def DeleteBasins(basins,pathout):
"""
===========================================================
DeleteBasins(basins,pathout)
===========================================================
this function deletes all the basins in a basin raster created when delineating
a catchment and leave only the first basin which is the biggest basin in the raster
Inputs:
----------
1- basins:
[gdal.dataset] raster you create during delineation of a catchment
values of its cells are the number of the basin it belongs to
2- pathout:
[String] path you want to save the resulted raster to it should include
the extension ".tif"
Outputs:
----------
1- raster with only one basin (the basin that its name is 1 )
Example:
----------
basins=gdal.Open("Data/basins.tif")
pathout="mask.tif"
DeleteBasins(basins,pathout)
"""
# input data validation
# data type
assert type(pathout) == str, "A_path input should be string type"
assert type(basins)==gdal.Dataset, "basins raster should be read using gdal (gdal dataset please read it using gdal library) "
# input values
# check wether the user wrote the extension of the raster or not
ext=pathout[-4:]
assert ext == ".tif", "please add the extension at the end of the path input"
# get number of rows
rows=basins.RasterYSize
# get number of columns
cols=basins.RasterXSize
# array
basins_A=basins.ReadAsArray()
# no data value
no_val=np.float32(basins.GetRasterBand(1).GetNoDataValue())
# get number of basins and there names
basins_val=list(set([int(basins_A[i,j]) for i in range(rows) for j in range(cols) if basins_A[i,j] != no_val]))
# keep the first basin and delete the others by filling their cells by nodata value
for i in range(rows):
for j in range(cols):
if basins_A[i,j] != no_val and basins_A[i,j] != basins_val[0]:
basins_A[i,j] = no_val
raster.RasterLike(basins,basins_A,pathout)
def SaveParameters(self, Path):
"""
SaveParameters method takes generated parameters by the calibration algorithm,
distributed them with a given function and save them as a rasters
Inputs:
----------
1-DistParFn:
[function] function to distribute the parameters (all functions are
in Hapi.DistParameters )
2-raster:
[gdal.dataset] raster to get the spatial information
3-Par
[list or numpy ndarray] parameters as 1D array or list
4-no_parameters:
[int] number of the parameters in the conceptual model
5-snow:
[integer] number to define whether to take parameters of
the conceptual model with snow subroutine or without
5-kub:
[numeric] upper bound for k parameter in muskingum function
6-klb:
[numeric] lower bound for k parameter in muskingum function
7-Path:
[string] path to the folder you want to save the parameters in
default value is None (parameters are going to be saved in the
current directory)
Outputs:
----------
Rasters for parameters of the distributed model
Examples:
----------
DemPath = path+"GIS/4000/dem4000.tif"
raster=gdal.Open(DemPath)
ParPath = "par15_7_2018.txt"
par=np.loadtxt(ParPath)
klb=0.5
kub=1
no_parameters=12
DistParFn=DP.par3dLumped
Path="parameters/"
snow=0
SaveParameters(DistParFn, raster, par, no_parameters,snow ,kub, klb,Path)
"""
assert type(Path) == str, "path should be of type string"
assert os.path.exists(Path), Path + " you have provided does not exist"
# save
if self.Snow == 0: # now snow subroutine
pnme=["01_rfcf","02_FC", "03_BETA", "04_ETF", "05_LP", "06_K0","07_K1",
"08_K2","09_UZL","10_PERC", "11_Kmuskingum", "12_Xmuskingum"]
else: # there is snow subtoutine
pnme=["01_ltt", "02_rfcf", "03_sfcf", "04_cfmax", "05_cwh", "06_cfr",
"07_fc", "08_beta","09_etf","10_lp", "11_k0", "12_k1", "13_k2",
"14_uzl", "18_perc"]
if Path != None:
pnme = [Path+i+"_"+str(dt.datetime.now())[0:10]+".tif" for i in pnme]
for i in range(np.shape(self.Par3d)[2]):
Raster.RasterLike(self.raster,self.Par3d[:,:,i],pnme[i])