Skip to content

M
mooc-rr
Commit 200b1356 authored by 504e2a253167de0c9e327e795bb40e5b's avatar 504e2a253167de0c9e327e795bb40e5b
Browse files
Options

En cours

parent 15adab6a
Hide whitespace changes
Inline Side-by-side
Showing with 139 additions and 12 deletions
module3/exo3/exercice_fr.ipynb
View file @ 200b1356
...@@ -807,16 +807,16 @@ ...@@ -807,16 +807,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f82cf75a828>" "<matplotlib.axes._subplots.AxesSubplot at 0x7f61e0990048>"
] ]
}, },
"execution_count": 13, "execution_count": 8,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -870,7 +870,7 @@ ...@@ -870,7 +870,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f82d59caa90>" "<matplotlib.axes._subplots.AxesSubplot at 0x7f61de904128>"
] ]
}, },
"execution_count": 9, "execution_count": 9,
...@@ -905,7 +905,7 @@ ...@@ -905,7 +905,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -924,16 +924,16 @@ ...@@ -924,16 +924,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f82cf6c3a20>" "<matplotlib.axes._subplots.AxesSubplot at 0x7f61de94aef0>"
] ]
}, },
"execution_count": 15, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -967,7 +967,7 @@ ...@@ -967,7 +967,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -983,10 +983,10 @@ ...@@ -983,10 +983,10 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f82cf5dac88>" "<matplotlib.axes._subplots.AxesSubplot at 0x7f61de83cf98>"
] ]
}, },
"execution_count": 16, "execution_count": 12,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -1023,7 +1023,7 @@ ...@@ -1023,7 +1023,7 @@
"\n", "\n",
"data_last_year_filtered -= f\n", "data_last_year_filtered -= f\n",
"data_last_year_filtered.index = data_last_year.index\n", "data_last_year_filtered.index = data_last_year.index\n",
"amp = data_last_year_filtered.max()\n", "amp = data_last_year_filtered.max() / 2\n",
"data_last_year_filtered.plot()" "data_last_year_filtered.plot()"
] ]
}, },
...@@ -1037,6 +1037,133 @@ ...@@ -1037,6 +1037,133 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [
"Nous pouvons désormais isoler l'évolution lente d'arrière-plan $f(t)$ en moyennant sur un an les valeurs (on recrée artificiellement un filtre passe-bas) :"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test = full_data.copy()\n",
"test[interpolated_marks == 1] = np.nan\n",
"print(np.mean)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1958-03-29 316.118235\n",
"1958-04-05 316.118235\n",
"1958-04-12 316.118235\n",
"1958-04-19 316.118235\n",
"1958-04-26 316.118235\n",
"1958-05-03 316.118235\n",
"1958-05-10 315.947778\n",
"1958-05-17 315.806316\n",
"1958-05-24 315.694500\n",
"1958-05-31 315.614286\n",
"1958-06-07 315.566364\n",
"1958-06-14 315.516087\n",
"1958-06-21 315.485000\n",
"1958-06-28 315.474000\n",
"1958-07-05 315.465000\n",
"1958-07-12 315.466296\n",
"1958-07-19 315.474286\n",
"1958-07-26 315.487586\n",
"1958-08-02 315.485333\n",
"1958-08-09 315.485333\n",
"1958-08-16 315.532258\n",
"1958-08-23 315.565938\n",
"1958-08-30 315.597879\n",
"1958-09-06 315.633529\n",
"1958-09-13 315.633529\n",
"1958-09-20 315.664857\n",
"1958-09-27 315.693889\n",
"1958-10-04 315.736389\n",
"1958-10-11 315.731111\n",
"1958-10-18 315.729722\n",
" ... \n",
"2024-04-20 423.620943\n",
"2024-04-27 423.687170\n",
"2024-05-04 423.772642\n",
"2024-05-11 423.850189\n",
"2024-05-18 NaN\n",
"2024-05-25 NaN\n",
"2024-06-01 NaN\n",
"2024-06-08 NaN\n",
"2024-06-15 NaN\n",
"2024-06-22 NaN\n",
"2024-06-29 NaN\n",
"2024-07-06 NaN\n",
"2024-07-13 NaN\n",
"2024-07-20 NaN\n",
"2024-07-27 NaN\n",
"2024-08-03 NaN\n",
"2024-08-10 NaN\n",
"2024-08-17 NaN\n",
"2024-08-24 NaN\n",
"2024-08-31 NaN\n",
"2024-09-07 NaN\n",
"2024-09-14 NaN\n",
"2024-09-21 NaN\n",
"2024-09-28 NaN\n",
"2024-10-05 NaN\n",
"2024-10-12 NaN\n",
"2024-10-19 NaN\n",
"2024-10-26 NaN\n",
"2024-11-02 NaN\n",
"2024-11-09 NaN\n",
"Freq: 7D, Length: 3477, dtype: float64\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f61da17fe10>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"rolling_base = full_data.copy()\n",
"rolling_base[interpolated_marks == 1] = np.nan\n",
"rolling_mean_with_offset = rolling_base.rolling(window='365D', min_periods=1).mean()\n",
"# This function computed the rolling mean for the year after each value,\n",
"# and not for the 6 month before to 6 month after period for each value\n",
"rolling_mean = rolling_mean_with_offset.shift(periods=-182, freq='D')\n",
"print(rolling_mean.reindex(full_index))\n",
"rolling_mean.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [] "source": []
} }
], ],
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment