def livestock_feed(output_folder, lu_fh, ndm_fhs, feed_dict, live_feed, cattle_fh, fraction_fhs, ndmdates):
"""
Calculate natural livestock feed production
INPUTS
----------
lu_fh : str
filehandle for land use map
ndm_fhs: nd array
array of filehandles of NDM maps
ndm_dates: nd array
array of dates for NDM maps
feed_dict: dict
dictionnary 'pasture class':[list of LULC]
feed_pct: dict
dictionnary 'pasture class':[percent available as feed]
cattle_fh : str
filehandle for cattle map
"""
Data_Path_Feed = "Feed"
out_folder = os.path.join(output_folder, Data_Path_Feed)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
area_ha = becgis.MapPixelAreakm(lu_fh) * 100
LULC = RC.Open_tiff_array(lu_fh)
# cattle = RC.Open_tiff_array(cattle_fh)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
f_pct = np.zeros(LULC.shape)
for lu_type in feed_dict.keys():
classes = feed_dict[lu_type]
mask = np.logical_or.reduce([LULC == value for value in classes])
f_pct[mask] = live_feed[lu_type]
feed_fhs_landscape = []
feed_fhs_incremental = []
for d in range(len(ndm_fhs)):
ndm_fh = ndm_fhs[d]
fraction_fh = fraction_fhs[d]
date1 = ndmdates[d]
year = '%d' %date1.year
month = '%02d' %date1.month
yield_fract = RC.Open_tiff_array(fraction_fh)
out_fh_l = out_folder+'\\feed_prod_landscape_%s_%s.tif' %(year, month)
out_fh_i = out_folder+'\\feed_prod_incremental_%s_%s.tif' %(year, month)
# out_fh2 = out_folder+'\\Feed_prod_pH_%s_%s.tif' %(year, month)
NDM = becgis.OpenAsArray(ndm_fh, nan_values=True)
NDM_feed = NDM * f_pct
NDM_feed_incremental = NDM_feed * yield_fract * area_ha/1e6
NDM_feed_landscape = (NDM_feed *(1-yield_fract)) * area_ha/1e6
DC.Save_as_tiff(out_fh_l, NDM_feed_landscape, geo_out)
DC.Save_as_tiff(out_fh_i, NDM_feed_incremental, geo_out)
# NDM_feed_perHead = NDM_feed / cattle
# DC.Save_as_tiff(out_fh2, NDM_feed, geo_out)
feed_fhs_landscape.append(out_fh_l)
feed_fhs_incremental.append(out_fh_i)
return feed_fhs_landscape, feed_fhs_incremental
def fuel_wood(output_folder, lu_fh, ndm_fhs, fraction_fhs, ndmdates):
"""
Calculate natural livestock feed production
INPUTS
----------
lu_fh : str
filehandle for land use map
ndm_fhs: nd array
array of filehandles of NDM maps
abv_grnd_biomass_ratio: dict
dictionnary 'LULC':[above ground biomass]
"""
Data_Path_Fuel = "Fuel"
out_folder = os.path.join(output_folder, Data_Path_Fuel)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
area_ha = becgis.MapPixelAreakm(lu_fh) * 100
LULC = RC.Open_tiff_array(lu_fh)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
fuel_classes = [1, 8, 9, 10, 11, 12, 13]
fuel_mask = np.zeros(LULC.shape)
for fc in fuel_classes:
fuel_mask[np.where(LULC == fc)] = 1
fuel_fhs_landscape = []
fuel_fhs_incremental = []
for d in range(len(ndm_fhs)):
ndm_fh = ndm_fhs[d]
fraction_fh = fraction_fhs[d]
yield_fract = RC.Open_tiff_array(fraction_fh)
date1 = ndmdates[d]
year = '%d' %date1.year
month = '%02d' %date1.month
# year = ndm_fh[-14:-10]
# month = ndm_fh[-9:-7]
out_fh_l = out_folder+'\\fuel_prod_landscape_%s_%s.tif' %(year, month)
out_fh_i = out_folder+'\\fuel_prod_incremental_%s_%s.tif' %(year, month)
NDM = becgis.OpenAsArray(ndm_fh, nan_values=True)
NDM_fuel_incremental = NDM * .05 * fuel_mask * yield_fract * area_ha/1e6
NDM_fuel_landscape = NDM * .05 * fuel_mask *(1-yield_fract) * area_ha/1e6
DC.Save_as_tiff(out_fh_i, NDM_fuel_incremental, geo_out)
DC.Save_as_tiff(out_fh_l, NDM_fuel_landscape, geo_out)
fuel_fhs_landscape.append(out_fh_l)
fuel_fhs_incremental.append(out_fh_i)
return fuel_fhs_landscape, fuel_fhs_incremental
def Clip_Dataset(local_filename, Filename_out, latlim, lonlim):
import wa.General.raster_conversions as RC
# Open Dataset
HiHydroSoil_Array = RC.Open_tiff_array(local_filename)
# Define area
XID = [
int(np.floor((180 + lonlim[0]) / 0.00833333)),
int(np.ceil((180 + lonlim[1]) / 0.00833333))
]
YID = [
int(np.ceil((90 - latlim[1]) / 0.00833333)),
int(np.floor((90 - latlim[0]) / 0.00833333))
]
# Define Georeference
geo = tuple([
-180 + 0.00833333 * XID[0], 0.00833333, 0, 90 - 0.00833333 * YID[0], 0,
-0.00833333
])
# Clip Array
HiHydroSoil_Array_clipped = HiHydroSoil_Array[YID[0]:YID[1], XID[0]:XID[1]]
# Save tiff file
DC.Save_as_tiff(Filename_out, HiHydroSoil_Array_clipped, geo, "WGS84")
def Download_ALEXI_from_WA_FTP(local_filename, DirFile, filename, lonlim,
latlim, yID, xID, TimeStep):
"""
This function retrieves ALEXI data for a given date from the
ftp.wateraccounting.unesco-ihe.org server.
Restrictions:
The data and this python file may not be distributed to others without
permission of the WA+ team due data restriction of the ALEXI developers.
Keyword arguments:
local_filename -- name of the temporary file which contains global ALEXI data
DirFile -- name of the end file with the weekly ALEXI data
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)
"""
# Collect account and FTP information
username, password = WebAccounts.Accounts(Type='FTP_WA')
ftpserver = "ftp.wateraccounting.unesco-ihe.org"
# Download data from FTP
ftp = FTP(ftpserver)
ftp.login(username, password)
if TimeStep is "weekly":
directory = "/WaterAccounting/Data_Satellite/Evaporation/ALEXI/World/"
if TimeStep is "daily":
directory = "/WaterAccounting/Data_Satellite/Evaporation/ALEXI/World_05182018/"
ftp.cwd(directory)
lf = open(local_filename, "wb")
ftp.retrbinary("RETR " + filename, lf.write)
lf.close()
if TimeStep is "weekly":
# Open global ALEXI data
dataset = RC.Open_tiff_array(local_filename)
# Clip extend out of world data
data = dataset[yID[0]:yID[1], xID[0]:xID[1]]
data[data < 0] = -9999
if TimeStep is "daily":
DC.Extract_Data_gz(local_filename, os.path.splitext(local_filename)[0])
raw_data = np.fromfile(os.path.splitext(local_filename)[0],
dtype="<f4")
dataset = np.flipud(np.resize(raw_data, [3000, 7200]))
data = dataset[yID[0]:yID[1], xID[0]:xID[1]]
data[data < 0] = -9999
# make geotiff file
geo = [lonlim[0], 0.05, 0, latlim[1], 0, -0.05]
DC.Save_as_tiff(name=DirFile, data=data, geo=geo, projection="WGS84")
return
def gap_filling(dataset, NoDataValue, method=1):
"""
This function fills the no data gaps in a numpy array
Keyword arguments:
dataset -- 'C:/' path to the source data (dataset that must be filled)
NoDataValue -- Value that must be filled
"""
import wa.General.data_conversions as DC
try:
if dataset.split('.')[-1] == 'tif':
# Open the numpy array
data = Open_tiff_array(dataset)
Save_as_tiff = 1
else:
data = dataset
Save_as_tiff = 0
except:
data = dataset
Save_as_tiff = 0
# fill the no data values
if NoDataValue is np.nan:
mask = ~(np.isnan(data))
else:
mask = ~(data == NoDataValue)
xx, yy = np.meshgrid(np.arange(data.shape[1]), np.arange(data.shape[0]))
xym = np.vstack((np.ravel(xx[mask]), np.ravel(yy[mask]))).T
data0 = np.ravel(data[:, :][mask])
if method == 1:
interp0 = scipy.interpolate.NearestNDInterpolator(xym, data0)
data_end = interp0(np.ravel(xx), np.ravel(yy)).reshape(xx.shape)
if method == 2:
interp0 = scipy.interpolate.LinearNDInterpolator(xym, data0)
data_end = interp0(np.ravel(xx), np.ravel(yy)).reshape(xx.shape)
if Save_as_tiff == 1:
EndProduct = dataset[:-4] + '_GF.tif'
# collect the geoinformation
geo_out, proj, size_X, size_Y = Open_array_info(dataset)
# Save the filled array as geotiff
DC.Save_as_tiff(name=EndProduct,
data=data_end,
geo=geo_out,
projection=proj)
else:
EndProduct = data_end
return (EndProduct)
def Download_ALEXI_from_WA_FTP(local_filename, DirFile, filename, lonlim,
latlim, yID, xID):
"""
This function retrieves ALEXI data for a given date from the
ftp.wateraccounting.unesco-ihe.org server.
Restrictions:
The data and this python file may not be distributed to others without
permission of the WA+ team due data restriction of the ALEXI developers.
Keyword arguments:
local_filename -- name of the temporary file which contains global ALEXI data
DirFile -- name of the end file with the weekly ALEXI data
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"
# Download data from FTP
ftp = FTP(ftpserver)
ftp.login(username, password)
directory = "/WaterAccounting/Data_Satellite/Evaporation/ALEXI/World/"
ftp.cwd(directory)
lf = open(local_filename, "wb")
ftp.retrbinary("RETR " + filename, lf.write)
lf.close()
# Open global ALEXI data
dataset = RC.Open_tiff_array(local_filename)
# Clip extend out of world data
data = dataset[yID[0]:yID[1], xID[0]:xID[1]]
data[data < 0] = -9999
# make geotiff file
geo = [lonlim[0], 0.05, 0, latlim[1], 0, -0.05]
DC.Save_as_tiff(name=DirFile, data=data, geo=geo, projection="WGS84")
# delete old tif file
os.remove(local_filename)
except:
print "file not exists"
return
def recycle(output_folder, et_bg_fhs, recy_ratio, lu_fh, et_type):
Data_Path_rec = "temp_et_recycle"
out_folder = os.path.join(output_folder, Data_Path_rec)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
recycle_fhs = []
for et_fh in et_bg_fhs:
out_fh = out_folder + "\\recycled_et_"+et_type+et_fh[-11:-4]+".tif"
et = becgis.OpenAsArray(et_fh, nan_values=True)
et_recy = et*recy_ratio
DC.Save_as_tiff(out_fh, et_recy, geo_out)
recycle_fhs.append(out_fh)
return recycle_fhs
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(name_collect, epsg_to)
# 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 split_yield(output_folder, p_fhs, et_blue_fhs, et_green_fhs, ab=(1.0, 1.0)):
Data_Path_split = "split_y"
out_folder = os.path.join(output_folder, Data_Path_split)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
sp_yield_fhs = []
geo_out, proj, size_X, size_Y = RC.Open_array_info(p_fhs[0])
for m in range(len(p_fhs)):
out_fh = out_folder+'\\split_yield'+et_blue_fhs[m][-12:]
P = RC.Open_tiff_array(p_fhs[m])
ETBLUE = RC.Open_tiff_array(et_blue_fhs[m])
ETGREEN = RC.Open_tiff_array(et_green_fhs[m])
etbfraction = ETBLUE / (ETBLUE + ETGREEN)
pfraction = P / np.nanmax(P)
fraction = sh3.split_Yield(pfraction, etbfraction, ab[0], ab[1])
DC.Save_as_tiff(out_fh, fraction, geo_out)
sp_yield_fhs.append(out_fh)
return sp_yield_fhs
def Clip_Dataset(local_filename, Filename_out, latlim, lonlim):
# Open Dataset
SEBS_Array = spio.loadmat(local_filename)['ETmon']
# Define area
XID = [
int(np.floor((180 + lonlim[0]) / 0.05)),
int(np.ceil((180 + lonlim[1]) / 0.05))
]
YID = [
int(np.ceil((90 - latlim[1]) / 0.05)),
int(np.floor((90 - latlim[0]) / 0.05))
]
# Define Georeference
geo = tuple([-180 + 0.05 * XID[0], 0.05, 0, 90 - 0.05 * YID[0], 0, -0.05])
# Clip Array
SEBS_Array_clipped = SEBS_Array[YID[0]:YID[1], XID[0]:XID[1]]
# Save tiff file
DC.Save_as_tiff(Filename_out, SEBS_Array_clipped, geo, "WGS84")
def dry_season_bf(output_folder, WPixOutFile, dry_months):
data_path = "dry_bf"
out_folder = os.path.join(output_folder, data_path)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
WPOut = nc.Dataset(WPixOutFile)
lat = WPOut.variables['latitude']
lon = WPOut.variables['longitude']
px_size_lat = np.mean([lat[i+1]-lat[i] for i in range(len(lat)-1)])
px_size_lon = np.mean([lon[i+1]-lon[i] for i in range(len(lon)-1)])
geo_out = (np.min(lon)-px_size_lon/2, px_size_lon,
0, np.max(lat)-px_size_lat/2, 0, px_size_lat)
times = WPOut.variables['time_yyyymm'][:]
years = WPOut.variables['time_yyyy'][:]
months = np.array(times) % 100 #might need this later if not a complete year etc.
dry_months2 = np.where([months[i] in dry_months for i in range(len(months))])[0]
bf = WPOut.variables['Baseflow_M'][:].data
dry_bf = bf[:, :, dry_months2]
dry_bf = np.nanmean(dry_bf, axis=2)
dry_bf_fh = out_folder + '\\dry_bf_%d.tif' %years
DC.Save_as_tiff(dry_bf_fh, dry_bf, geo_out)
return dry_bf_fh
def Calc_Rainy_Days(Dir_Basin, Data_Path_P, Startdate, Enddate):
"""
This functions calculates the amount of rainy days based on daily precipitation data.
Parameters
----------
Dir_Basin : str
Path to all the output data of the Basin
Data_Path_P : str
Path from the Dir_Basin to the daily rainfall data
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Returns
-------
Data_Path_RD : str
Path from the Dir_Basin to the rainy days data
"""
# import WA+ modules
import wa.General.data_conversions as DC
import wa.General.raster_conversions as RC
# Create an output directory to store the rainy days tiffs
Data_Path_RD = 'Rainy_Days'
Dir_RD = os.path.join(Dir_Basin, Data_Path_RD)
if not os.path.exists(Dir_RD):
os.mkdir(Dir_RD)
# Define the dates that must be created
Dates = pd.date_range(Startdate, Enddate, freq='MS')
# Set working directory to the rainfall folder
Dir_path_Prec = os.path.join(Dir_Basin, Data_Path_P)
os.chdir(Dir_path_Prec)
# Open all the daily data and store the data in a 3D array
for Date in Dates:
# Define the year and month and amount of days in month
year = Date.year
month = Date.month
daysinmonth = calendar.monthrange(year, month)[1]
# Set the third (time) dimension of array starting at 0
i = 0
# Find all files of that month
files = glob.glob('*daily_%d.%02d.*.tif' % (year, month))
# Check if the amount of files corresponds with the amount of days in month
if len(files) is not daysinmonth:
print 'ERROR: Not all Rainfall days for month %d and year %d are downloaded' % (
month, year)
# Loop over the days and store data in raster
for File in files:
dir_file = os.path.join(Dir_path_Prec, File)
# Get array information and create empty numpy array for daily rainfall when looping the first file
if File == files[0]:
# Open geolocation info and define projection
geo_out, proj, size_X, size_Y = RC.Open_array_info(dir_file)
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
# Create empty array for the whole month
P_Daily = np.zeros([daysinmonth, size_Y, size_X])
# Open data and put the data in 3D array
Data = RC.Open_tiff_array(dir_file)
# Remove the weird numbers
Data[Data < 0] = 0
# Add the precipitation to the monthly cube
P_Daily[i, :, :] = Data
i += 1
# Define a rainy day
P_Daily[P_Daily > 0.201] = 1
P_Daily[P_Daily != 1] = 0
# Sum the amount of rainy days
RD_one_month = np.nansum(P_Daily, 0)
# Define output name
Outname = os.path.join(
Dir_RD,
'Rainy_Days_NumOfDays_monthly_%d.%02d.01.tif' % (year, month))
# Save tiff file
DC.Save_as_tiff(Outname, RD_one_month, geo_out, proj)
return (Data_Path_RD)
def RetrieveData_monthly(Date, args):
"""
This function retrieves GLDAS monthly data for a given date.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[
path, url, Var, VarStr, VarInfo, TimeCase, xID, yID, lonlim, latlim,
CaseParameters, username, password
] = args
# Open variable info parameters
VarFactor = VarInfo.factors[Var]
# Check whether the file already exist or the worldfile is downloaded
BasinDir = path + '/' + VarStr + '_GLDAS-NOAH_' + \
VarInfo.units[Var] + '_monthly_' + Date.strftime('%Y.%m.%d') + \
'.tif'
# Define month and year of current month
Y = Date.year
M = Date.month
Mday = calendar.monthrange(Y, M)[1]
# Check GLDAS version
version = url[-3:]
# Check if the outputfile already excists
if not os.path.isfile(BasinDir):
# Reset the begin parameters for downloading
downloaded = 0
N = 0
# Create the time dimension
if version == '2.1':
zID = (Y - 2000) * 12 + (M - 1)
elif version == '2.0':
zID = (Y - 1948) * 12 + (M - 1)
# define total url
url_GLDAS = url + '.ascii?%s[%s][%s:1:%s][%s:1:%s]' % (
Var, zID, yID[0], yID[1], xID[0], xID[1])
# if not downloaded try to download file
while downloaded == 0:
try:
# open URL
try:
dataset = requests.get(url_GLDAS,
allow_redirects=False,
stream=True)
except:
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(
InsecureRequestWarning)
dataset = requests.get(url_GLDAS,
allow_redirects=False,
stream=True,
verify=False)
try:
get_dataset = requests.get(dataset.headers['location'],
auth=(username, password),
stream=True)
except:
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(
InsecureRequestWarning)
get_dataset = requests.get(dataset.headers['location'],
auth=(username, password),
stream=True,
verify=False)
# download data (first save as text file)
pathtext = os.path.join(path, 'temp%s.txt' % str(zID))
z = open(pathtext, 'w')
z.write(get_dataset.content)
z.close()
# Open text file and remove header and footer
data_start = np.genfromtxt(pathtext,
dtype=float,
skip_header=1,
skip_footer=6,
delimiter=',')
data = data_start[:, 1:]
# Add the VarFactor
if VarFactor < 0:
data = data + VarFactor
else:
data = data * VarFactor
if VarInfo.types[Var] == 'flux':
data = data * Mday
# Set Nan value for values lower than -9999
data[data < -9999] = -9999
# Say that download was succesfull
downloaded = 1
# If download was not succesfull
except:
data = []
# Try another time
N = N + 1
# Stop trying after 10 times
if N == 10:
print 'Data from ' + Date.strftime(
'%Y-%m-%d') + ' is not available'
downloaded = 1
# define geo
lonlimGLDAS = xID[0] * 0.25 - 180
latlimGLDAS = (yID[1] + 1) * 0.25 - 60
# Save to geotiff file
geo = [lonlimGLDAS, 0.25, 0, latlimGLDAS, 0, -0.25]
DC.Save_as_tiff(name=BasinDir,
data=np.flipud(data[:, :]),
geo=geo,
projection="WGS84")
# Delete data and text file
del data
os.remove(pathtext)
return True
def RetrieveData_daily(Date, args):
"""
This function retrieves GLDAS daily data for a given date.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Open all the parameters
[
path, url, Var, VarStr, VarInfo, TimeCase, xID, yID, lonlim, latlim,
CaseParameters, username, password
] = args
[selected, types] = CaseParameters
# Reset the begin parameters for downloading
downloaded = 0
N = 0
data_end = []
# Check GLDAS version
version = url[-3:]
# Open all variable info
for T in types:
if T == 'mean':
VarStr = VarInfo.names[Var]
else:
VarStr = VarInfo.names[Var] + '-' + T
# Check whether the file already exist or
# the worldfile is downloaded
BasinDir = path[T] + '/' + VarStr + '_GLDAS-NOAH_' + \
VarInfo.units[Var] + '_daily_' + Date.strftime('%Y.%m.%d') + \
'.tif'
# Check if the outputfile already excists
if not os.path.isfile(BasinDir):
# Create the time dimension
if version == '2.0':
zID_start = int(((Date - pd.Timestamp("1948-1-1")).days) * 8)
zID_end = zID_start + 7
elif version == '2.1':
zID_start = int(((Date - pd.Timestamp("2000-1-1")).days) * 8)
zID_end = zID_start + 7
# define total url
url_GLDAS = url + '.ascii?%s[%s:1:%s][%s:1:%s][%s:1:%s]' % (
Var, zID_start, zID_end, yID[0], yID[1], xID[0], xID[1])
# if not downloaded try to download file
while downloaded == 0:
try:
# open URL
try:
dataset = requests.get(url_GLDAS,
allow_redirects=False,
stream=True)
except:
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(
InsecureRequestWarning)
dataset = requests.get(url_GLDAS,
allow_redirects=False,
stream=True,
verify=False)
try:
get_dataset = requests.get(dataset.headers['location'],
auth=(username, password),
stream=True)
except:
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(
InsecureRequestWarning)
get_dataset = requests.get(dataset.headers['location'],
auth=(username, password),
stream=True,
verify=False)
# download data (first save as text file)
pathtext = os.path.join(path[T],
'temp%s.txt' % str(zID_start))
z = open(pathtext, 'w')
z.write(get_dataset.content)
z.close()
# Reshape data
datashape = [8, yID[1] - yID[0] + 1, xID[1] - xID[0] + 1]
data_start = np.genfromtxt(pathtext,
dtype=float,
skip_header=1,
skip_footer=6,
delimiter=',')
data_list = np.asarray(data_start[:, 1:])
data_end = np.resize(data_list,
(8, datashape[1], datashape[2]))
os.remove(pathtext)
# Add the VarFactor
if VarInfo.factors[Var] < 0:
data_end[data_end != -9999] = data_end[
data_end != -9999] + VarInfo.factors[Var]
else:
data_end[data_end != -9999] = data_end[
data_end != -9999] * VarInfo.factors[Var]
data_end[data_end < -9999] = -9999
# define geo
lonlimGLDAS = xID[0] * 0.25 - 180
latlimGLDAS = (yID[1] + 1) * 0.25 - 60
# Download was succesfull
downloaded = 1
# If download was not succesfull
except:
# Try another time
N = N + 1
# Stop trying after 10 times
if N == 10:
print 'Data from ' + Date.strftime(
'%Y-%m-%d') + ' is not available'
downloaded = 1
try:
# Save to geotiff file
if T == 'mean':
data = np.flipud(np.mean(data_end, axis=0))
if T == 'max':
data = np.flipud(np.max(data_end, axis=0))
if T == 'min':
data = np.flipud(np.min(data_end, axis=0))
geo = [lonlimGLDAS, 0.25, 0, latlimGLDAS, 0, -0.25]
DC.Save_as_tiff(name=BasinDir,
data=data,
geo=geo,
projection="WGS84")
except:
print 'GLDAS map from ' + Date.strftime(
'%Y-%m-%d') + ' is not created'
return True
def NPP_GPP_Based(Dir_Basin, Data_Path_GPP, Data_Path_NPP, Startdate, Enddate):
"""
This functions calculated monthly NDM based on the yearly NPP and monthly GPP.
Parameters
----------
Dir_Basin : str
Path to all the output data of the Basin
Data_Path_GPP : str
Path from the Dir_Basin to the GPP data
Data_Path_NPP : str
Path from the Dir_Basin to the NPP data
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Simulation : int
Defines the simulation
Returns
-------
Data_Path_NDM : str
Path from the Dir_Basin to the normalized dry matter data
"""
# import WA+ modules
import wa.General.data_conversions as DC
import wa.General.raster_conversions as RC
# Define output folder for Normalized Dry Matter
Data_Path_NDM = os.path.join(Dir_Basin, "NDM")
if not os.path.exists(Data_Path_NDM):
os.mkdir(Data_Path_NDM)
# Define monthly time steps that will be created
Dates = pd.date_range(Startdate, Enddate, freq = 'MS')
# Define the years that will be calculated
Year_Start = int(Startdate[0:4])
Year_End = int(Enddate[0:4])
Years = range(Year_Start, Year_End+1)
# Loop over the years
for year in Years:
# Change working directory to the NPP folder
os.chdir(Data_Path_NPP)
# Open yearly NPP data
yearly_NPP_File = glob.glob('*yearly*%d.01.01.tif' %int(year))[0]
Yearly_NPP = RC.Open_tiff_array(yearly_NPP_File)
# Get the No Data Value of the NPP file
dest = gdal.Open(yearly_NPP_File)
NDV = dest.GetRasterBand(1).GetNoDataValue()
# Set the No Data Value to Nan
Yearly_NPP[Yearly_NPP == NDV] = np.nan
# Change working directory to the GPP folder
os.chdir(Data_Path_GPP)
# Find all the monthly files of that year
monthly_GPP_Files = glob.glob('*monthly*%d.*.01.tif' %int(year))
# Check if it are 12 files otherwise something is wrong and send the ERROR
if not len(monthly_GPP_Files) == 12:
print 'ERROR: Some monthly GPP Files are missing'
# Get the projection information of the GPP inputs
geo_out, proj, size_X, size_Y = RC.Open_array_info(monthly_GPP_Files[0])
geo_out_NPP, proj_NPP, size_X_NPP, size_Y_NPP = RC.Open_array_info(os.path.join(Data_Path_NPP,yearly_NPP_File))
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
# Get the No Data Value of the GPP files
dest = gdal.Open(monthly_GPP_Files[0])
NDV = dest.GetRasterBand(1).GetNoDataValue()
# Create a empty numpy array
Yearly_GPP = np.zeros([size_Y, size_X])
# Calculte the total yearly GPP
for monthly_GPP_File in monthly_GPP_Files:
# Open array
Data = RC.Open_tiff_array(monthly_GPP_File)
# Remove nan values
Data[Data == NDV] = np.nan
# Add data to yearly sum
Yearly_GPP += Data
# Check if size is the same of NPP and GPP otherwise resize
if not (size_X_NPP is size_X or size_Y_NPP is size_Y):
Yearly_NPP = RC.resize_array_example(Yearly_NPP, Yearly_GPP)
# Loop over the monthly dates
for Date in Dates:
# If the Date is in the same year as the yearly NPP and GPP
if Date.year == year:
# Create empty GPP array
monthly_GPP = np.ones([size_Y, size_X]) * np.nan
# Get current month
month = Date.month
# Get the GPP file of the current year and month
monthly_GPP_File = glob.glob('*monthly_%d.%02d.01.tif' %(int(year), int(month)))[0]
monthly_GPP = RC.Open_tiff_array(monthly_GPP_File)
monthly_GPP[monthly_GPP == NDV] = np.nan
# Calculate the NDM based on the monthly and yearly NPP and GPP (fraction of GPP)
Monthly_NDM = Yearly_NPP * monthly_GPP / Yearly_GPP * (30./12.) *10000 # kg/ha
# Define output name
output_name = os.path.join(Data_Path_NDM, 'NDM_MOD17_kg_ha-1_monthly_%d.%02d.01.tif' %(int(year), int(month)))
# Save the NDM as tiff file
DC.Save_as_tiff(output_name, Monthly_NDM, geo_out, proj)
return(Data_Path_NDM)
def main(Dir,
Startdate='',
Enddate='',
latlim=[-60, 60],
lonlim=[-180, 180],
pixel_size=False,
cores=False,
LANDSAF=0,
SourceLANDSAF='',
Waitbar=1):
"""
This function downloads TRMM3B43 V7 (monthly) data
Keyword arguments:
Dir -- 'C:/file/to/path/'
Startdate -- 'yyyy-mm-dd'
Enddate -- 'yyyy-mm-dd'
latlim -- [ymin, ymax] (values must be between -50 and 50)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
cores -- The number of cores used to run the routine.
It can be 'False' to avoid using parallel computing
routines.
Waitbar -- 1 (Default) will print the waitbar
"""
print 'Create monthly Reference ET data for period %s till %s' % (
Startdate, Enddate)
# An array of monthly dates which will be calculated
Dates = pd.date_range(Startdate, Enddate, freq='MS')
# Create Waitbar
if Waitbar == 1:
import wa.Functions.Start.WaitbarConsole as WaitbarConsole
total_amount = len(Dates)
amount = 0
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
# Calculate the ETref day by day for every month
for Date in Dates:
# Collect date data
Y = Date.year
M = Date.month
Mday = calendar.monthrange(Y, M)[1]
Days = pd.date_range(Date, Date + pd.Timedelta(days=Mday), freq='D')
StartTime = Date.strftime('%Y') + '-' + Date.strftime('%m') + '-01'
EndTime = Date.strftime('%Y') + '-' + Date.strftime('%m') + '-' + str(
Mday)
# Get ETref on daily basis
daily(Dir=Dir,
Startdate=StartTime,
Enddate=EndTime,
latlim=latlim,
lonlim=lonlim,
pixel_size=pixel_size,
cores=cores,
LANDSAF=LANDSAF,
SourceLANDSAF=SourceLANDSAF,
Waitbar=0)
# Load DEM
if not pixel_size:
nameDEM = 'DEM_HydroShed_m_3s.tif'
DEMmap = os.path.join(Dir, 'HydroSHED', 'DEM', nameDEM)
else:
DEMmap = os.path.join(Dir, 'HydroSHED', 'DEM',
'DEM_HydroShed_m_reshaped_for_ETref.tif')
# Get some geo-data to save results
geo_ET, proj, size_X, size_Y = RC.Open_array_info(DEMmap)
dataMonth = np.zeros([size_Y, size_X])
for Day in Days[:-1]:
DirDay = os.path.join(
Dir, 'ETref', 'Daily',
'ETref_mm-day-1_daily_' + Day.strftime('%Y.%m.%d') + '.tif')
dataDay = gdal.Open(DirDay)
Dval = dataDay.GetRasterBand(1).ReadAsArray().astype(np.float32)
Dval[Dval < 0] = 0
dataMonth = dataMonth + Dval
dataDay = None
# make geotiff file
output_folder_month = os.path.join(Dir, 'ETref', 'Monthly')
if os.path.exists(output_folder_month) == False:
os.makedirs(output_folder_month)
DirMonth = os.path.join(
output_folder_month,
'ETref_mm-month-1_monthly_' + Date.strftime('%Y.%m.%d') + '.tif')
# Create the tiff file
DC.Save_as_tiff(DirMonth, dataMonth, geo_ET, proj)
# Create Waitbar
if Waitbar == 1:
amount += 1
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
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)
# -*- coding: utf-8 -*- """ Created on Mon Jun 19 10:09:38 2017 @author: tih """ Tfile = r"J:\Tyler\Input\Meteo\daily\avgsurft_inst\mean\T_GLDAS-NOAH_C_daily_2016.06.15.tif" Pfile = r"J:\Tyler\Input\Meteo\daily\psurf_f_inst\mean\P_GLDAS-NOAH_kpa_daily_2016.06.15.tif" Hfile = r"J:\Tyler\Input\Meteo\daily\qair_f_inst\mean\Hum_GLDAS-NOAH_kg-kg_daily_2016.06.15.tif" Outfilename = r"J:\Tyler\Input\Meteo\daily\Hum_Calculated\Humidity_percentage_Calculated_daily.tif" import gdal import os import wa.General.raster_conversions as RC import wa.General.data_conversions as DC import numpy as np geo_out, proj, size_X, size_Y = RC.Open_array_info(Tfile) Tdata = RC.Open_tiff_array(Tfile) Tdata[Tdata < -900] = np.nan Pdata = RC.Open_tiff_array(Pfile) Hdata = RC.Open_tiff_array(Hfile) Esdata = 0.6108 * np.exp((17.27 * Tdata) / (Tdata + 237.3)) HumData = np.minimum((1.6077717 * Hdata * Pdata / Esdata), 1) * 100 DC.Save_as_tiff(Outfilename, HumData, geo_out, "WGS84")
def RetrieveData(Date, args):
"""
This function retrieves TRMM data for a given date from the
ftp://disc2.nascom.nasa.gov server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[output_folder, TimeCase, xID, yID, lonlim, latlim] = args
year = Date.year
month = Date.month
day = Date.day
from wa import WebAccounts
username, password = WebAccounts.Accounts(Type='NASA')
# Create https
if TimeCase == 'daily':
URL = 'https://disc2.gesdisc.eosdis.nasa.gov/opendap/TRMM_L3/TRMM_3B42_Daily.7/%d/%02d/3B42_Daily.%d%02d%02d.7.nc4.ascii?precipitation[%d:1:%d][%d:1:%d]' % (
year, month, year, month, day, xID[0], xID[1] - 1, yID[0],
yID[1] - 1)
DirFile = os.path.join(
output_folder, "P_TRMM3B42.V7_mm-day-1_daily_%d.%02d.%02d.tif" %
(year, month, day))
Scaling = 1
if TimeCase == 'monthly':
if Date >= pd.Timestamp('2010-10-01'):
URL = 'https://disc2.gesdisc.eosdis.nasa.gov/opendap/TRMM_L3/TRMM_3B43.7/%d/3B43.%d%02d01.7.HDF.ascii?precipitation[%d:1:%d][%d:1:%d]' % (
year, year, month, xID[0], xID[1] - 1, yID[0], yID[1] - 1)
else:
URL = 'https://disc2.gesdisc.eosdis.nasa.gov/opendap/TRMM_L3/TRMM_3B43.7/%d/3B43.%d%02d01.7A.HDF.ascii?precipitation[%d:1:%d][%d:1:%d]' % (
year, year, month, xID[0], xID[1] - 1, yID[0], yID[1] - 1)
Scaling = calendar.monthrange(year, month)[1] * 24
DirFile = os.path.join(
output_folder,
"P_TRMM3B43.V7_mm-month-1_monthly_%d.%02d.01.tif" % (year, month))
if not os.path.isfile(DirFile):
dataset = requests.get(URL, allow_redirects=False, stream=True)
try:
get_dataset = requests.get(dataset.headers['location'],
auth=(username, password),
stream=True)
except:
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
get_dataset = requests.get(dataset.headers['location'],
auth=(username, password),
verify=False)
# download data (first save as text file)
pathtext = os.path.join(output_folder, 'temp.txt')
z = open(pathtext, 'w')
z.write(get_dataset.content)
z.close()
# Open text file and remove header and footer
data_start = np.genfromtxt(pathtext,
dtype=float,
skip_header=1,
delimiter=',')
data = data_start[:, 1:] * Scaling
data[data < 0] = -9999
data = data.transpose()
data = np.flipud(data)
# Delete .txt file
os.remove(pathtext)
# Make geotiff file
geo = [lonlim[0], 0.25, 0, latlim[1], 0, -0.25]
DC.Save_as_tiff(name=DirFile, data=data, geo=geo, projection="WGS84")
return True
def RetrieveData(Date, args):
"""
This function retrieves GLEAM ET data for a given date from the
www.gleam.eu server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[output_folder, latlim, lonlim, VarCode, TimeCase] = args
# Adjust latlim to GLEAM dataset
latlim1 = [latlim[1] * -1, latlim[0] * -1]
# select the spatial dataset
Ystart = int(np.floor((latlim1[0] + 90) / 0.25))
Yend = int(np.ceil((latlim1[1] + 90) / 0.25))
Xstart = int(np.floor((lonlim[0] + 180) / 0.25))
Xend = int(np.ceil((lonlim[1] + 180) / 0.25))
Year = Date.year
Month = Date.month
filename = 'E_' + str(Year) + '_GLEAM_v3.1b.nc'
local_filename = os.path.join(output_folder, filename)
f = Dataset(local_filename, mode='r')
if TimeCase == 'monthly':
# defines the start and end of the month
Datesend1 = str(Date)
Datesend2 = Datesend1.replace(Datesend1[8:10], "01")
Datesend3 = Datesend2[0:10]
Datesend4 = Datesend1[0:10]
Datestart = pd.date_range(Datesend3, Datesend4, freq='MS')
# determine the DOY-1 and DOYend (those are use to define the temporal boundaries of the yearly data)
DOY = int(Datestart[0].strftime('%j'))
DOYend = int(Date.strftime('%j'))
DOYDownload = DOY - 1
Day = 1
Data = f.variables['E'][DOYDownload:DOYend, Xstart:Xend, Ystart:Yend]
data = np.array(Data)
f.close()
# Sum ET data in time and change the no data value into -999
dataSum = sum(data, 1)
dataSum[dataSum < -100] = -999.000
dataCor = np.swapaxes(dataSum, 0, 1)
if TimeCase == 'daily':
Day = Date.day
# Define the DOY, DOY-1 is taken from the yearly dataset
DOY = int(Date.strftime('%j'))
DOYDownload = DOY - 1
Data = f.variables['E'][DOYDownload, Xstart:Xend, Ystart:Yend]
data = np.array(Data)
f.close()
data[data < -100] = -999.000
dataCor = np.swapaxes(data, 0, 1)
# The Georeference of the map
geo_in = [lonlim[0], 0.25, 0.0, latlim[1], 0.0, -0.25]
# Name of the map
dataset_name = VarCode + '_' + str(Year) + '.' + str(Month).zfill(
2) + '.' + str(Day).zfill(2) + '.tif'
output_file = os.path.join(output_folder, dataset_name)
# save data as tiff file
DC.Save_as_tiff(name=output_file,
data=dataCor,
geo=geo_in,
projection="WGS84")
return True
def Nearest_Interpolate(Dir_in, Startdate, Enddate, Dir_out=None):
"""
This functions calculates monthly tiff files based on the daily tiff files.
(will calculate the total sum)
Parameters
----------
Dir_in : str
Path to the input data
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Dir_out : str
Path to the output data, default is same as Dir_in
"""
# import WA+ modules
import wa.General.data_conversions as DC
import wa.General.raster_conversions as RC
# Change working directory
os.chdir(Dir_in)
# Define end and start date
Dates = pd.date_range(Startdate, Enddate, freq='MS')
# Find all monthly files
files = glob.glob('*daily*.tif')
# Get array information and define projection
geo_out, proj, size_X, size_Y = RC.Open_array_info(files[0])
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
# Get the No Data Value
dest = gdal.Open(files[0])
NDV = dest.GetRasterBand(1).GetNoDataValue()
for date in Dates:
Year = date.year
Month = date.month
files_one_year = glob.glob('*daily*%d.%02d*.tif' % (Year, Month))
# Create empty arrays
Month_data = np.zeros([size_Y, size_X])
# Get amount of days in month
Amount_days_in_month = int(calendar.monthrange(Year, Month)[1])
if len(files_one_year) is not Amount_days_in_month:
print("One day is missing!!!")
for file_one_year in files_one_year:
file_path = os.path.join(Dir_in, file_one_year)
Day_data = RC.Open_tiff_array(file_path)
Day_data[np.isnan(Day_data)] = 0.0
Day_data[Day_data == -9999] = 0.0
Month_data += Day_data
# Define output directory
if Dir_out is None:
Dir_out = Dir_in
# Define output name
output_name = os.path.join(Dir_out, file_one_year
.replace('daily', 'monthly')
.replace('day', 'month'))
output_name = output_name[:-14] + '%d.%02d.01.tif' % (date.year, date.month)
# Save tiff file
DC.Save_as_tiff(output_name, Month_data, geo_out, proj)
return
def DownloadData(Dir, Var, Startdate, Enddate, latlim, lonlim, Waitbar, cores,
TimeCase, CaseParameters):
"""
This function downloads ECMWF six-hourly, daily or monthly data
Keyword arguments:
"""
# correct latitude and longitude limits
latlim_corr_one = np.floor(latlim[0]/0.125) * 0.125
latlim_corr_two = np.ceil(latlim[1]/0.125) * 0.125
latlim_corr = [latlim_corr_one, latlim_corr_two]
# correct latitude and longitude limits
lonlim_corr_one = np.floor(lonlim[0]/0.125) * 0.125
lonlim_corr_two = np.ceil(lonlim[1]/0.125) * 0.125
lonlim_corr = [lonlim_corr_one, lonlim_corr_two]
# Load factors / unit / type of variables / accounts
VarInfo = VariablesInfo(TimeCase)
Varname_dir = VarInfo.file_name[Var]
# Create Out directory
out_dir = os.path.join(Dir, "Weather_Data", "Model", "ECMWF", TimeCase, Varname_dir, "mean")
if not os.path.exists(out_dir):
os.makedirs(out_dir)
DownloadType = VarInfo.DownloadType[Var]
# Set required data for the three hourly option
if TimeCase == 'six_hourly':
string1 = 'oper'
# Set required data for the daily option
elif TimeCase == 'daily':
Dates = pd.date_range(Startdate, Enddate, freq='D')
elif TimeCase == 'monthly':
Dates = pd.date_range(Startdate, Enddate, freq='MS')
if DownloadType == 1:
string1 = 'oper'
string4 = "0"
string6 = "00:00:00/06:00:00/12:00:00/18:00:00"
string2 = 'sfc'
string8 = 'an'
if DownloadType == 2:
string1 = 'oper'
string4 = "12"
string6 = "00:00:00/12:00:00"
string2 = 'sfc'
string8 = 'fc'
if DownloadType == 3:
string1 = 'oper'
string4 = "0"
string6 = "00:00:00/06:00:00/12:00:00/18:00:00"
string2 = 'pl'
string8 = 'an'
string7 = '%s/to/%s' %(Startdate, Enddate)
parameter_number = VarInfo.number_para[Var]
string3 = '%03d.128' %(parameter_number)
string5 = '0.125/0.125'
string9 = 'ei'
string10 = '%s/%s/%s/%s' %(latlim_corr[1], lonlim_corr[0], latlim_corr[0], lonlim_corr[1]) #N, W, S, E
# Download data by using the ECMWF API
import wa.Collect.ECMWF.ECMWFdownload as Download
print 'Use API ECMWF to collect the data, please wait'
Download.API(Dir, DownloadType, string1, string2, string3, string4, string5, string6, string7, string8, string9, string10)
# Open the downloaded data
NC_filename = os.path.join(Dir,'data_interim.nc')
fh = Dataset(NC_filename, mode='r')
# Get the NC variable parameter
parameter_var = VarInfo.var_name[Var]
Var_unit = VarInfo.units[Var]
factors_add = VarInfo.factors_add[Var]
factors_mul = VarInfo.factors_mul[Var]
# Open the NC data
Data = fh.variables[parameter_var][:]
Data_time = fh.variables['time'][:]
lons = fh.variables['longitude'][:]
lats = fh.variables['latitude'][:]
# Define the georeference information
Geo_four = np.nanmax(lats)
Geo_one = np.nanmin(lons)
Geo_out = tuple([Geo_one, 0.125, 0.0, Geo_four, 0.0, -0.125])
# Create Waitbar
if Waitbar == 1:
import wa.Functions.Start.WaitbarConsole as WaitbarConsole
total_amount = len(Dates)
amount = 0
WaitbarConsole.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
for date in Dates:
# Define the year, month and day
year = date.year
month = date.month
day = date.day
# Hours since 1900-01-01
start = datetime.datetime(year=1900, month=1, day=1)
end = datetime.datetime(year, month, day)
diff = end - start
hours_from_start_begin = diff.total_seconds()/60/60
Date_good = np.zeros(len(Data_time))
if TimeCase == 'daily':
days_later = 1
if TimeCase == 'monthly':
days_later = calendar.monthrange(year,month)[1]
Date_good[np.logical_and(Data_time>=hours_from_start_begin, Data_time<(hours_from_start_begin + 24 * days_later))] = 1
Data_one = np.zeros([int(np.sum(Date_good)),int(np.size(Data,1)),int(np.size(Data,2))])
Data_one = Data[np.int_(Date_good) == 1, :, :]
# Calculate the average temperature in celcius degrees
Data_end = factors_mul * np.nanmean(Data_one,0) + factors_add
if VarInfo.types[Var] == 'flux':
Data_end = Data_end * days_later
VarOutputname = VarInfo.file_name[Var]
# Define the out name
name_out = os.path.join(out_dir, "%s_ECMWF_ERA-Interim_%s_%s_%d.%02d.%02d.tif" %(VarOutputname, Var_unit, TimeCase, year,month,day))
# Create Tiff files
DC.Save_as_tiff(name_out, Data_end, Geo_out, "WGS84")
if Waitbar == 1:
amount += 1
WaitbarConsole.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
fh.close()
return()
def Nearest_Interpolate(Dir_in, Startdate, Enddate, Dir_out=None):
"""
This functions calculates monthly tiff files based on the 8 daily tiff files. (will calculate the average)
Parameters
----------
Dir_in : str
Path to the input data
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Dir_out : str
Path to the output data, default is same as Dir_in
"""
# import WA+ modules
import wa.General.data_conversions as DC
import wa.General.raster_conversions as RC
# Change working directory
os.chdir(Dir_in)
# Find all eight daily files
files = glob.glob('*8-daily*.tif')
# Create array with filename and keys (DOY and year) of all the 8 daily files
i = 0
DOY_Year = np.zeros([len(files), 3])
for File in files:
# Get the time characteristics from the filename
year = File.split('.')[-4][-4:]
month = File.split('.')[-3]
day = File.split('.')[-2]
# Create pandas Timestamp
date_file = '%s-%02s-%02s' % (year, month, day)
Datum = pd.Timestamp(date_file)
# Get day of year
DOY = Datum.strftime('%j')
# Save data in array
DOY_Year[i, 0] = i
DOY_Year[i, 1] = DOY
DOY_Year[i, 2] = year
# Loop over files
i += 1
# Check enddate:
Enddate_split = Enddate.split('-')
month_range = calendar.monthrange(int(Enddate_split[0]),
int(Enddate_split[1]))[1]
Enddate = '%d-%02d-%02d' % (int(Enddate_split[0]), int(
Enddate_split[1]), month_range)
# Check startdate:
Startdate_split = Startdate.split('-')
Startdate = '%d-%02d-01' % (int(Startdate_split[0]), int(
Startdate_split[1]))
# Define end and start date
Dates = pd.date_range(Startdate, Enddate, freq='MS')
DatesEnd = pd.date_range(Startdate, Enddate, freq='M')
# Get array information and define projection
geo_out, proj, size_X, size_Y = RC.Open_array_info(files[0])
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
# Get the No Data Value
dest = gdal.Open(files[0])
NDV = dest.GetRasterBand(1).GetNoDataValue()
# Loop over months and create monthly tiff files
i = 0
for date in Dates:
# Get Start and end DOY of the current month
DOY_month_start = date.strftime('%j')
DOY_month_end = DatesEnd[i].strftime('%j')
# Search for the files that are between those DOYs
year = date.year
DOYs = DOY_Year[DOY_Year[:, 2] == year]
DOYs_oneMonth = DOYs[np.logical_and(
(DOYs[:, 1] + 8) >= int(DOY_month_start),
DOYs[:, 1] <= int(DOY_month_end))]
# Create empty arrays
Monthly = np.zeros([size_Y, size_X])
Weight_tot = np.zeros([size_Y, size_X])
Data_one_month = np.ones([size_Y, size_X]) * np.nan
# Loop over the files that are within the DOYs
for EightDays in DOYs_oneMonth[:, 0]:
# Calculate the amount of days in this month of each file
Weight = np.ones([size_Y, size_X])
# For start of month
if EightDays == DOYs_oneMonth[:, 0][0]:
Weight = Weight * int(DOYs_oneMonth[:, 1][0] + 8 -
int(DOY_month_start))
# For end of month
elif EightDays == DOYs_oneMonth[:, 0][-1]:
Weight = Weight * (int(DOY_month_end) -
DOYs_oneMonth[:, 1][-1] + 1)
# For the middle of the month
else:
Weight = Weight * 8
# Open the array of current file
input_name = os.path.join(Dir_in, files[int(EightDays)])
Data = RC.Open_tiff_array(input_name)
# Remove NDV
Weight[Data == NDV] = 0
Data[Data == NDV] = np.nan
# Multiply weight time data
Data = Data * Weight
# Calculate the total weight and data
Weight_tot += Weight
Monthly[~np.isnan(Data)] += Data[~np.isnan(Data)]
# Go to next month
i += 1
# Calculate the average
Data_one_month[Weight_tot != 0.] = Monthly[
Weight_tot != 0.] / Weight_tot[Weight_tot != 0.]
# Define output directory
if Dir_out == None:
Dir_out = Dir_in
# Define output name
output_name = os.path.join(
Dir_out, files[int(EightDays)].replace('8-daily', 'monthly'))
output_name = output_name[:-6] + '01.tif'
# Save tiff file
DC.Save_as_tiff(output_name, Data_one_month, geo_out, proj)
return
def DownloadData(Dir, Startdate, Enddate, latlim, lonlim, Waitbar):
"""
This scripts downloads ASCAT SWI data from the VITO server.
The output files display the Surface Water Index.
Keyword arguments:
Dir -- 'C:/file/to/path/'
Startdate -- 'yyyy-mm-dd'
Enddate -- 'yyyy-mm-dd'
lonlim -- [ymin, ymax]
latlim -- [xmin, xmax]
"""
# Check the latitude and longitude and otherwise reset lat and lon.
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('2007-01-01')
if not Enddate:
Enddate = pd.Timestamp('2018-12-31')
# Make a panda timestamp of the date
try:
Enddate = pd.Timestamp(Enddate)
except:
Enddate = Enddate
# amount of Dates weekly
Dates = pd.date_range(Startdate, Enddate, freq='D')
# Create Waitbar
if Waitbar == 1:
import wa.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
output_folder = os.path.join(Dir, 'SWI', 'ASCAT', 'Daily')
if not os.path.exists(output_folder):
os.makedirs(output_folder)
output_folder_temp = os.path.join(Dir, 'SWI', 'ASCAT', 'Daily', 'Temp')
if not os.path.exists(output_folder_temp):
os.makedirs(output_folder_temp)
# loop over dates
for Date in Dates:
# Define end filename
End_filename = os.path.join(
output_folder, 'SWI_ASCAT_V3_Percentage_daily_%d.%02d.%02d.tif' %
(Date.year, Date.month, Date.day))
# Define IDs
xID = 1800 + np.int16(
np.array([np.ceil((lonlim[0]) * 10),
np.floor((lonlim[1]) * 10)]))
yID = np.int16(
np.array([np.floor((-latlim[1]) * 10),
np.ceil((-latlim[0]) * 10)])) + 900
# Download the data from FTP server if the file not exists
if not os.path.exists(End_filename):
try:
data = Download_ASCAT_from_VITO(End_filename,
output_folder_temp, Date, yID,
xID)
# make geotiff file
geo = [lonlim[0], 0.1, 0, latlim[1], 0, -0.1]
DC.Save_as_tiff(name=End_filename,
data=data,
geo=geo,
projection="WGS84")
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)
# remove the temporary folder
shutil.rmtree(output_folder_temp)
def Calculate(Basin, P_Product, ET_Product, Inflow_Text_Files,
Reservoirs_Lakes_Calculations, Startdate, Enddate, Simulation):
'''
This functions consists of the following sections:
1. Set General Parameters
2. Download Data
3. Convert the RAW data to NETCDF files
4. Create Mask based on LU map
5. Calculate Runoff based on Budyko
6. Add inflow in Runoff
7. Calculate River flow
7.1 Route Runoff
7.2 Add Reservoirs
7.3 Add surface water withdrawals
'''
# import General modules
import os
import gdal
import numpy as np
import pandas as pd
import copy
# import WA plus modules
from wa.General import raster_conversions as RC
from wa.General import data_conversions as DC
import wa.Functions.Five as Five
import wa.Functions.Start as Start
######################### 1. Set General Parameters ##############################
# Get environmental variable for the Home folder
WA_env_paths = os.environ["WA_HOME"].split(';')
Dir_Home = WA_env_paths[0]
# Create the Basin folder
Dir_Basin = os.path.join(Dir_Home, Basin)
if not os.path.exists(Dir_Basin):
os.makedirs(Dir_Basin)
# Get the boundaries of the basin based on the shapefile of the watershed
# Boundaries, Shape_file_name_shp = Start.Boundaries.Determine(Basin)
Boundaries, LU_dataset = Start.Boundaries.Determine_LU_Based(Basin)
LU_data = RC.Open_tiff_array(LU_dataset)
geo_out_LU, proj_LU, size_X_LU, size_Y_LU = RC.Open_array_info(LU_dataset)
# Define resolution of SRTM
Resolution = '15s'
# Get the amount of months
Amount_months = len(pd.date_range(Startdate, Enddate, freq='MS'))
Amount_months_reservoirs = Amount_months + 1
# Startdate for moving window Budyko
Startdate_2months_Timestamp = pd.Timestamp(Startdate) - pd.DateOffset(
months=2)
Startdate_2months = Startdate_2months_Timestamp.strftime('%Y-%m-%d')
############################# 2. Download Data ###################################
# Download data
Data_Path_P = Start.Download_Data.Precipitation(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_2months,
Enddate, P_Product)
Data_Path_ET = Start.Download_Data.Evapotranspiration(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_2months,
Enddate, ET_Product)
Data_Path_DEM = Start.Download_Data.DEM(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Resolution)
if Resolution is not '3s':
Data_Path_DEM = Start.Download_Data.DEM(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Resolution)
Data_Path_DEM_Dir = Start.Download_Data.DEM_Dir(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Resolution)
Data_Path_ETref = Start.Download_Data.ETreference(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_2months,
Enddate)
Data_Path_JRC_occurrence = Start.Download_Data.JRC_occurrence(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']])
Data_Path_P_Monthly = os.path.join(Data_Path_P, 'Monthly')
###################### 3. Convert the RAW data to NETCDF files ##############################
# The sequence of converting the data is:
# DEM
# DEM flow directions
# Precipitation
# Evapotranspiration
# Reference Evapotranspiration
#_____________________________________DEM__________________________________
# Get the data of DEM and save as nc, This dataset is also used as reference for others
Example_dataset = os.path.join(Dir_Basin, Data_Path_DEM,
'DEM_HydroShed_m_%s.tif' % Resolution)
DEMdest = gdal.Open(Example_dataset)
Xsize_CR = int(DEMdest.RasterXSize)
Ysize_CR = int(DEMdest.RasterYSize)
DataCube_DEM_CR = DEMdest.GetRasterBand(1).ReadAsArray()
Name_NC_DEM_CR = DC.Create_NC_name('DEM_CR', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_DEM_CR):
DC.Save_as_NC(Name_NC_DEM_CR, DataCube_DEM_CR, 'DEM_CR',
Example_dataset)
DEMdest = None
#___________________________________DEM Dir________________________________
# Get the data of flow direction and save as nc.
Dir_dataset = os.path.join(Dir_Basin, Data_Path_DEM_Dir,
'DIR_HydroShed_-_%s.tif' % Resolution)
DEMDirdest = gdal.Open(Dir_dataset)
DataCube_DEM_Dir_CR = DEMDirdest.GetRasterBand(1).ReadAsArray()
Name_NC_DEM_Dir_CR = DC.Create_NC_name('DEM_Dir_CR', Simulation, Dir_Basin,
5)
if not os.path.exists(Name_NC_DEM_Dir_CR):
DC.Save_as_NC(Name_NC_DEM_Dir_CR, DataCube_DEM_Dir_CR, 'DEM_Dir_CR',
Example_dataset)
DEMDirdest = None
del DataCube_DEM_Dir_CR
#______________________________ Precipitation______________________________
# Define info for the nc files
info = [
'monthly', 'mm',
''.join([Startdate_2months[5:7], Startdate_2months[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
# Precipitation data
Name_NC_Prec_CR = DC.Create_NC_name('Prec_CR', Simulation, Dir_Basin, 5,
info)
if not os.path.exists(Name_NC_Prec_CR):
# Get the data of Precipitation and save as nc
DataCube_Prec_CR = RC.Get3Darray_time_series_monthly(
Dir_Basin,
Data_Path_P_Monthly,
Startdate_2months,
Enddate,
Example_data=Example_dataset)
DC.Save_as_NC(Name_NC_Prec_CR, DataCube_Prec_CR, 'Prec_CR',
Example_dataset, Startdate_2months, Enddate, 'monthly',
0.01)
del DataCube_Prec_CR
#____________________________ Evapotranspiration___________________________
# Evapotranspiration data
info = [
'monthly', 'mm',
''.join([Startdate_2months[5:7], Startdate_2months[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
Name_NC_ET_CR = DC.Create_NC_name('ET_CR', Simulation, Dir_Basin, 5, info)
if not os.path.exists(Name_NC_ET_CR):
# Get the data of Evaporation and save as nc
DataCube_ET_CR = RC.Get3Darray_time_series_monthly(
Dir_Basin,
Data_Path_ET,
Startdate_2months,
Enddate,
Example_data=Example_dataset)
DC.Save_as_NC(Name_NC_ET_CR, DataCube_ET_CR, 'ET_CR', Example_dataset,
Startdate_2months, Enddate, 'monthly', 0.01)
del DataCube_ET_CR
#_______________________Reference Evapotranspiration_______________________
# Reference Evapotranspiration data
Name_NC_ETref_CR = DC.Create_NC_name('ETref_CR', Simulation, Dir_Basin, 5,
info)
if not os.path.exists(Name_NC_ETref_CR):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_ETref_CR = RC.Get3Darray_time_series_monthly(
Dir_Basin,
Data_Path_ETref,
Startdate_2months,
Enddate,
Example_data=Example_dataset)
DC.Save_as_NC(Name_NC_ETref_CR, DataCube_ETref_CR, 'ETref_CR',
Example_dataset, Startdate_2months, Enddate, 'monthly',
0.01)
del DataCube_ETref_CR
#_______________________fraction surface water _______________________
Name_NC_frac_sw_CR = DC.Create_NC_name('Fraction_SW_CR', Simulation,
Dir_Basin, 5)
if not os.path.exists(Name_NC_frac_sw_CR):
DataCube_frac_sw = np.ones_like(LU_data) * np.nan
import wa.Functions.Start.Get_Dictionaries as GD
# Get dictionaries and keys
lulc = GD.get_sheet5_classes()
lulc_dict = GD.get_sheet5_classes().keys()
consumed_frac_dict = GD.sw_supply_fractions_sheet5()
for key in lulc_dict:
Numbers = lulc[key]
for LU_nmbr in Numbers:
Mask = np.zeros_like(LU_data)
Mask[LU_data == LU_nmbr] = 1
DataCube_frac_sw[Mask == 1] = consumed_frac_dict[key]
dest_frac_sw = DC.Save_as_MEM(DataCube_frac_sw, geo_out_LU, proj_LU)
dest_frac_sw_CR = RC.reproject_dataset_example(dest_frac_sw,
Example_dataset)
DataCube_frac_sw_CR = dest_frac_sw_CR.ReadAsArray()
DataCube_frac_sw_CR[DataCube_frac_sw_CR == 0] = np.nan
DC.Save_as_NC(Name_NC_frac_sw_CR,
DataCube_frac_sw_CR,
'Fraction_SW_CR',
Example_dataset,
Scaling_factor=0.01)
del DataCube_frac_sw_CR
del DataCube_DEM_CR
##################### 4. Create Mask based on LU map ###########################
# Now a mask will be created to define the area of interest (pixels where there is a landuse defined)
#_____________________________________LU___________________________________
destLU = RC.reproject_dataset_example(LU_dataset,
Example_dataset,
method=1)
DataCube_LU_CR = destLU.GetRasterBand(1).ReadAsArray()
Raster_Basin_CR = np.zeros([Ysize_CR, Xsize_CR])
Raster_Basin_CR[DataCube_LU_CR > 0] = 1
Name_NC_Basin_CR = DC.Create_NC_name('Basin_CR', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_Basin_CR):
DC.Save_as_NC(Name_NC_Basin_CR, Raster_Basin_CR, 'Basin_CR',
Example_dataset)
#del Raster_Basin
'''
Name_NC_Basin = DC.Create_NC_name('Basin_CR', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_Basin):
Raster_Basin = RC.Vector_to_Raster(Dir_Basin, Shape_file_name_shp, Example_dataset)
Raster_Basin = np.clip(Raster_Basin, 0, 1)
DC.Save_as_NC(Name_NC_Basin, Raster_Basin, 'Basin_CR', Example_dataset)
#del Raster_Basin
'''
###################### 5. Calculate Runoff based on Budyko ###########################
# Define info for the nc files
info = [
'monthly', 'mm', ''.join([Startdate[5:7], Startdate[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
# Define the output names of section 5 and 6
Name_NC_Runoff_CR = DC.Create_NC_name('Runoff_CR', Simulation, Dir_Basin,
5, info)
Name_NC_Runoff_for_Routing_CR = Name_NC_Runoff_CR
if not os.path.exists(Name_NC_Runoff_CR):
# Calculate runoff based on Budyko
DataCube_Runoff_CR = Five.Budyko.Calc_runoff(Name_NC_ETref_CR,
Name_NC_Prec_CR)
# Save the runoff as netcdf
DC.Save_as_NC(Name_NC_Runoff_CR, DataCube_Runoff_CR, 'Runoff_CR',
Example_dataset, Startdate, Enddate, 'monthly', 0.01)
del DataCube_Runoff_CR
'''
###################### Calculate Runoff with P min ET ###########################
Name_NC_Runoff_CR = DC.Create_NC_name('Runoff_CR', Simulation, Dir_Basin, 5, info)
if not os.path.exists(Name_NC_Runoff_CR):
ET = RC.Open_nc_array(Name_NC_ET_CR)
P = RC.Open_nc_array(Name_NC_Prec_CR)
DataCube_Runoff_CR = P - ET
DataCube_Runoff_CR[:,:,:][DataCube_Runoff_CR<=0.1] = 0
DataCube_Runoff_CR[:,:,:][np.isnan(DataCube_Runoff_CR)] = 0
DC.Save_as_NC(Name_NC_Runoff_CR, DataCube_Runoff_CR, 'Runoff_CR', Example_dataset, Startdate, Enddate, 'monthly')
del DataCube_Runoff_CR
'''
############### 6. Add inflow in basin by using textfile #########################
# add inlets if there are textfiles defined
if len(Inflow_Text_Files) > 0:
# Create name of the Runoff with inlets
Name_NC_Runoff_with_Inlets_CR = DC.Create_NC_name(
'Runoff_with_Inlets_CR', Simulation, Dir_Basin, 5, info)
# Use this runoff name for the routing (it will overwrite the runoff without inlets)
Name_NC_Runoff_for_Routing_CR = Name_NC_Runoff_with_Inlets_CR
# Create the file if it not exists
if not os.path.exists(Name_NC_Runoff_with_Inlets_CR):
# Calculate the runoff that will be routed by including the inlets
DataCube_Runoff_with_Inlets_CR = Five.Inlets.Add_Inlets(
Name_NC_Runoff_CR, Inflow_Text_Files)
# Save this runoff as netcdf
DC.Save_as_NC(Name_NC_Runoff_with_Inlets_CR,
DataCube_Runoff_with_Inlets_CR,
'Runoff_with_Inlets_CR', Example_dataset, Startdate,
Enddate, 'monthly', 0.01)
del DataCube_Runoff_with_Inlets_CR
######################### 7. Now the surface water is calculated #################
# Names for dicionaries and nc files
# CR1 = Natural_flow with only green water
# CR2 = Natural_flow with only green water and reservoirs
# CR3 = Flow with green, blue and reservoirs
######################### 7.1 Apply Channel Routing ###############################
# Create the name for the netcdf outputs for section 7.1
info = [
'monthly', 'pixels', ''.join([Startdate[5:7], Startdate[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
Name_NC_Acc_Pixels_CR = DC.Create_NC_name('Acc_Pixels_CR', Simulation,
Dir_Basin, 5)
info = [
'monthly', 'm3', ''.join([Startdate[5:7], Startdate[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
Name_NC_Discharge_CR1 = DC.Create_NC_name('Discharge_CR1', Simulation,
Dir_Basin, 5, info)
# If one of the outputs does not exists, run this part
if not (os.path.exists(Name_NC_Acc_Pixels_CR)
and os.path.exists(Name_NC_Discharge_CR1)):
Accumulated_Pixels_CR, Discharge_CR1 = Five.Channel_Routing.Channel_Routing(
Name_NC_DEM_Dir_CR,
Name_NC_Runoff_for_Routing_CR,
Name_NC_Basin_CR,
Example_dataset,
Degrees=1)
# Save Results
DC.Save_as_NC(Name_NC_Acc_Pixels_CR, Accumulated_Pixels_CR,
'Acc_Pixels_CR', Example_dataset)
DC.Save_as_NC(Name_NC_Discharge_CR1, Discharge_CR1, 'Discharge_CR1',
Example_dataset, Startdate, Enddate, 'monthly')
################# Calculate the natural river and river zones #################
Name_NC_Rivers_CR = DC.Create_NC_name('Rivers_CR', Simulation, Dir_Basin,
5, info)
if not os.path.exists(Name_NC_Rivers_CR):
# Open routed discharge array
Discharge_CR1 = RC.Open_nc_array(Name_NC_Discharge_CR1)
Raster_Basin = RC.Open_nc_array(Name_NC_Basin_CR)
# Calculate mean average over the period
if len(np.shape(Discharge_CR1)) > 2:
Routed_Discharge_Ave = np.nanmean(Discharge_CR1, axis=0)
else:
Routed_Discharge_Ave = Discharge_CR1
# Define the 2% highest pixels as rivers
Rivers = np.zeros([
np.size(Routed_Discharge_Ave, 0),
np.size(Routed_Discharge_Ave, 1)
])
Routed_Discharge_Ave[Raster_Basin != 1] = np.nan
Routed_Discharge_Ave_number = np.nanpercentile(Routed_Discharge_Ave,
98)
Rivers[
Routed_Discharge_Ave >
Routed_Discharge_Ave_number] = 1 # if yearly average is larger than 5000km3/month that it is a river
# Save the river file as netcdf file
DC.Save_as_NC(Name_NC_Rivers_CR, Rivers, 'Rivers_CR', Example_dataset)
########################## Create river directories ###########################
Name_py_River_dict_CR1 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'River_dict_CR1_simulation%d.npy' % (Simulation))
Name_py_DEM_dict_CR1 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'DEM_dict_CR1_simulation%d.npy' % (Simulation))
Name_py_Distance_dict_CR1 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'Distance_dict_CR1_simulation%d.npy' % (Simulation))
if not (os.path.exists(Name_py_River_dict_CR1)
and os.path.exists(Name_py_DEM_dict_CR1)
and os.path.exists(Name_py_Distance_dict_CR1)):
# Get river and DEM dict
River_dict_CR1, DEM_dict_CR1, Distance_dict_CR1 = Five.Create_Dict.Rivers_General(
Name_NC_DEM_CR, Name_NC_DEM_Dir_CR, Name_NC_Acc_Pixels_CR,
Name_NC_Rivers_CR, Example_dataset)
np.save(Name_py_River_dict_CR1, River_dict_CR1)
np.save(Name_py_DEM_dict_CR1, DEM_dict_CR1)
np.save(Name_py_Distance_dict_CR1, Distance_dict_CR1)
else:
# Load
River_dict_CR1 = np.load(Name_py_River_dict_CR1).item()
DEM_dict_CR1 = np.load(Name_py_DEM_dict_CR1).item()
Distance_dict_CR1 = np.load(Name_py_Distance_dict_CR1).item()
Name_py_Discharge_dict_CR1 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'Discharge_dict_CR1_simulation%d.npy' % (Simulation))
if not os.path.exists(Name_py_Discharge_dict_CR1):
# Get discharge dict
Discharge_dict_CR1 = Five.Create_Dict.Discharge(
Name_NC_Discharge_CR1, River_dict_CR1, Amount_months,
Example_dataset)
np.save(Name_py_Discharge_dict_CR1, Discharge_dict_CR1)
else:
# Load
Discharge_dict_CR1 = np.load(Name_py_Discharge_dict_CR1).item()
###################### 7.2 Calculate surface water storage characteristics ######################
Name_py_Discharge_dict_CR2 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'Discharge_dict_CR2_simulation%d.npy' % (Simulation))
Name_py_River_dict_CR2 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'River_dict_CR2_simulation%d.npy' % (Simulation))
Name_py_DEM_dict_CR2 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'DEM_dict_CR2_simulation%d.npy' % (Simulation))
Name_py_Distance_dict_CR2 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'Distance_dict_CR2_simulation%d.npy' % (Simulation))
Name_py_Diff_Water_Volume = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'Diff_Water_Volume_CR2_simulation%d.npy' % (Simulation))
Name_py_Regions = os.path.join(Dir_Basin, 'Simulations',
'Simulation_%d' % Simulation, 'Sheet_5',
'Regions_simulation%d.npy' % (Simulation))
if not (os.path.exists(Name_py_Discharge_dict_CR2)
and os.path.exists(Name_py_River_dict_CR2)
and os.path.exists(Name_py_DEM_dict_CR2)
and os.path.exists(Name_py_Distance_dict_CR2)):
# Copy dicts as starting adding reservoir
Discharge_dict_CR2 = copy.deepcopy(Discharge_dict_CR1)
River_dict_CR2 = copy.deepcopy(River_dict_CR1)
DEM_dict_CR2 = copy.deepcopy(DEM_dict_CR1)
Distance_dict_CR2 = copy.deepcopy(Distance_dict_CR1)
if Reservoirs_Lakes_Calculations == 1:
# define input tiffs for surface water calculations
input_JRC = os.path.join(Dir_Basin, Data_Path_JRC_occurrence,
'JRC_Occurrence_percent.tif')
DEM_dataset = os.path.join(Dir_Basin, Data_Path_DEM,
'DEM_HydroShed_m_3s.tif')
sensitivity = 700 # 900 is less sensitive 1 is very sensitive
Regions = Five.Reservoirs.Calc_Regions(Name_NC_Basin_CR, input_JRC,
sensitivity, Boundaries)
Diff_Water_Volume = np.zeros(
[len(Regions), Amount_months_reservoirs - 1, 3])
reservoir = 0
for region in Regions:
popt = Five.Reservoirs.Find_Area_Volume_Relation(
region, input_JRC, DEM_dataset)
Area_Reservoir_Values = Five.Reservoirs.GEE_calc_reservoir_area(
region, Startdate, Enddate)
Diff_Water_Volume[
reservoir, :, :] = Five.Reservoirs.Calc_Diff_Storage(
Area_Reservoir_Values, popt)
reservoir += 1
################# 7.3 Add storage reservoirs and change outflows ##################
Discharge_dict_CR2, River_dict_CR2, DEM_dict_CR2, Distance_dict_CR2 = Five.Reservoirs.Add_Reservoirs(
Name_NC_Rivers_CR, Name_NC_Acc_Pixels_CR, Diff_Water_Volume,
River_dict_CR2, Discharge_dict_CR2, DEM_dict_CR2,
Distance_dict_CR2, Regions, Example_dataset)
np.save(Name_py_Regions, Regions)
np.save(Name_py_Diff_Water_Volume, Diff_Water_Volume)
np.save(Name_py_Discharge_dict_CR2, Discharge_dict_CR2)
np.save(Name_py_River_dict_CR2, River_dict_CR2)
np.save(Name_py_DEM_dict_CR2, DEM_dict_CR2)
np.save(Name_py_Distance_dict_CR2, Distance_dict_CR2)
else:
# Load
Discharge_dict_CR2 = np.load(Name_py_Discharge_dict_CR2).item()
River_dict_CR2 = np.load(Name_py_River_dict_CR2).item()
DEM_dict_CR2 = np.load(Name_py_DEM_dict_CR2).item()
Distance_dict_CR2 = np.load(Name_py_Distance_dict_CR2).item()
####################### 7.3 Add surface water withdrawals #############################
Name_py_Discharge_dict_CR3 = os.path.join(
Dir_Basin, 'Simulations', 'Simulation_%d' % Simulation, 'Sheet_5',
'Discharge_dict_CR3_simulation%d.npy' % (Simulation))
if not os.path.exists(Name_py_Discharge_dict_CR3):
Discharge_dict_CR3, DataCube_ETblue_m3 = Five.Irrigation.Add_irrigation(
Discharge_dict_CR2, River_dict_CR2, Name_NC_Rivers_CR,
Name_NC_ET_CR, Name_NC_ETref_CR, Name_NC_Prec_CR, Name_NC_Basin_CR,
Name_NC_frac_sw_CR, Startdate, Enddate, Example_dataset)
np.save(Name_py_Discharge_dict_CR3, Discharge_dict_CR3)
# save ETblue as nc
info = [
'monthly', 'm3-month-1', ''.join([Startdate[5:7], Startdate[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
Name_NC_ETblue = DC.Create_NC_name('ETblue', Simulation, Dir_Basin, 5,
info)
DC.Save_as_NC(Name_NC_ETblue, DataCube_ETblue_m3, 'ETblue',
Example_dataset, Startdate, Enddate, 'monthly')
else:
Discharge_dict_CR3 = np.load(Name_py_Discharge_dict_CR3).item()
################################# Plot graph ##################################
# Draw graph
Five.Channel_Routing.Graph_DEM_Distance_Discharge(
Discharge_dict_CR3, Distance_dict_CR2, DEM_dict_CR2, River_dict_CR2,
Startdate, Enddate, Example_dataset)
######################## Change data to fit the LU data #######################
# Discharge
# Define info for the nc files
info = [
'monthly', 'm3-month-1', ''.join([Startdate[5:7], Startdate[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
Name_NC_Discharge = DC.Create_NC_name('Discharge', Simulation, Dir_Basin,
5, info)
if not os.path.exists(Name_NC_Discharge):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_Discharge_CR = DC.Convert_dict_to_array(
River_dict_CR2, Discharge_dict_CR3, Example_dataset)
DC.Save_as_NC(Name_NC_Discharge, DataCube_Discharge_CR, 'Discharge',
Example_dataset, Startdate, Enddate, 'monthly')
del DataCube_Discharge_CR
# DEM
Name_NC_DEM = DC.Create_NC_name('DEM', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_DEM):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_DEM_CR = RC.Open_nc_array(Name_NC_DEM_CR)
DataCube_DEM = RC.resize_array_example(DataCube_DEM_CR,
LU_data,
method=1)
DC.Save_as_NC(Name_NC_DEM, DataCube_DEM, 'DEM', LU_dataset)
del DataCube_DEM
# flow direction
Name_NC_DEM_Dir = DC.Create_NC_name('DEM_Dir', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_DEM_Dir):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_DEM_Dir_CR = RC.Open_nc_array(Name_NC_DEM_Dir_CR)
DataCube_DEM_Dir = RC.resize_array_example(DataCube_DEM_Dir_CR,
LU_data,
method=1)
DC.Save_as_NC(Name_NC_DEM_Dir, DataCube_DEM_Dir, 'DEM_Dir', LU_dataset)
del DataCube_DEM_Dir
# Precipitation
# Define info for the nc files
info = [
'monthly', 'mm', ''.join([Startdate[5:7], Startdate[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
Name_NC_Prec = DC.Create_NC_name('Prec', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_Prec):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_Prec = RC.Get3Darray_time_series_monthly(
Dir_Basin, Data_Path_P_Monthly, Startdate, Enddate, LU_dataset)
DC.Save_as_NC(Name_NC_Prec, DataCube_Prec, 'Prec', LU_dataset,
Startdate, Enddate, 'monthly', 0.01)
del DataCube_Prec
# Evapotranspiration
Name_NC_ET = DC.Create_NC_name('ET', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_ET):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_ET = RC.Get3Darray_time_series_monthly(
Dir_Basin, Data_Path_ET, Startdate, Enddate, LU_dataset)
DC.Save_as_NC(Name_NC_ET, DataCube_ET, 'ET', LU_dataset, Startdate,
Enddate, 'monthly', 0.01)
del DataCube_ET
# Reference Evapotranspiration data
Name_NC_ETref = DC.Create_NC_name('ETref', Simulation, Dir_Basin, 5, info)
if not os.path.exists(Name_NC_ETref):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_ETref = RC.Get3Darray_time_series_monthly(
Dir_Basin, Data_Path_ETref, Startdate, Enddate, LU_dataset)
DC.Save_as_NC(Name_NC_ETref, DataCube_ETref, 'ETref', LU_dataset,
Startdate, Enddate, 'monthly', 0.01)
del DataCube_ETref
# Rivers
Name_NC_Rivers = DC.Create_NC_name('Rivers', Simulation, Dir_Basin, 5,
info)
if not os.path.exists(Name_NC_Rivers):
# Get the data of Reference Evapotranspiration and save as nc
Rivers_CR = RC.Open_nc_array(Name_NC_Rivers_CR)
DataCube_Rivers = RC.resize_array_example(Rivers_CR, LU_data)
DC.Save_as_NC(Name_NC_Rivers, DataCube_Rivers, 'Rivers', LU_dataset)
del DataCube_Rivers, Rivers_CR
# Discharge
# Define info for the nc files
info = [
'monthly', 'm3', ''.join([Startdate[5:7], Startdate[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
Name_NC_Routed_Discharge = DC.Create_NC_name('Routed_Discharge',
Simulation, Dir_Basin, 5,
info)
if not os.path.exists(Name_NC_Routed_Discharge):
# Get the data of Reference Evapotranspiration and save as nc
Routed_Discharge_CR = RC.Open_nc_array(Name_NC_Discharge)
DataCube_Routed_Discharge = RC.resize_array_example(
Routed_Discharge_CR, LU_data)
DC.Save_as_NC(Name_NC_Routed_Discharge, DataCube_Routed_Discharge,
'Routed_Discharge', LU_dataset, Startdate, Enddate,
'monthly')
del DataCube_Routed_Discharge, Routed_Discharge_CR
# Get raster information
geo_out, proj, size_X, size_Y = RC.Open_array_info(Example_dataset)
Rivers = RC.Open_nc_array(Name_NC_Rivers_CR)
# 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
# Get tiff array time dimension:
time_dimension = int(np.shape(Discharge_dict_CR3[0])[0])
# create an empty array
Result = np.zeros([time_dimension, size_Y, size_X])
for river_part in range(0, len(River_dict_CR2)):
for river_pixel in range(1, len(River_dict_CR2[river_part])):
river_pixel_ID = River_dict_CR2[river_part][river_pixel]
if len(np.argwhere(ID_Matrix == river_pixel_ID)) > 0:
row, col = np.argwhere(ID_Matrix == river_pixel_ID)[0][:]
Result[:, row,
col] = Discharge_dict_CR3[river_part][:, river_pixel]
print(river_part)
Outflow = Discharge_dict_CR3[0][:, 1]
for i in range(0, time_dimension):
output_name = r'C:/testmap/rtest_%s.tif' % i
Result_one = Result[i, :, :]
DC.Save_as_tiff(output_name, Result_one, geo_out, "WGS84")
import os
# Get environmental variable for the Home folder
WA_env_paths = os.environ["WA_HOME"].split(';')
Dir_Home = WA_env_paths[0]
# Create the Basin folder
Dir_Basin = os.path.join(Dir_Home, Basin)
info = [
'monthly', 'm3-month-1', ''.join([Startdate[5:7], Startdate[0:4]]),
''.join([Enddate[5:7], Enddate[0:4]])
]
Name_Result = DC.Create_NC_name('DischargeEnd', Simulation, Dir_Basin, 5,
info)
Result[np.logical_and(Result == 0.0, Rivers == 0.0)] = np.nan
DC.Save_as_NC(Name_Result, Result, 'DischargeEnd', Example_dataset,
Startdate, Enddate, 'monthly')
return ()
def CollectLANDSAF(SourceLANDSAF, Dir, Startdate, Enddate, latlim, lonlim):
"""
This function collects and clip LANDSAF data
Keyword arguments:
SourceLANDSAF -- 'C:/' path to the LANDSAF source data (The directory includes SIS and SID)
Dir -- 'C:/' path to the WA map
Startdate -- 'yyyy-mm-dd'
Enddate -- 'yyyy-mm-dd'
latlim -- [ymin, ymax] (values must be between -60 and 60)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
"""
# Make an array of the days of which the ET is taken
Dates = pd.date_range(Startdate, Enddate, freq='D')
# make directories
SISdir = os.path.join(Dir, 'Landsaf_Clipped', 'SIS')
if os.path.exists(SISdir) is False:
os.makedirs(SISdir)
SIDdir = os.path.join(Dir, 'Landsaf_Clipped', 'SID')
if os.path.exists(SIDdir) is False:
os.makedirs(SIDdir)
ShortwaveBasin(SourceLANDSAF,
Dir,
latlim,
lonlim,
Dates=[Startdate, Enddate])
DEMmap_str = os.path.join(Dir, 'HydroSHED', 'DEM',
'DEM_HydroShed_m_3s.tif')
geo_out, proj, size_X, size_Y = RC.Open_array_info(DEMmap_str)
# Open DEM map
demmap = RC.Open_tiff_array(DEMmap_str)
demmap[demmap < 0] = 0
# make lat and lon arrays)
dlat = geo_out[5]
dlon = geo_out[1]
lat = geo_out[3] + (np.arange(size_Y) + 0.5) * dlat
lon = geo_out[0] + (np.arange(size_X) + 0.5) * dlon
for date in Dates:
# day of year
day = date.dayofyear
Horizontal, Sloping, sinb, sinb_hor, fi, slope, ID = SlopeInfluence(
demmap, lat, lon, day)
SIDname = os.path.join(
SIDdir, 'SAF_SID_Daily_W-m2_' + date.strftime('%Y-%m-%d') + '.tif')
SISname = os.path.join(
SISdir, 'SAF_SIS_Daily_W-m2_' + date.strftime('%Y-%m-%d') + '.tif')
#PREPARE SID MAPS
SIDdest = RC.reproject_dataset_example(SIDname, DEMmap_str, method=3)
SIDdata = SIDdest.GetRasterBand(1).ReadAsArray()
#PREPARE SIS MAPS
SISdest = RC.reproject_dataset_example(SISname, DEMmap_str, method=3)
SISdata = SISdest.GetRasterBand(1).ReadAsArray()
# Calculate ShortWave net
Short_Wave_Net = SIDdata * (Sloping /
Horizontal) + SISdata * 86400 / 1e6
# Calculate ShortWave Clear
Short_Wave = Sloping
Short_Wave_Clear = Short_Wave * (0.75 + demmap * 2 * 10**-5)
# make directories
PathClear = os.path.join(Dir, 'Landsaf_Clipped', 'Shortwave_Clear_Sky')
if os.path.exists(PathClear) is False:
os.makedirs(PathClear)
PathNet = os.path.join(Dir, 'Landsaf_Clipped', 'Shortwave_Net')
if os.path.exists(PathNet) is False:
os.makedirs(PathNet)
# name Shortwave Clear and Net
nameFileNet = 'ShortWave_Net_Daily_W-m2_' + date.strftime(
'%Y-%m-%d') + '.tif'
nameNet = os.path.join(PathNet, nameFileNet)
nameFileClear = 'ShortWave_Clear_Daily_W-m2_' + date.strftime(
'%Y-%m-%d') + '.tif'
nameClear = os.path.join(PathClear, nameFileClear)
# Save net and clear short wave radiation
DC.Save_as_tiff(nameNet, Short_Wave_Net, geo_out, proj)
DC.Save_as_tiff(nameClear, Short_Wave_Clear, geo_out, proj)
return
def RetrieveData_monthly(Date, args):
"""
This function retrieves MSWEP precipitation monthly data for a given date.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[path, url, TimeCase, xID, yID, lonlim, latlim, username, password] = args
# Check whether the file already exist or the worldfile is downloaded
BasinDir = path + '/P_MSWEP_mm-month_monthly_' + Date.strftime('%Y.%m.%d') + '.tif'
# Define month and year of current month
Y = Date.year
M = Date.month
# Check if the outputfile already excists
if not os.path.isfile(BasinDir):
# Reset the begin parameters for downloading
downloaded = 0
N=0
# Create the time dimension
zID = (Y - 1979) * 12 + (M - 1)
# define total url
url_MSWEP = url + '.ascii?precipitation[%s][%s:1:%s][%s:1:%s]' %(zID,yID[0],yID[1],xID[0],xID[1])
# if not downloaded try to download file
while downloaded == 0:
try:
# open URL
try:
dataset = requests.get(url_MSWEP , allow_redirects=False,stream = True)
except:
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
dataset = requests.get(url_MSWEP , allow_redirects=False,stream = True, verify = False)
# download data (first save as text file)
pathtext = os.path.join(path,'temp%s.txt' %str(zID))
z = open(pathtext,'w')
z.write(dataset.content)
z.close()
# Open text file and remove header and footer
data_start = np.genfromtxt(pathtext,dtype = float,skip_header = 1,skip_footer = 6,delimiter=',')
data = data_start[1:,1:]
# Set Nan value for values lower than -9999
data[data < -9998] = np.nan
# Say that download was succesfull
downloaded = 1
# If download was not succesfull
except:
data=[]
# Try another time
N = N + 1
# Stop trying after 10 times
if N == 10:
print 'Data from ' + Date.strftime('%Y-%m-%d') + ' is not available'
downloaded = 1
# define geo
lonlimMSWEP = xID[0] * 0.10 - 180
latlimMSWEP = yID[1] * 0.10 - 90
# Save to geotiff file
geo = [lonlimMSWEP ,0.1,0,latlimMSWEP ,0,-0.1]
DC.Save_as_tiff(name=BasinDir, data = data, geo=geo, projection="WGS84")
# Delete data and text file
del data
os.remove(pathtext)
return True
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
