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": 1,
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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": 2,
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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.axes._subplots.AxesSubplot at 0x7fb4f5674790>"
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]
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},
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"execution_count": 2,
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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",
|
|
"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": "3.8.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|