Commit 3ae3dbd501ea60591f2a4fec1214f3efbfd140bc

Authored by Jordi Inglada
1 parent 4ecbec2d
Exists in master

exploration des jointures

Showing 1 changed file with 466 additions and 1823 deletions   Show diff stats
notebook/zonal_stats.org
@@ -6,17 +6,35 @@ @@ -6,17 +6,35 @@
6 6
7 Petit bout de python vide pour initialiser la session. Ca permet d'avoir la version de python utilisée 7 Petit bout de python vide pour initialiser la session. Ca permet d'avoir la version de python utilisée
8 #+begin_src python :results output :session :exports code 8 #+begin_src python :results output :session :exports code
  9 +import geopandas as gp
  10 +import pandas as pd
9 #+end_src 11 #+end_src
10 12
11 #+RESULTS: 13 #+RESULTS:
12 -: Python 3.7.1 (default, Oct 23 2018, 19:19:42)  
13 -: [GCC 7.3.0] :: Anaconda, Inc. on linux  
14 -: Type "help", "copyright", "credits" or "license" for more information.  
15 -: >>> python.el: native completion setup loaded  
16 14
17 15
18 * Import des fichiers dans des DF 16 * Import des fichiers dans des DF
19 17
  18 +Le fichier TERLAB qui contient le dept 31 est:
  19 +
  20 +#+begin_src python :results output :session :exports code
  21 +terlabdir = '/work/OT/theia/oso/shapes/TERLAB/'
  22 +terlab31 = terlabdir+'SURFACES-2017-PARCELLES-GRAPHIQUES-CONSTATEES_031_20180210.shp'
  23 +df_terlab = gp.read_file(terlab31)
  24 +print(df_terlab.head())
  25 +#+end_src
  26 +
  27 +#+RESULTS:
  28 +: CODE_CULTU SURF_ADM PRECISION SEMENCE DEST_ICHN CULTURE_D1 CULTURE_D2 ... TLENQ CODUTISOL LIBCULTURE SURUTISOL RENDNORME MMEAU geometry
  29 +: 0 ORH 1.42 None 0 None None None ... 1 03 03_ORGE_HIVER NaN NaN NaN POLYGON ((506016.382600002 6234988.195600003, ...
  30 +: 1 PPH 0.00 None 0 None None None ... 0 00 00_XXXXXXXXXX NaN NaN NaN POLYGON ((506032.0680000037 6235002.251000002,...
  31 +: 2 PPH 0.00 None 0 None None None ... 0 00 00_XXXXXXXXXX NaN NaN NaN POLYGON ((506108.3540000021 6234766.824000001,...
  32 +: 3 ORH 1.87 None 0 None None None ... 1 03 03_ORGE_HIVER NaN NaN NaN POLYGON ((506107.6996000037 6234765.226100001,...
  33 +: 4 PPH 0.00 None 0 None None None ... 0 00 00_XXXXXXXXXX NaN NaN NaN POLYGON ((506874.6119000018 6234495.103300001,...
  34 +:
  35 +: [5 rows x 18 columns]
  36 +
  37 +
20 Les fichiers shape avec les stats sont 38 Les fichiers shape avec les stats sont
21 39
22 #+begin_src python :results none :session :exports code 40 #+begin_src python :results none :session :exports code
@@ -30,10 +48,9 @@ zs_mask=zs_dir+'/zonal_stats_mask.shp' @@ -30,10 +48,9 @@ zs_mask=zs_dir+'/zonal_stats_mask.shp'
30 Lire les fichiers dans un DF pandas 48 Lire les fichiers dans un DF pandas
31 49
32 #+begin_src python :results output :session :exports code 50 #+begin_src python :results output :session :exports code
33 -import geopandas as gp  
34 df_rf1 = gp.read_file(zs_rf1) 51 df_rf1 = gp.read_file(zs_rf1)
35 df_rf2 = gp.read_file(zs_rf2) 52 df_rf2 = gp.read_file(zs_rf2)
36 -df_dem = gp.read_file(zs_dem) 53 +df_dem = gp.read_file(zs_dem).to_crs
37 df_mask = gp.read_file(zs_mask) 54 df_mask = gp.read_file(zs_mask)
38 print(len(df_rf1)) 55 print(len(df_rf1))
39 print(len(df_rf2)) 56 print(len(df_rf2))
@@ -49,1852 +66,478 @@ print(len(df_mask)) @@ -49,1852 +66,478 @@ print(len(df_mask))
49 66
50 Il paraît que le fichier de DEM a perdu quelques polygones? 67 Il paraît que le fichier de DEM a perdu quelques polygones?
51 68
52 -* Fusion dans un seul DF 69 +Vérification des projections :
53 70
54 -On utilise overlay avec intersection 71 +#+begin_src python :results output :session :exports code
  72 +print(df_dem.crs)
  73 +print(df_mask.crs)
  74 +print(df_rf1.crs)
  75 +print(df_rf1.crs)
  76 +#+end_src
  77 +
  78 +#+RESULTS:
  79 +: {'init': 'epsg:32631'}
  80 +: {'init': 'epsg:2154'}
  81 +: {'init': 'epsg:2154'}
  82 +: {'init': 'epsg:2154'}
  83 +
  84 +Reprojection du DEM
55 85
56 #+begin_src python :results output :session :exports code 86 #+begin_src python :results output :session :exports code
57 -res_intersection = gp.overlay(df_rf1, df_mask, how='identity') 87 +df_dem = df_dem.to_crs(df_mask.crs)
  88 +print(df_dem.crs)
58 #+end_src 89 #+end_src
59 90
60 #+RESULTS: 91 #+RESULTS:
  92 +: {'init': 'epsg:2154'}
  93 +
  94 +
  95 +* Fusion dans un seul DF
61 96
62 #+begin_src python :results output :session :exports code 97 #+begin_src python :results output :session :exports code
63 -print(len(df_rf1.columns))  
64 -print(len(df_mask.columns))  
65 -print(len(res_intersection.columns)) 98 +for c in df_terlab.columns:
  99 + print(c)
66 #+end_src 100 #+end_src
67 101
68 #+RESULTS: 102 #+RESULTS:
69 -: 1446  
70 -: 298  
71 -: 1743 103 +#+begin_example
  104 +CODE_CULTU
  105 +SURF_ADM
  106 +PRECISION
  107 +SEMENCE
  108 +DEST_ICHN
  109 +CULTURE_D1
  110 +CULTURE_D2
  111 +BIO
  112 +ENGAGEMENT
  113 +MARAICHAGE
  114 +AGROFOREST
  115 +TLENQ
  116 +CODUTISOL
  117 +LIBCULTURE
  118 +SURUTISOL
  119 +RENDNORME
  120 +MMEAU
  121 +geometry
  122 +#+end_example
72 123
73 -Pandas renomme les colonnes qui ont le mm nom dans les 2 DF  
74 #+begin_src python :results output :session :exports code 124 #+begin_src python :results output :session :exports code
75 -for c in res_intersection.columns: 125 +for c in df_mask.columns:
76 print(c) 126 print(c)
77 #+end_src 127 #+end_src
78 128
79 #+RESULTS: 129 #+RESULTS:
80 #+begin_example 130 #+begin_example
81 -CODE_CULTU_1  
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83 -SEMENCE_1  
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