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snoopy0328
2025-03-12 14:10:26 -04:00
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## HW3 Problem 3"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'numpy'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# Baseline parameters\u001b[39;00m\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'numpy'"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Baseline parameters\n",
"a = 2.0\n",
"beta = 0.5\n",
"tau = 0.5\n",
"scale = 1.0\n",
"max_time = 10.0\n",
"dt = 1e-3\n",
"num_sims = 2000\n",
"\n",
"# Vary drift rate\n",
"v_values = np.linspace(0.5, 1.5, 25)\n",
"mean_rt_upper = []\n",
"mean_rt_lower = []\n",
"\n",
"for v in v_values:\n",
" data = simulate_diffusion_n(num_sims, v, a, beta, tau, dt, scale, max_time)\n",
" mean_rt_upper.append(data[data[:, 1] == 1, 0].mean())\n",
" mean_rt_lower.append(data[data[:, 1] == 0, 0].mean())\n",
"\n",
"# Plot results\n",
"plt.figure(figsize=(8, 6))\n",
"plt.plot(v_values, mean_rt_upper, label='Upper Boundary (Correct)', color='maroon')\n",
"plt.plot(v_values, mean_rt_lower, label='Lower Boundary (Incorrect)', color='gray')\n",
"plt.xlabel('Drift Rate (v)')\n",
"plt.ylabel('Mean Response Time (s)')\n",
"plt.legend()\n",
"plt.title('Effect of Drift Rate on Mean RTs')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Vary boundary separation (a)\n",
"a_values = np.linspace(1.0, 3.0, 25)\n",
"mean_rt_upper = []\n",
"mean_rt_lower = []\n",
"std_rt_upper = []\n",
"std_rt_lower = []\n",
"\n",
"for a in a_values:\n",
" data = simulate_diffusion_n(num_sims, v, a, beta, tau, dt, scale, max_time)\n",
" mean_rt_upper.append(data[data[:, 1] == 1, 0].mean())\n",
" mean_rt_lower.append(data[data[:, 1] == 0, 0].mean())\n",
" std_rt_upper.append(data[data[:, 1] == 1, 0].std())\n",
" std_rt_lower.append(data[data[:, 1] == 0, 0].std())\n",
"\n",
"# Plot results\n",
"plt.figure(figsize=(12, 6))\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(a_values, mean_rt_upper, label='Upper Boundary (Correct)', color='maroon')\n",
"plt.plot(a_values, mean_rt_lower, label='Lower Boundary (Incorrect)', color='gray')\n",
"plt.xlabel('Boundary Separation (a)')\n",
"plt.ylabel('Mean Response Time (s)')\n",
"plt.legend()\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot(a_values, std_rt_upper, label='Upper Boundary (Correct)', color='maroon')\n",
"plt.plot(a_values, std_rt_lower, label='Lower Boundary (Incorrect)', color='gray')\n",
"plt.xlabel('Boundary Separation (a)')\n",
"plt.ylabel('Standard Deviation of RT (s)')\n",
"plt.legend()\n",
"\n",
"plt.suptitle('Effect of Boundary Separation on RT Distributions')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}