Commit 1c17797a93916357e3ac40896ffb39f4977947ff

Authored by Antoine Beaumont
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mET_weap 1.3

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  1 + GNU GENERAL PUBLIC LICENSE
  2 + Version 3, 29 June 2007
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  4 + Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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... ...
README.md 0 → 100644
  1 +++ a/README.md
... ... @@ -0,0 +1,229 @@
  1 +# mET_weap
  2 +
  3 +Trough the ANR Amethyst project, a python package has been developed to process MODIS-NDVI and Meteo ERA5 long time series to produce the input data for WEAP 21.
  4 +
  5 +## Documentation
  6 +
  7 +- Read the documentation "mET_weap Installation Procedure & User guide"
  8 +
  9 +## Quick Installation of mET weap
  10 +
  11 +For more informations, refer to the documentation "mET_weap Installation Procedure & User guide".
  12 +
  13 +### Requirements
  14 +
  15 +1) Download mET_weap on PyPI or on GitHub
  16 +2) Copy/paste the test file “mET_weap_test” on your computer
  17 +3) Subscription to earth data
  18 +4) Subscription to the CDS (Climate Data Store)
  19 +5) OSGeo4w installation
  20 +6) Create an environment variable for GDAL (Windows)
  21 +7) Download required files
  22 +8) Installation of Anaconda
  23 +
  24 +### Installation
  25 +
  26 +#### a) Users installation from local file:
  27 +
  28 +For example, if the source file of the setup.py = "C:\Users\VI\Documents\mET_weap".
  29 +In the anaconda prompt, execute:
  30 +
  31 +```shell
  32 +- cd "C:\Users\VI\Documents\mET_weap"
  33 +```
  34 +Then, execute:
  35 +```shell
  36 +- python setup.py sdist bdist_wheel
  37 +- cd .\dist
  38 +- pip install mET_weap-1.3.tar.gz
  39 +```
  40 +
  41 +Finally, because the installation of gdal doesn't work with install_requires in the setup.py, execute:
  42 +```shell
  43 +- conda install gdal
  44 +```
  45 +
  46 +#### b) Users installation from the PyPI
  47 +
  48 +In the anaconda prompt, execute:
  49 +```shell
  50 +- pip install mET_weap
  51 +- conda install gdal
  52 +```
  53 +
  54 +### Test folder
  55 +
  56 +Recover the “mET_weap_test” folder in the package downloaded on Pypi or Github.
  57 +This folder can be also recovered in the “sites-packages” folder ("C:\Users\VI\Anaconda3\Lib\site packages\mET_weap" for instance) if the installation is made directly from the Pypi.
  58 +To begin, copy/paste this test folder “mET_weap_test” on your desktop. The program will be executed from this “work” directory.
  59 +
  60 +## Configuration
  61 +
  62 +The configuration is made with the userconfig.py file. This is the only file that the user has to fill in to make his project works.
  63 +
  64 +First, the path root and the path to osgeo has to be defined :
  65 +
  66 +```python
  67 +mET_weap.path_root = os.path.join('C:'+os.sep+'HAOUZ'+os.sep+'mET_weap') # The root directory for output files
  68 +mET_weap.path_to_osgeo = os.path.join('C:'+os.sep+'OSGeo4W64'+os.sep+'bin') # the path for gdal binaries
  69 +```
  70 +
  71 +For instance, in the example above, path_root = 'C:\\HAOUZ\\mET_weap' and path_to_osgeo = 'C:\\OSGeo4W64\\bin'. The path root creates the directory for the outputs.
  72 +
  73 +The next part of the config file is the switchs for the modules. Before to execute the usermain, fill in "True" to activate and "False" to deactivate a module.
  74 +For instance, on the example below, 'do_get_modis' is the only module activated.
  75 +
  76 +```python
  77 +# ---- Switchs to activate or deactivate the modules ----
  78 +
  79 +# Each Modis tile covers an area of 5°x5°, it may not be necessary to download it again to process another area.
  80 +
  81 +'do_get_modis' : True, # download modis files
  82 +
  83 +# If you already downloaded ERA5 files for your area and domain, don't do it again.
  84 +
  85 +'do_download_ERA5_cds' : False, # download weather dataset from CDS ERA5
  86 +
  87 +# The following toggles are processing steps,
  88 +# Toggle the steps that already went ok to False
  89 +
  90 +'do_project_subset' : False, # projection and subset of the modis files
  91 +'do_classification' : False, # yearly classification
  92 +'do_kc_fc' : False, # compute Kc/Fc
  93 +'do_ET0_rain_ERA5' : False, # compute daily ET0 and rainfall from ERA5
  94 +'do_interp_ndvi_ERA5' : False, # temporal interpolation of NDVI and KC*ET0 with ERA5 data
  95 +'do_synthesis_ERA5' : False, # spatio-temporal synthesis with ERA5 data
  96 +```
  97 +
  98 +After the login to the server CDS API. Accept the license copernicus product.
  99 +Then, click on your name near the logout button to retrieve your API key on your user profile. Use the format "UID:API key" for your CDSAPI key.
  100 +```python
  101 +# ----- The CDSAPI key to download ERA5 data -----
  102 +'CDSAPI':{
  103 + 'url':"https://cds.climate.copernicus.eu/api/v2",
  104 + 'key':"xxxx:xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
  105 + },
  106 +```
  107 +
  108 +Next, fill in your MODIS username and password in the the userconfig.py file.
  109 +```python
  110 +# ---- The MODIS login to download MODIS data ----
  111 +'MODIS':{
  112 + 'username':"xxxxxxxx",
  113 + 'password':"xxxxxxxx"
  114 + },
  115 +```
  116 +
  117 +Then, the spatial domain has to be defined. Fill in the geographic coordinates of your studied area in the userconfig.py.
  118 +```python
  119 +# ----- the spatial domain to be processed -----
  120 +'domain' : {
  121 + "uly":31.7, # Upper left Y in dd (decimal degrees)
  122 + "ulx":-8.6, # Upper left X in dd
  123 + 'lry':30.85, # Lower right Y in dd
  124 + 'lrx':-7 # Lower Right X in dd
  125 + },
  126 +```
  127 +
  128 +After, the scales are defined. Normally, the user doesn't need to change these values.
  129 +```python
  130 +'scale' : {
  131 + "echelle" : 0.25,
  132 + "resolution_ERA5": 0.25
  133 + },
  134 +```
  135 +
  136 +Choose your temporal domain. For instance, from September 2015 to September 2016 in the example above.
  137 +```python
  138 +# ----- the temporal domain to be processed -----
  139 +'period' : {
  140 + 'begin': datetime(2015, 9, 1), # the date of beginning as a datetime (yyyy,m,j)
  141 + 'end': datetime(2016, 8, 30) # the date of ending as a datetime (yyyy,m,j)
  142 + },
  143 +```
  144 +
  145 +Define the agronomic year:
  146 +```python
  147 +# ----- definition of the agronomic year with the beginning month and ending month -----
  148 +'agroyear' : {
  149 + 'begin': 9, # generally the agronomic year begins in september (9)
  150 + 'end': 8 # generally the agronomic year begins in august of the next year (8)
  151 + },
  152 +```
  153 +
  154 +The next part allows to choose the functions use to compute the Kc and Fc factors.
  155 +```python
  156 +# ----- Kc and Fc to NDVI relationships -----
  157 +'Relation_NDVI': {
  158 + 'scale_factor' : 0.0001, # A scale factor to apply to NDVI data, typically 0.001 for MODIS
  159 + 'Ndvi_KcKcb' : os.path.join('Tab_ndvi_kc_fc', 'ndvi_kckcb.xlsx'), # the XLS file for relations NDVI->Kc (Crop Coefficient)
  160 + 'Ndvi_Fc' : os.path.join('Tab_ndvi_kc_fc', 'NDVI-FC.xlsx'), # the XLS file for relations NDVI->Fc (Fraction Cover)
  161 + 'do_senescence' : True, # Optional: To compute or not the fraction cover plateau for annual crops. The computation is time consuming
  162 + 'do_stack':False, # Optional: To output or not annual stacks. Those stacks may be useful for data vizualisation
  163 +
  164 + # rel OS is the table of relations between NDVI/Kcb and NDVI/Fraction covers
  165 + # the syntax is: 'class_name' : [output_value, line of xls file for Kcb, line of xls file for Fc]
  166 + # None if Kc is not computed for a particular class
  167 +
  168 + 'rel_OS' : {
  169 + 'No_data' : [0, None, None],
  170 + 'Advent' : [2, None, None],
  171 + 'Arb_Veg' : [3, 6, 5],
  172 + 'Arb_SN' : [4, 0, 5],
  173 + 'C_Anu' : [5, 6, 0],
  174 + 'Sol_Nu' : [8, None, None]
  175 + }
  176 + },
  177 +```
  178 +
  179 +This module allows to configure the link between the shapefile of the studied area and the synthesis module.
  180 +This shapefile has to be in the mET_weap directory.
  181 +For his project, the user need to fill in the name of his shapefile in ['Synth']['shapefile'].
  182 +In the test file, on the example above, 'shapefile' : 'zonage\\Atlas_Piemont_Haouz.shp'.
  183 +In 'shp_attrib', fill in the column name of the attribute table. In the example 'c_zone_irr'.
  184 +Finally, define the 'timerange' and the 'mode'.
  185 +```python
  186 +# ----- Synthesis -----
  187 +'Synth': {
  188 + 'shapefile' : os.path.join('zonage'+os.sep+'Atlas_Piemont_Haouz.shp'),
  189 + 'shp_attrib' : 'c_zone_irr', # column name of the attribute table
  190 + 'timerange' : "MONTH", # an accumulation time which is either DAY, WEEK or MONTH
  191 + 'mode' : "SUM", # MEAN or SUM
  192 +},
  193 +```
  194 +
  195 +Normally, the paths don't need to be changed because the directory Gtopo30 is located in the mET_weap_test directory.
  196 +'geopotential_cdsapi.nc' is downloaded in 'C:\\HAOUZ'
  197 +```python
  198 +# ----- The path for the DEM input file -----
  199 +'DEM': {
  200 + 'Description':'GTOPO30',
  201 + 'DEM_Filename': os.path.join('Gtopo30', 'gt30w020n40.tif'),
  202 + 'ERA_Filename_CDSAPI': os.path.join(''.join(os.path.split(mET_weap.path_root)[0]), 'geopotential_cdsapi.nc')
  203 +},
  204 +```
  205 +
  206 +These paths define the outputs directory. They are all base on the path_root. Normally, the user doesn't need to change them.
  207 +```python
  208 +# ----- The paths of the output files -----
  209 +'path': {
  210 + 'Temp_ERA5': os.path.join(''.join(mET_weap.path_root), "fishnet_era5"), # directory for temporary data
  211 +
  212 + 'Data' : os.path.join(''.join(mET_weap.path_root), "data"), # the root directory to download data
  213 + 'MOD13Q1': os.path.join(''.join(mET_weap.path_root), "data"+os.sep+ "MOD13Q1"), # the directory for downloading MODIS MOD13Q1
  214 + 'ERA5': os.path.join(''.join(mET_weap.path_root), os.path.join("data", "ERA5")), # the directory to download ERA5 data
  215 +
  216 + 'Tif' : os.path.join(''.join(mET_weap.path_root), "tif"), # the root directory for output data
  217 + 'MOD13Q1_tif': os.path.join(''.join(mET_weap.path_root), "tif"+os.sep+"MOD13Q1"), # the directory for NDVI Subsets
  218 + 'ERA5_tif': os.path.join(''.join(mET_weap.path_root), "tif"+os.sep+"ERA5"), # the directory for ERA5 processed input files
  219 +
  220 + 'outputs_ERA5': os.path.join(''.join(mET_weap.path_root), "tif"+os.sep+"outputs_ERA5"), # The directory for results used for synthesis ERA5
  221 +
  222 + 'synthesis_ERA5': os.path.join(''.join(mET_weap.path_root), "synthesis_ERA5") # The directory for synthesized results based on ERA5 meteorological data
  223 +},
  224 +```
  225 +
  226 +## License
  227 +
  228 +mET_weap uses the GPLv3 license. Copyright (C) 2014-2018 Michel Le Page. For more information see COPYING.
  229 +
... ...
mET_weap/Classification.py 0 → 100644
<
  1 +++ a/mET_weap/Classification.py
... ... @@ -0,0 +1,213 @@
  1 +# Copyright (C) 2014-2018 Michel Le Page, Oulad Sayad Younes, Cindy Gosset
  2 +#
  3 +# mET_weap is free software: you can redistribute it and/or modify
  4 +# it under the terms of the GNU General Public License as published by
  5 +# the Free Software Foundation, either version 3 of the License, or
  6 +# (at your option) any later version.
  7 +#
  8 +# mET_weap is distributed in the hope that it will be useful,
  9 +# but WITHOUT ANY WARRANTY; without even the implied warranty of
  10 +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
  11 +# GNU General Public License for more details.
  12 +#
  13 +# You should have received a copy of the GNU General Public License
  14 +# along with this program. If not, see <http://www.gnu.org/licenses/>.
  15 +
  16 +#--- external imports ---
  17 +from __future__ import division
  18 +from osgeo import gdal
  19 +import os
  20 +import numpy as np
  21 +#------------------------
  22 +
  23 +#--- config imports ---
  24 +from userconfig import mET_weap
  25 +from mET_weap import cf, path_to_osgeo, path_root
  26 +#----------------------
  27 +
  28 +"""
  29 +Script de determination d'occupation du sol d'une zone specifier
  30 +
  31 +Syntaxe : Python Classification.py [nom_dossier]
  32 +
  33 +-------------------------------------------------------------------------"
  34 +
  35 +ce script sert a determiner l'occupation du sol d'une zone precise en utilisant l'ensemble des images de cette zone dans une année precise
  36 +pour lancer le script veuillez utiliser la commande suivante : python Classification.py [2013.09.01 2014.08.31]
  37 +le dossier doit contenir les images de la zone a determiner l'occupation du sol
  38 +
  39 +Realiser par : Michel Le Page/ Oulad Sayad Younes
  40 +Date Debut : 01/12/2014
  41 +Modifié par Gosset Cindy (05/03/2015)
  42 +Entrée
  43 +Image tif MODIS du ndvi
  44 +Paramètres des seuil SN, ANU et DEV
  45 +Sortie image tif d'os par an à la taille des image MODIS (Etendue et pixel)
  46 +--------------------------------------------------------------------------
  47 +"""
  48 +
  49 +
  50 +def classif_ijrs1997(list_fnames, fname_out, valsn=0.22, valanu=0.38, valdev=0.32):
  51 +
  52 + do_stack = True
  53 + #Faire des seuils des paramètres pour la table de contingence
  54 + seuil_sn = float(valsn)
  55 + seuil_anu = float(valanu)
  56 + seuil_dev = float(valdev)
  57 +
  58 + Images = []
  59 + bands = 0
  60 +
  61 + for myfile in list_fnames: # on liste les fichiers HDF du dossier n un par un
  62 +
  63 + #decouper le chemin pour recuperer la date de l'image TIF
  64 + #date=myfile.split('\\')
  65 + #dateimage=date[len(date)-1].split('.tif')[0].split('_')[-1]
  66 + if os.path.exists(myfile):
  67 + bands += 1
  68 + Images.append(myfile)
  69 +
  70 + if len(Images) > 0:
  71 + #lecture des dimensions de la première image
  72 + raster = gdal.Open(Images[0])
  73 + cols = raster.RasterXSize
  74 + rows = raster.RasterYSize
  75 + driver = raster.GetDriver()
  76 + GT = raster.GetGeoTransform()
  77 + PJ = raster.GetProjection()
  78 + raster = None
  79 +
  80 + #initialisation de la matrice tri-dimensionelle
  81 + ndvis = np.zeros((bands, rows, cols)) # ndvis=np.zeros((nb_matrice,lignes,colonnes))
  82 + print("Dates=", bands, "Rows=", rows, "Cols=", cols, "seuil_sn=", seuil_sn, "seuil_anu=", seuil_anu, "seuil_dev=", seuil_dev)
  83 +
  84 + bande = 0
  85 + for myfile in Images:
  86 + print(myfile)
  87 +
  88 + #lecture de l'image avec GDAL
  89 + raster = gdal.Open(myfile)