217 lines
101 KiB
Plaintext
217 lines
101 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d859e6a3-9237-4c6c-adad-8a88917ec849",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from aligner import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "09cd94af-994f-4c36-a88a-530000ecce40",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "573034ed-a259-4ce9-96aa-5f07a440ada4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Titan's Call\n",
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"280 280 280\n",
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"a) total 282 selected 280\n",
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"b) total 281 selected 280\n"
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]
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}
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],
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"source": [
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"j = 0 # track id (0-based)\n",
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"i_a, i_b = 0, 1 # annotation run ids (0-based)\n",
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"\n",
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"annot_a = load_track_annot(fns_all[i_a][j])\n",
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"annot_b = load_track_annot(fns_all[i_b][j])\n",
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"assert annot_a['contentId'] == annot_b['contentId']\n",
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"\n",
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"print(annot_a['title'])\n",
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"\n",
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"ts_a, ts_b, tsa_a, tsa_b = align(annot_a, annot_b)\n",
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"\n",
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"print('a) total', len(ts_a), 'selected', len(tsa_a))\n",
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"print('b) total', len(ts_b), 'selected', len(tsa_b))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "c650bfa4-5c38-4b0a-81f0-0544ae711ec9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 800x200 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"#mn = np.min(np.diff(ts_a[idxs_a]))\n",
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"plt.figure(figsize=(8,2))\n",
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"plt.stem(np.diff(tsa_a) - np.diff(tsa_b))\n",
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"plt.title('between annotations: interval deviations'); None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "1e19feb7-b841-4163-8276-8ccfe40cac2f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 800x200 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Simple linear regression\n",
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"from scipy import stats\n",
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"slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(tsa_a.shape[0]), tsa_a)\n",
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"\n",
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"plt.figure(figsize=(8,2))\n",
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"plt.stem(tsa_a - np.arange(tsa_a.shape[0]) * slope - intercept)\n",
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"plt.title('linear regression on a (time stability vs. global beat)'); None"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"id": "db46ce9e-e237-4501-a90a-191e32f1d0a3",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(\"primary:Music/Rhythmology/01 Titan's Call.mp3\",\n",
|
||
|
|
" \"Titan's Call\",\n",
|
||
|
|
" 'Follow The Cipher')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"annot_a['contentId'], annot_a['title'], annot_a['artist']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"id": "db35c8f5-4258-47e1-ab19-64d5c0667930",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#import os\n",
|
||
|
|
"#os.makedirs('ref')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"id": "d9147484-6a1d-4639-9da9-8376e73b479f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"with open('ref/Rhythmology_001.json', 'w') as fout:\n",
|
||
|
|
" json.dump({\n",
|
||
|
|
" 'contentId': annot_a['contentId'],\n",
|
||
|
|
" 'title': annot_a['title'],\n",
|
||
|
|
" 'artist': annot_a['artist'],\n",
|
||
|
|
" 'beatTimesSec': list(tsa_a)\n",
|
||
|
|
" }, fout, indent=2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "85bb17d3-7c61-43c1-8700-135b8b1bd1cf",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"id": "1cdb8c06-406d-4861-a1d9-2a6809d92834",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAADcCAYAAABXo+ihAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABaQklEQVR4nO3deXxM5/4H8M9MJJNENhHZ0CSWIkIQkqa2lpCgSjdLKXJ7tURaa6/q/SFor7UttXalt6i1pVpSia2XprZQJSgaS8lCIgvZM8/vDz2nM5NZzpn1zOT7fr282pw5c85z1vme5zzP95ExxhgIIYQQQgiRILmtC0AIIYQQQoguFKwSQgghhBDJomCVEEIIIYRIFgWrhBBCCCFEsihYJYQQQgghkkXBKiGEEEIIkSwKVgkhhBBCiGRRsEoIIYQQQiSLglVCCCGEECJZFKwSs9mwYQNkMhmuX7/OT3vqqafw1FNP2axMRLxx48YhNDTU1sWwGO48PXXqlEnLSUpKQr9+/QTN6+j7VBeZTIbk5GSD8wm5d1y/fh0ymQwbNmwwf0HtyOHDhyGTyXD48GHR3x03bhw8PDzMWp7Q0FCMGzdO7zzcsVu2bJlZ122qESNGYNiwYbYuBhGAglVCCBEpOzsbn332Gd555x1+2p07d5CSkoKzZ8/armBmtHfvXqSkpNi6GHrZQxmJbem7LmfOnImdO3fi119/tX7BiCgUrBKL2r9/P/bv32/rYhARPv30U1y+fNnWxZC0FStWICwsDE8//TQ/7c6dO5g3b57WH0V73Kd79+7FvHnzrLKuV155BeXl5QgJCdE5T0hICMrLy/HKK6/w06xZRmKf9F2XnTt3RteuXfH+++9bv2BEFApWiUW5uLjAxcXF1sXQ6eHDh6Lmr6iogFKptFBp1CmVSlRUVFhlXaqcnZ2hUCisvl57UV1djU2bNol6fUj7VD8nJye4urpCJpPpnEcmk8HV1RVOTk5WLBlxdMOGDcM333yDBw8e2LooRA8KVolFabY749pbbdu2De+99x6aNWsGV1dX9O3bF1evXq3z/ePHjyMhIQHe3t5wd3dH7969cezYMbV5bty4gaSkJLRp0wZubm5o3LgxXnrpJbX2b8Df7eKOHDmCpKQk+Pv7o1mzZjrLzpV1y5Yt+L//+z80bdoU7u7uKCkpEVw2bjldu3aFq6srWrZsiY8//hgpKSl1fpi59n2bNm1C+/btoVAokJqaCgC4ffs2/vGPfyAgIAAKhQLt27fHF198UWddK1euRPv27eHu7o5GjRqha9eu2Lx5M/95aWkppkyZgtDQUCgUCvj7+6Nfv37IzMzk59HWvvLhw4eYPn06mjdvDoVCgTZt2mDZsmVgjGndhl27diEiIoIvK7cdhuTn5+PVV19FQEAAXF1dERkZiS+//FJtHtX2b5988glatmwJhUKBbt264eTJk4LWAwBlZWV4/fXX0bhxY3h5eWHMmDG4f/++we8dPXoU9+7dQ1xcHD/t8OHD6NatGwAgMTERMplMrX2l5j5V3YbVq1ejRYsWcHd3R//+/XHr1i0wxrBgwQI0a9YMbm5uGDJkCAoLC+uUZd++fejZsycaNmwIT09PDBo0CBcuXDC4DdXV1Zg3bx5at24NV1dXNG7cGD169EBaWhpf3tWrVwMAvy2q5+uyZcvw5JNPonHjxnBzc0NUVBR27Nihc32bNm1CmzZt4OrqiqioKPz0009qn2trs6pJs82qrjIyxhAaGoohQ4bUWUZFRQW8vb3x+uuv61xPRESEWo05R6lUomnTpnjxxRf5aVu2bEFUVBQ8PT3h5eWFDh06YMWKFTqXrY9SqURKSgqCg4Ph7u6Op59+GllZWYLahALA9u3bERUVBTc3N/j5+WH06NG4ffu21nn/+OMPxMfHo2HDhggODsb8+fPrXMtij7FQH374IUJCQuDm5obevXvj/Pnzdea5dOkSXnzxRfj6+sLV1RVdu3bFd999pzZPYWEhZsyYgQ4dOsDDwwNeXl4YMGCA2it9Q9clAPTr1w8PHz7kz30iTQ1sXQBSPy1atAhyuRwzZsxAcXExlixZglGjRuH48eP8PAcPHsSAAQMQFRWFuXPnQi6XY/369ejTpw/+97//ITo6GgBw8uRJ/PzzzxgxYgSaNWuG69evY+3atXjqqaeQlZUFd3d3tXUnJSWhSZMmmDNnjqCa1QULFsDFxQUzZsxAZWUlXFxcBJftzJkzSEhIQFBQEObNm4fa2lrMnz8fTZo00bqugwcPYtu2bUhOToafnx9CQ0ORl5eHJ554gg8EmzRpgn379uHVV19FSUkJpkyZAuDRq+Y333wTL774IiZPnoyKigqcO3cOx48fx8svvwwAmDBhAnbs2IHk5GSEh4ejoKAAR48excWLF9GlSxetZWKM4dlnn8WhQ4fw6quvolOnTvjxxx/x1ltv4fbt2/jwww/V5j969Ci++eYbJCUlwdPTEx999BFeeOEF3Lx5E40bN9a5n8vLy/HUU0/h6tWrSE5ORlhYGLZv345x48ahqKgIkydPVpt/8+bNKC0txeuvvw6ZTIYlS5bg+eefxx9//AFnZ2eDxzU5ORk+Pj5ISUnB5cuXsXbtWty4cYN/SNHl559/hkwmQ+fOnflp7dq1w/z58zFnzhy89tpr6NmzJwDgySef1FuGTZs2oaqqCm+88QYKCwuxZMkSDBs2DH369MHhw4cxc+ZMXL16FStXrsSMGTPUHlC++uorjB07FvHx8Vi8eDHKysqwdu1a9OjRA2fOnNHboSslJQULFy7EP//5T0RHR6OkpASnTp1CZmYm+vXrh9dffx137txBWloavvrqqzrfX7FiBZ599lmMGjUKVVVV2LJlC1566SV8//33GDRokNq8R44cwdatW/Hmm29CoVBgzZo1SEhIwIkTJxAREaF3/+ijq4wymQyjR4/GkiVLUFhYCF9fX/6zPXv2oKSkBKNHj9a53OHDhyMlJQW5ubkIDAzkpx89ehR37tzBiBEjAABpaWkYOXIk+vbti8WLFwMALl68iGPHjtU5V4WYNWsWlixZgsGDByM+Ph6//vor4uPjBb1d2bBhAxITE9GtWzcsXLgQeXl5WLFiBY4dO4YzZ87Ax8eHn7e2thYJCQl44oknsGTJEqSmpmLu3LmoqanB/Pnz+fnEHGOh/vvf/6K0tBSTJk1CRUUFVqxYgT59+uC3335DQEAAAODChQvo3r07mjZtirfffhsNGzbEtm3bMHToUOzcuRPPPfccgEcB965du/DSSy8hLCwMeXl5+Pjjj9G7d29kZWUhODhY0HUZHh4ONzc3HDt2jF82kSBGiJmsX7+eAWDZ2dn8tN69e7PevXvzfx86dIgBYO3atWOVlZX89BUrVjAA7LfffmOMMaZUKlnr1q1ZfHw8UyqV/HxlZWUsLCyM9evXT22apoyMDAaA/fe//61Tvh49erCamhqD28OVtUWLFmrrEFO2wYMHM3d3d3b79m1+2pUrV1iDBg2Y5uUHgMnlcnbhwgW16a+++ioLCgpi9+7dU5s+YsQI5u3tzZdtyJAhrH379nq3ydvbm02aNEnvPGPHjmUhISH837t27WIA2Lvvvqs234svvshkMhm7evWq2ja4uLioTfv1118ZALZy5Uq9612+fDkDwDZu3MhPq6qqYrGxsczDw4OVlJQwxhjLzs5mAFjjxo1ZYWEhP+/u3bsZALZnzx696+HOg6ioKFZVVcVPX7JkCQPAdu/erff7o0ePZo0bN64z/eTJkwwAW79+fZ3PNPcptw1NmjRhRUVF/PRZs2YxACwyMpJVV1fz00eOHMlcXFxYRUUFY4yx0tJS5uPjw8aPH6+2ntzcXObt7V1nuqbIyEg2aNAgvfNMmjSpzjnK0bzmqqqqWEREBOvTp4/adAAMADt16hQ/7caNG8zV1ZU999xz/DQh9w5un6nuX11lvHz5MgPA1q5dqzb92WefZaGhoWrXra7vap6vSUlJzMPDg9/2yZMnMy8vL0H3EkNyc3NZgwYN2NChQ9Wmp6SkMABs7Nix/DTuvnTo0CHG2KN97+/vzyIiIlh
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 800x200 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Simple linear regression\n",
|
||
|
|
"from scipy import stats\n",
|
||
|
|
"slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(tsa_b.shape[0]), tsa_b)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(8,2))\n",
|
||
|
|
"plt.stem(tsa_b - np.arange(tsa_b.shape[0]) * slope - intercept)\n",
|
||
|
|
"plt.title('linear regression on b (time stability vs. global beat)'); None"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.11.4"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|