From 849397e73008acbbb25411ff0962a91cd8d0673b Mon Sep 17 00:00:00 2001 From: 0db2f0554d3b3bbdf0f34a0c1240bdef <0db2f0554d3b3bbdf0f34a0c1240bdef@app-learninglab.inria.fr> Date: Wed, 17 Jun 2020 09:07:41 +0000 Subject: [PATCH] =?UTF-8?q?avec=20d=C3=A9termination=20des=20caract=C3=A9r?= =?UTF-8?q?istiques=20du=20ph=C3=A9nom=C3=A8ne=20rapide.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ans l'atmosph\303\250re depuis 1958.ipynb" | 753 +++--------------- 1 file changed, 100 insertions(+), 653 deletions(-) diff --git "a/module3/exo3/Concentration de CO2 dans l'atmosph\303\250re depuis 1958.ipynb" "b/module3/exo3/Concentration de CO2 dans l'atmosph\303\250re depuis 1958.ipynb" index d8fee86..d5aa2d2 100644 --- "a/module3/exo3/Concentration de CO2 dans l'atmosph\303\250re depuis 1958.ipynb" +++ "b/module3/exo3/Concentration de CO2 dans l'atmosph\303\250re depuis 1958.ipynb" @@ -2060,7 +2060,9 @@ { "cell_type": "code", "execution_count": 11, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -2081,6 +2083,66 @@ "plt.grid()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Date (année-mois) du minimum de 2009 à 2019" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min de l'année 2009 : 384.45 à la date: 614 2009-10\n", + "Name: date, dtype: object\n", + "min de l'année 2010 : 386.9 à la date: 625 2010-9\n", + "Name: date, dtype: object\n", + "min de l'année 2011 : 389.02 à la date: 638 2011-10\n", + "Name: date, dtype: object\n", + "min de l'année 2012 : 391.2 à la date: 649 2012-9\n", + "Name: date, dtype: object\n", + "min de l'année 2013 : 393.38 à la date: 661 2013-9\n", + "Name: date, dtype: object\n", + "min de l'année 2014 : 395.5 à la date: 673 2014-9\n", + "Name: date, dtype: object\n", + "min de l'année 2015 : 397.56 à la date: 685 2015-9\n", + "Name: date, dtype: object\n", + "min de l'année 2016 : 401.12 à la date: 697 2016-9\n", + "Name: date, dtype: object\n", + "min de l'année 2017 : 403.32 à la date: 709 2017-9\n", + "Name: date, dtype: object\n", + "min de l'année 2018 : 405.69 à la date: 721 2018-9\n", + "Name: date, dtype: object\n" + ] + } + ], + "source": [ + "annees = [2009+i for i in range(10)]\n", + "min_annees = []\n", + "date_min_annees = []\n", + "j = 0\n", + "for i in annees:\n", + " min_annees.append(Monthly_data['CO2_concentration_moyenne_mensuelle'][(Monthly_data.year == i)].min())\n", + " date_min_annees.append(Monthly_data['date'][(Monthly_data.year == i) & (Monthly_data['CO2_concentration_moyenne_mensuelle'] == min_annees[j])])\n", + " print('min de l\\'année',i,':', min_annees[j],'à la date:',date_min_annees[j])\n", + " j = j+1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "De la position des minima, on peut en deduire que la périodicité de la variation rapide est de l'ordre de l'année et que le minimum de C02 à Mauna Loa, Hawaii, États-Unis (lieu des mesures) est en septembre ou octobre." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2100,7 +2162,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -2119,7 +2181,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -2141,7 +2203,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -2150,7 +2212,7 @@ "(0, 4000)" ] }, - "execution_count": 14, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, @@ -2184,7 +2246,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2193,7 +2255,7 @@ "(0, 4000)" ] }, - "execution_count": 23, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, @@ -2228,13 +2290,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Cherchons l'index de la fréquence du maximum proche de fre=0.01 dans le tableau de la fft de départ\n", + "Cherchons l'index de la fréquence du maximum proche de freq=0.01 dans le tableau de la fft de départ\n", "Pour cela nous traçons cette fois le spectre en fonction de l'indice du tableau de valeurs. " ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -2243,7 +2305,7 @@ "(0, 4000)" ] }, - "execution_count": 16, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, @@ -2267,9 +2329,23 @@ "axes.set_ylim(0, 4000)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Conclusion sur le phénomène rapide**\n", + "\n", + "À partir de cette courbe nous pouvons déterminer précisément la fréquence des oscillations rapide.\n", + "Le résultat met en évidence une fréquence à $0.084 mois^{-1}= \\frac{1}{12} mois^{-1}$, soit une période d'une année.\n", + "\n", + "Nous retrouvons le résultat entrevue par la recherche des minima au départ. Nous pouvons préciser que le phénomène n'est pas purement sinusoïdal car nous avons clairement une composante à l'harmonique 2 soit $1.67 mois^{-1}= \\frac{2}{12} mois^{-1}$.\n", + "\n", + "***Hyp :*** Le phénomène n'est probablement pas directement lié à la différence d'activité humaine à Hawaï selon la période été/hiver, mais plus probablement à un stockage du C02 cyclique dans l'océan qui entoure la station de mesure sur l'île." + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 48, "metadata": { "scrolled": true }, @@ -2279,44 +2355,34 @@ "output_type": "stream", "text": [ "vmax = (178.51619865797664+1126.1445008434132j)\n", - "index = (array([62]),)\n" + "index = 62\n", + "frequence du maximum local = 0.08355795148247978\n" ] } ], "source": [ "vmax= fft_data[50:75].max()\n", "print('vmax = ',vmax)\n", - "imax = np.where(fft_data == vmax)\n", - "print('index = ',imax)" + "imax = np.where(fft_data == vmax)[0][0]\n", + "print('index = ',imax)\n", + "freqmax = freq_pos[imax]\n", + "print('frequence du maximum local =', freqmax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Nous pouvons maintenant filtrer grossièrement les données spectrales en *supprimant* les pics parasites, tant sur les fréquences positives que négatives." + "Nous pouvons maintenant filtrer grossièrement les données spectrales en *supprimant* les pics parasites, tant sur les fréquences positives que négatives, pour essayer de préciser la nature de la variation lente observée sous la variation rapide." ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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dateCO2_concentration_moyenne_mensuelleyearmonthfiltered_data
01958-3316.1919583312.80
11958-4317.2619584315.57
21958-5317.5019585318.40
31958-7315.6919587318.69
41958-8314.9919588318.58
51958-9313.5419589316.32
61958-11313.47195811314.99
71958-12314.74195812315.39
81959-1315.5419591315.71
91959-2316.7219592316.19
101959-3316.7519593314.88
111959-4317.7019594314.34
121959-5318.3819595314.49
131959-6318.0819596315.38
141959-7316.5819597316.40
151959-8314.9219598317.24
161959-9313.8719599317.39
171959-10313.44195910316.61
181959-11314.90195911316.93
191959-12315.58195912316.65
201960-1316.4219601316.95
211960-2317.0019602316.89
221960-3317.6419603316.26
231960-4319.1519604316.15
241960-5319.9719605316.03
251960-6319.5119606316.25
261960-7318.0919607317.05
271960-8315.8019608317.38
281960-9314.2319609317.46
291960-10313.88196010317.22
..................
7122017-12406.76201712407.10
7132018-1408.0920181407.51
7142018-2408.3020182407.17
7152018-3409.3020183407.43
7162018-4410.3620184407.56
7172018-5411.1520185407.95
7182018-6410.7920186408.48
7192018-7408.7320187408.60
7202018-8407.0820188409.37
7212018-9405.6920189409.36
7222018-10406.10201810409.52
7232018-11408.02201811410.09
7242018-12409.21201812409.84
7252019-1410.8220191410.53
7262019-2411.5820192410.70
7272019-3412.0620193410.37
7282019-4413.5520194410.80
7292019-5414.7820195411.39
7302019-6413.9120196411.16
7312019-7411.7720197411.05
7322019-8409.9720198411.76
7332019-9408.5520199411.99
7342019-10408.51201910412.02
7352019-11410.33201911412.70
7362019-12411.96201912412.99
7372020-1413.3120201413.47
7382020-2414.1420202413.78
7392020-3414.6220203413.46
7402020-4416.1420204413.72
7412020-5417.0820205413.58
\n", - "

742 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " date CO2_concentration_moyenne_mensuelle year month filtered_data\n", - "0 1958-3 316.19 1958 3 312.80\n", - "1 1958-4 317.26 1958 4 315.57\n", - "2 1958-5 317.50 1958 5 318.40\n", - "3 1958-7 315.69 1958 7 318.69\n", - "4 1958-8 314.99 1958 8 318.58\n", - "5 1958-9 313.54 1958 9 316.32\n", - "6 1958-11 313.47 1958 11 314.99\n", - "7 1958-12 314.74 1958 12 315.39\n", - "8 1959-1 315.54 1959 1 315.71\n", - "9 1959-2 316.72 1959 2 316.19\n", - "10 1959-3 316.75 1959 3 314.88\n", - "11 1959-4 317.70 1959 4 314.34\n", - "12 1959-5 318.38 1959 5 314.49\n", - "13 1959-6 318.08 1959 6 315.38\n", - "14 1959-7 316.58 1959 7 316.40\n", - "15 1959-8 314.92 1959 8 317.24\n", - "16 1959-9 313.87 1959 9 317.39\n", - "17 1959-10 313.44 1959 10 316.61\n", - "18 1959-11 314.90 1959 11 316.93\n", - "19 1959-12 315.58 1959 12 316.65\n", - "20 1960-1 316.42 1960 1 316.95\n", - "21 1960-2 317.00 1960 2 316.89\n", - "22 1960-3 317.64 1960 3 316.26\n", - "23 1960-4 319.15 1960 4 316.15\n", - "24 1960-5 319.97 1960 5 316.03\n", - "25 1960-6 319.51 1960 6 316.25\n", - "26 1960-7 318.09 1960 7 317.05\n", - "27 1960-8 315.80 1960 8 317.38\n", - "28 1960-9 314.23 1960 9 317.46\n", - "29 1960-10 313.88 1960 10 317.22\n", - ".. ... ... ... ... ...\n", - "712 2017-12 406.76 2017 12 407.10\n", - "713 2018-1 408.09 2018 1 407.51\n", - "714 2018-2 408.30 2018 2 407.17\n", - "715 2018-3 409.30 2018 3 407.43\n", - "716 2018-4 410.36 2018 4 407.56\n", - "717 2018-5 411.15 2018 5 407.95\n", - "718 2018-6 410.79 2018 6 408.48\n", - "719 2018-7 408.73 2018 7 408.60\n", - "720 2018-8 407.08 2018 8 409.37\n", - "721 2018-9 405.69 2018 9 409.36\n", - "722 2018-10 406.10 2018 10 409.52\n", - "723 2018-11 408.02 2018 11 410.09\n", - "724 2018-12 409.21 2018 12 409.84\n", - "725 2019-1 410.82 2019 1 410.53\n", - "726 2019-2 411.58 2019 2 410.70\n", - "727 2019-3 412.06 2019 3 410.37\n", - "728 2019-4 413.55 2019 4 410.80\n", - "729 2019-5 414.78 2019 5 411.39\n", - "730 2019-6 413.91 2019 6 411.16\n", - "731 2019-7 411.77 2019 7 411.05\n", - "732 2019-8 409.97 2019 8 411.76\n", - "733 2019-9 408.55 2019 9 411.99\n", - "734 2019-10 408.51 2019 10 412.02\n", - "735 2019-11 410.33 2019 11 412.70\n", - "736 2019-12 411.96 2019 12 412.99\n", - "737 2020-1 413.31 2020 1 413.47\n", - "738 2020-2 414.14 2020 2 413.78\n", - "739 2020-3 414.62 2020 3 413.46\n", - "740 2020-4 416.14 2020 4 413.72\n", - "741 2020-5 417.08 2020 5 413.58\n", - "\n", - "[742 rows x 5 columns]" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "filtered_data = np.fft.ifft(fft_data_filtree)\n", "Monthly_data['filtered_data'] = np.around(np.abs(filtered_data[0:len(filtered_data)]),decimals=2)\n", @@ -2944,34 +2416,9 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "trace=['CO2_concentration_moyenne_mensuelle','filtered_data']\n", "couleur = ['blue','red']\n", -- 2.18.1