101 lines
39 KiB
Plaintext
101 lines
39 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def to_quaternion(pan, tilt):\n",
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" pass\n",
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"\n",
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"def to_pan_tilt(q):\n",
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" pass"
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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": 29,
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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 0x7f68a28cf0d0>]"
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]
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},
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"execution_count": 29,
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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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",
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"text/plain": [
|
||
"<Figure size 432x288 with 3 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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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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||
"import matplotlib as mpl\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots()\n",
|
||
"\n",
|
||
"ax.set_xlim((-3/2 * np.pi, 3/2 * np.pi))\n",
|
||
"ax.set_xlabel(\"Pan (rad)\")\n",
|
||
"pan_dmx = ax.twiny()\n",
|
||
"pan_dmx.set_xlim((0, 255))\n",
|
||
"pan_dmx.set_xlabel(\"Pan (DMX)\")\n",
|
||
"\n",
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||
"ax.set_ylim((-1/2 * np.pi, 1/2 * np.pi))\n",
|
||
"ax.set_ylabel(\"Tilt (rad)\")\n",
|
||
"tilt_dmx = ax.twinx()\n",
|
||
"tilt_dmx.set_ylim((0, 255))\n",
|
||
"tilt_dmx.set_ylabel(\"Tilt (DMX)\")\n",
|
||
"\n",
|
||
"\n",
|
||
"ax.plot([-4, -4, -2, -2], [1,-1,-1,1])"
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||
]
|
||
}
|
||
],
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"metadata": {
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"interpreter": {
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"hash": "8949cd447f4051406d72dfa55357e47dbefb618694e663c2df1a04d5092ff1c9"
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},
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"kernelspec": {
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"display_name": "Python 3.9.5 64-bit ('venv': venv)",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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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": "3.9.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
|