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(DistParFn,
Raster,
Par,
No_parameters,
no_lumped_par,
lumped_par_pos,
snow,
kub,
klb,
Path=None):
"""
============================================================
SaveParameters(DistParFn, Raster, Par, No_parameters, snow, kub, klb, Path=None)
============================================================
this function 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 callable(
DistParFn), " please check the function to distribute your parameters"
assert type(
Raster
) == gdal.Dataset, "raster should be read using gdal (gdal dataset please read it using gdal library) "
assert type(Par) == np.ndarray or type(
Par) == list, "par_g should be of type 1d array or list"
assert type(No_parameters) == int, "No of parameters should be integer"
assert isinstance(kub, numbers.Number), " kub should be a number"
assert isinstance(klb, numbers.Number), " klb should be a number"
assert type(Path) == str, "path should be of type string"
assert os.path.exists(Path), Path + " you have provided does not exist"
par2d = DistParFn(Par, Raster, No_parameters, no_lumped_par,
lumped_par_pos, kub, klb)
# save
if snow == 0: # now snow subroutine
pnme = [
"01_rfcf.tif", "02_FC.tif", "03_BETA.tif", "04_ETF.tif",
"05_LP.tif", "06_CFLUX.tif", "07_K.tif", "08_K1.tif",
"09_ALPHA.tif", "10_PERC.tif", "11_Kmuskingum.tif",
"12_Xmuskingum.tif"
]
else: # there is snow subtoutine
pnme = [
"01_ltt.tif", "02_utt.tif", "03_rfcf.tif", "04_sfcf.tif",
"05_ttm.tif", "06_cfmax.tif", "07_cwh.tif", "08_cfr.tif",
"09_fc.tif", "10_fc.tif", "11_beta.tif", "12_etf.tif", "13_lp.tif",
"14_cflux.tif", "15_k.tif", "16_k1.tif", "17_alpha.tif",
"18_perc.tif"
]
if Path != None:
pnme = [Path + i for i in pnme]
for i in range(np.shape(par2d)[2]):
Raster.RasterLike(Raster, par2d[:, :, i], pnme[i])