Bertrand Benjamin
2ad2f8578b
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432 lines
28 KiB
Plaintext
432 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Mon hamster dans sa roue\n",
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"\n",
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"Mon hamster court dans sa roue durant toute la journée. J'aime l'étudier et aujourd'hui je voudrait savoir à quelle vitesse il avance dans sa roue.\n",
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"\n",
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"\n",
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"## Distance parcourue\n",
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"\n",
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"Lors d'anciennes expérimentation, j'ai enregistré sur un jour la position de sa roue. Je peux maintenant y avoir accès par la fonction `position`."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from hamster import position, graph"
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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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"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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"<AxesSubplot:title={'center':'Position de la roue'}, xlabel='Temps (en h)', ylabel='Position (en m)'>"
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]
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},
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"execution_count": 4,
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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": [
|
|
"graph()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Pour connaître la position de la roue à un moment donné, je dois le renseigner comme paramètre de la fonction.\n",
|
|
"\n",
|
|
"Par exemple au début de la journée (8h) à `t=0` la position de la roue est à"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"position(0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"À midi, donc `t=4` la position de la route est à"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"4"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"position(4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Quelle est la position de la roue à 10h (`t=2`)?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Quelle est la position de la roue à 16h?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Quelle est la position de la roue à 8h30?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Quelle est la position de la roue à 12h30?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Vitesse moyenne\n",
|
|
"\n",
|
|
"Comme je disais au début, ce n'est pas la position de la roue qui m'interesse mais bien la vitesse de mon hamster.\n",
|
|
"\n",
|
|
"Quelle est la **vitesse moyenne** de mon hamster entre 8h et 12h?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Quelle est la vitesse moyenne de mon hamster sur la journée entre 8h et 16h?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Entre quelles heures, mon hamster s'est-il montré le plus rapide?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Comme on commence un peu trop souvent écrire le même calcul, c'est qu'il est temps de programmer une fonction pour le faire à notre place.\n",
|
|
"\n",
|
|
"Ci-dessous, il y a le début de la définition de la fonction. À toi de compléter les ... pour que cette fonction calcule la vitesse du hamster entre les deux moments `t1` et `t2`.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def vitesse(t1,t2):\n",
|
|
" return ..."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Utilise ta nouvelle fonction vitesse pour calculer la vitesse moyenne de mon hamster entre 8h et 12h. Tu devrais trouver le même résultat que plus haut."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"vitesse(..., ...)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Calculer la vitesse de mon hamster entre 11h30 et 12h."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Puis entre 12h et 12h30."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Vitesse instantannée\n",
|
|
"\n",
|
|
"Les voitures ont un compteur de vitesse qui donne la **vitesse instantannée** du véhicule pas la vitesse moyenne entre 2 moments.\n",
|
|
"\n",
|
|
"J'aimerai connaître la **vitesse instantannée** de mon hamster à 12h.\n",
|
|
"\n",
|
|
"Pour faire cela, on va calculer la vitesse moyenne un peu avant 12h disons entre 11h45 et 12h puis la vitesse moyenne un peu après entre 12h et 12h15.\n",
|
|
"\n",
|
|
"Calcule ces deux vitesses moyennes sur 15min."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Ensuite on recommence mais cette fois-ci avec une vitesse moyenne sur 5minutes."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Et on continue avec une amplitude de 1min"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Pour avoir la vitesse instantannée, il faudrait continuer ainsi jusqu'à ce que l'amplitude soit nulle. Les vitesses moyennes se rapprocheraient de plus en plus autour d'une valeur que l'on appelle **vitesse instantannée**.\n",
|
|
"\n",
|
|
"\n",
|
|
"À toi de trouver la vitesse instantannée à 13h."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"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": "3.10.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|