def Calc_Humidity(Temp_format,
P_format,
Hum_format,
output_format,
Startdate,
Enddate,
freq="D"):
folder_dir_out = os.path.dirname(output_format)
if not os.path.exists(folder_dir_out):
os.makedirs(folder_dir_out)
Dates = pd.date_range(Startdate, Enddate, freq="D")
for Date in Dates:
print(Date)
Day = Date.day
Month = Date.month
Year = Date.year
Tempfile_one = Temp_format.format(yyyy=Year, mm=Month, dd=Day)
Presfile_one = P_format.format(yyyy=Year, mm=Month, dd=Day)
Humfile_one = Hum_format.format(yyyy=Year, mm=Month, dd=Day)
out_folder_one = output_format.format(yyyy=Year, mm=Month, dd=Day)
geo_out, proj, size_X, size_Y = RC.Open_array_info(Tempfile_one)
Tdata = RC.Open_tiff_array(Tempfile_one)
if "MERRA_K" in Temp_format:
Tdata = Tdata - 273.15
Tdata[Tdata < -900] = -9999
Pdata = RC.Open_tiff_array(Presfile_one)
Hdata = RC.Open_tiff_array(Humfile_one)
Pdata[Pdata < 0] = -9999
Hdata[Hdata < 0] = -9999
# gapfilling
Tdata = RC.gap_filling(Tdata, -9999)
Pdata = RC.gap_filling(Pdata, -9999)
Hdata = RC.gap_filling(Hdata, -9999)
Esdata = 0.6108 * np.exp((17.27 * Tdata) / (Tdata + 237.3))
HumData = np.minimum((1.6077717 * Hdata * Pdata / Esdata), 1) * 100
HumData = HumData.clip(0, 100)
DC.Save_as_tiff(out_folder_one, HumData, geo_out, "WGS84")
return ()
def Clip_Dataset(local_filename, Filename_out, latlim, lonlim):
import watertools.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]] / 2.45 # Values are in MJ/m2d so convert to mm/d
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 Calc_Humidity(Temp_format,
P_format,
Hum_format,
output_format,
Startdate,
Enddate,
freq="D"):
folder_dir_out = os.path.dirname(output_format)
if not os.path.exists(folder_dir_out):
os.makedirs(folder_dir_out)
Dates = pd.date_range(Startdate, Enddate, freq="D")
for Date in Dates:
print(Date)
Day = Date.day
Month = Date.month
Year = Date.year
Windyfile_one = wind_y_format.format(yyyy=Year, mm=Month, dd=Day)
Windxfile_one = wind_x_format.format(yyyy=Year, mm=Month, dd=Day)
out_folder_one = output_format.format(yyyy=Year, mm=Month, dd=Day)
geo_out, proj, size_X, size_Y = RC.Open_array_info(Windxfile_one)
Windxdata = RC.Open_tiff_array(Windxfile_one)
Windxdata[Windxdata < -900] = -9999
Windydata = RC.Open_tiff_array(Windyfile_one)
Windydata[Windxdata < -900] = -9999
# gapfilling
Windydata = RC.gap_filling(Windydata, -9999)
Windxdata = RC.gap_filling(Windxdata, -9999)
Wind = np.sqrt(Windxdata**2 + Windydata**2)
DC.Save_as_tiff(out_folder_one, Wind, geo_out, "WGS84")
return ()
def Calc_Property(Dir, latlim, lonlim, SL):
import watertools
# Define level
if SL == "sl3":
level = "Topsoil"
elif SL == "sl6":
level = "Subsoil"
# check if you need to download
filename_out_thetasat = os.path.join(
Dir, 'SoilGrids', 'Theta_Sat',
'Theta_Sat_%s_SoilGrids_kg-kg.tif' % level)
if not os.path.exists(filename_out_thetasat):
if SL == "sl3":
watertools.Products.SoilGrids.Theta_Sat.Topsoil(
Dir, latlim, lonlim)
elif SL == "sl6":
watertools.Products.SoilGrids.Theta_Sat.Subsoil(
Dir, latlim, lonlim)
filedir_out_whc = os.path.join(Dir, 'SoilGrids', 'Water_Holding_Capacity')
if not os.path.exists(filedir_out_whc):
os.makedirs(filedir_out_whc)
# Define theta field capacity output
filename_out_whc = os.path.join(
filedir_out_whc,
'Water_Holding_Capacity_%s_SoilGrids_mm-m.tif' % level)
if not os.path.exists(filename_out_whc):
# Get info layer
geo_out, proj, size_X, size_Y = RC.Open_array_info(
filename_out_thetasat)
# Open dataset
theta_sat = RC.Open_tiff_array(filename_out_thetasat)
# Calculate theta field capacity
theta_whc = np.ones(theta_sat.shape) * -9999
theta_whc = np.where(
theta_sat < 0.301, 80,
450 * np.arccosh(theta_sat + 0.7) - 2 * (theta_sat + 0.7) + 20)
# Save as tiff
DC.Save_as_tiff(filename_out_whc, theta_whc, geo_out, proj)
return
def Calc_Property(Dir, latlim, lonlim, SL):
import watertools
# Define level
if SL == "sl3":
level = "Topsoil"
elif SL == "sl6":
level = "Subsoil"
# check if you need to download
filename_out_thetasat = os.path.join(
Dir, 'SoilGrids', 'Theta_Sat',
'Theta_Sat2_%s_SoilGrids_kg-kg.tif' % level)
if not os.path.exists(filename_out_thetasat):
if SL == "sl3":
watertools.Products.SoilGrids.Theta_Sat2.Topsoil(
Dir, latlim, lonlim)
elif SL == "sl6":
watertools.Products.SoilGrids.Theta_Sat2.Subsoil(
Dir, latlim, lonlim)
filedir_out_n_genuchten = os.path.join(Dir, 'SoilGrids', 'N_van_genuchten')
if not os.path.exists(filedir_out_n_genuchten):
os.makedirs(filedir_out_n_genuchten)
# Define n van genuchten output
filename_out_ngenuchten = os.path.join(
filedir_out_n_genuchten, 'N_genuchten_%s_SoilGrids_-.tif' % level)
if not os.path.exists(filename_out_ngenuchten):
# Get info layer
geo_out, proj, size_X, size_Y = RC.Open_array_info(
filename_out_thetasat)
# Open dataset
theta_sat = RC.Open_tiff_array(filename_out_thetasat)
# Calculate n van genuchten
n_van_genuchten = np.ones(theta_sat.shape) * -9999
n_van_genuchten = 166.63 * theta_sat**4 - 387.72 * theta_sat**3 + 340.55 * theta_sat**2 - 133.07 * theta_sat + 20.739
# Save as tiff
DC.Save_as_tiff(filename_out_ngenuchten, n_van_genuchten, geo_out,
proj)
return
def lapse_rate(Dir,temperature_map, DEMmap):
"""
This function downscales the GLDAS temperature map by using the DEM map
Keyword arguments:
temperature_map -- 'C:/' path to the temperature map
DEMmap -- 'C:/' path to the DEM map
"""
# calculate average altitudes corresponding to T resolution
dest = RC.reproject_dataset_example(DEMmap, temperature_map,method = 4)
DEM_ave_out_name = os.path.join(Dir,'HydroSHED', 'DEM','DEM_ave.tif')
geo_out, proj, size_X, size_Y = RC.Open_array_info(temperature_map)
DEM_ave_data = dest.GetRasterBand(1).ReadAsArray()
DC.Save_as_tiff(DEM_ave_out_name, DEM_ave_data, geo_out, proj)
dest = None
# determine lapse-rate [degress Celcius per meter]
lapse_rate_number = 0.0065
# open maps as numpy arrays
dest = RC.reproject_dataset_example(DEM_ave_out_name, DEMmap, method = 2)
dem_avg=dest.GetRasterBand(1).ReadAsArray()
dem_avg[dem_avg<0]=0
dest = None
# Open the temperature dataset
dest = RC.reproject_dataset_example(temperature_map, DEMmap, method = 2)
T=dest.GetRasterBand(1).ReadAsArray()
dest = None
# Open Demmap
demmap = RC.Open_tiff_array(DEMmap)
dem_avg[demmap<=0]=0
demmap[demmap==-32768]=np.nan
# calculate first part
T = T + ((dem_avg-demmap) * lapse_rate_number)
return T
def Calc_Property(Dir, latlim, lonlim, SL):
import watertools
# Define level
if SL == "sl3":
level = "Topsoil"
elif SL == "sl6":
level = "Subsoil"
# check if you need to download
filename_out_thetasat = os.path.join(Dir, 'SoilGrids', 'Theta_Sat' ,'Theta_Sat2_%s_SoilGrids_kg-kg.tif' %level)
if not os.path.exists(filename_out_thetasat):
if SL == "sl3":
watertools.Products.SoilGrids.Theta_Sat2.Topsoil(Dir, latlim, lonlim)
elif SL == "sl6":
watertools.Products.SoilGrids.Theta_Sat2.Subsoil(Dir, latlim, lonlim)
filedir_out_thetares = os.path.join(Dir, 'SoilGrids', 'Theta_Res')
if not os.path.exists(filedir_out_thetares):
os.makedirs(filedir_out_thetares)
# Define theta field capacity output
filename_out_thetares = os.path.join(filedir_out_thetares ,'Theta_Res_%s_SoilGrids_kg-kg.tif' %level)
if not os.path.exists(filename_out_thetares):
# Get info layer
geo_out, proj, size_X, size_Y = RC.Open_array_info(filename_out_thetasat)
# Open dataset
theta_sat = RC.Open_tiff_array(filename_out_thetasat)
# Calculate theta field capacity
theta_Res = np.ones(theta_sat.shape) * -9999
#theta_Res = np.where(theta_sat < 0.351, 0.01, 0.4 * np.arccosh(theta_sat + 0.65) - 0.05 * np.power(theta_sat + 0.65, 2.5) + 0.02)
theta_Res = np.where(theta_sat < 0.351, 0.01, 0.271 * np.log(theta_sat) + 0.335)
# Save as tiff
DC.Save_as_tiff(filename_out_thetares, theta_Res, geo_out, proj)
return
def adjust_P(Dir, pressure_map, DEMmap):
"""
This function downscales the GLDAS air pressure map by using the DEM map
Keyword arguments:
pressure_map -- 'C:/' path to the pressure map
DEMmap -- 'C:/' path to the DEM map
"""
# calculate average latitudes
destDEMave = RC.reproject_dataset_example(DEMmap, pressure_map, method = 4)
DEM_ave_out_name = os.path.join(Dir, 'HydroSHED', 'DEM','DEM_ave.tif')
geo_out, proj, size_X, size_Y = RC.Open_array_info(pressure_map)
DEM_ave_data = destDEMave.GetRasterBand(1).ReadAsArray()
DC.Save_as_tiff(DEM_ave_out_name, DEM_ave_data, geo_out, proj)
# open maps as numpy arrays
dest = RC.reproject_dataset_example(DEM_ave_out_name, DEMmap, method = 2)
dem_avg=dest.GetRasterBand(1).ReadAsArray()
dest = None
# open maps as numpy arrays
dest = RC.reproject_dataset_example(pressure_map, DEMmap, method = 2)
P=dest.GetRasterBand(1).ReadAsArray()
dest = None
demmap = RC.Open_tiff_array(DEMmap)
dem_avg[demmap<=0]=0
demmap[demmap==-32768]=np.nan
# calculate second part
P = P + (101.3*((293-0.0065*(demmap-dem_avg))/293)**5.26 - 101.3)
os.remove(DEM_ave_out_name)
return P
Dates = pd.date_range(Startdate, Enddate, freq="D")
for Date in Dates:
Day = Date.day
Month = Date.month
Year = Date.year
Tempfile_one = Temp_folder.format(yyyy=Year, mm=Month, dd=Day)
Presfile_one = Pres_folder.format(yyyy=Year, mm=Month, dd=Day)
Humfile_one = Hum_folder.format(yyyy=Year, mm=Month, dd=Day)
out_folder_one = out_folder.format(yyyy=Year, mm=Month, dd=Day)
geo_out, proj, size_X, size_Y = RC.Open_array_info(Tempfile_one)
Tdata = RC.Open_tiff_array(Tempfile_one)
Tdata[Tdata < -900] = -9999
Pdata = RC.Open_tiff_array(Presfile_one)
Hdata = RC.Open_tiff_array(Humfile_one)
Pdata[Pdata < 0] = -9999
Hdata[Hdata < 0] = -9999
# gapfilling
Tdata = RC.gap_filling(Tdata, -9999)
Pdata = RC.gap_filling(Pdata, -9999)
Hdata = RC.gap_filling(Hdata, -9999)
Esdata = 0.6108 * np.exp((17.27 * Tdata) / (Tdata + 237.3))
HumData = np.minimum((1.6077717 * Hdata * Pdata / Esdata), 1) * 100
HumData = HumData.clip(0, 100)
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 Merge_DEM_15s_30s(output_folder_trash, output_file_merged, latlim, lonlim,
resolution):
os.chdir(output_folder_trash)
tiff_files = glob.glob('*.tif')
resolution_geo = []
lonmin = lonlim[0]
lonmax = lonlim[1]
latmin = latlim[0]
latmax = latlim[1]
if resolution == "15s":
resolution_geo = 0.00416667
if resolution == "30s":
resolution_geo = 0.00416667 * 2
size_x_tot = int(np.round((lonmax - lonmin) / resolution_geo))
size_y_tot = int(np.round((latmax - latmin) / resolution_geo))
data_tot = np.ones([size_y_tot, size_x_tot]) * -9999.
for tiff_file in tiff_files:
inFile = os.path.join(output_folder_trash, tiff_file)
geo, proj, size_X, size_Y = RC.Open_array_info(inFile)
resolution_geo = geo[1]
lonmin_one = geo[0]
lonmax_one = geo[0] + size_X * geo[1]
latmin_one = geo[3] + size_Y * geo[5]
latmax_one = geo[3]
if lonmin_one < lonmin:
lonmin_clip = lonmin
else:
lonmin_clip = lonmin_one
if lonmax_one > lonmax:
lonmax_clip = lonmax
else:
lonmax_clip = lonmax_one
if latmin_one < latmin:
latmin_clip = latmin
else:
latmin_clip = latmin_one
if latmax_one > latmax:
latmax_clip = latmax
else:
latmax_clip = latmax_one
size_x_clip = int(
np.round((lonmax_clip - lonmin_clip) / resolution_geo))
size_y_clip = int(
np.round((latmax_clip - latmin_clip) / resolution_geo))
inFile = os.path.join(output_folder_trash, tiff_file)
geo, proj, size_X, size_Y = RC.Open_array_info(inFile)
Data = RC.Open_tiff_array(inFile)
lonmin_tiff = geo[0]
latmax_tiff = geo[3]
lon_tiff_position = int(
np.round((lonmin_clip - lonmin_tiff) / resolution_geo))
lat_tiff_position = int(
np.round((latmax_tiff - latmax_clip) / resolution_geo))
lon_data_tot_position = int(
np.round((lonmin_clip - lonmin) / resolution_geo))
lat_data_tot_position = int(
np.round((latmax - latmax_clip) / resolution_geo))
Data[Data < -9999.] = -9999.
data_tot[lat_data_tot_position:lat_data_tot_position + size_y_clip,
lon_data_tot_position:lon_data_tot_position +
size_x_clip][data_tot[
lat_data_tot_position:lat_data_tot_position + size_y_clip,
lon_data_tot_position:lon_data_tot_position +
size_x_clip] == -9999] = Data[
lat_tiff_position:lat_tiff_position + size_y_clip,
lon_tiff_position:lon_tiff_position +
size_x_clip][data_tot[
lat_data_tot_position:lat_data_tot_position +
size_y_clip,
lon_data_tot_position:lon_data_tot_position +
size_x_clip] == -9999]
geo_out = [lonmin, resolution_geo, 0.0, latmax, 0.0, -1 * resolution_geo]
geo_out = tuple(geo_out)
data_tot[data_tot < -9999.] = -9999.
return (data_tot, geo_out)
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'
if parameter == "dir_30s":
para_name = "DIR"
unit = "-"
resolution = '30s'
parameter = 'dir'
if parameter == "dem_30s":
para_name = "DEM"
unit = "m"
resolution = '30s'
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' or resolution == '30s':
name = Find_Document_names_15s_30s(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'
if resolution == '30s':
file_name_extract2 = file_name_extract[
0] + '_' + file_name_extract[1] + '_30s'
output_tiff = os.path.join(output_folder_trash, file_name_tiff)
# convert data from adf to a tiff file
if (resolution == "15s" or resolution == "3s"):
input_adf = os.path.join(output_folder_trash,
file_name_extract2,
file_name_extract2, 'hdr.adf')
output_tiff = DC.Convert_adf_to_tiff(input_adf, output_tiff)
# convert data from adf to a tiff file
if resolution == "30s":
input_bil = os.path.join(output_folder_trash,
'%s.bil' % file_name_extract2)
output_tiff = DC.Convert_bil_to_tiff(input_bil, output_tiff)
geo_out, proj, size_X, size_Y = RC.Open_array_info(output_tiff)
if (resolution == "3s" and
(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_30s(output_folder_trash,
output_file_merged, latlim,
lonlim, resolution)
if resolution == '30s':
output_file_merged = os.path.join(output_folder_trash, 'merged.tif')
datasetTot, geo_out = Merge_DEM_15s_30s(output_folder_trash,
output_file_merged, latlim,
lonlim, resolution)
# 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 slope_correct(down_short_hor, pressure, ea, DEMmap, DOY):
"""
This function downscales the CFSR solar radiation by using the DEM map
The Slope correction is based on Allen et al. (2006)
'Analytical integrated functions for daily solar radiation on slope'
Keyword arguments:
down_short_hor -- numpy array with the horizontal downwards shortwave radiation
pressure -- numpy array with the air pressure
ea -- numpy array with the actual vapour pressure
DEMmap -- 'C:/' path to the DEM map
DOY -- day of the year
"""
# Get Geo Info
GeoT, Projection, xsize, ysize = RC.Open_array_info(DEMmap)
minx = GeoT[0]
miny = GeoT[3] + xsize*GeoT[4] + ysize*GeoT[5]
x = np.flipud(np.arange(xsize)*GeoT[1] + minx + GeoT[1]/2)
y = np.flipud(np.arange(ysize)*-GeoT[5] + miny + -GeoT[5]/2)
# Calculate Extraterrestrial Solar Radiation [W m-2]
demmap = RC.Open_tiff_array(DEMmap)
demmap[demmap<0]=0
# apply the slope correction
Ra_hor, Ra_slp, sinb, sinb_hor, fi, slope, ID = SlopeInfluence(demmap,y,x,DOY)
# Calculate atmospheric transmissivity
Rs_hor = down_short_hor
# EQ 39
tau = Rs_hor/Ra_hor
#EQ 41
KB_hor = np.zeros(tau.shape) * np.nan
indice = np.where(tau.flat >= 0.42)
KB_hor.flat[indice] = 1.56*tau.flat[indice] -0.55
indice = np.logical_and(tau.flat > 0.175, tau.flat < 0.42)
KB_hor.flat[indice] = 0.022 - 0.280*tau.flat[indice] + 0.828*tau.flat[indice]**2 + 0.765*tau.flat[indice]**3
indice = np.where(tau.flat <= 0.175)
KB_hor.flat[indice] = 0.016*tau.flat[indice]
# EQ 42
KD_hor = tau - KB_hor
Kt=0.7
#EQ 18
W = 0.14*ea*pressure + 2.1
KB0 = 0.98*np.exp((-0.00146*pressure/Kt/sinb)-0.075*(W/sinb)**0.4)
KB0_hor = 0.98*np.exp((-0.00146*pressure/Kt/sinb_hor)-0.075*(W/sinb_hor)**0.4)
#EQ 34
fB = KB0/KB0_hor * Ra_slp/Ra_hor
fia = (1-KB_hor) * (1 + (KB_hor/(KB_hor+KD_hor))**0.5 * np.sin(slope/2)**3)*fi + fB*KB_hor
Rs = Rs_hor*(fB*(KB_hor/tau) + fia*(KD_hor/tau) + 0.23*(1-fi))
Rs[np.isnan(Rs)] = Rs_hor[np.isnan(Rs)]
Rs_equiv = Rs / np.cos(slope)
bias = np.nansum(Rs_hor)/np.nansum(Rs_equiv)
return Rs_equiv, tau, bias
def Nearest_Interpolate(Dir_in, Startdate, Enddate, Dir_out=None):
"""
This functions calculates yearly tiff files based on the monthly 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 watertools.General.data_conversions as DC
import watertools.General.raster_conversions as RC
# Change working directory
os.chdir(Dir_in)
# Define end and start date
Dates = pd.date_range(Startdate, Enddate, freq='AS')
# Find all monthly files
files = glob.glob('*monthly*.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
files_one_year = glob.glob('*monthly*%d*.tif' % Year)
# Create empty arrays
Year_data = np.zeros([size_Y, size_X])
if len(files_one_year) is not int(12):
print("One month in year %s is missing!" % Year)
for file_one_year in files_one_year:
file_path = os.path.join(Dir_in, file_one_year)
Month_data = RC.Open_tiff_array(file_path)
Month_data[np.isnan(Month_data)] = 0.0
Month_data[Month_data == -9999] = 0.0
Year_data += Month_data
# Define output directory
if Dir_out == None:
Dir_out = Dir_in
if not os.path.exists(Dir_out):
os.makedirs(Dir_out)
# Define output name
output_name = os.path.join(
Dir_out,
file_one_year.replace('monthly',
'yearly').replace('month', 'year'))
output_name = output_name[:-14] + '%d.01.01.tif' % (date.year)
# Save tiff file
DC.Save_as_tiff(output_name, Year_data, geo_out, proj)
return
def RetrieveData(Date, args):
"""
This function retrieves RFE 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, lonlim, latlim, xID, yID] = args
# Create https
DirFile = os.path.join(
output_folder, 'P_RFE.v2.0_mm-day-1_daily_%s.%02s.%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'), Date.strftime('%d')))
if not os.path.isfile(DirFile):
# open ftp server
ftp = FTP("ftp.cpc.ncep.noaa.gov", "", "")
ftp.login()
# Define FTP path to directory
pathFTP = 'fews/fewsdata/africa/rfe2/geotiff/'
# find the document name in this directory
ftp.cwd(pathFTP)
listing = []
# read all the file names in the directory
ftp.retrlines("LIST", listing.append)
# create all the input name (filename) and output (outfilename, filetif, DiFileEnd) names
filename = 'africa_rfe.%s%02s%02s.tif.zip' % (
Date.strftime('%Y'), Date.strftime('%m'), Date.strftime('%d'))
outfilename = os.path.join(
output_folder, 'africa_rfe.%s%02s%02s.tif' %
(Date.strftime('%Y'), Date.strftime('%m'), Date.strftime('%d')))
try:
local_filename = os.path.join(output_folder, filename)
lf = open(local_filename, "wb")
ftp.retrbinary("RETR " + filename, lf.write)
lf.close()
# unzip the file
zip_filename = os.path.join(output_folder, filename)
DC.Extract_Data(zip_filename, output_folder)
# open tiff file
dataset = RC.Open_tiff_array(outfilename)
# clip dataset to the given extent
data = dataset[yID[0]:yID[1], xID[0]:xID[1]]
data[data < 0] = -9999
# save dataset as geotiff file
latlim_adj = 40.05 - 0.1 * yID[0]
lonlim_adj = -20.05 + 0.1 * xID[0]
geo = [lonlim_adj, 0.1, 0, latlim_adj, 0, -0.1]
DC.Save_as_tiff(name=DirFile,
data=data,
geo=geo,
projection="WGS84")
# delete old tif file
os.remove(outfilename)
os.remove(zip_filename)
except:
print("file not exists")
return True
def Calc_Property(Dir, latlim, lonlim, SL):
import watertools
# Define level
if SL == "sl3":
level = "Topsoil"
elif SL == "sl6":
level = "Subsoil"
# check if you need to download
filename_out_thetasat = os.path.join(
Dir, 'SoilGrids', 'Theta_Sat',
'Theta_Sat2_%s_SoilGrids_kg-kg.tif' % level)
if not os.path.exists(filename_out_thetasat):
if SL == "sl3":
watertools.Products.SoilGrids.Theta_Sat2.Topsoil(
Dir, latlim, lonlim)
elif SL == "sl6":
watertools.Products.SoilGrids.Theta_Sat2.Subsoil(
Dir, latlim, lonlim)
filename_out_thetares = os.path.join(
Dir, 'SoilGrids', 'Theta_Res',
'Theta_Res_%s_SoilGrids_kg-kg.tif' % level)
if not os.path.exists(filename_out_thetares):
if SL == "sl3":
watertools.Products.SoilGrids.Theta_Res.Topsoil(
Dir, latlim, lonlim)
elif SL == "sl6":
watertools.Products.SoilGrids.Theta_Res.Subsoil(
Dir, latlim, lonlim)
filename_out_n_genuchten = os.path.join(
Dir, 'SoilGrids', 'N_van_genuchten',
'N_genuchten_%s_SoilGrids_-.tif' % level)
if not os.path.exists(filename_out_n_genuchten):
if SL == "sl3":
watertools.Products.SoilGrids.n_van_genuchten.Topsoil(
Dir, latlim, lonlim)
elif SL == "sl6":
watertools.Products.SoilGrids.n_van_genuchten.Subsoil(
Dir, latlim, lonlim)
filedir_out_thetafc = os.path.join(Dir, 'SoilGrids', 'Theta_FC')
if not os.path.exists(filedir_out_thetafc):
os.makedirs(filedir_out_thetafc)
# Define theta field capacity output
filename_out_thetafc = os.path.join(
filedir_out_thetafc, 'Theta_FC2_%s_SoilGrids_cm3-cm3.tif' % level)
if not os.path.exists(filename_out_thetafc):
# Get info layer
geo_out, proj, size_X, size_Y = RC.Open_array_info(
filename_out_thetasat)
# Open dataset
theta_sat = RC.Open_tiff_array(filename_out_thetasat)
theta_res = RC.Open_tiff_array(filename_out_thetares)
n_genuchten = RC.Open_tiff_array(filename_out_n_genuchten)
# Calculate theta field capacity
theta_FC = np.ones(theta_sat.shape) * -9999
#theta_FC = np.where(theta_sat < 0.301, 0.042, np.arccosh(theta_sat + 0.7) - 0.32 * (theta_sat + 0.7) + 0.2)
#theta_FC = np.where(theta_sat < 0.301, 0.042, -2.95*theta_sat**2+3.96*theta_sat-0.871)
theta_FC = theta_res + (theta_sat - theta_res) / (
1 + (0.02 * 200)**n_genuchten)**(1 - 1 / n_genuchten)
# Save as tiff
DC.Save_as_tiff(filename_out_thetafc, theta_FC, geo_out, proj)
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 watertools.General.data_conversions as DC
import watertools.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[:, 1]:
# Calculate the amount of days in this month of each file
Weight = np.ones([size_Y, size_X])
# For start of month
if np.min(DOYs_oneMonth[:, 1]) == EightDays:
Weight = Weight * int(EightDays + 8 - int(DOY_month_start))
# For end of month
elif np.max(DOYs_oneMonth[:, 1]) == EightDays:
Weight = Weight * (int(DOY_month_end) - EightDays + 1)
# For the middle of the month
else:
Weight = Weight * 8
row = DOYs_oneMonth[np.argwhere(
DOYs_oneMonth[:, 1] == EightDays)[0][0], :][0]
# Open the array of current file
input_name = os.path.join(Dir_in, files[int(row)])
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(row)].replace('8-daily', 'monthly'))
output_name = output_name[:-9] + '%02d.01.tif' % (date.month)
# Save tiff file
DC.Save_as_tiff(output_name, Data_one_month, geo_out, proj)
return
def RetrieveData(Date, args):
"""
This function retrieves CHIRPS data for a given date from the
ftp://chg-ftpout.geog.ucsb.edu 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
# create all the input name (filename) and output (outfilename, filetif, DiFileEnd) names
if TimeCase == 'daily':
filename = 'chirps-v2.0.%s.%02s.%02s.tif.gz' %(Date.strftime('%Y'), Date.strftime('%m'), Date.strftime('%d'))
outfilename = os.path.join(output_folder,'chirps-v2.0.%s.%02s.%02s.tif' %(Date.strftime('%Y'), Date.strftime('%m'), Date.strftime('%d')))
DirFileEnd = os.path.join(output_folder,'P_CHIRPS.v2.0_mm-day-1_daily_%s.%02s.%02s.tif' %(Date.strftime('%Y'), Date.strftime('%m'), Date.strftime('%d')))
elif TimeCase == 'monthly':
filename = 'chirps-v2.0.%s.%02s.tif.gz' %(Date.strftime('%Y'), Date.strftime('%m'))
outfilename = os.path.join(output_folder,'chirps-v2.0.%s.%02s.tif' %(Date.strftime('%Y'), Date.strftime('%m')))
DirFileEnd = os.path.join(output_folder,'P_CHIRPS.v2.0_mm-month-1_monthly_%s.%02s.%02s.tif' %(Date.strftime('%Y'), Date.strftime('%m'), Date.strftime('%d')))
else:
raise KeyError("The input time interval is not supported")
if not os.path.exists(DirFileEnd):
# open ftp server
ftp = FTP("chg-ftpout.geog.ucsb.edu", "", "")
ftp.login()
# Define FTP path to directory
if TimeCase == 'daily':
pathFTP = 'pub/org/chg/products/CHIRPS-2.0/global_daily/tifs/p05/%s/' %Date.strftime('%Y')
elif TimeCase == 'monthly':
pathFTP = 'pub/org/chg/products/CHIRPS-2.0/global_monthly/tifs/'
else:
raise KeyError("The input time interval is not supported")
# find the document name in this directory
ftp.cwd(pathFTP)
listing = []
# read all the file names in the directory
ftp.retrlines("LIST", listing.append)
# download the global rainfall file
try:
local_filename = os.path.join(output_folder, filename)
lf = open(local_filename, "wb")
ftp.retrbinary("RETR " + filename, lf.write, 8192)
lf.close()
# unzip the file
zip_filename = os.path.join(output_folder, filename)
DC.Extract_Data_gz(zip_filename, outfilename)
# open tiff file
dataset = RC.Open_tiff_array(outfilename)
# clip dataset to the given extent
data = dataset[yID[0]:yID[1], xID[0]:xID[1]]
data[data < 0] = -9999
# save dataset as geotiff file
geo = [lonlim[0], 0.05, 0, latlim[1], 0, -0.05]
DC.Save_as_tiff(name=DirFileEnd, data=data, geo=geo, projection="WGS84")
# delete old tif file
os.remove(outfilename)
except:
print("file not exists")
return True
