241 lines
16 KiB
Plaintext
241 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "b5d8a856-d466-4102-8bfc-2a9014ab0767",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import importlib.util\n",
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"from ipywidgets import interactive\n",
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"\n",
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"%matplotlib inline"
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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": "c4fc1c81-3f08-4cd8-bb38-3566b033318a",
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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": 11,
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"id": "39d0f384-6533-41e0-8093-b1b2a9d59608",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_test(path):\n",
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" spec = importlib.util.spec_from_file_location(\"test\", path)\n",
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" module = importlib.util.module_from_spec(spec)\n",
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" spec.loader.exec_module(module)\n",
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" for sym in ['r', 'g', 'b', 'h', 's', 'v']:\n",
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" a = np.array(getattr(module, sym))\n",
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" setattr(module, sym, a)\n",
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" return module\n",
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"\n",
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"path = \"/home/martin/code/musicmouse/espmusicmouse/.pio/build/native/swipe.py\"\n",
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"path = \"/home/martin/code/musicmouse/espmusicmouse/.pio/build/native/reverse_swipe.py\"\n",
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"res = load_test(path)"
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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": 12,
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"id": "bb3bc44d-2523-43d5-87eb-2cded8d0d8c2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "09617018b02c449d912ef07757fcc8c6",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(IntSlider(value=99, description='t', max=199), Output(layout=Layout(height='350px'))), _…"
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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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"def plot_v(t):\n",
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" plt.figure(2)\n",
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" plt.ylim(-0.1, 1.1) \n",
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" plt.plot(res.v[t], label=f\"v at {t}\")\n",
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" plt.legend()\n",
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" plt.show()\n",
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"\n",
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"interactive_plot = interactive(plot_v, t=(0, len(res.v)-1))\n",
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"output = interactive_plot.children[-1]\n",
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"output.layout.height = '350px'\n",
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"interactive_plot"
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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": 9,
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"id": "2ef27efb-b021-42b7-8cb5-c84add88ab17",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2,\n",
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" 2, 1, 1, 1, 1, 1, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 1])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"res.r[70]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "817d4771-f210-48e9-a342-0b0d238d0e11",
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"metadata": {},
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"source": [
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"## Bell Curve"
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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": 14,
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"id": "763b5ffe-4224-4dec-a147-40be2aaa0d41",
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"metadata": {},
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"outputs": [],
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"source": [
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"def bell_curve_approximation_old(x, inverse_width):\n",
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" if x < 0:\n",
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" x = -x\n",
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" \n",
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" nx = x * inverse_width * 4\n",
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" if nx > 2:\n",
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" return 0\n",
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" \n",
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" x2 = nx * nx\n",
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" x3 = x2 * nx\n",
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" res = 1 + 0.2760695894 * x3 - 0.8021391 * x2\n",
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" \n",
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" return res if res > 0 else 0\n",
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"\n",
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"\n",
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"def bell_curve_approximation(x, inverse_width): \n",
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" nx = x * inverse_width * 4\n",
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" x2 = nx * nx\n",
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" x3 = x2 * nx\n",
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" res = 1 + 0.2760695894 * x3 - 0.8021391 * x2\n",
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" \n",
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" return res if res > 0 else 0\n",
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"\n",
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"# // Function start at 0 (x=0) and goes smoothly up to 1 and arrives 1 at x=width\n",
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"# // inverse width has to be 1 / width (should be cached outside)\n",
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"def step_function(x, width, inverse_width):\n",
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" if x < 0:\n",
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" return 0\n",
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" if x >= width:\n",
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" return 1\n",
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" bell_inv_width = inverse_width * 0.5\n",
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" nx = (-x + width) * bell_inv_width * 4\n",
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" x2 = nx * nx\n",
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" x3 = x2 * nx\n",
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" res = 1 + 0.2760695894 * x3 - 0.8021391 * x2\n",
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" return res"
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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": "be193428-3fec-4a2f-9903-4dbfa4ad19e3",
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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": 15,
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"id": "de8b9ee1-6232-4cab-8a1a-b62ec723b4f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.linspace(-1, 10, 20)\n",
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"y = np.array([step_function(x_i, 8, 1 / 8) for x_i in x])"
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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": 16,
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"id": "db9bddcc-ae89-462c-b8f4-56384a36a1df",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fa511346af0>]"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.plot(x, y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ad451aa5-8645-4432-a0e8-821eb4826f19",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
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"name": "python3"
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},
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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}
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