def RetrieveData(Date, args):
"""
This function retrieves MOD9 Reflectance data for a given date from the
http://e4ftl01.cr.usgs.gov/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[
output_folder, TilesVertical, TilesHorizontal, lonlim, latlim, band,
resolution, hdf_library
] = args
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal, TilesVertical, Date, output_folder, band,
resolution, hdf_library)
except:
print("Was not able to download the file")
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
try:
# Reproject the MODIS product to epsg_to
epsg_to = '4326'
name_reprojected = RC.reproject_MODIS(name_collect, epsg_to)
# Clip the data to the users extend
data, geo = RC.clip_data(name_reprojected, latlim, lonlim)
# Save results as Gtiff
ReffileName = os.path.join(
output_folder,
'ReflectanceBand%d_MOD09GQ_-_daily_' % band + Date.strftime('%Y') +
'.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
DC.Save_as_tiff(name=ReffileName,
data=data,
geo=geo,
projection='WGS84')
# remove the side products
os.remove(os.path.join(output_folder, name_collect))
os.remove(os.path.join(output_folder, name_reprojected))
except:
print('data for %02d-%02d-%d is not available' %
(Date.day, Date.month, Date.year))
return True
def RetrieveData(Date, args):
"""
This function retrieves MOD11 LST data for a given date from the
https://e4ftl01.cr.usgs.gov/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[
output_folder, TilesVertical, TilesHorizontal, lonlim, latlim,
TimeStep, hdf_library
] = args
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal, TilesVertical, Date, output_folder,
TimeStep, hdf_library)
except:
print("Was not able to download the file")
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
# Reproject the MODIS product to epsg_to
#epsg_to ='4326'
name_reprojected = RC.reproject_modis_wgs84(name_collect, method=2)
# Clip the data to the users extend
data, geo = RC.clip_data(name_reprojected, latlim, lonlim)
# Save results as Gtiff
if TimeStep == 8:
LSTfileName = os.path.join(
output_folder, 'LST_MOD11A2_K_8-daily_' + Date.strftime('%Y') +
'.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
if TimeStep == 1:
LSTfileName = os.path.join(
output_folder, 'LST_MOD11A1_K_daily_' + Date.strftime('%Y') + '.' +
Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
DC.Save_as_tiff(name=LSTfileName, data=data, geo=geo, projection='WGS84')
# remove the side products
os.remove(os.path.join(output_folder, name_collect))
os.remove(os.path.join(output_folder, name_reprojected))
return True
def RetrieveData(Date, args):
"""
This function retrieves MOD16 ET data for a given date from the
ftp://ftp.ntsg.umt.edu/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[
output_folder, TilesVertical, TilesHorizontal, latlim, lonlim,
timestep, hdf_library
] = args
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal, TilesVertical, Date, output_folder,
timestep, hdf_library)
except:
print("Was not able to download the file")
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
# Reproject the MODIS product to epsg_to
epsg_to = '4326'
name_reprojected = RC.reproject_MODIS(name_collect, epsg_to)
# Clip the data to the users extend
data, geo = RC.clip_data(name_reprojected, latlim, lonlim)
if timestep == 'monthly':
ETfileName = os.path.join(
output_folder, 'ET_MOD16A2_mm-month-1_monthly_' +
Date.strftime('%Y') + '.' + Date.strftime('%m') + '.01.tif')
elif timestep == '8-daily':
ETfileName = os.path.join(
output_folder,
'ET_MOD16A2_mm-8days-1_8-daily_' + Date.strftime('%Y') + '.' +
Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
DC.Save_as_tiff(name=ETfileName, data=data, geo=geo, projection='WGS84')
# remove the side products
os.remove(os.path.join(output_folder, name_collect))
os.remove(os.path.join(output_folder, name_reprojected))
return ()
def Download_GWF_from_WA_FTP(output_folder, filename_Out, lonlim, latlim):
"""
This function retrieves GWF data for a given date from the
ftp.wateraccounting.unesco-ihe.org server.
Keyword arguments:
output_folder -- name of the end file with the weekly ALEXI data
End_filename -- name of the end file
lonlim -- [ymin, ymax] (values must be between -60 and 70)
latlim -- [xmin, xmax] (values must be between -180 and 180)
"""
try:
# Collect account and FTP information
username, password = WebAccounts.Accounts(Type='FTP_WA')
ftpserver = "ftp.wateraccounting.unesco-ihe.org"
# Set the file names and directories
filename = "Gray_Water_Footprint.tif"
local_filename = os.path.join(output_folder, filename)
# Download data from FTP
ftp = FTP(ftpserver)
ftp.login(username, password)
directory = "/WaterAccounting_Guest/Static_WA_Datasets/"
ftp.cwd(directory)
lf = open(local_filename, "wb")
ftp.retrbinary("RETR " + filename, lf.write)
lf.close()
# Clip extend out of world data
dataset, Geo_out = RC.clip_data(local_filename, latlim, lonlim)
# make geotiff file
DC.Save_as_tiff(name=filename_Out,
data=dataset,
geo=Geo_out,
projection="WGS84")
# delete old tif file
os.remove(local_filename)
except:
print("file not exists")
return
def RetrieveData(args):
"""
This function retrieves JRC data for a given date from the
http://storage.googleapis.com/global-surface-water/downloads/ server.
Keyword arguments:
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[output_folder, Names_to_download, lonlim, latlim] = args
# Collect the data from the JRC webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(Names_to_download, output_folder)
except:
print("Was not able to download the file")
# Clip the data to the users extend
if len(Names_to_download) == 1:
trash_folder = os.path.join(output_folder, "Trash")
data_in = os.path.join(trash_folder, Names_to_download[0])
data_end, geo_end = RC.clip_data(data_in, latlim, lonlim)
else:
data_end = np.zeros([
int((latlim[1] - latlim[0]) / 0.00025),
int((lonlim[1] - lonlim[0]) / 0.00025)
])
for Name_to_merge in Names_to_download:
trash_folder = os.path.join(output_folder, "Trash")
data_in = os.path.join(trash_folder, Name_to_merge)
geo_out, proj, size_X, size_Y = RC.Open_array_info(data_in)
lat_min_merge = np.maximum(latlim[0],
geo_out[3] + size_Y * geo_out[5])
lat_max_merge = np.minimum(latlim[1], geo_out[3])
lon_min_merge = np.maximum(lonlim[0], geo_out[0])
lon_max_merge = np.minimum(lonlim[1],
geo_out[0] + size_X * geo_out[1])
lonmerge = [lon_min_merge, lon_max_merge]
latmerge = [lat_min_merge, lat_max_merge]
data_one, geo_one = RC.clip_data(data_in, latmerge, lonmerge)
Ystart = int((geo_one[3] - latlim[1]) / geo_one[5])
Yend = int(Ystart + np.shape(data_one)[0])
Xstart = int((geo_one[0] - lonlim[0]) / geo_one[1])
Xend = int(Xstart + np.shape(data_one)[1])
data_end[Ystart:Yend, Xstart:Xend] = data_one
geo_end = tuple([lonlim[0], geo_one[1], 0, latlim[1], 0, geo_one[5]])
# Save results as Gtiff
fileName_out = os.path.join(output_folder, 'JRC_Occurrence_percent.tif')
DC.Save_as_tiff(name=fileName_out,
data=data_end,
geo=geo_end,
projection='WGS84')
shutil.rmtree(trash_folder)
return True
def DownloadData(output_folder, latlim, lonlim, parameter, resolution):
"""
This function downloads DEM data from HydroSHED
Keyword arguments:
output_folder -- directory of the result
latlim -- [ymin, ymax] (values must be between -50 and 50)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
Resample -- 1 = The data will be resampled to 0.001 degree spatial
resolution
-- 0 = The data will have the same pixel size as the data obtained
from the internet
"""
# Define parameter depedent variables
if parameter == "dir_3s":
para_name = "DIR"
unit = "-"
resolution = '3s'
parameter = 'dir'
if parameter == "dem_3s":
para_name = "DEM"
unit = "m"
resolution = '3s'
parameter = 'dem'
if parameter == "dir_15s":
para_name = "DIR"
unit = "-"
resolution = '15s'
parameter = 'dir'
if parameter == "dem_15s":
para_name = "DEM"
unit = "m"
resolution = '15s'
parameter = 'dem'
# converts the latlim and lonlim into names of the tiles which must be
# downloaded
if resolution == '3s':
name, rangeLon, rangeLat = Find_Document_Names(latlim, lonlim,
parameter)
# Memory for the map x and y shape (starts with zero)
size_X_tot = 0
size_Y_tot = 0
if resolution == '15s':
name = Find_Document_names_15s(latlim, lonlim, parameter, resolution)
nameResults = []
# Create a temporary folder for processing
output_folder_trash = os.path.join(output_folder, "Temp")
if not os.path.exists(output_folder_trash):
os.makedirs(output_folder_trash)
# Download, extract, and converts all the files to tiff files
for nameFile in name:
try:
# Download the data from
# http://earlywarning.usgs.gov/hydrodata/
output_file, file_name = Download_Data(nameFile,
output_folder_trash,
parameter, para_name,
resolution)
# extract zip data
DC.Extract_Data(output_file, output_folder_trash)
# Converts the data with a adf extention to a tiff extension.
# The input is the file name and in which directory the data must be stored
file_name_tiff = file_name.split('.')[0] + '_trans_temporary.tif'
file_name_extract = file_name.split('_')[0:3]
if resolution == '3s':
file_name_extract2 = file_name_extract[
0] + '_' + file_name_extract[1]
if resolution == '15s':
file_name_extract2 = file_name_extract[
0] + '_' + file_name_extract[1] + '_15s'
input_adf = os.path.join(output_folder_trash, file_name_extract2,
file_name_extract2, 'hdr.adf')
output_tiff = os.path.join(output_folder_trash, file_name_tiff)
# convert data from adf to a tiff file
output_tiff = DC.Convert_adf_to_tiff(input_adf, output_tiff)
geo_out, proj, size_X, size_Y = RC.Open_array_info(output_tiff)
if int(size_X) != int(6000) or int(size_Y) != int(6000):
data = np.ones((6000, 6000)) * -9999
# Create the latitude bound
Vfile = str(nameFile)[1:3]
SignV = str(nameFile)[0]
SignVer = 1
# If the sign before the filename is a south sign than latitude is negative
if SignV is "s":
SignVer = -1
Bound2 = int(SignVer) * int(Vfile)
# Create the longitude bound
Hfile = str(nameFile)[4:7]
SignH = str(nameFile)[3]
SignHor = 1
# If the sign before the filename is a west sign than longitude is negative
if SignH is "w":
SignHor = -1
Bound1 = int(SignHor) * int(Hfile)
Expected_X_min = Bound1
Expected_Y_max = Bound2 + 5
Xid_start = int(
np.round((geo_out[0] - Expected_X_min) / geo_out[1]))
Xid_end = int(
np.round(
((geo_out[0] + size_X * geo_out[1]) - Expected_X_min) /
geo_out[1]))
Yid_start = int(
np.round((Expected_Y_max - geo_out[3]) / (-geo_out[5])))
Yid_end = int(
np.round((Expected_Y_max - (geo_out[3] +
(size_Y * geo_out[5]))) /
(-geo_out[5])))
data[Yid_start:Yid_end,
Xid_start:Xid_end] = RC.Open_tiff_array(output_tiff)
if np.max(data) == 255:
data[data == 255] = -9999
data[data < -9999] = -9999
geo_in = [
Bound1, 0.00083333333333333, 0.0,
int(Bound2 + 5), 0.0, -0.0008333333333333333333
]
# save chunk as tiff file
DC.Save_as_tiff(name=output_tiff,
data=data,
geo=geo_in,
projection="WGS84")
except:
if resolution == '3s':
# If tile not exist create a replacing zero tile (sea tiles)
output = nameFile.split('.')[0] + "_trans_temporary.tif"
output_tiff = os.path.join(output_folder_trash, output)
file_name = nameFile
data = np.ones((6000, 6000)) * -9999
data = data.astype(np.float32)
# Create the latitude bound
Vfile = str(file_name)[1:3]
SignV = str(file_name)[0]
SignVer = 1
# If the sign before the filename is a south sign than latitude is negative
if SignV is "s":
SignVer = -1
Bound2 = int(SignVer) * int(Vfile)
# Create the longitude bound
Hfile = str(file_name)[4:7]
SignH = str(file_name)[3]
SignHor = 1
# If the sign before the filename is a west sign than longitude is negative
if SignH is "w":
SignHor = -1
Bound1 = int(SignHor) * int(Hfile)
# Geospatial data for the tile
geo_in = [
Bound1, 0.00083333333333333, 0.0,
int(Bound2 + 5), 0.0, -0.0008333333333333333333
]
# save chunk as tiff file
DC.Save_as_tiff(name=output_tiff,
data=data,
geo=geo_in,
projection="WGS84")
if resolution == '15s':
print('no 15s data is in dataset')
if resolution == '3s':
# clip data
Data, Geo_data = RC.clip_data(output_tiff, latlim, lonlim)
size_Y_out = int(np.shape(Data)[0])
size_X_out = int(np.shape(Data)[1])
# Total size of the product so far
size_Y_tot = int(size_Y_tot + size_Y_out)
size_X_tot = int(size_X_tot + size_X_out)
if nameFile is name[0]:
Geo_x_end = Geo_data[0]
Geo_y_end = Geo_data[3]
else:
Geo_x_end = np.min([Geo_x_end, Geo_data[0]])
Geo_y_end = np.max([Geo_y_end, Geo_data[3]])
# create name for chunk
FileNameEnd = "%s_temporary.tif" % (nameFile)
nameForEnd = os.path.join(output_folder_trash, FileNameEnd)
nameResults.append(str(nameForEnd))
# save chunk as tiff file
DC.Save_as_tiff(name=nameForEnd,
data=Data,
geo=Geo_data,
projection="WGS84")
if resolution == '3s':
#size_X_end = int(size_X_tot) #!
#size_Y_end = int(size_Y_tot) #!
size_X_end = int(size_X_tot / len(rangeLat)) + 1 #!
size_Y_end = int(size_Y_tot / len(rangeLon)) + 1 #!
# Define the georeference of the end matrix
geo_out = [Geo_x_end, Geo_data[1], 0, Geo_y_end, 0, Geo_data[5]]
latlim_out = [geo_out[3] + geo_out[5] * size_Y_end, geo_out[3]]
lonlim_out = [geo_out[0], geo_out[0] + geo_out[1] * size_X_end]
# merge chunk together resulting in 1 tiff map
datasetTot = Merge_DEM(latlim_out, lonlim_out, nameResults, size_Y_end,
size_X_end)
datasetTot[datasetTot < -9999] = -9999
if resolution == '15s':
output_file_merged = os.path.join(output_folder_trash, 'merged.tif')
datasetTot, geo_out = Merge_DEM_15s(output_folder_trash,
output_file_merged, latlim, lonlim)
# name of the end result
output_DEM_name = "%s_HydroShed_%s_%s.tif" % (para_name, unit, resolution)
Save_name = os.path.join(output_folder, output_DEM_name)
# Make geotiff file
DC.Save_as_tiff(name=Save_name,
data=datasetTot,
geo=geo_out,
projection="WGS84")
os.chdir(output_folder)
# Delete the temporary folder
shutil.rmtree(output_folder_trash)
def DownloadData(Dir, Startdate, Enddate, latlim, lonlim, Waitbar, version,
Product):
"""
This scripts downloads SSEBop ET data from the UNESCO-IHE ftp server.
The output files display the total ET in mm for a period of one month.
The name of the file corresponds to the first day of the month.
Keyword arguments:
Dir -- 'C:/file/to/path/'
Startdate -- 'yyyy-mm-dd'
Enddate -- 'yyyy-mm-dd'
lonlim -- [ymin, ymax] (values must be between -90 and 90)
latlim -- [xmin, xmax] (values must be between -180 and 180)
"""
if version == "FTP":
# Check the latitude and longitude and otherwise set lat or lon on greatest extent
if latlim[0] < -59.2 or latlim[1] > 80:
print(
'Latitude above 80N or below -59.2S is not possible. Value set to maximum'
)
latlim[0] = np.max(latlim[0], -59.2)
latlim[1] = np.min(latlim[1], 80)
if lonlim[0] < -180 or lonlim[1] > 180:
print(
'Longitude must be between 180E and 180W. Now value is set to maximum'
)
lonlim[0] = np.max(lonlim[0], -180)
lonlim[1] = np.min(lonlim[1], 180)
# Check Startdate and Enddate
if not Startdate:
Startdate = pd.Timestamp('2003-01-01')
if not Enddate:
Enddate = pd.Timestamp('2014-10-31')
if version == "V4":
# Check the latitude and longitude and otherwise set lat or lon on greatest extent
if latlim[0] < -60 or latlim[1] > 80.0022588483988670:
print(
'Latitude above 80N or below -59.2S is not possible. Value set to maximum'
)
latlim[0] = np.max(latlim[0], -60)
latlim[1] = np.min(latlim[1], 80.0022588483988670)
if lonlim[0] < -180 or lonlim[1] > 180.0002930387853439:
print(
'Longitude must be between 180E and 180W. Now value is set to maximum'
)
lonlim[0] = np.max(lonlim[0], -180)
lonlim[1] = np.min(lonlim[1], 180.0002930387853439)
# Check Startdate and Enddate
if not Startdate:
Startdate = pd.Timestamp('2003-01-01')
if not Enddate:
import datetime
Enddate = pd.Timestamp(datetime.datetime.now())
# Define directory and create it if not exists
if Product == "ETact":
output_folder = os.path.join(Dir, 'Evaporation', 'SSEBop', 'Monthly')
freq_use = "MS"
if Product == "ETpot":
output_folder = os.path.join(Dir, 'Potential_Evapotranspiration',
'FEWS', 'Daily')
freq_use = "D"
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Creates dates library
Dates = pd.date_range(Startdate, Enddate, freq=freq_use)
# Create Waitbar
if Waitbar == 1:
import watools.Functions.Start.WaitbarConsole as WaitbarConsole
total_amount = len(Dates)
amount = 0
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
# Loop over the dates
for Date in Dates:
# Define year and month
year = Date.year
month = Date.month
day = Date.day
if version == "FTP":
# Date as printed in filename
Filename_out = os.path.join(
output_folder,
'ETa_SSEBop_FTP_mm-month-1_monthly_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
# Define end filename
Filename_dir = os.path.join("%s" % year,
"m%s%02d.tif" % (str(year)[2:], month))
Filename_only = "m%s%02d.tif" % (str(year)[2:], month)
if version == "V4":
# Date as printed in filename
if Product == "ETpot":
Filename_out = os.path.join(
output_folder,
'ETpot_FEWS_mm-day-1_daily_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
# Define the downloaded zip file
Filename_only_zip = 'et%02s%02d%02d.tar.gz' % (str(year)[2:],
month, day)
# The end file name after downloading and unzipping
Filename_only = "et%02s%02d%02d.bil" % (str(year)[2:], month,
day)
# Create bin folder
temp_folder = os.path.join(output_folder, "Temp")
if not os.path.exists(temp_folder):
os.makedirs(temp_folder)
local_filename = os.path.join(temp_folder, Filename_only)
if Product == "ETact":
Filename_out = os.path.join(
output_folder,
'ETa_SSEBop_V4_mm-month-1_monthly_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
# Define the downloaded zip file
Filename_only_zip = "m%s%02d.zip" % (str(year), month)
# The end file name after downloading and unzipping
Filename_only = "m%s%02d_modisSSEBopETv4_actual_mm.tif" % (
str(year), month)
# Temporary filename for the downloaded global file
local_filename = os.path.join(output_folder, Filename_only)
# Download the data from FTP server if the file not exists
if not os.path.exists(Filename_out):
try:
if version == "FTP":
Download_SSEBop_from_WA_FTP(local_filename, Filename_dir)
if version == "V4":
if Product == "ETpot":
Download_SSEBop_from_Web(temp_folder,
Filename_only_zip, Product)
if Product == "ETact":
Download_SSEBop_from_Web(output_folder,
Filename_only_zip, Product)
if Product == "ETpot":
Array_ETpot = RC.Open_bil_array(local_filename)
Array_ETpot = Array_ETpot / 100
Geo_out = tuple([-180.5, 1, 0, 90.5, 0, -1])
dest = DC.Save_as_MEM(Array_ETpot, Geo_out, "WGS84")
data, Geo_out = RC.clip_data(dest, latlim, lonlim)
DC.Save_as_tiff(Filename_out, data, Geo_out, "WGS84")
if Product == "ETact":
# Clip dataset
RC.Clip_Dataset_GDAL(local_filename, Filename_out, latlim,
lonlim)
os.remove(local_filename)
except:
print("Was not able to download file with date %s" % Date)
# Adjust waitbar
if Waitbar == 1:
amount += 1
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
if version == "V4":
import glob
os.chdir(output_folder)
if Product == "ETact":
zipfiles = glob.glob("*.zip")
for zipfile in zipfiles:
os.remove(os.path.join(output_folder, zipfile))
xmlfiles = glob.glob("*.xml")
for xmlfile in xmlfiles:
os.remove(os.path.join(output_folder, xmlfile))
if Product == "ETpot":
import shutil
Temp_dir = os.path.join(output_folder, "Temp")
shutil.rmtree(Temp_dir)
return
def DownloadData(output_folder, latlim, lonlim, dataset, level=None):
"""
This function downloads SoilGrids data from SoilGrids.org
Keyword arguments:
output_folder -- directory of the result
latlim -- [ymin, ymax] (values must be between -50 and 50)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
level -- "sl1" .... "sl7"
dataset -- ground dataset
"""
# Define parameter depedent variables
if dataset == "BLDFIE":
nameEnd = os.path.join(output_folder,
'BulkDensity_%s_SoilGrids_kg-m-3.tif' % level)
level_name = "%s_" % level
if dataset == "CLYPPT":
nameEnd = os.path.join(
output_folder,
'ClayContentMassFraction_%s_SoilGrids_percentage.tif' % level)
level_name = "%s_" % level
if dataset == "ORCDRC":
nameEnd = os.path.join(
output_folder,
'SoilOrganicCarbonContent_%s_SoilGrids_g_kg.tif' % level)
level_name = "%s_" % level
if dataset == "OCSTHA":
nameEnd = os.path.join(
output_folder,
'SoilOrganicCarbonStock_%s_SoilGrids_tonnes-ha-1.tif' % level)
level_name = "%s_" % level
if dataset == "CRFVOL":
nameEnd = os.path.join(
output_folder,
'CoarseFragmentVolumetric_%s_SoilGrids_percentage.tif' % level)
level_name = "%s_" % level
if dataset == "SLTPPT":
nameEnd = os.path.join(
output_folder,
'SiltContentMassFraction_%s_SoilGrids_percentage.tif' % level)
level_name = "%s_" % level
if dataset == "SNDPPT":
nameEnd = os.path.join(
output_folder,
'SandContentMassFraction_%s_SoilGrids_percentage.tif' % level)
level_name = "%s_" % level
if dataset == "BDRICM":
nameEnd = os.path.join(output_folder,
'DepthToBedrock_SoilGrids_cm.tif')
level_name = ""
if dataset == "BDTICM":
nameEnd = os.path.join(output_folder,
'AbsoluteDepthToBedrock_SoilGrids_cm.tif')
level_name = ""
if dataset == "BDRLOG":
nameEnd = os.path.join(
output_folder,
'PredictedProbabilityOfOccurrence_SoilGrids_percentage.tif')
level_name = ""
if not os.path.exists(nameEnd):
# Create trash folder
output_folder_trash = os.path.join(output_folder, "Trash")
if not os.path.exists(output_folder_trash):
os.makedirs(output_folder_trash)
# Download, extract, and converts all the files to tiff files
try:
# Download the data from
output_file = Download_Data(output_folder_trash, level_name,
dataset)
# Clip the data
RC.clip_data(output_filename, latlim, lonlim, output_name=nameEnd)
except:
print("Was not able to create the wanted dataset")
try:
shutil.rmtree(output_folder_trash)
except:
print("Was not able to remove the trash bin")
return ()
def DownloadData(Dir, Startdate, Enddate, latlim, lonlim, Type, Waitbar):
"""
This scripts downloads ETmonitor ET data from the UNESCO-IHE ftp server.
The output files display the total ET in mm for a period of one month.
The name of the file corresponds to the first day of the month.
Keyword arguments:
Dir -- 'C:/file/to/path/'
Startdate -- 'yyyy-mm-dd'
Enddate -- 'yyyy-mm-dd'
lonlim -- [ymin, ymax] (values must be between -90 and 90)
latlim -- [xmin, xmax] (values must be between -180 and 180)
"""
# Check the latitude and longitude and otherwise set lat or lon on greatest extent
if latlim[0] < -90 or latlim[1] > 90:
print(
'Latitude above 90N or below 90S is not possible. Value set to maximum'
)
latlim[0] = np.max(latlim[0], -90)
latlim[1] = np.min(latlim[1], 90)
if lonlim[0] < -180 or lonlim[1] > 180:
print(
'Longitude must be between 180E and 180W. Now value is set to maximum'
)
lonlim[0] = np.max(lonlim[0], -180)
lonlim[1] = np.min(lonlim[1], 180)
# Check Startdate and Enddate
if not Startdate:
Startdate = pd.Timestamp('2008-01-01')
if not Enddate:
Enddate = pd.Timestamp('2012-12-31')
# Creates dates library
Dates = pd.date_range(Startdate, Enddate, freq="MS")
# Create Waitbar
if Waitbar == 1:
import watools.Functions.Start.WaitbarConsole as WaitbarConsole
total_amount = len(Dates)
amount = 0
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
# Define directory and create it if not exists
if Type == "act":
output_folder = os.path.join(Dir, 'Evaporation', 'ETmonitor',
'Monthly')
if Type == "pot":
output_folder = os.path.join(Dir, 'ETpot', 'ETmonitor', 'Monthly')
if Type == "ei":
output_folder = os.path.join(Dir, 'Ei', 'ETmonitor', 'Monthly')
if Type == "es":
output_folder = os.path.join(Dir, 'Es', 'ETmonitor', 'Monthly')
if Type == "ew":
output_folder = os.path.join(Dir, 'Ew', 'ETmonitor', 'Monthly')
if Type == "tr":
output_folder = os.path.join(Dir, 'Transpiration', 'ETmonitor',
'Monthly')
if not os.path.exists(output_folder):
os.makedirs(output_folder)
for Date in Dates:
# Define year and month
year = Date.year
month = Date.month
# Define end filename and Date as printed in filename
if Type == "act":
Filename_in = "ET_ETmonitor_mm-month_%d_%02d_01.tif" % (year,
month)
Filename_out = os.path.join(
output_folder,
'ETa_ETmonitor_mm-month-1_monthly_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
if Type == "pot":
Filename_in = "ETpot_ETmonitor_mm-month_%d_%02d_01.tif" % (year,
month)
Filename_out = os.path.join(
output_folder,
'ETpot_ETmonitor_mm-month-1_monthly_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
if Type == "ei":
Filename_in = "Ei_ETmonitor_mm-month_%d_%02d_01.tif" % (year,
month)
Filename_out = os.path.join(
output_folder,
'Ei_ETmonitor_mm-month-1_monthly_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
if Type == "es":
Filename_in = "Es_ETmonitor_mm-month_%d_%02d_01.tif" % (year,
month)
Filename_out = os.path.join(
output_folder,
'Es_ETmonitor_mm-month-1_monthly_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
if Type == "ew":
Filename_in = "Ew_ETmonitor_mm-month_%d_%02d_01.tif" % (year,
month)
Filename_out = os.path.join(
output_folder,
'Ew_ETmonitor_mm-month-1_monthly_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
if Type == "tr":
Filename_in = "Tr_ETmonitor_mm-month_%d_%02d_01.tif" % (year,
month)
Filename_out = os.path.join(
output_folder,
'Tr_ETmonitor_mm-month-1_monthly_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'),
Date.strftime('%d')))
# Temporary filename for the downloaded global file
local_filename = os.path.join(output_folder, Filename_in)
# Download the data from FTP server if the file not exists
if not os.path.exists(Filename_out):
try:
Download_ETmonitor_from_WA_FTP(local_filename, Filename_in,
Type)
# Reproject dataset
epsg_to = '4326'
name_reprojected_ETmonitor = RC.reproject_modis_wgs84(
local_filename)
# Clip dataset
RC.clip_data(name_reprojected_ETmonitor,
latlim,
lonlim,
output_name=Filename_out)
os.remove(name_reprojected_ETmonitor)
os.remove(local_filename)
except:
print("Was not able to download file with date %s" % Date)
# Adjust waitbar
if Waitbar == 1:
amount += 1
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
return
def Add_Reservoirs(output_nc, Diff_Water_Volume, Regions):
import numpy as np
import watools.General.raster_conversions as RC
import watools.General.data_conversions as DC
# Extract data from NetCDF file
Discharge_dict = RC.Open_nc_dict(output_nc, "dischargedict_dynamic")
River_dict = RC.Open_nc_dict(output_nc, "riverdict_static")
DEM_dict = RC.Open_nc_dict(output_nc, "demdict_static")
Distance_dict = RC.Open_nc_dict(output_nc, "distancedict_static")
Rivers = RC.Open_nc_array(output_nc, "rivers")
acc_pixels = RC.Open_nc_array(output_nc, "accpix")
# Open data array info based on example data
geo_out, epsg, size_X, size_Y, size_Z, time = RC.Open_nc_info(output_nc)
# Create ID Matrix
y, x = np.indices((size_Y, size_X))
ID_Matrix = np.int32(
np.ravel_multi_index(np.vstack((y.ravel(), x.ravel())),
(size_Y, size_X),
mode='clip').reshape(x.shape)) + 1
del x, y
Acc_Pixels_Rivers = Rivers * acc_pixels
ID_Rivers = Rivers * ID_Matrix
Amount_of_Reservoirs = len(Regions)
Reservoir_is_in_River = np.ones([len(Regions), 3]) * -9999
for reservoir in range(0, Amount_of_Reservoirs):
region = Regions[reservoir, :]
dest = DC.Save_as_MEM(Acc_Pixels_Rivers, geo_out, projection='WGS84')
Rivers_Acc_Pixels_reservoir, Geo_out = RC.clip_data(
dest, latlim=[region[2], region[3]], lonlim=[region[0], region[1]])
dest = DC.Save_as_MEM(ID_Rivers, geo_out, projection='WGS84')
Rivers_ID_reservoir, Geo_out = RC.clip_data(
dest, latlim=[region[2], region[3]], lonlim=[region[0], region[1]])
size_Y_reservoir, size_X_reservoir = np.shape(
Rivers_Acc_Pixels_reservoir)
IDs_Edges = []
IDs_Edges = np.append(IDs_Edges, Rivers_Acc_Pixels_reservoir[0, :])
IDs_Edges = np.append(IDs_Edges, Rivers_Acc_Pixels_reservoir[:, 0])
IDs_Edges = np.append(
IDs_Edges,
Rivers_Acc_Pixels_reservoir[int(size_Y_reservoir) - 1, :])
IDs_Edges = np.append(
IDs_Edges, Rivers_Acc_Pixels_reservoir[:,
int(size_X_reservoir) - 1])
Value_Reservoir = np.max(np.unique(IDs_Edges))
y_pix_res, x_pix_res = np.argwhere(
Rivers_Acc_Pixels_reservoir == Value_Reservoir)[0]
ID_reservoir = Rivers_ID_reservoir[y_pix_res, x_pix_res]
# Find exact reservoir area in river directory
for River_part in River_dict.items():
if len(np.argwhere(River_part[1] == ID_reservoir)) > 0:
Reservoir_is_in_River[reservoir, 0] = np.argwhere(
River_part[1] == ID_reservoir) #River_part_good
Reservoir_is_in_River[reservoir,
1] = River_part[0] #River_Add_Reservoir
Reservoir_is_in_River[reservoir, 2] = 1 #Reservoir_is_in_River
numbers = abs(Reservoir_is_in_River[:, 1].argsort() -
len(Reservoir_is_in_River) + 1)
for number in range(0, len(Reservoir_is_in_River)):
row_reservoir = np.argwhere(numbers == number)[0][0]
if not Reservoir_is_in_River[row_reservoir, 2] == -9999:
# Get discharge into the reservoir:
Flow_in_res_m3 = Discharge_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][:,
int(Reservoir_is_in_River[row_reservoir,
0])]
# Get difference reservoir
Change_Reservoir_m3 = Diff_Water_Volume[row_reservoir, :, 2]
# Total Change outflow
Change_outflow_m3 = np.minimum(Flow_in_res_m3, Change_Reservoir_m3)
Difference = Change_outflow_m3 - Change_Reservoir_m3
if abs(np.sum(Difference)) > 10000 and np.sum(
Change_Reservoir_m3[Change_outflow_m3 > 0]) > 0:
Change_outflow_m3[Change_outflow_m3 < 0] = Change_outflow_m3[
Change_outflow_m3 < 0] * np.sum(
Change_outflow_m3[Change_outflow_m3 > 0]) / np.sum(
Change_Reservoir_m3[Change_outflow_m3 > 0])
# Find key name (which is also the lenght of the river dictionary)
i = len(River_dict)
#River_with_reservoirs_dict[i]=list((River_dict[River_Add_Reservoir][River_part_good[0][0]:]).flat) < MAAK DIRECTORIES ARRAYS OP DEZE MANIER DAN IS DE ARRAY 1D
River_dict[i] = River_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][int(Reservoir_is_in_River[row_reservoir,
0]):]
River_dict[int(
Reservoir_is_in_River[row_reservoir, 1])] = River_dict[int(
Reservoir_is_in_River[
row_reservoir,
1])][:int(Reservoir_is_in_River[row_reservoir, 0]) + 1]
DEM_dict[i] = DEM_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][int(Reservoir_is_in_River[row_reservoir,
0]):]
DEM_dict[int(
Reservoir_is_in_River[row_reservoir, 1])] = DEM_dict[int(
Reservoir_is_in_River[
row_reservoir,
1])][:int(Reservoir_is_in_River[row_reservoir, 0]) + 1]
Distance_dict[i] = Distance_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][int(Reservoir_is_in_River[row_reservoir,
0]):]
Distance_dict[int(
Reservoir_is_in_River[row_reservoir, 1])] = Distance_dict[int(
Reservoir_is_in_River[
row_reservoir,
1])][:int(Reservoir_is_in_River[row_reservoir, 0]) + 1]
Discharge_dict[i] = Discharge_dict[int(Reservoir_is_in_River[
row_reservoir, 1])][:,
int(Reservoir_is_in_River[row_reservoir,
0]):]
Discharge_dict[int(
Reservoir_is_in_River[row_reservoir, 1])] = Discharge_dict[int(
Reservoir_is_in_River[
row_reservoir,
1])][:, :int(Reservoir_is_in_River[row_reservoir, 0]) +
1]
Discharge_dict[int(Reservoir_is_in_River[
row_reservoir,
1])][:, 1:int(Reservoir_is_in_River[row_reservoir, 0]) +
1] = Discharge_dict[int(
Reservoir_is_in_River[row_reservoir, 1]
)][:, 1:int(Reservoir_is_in_River[row_reservoir, 0]) +
1] - Change_outflow_m3[:, None]
Next_ID = River_dict[int(Reservoir_is_in_River[row_reservoir,
1])][0]
times = 0
while len(River_dict) > times:
for River_part in River_dict.items():
if River_part[-1][-1] == Next_ID:
Next_ID = River_part[-1][0]
item = River_part[0]
#Always 10 procent of the incoming discharge will pass the dam
Change_outflow_m3[:, None] = np.minimum(
0.9 * Discharge_dict[item][:, -1:],
Change_outflow_m3[:, None])
Discharge_dict[item][:, 1:] = Discharge_dict[
item][:, 1:] - Change_outflow_m3[:, None]
print(item)
times = 0
times += 1
return (Discharge_dict, River_dict, DEM_dict, Distance_dict)
def Find_Area_Volume_Relation(region, input_JRC, input_nc):
# Find relation between V and A
import numpy as np
import watools.General.raster_conversions as RC
import watools.General.data_conversions as DC
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
def func(x, a, b):
"""
This function is used for finding relation area and volume
"""
return (a * x**b)
def func3(x, a, b, c, d):
"""
This function is used for finding relation area and volume
"""
return (a * (x - c)**b + d)
#Array, Geo_out = RC.clip_data(input_JRC,latlim=[14.528,14.985],lonlim =[35.810,36.005])
Array, Geo_out = RC.clip_data(
input_JRC,
latlim=[region[2], region[3]],
lonlim=[region[0], region[1]
]) # This reservoir was not filled when SRTM was taken
size_Y = int(np.shape([Array])[-2])
size_X = int(np.shape([Array])[-1])
Water_array = np.zeros(np.shape(Array))
buffer_zone = 4
Array[Array > 0] = 1
for i in range(0, size_Y):
for j in range(0, size_X):
Water_array[i, j] = np.max(Array[
np.maximum(0, i -
buffer_zone):np.minimum(size_Y, i + buffer_zone +
1),
np.maximum(0, j -
buffer_zone):np.minimum(size_X, j + buffer_zone +
1)])
del Array
# Open DEM and reproject
DEM_Array = RC.Open_nc_array(input_nc, "dem")
Geo_out_dem, proj_dem, size_X_dem, size_Y_dem, size_Z_dem, time = RC.Open_nc_info(
input_nc)
# Save Example as memory file
dest_example = DC.Save_as_MEM(Water_array, Geo_out, projection='WGS84')
dest_dem = DC.Save_as_MEM(DEM_Array, Geo_out_dem, projection='WGS84')
# reproject DEM by using example
dest_out = RC.reproject_dataset_example(dest_dem, dest_example, method=2)
DEM = dest_out.GetRasterBand(1).ReadAsArray()
# find DEM water heights
DEM_water = np.zeros(np.shape(Water_array))
DEM_water[Water_array != 1] = np.nan
DEM_water[Water_array == 1.] = DEM[Water_array == 1.]
# Get array with areas
import watools.Functions.Start.Area_converter as Area
dlat, dlon = Area.Calc_dlat_dlon(Geo_out, size_X, size_Y)
area_in_m2 = dlat * dlon
# find volume and Area
min_DEM_water = int(np.round(np.nanmin(DEM_water)))
max_DEM_water = int(np.round(np.nanmax(DEM_water)))
Reservoir_characteristics = np.zeros([1, 5])
i = 0
for height in range(min_DEM_water + 1, max_DEM_water):
DEM_water_below_height = np.zeros(np.shape(DEM_water))
DEM_water[np.isnan(DEM_water)] = 1000000
DEM_water_below_height[DEM_water < height] = 1
pixels = np.sum(DEM_water_below_height)
area = np.sum(DEM_water_below_height * area_in_m2)
if height == min_DEM_water + 1:
volume = 0.5 * area
histogram = pixels
Reservoir_characteristics[:] = [
height, pixels, area, volume, histogram
]
else:
area_previous = Reservoir_characteristics[i, 2]
volume_previous = Reservoir_characteristics[i, 3]
volume = volume_previous + 0.5 * (
area - area_previous) + 1 * area_previous
histogram_previous = Reservoir_characteristics[i, 1]
histogram = pixels - histogram_previous
Reservoir_characteristics_one = [
height, pixels, area, volume, histogram
]
Reservoir_characteristics = np.append(
Reservoir_characteristics, Reservoir_characteristics_one)
i += 1
Reservoir_characteristics = np.resize(Reservoir_characteristics,
(i + 1, 5))
maxi = int(len(Reservoir_characteristics[:, 3]))
# find minimum value for reservoirs height (DEM is same value if reservoir was already filled whe SRTM was created)
Historgram = Reservoir_characteristics[:, 4]
hist_mean = np.mean(Historgram)
hist_std = np.std(Historgram)
mini_tresh = hist_std * 5 + hist_mean
Check_hist = np.zeros([len(Historgram)])
Check_hist[Historgram > mini_tresh] = Historgram[Historgram > mini_tresh]
if np.max(Check_hist) != 0.0:
col = np.argwhere(Historgram == np.max(Check_hist))[0][0]
mini = col + 1
else:
mini = 0
fitted = 0
# find starting point reservoirs
V0 = Reservoir_characteristics[mini, 3]
A0 = Reservoir_characteristics[mini, 2]
# Calculate the best maxi reservoir characteristics, based on the normal V = a*x**b relation
while fitted == 0:
try:
if mini == 0:
popt1, pcov1 = curve_fit(
func, Reservoir_characteristics[mini:maxi, 2],
Reservoir_characteristics[mini:maxi, 3])
else:
popt1, pcov1 = curve_fit(
func, Reservoir_characteristics[mini:maxi, 2] - A0,
Reservoir_characteristics[mini:maxi, 3] - V0)
fitted = 1
except:
maxi -= 1
if maxi < mini:
print('ERROR: was not able to find optimal fit')
fitted = 1
# Remove last couple of pixels of maxi
maxi_end = int(np.round(maxi - 0.2 * (maxi - mini)))
done = 0
times = 0
while done == 0 and times > 20 and maxi_end < mini:
try:
if mini == 0:
popt, pcov = curve_fit(
func, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
else:
popt, pcov = curve_fit(
func3, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
except:
maxi_end = int(maxi)
if mini == 0:
popt, pcov = curve_fit(
func, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
else:
popt, pcov = curve_fit(
func3, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
if mini == 0:
plt.plot(Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3], 'ro')
t = np.arange(0., np.max(Reservoir_characteristics[:, 2]), 1000)
plt.plot(t, popt[0] * (t)**popt[1], 'g--')
plt.axis([
0,
np.max(Reservoir_characteristics[mini:maxi_end, 2]), 0,
np.max(Reservoir_characteristics[mini:maxi_end, 3])
])
plt.show()
done = 1
else:
plt.plot(Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3], 'ro')
t = np.arange(0., np.max(Reservoir_characteristics[:, 2]), 1000)
plt.plot(t, popt[0] * (t - popt[2])**popt[1] + popt[3], 'g--')
plt.axis([
0,
np.max(Reservoir_characteristics[mini:maxi_end, 2]), 0,
np.max(Reservoir_characteristics[mini:maxi_end, 3])
])
plt.show()
Volume_error = popt[3] / V0 * 100 - 100
print('error Volume = %s percent' % Volume_error)
print('error Area = %s percent' % (A0 / popt[2] * 100 - 100))
if Volume_error < 30 and Volume_error > -30:
done = 1
else:
times += 1
maxi_end -= 1
print('Another run is done in order to improve the result')
if done == 0:
popt = np.append(popt1, [A0, V0])
if len(popt) == 2:
popt = np.append(popt, [0, 0])
return (popt)
