hyena first try, trains
This commit is contained in:
martin committed
commit
ffd2d2f1a7
6 files changed
+1792
No files matched your search
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"cells": [],
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,176 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "5739f3c3-d71e-41f4-9408-3983e1fe7be2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0. , 0.05205668],\n",
|
||||
" [ 0.01257862, -0.05982817],\n",
|
||||
" [ 0.02515525, 0.17728985],\n",
|
||||
" [ 0.03772789, 0.08132162],\n",
|
||||
" [ 0.05029457, -0.20707477]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"def generate_sine_with_noise(n_points, frequency, phase, amplitude, noise_sd):\n",
|
||||
" # Generate an array of points from 0 to 2*pi\n",
|
||||
" x = np.linspace(0, 2*np.pi, n_points)\n",
|
||||
" \n",
|
||||
" # Generate the sine wave\n",
|
||||
" sine_wave = amplitude * np.sin(frequency * x + phase)\n",
|
||||
" \n",
|
||||
" # Generate Gaussian noise\n",
|
||||
" noise = np.random.normal(scale=noise_sd, size=n_points)\n",
|
||||
" \n",
|
||||
" # Add the noise to the sine wave\n",
|
||||
" sine_wave_noise = sine_wave + noise\n",
|
||||
" \n",
|
||||
" # Stack the sine wave and the noisy sine wave into a 2D array\n",
|
||||
" output = np.column_stack((sine_wave, sine_wave_noise))\n",
|
||||
" \n",
|
||||
" return output\n",
|
||||
"\n",
|
||||
"# Test the function\n",
|
||||
"output = generate_sine_with_noise(1000, 2, 0, 1, 0.1)\n",
|
||||
"output[:5] # display the first 5 rows\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "a5da0eae-330e-4391-b8a9-310bf9cb76be",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Unpack the sine wave and the noisy sine wave\n",
|
||||
"sine_wave, sine_wave_noise = output.T\n",
|
||||
"\n",
|
||||
"# Generate the x values\n",
|
||||
"x = np.linspace(0, 2*np.pi, len(sine_wave))\n",
|
||||
"\n",
|
||||
"# Create a figure and axis\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"\n",
|
||||
"# Plot the sine wave\n",
|
||||
"ax.plot(x, sine_wave, label='Sine wave')\n",
|
||||
"\n",
|
||||
"# Plot the noisy sine wave\n",
|
||||
"ax.plot(x, sine_wave_noise, label='Noisy sine wave', alpha=0.7)\n",
|
||||
"\n",
|
||||
"# Add a legend\n",
|
||||
"ax.legend()\n",
|
||||
"\n",
|
||||
"# Show the plot\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "b847578d-a39c-4f3a-b44c-5ad14a00629b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAfGCAYAAADhp2bOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXwcdf348dfsvZv7aJKmSe+WtlBaSrmPlsuCiKCAgKiIwtevys8D/aqooKiIqIgo3gqogCAioHhwVMvVKvSiLW3pQc/czZ3N3ju/P2Znd2Z3Ntm2OTbJ+/l45LG7c+xO2iQz73m/P++PoqqqihBCCCGEEEIIIYaFbbQPQAghhBBCCCGEGM8k8BZCCCGEEEIIIYaRBN5CCCGEEEIIIcQwksBbCCGEEEIIIYQYRhJ4CyGEEEIIIYQQw0gCbyGEEEIIIYQQYhhJ4C2EEEIIIYQQQgwjCbyFEEIIIYQQQohh5BjtAxhq8XicxsZGioqKUBRltA9HCCGEEEIIIcQ4pKoqvb291NbWYrMNnNMed4F3Y2Mj9fX1o30YQgghhBBCCCEmgAMHDlBXVzfgNuMu8C4qKgK0b764uHiUj0YIIYQQQgghxHjU09NDfX19MgYdyLgLvPXy8uLiYgm8hRBCCCGEEEIMq1yGOEtzNSGEEEIIIYQQYhhJ4C2EEEIIIYQQQgwjCbyFEEIIIYQQQohhNO7GeAshhBBCCCHGl1gsRiQSGe3DEBOM0+nEbrcPyXtJ4C2EEEIIIYTIS6qq0tzcTFdX12gfipigSktLqampyamB2kAk8BZCCCGEEELkJT3orqqqwufzHXXwI0SuVFWlv7+f1tZWACZPnnxU7yeBtxBCCCGEECLvxGKxZNBdUVEx2ocjJiCv1wtAa2srVVVVR1V2Ls3VhBBCCCGEEHlHH9Pt8/lG+UjERKb//B1tjwEJvIUQQgghhBB5S8rLxWgaqp8/CbyFEEIIIYQQQohhJIG3EEIIIYQQQowwRVF46qmnRvswxAiRwFsIIYQQQgghhlBbWxsf//jHmTp1Km63m5qaGlasWMGrr76a3KapqYmLLrpoFI9SjCTpai6EEEIIIYQQQ+jyyy8nHA7z29/+lpkzZ9LS0sLKlStpb29PblNTUzOKRyhGmmS8hRBCCCGEEGKIdHV18fLLL3PXXXdxzjnnMG3aNE4++WRuueUW3v3udye3M5aa7927F0VR+POf/8w555yDz+dj0aJFrFmzxvTer7zyCmeddRZer5f6+no+9alP4ff7LY+ju7sbu93O2rVrAYjH45SXl3Pqqacmt3nooYeor69Pvv7iF7/I3Llz8fl8zJw5k1tvvTXZzXvHjh0oisL27dtNn3PPPfcwa9as5OstW7Zw0UUXUVhYSHV1NR/84Ac5dOjQEfxLji8SeAshhBBCCCHGBFVV6Q9HR+VLVdWcjrGwsJDCwkKeeuopQqHQYX1/X/nKV/j85z/Pxo0bmTt3Ltdccw3RaBSA3bt3c+GFF3L55ZezadMmHnvsMV555RVuuukmy/cqKSlh8eLFrFq1CoDNmzejKAobNmygr68PgBdffJFly5Yl9ykqKuLBBx9k69at3HvvvfzqV7/innvuAWDu3LksXbqUhx9+2PQ5Dz/8MO9///sB7abDueeeywknnMDatWv55z//SUtLC+973/sO699hPFLUXH+Cxoienh5KSkro7u6muLh4tA9HCCGEEEIIcQSCwSB79uxhxowZeDweAPrDURbc9uyoHM/Wb6zA58ptpO4TTzzBjTfeSCAQYMmSJSxbtoyrr76a448/PrmNoig8+eSTXHbZZezdu5cZM2bw61//mo9+9KPa523dyrHHHsu2bduYN28eN9xwA3a7nV/84hfJ93jllVdYtmwZfr8/+W9k9LnPfY633nqLZ555hnvvvZc1a9awfft2vvOd73DhhRcyZ84cvvCFL3DjjTdafh/f//73efTRR5NZ8x/+8Ifcd9997Nq1C9Cy4Mccc0zyGL/1rW/x8ssv8+yzqf+jgwcPUl9fz1tvvcXcuXNz+vfLJ1Y/h7rDiT0l4y2EEEIIMVGEeuHA6xCLjvaRCDGuXX755TQ2NvKXv/yFCy+8kFWrVrFkyRIefPDBAfczBuaTJ08GoLW1FYA33niDBx98MJlRLywsZMWKFcTjcfbs2WP5fsuWLeOVV14hFovx4osvsnz5cpYvX86qVatobGxk165dLF++PLn9Y489xhlnnEFNTQ2FhYV89atfZf/+/cn1V199NXv37uU///kPoGW7lyxZwrx585LH+O9//9t0jPq63bt3H94/4jgjzdWEEEIIISaK52+DnkY4/io47r2jfTRCHDav087Wb6wYtc8+HB6PhwsuuIALLriAW2+9lRtuuIGvfe1rfPjDH866j9PpTD5XFAXQxmYD9PX18bGPfYxPfepTGftNnTrV8v3OPvtsent7Wb9+PS+99BLf/va3qamp4Tvf+Q6LFi2itraWOXPmALBmzRquvfZabr/9dlasWEFJSQmPPvood999d/L9ampqOPfcc3nkkUc49dRTeeSRR/j4xz+eXN/X18cll1zCXXfdlXEs+o2EiUoCbyGORvtuaFgHCy4Dh2u0j0YIIYQYWE+j9rh/jQTeYkxSFCXncu98s2DBgqOat3vJkiVs3bqV2bNn57xPaWkpxx9/PPfddx9Op5N58+ZRVVXFVVddxTPPPGMa37169WqmTZvGV77yleSyffv2Zbzntddeyxe+8AWuueYa3n77ba6++mrTMT7xxBNMnz4dh2Ns/j8NFyk1F+JoPPtl2PIEvPnkaB+JEEIIcRiU0T4AIcat9vZ2zj33XB566CE2bdrEnj17ePzxx/nud7/LpZdeesTv+8UvfpHVq1dz0003sXHjRnbu3MnTTz+dtbmabvny5Tz88MPJILu8vJz58+fz2GOPmQLvOXPmsH//fh599FF2797Nj370I558MvMa973vfS+9vb18/OMf55xzzqG2tja57pOf/CQdHR1cc801vP766+zevZtnn32W66+/nlgsdsTf+3gggbcQQ6HTelyNEEIIkZcUCbyFGC6FhYWccsop3HPPPZx99tkcd9xx3Hrrrdx4443cd999R/y+xx9/PC+++CI7duzgrLPO4oQTTuC2224zBb5Wli1bRiwWM43lXr58ecayd7/73Xz2s5/lpptuYvHixaxevZpbb7014/2Kioq45JJLeOONN7j22mtN62pra3n11VeJxWK84x3vYOHChXzmM5+htLQUm21ih57S1VyIo/HIVdpjzfFw7lcG3lYIIYQ4GvE4HO2Fq37eKpsOF2WOwRQinwzUTVqIkSJdzYXIJ+rELp0RQggxzDr2wJ+uh61PD9EbSsZbCCFGkgTeQgwFNT7aRyCEEGI8e/3XEA3CxkeG5v2k1FwIIUaUBN5CDIW4ZLyFEEIMo/hQz7stgbcQQowkCbyFGAqS8RZCCDGcYpHRPgIhhBBHQQJvIYbC+OpRKIQQIt9YZbxjUXjzKW389+GSUnMhhBhREngLMRSkuZoQQojhZDWk6a2/wxt/gH9+aeSPRwghxGFxjPYBCDEuSKm5EEKI4eA/BMFuiFuUmnceQaZbCCHEqJDAW4ihIM3VhBBCDIenPznaRyCEEGIISKm5mJiGeky2ZLyFEEKMuMMcp20898kYbyHGjOnTp/PDH/5wxD937969KIrCxo0bR/yzxyMJvMXE098BT35s6OZCBRnjLYQQYuQc6c1jU2d0CbyFGE4f/vCHURSF73znO6blTz31FMph3vh6/fXX+Z//+Z+hPLyc1NfX09TUxHHHHTfinz0eSeAtJp7tf9PGy219eujeUzLeQgghRsqfPgKd+w4/az3kc4ELIQbi8Xi466676OzsPKr3mTRpEj6fb4iOKnd2u52amhocDhmdPBQk8BZjV18rbHkCQn2Ht99wlNfFJfAWQggxxLJltiP9sPZ+DjtrbQy8pdRciGF3/vnnU1NTw5133jngdk888QTHHnssbreb6dOnc/fdd5vWG0vNVVXl61//OlOnTsXtdlNbW8unPvUpAL7xjW9YZqcXL17MrbfeavnZnZ2dXHvttUyaNAmv18ucOXN44IEHgMxS81WrVqEoCitXrmTp0qX4fD5OP/103nrrLdN7Pv300yxZsgSPx8PMmTO5/fbbiUatb/xt2bIFm81GW1sbAB0dHdhsNq6++urkNt/61rc488wzAYjFYnz0ox9lxowZeL1ejjnmGO69997kts899xwej4euri7T53z605/m3HPPTb5+5ZVXOOuss/B6vdTX1/OpT30Kv99veYxDRQJvMXY9+xXY9EdY+5vD28/mHPpjkYy3EEII0DLR/vahea+hbtxpDLylKagQw85ut/Ptb3+bH//4xxw8eNBym3Xr1vG+972Pq6++ms2bN/P1r3+dW2+9lQcffNBy+yeeeIJ77rmHX/ziF+zcuZOnnnqKhQsXAvCRj3yEbdu28frrrye337BhA5s2beL666+3fL9bb72VrVu38o9//INt27bxs5/9jMrKygG/r6985SvcfffdrF27FofDwUc+8pHkupdffpkPfehDfPrTn2br1q384he/4MEHH+SOO+6wfK9jjz2WiooKXnzxxeT+xtcAL774IsuXLwcgHo9TV1fH448/ztatW7ntttv48pe/zB//+EcAzjvvPEpLS3niiSeS+8diMR577DGuvfZaAHbv3s2FF17I5ZdfzqZNm3jsscd45ZVXuOmmmwb8vo+WBN5i7Ar1aI+t2w5vP7uhXCYe1zIKR9tsTcZ4CyHExKKqsONZaN2eWhbshme/DP+2vsA8bAOVhiu2oys1l8BbjFWqCpHg6HwdwfXie97zHhYvXszXvvY1y/U/+MEPOO+887j11luZO3cuH/7wh7npppv43ve+Z7n9/v37qamp4fzzz2fq1KmcfPLJ3HjjjQDU1dWxYsWKZMYa4IEHHmDLine truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x2000 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Define a list of parameters for generating different sine waves\n",
|
||||
"parameters = [\n",
|
||||
" {'n_points': 1000, 'frequency': 2, 'phase': 0, 'amplitude': 1, 'noise_sd': 0.1},\n",
|
||||
" {'n_points': 1000, 'frequency': 4, 'phase': 0, 'amplitude': 2, 'noise_sd': 0.2},\n",
|
||||
" {'n_points': 1000, 'frequency': 1, 'phase': np.pi / 4, 'amplitude': 1, 'noise_sd': 0.3},\n",
|
||||
" {'n_points': 1000, 'frequency': 3, 'phase': np.pi / 2, 'amplitude': 3, 'noise_sd': 0.4}\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Create a figure and axes\n",
|
||||
"fig, axes = plt.subplots(nrows=len(parameters), figsize=(10, 20))\n",
|
||||
"\n",
|
||||
"# Generate and plot sine waves for each set of parameters\n",
|
||||
"for ax, params in zip(axes, parameters):\n",
|
||||
" # Generate the sine wave and the noisy sine wave\n",
|
||||
" output = generate_sine_with_noise(**params)\n",
|
||||
" \n",
|
||||
" # Unpack the sine wave and the noisy sine wave\n",
|
||||
" sine_wave, sine_wave_noise = output.T\n",
|
||||
" \n",
|
||||
" # Generate the x values\n",
|
||||
" x = np.linspace(0, 2*np.pi, len(sine_wave))\n",
|
||||
"\n",
|
||||
" # Plot the sine wave\n",
|
||||
" ax.plot(x, sine_wave, label='Sine wave')\n",
|
||||
"\n",
|
||||
" # Plot the noisy sine wave\n",
|
||||
" ax.plot(x, sine_wave_noise, label='Noisy sine wave', alpha=0.7)\n",
|
||||
"\n",
|
||||
" # Add a legend\n",
|
||||
" ax.legend()\n",
|
||||
"\n",
|
||||
"# Show the plots\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5db8166f-6cb0-43f8-abe0-22f71400c715",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in new issue
Block a user