{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence du syndrome grippal"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://www.sentiweb.fr/datasets/all/inc-3-PAY.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
"\n",
"| Nom de colonne | Libellé de colonne |\n",
"|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
"| week | Semaine calendaire (ISO 8601) |\n",
"| indicator | Code de l'indicateur de surveillance |\n",
"| inc | Estimation de l'incidence de consultations en nombre de cas |\n",
"| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n",
"| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n",
"| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n",
"| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n",
"\n",
"La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
week
\n",
"
indicator
\n",
"
inc
\n",
"
inc_low
\n",
"
inc_up
\n",
"
inc100
\n",
"
inc100_low
\n",
"
inc100_up
\n",
"
geo_insee
\n",
"
geo_name
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
202437
\n",
"
3
\n",
"
59356
\n",
"
50585.0
\n",
"
68127.0
\n",
"
89
\n",
"
76.0
\n",
"
102.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1
\n",
"
202436
\n",
"
3
\n",
"
33435
\n",
"
27654.0
\n",
"
39216.0
\n",
"
50
\n",
"
41.0
\n",
"
59.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2
\n",
"
202435
\n",
"
3
\n",
"
27404
\n",
"
22036.0
\n",
"
32772.0
\n",
"
41
\n",
"
33.0
\n",
"
49.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
3
\n",
"
202434
\n",
"
3
\n",
"
26717
\n",
"
21003.0
\n",
"
32431.0
\n",
"
40
\n",
"
31.0
\n",
"
49.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
4
\n",
"
202433
\n",
"
3
\n",
"
20623
\n",
"
15349.0
\n",
"
25897.0
\n",
"
31
\n",
"
23.0
\n",
"
39.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
5
\n",
"
202432
\n",
"
3
\n",
"
23187
\n",
"
17532.0
\n",
"
28842.0
\n",
"
35
\n",
"
27.0
\n",
"
43.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
6
\n",
"
202431
\n",
"
3
\n",
"
26035
\n",
"
20267.0
\n",
"
31803.0
\n",
"
39
\n",
"
30.0
\n",
"
48.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
7
\n",
"
202430
\n",
"
3
\n",
"
36393
\n",
"
28593.0
\n",
"
44193.0
\n",
"
55
\n",
"
43.0
\n",
"
67.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
8
\n",
"
202429
\n",
"
3
\n",
"
39560
\n",
"
32592.0
\n",
"
46528.0
\n",
"
59
\n",
"
49.0
\n",
"
69.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
9
\n",
"
202428
\n",
"
3
\n",
"
54342
\n",
"
45781.0
\n",
"
62903.0
\n",
"
81
\n",
"
68.0
\n",
"
94.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
10
\n",
"
202427
\n",
"
3
\n",
"
47364
\n",
"
40234.0
\n",
"
54494.0
\n",
"
71
\n",
"
60.0
\n",
"
82.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
11
\n",
"
202426
\n",
"
3
\n",
"
44219
\n",
"
36956.0
\n",
"
51482.0
\n",
"
66
\n",
"
55.0
\n",
"
77.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
12
\n",
"
202425
\n",
"
3
\n",
"
47204
\n",
"
40300.0
\n",
"
54108.0
\n",
"
71
\n",
"
61.0
\n",
"
81.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
13
\n",
"
202424
\n",
"
3
\n",
"
41110
\n",
"
34671.0
\n",
"
47549.0
\n",
"
62
\n",
"
52.0
\n",
"
72.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
14
\n",
"
202423
\n",
"
3
\n",
"
35875
\n",
"
30610.0
\n",
"
41140.0
\n",
"
54
\n",
"
46.0
\n",
"
62.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
15
\n",
"
202422
\n",
"
3
\n",
"
33772
\n",
"
28274.0
\n",
"
39270.0
\n",
"
51
\n",
"
43.0
\n",
"
59.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
16
\n",
"
202421
\n",
"
3
\n",
"
21963
\n",
"
17556.0
\n",
"
26370.0
\n",
"
33
\n",
"
26.0
\n",
"
40.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
17
\n",
"
202420
\n",
"
3
\n",
"
20057
\n",
"
15780.0
\n",
"
24334.0
\n",
"
30
\n",
"
24.0
\n",
"
36.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
18
\n",
"
202419
\n",
"
3
\n",
"
15375
\n",
"
11274.0
\n",
"
19476.0
\n",
"
23
\n",
"
17.0
\n",
"
29.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
19
\n",
"
202418
\n",
"
3
\n",
"
22409
\n",
"
17653.0
\n",
"
27165.0
\n",
"
34
\n",
"
27.0
\n",
"
41.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
20
\n",
"
202417
\n",
"
3
\n",
"
27042
\n",
"
21410.0
\n",
"
32674.0
\n",
"
41
\n",
"
33.0
\n",
"
49.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
21
\n",
"
202416
\n",
"
3
\n",
"
28882
\n",
"
23305.0
\n",
"
34459.0
\n",
"
43
\n",
"
35.0
\n",
"
51.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
22
\n",
"
202415
\n",
"
3
\n",
"
30229
\n",
"
24648.0
\n",
"
35810.0
\n",
"
45
\n",
"
37.0
\n",
"
53.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
23
\n",
"
202414
\n",
"
3
\n",
"
31813
\n",
"
26529.0
\n",
"
37097.0
\n",
"
48
\n",
"
40.0
\n",
"
56.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
24
\n",
"
202413
\n",
"
3
\n",
"
35090
\n",
"
29607.0
\n",
"
40573.0
\n",
"
53
\n",
"
45.0
\n",
"
61.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
25
\n",
"
202412
\n",
"
3
\n",
"
40639
\n",
"
34582.0
\n",
"
46696.0
\n",
"
61
\n",
"
52.0
\n",
"
70.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
26
\n",
"
202411
\n",
"
3
\n",
"
50268
\n",
"
43331.0
\n",
"
57205.0
\n",
"
75
\n",
"
65.0
\n",
"
85.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
27
\n",
"
202410
\n",
"
3
\n",
"
60107
\n",
"
52623.0
\n",
"
67591.0
\n",
"
90
\n",
"
79.0
\n",
"
101.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
28
\n",
"
202409
\n",
"
3
\n",
"
71121
\n",
"
62920.0
\n",
"
79322.0
\n",
"
107
\n",
"
95.0
\n",
"
119.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
29
\n",
"
202408
\n",
"
3
\n",
"
104566
\n",
"
94520.0
\n",
"
114612.0
\n",
"
157
\n",
"
142.0
\n",
"
172.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
\n",
"
\n",
"
2051
\n",
"
198521
\n",
"
3
\n",
"
26096
\n",
"
19621.0
\n",
"
32571.0
\n",
"
47
\n",
"
35.0
\n",
"
59.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2052
\n",
"
198520
\n",
"
3
\n",
"
27896
\n",
"
20885.0
\n",
"
34907.0
\n",
"
51
\n",
"
38.0
\n",
"
64.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2053
\n",
"
198519
\n",
"
3
\n",
"
43154
\n",
"
32821.0
\n",
"
53487.0
\n",
"
78
\n",
"
59.0
\n",
"
97.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2054
\n",
"
198518
\n",
"
3
\n",
"
40555
\n",
"
29935.0
\n",
"
51175.0
\n",
"
74
\n",
"
55.0
\n",
"
93.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2055
\n",
"
198517
\n",
"
3
\n",
"
34053
\n",
"
24366.0
\n",
"
43740.0
\n",
"
62
\n",
"
44.0
\n",
"
80.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2056
\n",
"
198516
\n",
"
3
\n",
"
50362
\n",
"
36451.0
\n",
"
64273.0
\n",
"
91
\n",
"
66.0
\n",
"
116.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2057
\n",
"
198515
\n",
"
3
\n",
"
63881
\n",
"
45538.0
\n",
"
82224.0
\n",
"
116
\n",
"
83.0
\n",
"
149.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2058
\n",
"
198514
\n",
"
3
\n",
"
134545
\n",
"
114400.0
\n",
"
154690.0
\n",
"
244
\n",
"
207.0
\n",
"
281.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2059
\n",
"
198513
\n",
"
3
\n",
"
197206
\n",
"
176080.0
\n",
"
218332.0
\n",
"
357
\n",
"
319.0
\n",
"
395.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2060
\n",
"
198512
\n",
"
3
\n",
"
245240
\n",
"
223304.0
\n",
"
267176.0
\n",
"
445
\n",
"
405.0
\n",
"
485.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2061
\n",
"
198511
\n",
"
3
\n",
"
276205
\n",
"
252399.0
\n",
"
300011.0
\n",
"
501
\n",
"
458.0
\n",
"
544.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2062
\n",
"
198510
\n",
"
3
\n",
"
353231
\n",
"
326279.0
\n",
"
380183.0
\n",
"
640
\n",
"
591.0
\n",
"
689.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2063
\n",
"
198509
\n",
"
3
\n",
"
369895
\n",
"
341109.0
\n",
"
398681.0
\n",
"
670
\n",
"
618.0
\n",
"
722.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2064
\n",
"
198508
\n",
"
3
\n",
"
389886
\n",
"
359529.0
\n",
"
420243.0
\n",
"
707
\n",
"
652.0
\n",
"
762.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2065
\n",
"
198507
\n",
"
3
\n",
"
471852
\n",
"
432599.0
\n",
"
511105.0
\n",
"
855
\n",
"
784.0
\n",
"
926.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2066
\n",
"
198506
\n",
"
3
\n",
"
565825
\n",
"
518011.0
\n",
"
613639.0
\n",
"
1026
\n",
"
939.0
\n",
"
1113.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2067
\n",
"
198505
\n",
"
3
\n",
"
637302
\n",
"
592795.0
\n",
"
681809.0
\n",
"
1155
\n",
"
1074.0
\n",
"
1236.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2068
\n",
"
198504
\n",
"
3
\n",
"
424937
\n",
"
390794.0
\n",
"
459080.0
\n",
"
770
\n",
"
708.0
\n",
"
832.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2069
\n",
"
198503
\n",
"
3
\n",
"
213901
\n",
"
174689.0
\n",
"
253113.0
\n",
"
388
\n",
"
317.0
\n",
"
459.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2070
\n",
"
198502
\n",
"
3
\n",
"
97586
\n",
"
80949.0
\n",
"
114223.0
\n",
"
177
\n",
"
147.0
\n",
"
207.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2071
\n",
"
198501
\n",
"
3
\n",
"
85489
\n",
"
65918.0
\n",
"
105060.0
\n",
"
155
\n",
"
120.0
\n",
"
190.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2072
\n",
"
198452
\n",
"
3
\n",
"
84830
\n",
"
60602.0
\n",
"
109058.0
\n",
"
154
\n",
"
110.0
\n",
"
198.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2073
\n",
"
198451
\n",
"
3
\n",
"
101726
\n",
"
80242.0
\n",
"
123210.0
\n",
"
185
\n",
"
146.0
\n",
"
224.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2074
\n",
"
198450
\n",
"
3
\n",
"
123680
\n",
"
101401.0
\n",
"
145959.0
\n",
"
225
\n",
"
184.0
\n",
"
266.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2075
\n",
"
198449
\n",
"
3
\n",
"
101073
\n",
"
81684.0
\n",
"
120462.0
\n",
"
184
\n",
"
149.0
\n",
"
219.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2076
\n",
"
198448
\n",
"
3
\n",
"
78620
\n",
"
60634.0
\n",
"
96606.0
\n",
"
143
\n",
"
110.0
\n",
"
176.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2077
\n",
"
198447
\n",
"
3
\n",
"
72029
\n",
"
54274.0
\n",
"
89784.0
\n",
"
131
\n",
"
99.0
\n",
"
163.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2078
\n",
"
198446
\n",
"
3
\n",
"
87330
\n",
"
67686.0
\n",
"
106974.0
\n",
"
159
\n",
"
123.0
\n",
"
195.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2079
\n",
"
198445
\n",
"
3
\n",
"
135223
\n",
"
101414.0
\n",
"
169032.0
\n",
"
246
\n",
"
184.0
\n",
"
308.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2080
\n",
"
198444
\n",
"
3
\n",
"
68422
\n",
"
20056.0
\n",
"
116788.0
\n",
"
125
\n",
"
37.0
\n",
"
213.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
2081 rows × 10 columns
\n",
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202437 3 59356 50585.0 68127.0 89 76.0 \n",
"1 202436 3 33435 27654.0 39216.0 50 41.0 \n",
"2 202435 3 27404 22036.0 32772.0 41 33.0 \n",
"3 202434 3 26717 21003.0 32431.0 40 31.0 \n",
"4 202433 3 20623 15349.0 25897.0 31 23.0 \n",
"5 202432 3 23187 17532.0 28842.0 35 27.0 \n",
"6 202431 3 26035 20267.0 31803.0 39 30.0 \n",
"7 202430 3 36393 28593.0 44193.0 55 43.0 \n",
"8 202429 3 39560 32592.0 46528.0 59 49.0 \n",
"9 202428 3 54342 45781.0 62903.0 81 68.0 \n",
"10 202427 3 47364 40234.0 54494.0 71 60.0 \n",
"11 202426 3 44219 36956.0 51482.0 66 55.0 \n",
"12 202425 3 47204 40300.0 54108.0 71 61.0 \n",
"13 202424 3 41110 34671.0 47549.0 62 52.0 \n",
"14 202423 3 35875 30610.0 41140.0 54 46.0 \n",
"15 202422 3 33772 28274.0 39270.0 51 43.0 \n",
"16 202421 3 21963 17556.0 26370.0 33 26.0 \n",
"17 202420 3 20057 15780.0 24334.0 30 24.0 \n",
"18 202419 3 15375 11274.0 19476.0 23 17.0 \n",
"19 202418 3 22409 17653.0 27165.0 34 27.0 \n",
"20 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"21 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"22 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"23 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"24 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"25 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"26 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"27 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"28 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"29 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"... ... ... ... ... ... ... ... \n",
"2051 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2052 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2053 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2054 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2055 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2056 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2057 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2058 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2059 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2060 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2061 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2062 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2063 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2064 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2065 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2066 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2067 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2068 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2069 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2070 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2071 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2072 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2073 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2074 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2075 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2076 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2077 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2078 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2079 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2080 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 102.0 FR France \n",
"1 59.0 FR France \n",
"2 49.0 FR France \n",
"3 49.0 FR France \n",
"4 39.0 FR France \n",
"5 43.0 FR France \n",
"6 48.0 FR France \n",
"7 67.0 FR France \n",
"8 69.0 FR France \n",
"9 94.0 FR France \n",
"10 82.0 FR France \n",
"11 77.0 FR France \n",
"12 81.0 FR France \n",
"13 72.0 FR France \n",
"14 62.0 FR France \n",
"15 59.0 FR France \n",
"16 40.0 FR France \n",
"17 36.0 FR France \n",
"18 29.0 FR France \n",
"19 41.0 FR France \n",
"20 49.0 FR France \n",
"21 51.0 FR France \n",
"22 53.0 FR France \n",
"23 56.0 FR France \n",
"24 61.0 FR France \n",
"25 70.0 FR France \n",
"26 85.0 FR France \n",
"27 101.0 FR France \n",
"28 119.0 FR France \n",
"29 172.0 FR France \n",
"... ... ... ... \n",
"2051 59.0 FR France \n",
"2052 64.0 FR France \n",
"2053 97.0 FR France \n",
"2054 93.0 FR France \n",
"2055 80.0 FR France \n",
"2056 116.0 FR France \n",
"2057 149.0 FR France \n",
"2058 281.0 FR France \n",
"2059 395.0 FR France \n",
"2060 485.0 FR France \n",
"2061 544.0 FR France \n",
"2062 689.0 FR France \n",
"2063 722.0 FR France \n",
"2064 762.0 FR France \n",
"2065 926.0 FR France \n",
"2066 1113.0 FR France \n",
"2067 1236.0 FR France \n",
"2068 832.0 FR France \n",
"2069 459.0 FR France \n",
"2070 207.0 FR France \n",
"2071 190.0 FR France \n",
"2072 198.0 FR France \n",
"2073 224.0 FR France \n",
"2074 266.0 FR France \n",
"2075 219.0 FR France \n",
"2076 176.0 FR France \n",
"2077 163.0 FR France \n",
"2078 195.0 FR France \n",
"2079 308.0 FR France \n",
"2080 213.0 FR France \n",
"\n",
"[2081 rows x 10 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from os import path as pth\n",
"import requests\n",
"# Si le fichier csv des données d'incidence existe en local\n",
"# il n'est pas nécessaire de le télécharger par l'URL\n",
"local_filename = \"incidence-PAY-3.csv\"\n",
"if pth.exists(local_filename):\n",
" # Le fichier existe en local dans le dossier courant\n",
" raw_data = pd.read_csv(local_filename, skiprows=1)\n",
"else:\n",
" # le fichier de données n'existe pas en local,\n",
" # nous allons télécharger les données et les écrire\n",
" # dans un fichier en local\n",
" # Téléchargement des données\n",
" response = requests.get(data_url)\n",
" # Ecriture des données téléchargées dans le fichier local\n",
" with open(local_filename, \"wb\") as f:\n",
" f.write(response.content)\n",
" raw_data = pd.read_csv(local_filename, skiprows=1)\n",
"raw_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
week
\n",
"
indicator
\n",
"
inc
\n",
"
inc_low
\n",
"
inc_up
\n",
"
inc100
\n",
"
inc100_low
\n",
"
inc100_up
\n",
"
geo_insee
\n",
"
geo_name
\n",
"
\n",
" \n",
" \n",
"
\n",
"
1844
\n",
"
198919
\n",
"
3
\n",
"
-
\n",
"
NaN
\n",
"
NaN
\n",
"
-
\n",
"
NaN
\n",
"
NaN
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1844 198919 3 - NaN NaN - NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1844 FR France "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
week
\n",
"
indicator
\n",
"
inc
\n",
"
inc_low
\n",
"
inc_up
\n",
"
inc100
\n",
"
inc100_low
\n",
"
inc100_up
\n",
"
geo_insee
\n",
"
geo_name
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
202437
\n",
"
3
\n",
"
59356
\n",
"
50585.0
\n",
"
68127.0
\n",
"
89
\n",
"
76.0
\n",
"
102.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
1
\n",
"
202436
\n",
"
3
\n",
"
33435
\n",
"
27654.0
\n",
"
39216.0
\n",
"
50
\n",
"
41.0
\n",
"
59.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2
\n",
"
202435
\n",
"
3
\n",
"
27404
\n",
"
22036.0
\n",
"
32772.0
\n",
"
41
\n",
"
33.0
\n",
"
49.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
3
\n",
"
202434
\n",
"
3
\n",
"
26717
\n",
"
21003.0
\n",
"
32431.0
\n",
"
40
\n",
"
31.0
\n",
"
49.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
4
\n",
"
202433
\n",
"
3
\n",
"
20623
\n",
"
15349.0
\n",
"
25897.0
\n",
"
31
\n",
"
23.0
\n",
"
39.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
5
\n",
"
202432
\n",
"
3
\n",
"
23187
\n",
"
17532.0
\n",
"
28842.0
\n",
"
35
\n",
"
27.0
\n",
"
43.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
6
\n",
"
202431
\n",
"
3
\n",
"
26035
\n",
"
20267.0
\n",
"
31803.0
\n",
"
39
\n",
"
30.0
\n",
"
48.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
7
\n",
"
202430
\n",
"
3
\n",
"
36393
\n",
"
28593.0
\n",
"
44193.0
\n",
"
55
\n",
"
43.0
\n",
"
67.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
8
\n",
"
202429
\n",
"
3
\n",
"
39560
\n",
"
32592.0
\n",
"
46528.0
\n",
"
59
\n",
"
49.0
\n",
"
69.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
9
\n",
"
202428
\n",
"
3
\n",
"
54342
\n",
"
45781.0
\n",
"
62903.0
\n",
"
81
\n",
"
68.0
\n",
"
94.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
10
\n",
"
202427
\n",
"
3
\n",
"
47364
\n",
"
40234.0
\n",
"
54494.0
\n",
"
71
\n",
"
60.0
\n",
"
82.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
11
\n",
"
202426
\n",
"
3
\n",
"
44219
\n",
"
36956.0
\n",
"
51482.0
\n",
"
66
\n",
"
55.0
\n",
"
77.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
12
\n",
"
202425
\n",
"
3
\n",
"
47204
\n",
"
40300.0
\n",
"
54108.0
\n",
"
71
\n",
"
61.0
\n",
"
81.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
13
\n",
"
202424
\n",
"
3
\n",
"
41110
\n",
"
34671.0
\n",
"
47549.0
\n",
"
62
\n",
"
52.0
\n",
"
72.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
14
\n",
"
202423
\n",
"
3
\n",
"
35875
\n",
"
30610.0
\n",
"
41140.0
\n",
"
54
\n",
"
46.0
\n",
"
62.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
15
\n",
"
202422
\n",
"
3
\n",
"
33772
\n",
"
28274.0
\n",
"
39270.0
\n",
"
51
\n",
"
43.0
\n",
"
59.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
16
\n",
"
202421
\n",
"
3
\n",
"
21963
\n",
"
17556.0
\n",
"
26370.0
\n",
"
33
\n",
"
26.0
\n",
"
40.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
17
\n",
"
202420
\n",
"
3
\n",
"
20057
\n",
"
15780.0
\n",
"
24334.0
\n",
"
30
\n",
"
24.0
\n",
"
36.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
18
\n",
"
202419
\n",
"
3
\n",
"
15375
\n",
"
11274.0
\n",
"
19476.0
\n",
"
23
\n",
"
17.0
\n",
"
29.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
19
\n",
"
202418
\n",
"
3
\n",
"
22409
\n",
"
17653.0
\n",
"
27165.0
\n",
"
34
\n",
"
27.0
\n",
"
41.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
20
\n",
"
202417
\n",
"
3
\n",
"
27042
\n",
"
21410.0
\n",
"
32674.0
\n",
"
41
\n",
"
33.0
\n",
"
49.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
21
\n",
"
202416
\n",
"
3
\n",
"
28882
\n",
"
23305.0
\n",
"
34459.0
\n",
"
43
\n",
"
35.0
\n",
"
51.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
22
\n",
"
202415
\n",
"
3
\n",
"
30229
\n",
"
24648.0
\n",
"
35810.0
\n",
"
45
\n",
"
37.0
\n",
"
53.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
23
\n",
"
202414
\n",
"
3
\n",
"
31813
\n",
"
26529.0
\n",
"
37097.0
\n",
"
48
\n",
"
40.0
\n",
"
56.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
24
\n",
"
202413
\n",
"
3
\n",
"
35090
\n",
"
29607.0
\n",
"
40573.0
\n",
"
53
\n",
"
45.0
\n",
"
61.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
25
\n",
"
202412
\n",
"
3
\n",
"
40639
\n",
"
34582.0
\n",
"
46696.0
\n",
"
61
\n",
"
52.0
\n",
"
70.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
26
\n",
"
202411
\n",
"
3
\n",
"
50268
\n",
"
43331.0
\n",
"
57205.0
\n",
"
75
\n",
"
65.0
\n",
"
85.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
27
\n",
"
202410
\n",
"
3
\n",
"
60107
\n",
"
52623.0
\n",
"
67591.0
\n",
"
90
\n",
"
79.0
\n",
"
101.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
28
\n",
"
202409
\n",
"
3
\n",
"
71121
\n",
"
62920.0
\n",
"
79322.0
\n",
"
107
\n",
"
95.0
\n",
"
119.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
29
\n",
"
202408
\n",
"
3
\n",
"
104566
\n",
"
94520.0
\n",
"
114612.0
\n",
"
157
\n",
"
142.0
\n",
"
172.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
...
\n",
"
\n",
"
\n",
"
2051
\n",
"
198521
\n",
"
3
\n",
"
26096
\n",
"
19621.0
\n",
"
32571.0
\n",
"
47
\n",
"
35.0
\n",
"
59.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2052
\n",
"
198520
\n",
"
3
\n",
"
27896
\n",
"
20885.0
\n",
"
34907.0
\n",
"
51
\n",
"
38.0
\n",
"
64.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2053
\n",
"
198519
\n",
"
3
\n",
"
43154
\n",
"
32821.0
\n",
"
53487.0
\n",
"
78
\n",
"
59.0
\n",
"
97.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2054
\n",
"
198518
\n",
"
3
\n",
"
40555
\n",
"
29935.0
\n",
"
51175.0
\n",
"
74
\n",
"
55.0
\n",
"
93.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2055
\n",
"
198517
\n",
"
3
\n",
"
34053
\n",
"
24366.0
\n",
"
43740.0
\n",
"
62
\n",
"
44.0
\n",
"
80.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2056
\n",
"
198516
\n",
"
3
\n",
"
50362
\n",
"
36451.0
\n",
"
64273.0
\n",
"
91
\n",
"
66.0
\n",
"
116.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2057
\n",
"
198515
\n",
"
3
\n",
"
63881
\n",
"
45538.0
\n",
"
82224.0
\n",
"
116
\n",
"
83.0
\n",
"
149.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2058
\n",
"
198514
\n",
"
3
\n",
"
134545
\n",
"
114400.0
\n",
"
154690.0
\n",
"
244
\n",
"
207.0
\n",
"
281.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2059
\n",
"
198513
\n",
"
3
\n",
"
197206
\n",
"
176080.0
\n",
"
218332.0
\n",
"
357
\n",
"
319.0
\n",
"
395.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2060
\n",
"
198512
\n",
"
3
\n",
"
245240
\n",
"
223304.0
\n",
"
267176.0
\n",
"
445
\n",
"
405.0
\n",
"
485.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2061
\n",
"
198511
\n",
"
3
\n",
"
276205
\n",
"
252399.0
\n",
"
300011.0
\n",
"
501
\n",
"
458.0
\n",
"
544.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2062
\n",
"
198510
\n",
"
3
\n",
"
353231
\n",
"
326279.0
\n",
"
380183.0
\n",
"
640
\n",
"
591.0
\n",
"
689.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2063
\n",
"
198509
\n",
"
3
\n",
"
369895
\n",
"
341109.0
\n",
"
398681.0
\n",
"
670
\n",
"
618.0
\n",
"
722.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2064
\n",
"
198508
\n",
"
3
\n",
"
389886
\n",
"
359529.0
\n",
"
420243.0
\n",
"
707
\n",
"
652.0
\n",
"
762.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2065
\n",
"
198507
\n",
"
3
\n",
"
471852
\n",
"
432599.0
\n",
"
511105.0
\n",
"
855
\n",
"
784.0
\n",
"
926.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2066
\n",
"
198506
\n",
"
3
\n",
"
565825
\n",
"
518011.0
\n",
"
613639.0
\n",
"
1026
\n",
"
939.0
\n",
"
1113.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2067
\n",
"
198505
\n",
"
3
\n",
"
637302
\n",
"
592795.0
\n",
"
681809.0
\n",
"
1155
\n",
"
1074.0
\n",
"
1236.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2068
\n",
"
198504
\n",
"
3
\n",
"
424937
\n",
"
390794.0
\n",
"
459080.0
\n",
"
770
\n",
"
708.0
\n",
"
832.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2069
\n",
"
198503
\n",
"
3
\n",
"
213901
\n",
"
174689.0
\n",
"
253113.0
\n",
"
388
\n",
"
317.0
\n",
"
459.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2070
\n",
"
198502
\n",
"
3
\n",
"
97586
\n",
"
80949.0
\n",
"
114223.0
\n",
"
177
\n",
"
147.0
\n",
"
207.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2071
\n",
"
198501
\n",
"
3
\n",
"
85489
\n",
"
65918.0
\n",
"
105060.0
\n",
"
155
\n",
"
120.0
\n",
"
190.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2072
\n",
"
198452
\n",
"
3
\n",
"
84830
\n",
"
60602.0
\n",
"
109058.0
\n",
"
154
\n",
"
110.0
\n",
"
198.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2073
\n",
"
198451
\n",
"
3
\n",
"
101726
\n",
"
80242.0
\n",
"
123210.0
\n",
"
185
\n",
"
146.0
\n",
"
224.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2074
\n",
"
198450
\n",
"
3
\n",
"
123680
\n",
"
101401.0
\n",
"
145959.0
\n",
"
225
\n",
"
184.0
\n",
"
266.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2075
\n",
"
198449
\n",
"
3
\n",
"
101073
\n",
"
81684.0
\n",
"
120462.0
\n",
"
184
\n",
"
149.0
\n",
"
219.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2076
\n",
"
198448
\n",
"
3
\n",
"
78620
\n",
"
60634.0
\n",
"
96606.0
\n",
"
143
\n",
"
110.0
\n",
"
176.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2077
\n",
"
198447
\n",
"
3
\n",
"
72029
\n",
"
54274.0
\n",
"
89784.0
\n",
"
131
\n",
"
99.0
\n",
"
163.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2078
\n",
"
198446
\n",
"
3
\n",
"
87330
\n",
"
67686.0
\n",
"
106974.0
\n",
"
159
\n",
"
123.0
\n",
"
195.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2079
\n",
"
198445
\n",
"
3
\n",
"
135223
\n",
"
101414.0
\n",
"
169032.0
\n",
"
246
\n",
"
184.0
\n",
"
308.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
"
\n",
"
2080
\n",
"
198444
\n",
"
3
\n",
"
68422
\n",
"
20056.0
\n",
"
116788.0
\n",
"
125
\n",
"
37.0
\n",
"
213.0
\n",
"
FR
\n",
"
France
\n",
"
\n",
" \n",
"
\n",
"
2080 rows × 10 columns
\n",
"
"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202437 3 59356 50585.0 68127.0 89 76.0 \n",
"1 202436 3 33435 27654.0 39216.0 50 41.0 \n",
"2 202435 3 27404 22036.0 32772.0 41 33.0 \n",
"3 202434 3 26717 21003.0 32431.0 40 31.0 \n",
"4 202433 3 20623 15349.0 25897.0 31 23.0 \n",
"5 202432 3 23187 17532.0 28842.0 35 27.0 \n",
"6 202431 3 26035 20267.0 31803.0 39 30.0 \n",
"7 202430 3 36393 28593.0 44193.0 55 43.0 \n",
"8 202429 3 39560 32592.0 46528.0 59 49.0 \n",
"9 202428 3 54342 45781.0 62903.0 81 68.0 \n",
"10 202427 3 47364 40234.0 54494.0 71 60.0 \n",
"11 202426 3 44219 36956.0 51482.0 66 55.0 \n",
"12 202425 3 47204 40300.0 54108.0 71 61.0 \n",
"13 202424 3 41110 34671.0 47549.0 62 52.0 \n",
"14 202423 3 35875 30610.0 41140.0 54 46.0 \n",
"15 202422 3 33772 28274.0 39270.0 51 43.0 \n",
"16 202421 3 21963 17556.0 26370.0 33 26.0 \n",
"17 202420 3 20057 15780.0 24334.0 30 24.0 \n",
"18 202419 3 15375 11274.0 19476.0 23 17.0 \n",
"19 202418 3 22409 17653.0 27165.0 34 27.0 \n",
"20 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"21 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"22 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"23 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"24 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"25 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"26 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"27 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"28 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"29 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"... ... ... ... ... ... ... ... \n",
"2051 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2052 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2053 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2054 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2055 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2056 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2057 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2058 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2059 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2060 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2061 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2062 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2063 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2064 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2065 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2066 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2067 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2068 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2069 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2070 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2071 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2072 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2073 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2074 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2075 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2076 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2077 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2078 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2079 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2080 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 102.0 FR France \n",
"1 59.0 FR France \n",
"2 49.0 FR France \n",
"3 49.0 FR France \n",
"4 39.0 FR France \n",
"5 43.0 FR France \n",
"6 48.0 FR France \n",
"7 67.0 FR France \n",
"8 69.0 FR France \n",
"9 94.0 FR France \n",
"10 82.0 FR France \n",
"11 77.0 FR France \n",
"12 81.0 FR France \n",
"13 72.0 FR France \n",
"14 62.0 FR France \n",
"15 59.0 FR France \n",
"16 40.0 FR France \n",
"17 36.0 FR France \n",
"18 29.0 FR France \n",
"19 41.0 FR France \n",
"20 49.0 FR France \n",
"21 51.0 FR France \n",
"22 53.0 FR France \n",
"23 56.0 FR France \n",
"24 61.0 FR France \n",
"25 70.0 FR France \n",
"26 85.0 FR France \n",
"27 101.0 FR France \n",
"28 119.0 FR France \n",
"29 172.0 FR France \n",
"... ... ... ... \n",
"2051 59.0 FR France \n",
"2052 64.0 FR France \n",
"2053 97.0 FR France \n",
"2054 93.0 FR France \n",
"2055 80.0 FR France \n",
"2056 116.0 FR France \n",
"2057 149.0 FR France \n",
"2058 281.0 FR France \n",
"2059 395.0 FR France \n",
"2060 485.0 FR France \n",
"2061 544.0 FR France \n",
"2062 689.0 FR France \n",
"2063 722.0 FR France \n",
"2064 762.0 FR France \n",
"2065 926.0 FR France \n",
"2066 1113.0 FR France \n",
"2067 1236.0 FR France \n",
"2068 832.0 FR France \n",
"2069 459.0 FR France \n",
"2070 207.0 FR France \n",
"2071 190.0 FR France \n",
"2072 198.0 FR France \n",
"2073 224.0 FR France \n",
"2074 266.0 FR France \n",
"2075 219.0 FR France \n",
"2076 176.0 FR France \n",
"2077 163.0 FR France \n",
"2078 195.0 FR France \n",
"2079 308.0 FR France \n",
"2080 213.0 FR France \n",
"\n",
"[2080 rows x 10 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nos données utilisent une convention inhabituelle: le numéro de\n",
"semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
"de nombre entier. C'est comme ça que Pandas les interprète.\n",
" \n",
"Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
"semaine. Il faut lui fournir les dates de début et de fin de\n",
"semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
"\n",
"Comme la conversion des semaines est devenu assez complexe, nous\n",
"écrivons une petite fonction Python pour cela. Ensuite, nous\n",
"l'appliquons à tous les points de nos donnés. Les résultats vont\n",
"dans une nouvelle colonne 'period'."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def convert_week(year_and_week_int):\n",
" year_and_week_str = str(year_and_week_int)\n",
" year = int(year_and_week_str[:4])\n",
" week = int(year_and_week_str[4:])\n",
" w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n",
"\n",
"data['period'] = [convert_week(yw) for yw in data['week']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il restent deux petites modifications à faire.\n",
"\n",
"Premièrement, nous définissons les périodes d'observation\n",
"comme nouvel index de notre jeux de données. Ceci en fait\n",
"une suite chronologique, ce qui sera pratique par la suite.\n",
"\n",
"Deuxièmement, nous trions les points par période, dans\n",
"le sens chronologique."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
"le début de la période qui suit, la différence temporelle doit être\n",
"zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
"d'une seconde.\n",
"\n",
"Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
"entre lesquelles il manque une semaine.\n",
"\n",
"Nous reconnaissons ces dates: c'est la semaine sans observations\n",
"que nous avions supprimées !"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
}
],
"source": [
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
" delta = p2.to_timestamp() - p1.end_time\n",
" if delta > pd.Timedelta('1s'):\n",
" print(p1, p2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un premier regard sur les données !"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Empty 'DataFrame': no numeric data to plot",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
]
}
],
"source": [
"sorted_data['inc'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'68422'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_data['inc'][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous constatons que les données de la colonne 'inc' ne sont pas numériques mais du texte. Il faut donc convertir ses données."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"sorted_data['inc'] = sorted_data['inc'].astype('int')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sorted_data['inc'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJztvXmYXHWZt38/tXRX71uSzr5vkECCiQkIKIgsOiouoDCO4DY4I/5Gf+Ms6Dvv6KiMy4iOOiOKgOI2wCAgCojIvoeEBELI1tn33reqrv37/lHnVFd319adqq4lz31dfXXlW+ecPnXSfT7n2cUYg6IoiqJkg6PQJ6AoiqKUDioaiqIoStaoaCiKoihZo6KhKIqiZI2KhqIoipI1KhqKoihK1qhoKIqiKFmjoqEoiqJkjYqGoiiKkjWuQp9ArpkyZYqZP39+oU9DURSlpNi0aVOnMWZqpu3KTjTmz5/Pxo0bC30aiqIoJYWIHMhmO3VPKYqiKFmjoqEoiqJkjYqGoiiKkjUZRUNE5ojIEyKyXUS2icjnrPWviMgREdlifb0rYZ8vikibiOwUkUsT1teIyFbrvR+IiFjrlSJyl7X+kojMT9jnWhHZbX1dm8sPryiKooyPbALhYeALxphXRKQO2CQij1rvfc8Y853EjUXkdOAqYAUwE/iziCw1xkSAm4HrgBeBh4DLgIeBTwI9xpjFInIV8C3gwyLSDHwZWAsY62c/YIzpObmPrSiKokyEjJaGMeaYMeYV6/UAsB2YlWaXy4E7jTEBY8w+oA1YJyIzgHpjzAsmNvnpF8D7Eva5w3p9D3CRZYVcCjxqjOm2hOJRYkKjKIqiFIBxxTQst9FZwEvW0mdF5DURuV1Emqy1WcChhN0OW2uzrNej10fsY4wJA31AS5pjKYqiKAUga9EQkVrgt8DnjTH9xFxNi4DVwDHgJnvTJLubNOsT3Sfx3K4TkY0isrGjoyPt51CUUsMbCHPvK4czb6gok0BWoiEibmKC8WtjzL0AxpgTxpiIMSYK/BRYZ21+GJiTsPts4Ki1PjvJ+oh9RMQFNADdaY41AmPMLcaYtcaYtVOnZixoVJSS4r7NR/j7u1/lcI+v0KeiKFllTwlwG7DdGPPdhPUZCZu9H3jdev0AcJWVEbUAWAJsMMYcAwZE5GzrmNcAv0vYx86MugJ43Ip7PAJcIiJNlvvrEmtNUU4ZDnR5AfCHogU+E0XJLnvqXOCjwFYR2WKtfQm4WkRWE3MX7Qc+DWCM2SYidwNvEMu8ut7KnAL4W+DnQBWxrKmHrfXbgF+KSBsxC+Mq61jdIvI14GVru68aY7on9lEVpTQ52B2zMMJRFQ2l8GQUDWPMsySPLTyUZp8bgRuTrG8EViZZ9wNXpjjW7cDtmc5TUcqVA12WaETGhPMUZdLRinBFKWKMMRyyLI1QRC0NpfCoaChKEdPtDeINxry74ahaGkrhUdFQlCLmQPdwxlQorJaGUnhUNBSliDmUKBpqaShFgIqGohQxB7uGRSOsMQ2lCFDRUJQiZoR7SrOnlCJARUNRipiD3T4aq92AZk8pxYGKhqIUMUd6hlgwpQbQ4j6lOFDRUJQixhsM01xdAah7SikOVDQUpYgJhqNUV8YaN2hFuFIMqGgoShETDEepqXACGtNQigMVDUUpUqJRQzhqqLEsDRUNpRhQ0VCUIiVoiYRtaWgbEaUYUNFQlCLFFo2qCjumoZaGUnhUNBSlSAlavaaq4zENtTSUwqOioShFii0alS4HLodoTEMpClQ0FKVIsUWjwuXA5RSNaShFgYqGohQpdkyjwuXA7XCopaEUBSoailKkxC0Np2VpaExDKQJUNBSlSBlhaTjV0lCKAxUNRSlSEmMaMdFQS0MpPCoailKkjHFPaZfbvPHEznbufeVwoU+jJHAV+gQURUnOiOwph8Y08sWJfj9/95vNNNa4+cCbZhf6dIoeFQ1FKVJGxzSCGtPIC1/9/RsMBMKFPo2SQd1TilKkJLqn3E6HthHJA3s7Bnlw6zFaaioYCIQ12SALVDQUpUjR4r780+0NAnDW3CYA+oZChTydkkBFQ1GKFC3uyz9DoQgAMxo8APT6VDQyoaKhKEVKvPeU06nFfXliKGiJRqMtGsFCnk5JoKKhKEWKbWm4XaLFfXnCbwmzWhrZo6KhKEXKyEC4aHFfHvBblsb0+ioAetTSyIiKhqIUKcFwFIeAy+nA5XBocV8eGB3T0EB4ZlQ0FKVICUaiVLhif6Ia08gPtmhMravE6RC1NLIgo2iIyBwReUJEtovINhH5nLXeLCKPishu63tTwj5fFJE2EdkpIpcmrK8Rka3Wez8QEbHWK0XkLmv9JRGZn7DPtdbP2C0i1+bywytKMRMMR6lwxv5EK7S4Ly/4LdGocjtprHLTozGNjGRjaYSBLxhjTgPOBq4XkdOBG4DHjDFLgMesf2O9dxWwArgM+JGIOK1j3QxcByyxvi6z1j8J9BhjFgPfA75lHasZ+DKwHlgHfDlRnBSlnAmEo1S4Yn86amnkh6FQhAqXA4dDaKx206eikZGMomGMOWaMecV6PQBsB2YBlwN3WJvdAbzPen05cKcxJmCM2Qe0AetEZAZQb4x5wRhjgF+M2sc+1j3ARZYVcinwqDGm2xjTAzzKsNAoSlkTikSpjLunNKaRD/zBCFXumDA3VVeoeyoLxhXTsNxGZwEvAa3GmGMQExZgmrXZLOBQwm6HrbVZ1uvR6yP2McaEgT6gJc2xRp/XdSKyUUQ2dnR0jOcjKUrREgxHcTsFALdDs6fywVBoWDQaq92acpsFWYuGiNQCvwU+b4zpT7dpkjWTZn2i+wwvGHOLMWatMWbt1KlT05yaopQOwfBwIFzrNPKDPxSlqsIWjQot7suCrERDRNzEBOPXxph7reUTlssJ63u7tX4YmJOw+2zgqLU+O8n6iH1ExAU0AN1pjqUoZc/I7CmHxjTywFAogse2NKrc9GrKbUayyZ4S4DZguzHmuwlvPQDY2UzXAr9LWL/KyohaQCzgvcFyYQ2IyNnWMa8ZtY99rCuAx624xyPAJSLSZAXAL7HWFKXsScyecjuFkMY0co4/FMHjjl3jppoKfMEIgXCkwGdV3GQzT+Nc4KPAVhHZYq19CfgmcLeIfBI4CFwJYIzZJiJ3A28Qy7y63hhj/y/8LfBzoAp42PqCmCj9UkTaiFkYV1nH6haRrwEvW9t91RjTPcHPqiglRaJ7yuVwYAxEoganI5nXVpkIQwmB8IYqNxBrJdJa70y32ylNRtEwxjxL8tgCwEUp9rkRuDHJ+kZgZZJ1P5boJHnvduD2TOepKOVGIBKloSJ2I3NZAfFQJIrToTe0XOEPR+Ji0VRdAdii4SnkaRU1WhGuKEXK6OI+QIPhOWYoGMFTYafcxsRD027To6KhKEVKKBKlwhWzMGxLQ4PhucUfiuKxCigbqofdU0pqVDQUpUhJtDRctqWhwfCcMhSKUFURu7b1npho9PtVNNKhoqGULIOBMG8cTVcyVNqMqNNwqKWRD/wJxX12vUYgpNlT6VDRUEqWX7ywn/f/6Lmy9fMn1mm4NaaRc4wxIyrC7XqNIRWNtKhoKCVLe3+AQDhatjMQYu6p4YaFgLYSySGBcBRjiAfCPZZA+0MqzOlQ0VBKFrvlQ1mLxihLQ5sW5g67Lbon3kk4NiHRr5ZGWlQ0lJLFFotyzHYxxoxsI6IxjZxjWxR2LANiAqLuqfSoaCgli90nqG+o/PLqbTdUhd3l1hIPHcSUO4YSBjDZVLqd6p7KgIqGUrLYA3PK0T1li8Nw9pTlnlJLI2cMBS33VIJoVFU4NHsqAyoaSslSzu6pYNgSjXidhu2e0qfgXGFbGnbDQlD3VDaoaCgliTEmwT1VxqJhBWntYUyhqFoauSKQxD3lcTs1EJ4BFQ2lJPEGI0SsG2hZWxoJXW5BLY1cEo9pJATCqzSmkREVDaUkSZyw1l+OlkYkdkPT4r78kTwQ7lD3VAZUNJSSJNG6KMdpa4FRMQ23FvflnGSBcHVPZUZFQylJbOuiwuko85iG3eVWi/tyjd+6xiOyp9zOuGAryVHRUEoS27qY3Vw1wlVVLgzXaVjVyg61NHKNPzg2puFxO+IWiJIcFQ2lJLHdU/Oaq+kbChf4bHLP6EC4/V1jGrkjnnLrSki5dTvx64zwtKhoKCWJ7ZKa11JD31AQY8rrCXx0IFzbiOSeoVCECqcj7voDO3tKRSMdKhpKSdI7FKTC6aC13kMoYsou42VscZ9aGrnGH4pQ6R55C7TbiJTbQ0guUdFQSpI+X4iGajeNZTqiMzDKPWVnT4W1uC9nJA5gsrGrwzUYnhoVDaUk6RsK0VjlprHKHf93OWFbGpWj6zT0ZpYzhoKREUFwGK7Z0GB4alQ0lJKk1xeiocpNQ1V5Whp2w0JbLOLZU2pp5IyhpJZG7N8aDE+NioZSkvQOhWisdtNQXd6Whu2eEhFcDtE2IjlkKBSlMoV7SluJpEZFQylJ+odC1CdYGuU2U8MO7FcnuE9cTtGYRg7xBsLUVbpGrKl7KjMqGkpJ0usL0lhVQWN1hfXv8rI0fIEIDhmOaUBspkZQYxo5Y9AfpnaUaFSqeyojKhpKyRGORPEGIzRUuampcOJySNm5p3zBCNUVLkQkvuZ2ObSNSA4Z8Ieo9YwUDXteuNZqpEZFQyk5fNYfdE2lExGhqsKJr8zcCb5geIRrCrBiGuqeyhUDgbGWhp1NpaKRGhUNpeQYGtUzyON2Eigzd4I3GKFm1A3N7XRo76kcYYxhMBCmfrSloYHwjKhoKCVHXDTctmiUX5O5oWB4TDpoLBCuN7Nc4AtGMAZ1T00AFQ2l5PCNFg1X+U1b8wYi1FSOSgd1aV+kXDHgjzW5rK10j1i3rddya0uTSzKKhojcLiLtIvJ6wtpXROSIiGyxvt6V8N4XRaRNRHaKyKUJ62tEZKv13g/EivCJSKWI3GWtvyQi8xP2uVZEdltf1+bqQyulzegxnVUV5deZ1BeKBcITqfW4GAyUX0ffQjAYiCVOpLY0yushJJdkY2n8HLgsyfr3jDGrra+HAETkdOAqYIW1z49ExH5cuhm4DlhifdnH/CTQY4xZDHwP+JZ1rGbgy8B6YB3wZRFpGvcnVMoOfyiZpVFmohEYGwivrXTFn5CVk8O+jnWe0Sm3dkyjvH6fcklG0TDGPA10Z3m8y4E7jTEBY8w+oA1YJyIzgHpjzAsm1j7yF8D7Eva5w3p9D3CRZYVcCjxqjOk2xvQAj5JcvJRTDNs9ZT+Jx+Y6l9eToZ1ym0idx8WgikZOiIvG6DoNlwMRFY10nExM47Mi8prlvrItgFnAoYRtDltrs6zXo9dH7GOMCQN9QEuaYymnOMPuqdivr8ftJFBmf+S+YHhMTKPO42JA3VPjpn3Ajy848rrZbr7R7ikRKUvLNZdMVDRuBhYBq4FjwE3WuiTZ1qRZn+g+IxCR60Rko4hs7OjoSHfeShkwZN0Aqqwn8XIcnONN0oG1zuNmwF9eRYz54mCXjzue38/HfraB9f/+GF/7wxsj3h+MB8JdY/b1uB0a00jD2CuWBcaYE/ZrEfkp8Afrn4eBOQmbzgaOWuuzk6wn7nNYRFxAAzF32GHgglH7PJnifG4BbgFYu3atJrKXOclSbsvpjzwciRIMR6kZHQivdOEPRQlFovHut0py/vLWFzncM8SMBg8NVW6O9PpHvG9bbHUe95h9q9xOzZ5Kw4R+86wYhc37ATuz6gHgKisjagGxgPcGY8wxYEBEzrbiFdcAv0vYx86MugJ43Ip7PAJcIiJNlvvrEmtNOcXxjWrmV25znUd/Phs7aKtxjfQYYzje5+evz1/A8ze8nZUzGxgcZaHZFltyS6P8LNdcktHSEJH/IfbEP0VEDhPLaLpARFYTcxftBz4NYIzZJiJ3A28AYeB6Y4x99f+WWCZWFfCw9QVwG/BLEWkjZmFcZR2rW0S+BrxsbfdVY0y2AXmljPEHI0hCMz+P21lWxX2+wMhAv419gxsMhGmqqZj08yoV+ofChKOG1noPIkKdx8WJ/pGWxqA/lp3mdIz1gtsjX5XkZBQNY8zVSZZvS7P9jcCNSdY3AiuTrPuBK1Mc63bg9kznqJxa2MNz7GZ+sTYisbnOiQ3+ShWvFbMZGwiPuVL6Na6Rli5vAICW2piw1laOrW8ZTNJ3yqbK7VBLIw3qGFVKDl9w5MS1cpvrPDpmY6Puqezo8cVmqzTXVAJ2AsHIazbgD4+p0bBR91R6VDSUkmMoFImP5YTy6xfkDdiWRmr3lJKarkFLNKxZK3YlfSRhgNVAIExtkiA4WO7OMvldygcqGkrJMRSMjAgS2wJSLn/omQLhWhWenm6vJRqWe8ruZOtNqNUY9IfGFPbZlGMKdy5R0VBKjqHQyBoGu8ivXIKXKQPhtmiopZGWLks0WmqGYxowUmzTxTQqyyyFO9eoaCglx5iYRrm5p6wn4tGWRr3lTtECv/R0e4NUVzjjFqidQJAYC0oX06iucI6pIFeGUdFQSg7/KEvDvjmUi2jYgfDRMY1KlwOXQzQQnoEeb5Cm6uGU5GG33rDYDvrDY1qIDG/vZjAQJlYupoxGRUMpOUbHNOzOpOUS00hladg1BxrTSE+XNxhPt4UEt5513aJRw2AwnDKmUVvpIhQxZZONl2tUNJSSwxcclT1lvQ6UiR/aF4jgSCheTERnamSm2xukOaH4sX5ULMgbDCed2jdmexXnpKhoKCWHPzTS0qgqM/eULxihpsKVtFCxrlKbFmZitGjY0/ns6zaYpu8UJFomep2ToaKhlBxji/ss0SiT/lO+YHhMh1ubWnVPZaTLG4hnTsHYosh0HW4hJsyg9TCpUNFQSgpjTLyNiI3HXV4pt95gZEwQ3KZe3VNpGQpG8IeiI3pzVVc4cciwu2kgxSwNm9ExEGUkKhpKSWEHJ6sSahjslNtyaVo4FBw76tVGR76mJ953KkE0RMS6bjF308EuHwDT6z1Jj6FFlOlR0VBKCl+8L9Pwr67tyikX95Q3EEkpGnY6qJKceDW41XfKps7jjlsYmw/2UF3hZGlrXdJj1FVqPUw6VDSUkmIoNLZa2s4yKhf3lC80dj64TSymEdIaghR0xUVjZOv4xFTlzYd6WTW7MWlbdHtb0JhGKlQ0lJLCHvXqSXgSFxEqXY6ymRPuC4ydD25T59EagnR0D6YWjUF/GH8owhtH+zlrbmPKY2hMIz0qGkpJMRSM3SyrR7UNL6fOpLHssFSZPXpDS0f7QCymMaV2pGjUVroYCIR4/Ugf4ajhrLlNKY/hdjrwuB1qaaRARUMpKeyeQKNTUsupM6kvmNrSqFXXSVoOdHlpqakYU4Nhz9TYfLAXgNVzUlsaEKvt0JhGclQ0lJLCtiZGi4anTDqTGmMYDIRTxzQqxzbfU4bZ1+ll/pSaMeu1lnvqlYM9zGmuYmpdZZK9h6nXepiUqGgoJYVtTYyealcu09a8wQihiKGpOnm1sp1VpV1Yk3Ogy8f8lrGiUedx0e8P8ezuTs5e0JLxOFpEmZqMM8IVpZjwpRiFWlkmMY0eK/unaVQg18a2sHxl8FlzzVAwwvF+P/Nbqse8V+9xE4oYQpEwl6+elfFYdVpEmRK1NJSSYijFVLsqt6MsGhb2+mJ+9MTW3onUWG6rcilkzCX7u7wAyd1TVgLBtLpKzlmUhaWRUAyojERFQykp7JulZ0xMw1kWxX09PsvSyOieKv3PmmsOWKKxIIlo2LUX7101M2V9xsjt3Ro3SoGKhlJSDKVwT3lc5RHTsEWjMYWlUaUxjZTs64y1B5mXxD01r6UGt1O4Yu3srI6l7VpSozENpaQYCkVwOwW3c+TzTrlkT8VjGmppjJv9nV6m1I5NtwVYM6+JrV+5dMQclnTUe1wMBsNEowZHFpbJqYRaGkpJ4U2RjlpVUSaBcCum0VCVXDTs5owqGmPZ3+VNmjllk61gQCx7ypjhKYrKMCoaSkkxEAjH/dOJVJaJe6rXF6Shyo3LmfxP0+EQqtzOeDsVZZj9XclrNCaCba1oBtVYVDSUkmLAH046PMfjdpZF9lSPL5TSNWVTXeFUS2MU0aihfSDAjIbk7c7HS622a0mJioZSUgz4Q9Qn8Vl73A6CkSiRaGl3f+3xBVMGwW2qK52acjuKAX9s7nema5ctOlMjNSoaSkkx4E/unvKUyZzwHl8ws6XhdqmvfRS9Q1bWWYpY0Hip0znhKVHRUEqKwUA46ZjOqnIRDW8oZWGfTZW6p8ZgF0U2ZhDcbNGYRmpUNJSSIrWlYQ1iKvE5E73ZuKcq1D01mr6h9Fln40VjGqnJKBoicruItIvI6wlrzSLyqIjstr43Jbz3RRFpE5GdInJpwvoaEdlqvfcDERFrvVJE7rLWXxKR+Qn7XGv9jN0icm2uPrRSmhhjGPCHkubhl4N7KhCO4A1GaK7RQPh46R3KtaVhtaBX0RhDNpbGz4HLRq3dADxmjFkCPGb9GxE5HbgKWGHt8yMRsZOjbwauA5ZYX/YxPwn0GGMWA98DvmUdqxn4MrAeWAd8OVGclFOPQDhKKGKSZk9VukpfNIZdLJncU66yqEnJJX1WJX1DVW4C4TUVLkQ0ppGMjKJhjHka6B61fDlwh/X6DuB9Cet3GmMCxph9QBuwTkRmAPXGmBdMbLjxL0btYx/rHuAiywq5FHjUGNNtjOkBHmWseCmnELaroD5ZTKOi9EVjuO9UBveU26ltREbRm6Eocrw4HEJthYsBjWmMYaIxjVZjzDEA6/s0a30WcChhu8PW2izr9ej1EfsYY8JAH9CS5ljKKYr91JcsEO5xWTGNEq7V6PHaHW4zuKcqnfgCpSuO+aB3KERNhZMKV+7CtPbgJmUkuQ6EJ2vSYtKsT3SfkT9U5DoR2SgiGzs6OrI6UaX0sDNZ6irLM6bRm6FZoU11hRNfKELMaFcgZmnkqkbDpk4HMSVloqJxwnI5YX1vt9YPA3MStpsNHLXWZydZH7GPiLiABmLusFTHGoMx5hZjzFpjzNqpU6dO8CMpxY79B5y+TqOELQ3LxdKcYgCTTXWFi0jUEIyM/KzGGG59Zi///URb3s6xWOkbCubMNWVTW6mDmJIxUdF4ALCzma4FfpewfpWVEbWAWMB7g+XCGhCRs614xTWj9rGPdQXwuBX3eAS4RESarAD4JdaacoqSzj1l12mUcoB4uC16+ptf/LOOyqC66U+7+PqD2/n+Y7tL2uKaCDFLI7eiUedxayA8Cdmk3P4P8AKwTEQOi8gngW8CF4vIbuBi698YY7YBdwNvAH8ErjfG2L+9fwvcSiw4vgd42Fq/DWgRkTbg77EysYwx3cDXgJetr69aa8opynAgPHkbESht91TfUIhKlyNjN9Zk7dF3nRjgv55o4/QZ9QTDUbYc6s3ruRYbvUO5F41ajwbCk5FxnoYx5uoUb12UYvsbgRuTrG8EViZZ9wNXpjjW7cDtmc5ROTVI556qLIOYRp8vlJWLpSqJaBzuiQ0guuGdy/nYzzbwwp4uzl6YeaxpudDrC+Us3damXmMaSdGKcKVksP+Aa5J2uY39KgdKuCK8byg70bDniSSm3XYMBABYOLWGFTMbeGFvV35OsggxxtA3FMy9pVGp2VPJUNFQSobBQIgqt3PM1D6ACqcDh4z185cS2YpGTRJLo3MwFg+ZUlvJOYta2HKwt6StrvHgC0YIRUzOmhXa1HncDIUihCKl+yCSD1Q0lJIhVd8pABHB4y7tQUzZiobtnkoUyI6BAHUeFx63k7MXNhOMRHnlQE/ezrWYyHULERu784BX4xojUNFQSoYBf/IOtzYetxN/uPxFY9g9NVI0ptZVArB6TqzbzvbjA3k4y+KjN95CJNeWhjYtTIaKhlIy9KdoVmjjcTlKuk6jfyhEfVaiYbunEmIagwGm1MZEo6naTV2li4Nd3vycaJEx3OE298V9oKIxGhUNpWQYDIST9p2y8VSUrnsqEjUMBMLjc08lfNbOBEtDRJjbUs2Bbl9+TrbI6MvxLA0b+wFFazVGoqKhlAyp5oPbeFylKxr945gHYVsa3oT+Ux2DAaZalgbAvJZqDnadGqKR75iGVoWPREVDKRliszTSxTRK1z01niFCHpcdCI/dzPyhCAP+cNzSAJjTXM2hHl/Jz0zPhh6NaUwqKhpKyTDoD6ePaZRw9lS/P3vRcDhkxCCmzsFYjcYIS6O5hlDEcKxvKA9nW1wc7PLRXFMRTxDIFXbShVaFj0RFQykJIlGDNxhJ754q4eypuKWRpYvF7nQLw4V9U+qGA8HzWqoBTgkXVVv7IIun1ub8uPUa00iKioZSEgymaSFiU+Uu3dnZ451xXZUwJ9wWjam1nvj7c5tjolHuwXBjDG0dgyyalnvRqHQ5cDlEq8JHoaKhlASDwcyiUXmKxDQAqt2ueMptvBo8wdKY2ViF2ykcKHNLo9sbpNcXYnEeRENEdKZGElQ0lJJgME3fKRuP20mgxN1TyTr4JqOh2h23MOzvLTXDMQ2nQ5jdVM3B7vKu1WhrHwTIi2iANb1PYxojUNFQSgL7DzetaLicJW1pVDgd8caLmThjVgPbjvYTikTpHAzQWO0eM+p0bnN13NJ4eOsxPv6zDXRZQfNyoa0jJhqLptbk5fh1lTpTYzQqGkpJ4I2Pek0T06hwlOwQJrsaPDajLDNnzW0kEI6y49hArIVIQuaUjV2rYYzhT2+c4ImdHVx1y4uc6Pfn+vQLRlv7IFVuJzMbqvJy/Fp1T41BRUMpCbK1NCJRU5JdSWN9p7JPGT1rbqy/1Mv7u9l4oJulrXVjtpnbXM1AIEyPL8Shbh+zGqs40jvEh37yQnz+RqnT1j7Iomk1OBzZie140ZkaY1HRUEoCWzQypdxCaQ5iyrZZoc3MBg/T6iq59Zm9dA4Gec+qmWO2mdcSc9kc6PJysNvHuYtb+NWn1tPjDfKXP32JcJGL68v7uzMWJ+7t8OYl3dZG54SPRUVDKQm8WYmGPfK1uG+GyRivaIgIZ81t5Gifn3qPiwuXTx2zjV2rsevEAO0DAeY2V/OmuU382+UrONjtY/ux4u2Cu/lgD1f++AX+6/G2lNsEwhGO9A6xYEr+REPnhI9FRUMpCbJGYnN7AAAgAElEQVTNnoJTw9KAYRfVX5w5g0rX2Lnidq3Gc22xKX5zrH+fs3AKAC/tK97pfhv3x2aB/PeTbRxI0a23y0o1nlY/Np6TK6bWVdI7FBrRUfhUR0VDKQkGg2EqnI4xGUKJlLRoZDkfPJHzFk/B5RCuXDsn6fset5PW+kqea+sEhkVkeoOHuc3VvLy/++ROOo9sOdTLlNoKKpwO/v2h7Um3sUWjpSa3LdETWdpahzHDqb2KioZSYP74+nF+9eKBjP51byD9ACZIFI3Sck9Fx9EWPZGVsxrY+pVLeZNlcSRjbnM1Xd5g/LXNm+c3s2FfN8YUZ0PDLYd6OXthCx940yye3d1JNElso9Nr1ackyRzLFcumxxIMdp4iA62yQUVDKSj/8cgO/uX+13n3D5+l27q5JWPQH6amcqwLJpF4TKPECvx6h0IYA00TeGK2Z2ukYm5zLBheU+GkOeH46xc00+MLFeUTdHu/nyO9Q6ye08jpM+rxBiMc7hnbeLErPhc9f5bG3OZqKl0Odp1Q0bBR0VAKhjGGwz1DrJnXxI7jA9z7yuGU2w4GItRk6GJaVaLuKbvgLh9PzHYwfE5z9YgakDcvaAbgpX3F56LafKgXiNWiLJ9RD8D24/1jtrOv25Q8WhpOh7CktZadJ4pPXAuFioZSMDoHgwTCUd67aiZnzGrg968eTbmtNxBO23cKht1Tpda00HYf5cM3b4tGomsKYH5LNfUeFzuS3IwLzZZDvbgcwoqZDSxtrUUEdiTJ9OryBvG4HfGhVPliaWsdu9Q9FUdFQykYR3pjLofZTVW8Z9UMXj3cx/7O5Jkyg4Fw2swpSHRPlVZMIx7QzYObxRaL0aIhIsxsrOJYb/FVh79+pI9l0+vwuJ1UV7iY31KTVNw6BwO01FRmXUU/UZa11nG83x8fK3uqo6KhFAy7KnlWUxXvPjNWnPaH15JbG94sRMNOOy0595R3bMPBXLFwai3VFU5WzKof897MxiqO9hWfaOzv8rIwoWBv+fQ6diR50u8aDOY1nmGz1AqG72pXawNUNJQCYgc3ZzVWMbOxirPmNvLEzo6k2w4Gwmn7TsFwUDhQaqJhWRpNOZ5xDbFW6y/ccBGXr5o15r0ZDR6OF9lkv1AkytFeP/MSLKPl0+vZ3+UdUyvR5Q3kNXPKZpnVoiWZcBUTR3qHaJ+EvmIqGkrBONzjo7HaHR/hunBKLcd6k9/EsnNPWTGNUhMNb4CmajcuZ37+HBuq3Ul7M81o8NDjCxVVDOhYr59I1Ixwpy2fEauV2DUqGN01GMxrjYbNjAYPDVVuth8rvvhPIl+6dyuf+sXGvP8cFQ2lYBzuGWJ203B30ukNlbQPBMbk5EejBl8wklk0XKXZRqRrMDgpT8yjmWF1hi2mOeIHrPkfcxJE48zZDTgdwv+5b2vcpWmMmbTrJiKsmFnPtiN9ef9ZJ0OvL0hjdf5FVEVDKRhHeoaY3Th8c2it9xCOmnjRlo03mLktOoDLGRvPWXoxjeCIGorJYkZjbDzssSKKaxy0xtPaWV8QE7efXrOGg90+PvHzlwEYCIQJRqKTEtOAWCHl9uMDRd1BuXcolBcX52hUNJSCYNdozEqwNFrrYzex9v6RopFNW3Qbj7v0BjF1DQYm7eaXiD2D4mgKl2AhONjto8LpiP8u2Lx9eSt/87ZF7DoxiDcQzmvGWTJWzKwnGI5OSjGkMWZCHYh7vEEax9lVYCKclGiIyH4R2SoiW0Rko7XWLCKPishu63tTwvZfFJE2EdkpIpcmrK+xjtMmIj8QK4dORCpF5C5r/SURmX8y56sUB/98z2v8w/++xlAoMtI9Zd0ojo968vXGRSNzPn5VhZOhUGk1l+vyBvOSOZWJ6Q1FaGl0+ZjdXIUzSQxmkZVRtadjcLggcpKu28pZDUAsHTjf/Oqlg5z9jcfHZdWEI1H6/eGScU9daIxZbYxZa/37BuAxY8wS4DHr34jI6cBVwArgMuBHImLfBW4GrgOWWF+XWeufBHqMMYuB7wHfysH5KgWkczDA3ZsO8Vur+nt200j3FMDxURkgg4GYuylTcR/EqoPtmdmlQDgSpdcXKoh7yuN20lJTUVyi0e0bU1NiY88Bb2sfpHOSLY0FLTXUVDjZdjT/wfCHXjtG52BgXDU09oz5UnVPXQ7cYb2+A3hfwvqdxpiAMWYf0AasE5EZQL0x5gUT6572i1H72Me6B7hI8l3Jo+SVJ3d2YAxctmI6AEumDefjT6mtwCGMSRuMt0XP0EYEoLW+khP9pSMa3b78909Kx4xGT9EEwo0xHOxKLRrzWqpxOYS29sF4bUs+W4gk4nAIp8+sz7ul4QuG2Xgg1trl0DimK/baojEJDx/Zz5dMjgH+JCIG+Ikx5hag1RhzDMAYc0xEplnbzgJeTNj3sLUWsl6PXrf3OWQdKywifUAL0HmS560UiMd3nKC1vpKb/+pNdHtHZr+4nA6m1lUmsTSyj2m01nl4YxKeBnPFsG9+8t1TEAsyH+wqjtGvvb4QA4FwStFwOx3Mn1JDW/tgPL26aRLcMTYrZjZw98ZDRKImqfssF7y0t5tQJJY9eKh7HKJhPXyMt1PyRDhZS+NcY8ybgHcC14vIW9Nsm+wqmzTr6fYZeWCR60Rko4hs7OhIXhymFJ5gOMrTuzp5+/JpiEjSG+X0eg/HR1kKdkwjG/dUa4OHzsFA0Y0y3dMxyFO7Osa0Irc7+xbCPQWxsbFHi8TSOGDdJFOJBsDiqbW0dQyy6UAPs5uq0s5XyTUrZtbjC0bYl6LVTS54ZncnFS4HTock7eybih6v7Z4q8piGMeao9b0duA9YB5ywXE5Y39utzQ8DidNiZgNHrfXZSdZH7CMiLqABGNOW0xhzizFmrTFm7dSpY8deKsXBxv3dDAbCvH15a8ptptV7xrin7JTbrCyN+kqihrjPu1j44r1bufb2DXzk1pdGfL7OeKfWQrmnqhjwh4tipKmdmbQwzczvxdNqOdDl4+ndHXzgrLFV7vnEDoZvO5pbF9UrB3viaeLP7O5g/YJmZjR4xuWe6vHZXQWKWDREpEZE6uzXwCXA68ADwLXWZtcCv7NePwBcZWVELSAW8N5gubIGRORsK15xzah97GNdATxuinVqjJKR1yx/8DqrLXcyYpbGSNEY8GeeD27TWhcLpp+YhHYK2dI3FGLTgR7WzW9m88FePn/XlngB4/D0ucK4p+yMpN1FMFdj5/F+KlwO5reksTSm1RKJGoyBK9Ykn1iYLxZPq6XC5chpMPxAl5cP/Oh5vnTfVp7a1cHu9kEuWj6NOU3V43JP2YHwxpridk+1As+KyKvABuBBY8wfgW8CF4vIbuBi698YY7YBdwNvAH8ErjfG2FVYfwvcSiw4vgd42Fq/DWgRkTbg77EysZTSZG/HIFNqK9P6Xac3eOj1hUYU6HkDYVwOoTILV0SqDKxC8lxbJ5Go4R8vW8ZX3ns6z+/p4rZn9wGxFiJOh0yKLzoZy61mfMlaj082O44PsGRabdp2KnYG1dkLm5mbRlzygdvp4LTpdTkNhv9p2wkA7n3lCF+4ewsLptRw9fq5zGmu4tB43FO+IE6HZCyAzQUT/gnGmL3AqiTrXcBFKfa5EbgxyfpGYGWSdT9w5UTPUSku9nZ4WTS1Ju020+piT9zt/YH4TcHucJtN4lxrg71/8YjGkzvbqfO4OGtOI2vnNfHw68f57yfb+NT5C9jf6WNmoydpb6jJYHZTFbWVxTFXY+fxAc5bMiXtNoun1bJiZj2ffuuiSTqrkayY1cAfXj2KMSYnLdn/9MZxlkyrJRw17Ov0ctOHVlPpcjKnqZqOgQD+UCQe9E9Hjy9EY5U7723iQSvClUlkT8dgWn81DBecJVoK3b4QjVnmn7fUVOJ0SNGk3RpjeGpXB+cvmYLL6UBEeMdprfT6QhzpHWLH8X5Omz62bflkISKx1uMFtjR6vEHaBwJxyycVHreTB//ufC5cPi3tdvli5cwG+v3hcQWpU9E5GGDjgR7edcYMfnrNWm66chVvWxqLyc5ujhW9Zvtz+sbxN3KyqGgok0KPN0iPL5TR0pjVGPtjOdA1nKFypMcXb3mRCadDmFo7Nm23UDzw6lFO9Ae4YNnwTe70mTGReOVgL/s6vfGRpoVi+Yw6th/vH5PZNZnstGZwLyuggGbDCuv/Lhcuqse3t2MMXLKilcXTavngmuF8oDlW0Wu2wfAeX3DS0o9VNJRJYW+nnRmTXjTmtdRQ6XKw68Twk++R3pE9qjLR2uApikD407s6+MLdr/Lm+U28d9XM+Pry6XWIwANbjhA1cFqGp+t8s2x6PQP+cEEHMu20ZlVksjQKzbLpdbgcwhZrjvnJ8Ietx5jdVMXpSR4a7C6/h7MMhveopaGUG3s6YpbDogzuKadDWNJaGx94EwxHaR8IxC2QbGitqxzT9LAQfP+x3cxuquK2j715hF+6usLFgik1PGkNnCq0pXFaPBheuLjGjuMDNFa74zGtYsXjdnLOohYefv34SVlm7QN+nt3dweWrZyaNQ0ytraTK7WTD/p6sjjdZbdFBRUOZJPZ0DFLhdIzoNZWKpa11cUvjWN8QxjA+SyNJ2u5kY4xh1/EB3rp0KvWesU+AK2Y2EI4aqtzOtMVsk4E9zrSQk+neONrH0ta6SQnknizvPnMGB7t9bD0JF9UDW44SNfD+s2Ynfd/hED5+7nx+/+pRNu4fU5o2hl7f5LRFBxUNZZLY2+FlXkt1Vu0Xlk+v40R/gF5fkCNWIHD2OCyN6Q0e+oZCE56r8cbRfgLhk5vJcazPz0AgzJLW5O4W2yWxtLU2by0psqXe42ZOc1XB2q90e4O8dqSPcxa2FOTnj5dLV0zH7RR+/2ryefbZcN/mI5w5uyGeQpyMz759MTMbPPzf320bM5gsEX8owlAoopaGUroMBsK8sKcr3sqjYyDA60f6MsYzbJZaN9qdxwc4bM16GI+lYbdYH09xlM2hbh/v/uEz3PXyoXHvm0g8sJtKNKyA6vIiCfyunNnA6zmudM6Wp3fFmli+vUAZUeOlsbqC85dM5cHXjqW9mafi3lcOs+1of8aK9uoKF9e/fTHbj/WzN03rkl6fVdinloZSqnzz4e1c/dMXeeu3n+ATP3+Zy/7zabq8QT60NrsK3mWWu2TXiQGO9AwhMjyaNBtWzYm1e9g8gWDlEzvbiZrhwOxE2WXtv7Q1+ZPkGbMaqHA5OGtu40n9nFyxclYDB7p88criyeSJne201FRwhtWmoxS4Ys1sjvb5ueeVw5k3TuDZ3Z380z2v8ZZFLVy9fm7G7VfPif1+pJtPPpktREBFQ8kx3kCY+zcfZf2CZpbPqKd9wM/pM+v5/WfP46LTUvecSmR6vYd6j4sdxwc40jvEtLrKcTWmWzilloYqN5sPZhdETOSJHbFWaXs7Tq4p3a4Tg0yrq0zpMmiuqeCpf7yAK7MU0nwT76s0yXOwI9FYHcvblk0tWIHjRHjnyum8aW4j3/7jznH17frpM3tprffwk4+uodKVuWhv8bRaXA5JKxq2C7dlkppe5r/mXDml+P2rRxkMhPmny5axZl7qHlPpEBGWTa/jjWP9eFxOZo4jngGxIOJZcxvZdGB8ouEPRXhhbxfASXcy3XViIG4xpWI81lO+WWnXHxzt4y2L01dl55LNB3vo9YW4cFlpuKZsRIQvv2cFl//3c3zpvtf5zpVnZiUCbe2DrJnXRF2S5IhkVLqcLJ5Wm1Y0HtvRTk2Fk1VzJsdqVUtDySm/2XCQZa11vGluU+aN03DBsmlsPtjLlkO940q3tVkzt4ldJwbH5W55cW8X/lCUdfObOd7vj7dkHy/RqGF3+0A8NlMKtNRWMrPBw9YjkxsMv3/LESpdDt62rPS6U6+a08g/XrqM3796lL+69aUxY4pH4w2EOdI7lDb4nYzl0+vYnqJiPxI1PPrGCS5YPi2rdiO5QEVDyRn7O728driPK9fOPunUyY+9ZT5TaisYCkXGFQS3WTMvJlrjcVE9ubMDj9vB1etjLqOJWhuHenz4Q9GU8YxiZeWshkmZgW3jD0V4YMtR3rlyetK05FLg+gsX88Orz2Lb0X4u/c+neXzHiZTb2i7PJeMUjdNm1HO830+Pd2y7/80He+gcDHDJ6dm5fnOBioaSM/68PfYHc6k1yvVkqKl0cf2Fi4HxpdvarJrTiEPglXG4qJ7Y2c5bFk3hNCsdNl3GSjpePRy78Z5W4KK98XLGrAb2dXp5aOsxIhPIChovf95+gn5/eNJbnOea96yayYN/dz5zmqv4619sSpmK29YRsxbGa2nYv0ejXVTRqOG+zUdwO2VSe3GpaBSIUCRakEyVfPLn7SdYPr0u3gLhZPnL9XP5/9+xlMtWzhj3vjWVLpZNr4/fwDOxr9PLgS4fFyybyvyWGkRg3wSD4S/s6aTO40raHqKYuXz1LOY2V/OZX7/C1x98I+8/755Nh5nZ4OGcRaVRn5GOBVNquPO6c1gzt4nP3bk5aQyirX0Qp0OY15Jd6rmNLRpvJBzzQJeX87/9BL9+6SAXn946qZaaikaB+M6fdnLhd54sG+Ho84V4eX8PF52WuyeeSpeTz71jCVMn2FritBl1aQOIidhZUxcsjfmGZzZUxftljZfn93SxfkFL2rkQxcjclmqe+IcLeMdprTz42rG8NjD0hyI8v6eLd54xo+DFjbmittLFLdesocLl4JcvHhjzflv7IPNaqsc9onZqXSUzGjw8mGAB3vXyIY73+/n+Vav57odW5+T8s6W0fqvLhGjU8LvNR+n2BuPDeEqdJ3e1E4ka3pFlWu1kcPqMetoHAnQNZu5D9cTOdhZNrYnP8Fg4tWZCMY3DPT4OdPl4S4k+PTsdwiWnt9I+EMjrNL9XDvYQDEdL9jqlorG6gvecOZP7Nx8Zk4rb1j7I4gy911LxD5csY/PBXn72XOx+8ac3TrB+QTOXr541aQFwGxWNHHH3xkM8vPVYVttuPtTD8X4/U2oruP3ZffT6imuedTpePdTLY9vHBvue3tVJc00Fq2YXR7EaDFdbZ+qpNBgI89Le7hHtyxdNrY1VpI9jTjPErAyAcycxbTXXnGsNQnpmd2fefsaLe7txCLw5zejfUuUjZ8/DF4xw/+Yj8bVQJMqBLt+44xk2H3jTLC5aPo3/eGQnf9p2nLb2wUkNfieiopEDthzq5Z9/+xr/8L+v0p0kw2E0D209ToXTwU8+uobBQJg7nh9ryhYj/lCET/9yE3/zq01jnsI3HehmzbymoirQOm1GLOU1k4vq58/tIxiJ8p6E9uWfOHcBbqeDz925Jd4OJRte2NNFS01FyWVOJTKrsYqFU2p4dndH3n7Gi3u6WDmroWSzptKxanYDp8+o594E0XhiRzvhqGHJBH8vRIRvfOAMaitdfObXrwBwcQ4STiaCisZJEopE+eK9W2mursAXinDL03vxhyIpm+UZY/jj68c5f8kU1sxr5vwlU7h746FJyVY5WX7z0kGO9/sRhH9/aHt8vXMwwP4uH2vnnVxtRq5pqa1kWl3liADiaHq8QX7y1F4uPr013rIBYv79G9+/kk0HepL6p5NhjOH5PZ2cs6ilJLq1puPcxVN4aV83wXD2gpktQ8EIWw71cnaJNCgcLyLCRadN49VDvfQNhXiurZPP/s9mTptRf1Lu22n1Hm760CrCUcPKWfUTql/KBSoaE+DVQ7185NYX6RoM8OsXD7D9WD83vn8l7101k9uf3ceZ//Yn1nztUb5039Yxge77txzhSO8Q710de6r98JvncKR3iGfb8ucKyAX7O7386Mk9nLOwhc9fvIRH3zjBi1b1tF15vabIRANimSepCqMAvv3ITrzBMP946bIx712+ehZnzGrgd1uy62a6t9PLif4Ab1lUuq4pmwuXT8UXjHBvkt5KJxsg33Sgh2AkWjJdbSfCeYunEDWxTLov3beVuc3V/OZT67OuBE/FBcum8Z8fXs3//YvTc3Sm40dFYwLc/OQenmvr4v/c9zo/eLyNcxa2cOmK6Xzh4mWsntvIX62fxzvPmMHdLx/iC3e/Gv8jGwyE+cZDOzhzdgPvOTMmGhef3kpTtZu7Xj5YyI+Ulh8+tpuLvvsU3kCYf37ncj5x7gKm1Fbyoyf3ALGbQIXTEe9fVEycNqOetvYBXtrbReeogPitz+zlfzYc5FPnL0xZvX3ZyulsOdTLkd7Ms5qft4T/3MWlfzO8cNk01i1o5hsP7xhx3R7aeow1X/8zz++Z+EPOva8cpqbCyboyjGfYnDW3ieoKJ999dBcHunz8f29fTFOOekO976xZrC+g4KpojJMT/X4e3X6C6fUe/rjtON3eIF9813JEhLkt1dz96XP41/eczneuXMWX3nUaf95+gp8+sxeAHz3RRvtAgK+8d0Xc91/pcnLFmtn88fXj3Ld5fB0zJ4NNB7r57p93cemKVp7+pwtZPacRj9vJx8+dz9O7Oth+rJ9NB3o4Y3bDpGdxZMPKWfWEIoYP3/Iin/nVK/H159s6+fqD23nXGdO54bLlKfd/58qY3/iPrx/P+LOe39PFrMaqgg9VygUiwr+/fyW+YJhrbtvAL17Yz7f+uIPP/uYVur1Bvvforgkdt2swwB9eO8YH18ymprJ8W99VuBycvbCFXScGaa6p4LKVhYk/5AMVjQR+9ty+jIHsu16OxR9+8cl1nDm7gavXzeXMFBlDHz93PpeuaOU7f9rFpgM9/Oy5/bx31cwxfZk+/46lrF/Qwt/f/SoPnMRgl1wTCEf4p3teY2ZDFf9xxaoR9RJ/tX4e1RVOPn/nFl473FuUrimIVaf/+K/WcPW6uWzY383R3iF8wTA33LuV+S3VfPdDq9MG7xdOrWX59DoeypAZF40aXtjbVRbxDJvF0+q46UOrGQpF+NffbePmJ/fw1qVT+cdLl/Hy/h427Ms8Uc5m94kBvvHwdv7jkZ0EI1GuOWd+/k68SDjPyqC7cs3srJoZlgoqGhZt7YP8+0Pbuew/n+bZFKmG/lCE37x0kPMWT2Fpax33f+Zc/v39K1MeU0T46uUrcTuEj9z6IsFIlM+/Y8mY7WoqXfzs42/mrDmN/OvvXs8qA2sy+OULB9jT4eXr71855qmwodrNp9+6iC5vkPULWrhiTfKxlYXG7XRw2crp/M3bFgLw4GvH+I9HdnKw28c3P3hmVtbR+86axaYDPdz0p50p/flvHOun1xcqu7qD966ayeNfeBtP/sMFbPu3S/n5x9fxiXMX0FxTwQ8f351VfONQt4+P3PoSP3lqL3e+fIhzF7dMOPW0lPiLM2fwtqVTufYt8wt9KjlFRcNi8bRa7r/+XOqr3HzsZxt4YU8XfUMhXtzbxYl+P8YYfm1lD/3tBYuAWAvuTE+VrfUePveOJfhDUT5w1iwWpiju8bidfPODZzLoD/ONhMykQjEYCPOjJ/dw3uIpKdtWf+4dS9j4L+/gV59aX/QdXee11HDGrAZufmoPP3tuPx97y/yss3c+dd4CPrx2Dj98vI1P3bGRtvaxgfXfv3YUp0M4b0npB8FHIyLMn1ITf3CoqnDymQsW8czuTh7amt5t1zEQ4JrbN+APRbj/+nO55aNr+PYVqybjtAtOa72HOz6xbtyt/Yud8nUqToAVMxu49zNv4QM/ep5P/3IjBhjwx9pjr1vQTFv7IOctnjLuwq2Pn7uAKreTvzhzZtrtlrbW8anzF/Ljp/bwqfMXZpzHkE9ufWYv3d4g/5Akq6hUefeZM/jGwzs4a24jX3rXaVnv53I6+OYHz2DRtBp+8Fgb7/nhczz5jxfQao2VDUWi/HbTYd6+fBrT6jz5Ov2i4mNvmc/9W47w5Qe2sX5hM1Nqx7Z66fOF+OhtsZbhv/rUuhEpzUrpopbGKOo9bm6/9s3UV7lZv6CZn16zlhveuZy29kG6vcGkqZmZcDsdfPSc+TRnkT3x6bcuxON2cNuzeydy+mm5c8NB/vKnL9Len77v/50bDvL9x3bzF2fMKKs/9A+/eQ6fOHcBN39kzbj7/4gI1711Eb/77LkMhSLcuWF4hvhj29vpHAxy1ZtLu1vreHA5HXzzA2fSPxTi4u8+xZ0bDo6Yl+0NhPnYzzewt8PLLdesmfBALqX4kHw2JSsEa9euNRs3bsz5cfv9IQ52+SYlrfT/3v86d718iGdvuPCkn1yNMRzs9vHi3i5uuHcrxsT6+d953dm01FbSNxQiGjXxdMDbnt3H1/7wBhcsm8rNH1lDVUX5BPByxUdve4ndJwZ59p8vxAAf+elLHOz28ew/X1hyTQpPlh3H+/nX+7exYX83q+Y08uZ5TYQiUTYe6GHH8QH++y/fVFaZQ+WMiGwyxqzNuJ2KRvGxr9PL2296kivXzOabHzgTh0PwBcNsPzbAm+Y2Zp2dY4zhC//7Kve+Emtn8Ob5TXzmwsX8zS83sWhqLZ9/xxI+f9cW/KEIK2c10FJTwRM7O3jnyul8/6qzxv00fqrwyLbjfPqXm/jshYvZdrSPJ3Z28I0PnMHV6+YW+tQKgjGG+7cc4aY/7aJrMIjLKUyrq+Rz71jKe1eld8kqxYOKRonzzYd38OOn9nDJ6a00VLl5ZNtx+v1h/vr8BXzpXadlJRx3v3yIf/rta3z07Hmct2QKb10ylaoKJ0/t6uCv79hIMBJl8bRa3nXGDF7e182R3iHetnQqX37P6afcE/N4CEeivP2mpzjY7UMEvv6+lXxk/bxCn5ainBQqGiWOMYbvP7ab/3q8jcZqN+sXtlDldnLPpsO8+8wZ/M3bFiV1lRlj+Nlz+/nNhoPs6RjkLYta+MUn1o+ZWfDEjnbu2XSYr7x3xYTnVZzK+EMR2vsDeNwOptWfGsFvpbxR0SgTIlETv+EbY/juo7u49Zl9DIUirJ3XxDVvmc9lK6bT7w/x8r5u7tl0mMd2tLNufqwZ4kfPmUdjdW7aFyiKUr6UlWiIyGXA93ggXXAAAAaDSURBVAEncKsx5pupti030UhG31CIezYd5pcv7Gd/l4+aCifeYKyrbpXbyRcuWconz1tQNpXJiqLkn7IRDRFxAruAi4HDwMvA1caYpEOMTwXRsIlGDU/v7uCRbceZ11LDugXNrJzZoAFsRVHGTbaiUQrFfeuANmPMXgARuRO4HEgqGqcSDodwwbJpIybOKYqi5JNSeCSdBRxK+Pdhay2OiFwnIhtFZGNHR/6mjSmKopzqlIJoJHPMj/CpGWNuMcasNcasnTp16iSdlqIoyqlHKYjGYSCxP8NsoHj6hyuKopxClIJovAwsEZEFIlIBXAU8UOBzUhRFOSUp+kC4MSYsIp8FHiGWcnu7MWZbgU9LURTllKToRQPAGPMQ8FChz0NRFOVUpxTcU4qiKEqRoKKhKIqiZE3RV4SPFxEZAHaOWm4A+nL4Y6YAyQeJT4xcn18uj6fXrriOp9fv5NFrOJYpQI0xJnPNgjGmrL6AjUnWbsn3zzjJ4+X6/HJ2PL12RXc8vX56DXN+vPFck1PFPfX7Qp9ABnJ9frk8nl674jperin2z1vs1w+K/zPn9Hjl6J7aaLJoulXsP6Nc0Wt3cuj1O3n0Go5lPNekHC2NW8rkZ5Qreu1ODr1+J49ew7FkfU3KztJQFEVR8kc5WhqKoihKnlDRAERkjog8ISLbRWSbiHzOWm8WkUdFZLf1vclav1hENonIVuv72xOOdaOIHBKRwUJ9nskkV9dORKpF5EER2WEdJ+V0xnIix797fxSRV63j/NgaYFb25PIaJhzzARF5fbI/S0mQ63S2UvwCZgBvsl7XEZsUeDrwbeAGa/0G4FvW67OAmdbrlcCRhGOdbR1vsNCfq5SuHVANXGi9rgCeAd5Z6M9XKtfP+ne99V2A3wJXFfrzldo1tNY+APwGeL3Qn60Yvwp+AsX4BfyO2HjZncAMa20GsDPJtgJ0AZWj1k8J0cjHtbPe+z7w14X+PKV4/QA3sTTLDxf685TaNQRqgWct0VHRSPKl7qlRiMh8Yk8iLwGtxphjANb3ZHNVPwhsNsYEJusci5VcXTsRaQTeAzyWz/MtNnJx/UTkEaAdGADuyfMpFx05uIZfA24CfHk/2RJFRSMBEaklZtZ/3hjTn8X2K4BvAZ/O97kVO7m6diLiAv4H+IGx5sKfCuTq+hljLiX2VF0JjPHVlzMnew1FZDWw2BhzX15PtMRR0bAQETexX7hfG2PutZZPiMgM6/0ZxJ7g7O1nA/cB1xhj9kz2+RYTOb52twC7jTH/mf8zLw5y/btnjPETG1R2eb7PvVjI0TU8B1gjIvuJuaiWisiTk/MJSgcVDUBEBLgN2G6M+W7CWw8A11qvryXmK7XdJw8CXzTGPDeZ51ps5PLaicjXiTVX+3y+z7tYyNX1E5HahBukC3gXsCP/n6Dw5OoaGmNuNsbMNMbMB84DdhljLsj/JygxCh1UKYYvYr8gBngN2GJ9vQtoIeZX3219b7a2/xfAm7DtFmCa9d63ic01j1rfv1Loz1cK147Y7HcDbE9Y/1ShP18JXb9WYqORXwO2AT8EXIX+fKV0DUcdcz4aCE/6pRXhiqIoStaoe0pRFEXJGhUNRVEUJWtUNBRFUZSsUdFQFEVRskZFQ1EURckaFQ1FmWRE5G9E5JpxbD9fO64qxYKr0CegKKcSIuIyxvy40OehKBNFRUNRxonVFO+PxJrinUWsFfc1wGnAd4l1Su0EPmaMOWa1ongeOBd4QETqiHVB/o7V7+jHxFrD7wE+YYzpEZE1wO3EGuc9O3mfTlHSo+4pRZkYy4BbjDFnAv3A9cSqsK8wxtg3/BsTtm80xrzNGHPTqOP8Avhn6zhbgS9b6z8D/s4Yc04+P4SijBe1NBRlYhwyw32LfgV8idhAn0djrZBwAscStr9r9AFEpIGYmDxlLd0B/G+S9V8C78z9R1CU8aOioSgTY3T/nQFgWxrLwDuOY0uS4ytKUaDuKUWZGHNFxBaIq4EXgan2moi4rXkNKTHG9AE9InK+tfRR4CljTC/QJyLnWesfyf3pK8rEUEtDUSbGduBaEfkJsS6qPwQeAX5guZdcwH8S6zibjmuBH4tINbAX+Li1/nHgdhHxWcdVlKJAu9wqyjixsqf+YIxZWeBTUZRJR91TiqIoStaopaEoiqJkjVoaiqIoStaoaCiKoihZo6KhKIqiZI2KhqIoipI1KhqKoihK1qhoKIqiKFnz/wCsNQkNwtamhAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sorted_data['inc'][-200:].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Etude de l'incidence annuelle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n",
"entre deux années civiles, nous définissons la période de référence\n",
"entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
"1er août de l'année $N+1$.\n",
"\n",
"Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
"pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
"de référence: à la place du 1er août de chaque année, nous utilisons le\n",
"premier jour de la semaine qui contient le 1er août.\n",
"\n",
"Comme l'incidence de syndrome grippal est très faible en été, cette\n",
"modification ne risque pas de fausser nos conclusions.\n",
"\n",
"Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
"rend la première année incomplète. Nous commençons donc l'analyse en 1985."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
" for y in range(1985,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
"\n",
"Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_august_week[:-1],\n",
" first_august_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2\n",
" yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n",
"yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici les incidences annuelles."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.plot(style='*')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2021 743449\n",
"2014 1600941\n",
"1991 1659249\n",
"1995 1840410\n",
"2020 2010315\n",
"2022 2060304\n",
"2012 2175217\n",
"2003 2234584\n",
"2019 2254386\n",
"2006 2307352\n",
"2017 2321583\n",
"2001 2529279\n",
"1992 2574578\n",
"1993 2703886\n",
"2018 2705325\n",
"1988 2765617\n",
"2007 2780164\n",
"1987 2855570\n",
"2016 2856393\n",
"2011 2857040\n",
"2023 2873501\n",
"2008 2973918\n",
"1998 3034904\n",
"2002 3125418\n",
"2009 3444020\n",
"1994 3514763\n",
"1996 3539413\n",
"2004 3567744\n",
"1997 3620066\n",
"2015 3654892\n",
"2000 3826372\n",
"2005 3835025\n",
"1999 3908112\n",
"2010 4111392\n",
"2013 4182691\n",
"1986 5115251\n",
"1990 5235827\n",
"1989 5466192\n",
"dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
" française, sont assez rares: il y en eu trois au cours des 35 dernières années."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"yearly_incidence.hist(xrot=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}