RHSjffOiz+XoeTvKaIeNdqlEd9zS3AIDFtBNdBF/c3Y/swLjAFkZ17XvtW16SJHnvW379kjOxXEZ1WASAvUybdgAA9pyfOrx4OwFWD98xZJz7j9w0ZBy+M6M/g9VJPlFVx6vq4OCxAQAAZm30Ctb13f1gVT09ySer6gvd/anTT24WXQeTZP/+/YNPDQAAMK2hK1jd/eDm94eT3Jbk2rOeP9rda929trKyMvLUAAAAkxu2glVVT0nypO5+dPP2TyT5hVHjA1sb1bVPcwu+U5pbAMDYLYLPSHJbVZ0e90Pd/bGB48MwozrujTK3PItIhzsAYDcMK7C6+0tJnj9qPAAAgL1mdBdBAACApaXAAgAAGESBBcBSqKqrq+o3qureqrqnqt4wdSYAFs/o62DBnjC3ZhJzy7OINLcgyckkb+zuu6rqaUmOV9Unu/vzUwcDYHEosIAdMaoz4lvXfzDfyjfyo4dGpJoPXQ13X3c/lOShzduPVtW9Sa5MosACYBhbBAFYOlW1muQFST4zcRQAFowVLACWSlU9NclHktzS3V87x/MHkxxMkv379+9yOvhf/dThvzpknA8d+fiQcUYZ9bo4v9XDd0wdYalZwQJgaVTVFTlVXH2wuz96rmO6+2h3r3X32srKyu4GBGDPU2ABsBSqqpK8L8m93f32qfMAsJhsEQR2xKjOiG8+9Nl8PQ8nec2Q8eZCc4tJXJ/kVUk+V1V3bz72s91953SRAFg0CiwAlkJ3fzpJTZ0DgMW2p7cIHlg/9nirY2Be3rr+g4+3aueJFvH31yK+JgC4WHu6wAIAAJgTBRYAAMAgCiwAAIBB9nSTC124YL5GdRFcVIv4+2sRXxMAXCwrWAAAAIPs6RUsYPG99i0vSadz71umTnLK6S55VmsAgHNRYAHADls9fMeQce4/ctOQcUYY9Zred2TIMPmpw391yDgfOvLxIeMsolHvMctn1O+LvcIWQQAAgEEUWAAAAIMosAAAAAYZ9hmsqrohyTuTXJbkvd09aFc1sMze+5Zfz9fzcJL/09RRkmhuAQBsbUiBVVWXJXl3kh9P8kCSz1bV7d39+RHjw2hvXf/BJK7VdC5ze2/euv6D+Va+kR89NHUSAIDtjdoieG2S+7r7S939jSQfTvLyQWMDAADsCaMKrCuTfOWM+w9sPvZtqupgVW1U1caJEycGnRoAAGAeRhVYdY7H+gkPdB/t7rXuXltZWRl0agAAgHkYVWA9kOTqM+5fleTBQWMDAADsCaO6CH42yTVV9Zwkf5Dk5iQ/NWhsGG4uDRzmaG7vzZsPfXazi+Brpo7CAtDxFoCdNmQFq7tPJnl9ko8nuTfJL3f3PSPGBoARzuh4e2OS5yV5ZVU9b9pUACyaYdfB6u47k9w5ajwAGOzxjrdJUlWnO966pAgAw1T3E3pR7M6Jq04k+fKAofYleWTAOKPMKc+csiTybGdOeeaUJZFnK3PKkozL8+zuHtoNqapekeSG7n7t5v1XJXlRd7/+rOMOJjm4efe5Sb444PRz+nuaU5ZEnq3MKUsiz3bmlGdOWZLFzXPOuWrYCtbFGjVxVtVGd6+NGGuEOeWZU5ZEnu3MKc+csiTybGVOWZL55TnLBXe8TXJ06Iln9L7MKUsiz1bmlCWRZztzyjOnLMny5RnVRRAA5k7HWwB2nAILgGXxeMfbqnpyTnW8vX3iTAAsmMm2CA40dBvHAHPKM6csiTzbmVOeOWVJ5NnKnLIk88vzuO4+WVWnO95eluT9u9jxdk7vy5yyJPJsZU5ZEnm2M6c8c8qSLFmeyZpcAAAALBpbBAEAAAZRYAEAAAyiwAIAABhEgQUAADCIAgsAAGAQBRYAAMAgCiwAAIBBFFgAAACDKLAAAAAGUWABAAAMosACAAAYRIEFAAAwyOVTnXjfvn29uro61emXwqO5P9/Kn0wdAy7Zk3JFnpbVqWOwi44fP/5Id69MncNctfPMVSwKc9XyOd9cNVmBtbq6mo2NjalOvxR+LTfmT+XPTB0DLtkf5z/lxvza1DHYRVX15akzJOaq3WCuYlGYq5bP+eYqWwQBAAAGmWwFC/hfvW39x5Ikbzr0rydOMj9vW/+xfCsnc+OhqZMALLe/ffhvDBnn/Uc+OGQcmCsrWAAAAIMosAAAAAZRYAEAAAyiwAIAABhEkwuYAc0tzu9Nh/51/jj/KcnBqaMAAGzLChbMwOve8tfzurf89Usa423rP/Z4N0K4GAfWj+XA+rGpYwDAQlBgAQAADGKLIAAAMHurh+8YMs79R24aMs75WMECAAAYRIEFAAAwiC2CMAPvfsu/vOQxdCLkO3XroeumjgAAC0OBBTNwuvvfpRRJI8aYo7et/1i+lZO58dDUScY63bVPcQMAi8UWQQAAgEEUWAAAAIMosAAAAAbxGSwAALhEP3P49Uku7TpNO319JnaHAgtmYERjikVrbnHamw796/xx/lOSg1NHGUpzCwBYTLYIsqe8bf3HHu+Wx7eb23sztzyc34H1Y493NZzDOACwlymwAAAABrFFEIClUVX3J3k0yTeTnOzutWkTAbBoFFgALJsXd/cjU4cAYDHZIggAADCIFSz2lEXtlDfC3N6bueXh/EZ1NNwjnRE7ySeqqpOsd/fRqQMBsFiGFlj2trPTTnelm8v/vM8tz5x4b7Z2utveHilKLsgeeU3Xd/eDVfX0JJ+sqi9096fOPKCqDmbzugD79++fIiMstL99+G8MGef9Rz44ZBzOb/XwpV3X67Rlu77XTmwRfHF3/4DiCoC56e4HN78/nOS2JNee45ij3b3W3WsrKyu7HRGAPc5nsABYClX1lKp62unbSX4iye9NmwqARTO6wDq9t/345haLb1NVB6tqo6o2Tpw4MfjUALClZyT5dFX9TpL/Ickd3f2xiTMBsGBGN7nYcm/75oeJjybJ2tpaDz43AJxXd38pyfOnzgHAYhtaYJ25t72qTu9t/9TWfwou3NwaJswtz5x4b7Y280YQ35FFfE0AcLGGbRG0tx0AAFh2I1ewnpHktqo6Pe6H7G3/zuyRVscMpKX5+b1t/cfyrZzMjYemTgIAsL1hBZa97QAAwLLTph0AAGAQBRYAAMAgCiwAAIBBRl8HiwE0t1g+mluc35sO/ev8cf5TkidcuxwAYHasYLEr3rb+Y493ymNnzO09nlsezu/A+rHHu5fOYRwA2MsUWAAAAIPYIggAsMD+9uG/MXWEWfP+MJoVLAAAgEEUWAAAAIPYIsiu0CVv583tPZ5bHs5vVOdSHVABQIHFkjrd3e5Si4C5jbOI3rb+Y/lWTubGQ1MnOeV0l7xLLSZGjTPCnLIAwF5niyAAAMAgCiwAAIBBFFgAAACD+AwWAMBgI66t9P4jHxyQhGW0eviOqSMsNQUWS2lUM4m5jbOI3nToX+eP85+SHJw6SpLF7Lg3pywAsNcpsOASzKn735yyjDS3LoKj6NwHAIvJZ7AAAAAGUWABAAAMosACAAAYRIEFAAAwyLAmF1V1Q5J3JrksyXu7+8iosWGu5tRQYk5ZRppbF8FRNLcAgMU0pMCqqsuSvDvJjyd5IMlnq+r27v78iPFh0b3uLX89SfLut/zLiZPMrxvh3LoIjur+p4sge92o6+z84sz+OXbE9atGmVOWOfL+MFejtghem+S+7v5Sd38jyYeTvHzQ2AAAAHvCqALryiRfOeP+A5uPAQAALI1RBVad47F+wkFVB6tqo6o2Tpw4MejUAAAA8zCqwHogydVn3L8qyYNnH9TdR7t7rbvXVlZWBp0aAABgHkZ1Efxskmuq6jlJ/iDJzUl+atDYsPDm0NzitLk0tzhtbl0ERzWl0NxiGjreArDThqxgdffJJK9P8vEk9yb55e6+Z8TYADDCGR1vb0zyvCSvrKrnTZsKgEUz7DpY3X1nkjtHjQcAgz3e8TZJqup0x1uXFAFgmOp+Qi+K3Tlx1YkkXx4w1L4kjwwYZ5Q55ZlTlkSe7cwpz5yyJPJsZU5ZknF5nt3dQz+sW1WvSHJDd7928/6rkryou19/1nEH87/uSX1uki8OOP2c/p7mlCWRZytzypLIs5055ZlTlmRx85xzrhq2gnWxRk2cVbXR3WsjxhphTnnmlCWRZztzyjOnLIk8W5lTlmR+ec5yQR1vu/tokqNDTzyj92VOWRJ5tjKnLIk825lTnjllSZYvz6guggAwdxfU8RYALoUCC4Bl8XjH26p6ck51vL194kwALJjJtggONHQbxwBzyjOnLIk825lTnjllSeTZypyyJPPL87juPllVpzveXpbk/bvY8XZO78ucsiTybGVOWRJ5tjOnPHPKkixZnsmaXAAAACwaWwQBAAAGUWABAAAMosACAAAYRIEFAAAwiAILAABgEAUWAADAIAosAACAQRRYAAAAgyiwAAAABlFgAQAADKLAAgAAGESBBQAAMMjlU5143759vbq6OtXpl8LX8u/yrfzJ1DHgkj0pV+R78p9NHYNddPz48Ue6e2XqHOaqnWeuYlGYq5bP+eaqyQqs1dXVbGxsTHX6pXB7rs/l+e6pY8AlO5nH8pP5/04dg11UVV+eOkNirtoN5ioWhblq+ZxvrrJFEAAAYJDJVrBgSv94/RVJkr936FcWapw5GfnedL6Znzw0ItV8HFg/liS59dB1k44BLL6/c/iWIeP8syPvGDIOLDorWAAAAIMosAAAAAZRYAEAAAyiwAIAABhEkwuW0qhmEnMbZ05Gvjcn81iSvzNkvLkY0ZhCcwsAmB8rWCylf7z+ise73M1hnDnx3mztwPqxx7v3zWEcAGBeFFgAAACDKLAAAAAGUWABAAAMosACAAAYRBdBlpLuf+fnvdnaqM59OgACwGKyggUAADCIAgsuwZxakc8pSzK/PHOjTTsALCYFFgAAwCAKLAAAgEE0uQAAmKG/c/iWqSPsiFGv658deceQcWA0BRZcgjl1yptTlmR+eeZGF0EAWEy2CAIAAAyiwGIpjepwN6dOeXPKwu7QiRAA5keBBQAAMIjPYAGwNKrq/iSPJvlmkpPdvTZtIgAWjQILgGXz4u5+ZOoQACwmWwQBAAAGGbqCZesFe8WoFuJzakU+pyzsDq3evyOd5BNV1UnWu/vo2QdU1cEkB5Nk//79uxwP2G2jrsv1T47834aMMyerh++YOsK3uf/ITVNHuCA7sUVw17ZenO6e5X8y2OtOd/+7lCJpxBhsz++dPe/67n6wqp6e5JNV9YXu/tSZB2wWXUeTZG1tracICcDeZYsgAEujux/c/P5wktuSXDttIgAWzegC6/TWi+ObWyy+TVUdrKqNqto4ceLE4FMDwPlV1VOq6mmnbyf5iSS/N20qABbN6C2CW269sO0CgAk9I8ltVZWcmv8+1N0fmzYSAItmaIF15taLqjq99eJTW/8pANh53f2lJM+fOgcAi21YgbW53eJJ3f3oGVsvfmHU+OfiQ+YsihGNKTS32B1+7wAAWxm5gmXrBXvGqI57c+rcN6cs7A4dDQFgfoYVWLZeAAAAy06bdgAAgEEUWAAAAIMosAAAAAYZfR0s2BNGNYKYU0OJOWVhd2huAQDzYwWLpfSP11/xeNe9OYwzJ96brR1YP/Z49745jAMAzIsCCwAAYBAFFgAAwCA+gwUAwLb+zuFbpo7ABVo9fMfUEZaaFSwAAIBBrGCxlBaxi+Ao3putjercpwMgACwmK1jRzYvpLWLHvUV8TYvK70AAGEeBBQAAMIgCCwAAYBAFFgAAwCAKLAAAgEF0EYxuXkxvETvuLeJrWlR+BwJ7ketyMVdWsFhKozrczalT3pyysDt0/wOA+VFgAQAADKLAAgAAGESBBQAAMIgCCwAAYBBdBFlKozrczalT3pyysDt0/wOA+Rm2glVVN1TVF6vqvqo6PGpcAACAvWLIClZVXZbk3Ul+PMkDST5bVbd39+dHjM/ed7p9+KKtsszpdc0pC9s73V79UlahRozB7lg9fMfUER53/5Gbpo4wa6OurfTPjrxjyDjsHX/38M/m7+bS/lv33+fWRv0u3en3edQK1rVJ7uvuL3X3N5J8OMnLB40NAACwJ4wqsK5M8pUz7j+w+di3qaqDVbVRVRsnTpwYdGoAAIB5GFVg1Tke6yc80H20u9e6e21lZWXQqQEAAOZhVBfBB5Jcfcb9q5I8OGhsFsCifi5oTq9rTlnY3ojPTfns1cWrqhuSvDPJZUne291HJo4EwIIZtYL12STXVNVzqurJSW5OcvugsQHgkp3RkOnGJM9L8sqqet60qQBYNEMKrO4+meT1ST6e5N4kv9zd94wYGwAG0ZAJgB1X3U/4qNTunLjqRJIvDxhqX5JHBowzypzyzClLIs925pRnTlkSebYypyzJuDzP7u6hH9atqlckuaG7X7t5/1VJXtTdrz/ruINJDm7efW6SLw44/Zz+nuaUJZFnK3PKksiznTnlmVOWZHHznHOuGvUZrIs2auKsqo3uXhsx1ghzyjOnLIk825lTnjllSeTZypyyJPPLc5YLbsiU5OjQE8/ofZlTlkSercwpSyLPduaUZ05ZkuXLM+ozWAAwdxoyAbDjFFgALAsNmQDYcZNtERxo6DaOAeaUZ05ZEnm2M6c8c8qSyLOVOWVJ5pfncd19sqpON2S6LMn7d7Eh05zelzllSeTZypyyJPJsZ0555pQlWbI8kzW5AAAAWDS2CAIAAAyiwAIAABhEgQUAADCIAgsAAGAQBRYAAMAgCiwAAIBBFFgAAACDKLAAAAAGUWABAAAMosACAAAYRIEFAAAwiAILAABgEAUWAADAIJdPdeJ9+/b16urqVKdfCl/N/5jOyaljwCWrXJ4/nb84dQx20fHjxx/p7pWpc5irdp65ikVhrlo+55urJiuwVldXs7GxMdXpl8JH8wNTR4Bh/lr8vlgmVfXlqTMk5qrdYK5ikZirlsv55ipbBAEAAAaZbAULvhPvXn9NkuR1h963UOPMifdmawfWjyVJbj103SzGARbX3z/8j4aM8/YjPzdkHODCWMECAAAYRIEFAAAwiAILAABgEAUWAADAIJpcsKeMapgwt3HmxHuztVFNKTS3AIDFtKdXsA6sH3u8ExdM4d3rr3m8W96iWMTXtKj8DgSA+dnTBRYAAMCcKLAAAAAGUWABAAAMosACAAAYZE93EdSFi6ktYqe8RXxNi8rvQACYHytYLKVF7JS3iK8JAGCvUWABAAAMosACAAAYRIEFwEKoqvur6nNVdXdVbZzj+aqqf1JV91XV71bVC6fICcBi29NNLgDgLC/u7kfO89yNSa7Z/HpRkl/c/A4AwyiwWEqL2ClvEV8TDPbyJB/o7k7y21X1vVX1rO5+aOpgACwOWwRZSovYcW8RXxNcpE7yiao6XlUHz/H8lUm+csb9BzYf+zZVdbCqNqpq48SJEzsUFYBFpcACYFFc390vzKmtgK+rqh8+6/k6x5/pJzzQfbS717p7bWVlZSdyArDAFFgALITufnDz+8NJbkty7VmHPJDk6jPuX5Xkwd1JB8CyUGABsOdV1VOq6mmnbyf5iSS/d9Zhtyd59WY3wR9K8lWfvwJgNE0uAFgEz0hyW1Ulp+a2D3X3x6rqp5Oku9+T5M4kL01yX5LHkvytibICsMCGFlhVdX+SR5N8M8nJ7l4bOT6Msogd9xbxNcGF6u4vJXn+OR5/zxm3O8nrdjMXAMtnJ1awtroGyVAH1o8lSW49dN1unA6e4HTXvkUqbhbxNS0qvwMBYH58BgsAAGCQ0QXWdtcgAQAAWFijtwhe390PVtXTk3yyqr7Q3Z86/eRm0XUwSfbv3z/41AAAnO3vH/5HQ8Z5+5GfGzLOqDyjjHpdcNrQFaztrkHi4o0AAMAiG7aCtXndkSd196NnXIPkF0aNfy4+2M3UFrERxCK+pkXldyAAzM/ILYLnvAbJwPFhmEXslLeIrwkAYK8ZVmCd7xokAAAAy0KbdgAAgEEUWAAAAIMosAAAAAYZfR0s2BMWsRHEIr4mAIC9Zk8XWAfWjyXRqpjpLGLnvkV8TYvK78C9Y/XwHUPGuf/ITUPGAbgQo353jbJXfgfaIgjAnldVV1fVb1TVvVV1T1W94RzH/EhVfbWq7t78+vkpsgKw2Pb0ChYAbDqZ5I3dfVdVPS3J8ar6ZHd//qzjfqu7XzZBPgCWhBUsAPa87n6ou+/avP1oknuTXDltKgCWkQILgIVSVatJXpDkM+d4+rqq+p2q+rWq+r7z/PmDVbVRVRsnTpzYyagALKA9vUXQB7uZ2iI2gljE17So/A58oqp6apKPJLmlu7921tN3JXl2d/9RVb00ya8muebsMbr7aJKjSbK2ttY7mxiARWMFC4CFUFVX5FRx9cHu/ujZz3f317r7jzZv35nkiqrat8sxAVhwCiz2lHevv+bxNuJzGGeUueVh5x1YP/Z4m3UuXVVVkvclube7336eY565eVyq6tqcmgP/cPdSArAM9vQWQQDYdH2SVyX5XFXdvfnYzybZnyTd/Z4kr0jyM1V1MsnXk9zc3bYAAjCUAguAPa+7P52ktjnmXUnetTuJWHZ///A/mjrCcIv4mhbV3C4QvGxsEQQAABhEgQUAADCILYLsKaNaiM+tFfnc8rDztFgHgMVkBYultIhd+xbxNQEA7DUKLAAAgEEUWAAAAIMosAAAAAZRYAEAAAwyrItgVd2Q5J1JLkvy3u4+MmpsGG0Ru/Yt4msCuFCjLoL79iM/N2Qc9g4/O4w2ZAWrqi5L8u4kNyZ5XpJXVtXzRozNtBa1M93cXtfc8nB+B9aP5cD6sdmMAwDMy6gtgtcmua+7v9Td30jy4SQvHzQ2AADAnjCqwLoyyVfOuP/A5mPfpqoOVtVGVW2cOHFi0KkBAADmYVSBVed4rJ/wQPfR7l7r7rWVlZVBpwYAAJiHUQXWA0muPuP+VUkeHDQ2AADAnjCqi+Bnk1xTVc9J8gdJbk7yU4PGZkKL2plubq9rbnk4v1sPXTercThlu062VVWbz780yWNJ/i/dfdeuBwVg4Q1Zweruk0len+TjSe5N8svdfc+IsQFgKxfYyfbGJNdsfh1M8ou7GhKApTHsOljdfWeSO0eNBwAX6PFOtklSVac72X7+jGNenuQD3d1JfruqvreqntXdD+1+XAAWWZ2aayY4cdWJJF8eMNS+JI8MGGeUOeWZU5ZEnu3MKc+csiTybGVOWZJxeZ7d3RfUDamqXpHkhu5+7eb9VyV5UXe//oxj/lWSI9396c37/ybJm7p74xzjHcypVa4keW6SL17SKzllTn9Pc8qSyLOVOWVJ5NnOnPLMKUuyuHnOOVcNW8G6WBc6cW6nqja6e23EWCPMKc+csiTybGdOeeaUJZFnK3PKkkyW50I62V5Qt9vkVMfbJEcvNdS3nXxGf09zypLIs5U5ZUnk2c6c8swpS7J8eUZ1EQSAqVxIJ1vdbgHYFQosAPa6xzvZVtWTc6qT7e1nHXN7klfXKT+U5Ks+fwXATphsi+BAQ7dxDDCnPHPKksiznTnlmVOWRJ6tzClLMkGe7j5ZVac72V6W5P3dfU9V/fTm8+/JqSZML01yX061af9buxxzTn9Pc8qSyLOVOWVJ5NnOnPLMKUuyZHkma3IBAACwaGwRBAAAGESBBQAAMMieLbCq6oaq+mJV3VdVhyfOcnVV/UZV3VtV91TVG6bMc1pVXVZV/3bz+i9TZ/neqvqVqvrC5vt03YRZ/t7m39PvVdUvVdX/ZpfP//6qeriqfu+Mx/5sVX2yqn5/8/ufmTjPf7f5d/W7VXVbVX3vlHnOeO6/rqquqn1TZqmq/2rz9889VfXf7kaW8+Wpqh+oqt+uqruraqOqrt3FPOf83Tflz/PcmKu2zWSeOn8ec9X2eSaZq+Y0T22Vx1w13Ty1JwusqrosybuT3JjkeUleWVXPmzDSySRv7O7/PMkPJXndxHlOe0OSe6cOsemdST7W3X8pyfMzUa6qujLJ302y1t1/Oac+EH/zLsf450luOOuxw0n+TXdfk+TfbN6fMs8nk/zl7v4vkvyPSf7hxHlSVVcn+fEk/37KLFX14iQvT/JfdPf3Jfm/T5knyX+b5L/p7h9I8vOb93fL+X73TfnzPBvmqgtinjoHc9UF55lqrjpXlqnmqXPmMVc9bpJ5ak8WWEmuTXJfd3+pu7+R5MM59UM0ie5+qLvv2rz9aE79Ur5yqjxJUlVXJbkpyXunzLGZ5XuS/HCS9yVJd3+ju/9/E0a6PMl3VdXlSb47u3wtnO7+VJL/eNbDL0/y32/e/u+T/B+nzNPdn+juk5t3fzunrhk0WZ5N/zjJP8h5Lg67i1l+JsmR7v7jzWMenjhPJ/mezdt/Orv487zF777Jfp5nxly1BfPUtsxV2+SZaq6a0zy1RR5zVaabp/ZqgXVlkq+ccf+BTFzQnFZVq0lekOQzE0d5R079R/6tiXMkyV9IciLJ/2NzK8h7q+opUwTp7j/IqX/F+fdJHsqpa+F8YoosZ3nG6WvybH5/+sR5zvS3k/zalAGq6ieT/EF3/86UOTb9xST/+6r6TFX9f6rqByfOc0uS/66qvpJTP9u7udr4uLN+983553k3mau29o6Yp87JXPUdmXSumtk8lZirnmA356m9WmDVOR6bvN98VT01yUeS3NLdX5swx8uSPNzdx6fKcJbLk7wwyS929wuS/C+ZaMvQ5h7blyd5TpI/n+QpVfU3p8iyF1TVz+XU8voHJ8zw3Ul+Lqe2FMzB5Un+TE5tNfi/JvnlqjrX76Td8jNJ/l53X53k72XzX+B301x+982Quer8GcxTWzBXXZyp56oZzlOJuerb7Pbvvb1aYD2Q5Ooz7l+VXV46P1tVXZFTf3Ef7O6PTpklyfVJfrKq7s+pLSkvqap/MWGeB5I80N2n/6X0V3JqIpvCjyX5n7r7RHf/SZKPJvnfTZTlTP+hqp6VJJvfd20p/3yq6r9M8rIkf6OnvWDef5ZT/5PxO5s/01cluauqnjlRngeSfLRP+R9y6l/fd+3DzOfwX+bUz3GS/Muc2pa2a87zu292P88TMVedn3lqa+aqCzSTuWpu81RirnrcFPPUXi2wPpvkmqp6TlU9Oac++Hn7VGE2/0XgfUnu7e63T5XjtO7+h919VXev5tR78+vdPdm/fHX3/5zkK1X13M2HfjTJ5yeK8++T/FBVfffm39uPZh4fsL49p375ZPP7/2vCLKmqG5K8KclPdvdjU2bp7s9199O7e3XzZ/qBJC/c/Lmawq8meUmSVNVfTPLkJI9MlCU59T/s/4fN2y9J8vu7deItfvfN6ud5Quaq8zBPbctcdQHmMlfNcJ5KzFVJJpynuntPfiV5aU51jPl3SX5u4ix/Jae2ffxukrs3v1469Xu0me1HkvyrGeT4gSQbm+/Rryb5MxNm+W+SfCHJ7yX5fyb5U7t8/l/KqT31f5JTv4Rfk+TP5VQXm9/f/P5nJ85zX059duT0z/N7psxz1vP3J9k34Xvz5CT/YvPn564kL5n47+qvJDme5Hdyal/5/3YX85zzd9+UP89z+zJXXVAu89S585irts8zyVw1p3lqi/fGXNXTzVO1eXIAAAAu0V7dIggAADA7CiwAAIBBFFgAAACDKLAAAAAGUWABAAAMosACAAAYRIEFAAAwyP8fq5oln44j5VEAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(len(int_levels), 2, figsize=[12, 12], sharex=True)\n",
"\n",
"for i in int_levels:\n",
" \n",
" ## Raster plot\n",
" # select just data for current intensity level\n",
" # this is convenient since we'll refer to this subset of data a few times below\n",
" dat = spikes[spikes['Intensity'] == i]\n",
"\n",
" # Draw the raster one trial at a time\n",
" for trial in trials:\n",
" # get spike times for this trial\n",
" spike_times = dat[dat['Trial'] == trial]['SpikeTime']\n",
" # Draw the raster\n",
" axs[i, 0].vlines(spike_times, \n",
" trial - 0.4, trial + 0.4)\n",
" # Shade time when stimulus was on\n",
" axs[i, 0].axvspan(stim_on, stim_off, \n",
" alpha= i / 10 + .1, # Base intensity of shading (alpha) on intensity level\n",
" color='greenyellow')\n",
"\n",
" ## PSTH\n",
" # Shade time when stimulus was on\n",
" axs[i, 1].axvspan(stim_on, stim_off, \n",
" alpha= i / 10 + .1, \n",
" color='greenyellow')\n",
" \n",
" axs[i, 1].hist(dat['SpikeTime'], bins=range(0, num_tp, 1))\n",
"\n",
" # Set the x tickmarks to every 2 ms\n",
" axs[i, 1].set_xticks(range(0, num_tp + 1, 2))\n",
" \n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scale the *y* axes\n",
"\n",
"Although there is also a `sharey` kwarg for the *y* axis, this isn't appropriate for the current figure because the raster and PSTH plots necessarily will have different scales. The rasters are already on the same scale, since there were 10 trials at each intensity level. However, note that the *y* scales for the PSTHs are all different, which makes it very hard to compare between different intensity levels. \n",
"\n",
"There are (at least) two different ways we could do this: we could normalize the histograms (making the *y* axis the proportion of total spikes in each bin), or we could simply scale them all to the maximum number of spikes in any bin, across the entire data set. \n",
"\n",
"In this case, the latter option makes more sense. We want to compare the amount of spiking activity between different intensity levels, so we want to compare \"apples to apples\" by using the absolute number of spikes in each bin, rather than the proportion of spikes in each bin, at a particular intensity level. \n",
"\n",
"The way we figure out what the maximum value on the *y* axis should be, is by using an [accumulator variable](../3-python/for_loops). We initialize `y_max` before we start the plotting loop, and then as we plot the data for each intensity level we compare the upper limit of the *y* axis for the current intensity against the current value of `y_max`, and update `y_max` if the current value is larger. By the time we have looped through all intensity levels, `y_max` will contain the largest number of spikes found in the data set."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# set the number of subplot rows based on number of intensity levels in data\n",
"# specify the figure dimensions since it will need to be a big figure\n",
"fig, axs = plt.subplots(len(int_levels), 2, figsize=[12, 12], sharex=True)\n",
"\n",
"y_max = 0 # Accumulator variable\n",
"\n",
"for i in int_levels:\n",
" \n",
" ## Raster plot\n",
" # select just data for current intensity level\n",
" # this is convenient since we'll refer to this subset of data a few times below\n",
" dat = spikes[spikes['Intensity'] == i]\n",
"\n",
" # Draw the raster one trial at a time\n",
" for trial in trials:\n",
" # get spike times for this trial\n",
" spike_times = dat[dat['Trial'] == trial]['SpikeTime']\n",
" # Draw the raster\n",
" axs[i, 0].vlines(spike_times, \n",
" trial - 0.4, trial + 0.4)\n",
"\n",
" # Shade time when stimulus was on\n",
" axs[i, 0].axvspan(stim_on, stim_off, \n",
" alpha= i / 10 + .1, # Base intensity of shading (alpha) on intensity level\n",
" color='greenyellow')\n",
"\n",
" ## PSTH\n",
" # Shade time when stimulus was on\n",
" axs[i, 1].axvspan(stim_on, stim_off, \n",
" alpha= i / 10 + .1, \n",
" color='greenyellow')\n",
" \n",
" # Plot histogram\n",
" axs[i, 1].hist(dat['SpikeTime'], bins=range(0, num_tp, 1))\n",
" \n",
" # Set the x tickmarks to every 2 ms\n",
" axs[i, 1].set_xticks(range(0, num_tp + 1, 2))\n",
" \n",
" # find y max of current plot\n",
" cur_min, cur_max = axs[i, 1].get_ylim()\n",
" \n",
" # update y_max if necessary\n",
" if cur_max > y_max:\n",
" y_max = cur_max\n",
" \n",
"for a in int_levels:\n",
" axs[a, 1].set_ylim(0, y_max)\n",
" \n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the PSTHs more clearly show us that the low intensity levels elicited very few spikes, while the higher intensity levels elicited a lot more."
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Label the plot\n",
"\n",
"Finally, we can properly annotate (label) the graph for easy interpretation, adding things like axis labels and titles. We'll also make sure the axes of all the plots are the same:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# set the number of subplot rows based on number of intensity levels in data\n",
"# specify the figure dimensions since it will need to be a big figure\n",
"fig, axs = plt.subplots(len(int_levels), 2, figsize=[12, 12], sharex=True)\n",
"\n",
"y_max = 0 # Accumulator variable\n",
"\n",
"for i in int_levels:\n",
" ## Raster plot\n",
" # select just data for current intensity level\n",
" # this is convenient since we'll refer to this subset of data a few times below\n",
" dat = spikes[spikes['Intensity'] == i]\n",
"\n",
" # Draw the raster one trial at a time\n",
" for trial in trials:\n",
" # get spike times for this trial\n",
" spike_times = dat[dat['Trial'] == trial]['SpikeTime']\n",
" # Draw the raster\n",
" axs[i, 0].vlines(spike_times, \n",
" trial - 0.4, trial + 0.4)\n",
"\n",
" # Shade time when stimulus was on\n",
" axs[i, 0].axvspan(stim_on, stim_off, \n",
" alpha= i / 10 + .1, # Base intensity of shading (alpha) on intensity level\n",
" color='greenyellow')\n",
"\n",
" # Label the y axis with intensity level\n",
" axs[i, 0].set_ylabel('Intensity ' + str(i))\n",
" \n",
" # place title only above the first row of plots:\n",
" if i == 0:\n",
" axs[i, 0].set_title('Raster Plot for each intensity', fontsize=10)\n",
" \n",
"\n",
" ## PSTH\n",
" # Shade time when stimulus was on\n",
" axs[i, 1].axvspan(stim_on, stim_off, \n",
" alpha= i / 10 + .1, \n",
" color='greenyellow')\n",
" \n",
" # Plot histogram\n",
" axs[i, 1].hist(dat['SpikeTime'], bins=range(0, num_tp, 1))\n",
"\n",
" # Set the x tickmarks to every 2 ms\n",
" axs[i, 1].set_xticks(range(0, num_tp + 1, 2))\n",
" \n",
" # Label the y axis \n",
" axs[i, 1].set_ylabel('Num. Spikes')\n",
"\n",
" # find y max of current plot\n",
" cur_min, cur_max = axs[i, 1].get_ylim()\n",
" \n",
" # update y_max if necessary\n",
" if cur_max > y_max:\n",
" y_max = cur_max\n",
" \n",
" # place title only above the first row of plots:\n",
" if i == 0:\n",
" axs[i, 1].set_title('PSTH for each intensity', fontsize=10)\n",
" \n",
" # Place x label only below bottom row of plots: \n",
" if i == max(int_levels):\n",
" axs[i, 1].set_xlabel('Time (ms)')\n",
" axs[i, 0].set_xlabel('Time (ms)')\n",
"\n",
"# Having plotted all intensity levels, re-scale y axis to the max we found\n",
"# Also apply the same scale to all rasters\n",
"for a in int_levels:\n",
" axs[a, 0].set_ylim(0, num_trials)\n",
" axs[a, 1].set_ylim(0, y_max)\n",
" \n",
"# Add an overall figure title\n",
"fig.suptitle('Effects of 550 nm light intensity on spike rate of neuron')\n",
" \n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interpreting the data\n",
"\n",
"Now that we have a nice visualization, we can use it to make sense of the data. Take some time to look at the figure above and think about what it shows you, about the relationship between light intensity and the activity of the neuron, then read on below.\n",
"\n",
"It is clear from the figure that more intense light stimulation elicits a stronger response from the neuron. Furthermore, it doesn't look like there is a *linear* relationship between intensity and neuronal activity. If the relationship was linear, we would see a steady increase in spiking with increasing intensity level. Instead, it looks like the light elicits very little response at levels 0–2, a weak response at levels 3–5, and a stronger response at levels 6–9. So it may be that the neuron has a *threshold* level of light intensity, below which it doesn't respond, and above which it does respond. From levels 3 to 8, spiking intensity seems to increase with light intensity, however at level 9 the peak number of spikes is lower than for levels 7 or 8. \n",
"\n",
"This relationship also appears to involve both the intensity of the neuron's response (spike rate), and its timing. As intensity increases, especially from level 3 upwards, the time at which spiking activity is evident relative to the onset of the light stimulation seems to get earlier, as does the time at which peak spiking occurs. So it seems that the latency to first spike property we mentioned in the previous lesson is influenced by light intensity.\n",
"\n",
"Finally, the neuron seems. to show offset as well as onset effects. That is, at least at the highest intensity levels (7–9) there is a clear decrease in spiking towards the end of the stimulation period, followed by a brief increase in spiking after the light turns off. \n",
"\n",
"---"
]
}
],
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"language": "python",
"name": "python3"
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