diff --git a/bindsnet/analysis/plotting.py b/bindsnet/analysis/plotting.py new file mode 100644 index 00000000..92c7cfae --- /dev/null +++ b/bindsnet/analysis/plotting.py @@ -0,0 +1,139 @@ +import torch +import numpy as np +import matplotlib.pyplot as plt +import sys +import numpy as np +from mpl_toolkits.axes_grid1 import make_axes_locatable + +plt.ion() + +def plot_input(image, inpt, ims=None, figsize=(12, 6)): + if not ims: + f, axes = plt.subplots(1, 2, figsize=figsize) + ims = axes[0].imshow(image, cmap='binary'), axes[1].imshow(inpt, cmap='binary') + axes[0].set_title('Current image') + axes[1].set_title('Poisson spiking representation') + f.tight_layout() + else: + ims[0].set_data(image) + ims[1].set_data(inpt) + + return ims + +def plot_spikes(data, ims=None, time=None, figsize=(12, 7)): + """ + Plot spikes for any group of neuron + + Inputs: + data (dict): Contains spiking data for groups of neurons of interest. + + ims (matplotlib.figure.Figure): Figure to plot on. Otherwise, a new + figure is created. + + time (tuple): Plot spiking activity of neurons between the given range + of time. + + Default is the entire simulation time. + + Ex: time = (40, 80) will plot spiking activity of + neurons from 40 ms to 80 ms. Plotting ticks are multiples + of 5. + + figsize (tuple): Figure size. + + Returns: + Nothing + """ + + n_subplots = len(data.keys()) + # Confirm that only 2 values for time were given + if time is not None: + assert(len(time) == 2) + assert(time[0] < time[1]) + + else: # Set it for entire duration + for key in data.keys(): + time = (0, data[key].shape[1]) + n = data[key].shape[0] # plot for a certain set of neurons? + break + + if not ims: + locs, ticks = [t for t in range(0, time[1]-time[0]+5, 5)], [t for t in range(time[0], time[1]+5, 5)] + if n_subplots == 1: # Plotting only one image + plt.figure(figsize=figsize) + for key in data.keys(): + ims = plt.imshow(data[key][:, time[0]:time[1]], cmap='binary') + plt.title('%s spikes from t = %1.2f ms to %1.2f ms'%(key, time[0], time[1])) + plt.xlabel('Time (ms)'); plt.ylabel('Neuron index') + + plt.xticks(locs,ticks) + + else: # Multiple subplots + f, axes = plt.subplots(n_subplots, 1, figsize=figsize) + plt.setp(axes, xticks=locs, xticklabels=ticks) + + # Plot each layer at a time + for plot_ind, layer_data in enumerate(data.items()): + ims = axes[plot_ind].imshow(layer_data[1][:, time[0]:time[1]], cmap='binary') + axes[plot_ind].set_title('%s spikes from t = %1.2f ms to %1.2f ms'%(layer_data[0], time[0], time[1])) + # axes[plot_ind].axis('off') + + f.tight_layout() + + else: #plotting figure given + assert(len(ims) == n_subplots) + for plot_ind, layer_data in enumerate(data.items()): + if time is None: + ims[plot_ind].set_data(layer_data[1]) + ims[plot_ind].set_title('%s spikes from t = %1.2f ms to %1.2f ms'%(layer_data[0], time[0], time[1])) + else:#plot for given time + ims[plot_ind].set_data(layer_data[1][time[0], time[1]]) + ims[plot_ind].set_title('%s spikes from t = %1.2f ms to %1.2f ms'%(layer_data[0], time[0], time[1])) + +def plot_weights(weights, assignments, wmax=1, ims=None, figsize=(10, 6)): + if not ims: + f, axes = plt.subplots(1, 2, figsize=figsize) + + color = plt.get_cmap('RdBu', 11) + ims = axes[0].imshow(weights, cmap='hot_r', vmin=0, vmax=wmax), axes[1].matshow(assignments, cmap=color, vmin=-1.5, vmax=9.5) + divs = make_axes_locatable(axes[0]), make_axes_locatable(axes[1]) + caxs = divs[0].append_axes("right", size="5%", pad=0.05), divs[1].append_axes("right", size="5%", pad=0.05) + + plt.colorbar(ims[0], cax=caxs[0]) + plt.colorbar(ims[0], cax=caxs[1], ticks=np.arange(-1, 10)) + f.tight_layout() + else: + ims[0].set_data(weights) + ims[1].set_data(assignments) + + return ims + + +def plot_performance(performances, ax=None, figsize=(6, 6)): + if not ax: + _, ax = plt.subplots(figsize=figsize) + else: + ax.clear() + + for scheme in performances: + ax.plot(range(len(performances[scheme])), [100 * p for p in performances[scheme]], label=scheme) + + ax.set_ylim([0, 100]) + ax.set_title('Estimated classification accuracy') + ax.set_xlabel('No. of examples') + ax.set_ylabel('Accuracy') + ax.legend() + + return ax + + +def plot_voltages(exc, inh, axes=None, figsize=(8, 8)): + if axes is None: + _, axes = plt.subplots(2, 1, figsize=figsize) + axes[0].set_title('Excitatory voltages') + axes[1].set_title('Inhibitory voltages') + + axes[0].clear(); axes[1].clear() + axes[0].plot(exc), axes[1].plot(inh) + + return axes \ No newline at end of file diff --git a/bindsnet/network/nodes.py b/bindsnet/network/nodes.py index 98479fb1..5b1c89c8 100644 --- a/bindsnet/network/nodes.py +++ b/bindsnet/network/nodes.py @@ -2,7 +2,6 @@ from abc import ABC, abstractmethod - class Nodes(ABC): ''' Abstract base class for groups of neurons. @@ -277,4 +276,4 @@ def reset(self): self.refrac_count = torch.zeros(n) # Refractory period counters. if self.traces: - self.x = torch.zeros_like(torch.Tensor(n)) # Firing traces. \ No newline at end of file + self.x = torch.zeros_like(torch.Tensor(n)) # Firing traces. diff --git a/examples/notebooks/Simple test network.ipynb b/examples/notebooks/Simple test network.ipynb index 559c301b..b6ba533d 100644 --- a/examples/notebooks/Simple test network.ipynb +++ b/examples/notebooks/Simple test network.ipynb @@ -20,12 +20,14 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "sys.path.append(os.path.abspath(os.path.join('..', '..', 'bindsnet')))\n", - "sys.path.append(os.path.abspath(os.path.join('..', '..', 'bindsnet', 'network')))\n", + "sys.path.append(os.path.abspath(os.path.join('..', 'bindsnet')))\n", + "sys.path.append(os.path.abspath(os.path.join('..', 'bindsnet', 'network')))\n", + "sys.path.append(os.path.abspath(os.path.join('..', 'bindsnet', 'analysis')))\n", "\n", "from network import Network\n", "from connections import Connection\n", "from nodes import McCullochPitts, Input\n", + "import plotting as pl\n", "\n", "%matplotlib inline" ] @@ -43,7 +45,8 @@ "metadata": {}, "outputs": [], "source": [ - "network = Network(dt=1.0) # Initialize network" + "network = Network(dt=1.0) # Init network with simulation timestep 1\n", + "dt = 1.0" ] }, { @@ -52,7 +55,7 @@ "source": [ "That was easy!\n", "\n", - "### Add layer of McCulloch-Pitts (MP) neurons" + "### Add layer of McCulluch-Pitts (MP) neurons" ] }, { @@ -104,7 +107,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'A': , 'X': }\n", + "{'A': , 'X': }\n", "\n", "No. of neurons in layer A: 25\n", "Spike traces in layer A? False\n", @@ -147,7 +150,7 @@ "metadata": {}, "outputs": [], "source": [ - "w = torch.rand(input_layer.n, mp_layer.n) # Create random uniform weight matrix between layers.\n", + "w = torch.rand(input_layer.n, input_layer.n) # Create random uniform weight matrix between layers.\n", "\n", "# Create connection object from input to MP layer with specified weights.\n", "connection = Connection(input_layer, mp_layer, update_rule=None, w=w)\n", @@ -161,9 +164,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -214,7 +217,8 @@ "metadata": {}, "outputs": [], "source": [ - "output = network.run(inpts={'X' : inpt}, time=100)" + "simulation_time = 100\n", + "output = network.run(inpts={'X' : inpt}, time=simulation_time)" ] }, { @@ -226,16 +230,14 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -296,6 +298,89 @@ "plt.title('Summed intensity of McCulloch-Pitts spikes')\n", "plt.xticks(()); plt.yticks(());" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting all neurons' spiking activity" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot_spikes(output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot all layer's spiking activity between a specific range of time" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3dizbKyKuiIjv1l/3HGyZkiRpUPo5TLASOG7WstOAL2XmE4Ev1dclSVILzRsGMvPLwD2zFp8AXFh/fyFwYsN1SZKkIVnsCYT7ZuY6gPrrPs2VJEmShmngnyaIiBURMRURU9PT04O+O0mStECLDQPrI2I/gPrrhl4rZua5mTmZmZMTExOLvDtJkjQoiw0DnwFOqb8/Bfh0M+VIkqRh6+ejhRcDXwUOjYg1EfE64P3ACyLiu8AL6uuSJKmF5p10KDNf2eOmYxuuRdKYchIXjQv3xcFwOmJJkgpnGJAkqXCGAUmSCmcYkCSpcIYBSZIKZxiQJKlwhgFJkgpnGJAkqXCGAUmSCjfvDIRSNxEx7zrOFKZB62c/bHpb7tcL03S//NszGI4MSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFc9IhLYqTeoxOv71vckKecdXkfthkv0r5/ejncTY9mVMpvR02RwYkSSqcYUCSpMIZBiRJKpxhQJKkwhkGJEkqnGFAkqTCGQYkSSqcYUCSpMIZBiRJKpwzEOLMY2qXEmYWbFo/PRvFbIZt/3sxin1x2M9lKRwZkCSpcIYBSZIKZxiQJKlwhgFJkgpnGJAkqXCGAUmSCmcYkCSpcIYBSZIK56RDC+BEFps48cfo9NtXJydaGPu1cP3si/a1HRwZkCSpcPOGgYi4ICI2RMSNHcvOiIi1EXF9fXnRYMuUJEmD0s/IwErguC7LP5iZR9aXzzVbliRJGpZ5w0Bmfhm4Zwi1SJKkEVjKOQNviogb6sMIe/ZaKSJWRMRURExNT08v4e4kSdIgLDYMnAMcAhwJrAM+0GvFzDw3Myczc3JiYmKRdydJkgZlUWEgM9dn5qOZ+RhwHvDMZsuSJEnDsqgwEBH7dVx9KXBjr3UlSdJ4m3fSoYi4GDgG2Dsi1gDvAY6JiCOBBG4D3jDAGiVJ0gDNGwYy85VdFn90ALWMjDPlLZw9G51+Z3TzOVqYJvtVyqx7o3ic7teD4QyEkiQVzjAgSVLhDAOSJBXOMCBJUuEMA5IkFc4wIElS4QwDkiQVzjAgSVLhDAOSJBVu3hkIJamEWQ9LeIxN66cXpczG2HaODEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhXPSIZqdFMMJSbQ1anK/HtfJffzdXTgnFNp6ODIgSVLhDAOSJBXOMCBJUuEMA5IkFc4wIElS4QwDkiQVzjAgSVLhDAOSJBXOMCBJUuGcgXABnKFsk35mHrNfo+VzNDr2VW3jyIAkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDgnHaL/CUKcxGWTUh6nmjeu+04/v99NG9de9Kuf+pvuq3+HB8ORAUmSCjdvGIiIX4iIqyLipohYHRF/WC/fKyKuiIjv1l/3HHy5kiSpaf2MDDwCvC0zDwOeDbwxIg4HTgO+lJlPBL5UX5ckSS0zbxjIzHWZ+fX6+weAm4D9gROAC+vVLgROHFSRkiRpcBZ0zkBELAeeBlwH7JuZ66AKDMA+PX5mRURMRcTU9PT00qqVJEmN6zsMRMSuwD8Cb87M+/v9ucw8NzMnM3NyYmJiMTVKkqQB6isMRMQyqiDwfzLzk/Xi9RGxX337fsCGwZQoSZIGqZ9PEwTwUeCmzPyrjps+A5xSf38K8Onmy5MkSYPWz6RDRwOvBr4VEdfXy04H3g9cGhGvA34AnDSYEiVJ0iDNGwYy8xqg15RPxzZbznhzVqtNnAVM6s19fxN70Q7OQChJUuEMA5IkFc4wIElS4QwDkiQVzjAgSVLhDAOSJBXOMCBJUuEMA5IkFa6fGQilLTiRyMI02S97P/76mZQLfC4Xw54NhiMDkiQVzjAgSVLhDAOSJBXOMCBJUuEMA5IkFc4wIElS4QwDkiQVzjAgSVLhDAOSJBXOGQilIWhyRjpntxuMfvvVb/9VaXp/7Wd77vsL58iAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4Jx3Sojjxx/jzORoMe7aJEzBtPRwZkCSpcIYBSZIKZxiQJKlwhgFJkgpnGJAkqXCGAUmSCmcYkCSpcIYBSZIKZxiQJKlwMczZtCJiGrh91uK9gY1DK6JZba4drH/UrH902lw7WP+otan+gzJzYr6VhhoGuhYQMZWZkyMtYpHaXDtY/6hZ/+i0uXaw/lFre/3deJhAkqTCGQYkSSrcOISBc0ddwBK0uXaw/lGz/tFpc+1g/aPW9vq3MPJzBiRJ0miNw8iAJEkaIcOAJEmFG1oYiIgdI+I/IuKbEbE6It5bL//FiLguIr4bEZ+IiO2HVdNCzFH/yoj4fkRcX1+OHHWtvUTEthHxjYj4bH29Fb2f0aX+NvX+toj4Vl3nVL1sr4i4ou7/FRGx56jr7KVH/WdExNqO/r9o1HX2EhF7RMRlEXFzRNwUEb/Slv73qL0VvY+IQztqvD4i7o+IN7eo973qb0X/F2Jo5wxERAC7ZOaDEbEMuAb4Q+CtwCcz85KI+Ajwzcw8ZyhFLcAc9Z8KfDYzLxtpgX2IiLcCk8DumfmbEXEpLej9jC71r6Q9vb8NmMzMjR3L/hy4JzPfHxGnAXtm5jtGVeNcetR/BvBgZv7lqOrqV0RcCPxbZp5fh96dgdNpQf971P5mWtL7GRGxLbAWeBbwRlrQ+06z6n8tLev/fIY2MpCVB+ury+pLAr8OzPwxvxA4cVg1LcQc9bdCRBwAvBg4v74etKT3sGX9W4kTqPoOY97/NouI3YHnAR8FyMyfZeZ9tKD/c9TeRscC38vM22lB77vorH+rM9RzBuph3uuBDcAVwPeA+zLzkXqVNcD+w6xpIWbXn5nX1TedFRE3RMQHI2KHEZY4l7OBtwOP1dcfT4t6z5b1z2hD76EKjl+MiFURsaJetm9mrgOov+4zsurm161+gDfV/b9gXId6gYOBaeBj9WGm8yNiF9rR/161Qzt63+kVwMX1923o/Wyd9UP7+j+noYaBzHw0M48EDgCeCRzWbbVh1rQQs+uPiKcA7wSeBDwD2AsYu6GuiPhNYENmrupc3GXVsex9j/qhBb3vcHRmPh04HnhjRDxv1AUtULf6zwEOAY4E1gEfGGF9c9kOeDpwTmY+DfgRcNpoS+pbr9rb0nsA6sMbLwH+76hrWYwu9beq//0YyacJ6mGuq4FnA3tExHb1TQcAd46ipoXoqP+4zFxXH0L4KfAxqpAzbo4GXlIf972E6vDA2bSn91vUHxH/0JLeA5CZd9ZfNwCXU9W6PiL2A6i/bhhdhXPrVn9mrq8D8mPAeYxv/9cAazpG8i6jeoFtQ/+71t6i3s84Hvh6Zq6vr7eh9502q7+F/Z/XMD9NMBERe9Tf7wQ8H7gJuAp4eb3aKcCnh1XTQvSo/+aOHTqojnvdOLoqu8vMd2bmAZm5nGqo68rMPJmW9L5H/a9qQ+8BImKXiNht5nvghVS1foaq7zDG/e9V/0z/ay9lTPufmXcBd0TEofWiY4Fv04L+96q9Lb3v8Eo2H2If+97Psln9Lez/vIb5aYKnUp0osi1VCLk0M98XEQdTvdvbC/gG8Kr6nd5YmaP+K4EJqmH364FTO040HDsRcQzwR/XZ+K3ofadZ9bei93WfL6+vbgdclJlnRcTjgUuBA4EfACdl5j0jKrOnOer/e6ph0gRuA94wcxx43ET1sdPzge2BW6nOBt+GdvS/W+1/TXt6vzNwB3BwZv6wXtaKfR961t+afb9fTkcsSVLhnIFQkqTCGQYkSSqcYUCSpMIZBiRJKpxhQJKkwhkGJEkqnGFAkqTCGQYkSSqcYUCSpMIZBiRJKpxhQJKkwhkGJEkqnGFArRMRn4+IU+rvXxMR1wz4/vaNiC9HxAMR8YFB3pckjYJhQI2IiF0j4raI+J2OZbtFxA8i4uVN3ldmHp+ZFza5zXmsADYCu2fm24Z4vwBExMqIOHMA2907Iq6NiLsj4r6I+GpEHD1rnbdExF0R8cOIuCAidphje8dGxM0R8eOIuCoiDuq4bYf65++vt/fWph/PfJYaHKNyZkSsrftxdUQ8ueP2BT3GuXobEcvrHv647unzF1u31A/DgBqRmQ9SvWh+KCIm6sV/Dkxl5mWjq6wRBwHfzh7/7zsithtyPU15EPg9YALYE/gz4J9mHk9E/AZwGnAssBw4GHhvtw1FxN7AJ4H/DewFTAGf6FjlDOCJVL38NeDtEXFc0w9owE6i6tdzqR7jV4G/77j9DPp8jH309mLgG8Djgf8FXNbxeyU1LzO9eGnsAqyk+kN2DHA3sN8c674GuBV4APg+cHLH8muBvwF+CNwMHNvxc1cDv9+x7jUdt/0FcA3wuPr67wE3AfcCXwAOqpcH8EFgQ30fNwBP6fF4HgZ+RvXi+XyqP/qXAf8A3A/8PrADcDZwZ305G9ih3sYxwBrg7fX9rQNOBF4E/CdwD3B6jx6tmHX//zSg520b4LeABPapl10E/EnHOscCd81R51c6ru8C/AR4Un19LfDCjtv/GLhkjv3i2vr5ua/eR/5rvfyOuoendKz/IuDb9X60FvijLts8DHgIeLTu43318scBHwemgduBdwHb9KjrHcClHdefDDzUcX0hj7Fnb4FfBn4K7NZx+78Bp87xO/d3wOfrx3Yt8F/qffBeqt+fp816HGvrfn2Hjt8tL+VeRl6Al63rQvUOcx3VsPpr51hvF6oX0kPr6/sBT66/fw3wCPAWYBnw21Qv2HvVt1/NrDBQv5idR/WCv3N924nALfULwXb1H/qv1Lf9BrAK2IMqGBxGj+BS/7E9s+P6GVQv0CfW97sT8D7g34F9qN5pfwX443r9Y+rH8+768by+fvG5CNht5kUFOLif+++xzg1UL5zdLn/Xx8/+jCoInNex/JvAb3dc37te5/FdtvEh4JxZy24EXlbvEwns23Hby4Fv9ahn5vl/LbAtcCbwA+DDVKHrhfUL2a71+uuA53bsf0+fY7vXzFr2ceDT9fOwnCqcva7Hzx8EfJ3qxXoZ1cjXpzrudyGPsWdvgZcCN81a/2+Bv5lj/9gIHAXsCFxJFa5/t6N/V9XrHkoVqJ5QX18OHDLqvxteRn/xMIEalZn3AquBnamGjefyGPCUiNgpM9dl5uqO2zYAZ2fmw5n5Cap3MC/usZ1lVKMRewG/lZk/rpe/AfjTzLwpMx8B/gQ4sj6W/TDVC8CTgKjXWbeAh/rVzPxUZj6WmT8BTgbel5kbMnOaasj31R3rPwyclZkPA5dQ/fH/UGY+UD/u1cBTF3D/m8nMp2bmHj0ufzDfzwK7A79DFaxm7EoVwmbMfL9bl83MXndm/d3q22DLbXXbzozvZ+bHMvNRqsMNv0DV359m5hepwssv1es+DBweEbtn5r2Z+fU5tvtzEbEtVdB8Z/083AZ8gM2ft07rqN6hf4dq1OMkqsDKIh7jXL2dq5e9XJ6ZqzLzIeByqhGLj3f072n1eo9SBarDI2JZZt6Wmd+bY7sqhGFAjYqIV1G92/gXqmPQXWXmj6j+EJ8KrIuI/xcRT+pYZW1mdh6jvx14Qo/N/RJwAvDezPxZx/KDqM5huC8i7qMajg9g/8y8kurd1oeB9RFxbkTsvoCHeses60+oa+xV7931H2aoXkgA1nfc/hM2vaAMXWY+lJkXA6dFxBH14gepQsKMme8f6LKJ2evOrP9AfRtsua1u25kxuzdkZq9+vYzqUMHtEfGvEfErc2y3097A9mz5vO3fY/33AM+gCiY7UgW+KyNiZxb+GOfq7Vy97GV2b7r2KjNvAd5MNbq1ISIuiYhev1cqiGFAjYmIfaiO876e6l35f4uI5/VaPzO/kJkvoDpEcDPVMP+M/SMiOq4fSHUsvpubqIaUPx8Rh3YsvwN4w6x3yTtl5lfq+//rzDyKapj+l4H/uYCHO/tkwjupwkc/9S5U1xMXO0XE6oh4sMflIwu4r2VUJ7NBNVpxRMdtRwDrM/PuLj+32boRsQtwCLC6Hi1a12Vbq2lAZn4tM0+gOkTzKeDSXqvOur6RalRh9vO2tsfPHwF8IjPXZOYjmbmS6vDA4Yt4jHPKmqI+AAASp0lEQVT1djVwcETsNuv2pvp1UWY+h+pxJ3OEdpXDMKAm/S3VMdSr6iH3twPndfs4Wv3Z/ZfULxo/pXo39GjHKvsA/yMilkXESVTH9D/X647rd7WnA/8SEYfUiz8CvHPm418R8bh6W0TEMyLiWRGxDPgRm04uW6yLgXdFxER9Zv27qU4wbMJ6Nr1Ad5WZT87MXXtcTu32MxHx7Ih4TkRsHxE7RcQ7gH2B6+pVPg68LiIOj4g9qc65WNmjhMupDvm8LCJ2pHr8N2TmzR3beldE7FmPAL1+jm31ra795Ih4XH0I5n56P4/rgQMiYnuAeqTmUuCs+mOwBwFvpffz9jXgpHrf3SYiXk0Vnm5ZxGPs2dvM/E/geuA9EbFjRLyU6hDSP/bTk7lExKER8ev17+RDVKMGS9nvtZUwDKgREXEi8Bw63l1n5vlUZ9G/u8uPbAO8jerd8z3ArwKdx7avo/qY1kbgLODlPd6R/lxWcw+8j2rodnlmXk71rueSiLif6oS24+vVd6caibiXamj4buAvF/CQZzuT6uN0NwDfojrRrKm5AT5KdYz3voj4VEPbhOrY8YepHvtaqqH2F2fmnQCZ+c9UJ8ldRdWj26mGyoGfj0acXK87TTVcfxZVT58FvKLjvt4DfK/exr8Cf1FvvwmvBm6rn+NTgVf1WO9KqnfXd0XExnrZf6cKg7dSnS9xEXBBj5//M6oT/66nOjHzLcDLMvO++vaejzEiDqxHaQ6E+XtL1btJql6+n2r/n+6rG3Pbod7eRuAuqtB9egPbVcvF5odlpdGLiNdQfVrgOaOuRZJK4MiAJEmFMwxIklQ4DxNIklQ4RwYkSSrcUP/Byt57753Lly8f5l32ZdWqVX2td9RRRw24EkkLMYrf3X7us+1/K9r+N7Ht9Tdp1apVGzNz3n9yNdTDBJOTkzk1NTW0++vX5nPb9OYhFWm8jOJ3t5/7bPvfirb/TWx7/U2KiFWZOTnfeks6TBARx0XEdyLilog4bSnbkiRJo7HoMFD/k48PU03icjjwyog4vKnCJEnScCxlZOCZwC2ZeWv9z2EuofpnMZIkqUWWEgb2Z/P/3LaGLv/tKyJWRMRURExNTzcxm6YkSWrSUsJAtzM0tjgbIzPPzczJzJycmJj3hEZJkjRkSwkDa6j+r/eMA2juX7ZKkqQhWUoY+BrwxIj4xfpfgr4C+EwzZUmSpGFZ9KRDmflIRLwJ+AKwLXBBZq5urDJJkjQUQ510KCL6ujMnCBkdJ+vQUozr79u41iUN2lAmHZIkSe1nGJAkqXCGAUmSCmcYkCSpcIYBSZIKZxiQJKlwhgFJkgpnGJAkqXCLnoFwMY466iimpqaGeZetnkhkFBMAtblfpRjniaH6uc8m6x/XbUlt48iAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYWLYc6mFRGN3VmTdTvzmMZFP/ui++Fo+RxtMope2P+FiYhVmTk533qODEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhdtumHd21FFHMTU1Ncy77EvbJ6hw0qSth8/R+PM52mQUvbD/lX7/7vfLkQFJkgpnGJAkqXCGAUmSCmcYkCSpcIYBSZIKZxiQJKlwhgFJkgpnGJAkqXCGAUmSCjfUGQg1GKOYkauf2a+cKUyS2mFJYSAibgMeAB4FHsnMySaKkiRJw9PEyMCvZebGBrYjSZJGwHMGJEkq3FLDQAJfjIhVEbGiiYIkSdJwLfUwwdGZeWdE7ANcERE3Z+aXO1eoQ8IKgAMPPHCJdydJkpq2pJGBzLyz/roBuBx4Zpd1zs3MycycnJiYWMrdSZKkAVh0GIiIXSJit5nvgRcCNzZVmCRJGo6lHCbYF7i8/rz5dsBFmfnPjVQlSZKGZtFhIDNvBY5osBa1iBMKSVuvfiYV69e4TooG7f471m/t/fbCjxZKklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVLil/tdCSRqIfmZOa/MMcluDce3/uNY1zhwZkCSpcIYBSZIKZxiQJKlwhgFJkgpnGJAkqXCGAUmSCmcYkCSpcIYBSZIKt9VPOuTEJdLw+Pu2dfA5Ko8jA5IkFc4wIElS4QwDkiQVzjAgSVLhDAOSJBXOMCBJUuEMA5IkFc4wIElS4QwDkiQVbqufgdCZtNSNM+UNRpM9a3Jb/TzfTXLfUds4MiBJUuEMA5IkFc4wIElS4QwDkiQVzjAgSVLhDAOSJBXOMCBJUuEMA5IkFc4wIElS4bb6GQhVln5nmnOGuPHX5CyRPt/a2jQ9q+a8IwMRcUFEbIiIGzuW7RURV0TEd+uvezZalSRJGpp+DhOsBI6btew04EuZ+UTgS/V1SZLUQvOGgcz8MnDPrMUnABfW318InNhwXZIkaUgWewLhvpm5DqD+uk+vFSNiRURMRcTU9PT0Iu9OkiQNysA/TZCZ52bmZGZOTkxMDPruJEnSAi02DKyPiP0A6q8bmitJkiQN02LDwGeAU+rvTwE+3Uw5kiRp2Pr5aOHFwFeBQyNiTUS8Dng/8IKI+C7wgvq6JElqoXknHcrMV/a46diGa5GWzMllth5NPpdNT9AyH/dDLUWTE271u+87HbEkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhZt3BkKVpd/ZqoY9w9q41gXNzhbW1P01fZ9tZy/G3yj262H/7vZrFPfpyIAkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVLjWTjrkxCuDMa79Gte6YPi1jXMvhs2/A1uPUib3GVeODEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUuNbOQNjvzFH9zFDmLFSD4exwgzGKvo7rc+m+M1ol/H0d132/aY4MSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFa+2kQ/3qZyKIcZ3Epe2TWLS9/n4N+7kcRV9LeS6l2UrZ9x0ZkCSpcPOGgYi4ICI2RMSNHcvOiIi1EXF9fXnRYMuUJEmD0s/IwErguC7LP5iZR9aXzzVbliRJGpZ5w0Bmfhm4Zwi1SJKkEVjKOQNviogb6sMIe/ZaKSJWRMRURExNT08v4e4kSdIgLDYMnAMcAhwJrAM+0GvFzDw3Myczc3JiYmKRdydJkgZlUWEgM9dn5qOZ+RhwHvDMZsuSJEnDsqgwEBH7dVx9KXBjr3UlSdJ4m3fSoYi4GDgG2Dsi1gDvAY6JiCOBBG4D3jDAGiVJ0gDNGwYy85VdFn90ALWMjDO6aSnGdWbKEma5lLZG/c6K2yRnIJQkqXCGAUmSCmcYkCSpcIYBSZIKZxiQJKlwhgFJkgpnGJAkqXCGAUmSCmcYkCSpcPPOQChpbqOYLawfzi5Yln73w372iya3pYUb9qym4MiAJEnFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4Jx2SlsiJV7YOTU4eNYp9osn7HMWkN/3y963SdF8dGZAkqXCGAUmSCmcYkCSpcIYBSZIKZxiQJKlwhgFJkgpnGJAkqXCGAUmSCmcYkCSpcM5AuAD9zPjk7Fib9DtDVj89a3JbTWvzfjHOfe1H22cNLIF9HYx++9rv74gjA5IkFc4wIElS4QwDkiQVzjAgSVLhDAOSJBXOMCBJUuEMA5IkFc4wIElS4Zx0aAHaPjlOP5qsv8nHOK79gvGubT5trn0hSnmc0mI5MiBJUuHmDQMR8QsRcVVE3BQRqyPiD+vle0XEFRHx3frrnoMvV5IkNa2fkYFHgLdl5mHAs4E3RsThwGnAlzLzicCX6uuSJKll5g0DmbkuM79ef/8AcBOwP3ACcGG92oXAiYMqUpIkDc6CzhmIiOXA04DrgH0zcx1UgQHYp8fPrIiIqYiYmp6eXlq1kiSpcX2HgYjYFfhH4M2ZeX+/P5eZ52bmZGZOTkxMLKZGSZI0QH2FgYhYRhUE/k9mfrJevD4i9qtv3w/YMJgSJUnSIPXzaYIAPgrclJl/1XHTZ4BT6u9PAT7dfHmSJGnQ+pl06Gjg1cC3IuL6etnpwPuBSyPidcAPgJMGU6IkSRqkecNAZl4D9JqW7thmy2m/ts901vb6tTD9zjjZL/efyihmInX2Uy2FMxBKklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYXrZwZCSUPS5CRA/UzO0vYJXMa1/n7rGvbzLfXiyIAkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhXMGwob1O6OYs4VtPfp5zpt8vpvcVtP767B7Ma7a/ndgFPWPay/61fZ935EBSZIKZxiQJKlwhgFJkgpnGJAkqXCGAUmSCmcYkCSpcIYBSZIKZxiQJKlwTjrUsHGeVEKD0eaJV5q+P/f/Stv7MM71j+vkPv3c5zhPRuXIgCRJhTMMSJJUOMOAJEmFMwxIklQ4w4AkSYUzDEiSVDjDgCRJhTMMSJJUOMOAJEmFi2HOdBQR08DtsxbvDWwcWhHNanPtYP2jZv2j0+bawfpHrU31H5SZE/OtNNQw0LWAiKnMnBxpEYvU5trB+kfN+kenzbWD9Y9a2+vvxsMEkiQVzjAgSVLhxiEMnDvqApagzbWD9Y+a9Y9Om2sH6x+1tte/hZGfMyBJkkZrHEYGJEnSCBkGJEkq3NDCQETsGBH/ERHfjIjVEfHeevkvRsR1EfHdiPhERGw/rJoWYo76V0bE9yPi+vpy5Khr7SUito2Ib0TEZ+vrrej9jC71t6n3t0XEt+o6p+ple0XEFXX/r4iIPUddZy896j8jItZ29P9Fo66zl4jYIyIui4ibI+KmiPiVtvS/R+2t6H1EHNpR4/URcX9EvLlFve9Vfyv6vxBDO2cgIgLYJTMfjIhlwDXAHwJvBT6ZmZdExEeAb2bmOUMpagHmqP9U4LOZedlIC+xDRLwVmAR2z8zfjIhLaUHvZ3SpfyXt6f1twGRmbuxY9ufAPZn5/og4DdgzM98xqhrn0qP+M4AHM/MvR1VXvyLiQuDfMvP8OvTuDJxOC/rfo/Y305Lez4iIbYG1wLOAN9KC3neaVf9raVn/5zO0kYGsPFhfXVZfEvh1YOaP+YXAicOqaSHmqL8VIuIA4MXA+fX1oCW9hy3r30qcQNV3GPP+t1lE7A48D/goQGb+LDPvowX9n6P2NjoW+F5m3k4Let9FZ/1bnaGeM1AP814PbACuAL4H3JeZj9SrrAH2H2ZNCzG7/sy8rr7prIi4ISI+GBE7jLDEuZwNvB14rL7+eFrUe7asf0Ybeg9VcPxiRKyKiBX1sn0zcx1A/XWfkVU3v271A7yp7v8F4zrUCxwMTAMfqw8znR8Ru9CO/veqHdrR+06vAC6uv29D72frrB/a1/85DTUMZOajmXkkcADwTOCwbqsNs6aFmF1/RDwFeCfwJOAZwF7A2A11RcRvAhsyc1Xn4i6rjmXve9QPLeh9h6Mz8+nA8cAbI+J5oy5ogbrVfw5wCHAksA74wAjrm8t2wNOBczLzacCPgNNGW1LfetXelt4DUB/eeAnwf0ddy2J0qb9V/e/HSD5NUA9zXQ08G9gjIrarbzoAuHMUNS1ER/3HZea6+hDCT4GPUYWccXM08JL6uO8lVIcHzqY9vd+i/oj4h5b0HoDMvLP+ugG4nKrW9RGxH0D9dcPoKpxbt/ozc30dkB8DzmN8+78GWNMxkncZ1QtsG/rftfYW9X7G8cDXM3N9fb0Nve+0Wf0t7P+8hvlpgomI2KP+fifg+cBNwFXAy+vVTgE+PayaFqJH/Td37NBBddzrxtFV2V1mvjMzD8jM5VRDXVdm5sm0pPc96n9VG3oPEBG7RMRuM98DL6Sq9TNUfYcx7n+v+mf6X3spY9r/zLwLuCMiDq0XHQt8mxb0v1ftbel9h1ey+RD72Pd+ls3qb2H/5zXMTxM8lepEkW2pQsilmfm+iDiY6t3eXsA3gFfV7/TGyhz1XwlMUA27Xw+c2nGi4diJiGOAP6rPxm9F7zvNqr8Vva/7fHl9dTvgosw8KyIeD1wKHAj8ADgpM+8ZUZk9zVH/31MNkyZwG/CGmePA4yaqj52eD2wP3Ep1Nvg2tKP/3Wr/a9rT+52BO4CDM/OH9bJW7PvQs/7W7Pv9cjpiSZIK5wyEkiQVzjAgSVLhDAOSJBXOMCBJUuEMA5IkFc4wIElS4QwDkiQV7v8DwleS2GRVstsAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot_spikes(output, time=(30, 80), figsize=(15, 8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot input layer's spiking activity for a certain amount of time" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.plot_spikes(data={'Input': output['X'].numpy()}, time=(50, 100))" + ] } ], "metadata": { @@ -315,7 +400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.3" } }, "nbformat": 4,