def lu_type_average(data_fh, lu_fh, lu_dict):
LULC = RC.Open_tiff_array(lu_fh)
in_data = RC.Open_tiff_array(data_fh)
out_data = {}
for lu_class in list(lu_dict.keys()):
mask = [LULC == value for value in lu_dict[lu_class]]
mask = (np.sum(mask, axis=0)).astype(bool)
out_data[lu_class] = np.nanmean(in_data[mask])
return out_data
def fuel_wood(output_folder, lu_fh, AREA, 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 = AREA * 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.open_as_array(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 watools.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_Property(Dir, latlim, lonlim, SL):
import watools
# 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":
watools.Products.SoilGrids.Theta_Sat2.Topsoil(Dir, latlim, lonlim)
elif SL == "sl6":
watools.Products.SoilGrids.Theta_Sat2.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)
# 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)
# Save as tiff
DC.Save_as_tiff(filename_out_thetafc, theta_FC, geo_out, proj)
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 lu_type_sum(data_fh, lu_fh, AREA, lu_dict, convert=None):
LULC = RC.Open_tiff_array(lu_fh)
in_data = becgis.open_as_array(data_fh, nan_values=True)
# in_data = RC.Open_tiff_array(data_fh)
if convert == 'mm_to_km3':
in_data *= AREA / 1e6
out_data = {}
for lu_class in list(lu_dict.keys()):
mask = [LULC == value for value in lu_dict[lu_class]]
mask = (np.sum(mask, axis=0)).astype(bool)
out_data[lu_class] = np.nansum(in_data[mask])
return out_data
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 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
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 watools.General.data_conversions as DC
import watools.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()
# Define output directory
if Dir_out is None:
Dir_out = Dir_in
if not os.path.exists(Dir_out):
os.makedirs(Dir_out)
# loop over the months and sum the days
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!!! month %s year %s" %(Month, Year))
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 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 Merge_DEM_15s(output_folder_trash, output_file_merged, latlim, lonlim):
os.chdir(output_folder_trash)
tiff_files = glob.glob('*.tif')
resolution_geo = []
lonmin = lonlim[0]
lonmax = lonlim[1]
latmin = latlim[0]
latmax = latlim[1]
resolution_geo = 0.00416667
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'
# converts the latlim and lonlim into names of the tiles which must be
# downloaded
if resolution == '3s':
name, rangeLon, rangeLat = Find_Document_Names(latlim, lonlim,
parameter)
# Memory for the map x and y shape (starts with zero)
size_X_tot = 0
size_Y_tot = 0
if resolution == '15s':
name = Find_Document_names_15s(latlim, lonlim, parameter, resolution)
nameResults = []
# Create a temporary folder for processing
output_folder_trash = os.path.join(output_folder, "Temp")
if not os.path.exists(output_folder_trash):
os.makedirs(output_folder_trash)
# Download, extract, and converts all the files to tiff files
for nameFile in name:
try:
# Download the data from
# http://earlywarning.usgs.gov/hydrodata/
output_file, file_name = Download_Data(nameFile,
output_folder_trash,
parameter, para_name,
resolution)
# extract zip data
DC.Extract_Data(output_file, output_folder_trash)
# Converts the data with a adf extention to a tiff extension.
# The input is the file name and in which directory the data must be stored
file_name_tiff = file_name.split('.')[0] + '_trans_temporary.tif'
file_name_extract = file_name.split('_')[0:3]
if resolution == '3s':
file_name_extract2 = file_name_extract[
0] + '_' + file_name_extract[1]
if resolution == '15s':
file_name_extract2 = file_name_extract[
0] + '_' + file_name_extract[1] + '_15s'
input_adf = os.path.join(output_folder_trash, file_name_extract2,
file_name_extract2, 'hdr.adf')
output_tiff = os.path.join(output_folder_trash, file_name_tiff)
# convert data from adf to a tiff file
output_tiff = DC.Convert_adf_to_tiff(input_adf, output_tiff)
geo_out, proj, size_X, size_Y = RC.Open_array_info(output_tiff)
if int(size_X) != int(6000) or int(size_Y) != int(6000):
data = np.ones((6000, 6000)) * -9999
# Create the latitude bound
Vfile = str(nameFile)[1:3]
SignV = str(nameFile)[0]
SignVer = 1
# If the sign before the filename is a south sign than latitude is negative
if SignV is "s":
SignVer = -1
Bound2 = int(SignVer) * int(Vfile)
# Create the longitude bound
Hfile = str(nameFile)[4:7]
SignH = str(nameFile)[3]
SignHor = 1
# If the sign before the filename is a west sign than longitude is negative
if SignH is "w":
SignHor = -1
Bound1 = int(SignHor) * int(Hfile)
Expected_X_min = Bound1
Expected_Y_max = Bound2 + 5
Xid_start = int(
np.round((geo_out[0] - Expected_X_min) / geo_out[1]))
Xid_end = int(
np.round(
((geo_out[0] + size_X * geo_out[1]) - Expected_X_min) /
geo_out[1]))
Yid_start = int(
np.round((Expected_Y_max - geo_out[3]) / (-geo_out[5])))
Yid_end = int(
np.round((Expected_Y_max - (geo_out[3] +
(size_Y * geo_out[5]))) /
(-geo_out[5])))
data[Yid_start:Yid_end,
Xid_start:Xid_end] = RC.Open_tiff_array(output_tiff)
if np.max(data) == 255:
data[data == 255] = -9999
data[data < -9999] = -9999
geo_in = [
Bound1, 0.00083333333333333, 0.0,
int(Bound2 + 5), 0.0, -0.0008333333333333333333
]
# save chunk as tiff file
DC.Save_as_tiff(name=output_tiff,
data=data,
geo=geo_in,
projection="WGS84")
except:
if resolution == '3s':
# If tile not exist create a replacing zero tile (sea tiles)
output = nameFile.split('.')[0] + "_trans_temporary.tif"
output_tiff = os.path.join(output_folder_trash, output)
file_name = nameFile
data = np.ones((6000, 6000)) * -9999
data = data.astype(np.float32)
# Create the latitude bound
Vfile = str(file_name)[1:3]
SignV = str(file_name)[0]
SignVer = 1
# If the sign before the filename is a south sign than latitude is negative
if SignV is "s":
SignVer = -1
Bound2 = int(SignVer) * int(Vfile)
# Create the longitude bound
Hfile = str(file_name)[4:7]
SignH = str(file_name)[3]
SignHor = 1
# If the sign before the filename is a west sign than longitude is negative
if SignH is "w":
SignHor = -1
Bound1 = int(SignHor) * int(Hfile)
# Geospatial data for the tile
geo_in = [
Bound1, 0.00083333333333333, 0.0,
int(Bound2 + 5), 0.0, -0.0008333333333333333333
]
# save chunk as tiff file
DC.Save_as_tiff(name=output_tiff,
data=data,
geo=geo_in,
projection="WGS84")
if resolution == '15s':
print('no 15s data is in dataset')
if resolution == '3s':
# clip data
Data, Geo_data = RC.clip_data(output_tiff, latlim, lonlim)
size_Y_out = int(np.shape(Data)[0])
size_X_out = int(np.shape(Data)[1])
# Total size of the product so far
size_Y_tot = int(size_Y_tot + size_Y_out)
size_X_tot = int(size_X_tot + size_X_out)
if nameFile is name[0]:
Geo_x_end = Geo_data[0]
Geo_y_end = Geo_data[3]
else:
Geo_x_end = np.min([Geo_x_end, Geo_data[0]])
Geo_y_end = np.max([Geo_y_end, Geo_data[3]])
# create name for chunk
FileNameEnd = "%s_temporary.tif" % (nameFile)
nameForEnd = os.path.join(output_folder_trash, FileNameEnd)
nameResults.append(str(nameForEnd))
# save chunk as tiff file
DC.Save_as_tiff(name=nameForEnd,
data=Data,
geo=Geo_data,
projection="WGS84")
if resolution == '3s':
#size_X_end = int(size_X_tot) #!
#size_Y_end = int(size_Y_tot) #!
size_X_end = int(size_X_tot / len(rangeLat)) + 1 #!
size_Y_end = int(size_Y_tot / len(rangeLon)) + 1 #!
# Define the georeference of the end matrix
geo_out = [Geo_x_end, Geo_data[1], 0, Geo_y_end, 0, Geo_data[5]]
latlim_out = [geo_out[3] + geo_out[5] * size_Y_end, geo_out[3]]
lonlim_out = [geo_out[0], geo_out[0] + geo_out[1] * size_X_end]
# merge chunk together resulting in 1 tiff map
datasetTot = Merge_DEM(latlim_out, lonlim_out, nameResults, size_Y_end,
size_X_end)
datasetTot[datasetTot < -9999] = -9999
if resolution == '15s':
output_file_merged = os.path.join(output_folder_trash, 'merged.tif')
datasetTot, geo_out = Merge_DEM_15s(output_folder_trash,
output_file_merged, latlim, lonlim)
# name of the end result
output_DEM_name = "%s_HydroShed_%s_%s.tif" % (para_name, unit, resolution)
Save_name = os.path.join(output_folder, output_DEM_name)
# Make geotiff file
DC.Save_as_tiff(name=Save_name,
data=datasetTot,
geo=geo_out,
projection="WGS84")
os.chdir(output_folder)
# Delete the temporary folder
shutil.rmtree(output_folder_trash)
def main(files_DEM_dir, files_DEM, files_Basin, files_Runoff, files_Extraction, startdate, enddate, input_nc, resolution, Format_DEM_dir, Format_DEM, Format_Basin, Format_Runoff, Format_Extraction):
# Define a year to get the epsg and geo
Startdate_timestamp = pd.Timestamp(startdate)
year = Startdate_timestamp.year
############################## Drainage Direction #####################################
# Open Array DEM dir as netCDF
if Format_DEM_dir == "NetCDF":
file_DEM_dir = os.path.join(files_DEM_dir, "%d.nc" %year)
DataCube_DEM_dir = RC.Open_nc_array(file_DEM_dir, "Drainage_Direction")
geo_out_example, epsg_example, size_X_example, size_Y_example, size_Z_example, Time_example = RC.Open_nc_info(files_DEM_dir)
# Create memory file for reprojection
gland = DC.Save_as_MEM(DataCube_DEM_dir, geo_out_example, epsg_example)
dataset_example = file_name_DEM_dir = gland
# Open Array DEM dir as TIFF
if Format_DEM_dir == "TIFF":
file_name_DEM_dir = os.path.join(files_DEM_dir,"DIR_HydroShed_-_%s.tif" %resolution)
DataCube_DEM_dir = RC.Open_tiff_array(file_name_DEM_dir)
geo_out_example, epsg_example, size_X_example, size_Y_example = RC.Open_array_info(file_name_DEM_dir)
dataset_example = file_name_DEM_dir
# Calculate Area per pixel in m2
import watools.Functions.Start.Area_converter as AC
DataCube_Area = AC.Degrees_to_m2(file_name_DEM_dir)
################################## DEM ##########################################
# Open Array DEM as netCDF
if Format_DEM == "NetCDF":
file_DEM = os.path.join(files_DEM, "%d.nc" %year)
DataCube_DEM = RC.Open_nc_array(file_DEM, "Elevation")
# Open Array DEM as TIFF
if Format_DEM == "TIFF":
file_name_DEM = os.path.join(files_DEM,"DEM_HydroShed_m_%s.tif" %resolution)
destDEM = RC.reproject_dataset_example(file_name_DEM, dataset_example, method=1)
DataCube_DEM = destDEM.GetRasterBand(1).ReadAsArray()
################################ Landuse ##########################################
# Open Array Basin as netCDF
if Format_Basin == "NetCDF":
file_Basin = os.path.join(files_Basin, "%d.nc" %year)
DataCube_Basin = RC.Open_nc_array(file_Basin, "Landuse")
geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(file_Basin, "Landuse")
dest_basin = DC.Save_as_MEM(DataCube_Basin, geo_out, str(epsg))
destLU = RC.reproject_dataset_example(dest_basin, dataset_example, method=1)
DataCube_LU_CR = destLU.GetRasterBand(1).ReadAsArray()
DataCube_Basin = np.zeros([size_Y_example, size_X_example])
DataCube_Basin[DataCube_LU_CR > 0] = 1
# Open Array Basin as TIFF
if Format_Basin == "TIFF":
file_name_Basin = files_Basin
destLU = RC.reproject_dataset_example(file_name_Basin, dataset_example, method=1)
DataCube_LU_CR = destLU.GetRasterBand(1).ReadAsArray()
DataCube_Basin = np.zeros([size_Y_example, size_X_example])
DataCube_Basin[DataCube_LU_CR > 0] = 1
################################ Surface Runoff ##########################################
# Open Array runoff as netCDF
if Format_Runoff == "NetCDF":
DataCube_Runoff = RC.Open_ncs_array(files_Runoff, "Surface_Runoff", startdate, enddate)
size_Z_example = DataCube_Runoff.shape[0]
file_Runoff = os.path.join(files_Runoff, "%d.nc" %year)
geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(file_Runoff, "Surface_Runoff")
DataCube_Runoff_CR = np.ones([size_Z_example, size_Y_example, size_X_example]) * np.nan
for i in range(0, size_Z):
DataCube_Runoff_one = DataCube_Runoff[i,:,:]
dest_Runoff_one = DC.Save_as_MEM(DataCube_Runoff_one, geo_out, str(epsg))
dest_Runoff = RC.reproject_dataset_example(dest_Runoff_one, dataset_example, method=4)
DataCube_Runoff_CR[i,:,:] = dest_Runoff.GetRasterBand(1).ReadAsArray()
DataCube_Runoff_CR[:, DataCube_LU_CR == 0] = -9999
DataCube_Runoff_CR[DataCube_Runoff_CR < 0] = -9999
# Open Array runoff as TIFF
if Format_Runoff == "TIFF":
DataCube_Runoff_CR = RC.Get3Darray_time_series_monthly(files_Runoff, startdate, enddate, Example_data = dataset_example)
################################ Surface Withdrawal ##########################################
# Open Array Extraction as netCDF
if Format_Extraction == "NetCDF":
DataCube_Extraction = RC.Open_ncs_array(files_Extraction, "Surface_Withdrawal", startdate, enddate)
size_Z_example = DataCube_Extraction.shape[0]
file_Extraction = os.path.join(files_Extraction, "%d.nc" %year)
geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(file_Extraction, "Surface_Withdrawal")
DataCube_Extraction_CR = np.ones([size_Z_example, size_Y_example, size_X_example]) * np.nan
for i in range(0, size_Z):
DataCube_Extraction_one = DataCube_Extraction[i,:,:]
dest_Extraction_one = DC.Save_as_MEM(DataCube_Extraction_one, geo_out, str(epsg))
dest_Extraction = RC.reproject_dataset_example(dest_Extraction_one, dataset_example, method=4)
DataCube_Extraction_CR[i,:,:] = dest_Extraction.GetRasterBand(1).ReadAsArray()
DataCube_Extraction_CR[:, DataCube_LU_CR == 0] = -9999
DataCube_Extraction_CR[DataCube_Extraction_CR < 0] = -9999
# Open Array Extraction as TIFF
if Format_Extraction == "TIFF":
DataCube_Extraction_CR = RC.Get3Darray_time_series_monthly(files_Extraction, startdate, enddate, Example_data = dataset_example)
################################ Create input netcdf ##########################################
# Save data in one NetCDF file
geo_out_example = np.array(geo_out_example)
# Latitude and longitude
lon_ls = np.arange(size_X_example)*geo_out_example[1]+geo_out_example[0] + 0.5 * geo_out_example[1]
lat_ls = np.arange(size_Y_example)*geo_out_example[5]+geo_out_example[3] - 0.5 * geo_out_example[5]
lat_n = len(lat_ls)
lon_n = len(lon_ls)
# Create NetCDF file
nc_file = netCDF4.Dataset(input_nc, 'w')
nc_file.set_fill_on()
# Create dimensions
lat_dim = nc_file.createDimension('latitude', lat_n)
lon_dim = nc_file.createDimension('longitude', lon_n)
# Create NetCDF variables
crso = nc_file.createVariable('crs', 'i4')
crso.long_name = 'Lon/Lat Coords in WGS84'
crso.standard_name = 'crs'
crso.grid_mapping_name = 'latitude_longitude'
crso.projection = epsg_example
crso.longitude_of_prime_meridian = 0.0
crso.semi_major_axis = 6378137.0
crso.inverse_flattening = 298.257223563
crso.geo_reference = geo_out_example
lat_var = nc_file.createVariable('latitude', 'f8', ('latitude',))
lat_var.units = 'degrees_north'
lat_var.standard_name = 'latitude'
lat_var.pixel_size = geo_out_example[5]
lon_var = nc_file.createVariable('longitude', 'f8', ('longitude',))
lon_var.units = 'degrees_east'
lon_var.standard_name = 'longitude'
lon_var.pixel_size = geo_out_example[1]
Dates = pd.date_range(startdate,enddate,freq = 'MS')
time_or=np.zeros(len(Dates))
i = 0
for Date in Dates:
time_or[i] = Date.toordinal()
i += 1
nc_file.createDimension('time', None)
timeo = nc_file.createVariable('time', 'f4', ('time',))
timeo.units = 'Monthly'
timeo.standard_name = 'time'
# Variables
demdir_var = nc_file.createVariable('demdir', 'i',
('latitude', 'longitude'),
fill_value=-9999)
demdir_var.long_name = 'Flow Direction Map'
demdir_var.grid_mapping = 'crs'
dem_var = nc_file.createVariable('dem', 'f8',
('latitude', 'longitude'),
fill_value=-9999)
dem_var.long_name = 'Altitude'
dem_var.units = 'meters'
dem_var.grid_mapping = 'crs'
basin_var = nc_file.createVariable('basin', 'i',
('latitude', 'longitude'),
fill_value=-9999)
basin_var.long_name = 'Altitude'
basin_var.units = 'meters'
basin_var.grid_mapping = 'crs'
area_var = nc_file.createVariable('area', 'f8',
('latitude', 'longitude'),
fill_value=-9999)
area_var.long_name = 'area in squared meters'
area_var.units = 'squared_meters'
area_var.grid_mapping = 'crs'
runoff_var = nc_file.createVariable('Runoff_M', 'f8',
('time', 'latitude', 'longitude'),
fill_value=-9999)
runoff_var.long_name = 'Runoff'
runoff_var.units = 'm3/month'
runoff_var.grid_mapping = 'crs'
extraction_var = nc_file.createVariable('Extraction_M', 'f8',
('time', 'latitude', 'longitude'),
fill_value=-9999)
extraction_var.long_name = 'Surface water Extraction'
extraction_var.units = 'm3/month'
extraction_var.grid_mapping = 'crs'
# Load data
lat_var[:] = lat_ls
lon_var[:] = lon_ls
timeo[:] = time_or
# Static variables
demdir_var[:, :] = DataCube_DEM_dir[:, :]
dem_var[:, :] = DataCube_DEM[:, :]
basin_var[:, :] = DataCube_Basin[:, :]
area_var[:, :] = DataCube_Area[:, :]
for i in range(len(Dates)):
runoff_var[i,:,:] = DataCube_Runoff_CR[i,:,:]
for i in range(len(Dates)):
extraction_var[i,:,:] = DataCube_Extraction_CR[i,:,:]
# Close file
nc_file.close()
return()
def main(Dir,
Startdate='',
Enddate='',
latlim=[-50, 50],
lonlim=[-180, 180],
cores=False,
Waitbar=1):
"""
This function downloads RFE V2.0 (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 a waitbar
"""
# Download data
print('\nDownload monthly RFE precipitation data for period %s till %s' %
(Startdate, Enddate))
# Check variables
if not Startdate:
Startdate = pd.Timestamp('2001-01-01')
if not Enddate:
Enddate = pd.Timestamp('Now')
Dates = pd.date_range(Startdate, Enddate, freq='MS')
# Make directory
output_folder = os.path.join(Dir, 'Precipitation', 'RFE', 'Monthly/')
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Create Waitbar
if Waitbar == 1:
import watools.Functions.Start.WaitbarConsole as WaitbarConsole
total_amount = len(Dates)
amount = 0
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
for Date in Dates:
month = Date.month
year = Date.year
end_day = calendar.monthrange(year, month)[1]
Startdate_one_month = '%s-%02s-01' % (year, month)
Enddate_one_month = '%s-%02s-%02s' % (year, month, end_day)
DownloadData(Dir, Startdate_one_month, Enddate_one_month, latlim,
lonlim, 0, cores)
Dates_daily = pd.date_range(Startdate_one_month,
Enddate_one_month,
freq='D')
# Make directory
input_folder_daily = os.path.join(Dir, 'Precipitation', 'RFE',
'Daily/')
i = 0
for Date_daily in Dates_daily:
file_name = 'P_RFE.v2.0_mm-day-1_daily_%s.%02s.%02s.tif' % (
Date_daily.strftime('%Y'), Date_daily.strftime('%m'),
Date_daily.strftime('%d'))
file_name_daily_path = os.path.join(input_folder_daily, file_name)
if os.path.exists(file_name_daily_path):
if Date_daily == Dates_daily[i]:
Raster_monthly = RC.Open_tiff_array(file_name_daily_path)
else:
Raster_monthly += RC.Open_tiff_array(file_name_daily_path)
else:
if Date_daily == Dates_daily[i]:
i += 1
geo_out, proj, size_X, size_Y = RC.Open_array_info(
file_name_daily_path)
file_name = 'P_RFE.v2.0_mm-month-1_monthly_%s.%02s.01.tif' % (
Date.strftime('%Y'), Date.strftime('%m'))
file_name_output = os.path.join(output_folder, file_name)
DC.Save_as_tiff(file_name_output,
Raster_monthly,
geo_out,
projection="WGS84")
if Waitbar == 1:
amount += 1
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
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 Calc_Regions(input_nc, output_nc, input_JRC, Boundaries):
import numpy as np
import watools.General.raster_conversions as RC
sensitivity = 700 # 900 is less sensitive 1 is very sensitive
# Get JRC array and information
Array_JRC_occ = RC.Open_tiff_array(input_JRC)
Geo_out, proj, size_X, size_Y = RC.Open_array_info(input_JRC)
# Get Basin boundary based on LU
Array_LU = RC.Open_nc_array(input_nc, "basin")
LU_array = RC.resize_array_example(Array_LU, Array_JRC_occ)
basin_array = np.zeros(np.shape(LU_array))
basin_array[LU_array > 0] = 1
del LU_array
# find all pixels with water occurence
Array_JRC_occ[basin_array < 1] = 0
Array_JRC_occ[Array_JRC_occ < 30] = 0
Array_JRC_occ[Array_JRC_occ >= 30] = 1
del basin_array
# sum larger areas to find lakes
x_size = np.round(int(np.shape(Array_JRC_occ)[0]) / 30)
y_size = np.round(int(np.shape(Array_JRC_occ)[1]) / 30)
sum_array = np.zeros([x_size, y_size])
for i in range(0, len(sum_array)):
for j in range(0, len(sum_array[1])):
sum_array[i, j] = np.sum(Array_JRC_occ[i * 30:(i + 1) * 30,
j * 30:(j + 1) * 30])
del Array_JRC_occ
lakes = np.argwhere(sum_array >= sensitivity)
lake_info = np.zeros([1, 4])
i = 0
k = 1
# find all neighboring pixels
for lake in lakes:
added = 0
for j in range(0, k):
if (lake[0] >= lake_info[j, 0] and lake[0] <= lake_info[j, 1]
and lake[1] >= lake_info[j, 2]
and lake[1] <= lake_info[j, 3]):
lake_info[j, 0] = np.maximum(
np.minimum(lake_info[j, 0], lake[0] - 8), 0)
lake_info[j, 1] = np.minimum(
np.maximum(lake_info[j, 1], lake[0] + 8), x_size)
lake_info[j, 2] = np.maximum(
np.minimum(lake_info[j, 2], lake[1] - 8), 0)
lake_info[j, 3] = np.minimum(
np.maximum(lake_info[j, 3], lake[1] + 8), y_size)
added = 1
if added == 0:
lake_info_one = np.zeros([4])
lake_info_one[0] = np.maximum(0, lake[0] - 8)
lake_info_one[1] = np.minimum(x_size, lake[0] + 8)
lake_info_one[2] = np.maximum(0, lake[1] - 8)
lake_info_one[3] = np.minimum(y_size, lake[1] + 8)
lake_info = np.append(lake_info, lake_info_one)
lake_info = np.resize(lake_info, (k + 1, 4))
k += 1
# merge all overlaping regions
p = 0
lake_info_end = np.zeros([1, 4])
for i in range(1, k):
added = 0
lake_info_one = lake_info[i, :]
lake_y_region = list(
range(int(lake_info_one[0]), int(lake_info_one[1] + 1)))
lake_x_region = list(
range(int(lake_info_one[2]), int(lake_info_one[3] + 1)))
for j in range(0, p + 1):
if len(lake_y_region) + len(
list(
range(int(lake_info_end[j, 0]),
int(lake_info_end[j, 1] + 1)))
) is not len(
np.unique(
np.append(
lake_y_region,
list(
range(int(lake_info_end[j, 0]),
int(lake_info_end[j, 1] + 1)))))
) and len(lake_x_region) + len(
list(
range(int(lake_info_end[j, 2]),
int(lake_info_end[j, 3] + 1)))) is not len(
np.unique(
np.append(
lake_x_region,
list(
range(
int(lake_info_end[j, 2]),
int(lake_info_end[j, 3] +
1)))))):
lake_info_end[j, 0] = np.min(
np.unique(
np.append(
lake_y_region,
list(
range(int(lake_info_end[j, 0]),
int(lake_info_end[j, 1] + 1))))))
lake_info_end[j, 1] = np.max(
np.unique(
np.append(
lake_y_region,
list(
range(int(lake_info_end[j, 0]),
int(lake_info_end[j, 1] + 1))))))
lake_info_end[j, 2] = np.min(
np.unique(
np.append(
lake_x_region,
list(
range(int(lake_info_end[j, 2]),
int(lake_info_end[j, 3] + 1))))))
lake_info_end[j, 3] = np.max(
np.unique(
np.append(
lake_x_region,
list(
range(int(lake_info_end[j, 2]),
int(lake_info_end[j, 3] + 1))))))
added = 1
if added == 0:
lake_info_one = lake_info[i, :]
lake_info_end = np.append(lake_info_end, lake_info_one)
lake_info_end = np.resize(lake_info_end, (p + 2, 4))
p += 1
# calculate the area
Regions = np.zeros([p, 4])
pixel_x_size = Geo_out[1] * 30
pixel_y_size = Geo_out[5] * 30
for region in range(1, p + 1):
Regions[region - 1,
0] = Geo_out[0] + pixel_x_size * lake_info_end[region, 2]
Regions[region - 1,
1] = Geo_out[0] + pixel_x_size * (lake_info_end[region, 3] + 1)
Regions[region - 1,
2] = Geo_out[3] + pixel_y_size * (lake_info_end[region, 1] + 1)
Regions[region - 1,
3] = Geo_out[3] + pixel_y_size * lake_info_end[region, 0]
return (Regions)
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 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 watools.General.data_conversions as DC
import watools.General.raster_conversions as RC
# Create an output directory to store the rainy days tiffs
Data_Path_RD = os.path.join(Dir_Basin, 'Rainy_Days')
if not os.path.exists(Data_Path_RD):
os.mkdir(Data_Path_RD)
# Define the dates that must be created
Dates = pd.date_range(Startdate, Enddate, freq ='MS')
# Set working directory to the rainfall folder
os.chdir(Data_Path_P)
# 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(Data_Path_P, 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(Data_Path_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 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 watools.General.data_conversions as DC
import watools.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 = list(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
Data[np.isnan(Data)] = 0
# 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 == size_X and size_Y_NPP == 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 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 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 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
# 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)
# 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")
# 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
def Nearest_Interpolate(Dir_in, Startdate, Enddate, Dir_out=None):
"""
This functions calculates monthly tiff files based on the 16 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 watools.General.data_conversions as DC
import watools.General.raster_conversions as RC
# Change working directory
os.chdir(Dir_in)
# Find all eight daily files
files = glob.glob('*16-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] + 16) >= 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 + 16 - 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 * 16
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 (per day)
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('16-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 livestock_feed(output_folder, lu_fh, AREA, 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 = AREA * 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 list(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.open_as_array(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
