def getgridparams():
""" return grid parameters in a python friendly way
Output:
[ Xul, Yul, xsize, ysize, rows, cols]
- xul - x upper left centre
- yul - y upper left centre
- xsize - size of a cell in x direction
- ysize - size of a cell in y direction
- cols - number of columns
- rows - number of rows
- xlr - x lower right centre
- ylr - y lower right centre
"""
# This is the default, but add for safety...
pcr.setglobaloption("coorcentre")
# x and Y are the same for now
xy = pcr.pcr2numpy(pcr.celllength(), np.nan)[0, 0]
xu = pcr.pcr2numpy(pcr.xcoordinate(1), np.nan)[0, 0]
yu = pcr.pcr2numpy(pcr.ycoordinate(1), np.nan)[0, 0]
ylr = pcr.pcr2numpy(pcr.ycoordinate(1), np.nan)[getrows() - 1, getcols() - 1]
xlr = pcr.pcr2numpy(pcr.xcoordinate(1), np.nan)[getrows() - 1, getcols() - 1]
return [xu, yu, xy, xy, getrows(), getcols(), xlr, ylr]
def __init__(self,configuration,model,specificAttributeDictionary=None):
# Set clone map
pcr.setclone(configuration.cloneMap)
cloneMap = pcr.boolean(1.0) # map with all cell values equal to 1
# Retrieve latitudes and longitudes from clone map
self.latitudes = np.unique(pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
self.crops = np.arange(1, model.nCrop + 1)
self.depths = np.arange(1, model.nComp + 1)
# Let users decide what their preference regarding latitude order
self.netcdf_y_orientation_follow_cf_convention = False
if 'netcdf_y_orientation_follow_cf_convention' in configuration.reportingOptions.keys() and\
configuration.reportingOptions['netcdf_y_orientation_follow_cf_convention'] == "True":
msg = "Latitude (y) orientation for output netcdf files start from the bottom to top."
self.netcdf_y_orientation_follow_cf_convention = True
self.latitudes = np.unique(pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))
# Set general netcdf attributes (based on the information given in the ini/configuration file)
self.set_general_netcdf_attributes(configuration, specificAttributeDictionary)
# netcdf format and zlib setup
self.format = 'NETCDF3_CLASSIC'
self.zlib = False
if "formatNetCDF" in configuration.reportingOptions.keys():
self.format = str(configuration.reportingOptions['formatNetCDF'])
if "zlib" in configuration.reportingOptions.keys():
if configuration.reportingOptions['zlib'] == "True": self.zlib = True
def getgridparams():
""" return grid parameters in a python friendly way
Output:
[ Xul, Yul, xsize, ysize, rows, cols]
- xul - x upper left centre
- yul - y upper left centre
- xsize - size of a cell in x direction
- ysize - size of a cell in y direction
- cols - number of columns
- rows - number of rows
- xlr - x lower right centre
- ylr - y lower right centre
"""
# This is the default, but add for safety...
pcr.setglobaloption("coorcentre")
# x and Y are the same for now
xy = pcr.pcr2numpy(pcr.celllength(), np.nan)[0, 0]
xu = pcr.pcr2numpy(pcr.xcoordinate(1), np.nan)[0, 0]
yu = pcr.pcr2numpy(pcr.ycoordinate(1), np.nan)[0, 0]
ylr = pcr.pcr2numpy(pcr.ycoordinate(1), np.nan)[getrows() - 1, getcols() - 1]
xlr = pcr.pcr2numpy(pcr.xcoordinate(1), np.nan)[getrows() - 1, getcols() - 1]
return [xu, yu, xy, xy, getrows(), getcols(), xlr, ylr]
def checkerboard(mapin, fcc):
"""
checkerboard create a checkerboard map with unique id's in a
fcc*fcc cells area. The resulting map can be used
to derive statistics for (later) upscaling of maps (using the fcc factor)
.. warning: use with unitcell to get most reliable results!
Input:
- map (used to determine coordinates)
- fcc (size of the areas in cells)
Output:
- checkerboard type map
"""
msker = pcr.defined(mapin)
ymin = pcr.mapminimum(pcr.ycoordinate(msker))
yc = (pcr.ycoordinate((msker)) - ymin) / pcr.celllength()
yc = pcr.rounddown(yc / fcc)
# yc = yc/fcc
xmin = pcr.mapminimum(pcr.xcoordinate((msker)))
xc = (pcr.xcoordinate((msker)) - xmin) / pcr.celllength()
xc = pcr.rounddown(xc / fcc)
# xc = xc/fcc
yc = yc * (pcr.mapmaximum(xc) + 1.0)
xy = pcr.ordinal(xc + yc)
return xy
def checkerboard(mapin, fcc):
"""
checkerboard create a checkerboard map with unique id's in a
fcc*fcc cells area. The resulting map can be used
to derive statistics for (later) upscaling of maps (using the fcc factor)
.. warning: use with unitcell to get most reliable results!
Input:
- map (used to determine coordinates)
- fcc (size of the areas in cells)
Output:
- checkerboard type map
"""
msker = pcr.defined(mapin)
ymin = pcr.mapminimum(pcr.ycoordinate(msker))
yc = (pcr.ycoordinate((msker)) - ymin) / pcr.celllength()
yc = pcr.rounddown(yc / fcc)
# yc = yc/fcc
xmin = pcr.mapminimum(pcr.xcoordinate((msker)))
xc = (pcr.xcoordinate((msker)) - xmin) / pcr.celllength()
xc = pcr.rounddown(xc / fcc)
# xc = xc/fcc
yc = yc * (pcr.mapmaximum(xc) + 1.0)
xy = pcr.ordinal(xc + yc)
return xy
def __init__(self, iniItems, specificAttributeDictionary=None):
# cloneMap
pcr.setclone(iniItems.cloneMap)
cloneMap = pcr.boolean(1.0)
# latitudes and longitudes
self.latitudes = np.unique(pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[
::-1
]
self.longitudes = np.unique(pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
# Let users decide what their preference regarding latitude order.
self.netcdf_y_orientation_follow_cf_convention = False
if (
"netcdf_y_orientation_follow_cf_convention"
in list(iniItems.reportingOptions.keys())
and iniItems.reportingOptions["netcdf_y_orientation_follow_cf_convention"]
== "True"
):
msg = "Latitude (y) orientation for output netcdf files start from the bottom to top."
self.netcdf_y_orientation_follow_cf_convention = True
self.latitudes = np.unique(pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))
# set the general netcdf attributes (based on the information given in the ini/configuration file)
self.set_general_netcdf_attributes(iniItems, specificAttributeDictionary)
# netcdf format and zlib setup
self.format = "NETCDF3_CLASSIC"
self.zlib = False
if "formatNetCDF" in list(iniItems.reportingOptions.keys()):
self.format = str(iniItems.reportingOptions["formatNetCDF"])
if "zlib" in list(iniItems.reportingOptions.keys()):
if iniItems.reportingOptions["zlib"] == "True":
self.zlib = True
# if given in the ini file, use the netcdf as given in the section 'specific_attributes_for_netcdf_output_files'
if "specific_attributes_for_netcdf_output_files" in iniItems.allSections:
for key in list(
iniItems.specific_attributes_for_netcdf_output_files.keys()
):
self.attributeDictionary[
key
] = iniItems.specific_attributes_for_netcdf_output_files[key]
if self.attributeDictionary[key] == "None":
self.attributeDictionary[key] = ""
if key == "history" and self.attributeDictionary[key] == "Default":
self.attributeDictionary[
key
] = "created on " + datetime.datetime.today().isoformat(" ")
if self.attributeDictionary[key] == "Default" and (
key == "date_created" or key == "date_issued"
):
self.attributeDictionary[key] = datetime.datetime.today().isoformat(
" "
)
def __init__(self, iniItems, specificAttributeDictionary=None):
# cloneMap
pcr.setclone(iniItems.cloneMap)
cloneMap = pcr.boolean(1.0)
# latitudes and longitudes
self.latitudes = np.unique(
pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(
pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
# Let users decide what their preference regarding latitude order.
self.netcdf_y_orientation_follow_cf_convention = False
if ("netcdf_y_orientation_follow_cf_convention" in list(
iniItems.reportingOptions.keys()) and iniItems.
reportingOptions["netcdf_y_orientation_follow_cf_convention"]
== "True"):
msg = "Latitude (y) orientation for output netcdf files start from the bottom to top."
self.netcdf_y_orientation_follow_cf_convention = True
self.latitudes = np.unique(
pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))
# set the general netcdf attributes (based on the information given in the ini/configuration file)
self.set_general_netcdf_attributes(iniItems,
specificAttributeDictionary)
# netcdf format and zlib setup
self.format = "NETCDF3_CLASSIC"
self.zlib = False
if "formatNetCDF" in list(iniItems.reportingOptions.keys()):
self.format = str(iniItems.reportingOptions["formatNetCDF"])
if "zlib" in list(iniItems.reportingOptions.keys()):
if iniItems.reportingOptions["zlib"] == "True":
self.zlib = True
# if given in the ini file, use the netcdf as given in the section 'specific_attributes_for_netcdf_output_files'
if "specific_attributes_for_netcdf_output_files" in iniItems.allSections:
for key in list(
iniItems.specific_attributes_for_netcdf_output_files.keys(
)):
self.attributeDictionary[
key] = iniItems.specific_attributes_for_netcdf_output_files[
key]
if self.attributeDictionary[key] == "None":
self.attributeDictionary[key] = ""
if key == "history" and self.attributeDictionary[
key] == "Default":
self.attributeDictionary[
key] = "created on " + datetime.datetime.today(
).isoformat(" ")
if self.attributeDictionary[key] == "Default" and (
key == "date_created" or key == "date_issued"):
self.attributeDictionary[key] = datetime.datetime.today(
).isoformat(" ")
def getRowColPoint(in_map, xcor, ycor):
"""
returns the row and col in a map at the point given.
Works but is rather slow.
Input:
- in_map - map to determine coordinates from
- xcor - x coordinate
- ycor - y coordinate
Output:
- row, column
"""
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.boolean(pcr.scalar(in_map) + 1.0)), np.nan)
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.boolean(pcr.scalar(in_map) + 1.0)), np.nan)
XX = pcr.pcr2numpy(pcr.celllength(), 0.0)
tolerance = 0.5 # takes a single point
diffx = x - xcor
diffy = y - ycor
col_ = np.absolute(diffx) <= (XX[0, 0] * tolerance) # cellsize
row_ = np.absolute(diffy) <= (XX[0, 0] * tolerance) # cellsize
point = col_ * row_
return point.argmax(0).max(), point.argmax(1).max()
def getValAtPoint(in_map, xcor, ycor):
"""
returns the value in a map at the point given.
works but is rather slow.
Input:
- in_map - map to determine coordinates from
- xcor - x coordinate
- ycor - y coordinate
Output:
- value
"""
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.defined(in_map)), np.nan)
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.defined(in_map)), np.nan)
XX = pcr.pcr2numpy(pcr.celllength(), 0.0)
themap = pcr.pcr2numpy(in_map, np.nan)
tolerance = 0.5 # takes a single point
diffx = x - xcor
diffy = y - ycor
col_ = np.absolute(diffx) <= (XX[0, 0] * tolerance) # cellsize
row_ = np.absolute(diffy) <= (XX[0, 0] * tolerance) # cellsize
point = col_ * row_
pt = point.argmax()
return themap.ravel()[pt]
def getValAtPoint(in_map, xcor, ycor):
"""
returns the value in a map at the point given.
works but is rather slow.
Input:
- in_map - map to determine coordinates from
- xcor - x coordinate
- ycor - y coordinate
Output:
- value
"""
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.defined(in_map)), np.nan)
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.defined(in_map)), np.nan)
XX = pcr.pcr2numpy(pcr.celllength(), 0.0)
themap = pcr.pcr2numpy(in_map, np.nan)
tolerance = 0.5 # takes a single point
diffx = x - xcor
diffy = y - ycor
col_ = np.absolute(diffx) <= (XX[0, 0] * tolerance) # cellsize
row_ = np.absolute(diffy) <= (XX[0, 0] * tolerance) # cellsize
point = col_ * row_
pt = point.argmax()
return themap.ravel()[pt]
def getRowColPoint(in_map, xcor, ycor):
"""
returns the row and col in a map at the point given.
Works but is rather slow.
Input:
- in_map - map to determine coordinates from
- xcor - x coordinate
- ycor - y coordinate
Output:
- row, column
"""
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.boolean(pcr.scalar(in_map) + 1.0)), np.nan)
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.boolean(pcr.scalar(in_map) + 1.0)), np.nan)
XX = pcr.pcr2numpy(pcr.celllength(), 0.0)
tolerance = 0.5 # takes a single point
diffx = x - xcor
diffy = y - ycor
col_ = np.absolute(diffx) <= (XX[0, 0] * tolerance) # cellsize
row_ = np.absolute(diffy) <= (XX[0, 0] * tolerance) # cellsize
point = col_ * row_
return point.argmax(0).max(), point.argmax(1).max()
def __init__(self, netcdffile, logging):
"""
First try to setup a class read netcdf files
(converted with pcr2netcdf.py)
netcdffile: file to read the forcing data from
logging: python logging object
vars: list of variables to get from file
"""
if os.path.exists(netcdffile):
self.dataset = netCDF4.Dataset(netcdffile, mode="r")
else:
msg = os.path.abspath(netcdffile) + " not found!"
logging.error(msg)
raise ValueError(msg)
try:
self.x = self.dataset.variables["x"][:]
except:
self.x = self.dataset.variables["lon"][:]
# Now check Y values to see if we must flip the data
try:
self.y = self.dataset.variables["y"][:]
except:
self.y = self.dataset.variables["lat"][:]
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.boolean(pcr.cover(1.0))), np.nan)[0, :]
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.boolean(pcr.cover(1.0))), np.nan)[:, 0]
(self.latidx,) = np.logical_and(self.x >= x.min(), self.x < x.max()).nonzero()
(self.lonidx,) = np.logical_and(self.y >= x.min(), self.y < y.max()).nonzero()
logging.info("Reading static input from netCDF file: " + netcdffile)
def getCoordinates(cloneMap, MV=-9999):
'''returns cell centre coordinates for a clone map as numpy array
return longitudes, latitudes '''
cln = pcr.cover(pcr.boolean(cloneMap), pcr.boolean(1))
xMap = pcr.xcoordinate(cln)
yMap = pcr.ycoordinate(cln)
return pcr.pcr2numpy(xMap, MV)[1, :], pcr.pcr2numpy(yMap, MV)[:, 1]
def __init__(self, cloneMapFileName, netcdf_attribute_description):
# cloneMap
cloneMap = pcr.boolean(pcr.readmap(cloneMapFileName))
cloneMap = pcr.boolean(pcr.scalar(1.0))
# latitudes and longitudes
self.latitudes = np.unique(
pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(
pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
# netCDF format and attributes:
self.format = 'NETCDF3_CLASSIC'
self.attributeDictionary = {}
self.attributeDictionary['institution'] = "European Commission - JRC"
self.attributeDictionary[
'title'] = "EFAS-Meteo 5km for the Rhine-Meuse basin"
self.attributeDictionary[
'source'] = "5km Gridded Meteo Database (C) European Commission - JRDC, 2014"
self.attributeDictionary[
'history'] = "The data were provided by Ad de Roo ([email protected]) on 19 November 2014 and then converted by Edwin H. Sutanudjaja ([email protected]) to netcdf files on 27 November 2014."
self.attributeDictionary[
'references'] = "Ntegeka et al., 2013. EFAS-Meteo: A European daily high-resolution gridded meteorological data set. JRC Technical Reports. doi: 10.2788/51262"
self.attributeDictionary[
'comment'] = "Please use this dataset only for Hyper-Hydro test bed experiments. "
self.attributeDictionary[
'comment'] += "For using it and publishing it, please acknowledge its source: 5km Gridded Meteo Database (C) European Commission - JRDC, 2014 and its reference: Ntegeka et al., 2013 (doi: 10.2788/51262). "
self.attributeDictionary[
'comment'] += "The data are in European ETRS projection, 5km grid; http://en.wikipedia.org/wiki/European_grid. "
self.attributeDictionary['description'] = netcdf_attribute_description
def __init__(self, iniItems):
# cloneMap
pcr.setclone(iniItems.cloneMap)
cloneMap = pcr.boolean(1.0)
# latitudes and longitudes
self.latitudes = np.unique(pcr2numpy(pcr.ycoordinate(cloneMap),
vos.MV))[::-1]
self.longitudes = np.unique(
pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
# TODO: Let users decide what their preference regarding latitude order.
# Consult with Stefanie regarding CF convention.
# netCDF format and attributes:
self.attributeDictionary = {}
self.attributeDictionary['institution'] = iniItems.globalOptions[
'institution']
self.attributeDictionary['title'] = iniItems.globalOptions['title']
self.attributeDictionary['description'] = iniItems.globalOptions[
'description']
# netcdf format and zlib setup
self.format = 'NETCDF3_CLASSIC'
self.zlib = False
if "formatNetCDF" in iniItems.reportingOptions.keys():
self.format = str(iniItems.reportingOptions['formatNetCDF'])
if "zlib" in iniItems.reportingOptions.keys():
if iniItems.reportingOptions['zlib'] == "True": self.zlib = True
def spatial(self):
"""Computes requruired biosafe output for a spatial domain"""
#-determine a representative points for each floodplain section
points = pcrr.representativePoint(self.sections)
clone = pcr.defined(self.sections)
pcr.setglobaloption('unittrue')
xcoor = pcr.xcoordinate(clone)
ycoor = pcr.ycoordinate(clone)
geoDf = pcrr.getCellValues(points, \
mapList = [points, xcoor, ycoor],\
columns = ['ID', 'xcoor', 'ycoor'])
geoDf.set_index('ID', inplace=True, drop=False)
geoDf.drop(['rowIdx', 'colIdx', 'ID'], axis=1, inplace=True)
#-compupte the required biosafe parameters for all sections
sectionIDs = np.unique(pcr.pcr2numpy(self.sections,-9999))[1:]
ll = []
for sectionID in sectionIDs:
ll.append(self.sectionScores(sectionID))
paramLL = zip(*ll)
dfParamLL = []
for ii in range(len(self.params)):
bsScores = pd.concat(paramLL[ii], axis=1).T
bsScores = bsScores.join(geoDf)
bsScores.index.name = 'ID'
bsScores.columns.name = self.params[ii]
dfParamLL.append(bsScores)
return dfParamLL
def __init__(self,
cloneMapFileName,
resetClone=None,
attributeDictionary=None):
# cloneMap
if resetClone != None: pcr.setclone(cloneMapFileName)
cloneMap = pcr.boolean(pcr.readmap(cloneMapFileName))
cloneMap = pcr.boolean(pcr.scalar(1.0))
# latitudes and longitudes
self.latitudes = np.unique(
pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(
pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
# reset clone (if necessary)
if resetClone != None: pcr.setclone(resetClone)
# netcdf format:
self.format = 'NETCDF3_CLASSIC'
self.attributeDictionary = {}
if attributeDictionary == None:
self.attributeDictionary['institution'] = "None"
self.attributeDictionary['title'] = "None"
self.attributeDictionary['source'] = "None"
self.attributeDictionary['history'] = "None"
self.attributeDictionary['references'] = "None"
self.attributeDictionary['description'] = "None"
self.attributeDictionary['comment'] = "None"
else:
self.attributeDictionary = attributeDictionary
def boundingBox(pcrmap):
''' derive the bounding box for a map, return xmin,ymin,xmax,ymax '''
bb = []
xcoor = pcr.xcoordinate(pcrmap)
ycoor = pcr.ycoordinate(pcrmap)
xmin = pcr.cellvalue(pcr.mapminimum(xcoor), 1, 1)[0]
xmax = pcr.cellvalue(pcr.mapmaximum(xcoor), 1, 1)[0]
ymin = pcr.cellvalue(pcr.mapminimum(ycoor), 1, 1)[0]
ymax = pcr.cellvalue(pcr.mapmaximum(ycoor), 1, 1)[0]
return [math.floor(xmin), math.floor(ymin), math.ceil(xmax), math.ceil(ymax)]
def get_model_dimensions(self):
"""Function to set model dimensions"""
self.nLat = int(self.cloneMapAttributes['rows'])
self.latitudes = np.unique(pcr.pcr2numpy(pcr.ycoordinate(self.cloneMap), vos.MV))[::-1]
self.nLon = int(self.cloneMapAttributes['cols'])
self.longitudes = np.unique(pcr.pcr2numpy(pcr.xcoordinate(self.cloneMap), vos.MV))
self.nCell = int(np.sum(self.landmask))
self.nLayer = 3 # FIXED
self.dimensions = {
'time' : None,
'depth' : np.arange(self.nLayer), # TODO - put nComp in config section [SOIL]
'lat' : self.latitudes,
'lon' : self.longitudes,
}
def map_edges(clone):
"""Boolean map true map edges, false elsewhere"""
pcr.setglobaloption('unittrue')
xmin, xmax, ymin, ymax, nr_rows, nr_cols, cell_size = clone_attributes()
clone = pcr.ifthenelse(pcr.defined(clone), pcr.boolean(1), pcr.boolean(1))
x_coor = pcr.xcoordinate(clone)
y_coor = pcr.ycoordinate(clone)
north = y_coor > (ymax - cell_size)
south = y_coor < (ymin + cell_size)
west = x_coor < (xmin + cell_size)
east = x_coor > (xmax - cell_size)
edges = north | south | west | east
return edges
def pcr2col(listOfMaps, MV, selection='ONE_TRUE'):
"""converts a set of maps to a column array: X, Y, map values
selection can be set to ALL, ALL_TRUE, ONE_TRUE"""
#-intersect all maps and get X and Y coordinates
intersection = pcr.boolean(pcr.cover(listOfMaps[0], 0))
for mapX in listOfMaps[1:]:
intersection = intersection | pcr.boolean(pcr.cover(mapX, 0))
pcr.setglobaloption("unittrue")
xCoor = pcr.ifthen(intersection, pcr.xcoordinate(intersection))
yCoor = pcr.ifthen(intersection, pcr.ycoordinate(intersection))
pcr.setglobaloption("unitcell")
#-initiate outArray with xCoor and yCoor
xCoorArr = pcr.pcr2numpy(xCoor, MV)
yCoorArr = pcr.pcr2numpy(yCoor, MV)
nRows, nCols = xCoorArr.shape
nrCells = nRows * nCols
outArray = np.hstack((xCoorArr.reshape(nrCells,
1), yCoorArr.reshape(nrCells, 1)))
#-add subsequent maps
for mapX in listOfMaps:
arr = pcr.pcr2numpy(mapX, MV).reshape(nrCells, 1)
outArray = np.hstack((outArray, arr))
#-subset output based on selection criterium
ll = []
nrMaps = len(listOfMaps)
if selection == 'ONE_TRUE':
for line in outArray:
nrMV = len(line[line == MV])
if nrMV < nrMaps:
ll.append(line)
else:
pass
outArray = np.array(ll)
elif selection == 'ALL_TRUE':
for line in outArray:
if MV not in line:
ll.append(line)
else:
pass
outArray = np.array(ll)
elif selection == 'ALL':
pass
return outArray
def points_to_map(in_map, xcor, ycor, tolerance):
"""
Returns a map with non zero values at the points defined
in X, Y pairs. It's goal is to replace the pcraster col2map program.
tolerance should be 0.5 to select single points
Performance is not very good and scales linear with the number of points
Input:
- in_map - map to determine coordinates from
- xcor - x coordinate (array or single value)
- ycor - y coordinate (array or single value)
- tolerance - tolerance in cell units. 0.5 selects a single cell\
10 would select a 10x10 block of cells
Output:
- Map with values burned in. 1 for first point, 2 for second and so on
"""
point = in_map * 0.0
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.defined(in_map)), np.nan)
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.defined(in_map)), np.nan)
cell_length = float(pcr.celllength())
# simple check to use both floats and numpy arrays
try:
c = xcor.ndim
except:
xcor = np.array([xcor])
ycor = np.array([ycor])
# Loop over points and "burn in" map
for n in range(0, xcor.size):
if Verbose:
print(n)
diffx = x - xcor[n]
diffy = y - ycor[n]
col_ = np.absolute(diffx) <= (cell_length * tolerance) # cellsize
row_ = np.absolute(diffy) <= (cell_length * tolerance) # cellsize
point = point + pcr.numpy2pcr(pcr.Scalar,
((col_ * row_) * (n + 1)), np.nan)
return pcr.ordinal(point)
def points_to_map(in_map, xcor, ycor, tolerance):
"""
Returns a map with non zero values at the points defined
in X, Y pairs. It's goal is to replace the pcraster col2map program.
tolerance should be 0.5 to select single points
Performance is not very good and scales linear with the number of points
Input:
- in_map - map to determine coordinates from
- xcor - x coordinate (array or single value)
- ycor - y coordinate (array or single value)
- tolerance - tolerance in cell units. 0.5 selects a single cell\
10 would select a 10x10 block of cells
Output:
- Map with values burned in. 1 for first point, 2 for second and so on
"""
point = in_map * 0.0
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.defined(in_map)), np.nan)
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.defined(in_map)), np.nan)
cell_length = float(pcr.celllength())
# simple check to use both floats and numpy arrays
try:
c = xcor.ndim
except:
xcor = np.array([xcor])
ycor = np.array([ycor])
# Loop over points and "burn in" map
for n in range(0, xcor.size):
if Verbose:
print(n)
diffx = x - xcor[n]
diffy = y - ycor[n]
col_ = np.absolute(diffx) <= (cell_length * tolerance) # cellsize
row_ = np.absolute(diffy) <= (cell_length * tolerance) # cellsize
point = point + pcr.numpy2pcr(pcr.Scalar, ((col_ * row_) * (n + 1)), np.nan)
return pcr.ordinal(point)
def __init__(self, cloneMapFile, attribute=None, cellSizeInArcMinutes=None):
# cloneMap
# - the cloneMap must be at 5 arc min resolution
cloneMap = pcr.readmap(cloneMapFile)
cloneMap = pcr.boolean(1.0)
# latitudes and longitudes
self.latitudes = np.unique(pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
#~ # properties of the clone map
#~ # - number of rows and columns
#~ self.nrRows = np.round(pcr.clone().nrRows())
#~ self.nrCols = np.round(pcr.clone().nrCols())
#~ # - upper right coordinate, unit: arc degree ; must be integer (without decimals)
#~ self.minLongitude = np.round(pcr.clone().west() , 0)
#~ self.maxLatitude = np.round(pcr.clone().north(), 0)
#~ # - cell resolution, unit: arc degree
#~ self.cellSize = pcr.clone().cellSize()
#~ if cellSizeInArcMinutes != None: self.cellSize = cellSizeInArcMinutes / 60.0
#~ # - lower right coordinate, unit: arc degree ; must be integer (without decimals)
#~ self.maxLongitude = np.round(self.minLongitude + self.cellSize*self.nrCols, 0)
#~ self.minLatitude = np.round(self.maxLatitude - self.cellSize*self.nrRows, 0)
#~
#~ # latitudes and longitudes for netcdf files
#~ latMin = self.minLatitude + self.cellSize / 2
#~ latMax = self.maxLatitude - self.cellSize / 2
#~ lonMin = self.minLongitude + self.cellSize / 2
#~ lonMax = self.maxLongitude - self.cellSize / 2
#~ self.longitudes = np.arange(lonMin,lonMax+self.cellSize, self.cellSize)
#~ self.latitudes= np.arange(latMax,latMin-self.cellSize,-self.cellSize)
# netCDF format and attributes:
self.format = 'NETCDF4'
self.attributeDictionary = {}
if attribute == None:
self.attributeDictionary['institution'] = "None"
self.attributeDictionary['title' ] = "None"
self.attributeDictionary['description'] = "None"
else:
self.attributeDictionary = attribute
def __init__(self,
cloneMapFileName_or_latlonDict,
attributeDictionary=None):
# cloneMap
if isinstance(cloneMapFileName_or_latlonDict, str):
# define latitudes and longitudes based on cloneMap
cloneMapFileName = cloneMapFileName_or_latlonDict
pcr.setclone(cloneMapFileName)
cloneMap = pcr.boolean(1.0)
# latitudes and longitudes
self.latitudes = np.unique(
pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(
pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
else:
# define latitudes and longitudes based on latlonDict # NOT TESTED YET
latlonDict = cloneMapFileName_or_latlonDict
self.latitudes = latlonDict['lat']
self.longitudes = latlonDict['lon']
# make sure that latitudes are from high to low
if self.latitudes[-1] > self.latitudes[0]:
self.latitudes = self.latitudes[::-1]
if self.longitudes[-1] < self.longitudes[0]:
self.longitudes = self.longitudes[::-1]
# netcdf format:
self.format = 'NETCDF3_CLASSIC'
self.attributeDictionary = {}
if attributeDictionary == None:
self.attributeDictionary['institution'] = "None"
self.attributeDictionary['title'] = "None"
self.attributeDictionary['source'] = "None"
self.attributeDictionary['history'] = "None"
self.attributeDictionary['references'] = "None"
self.attributeDictionary['description'] = "None"
self.attributeDictionary['comment'] = "None"
else:
self.attributeDictionary = attributeDictionary
def detRealCellLength(ZeroMap, sizeinmetres):
"""
Determine cellength. Always returns the length
in meters.
"""
if sizeinmetres:
reallength = pcr.celllength()
xl = pcr.celllength()
yl = pcr.celllength()
else:
aa = pcr.ycoordinate(pcr.boolean(pcr.cover(ZeroMap + 1, 1)))
yl, xl = lattometres(aa)
xl = xl * pcr.celllength()
yl = yl * pcr.celllength()
# Average length for surface area calculations.
reallength = (xl + yl) * 0.5
return xl, yl, reallength
def detRealCellLength(ZeroMap, sizeinmetres):
"""
Determine cellength. Always returns the length
in meters.
"""
if sizeinmetres:
reallength = pcr.celllength()
xl = pcr.celllength()
yl = pcr.celllength()
else:
aa = pcr.ycoordinate(pcr.boolean(pcr.cover(ZeroMap + 1, 1)))
yl, xl = lattometres(aa)
xl = xl * pcr.celllength()
yl = yl * pcr.celllength()
# Average length for surface area calculations.
reallength = (xl + yl) * 0.5
return xl, yl, reallength
def __init__(self, clone_map_file_name):
# cloneMap
pcr.setclone(clone_map_file_name)
cloneMap = pcr.boolean(1.0)
# latitudes and longitudes
self.latitudes = np.unique(
pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(
pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
# Let users decide what their preference regarding latitude order.
self.netcdf_y_orientation_follow_cf_convention = False
# set the general netcdf attributes
self.set_general_netcdf_attributes()
# netcdf format and zlib setup
self.format = 'NETCDF4' # 'NETCDF3_CLASSIC'
self.zlib = True
def __init__(self, iniItems):
# cloneMap
pcr.setclone(iniItems.cloneMap)
cloneMap = pcr.boolean(1.0)
# latitudes and longitudes
self.latitudes = np.unique(
pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(
pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
# TODO: Let users decide what their preference regarding latitude order.
# Consult with Stefanie regarding CF convention.
# netCDF format and attributes:
self.format = 'NETCDF3_CLASSIC'
self.attributeDictionary = {}
self.attributeDictionary['institution'] = iniItems.globalOptions['institution']
self.attributeDictionary['title'] = iniItems.globalOptions['title']
self.attributeDictionary['description'] = iniItems.globalOptions['description']
def __init__(self, netcdffile, logging):
"""
First try to setup a class read netcdf files
(converted with pcr2netcdf.py)
netcdffile: file to read the forcing data from
logging: python logging object
vars: list of variables to get from file
"""
if os.path.exists(netcdffile):
self.dataset = netCDF4.Dataset(netcdffile, mode="r")
else:
msg = os.path.abspath(netcdffile) + " not found!"
logging.error(msg)
raise ValueError(msg)
try:
self.x = self.dataset.variables["x"][:]
except:
self.x = self.dataset.variables["lon"][:]
# Now check Y values to see if we must flip the data
try:
self.y = self.dataset.variables["y"][:]
except:
self.y = self.dataset.variables["lat"][:]
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.boolean(pcr.cover(1.0))),
np.nan)[0, :]
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.boolean(pcr.cover(1.0))),
np.nan)[:, 0]
(self.latidx, ) = np.logical_and(self.x >= x.min(),
self.x < x.max()).nonzero()
(self.lonidx, ) = np.logical_and(self.y >= x.min(),
self.y < y.max()).nonzero()
logging.info("Reading static input from netCDF file: " + netcdffile)
def __init__(self,cloneMapFileName,netcdf_attribute_description):
# cloneMap
cloneMap = pcr.boolean(pcr.readmap(cloneMapFileName))
cloneMap = pcr.boolean(pcr.scalar(1.0))
# latitudes and longitudes
self.latitudes = np.unique(pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
self.longitudes = np.unique(pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
# netCDF format and attributes:
self.format = 'NETCDF3_CLASSIC'
self.attributeDictionary = {}
self.attributeDictionary['institution'] = "European Commission - JRC"
self.attributeDictionary['title' ] = "EFAS-Meteo 5km for the Rhine-Meuse basin"
self.attributeDictionary['source' ] = "5km Gridded Meteo Database (C) European Commission - JRDC, 2014"
self.attributeDictionary['history' ] = "The data were provided by Ad de Roo ([email protected]) on 19 November 2014 and then converted by Edwin H. Sutanudjaja ([email protected]) to netcdf files on 27 November 2014."
self.attributeDictionary['references' ] = "Ntegeka et al., 2013. EFAS-Meteo: A European daily high-resolution gridded meteorological data set. JRC Technical Reports. doi: 10.2788/51262"
self.attributeDictionary['comment' ] = "Please use this dataset only for Hyper-Hydro test bed experiments. "
self.attributeDictionary['comment' ] += "For using it and publishing it, please acknowledge its source: 5km Gridded Meteo Database (C) European Commission - JRDC, 2014 and its reference: Ntegeka et al., 2013 (doi: 10.2788/51262). "
self.attributeDictionary['comment' ] += "The data are in European ETRS projection, 5km grid; http://en.wikipedia.org/wiki/European_grid. "
self.attributeDictionary['description'] = netcdf_attribute_description
def __init__(self, netcdffile, logging, vars=[]):
"""
First try to setup a class read netcdf files
(converted with pcr2netcdf.py)
netcdffile: file to read the forcing data from
logging: python logging object
vars: list of variables to get from file
"""
self.fname = netcdffile
if os.path.exists(netcdffile):
self.dataset = netCDF4.Dataset(netcdffile, mode="r")
else:
msg = os.path.abspath(netcdffile) + " not found!"
logging.error(msg)
raise ValueError(msg)
logging.info("Reading state input from netCDF file: " + netcdffile)
self.alldat = {}
a = pcr.pcr2numpy(pcr.cover(0.0), 0.0).flatten()
# Determine steps to load in mem based on estimated memory usage
floatspermb = 1048576 / 4
maxmb = 40
self.maxsteps = maxmb * len(a) / floatspermb + 1
self.fstep = 0
self.lstep = self.fstep + self.maxsteps
self.datetime = self.dataset.variables["time"][:]
if hasattr(self.dataset.variables["time"], "units"):
self.timeunits = self.dataset.variables["time"].units
else:
self.timeunits = "Seconds since 1970-01-01 00:00:00"
if hasattr(self.dataset.variables["time"], "calendar"):
self.calendar = self.dataset.variables["time"].calendar
else:
self.calendar = "gregorian"
self.datetimelist = cftime.num2date(self.datetime,
self.timeunits,
calendar=self.calendar)
try:
self.x = self.dataset.variables["x"][:]
except:
self.x = self.dataset.variables["lon"][:]
# Now check Y values to see if we must flip the data
try:
self.y = self.dataset.variables["y"][:]
except:
self.y = self.dataset.variables["lat"][:]
# test if 1D or 2D array
if len(self.y.shape) == 1:
if self.y[0] > self.y[-1]:
self.flip = False
else:
self.flip = True
else: # not sure if this works
self.y = self.y[:][0]
if self.y[0] > self.y[-1]:
self.flip = False
else:
self.flip = True
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.boolean(pcr.cover(1.0))),
np.nan)[0, :]
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.boolean(pcr.cover(1.0))),
np.nan)[:, 0]
# Get average cell size
acc = (
np.diff(x).mean() * 0.25
) # non-exact match needed becuase of possible rounding problems
if self.flip:
(self.latidx, ) = np.logical_and(
self.y[::-1] + acc >= y.min(),
self.y[::-1] <= y.max() + acc).nonzero()
(self.lonidx, ) = np.logical_and(
self.x + acc >= x.min(), self.x <= x.max() + acc).nonzero()
else:
(self.latidx, ) = np.logical_and(
self.y + acc >= y.min(), self.y <= y.max() + acc).nonzero()
(self.lonidx, ) = np.logical_and(
self.x + acc >= x.min(), self.x <= x.max() + acc).nonzero()
if len(self.lonidx) != len(x):
logging.error("error in determining X coordinates in netcdf...")
logging.error("model expects: " + str(x.min()) + " to " +
str(x.max()))
logging.error("got coordinates netcdf: " + str(self.x.min()) +
" to " + str(self.x.max()))
logging.error("got len from netcdf x: " + str(len(x)) +
" expected " + str(len(self.lonidx)))
raise ValueError("X coordinates in netcdf do not match model")
if len(self.latidx) != len(y):
logging.error("error in determining Y coordinates in netcdf...")
logging.error("model expects: " + str(y.min()) + " to " +
str(y.max()))
logging.error("got from netcdf: " + str(self.y.min()) + " to " +
str(self.y.max()))
logging.error("got len from netcdf y: " + str(len(y)) +
" expected " + str(len(self.latidx)))
raise ValueError("Y coordinates in netcdf do not match model")
for var in vars:
try:
self.alldat[var] = self.dataset.variables[var][self.fstep:self.
maxsteps]
except:
self.alldat.pop(var, None)
logging.warning("Variable " + var +
" not found in netcdf file: " + netcdffile)
def __init__(
self,
netcdffile,
logger,
starttime,
timesteps,
EPSG="EPSG:4326",
timestepsecs=86400,
metadata={},
zlib=True,
Format="NETCDF4",
maxbuf=25,
least_significant_digit=None,
):
"""
Under construction
"""
self.EPSG = EPSG
self.zlib = zlib
self.Format = Format
self.least_significant_digit = least_significant_digit
def date_range(start, end, timestepsecs):
r = int(
(end + dt.timedelta(seconds=timestepsecs) - start).total_seconds()
/ timestepsecs
)
return [start + dt.timedelta(seconds=(timestepsecs * i)) for i in range(r)]
self.logger = logger
# Do not allow a max buffer larger than the number of timesteps
self.maxbuf = maxbuf if timesteps >= maxbuf else timesteps
self.ncfile = netcdffile
self.timesteps = timesteps
rows = pcr.clone().nrRows()
cols = pcr.clone().nrCols()
cellsize = pcr.clone().cellSize()
yupper = pcr.clone().north()
xupper = pcr.clone().west()
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.boolean(pcr.cover(1.0))), np.nan)[0, :]
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.boolean(pcr.cover(1.0))), np.nan)[:, 0]
# Shift one timestep as we output at the end
# starttime = starttime + dt.timedelta(seconds=timestepsecs)
end = starttime + dt.timedelta(seconds=timestepsecs * (self.timesteps - 1))
timeList = date_range(starttime, end, timestepsecs)
self.timestepbuffer = np.zeros((self.maxbuf, len(y), len(x)))
self.bufflst = {}
self.buffdirty = False
globmetadata.update(metadata)
prepare_nc(
self.ncfile,
timeList,
x,
y,
globmetadata,
logger,
Format=self.Format,
EPSG=EPSG,
zlib=self.zlib,
least_significant_digit=self.least_significant_digit,
)
def __init__(self, netcdffile, logging, vars=[]):
"""
First try to setup a class read netcdf files
(converted with pcr2netcdf.py)
netcdffile: file to read the forcing data from
logging: python logging object
vars: list of variables to get from file
"""
self.fname = netcdffile
if os.path.exists(netcdffile):
self.dataset = netCDF4.Dataset(netcdffile, mode="r")
else:
msg = os.path.abspath(netcdffile) + " not found!"
logging.error(msg)
raise ValueError(msg)
logging.info("Reading state input from netCDF file: " + netcdffile)
self.alldat = {}
a = pcr.pcr2numpy(pcr.cover(0.0), 0.0).flatten()
# Determine steps to load in mem based on estimated memory usage
floatspermb = 1048576 / 4
maxmb = 40
self.maxsteps = maxmb * len(a) / floatspermb + 1
self.fstep = 0
self.lstep = self.fstep + self.maxsteps
self.datetime = self.dataset.variables["time"][:]
if hasattr(self.dataset.variables["time"], "units"):
self.timeunits = self.dataset.variables["time"].units
else:
self.timeunits = "Seconds since 1970-01-01 00:00:00"
if hasattr(self.dataset.variables["time"], "calendar"):
self.calendar = self.dataset.variables["time"].calendar
else:
self.calendar = "gregorian"
self.datetimelist = cftime.num2date(
self.datetime, self.timeunits, calendar=self.calendar
)
try:
self.x = self.dataset.variables["x"][:]
except:
self.x = self.dataset.variables["lon"][:]
# Now check Y values to see if we must flip the data
try:
self.y = self.dataset.variables["y"][:]
except:
self.y = self.dataset.variables["lat"][:]
# test if 1D or 2D array
if len(self.y.shape) == 1:
if self.y[0] > self.y[-1]:
self.flip = False
else:
self.flip = True
else: # not sure if this works
self.y = self.y[:][0]
if self.y[0] > self.y[-1]:
self.flip = False
else:
self.flip = True
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.boolean(pcr.cover(1.0))), np.nan)[0, :]
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.boolean(pcr.cover(1.0))), np.nan)[:, 0]
# Get average cell size
acc = (
np.diff(x).mean() * 0.25
) # non-exact match needed becuase of possible rounding problems
if self.flip:
(self.latidx,) = np.logical_and(
self.y[::-1] + acc >= y.min(), self.y[::-1] <= y.max() + acc
).nonzero()
(self.lonidx,) = np.logical_and(
self.x + acc >= x.min(), self.x <= x.max() + acc
).nonzero()
else:
(self.latidx,) = np.logical_and(
self.y + acc >= y.min(), self.y <= y.max() + acc
).nonzero()
(self.lonidx,) = np.logical_and(
self.x + acc >= x.min(), self.x <= x.max() + acc
).nonzero()
if len(self.lonidx) != len(x):
logging.error("error in determining X coordinates in netcdf...")
logging.error("model expects: " + str(x.min()) + " to " + str(x.max()))
logging.error(
"got coordinates netcdf: "
+ str(self.x.min())
+ " to "
+ str(self.x.max())
)
logging.error(
"got len from netcdf x: "
+ str(len(x))
+ " expected "
+ str(len(self.lonidx))
)
raise ValueError("X coordinates in netcdf do not match model")
if len(self.latidx) != len(y):
logging.error("error in determining Y coordinates in netcdf...")
logging.error("model expects: " + str(y.min()) + " to " + str(y.max()))
logging.error(
"got from netcdf: " + str(self.y.min()) + " to " + str(self.y.max())
)
logging.error(
"got len from netcdf y: "
+ str(len(y))
+ " expected "
+ str(len(self.latidx))
)
raise ValueError("Y coordinates in netcdf do not match model")
for var in vars:
try:
self.alldat[var] = self.dataset.variables[var][
self.fstep : self.maxsteps
]
except:
self.alldat.pop(var, None)
logging.warning(
"Variable " + var + " not found in netcdf file: " + netcdffile
)
cmd = "Extending class: step "+str(i)+" from " + str(max_step)
print(cmd)
uniqueIDs = pcr.cover(uniqueIDs, pcr.windowmajority(uniqueIDs, 0.5))
# - use only cells within the landmask
uniqueIDs = pcr.ifthen(landmask, uniqueIDs)
pcr.report(uniqueIDs, "class_ids.map")
# cell area at 5 arc min resolution
cellArea = vos.readPCRmapClone(cellArea05minFile,
cloneMapFileName, tmp_directory)
cellArea = pcr.ifthen(landmask, cellArea)
# get a sample cell for every id
x_min_for_each_id = pcr.areaminimum(pcr.xcoordinate(pcr.boolean(1.0)), uniqueIDs)
sample_cells = pcr.xcoordinate(pcr.boolean(1.0)) == x_min_for_each_id
y_min_for_each_id = pcr.areaminimum(pcr.ycoordinate(sample_cells), uniqueIDs)
sample_cells = pcr.ycoordinate(sample_cells) == y_min_for_each_id
uniqueIDs_sample = pcr.ifthen(sample_cells, uniqueIDs)
# - save it to a pcraster map file
pcr.report(uniqueIDs_sample, "sample.ids")
# calculate the country values
index = 0 # for posCnt
for iYear in range(staYear,endYear+1):
# time stamp and index for netcdf files:
index = index + 1
timeStamp = datetime.datetime(int(iYear), int(12), int(31), int(0))
fulldate = '%4i-%02i-%02i' %(int(iYear), int(12), int(31))
print fulldate
cell_area_05min = vos.readPCRmapClone(cell_area_05min_file, clone_map_05min_file, \
tmp_directory)
# 30 min cell ids
cell_ids_30min_file = "/data/hydroworld/others/irrigationZones/half_arc_degree/uniqueIds30min.nom.map"
cell_ids_30min = vos.readPCRmapClone(cell_ids_30min_file , clone_map_05min_file, \
tmp_directory, \
None, False, None, True)
cell_ids_30min = pcr.nominal(cell_ids_30min)
# reporting objects
# - for 5 arcmin resolution
latlonDict05min = {}
cloneMap = pcr.boolean(1.0)
latlonDict05min['lat'] = np.unique(
pcr.pcr2numpy(pcr.ycoordinate(cloneMap), vos.MV))[::-1]
latlonDict05min['lon'] = np.unique(
pcr.pcr2numpy(pcr.xcoordinate(cloneMap), vos.MV))
report_netcdf_05min = outputNetCDF.OutputNetCDF(latlonDict05min)
# - for 30 arcmin resolution
latlonDict30min = {}
latlonDict30min['lat'] = np.arange(
np.round(latlonDict05min['lat'][0] + 2.5 / 60 - 0.25, 2),
latlonDict05min['lat'][-1] - 2.5 / 60, -0.5)
latlonDict30min['lon'] = np.arange(
np.round(latlonDict05min['lon'][0] - 2.5 / 60 + 0.25, 2),
latlonDict05min['lon'][-1] + 2.5 / 60, 0.5)
report_netcdf_30min = outputNetCDF.OutputNetCDF(latlonDict30min)
# TODO: Make this module writes for CF convention (see Hessel's document)
# preparing the file at 5 arcmin resolution:
def initial(self):
"""
*Required*
Initial part of the model, executed only once. It reads all static model
information (parameters) and sets-up the variables used in modelling.
This function is required. The contents is free. However, in order to
easily connect to other models it is advised to adhere to the directory
structure used in the other models.
"""
#: pcraster option to calculate with units or cells. Not really an issue
#: in this model but always good to keep in mind.
pcr.setglobaloption("unittrue")
pcr.setglobaloption(
"radians"
) # Needed as W3RA was originally written in matlab
# SET GLBOAL PARAMETER VALUES (however not used in original script)
# Nhru=2
# K_gw_scale=0.0146
# K_gw_shape=0.0709
# K_rout_scale=0.1943
# K_rout_int=0.0589
# FdrainFC_scale=0.2909
# FdrainFC_shape=0.5154
# Sgref_scale=3.2220
# Sgref_shape=3.2860
# fday=0.5000
self.timestepsecs = int(
configget(self.config, "model", "timestepsecs", "86400")
)
self.reinit = int(configget(self.config, "run", "reinit", "0"))
self.OverWriteInit = int(configget(self.config, "model", "OverWriteInit", "0"))
self.UseETPdata = int(
configget(self.config, "model", "UseETPdata", "1")
) # 1: Use ETP data, 0: Compute ETP from meteorological variables
self.logger.debug("use DATA: " + str(self.UseETPdata))
self.basetimestep = 86400
self.SaveMapDir = self.Dir + "/" + self.runId + "/outmaps"
# Define here the W3RA mapstacks (best to read these via netcdf)
self.TMAX_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "TMAX", "/inmaps/TMAX"
)
self.TMIN_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "TMIN", "/inmaps/TMIN"
)
self.TDAY_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "TDAY", "/inmaps/TDAY"
)
self.EPOT_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "EPOT", "/inmaps/EPOT"
)
self.PRECIP_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "PRECIP", "/inmaps/PRECIP"
)
self.RAD_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "RAD", "/inmaps/RAD"
)
# self.WINDSPEED_mapstack=self.Dir + configget(self.config,"inputmapstacks","WINDSPEED","/inmaps/ClimatologyMapFiles/WINDS/WNDSPEED")
# self.AIRPRESS_mapstack=self.Dir + configget(self.config,"inputmapstacks","AIRPRESS","/inmaps/ClimatologyMapFiles/AIRPRESS/AIRPRESS")
self.ALBEDO_mapstack = self.Dir + configget(
self.config,
"inputmapstacks",
"ALBEDO",
"/inmaps/ClimatologyMapFiles/ALBEDO/ALBEDO",
)
self.WINDSPEED_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "WINDSPEED", "/inmaps/WIND"
)
self.AIRPRESS_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "AIRPRESS", "/inmaps/PRES"
)
self.Altitude = pcr.readmap(self.Dir + "/staticmaps/wflow_dem")
self.latitude = pcr.ycoordinate(pcr.boolean(self.Altitude))
# Add reading of parameters here
self.K_gw = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/k_gw.map"), 0.0, fail=True
)
self.K_rout = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/k_rout.map"), 0.0, fail=True
)
self.Sgref = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sgref.map"), 0.0, fail=True
)
self.alb_dry1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/alb_dry.map"), 0.0, fail=True
)
self.alb_wet1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/alb_wet.map"), 0.0, fail=True
)
self.beta1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/beta.map"), 0.0, fail=True
)
self.cGsmax1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/cgsmax.map"), 0.0, fail=True
)
self.ER_frac_ref1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/er_frac_ref.map"), 0.0, fail=True
)
self.FdrainFC1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fdrainfc.map"), 0.0, fail=True
)
self.Fgw_conn1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fgw_conn.map"), 0.0, fail=True
)
self.Fhru1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fhru.map"), 0.0, fail=True
)
self.SLA1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sla.map"), 0.0, fail=True
)
self.LAIref1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/lairef.map"), 0.0, fail=True
)
self.FsoilEmax1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fsoilemax.map"), 0.0, fail=True
)
self.fvegref_G1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fvegref_g.map"), 0.0, fail=True
)
self.FwaterE1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fwatere.map"), 0.0, fail=True
)
self.Gfrac_max1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/gfrac_max.map"), 0.0, fail=True
)
self.hveg1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/hveg.map"), 0.0, fail=True
)
self.InitLoss1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/initloss.map"), 0.0, fail=True
)
self.LAImax1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/laimax.map"), 0.0, fail=True
)
self.PrefR1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/prefr.map"), 0.0, fail=True
)
self.S_sls1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/s_sls.map"), 0.0, fail=True
)
self.S0FC1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/s0fc.map"), 0.0, fail=True
)
self.SsFC1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/ssfc.map"), 0.0, fail=True
)
self.SdFC1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sdfc.map"), 0.0, fail=True
)
self.Vc1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/vc.map"), 0.0, fail=True
)
self.w0ref_alb1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/w0ref_alb.map"), 0.0, fail=True
)
self.Us01 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/us0.map"), 0.0, fail=True
)
self.Ud01 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/ud0.map"), 0.0, fail=True
)
self.wslimU1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/wslimu.map"), 0.0, fail=True
)
self.wdlimU1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/wdlimu.map"), 0.0, fail=True
)
self.w0limE1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/w0lime.map"), 0.0, fail=True
)
self.Tgrow1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/tgrow.map"), 0.0, fail=True
)
self.Tsenc1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/tsenc.map"), 0.0, fail=True
)
self.alb_dry2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/alb_dry2.map"), 0.0, fail=True
)
self.alb_wet2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/alb_wet2.map"), 0.0, fail=True
)
self.beta2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/beta2.map"), 0.0, fail=True
)
self.cGsmax2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/cgsmax2.map"), 0.0, fail=True
)
self.ER_frac_ref2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/er_frac_ref2.map"), 0.0, fail=True
)
self.FdrainFC2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fdrainfc2.map"), 0.0, fail=True
)
self.Fgw_conn2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fgw_conn2.map"), 0.0, fail=True
)
self.Fhru2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fhru2.map"), 0.0, fail=True
)
self.SLA2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sla2.map"), 0.0, fail=True
)
self.LAIref2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/lairef2.map"), 0.0, fail=True
)
self.FsoilEmax2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fsoilemax2.map"), 0.0, fail=True
)
self.fvegref_G2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fvegref_g2.map"), 0.0, fail=True
)
self.FwaterE2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fwatere2.map"), 0.0, fail=True
)
self.Gfrac_max2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/gfrac_max2.map"), 0.0, fail=True
)
self.hveg2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/hveg2.map"), 0.0, fail=True
)
self.InitLoss2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/initloss2.map"), 0.0, fail=True
)
self.LAImax2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/laimax2.map"), 0.0, fail=True
)
self.PrefR2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/prefr2.map"), 0.0, fail=True
)
self.S_sls2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/s_sls2.map"), 0.0, fail=True
)
self.S0FC2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/s0fc2.map"), 0.0, fail=True
)
self.SsFC2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/ssfc2.map"), 0.0, fail=True
)
self.SdFC2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sdfc2.map"), 0.0, fail=True
)
self.Vc2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/vc2.map"), 0.0, fail=True
)
self.w0ref_alb2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/w0ref_alb2.map"), 0.0, fail=True
)
self.Us02 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/us02.map"), 0.0, fail=True
)
self.Ud02 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/ud02.map"), 0.0, fail=True
)
self.wslimU2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/wslimu2.map"), 0.0, fail=True
)
self.wdlimU2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/wdlimu2.map"), 0.0, fail=True
)
self.w0limE2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/w0lime2.map"), 0.0, fail=True
)
self.Tgrow2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/tgrow2.map"), 0.0, fail=True
)
self.Tsenc2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/tsenc2.map"), 0.0, fail=True
)
self.wf_multparameters()
# Static, for the computation of Aerodynamic conductance (3.7)
self.fh1 = pcr.ln(813.0 / self.hveg1 - 5.45)
self.fh2 = pcr.ln(813.0 / self.hveg2 - 5.45)
self.ku2_1 = 0.305 / (self.fh1 * (self.fh1 + 2.3))
self.ku2_2 = 0.305 / (self.fh2 * (self.fh2 + 2.3))
self.logger.info("Starting Dynamic run...")
def main():
#-initialization
# MVs
MV= -999.
# minimum catchment size to process
catchmentSizeLimit= 0.0
# period of interest, start and end year
startYear= 1961
endYear= 2010
# maps
cloneMapFileName= '/data/hydroworld/PCRGLOBWB20/input30min/global/Global_CloneMap_30min.map'
lddFileName= '/data/hydroworld/PCRGLOBWB20/input30min/routing/lddsound_30min.map'
cellAreaFileName= '/data/hydroworld/PCRGLOBWB20/input30min/routing/cellarea30min.map'
# set clone
pcr.setclone(cloneMapFileName)
# output
outputPath= '/scratch/rens/reservedrecharge'
percentileMapFileName= os.path.join(outputPath,'q%03d_cumsec.map')
textFileName= os.path.join(outputPath,'groundwater_environmentalflow_%d.txt')
fractionReservedRechargeMapFileName= os.path.join(outputPath,'fraction_reserved_recharge%d.map')
fractionMinimumReservedRechargeMapFileName= os.path.join(outputPath,'minimum_fraction_reserved_recharge%d.map')
# input
inputPath= '/nfsarchive/edwin-emergency-backup-DO-NOT-DELETE/rapid/edwin/05min_runs_results/2015_04_27/non_natural_2015_04_27/global/netcdf/'
# define data to be read from netCDF files
ncData= {}
variableName= 'totalRunoff'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'totalRunoff_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'qloc')
ncData[variableName]['annualAverage']= pcr.scalar(0)
variableName= 'gwRecharge'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'gwRecharge_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'gwrec')
ncData[variableName]['annualAverage']= pcr.scalar(0)
variableName= 'discharge'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'totalRunoff_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'qc')
ncData[variableName]['annualAverage']= pcr.scalar(0)
ncData[variableName]['mapStack']= np.array([])
# percents and environmental flow condition set as percentile
percents= range(10,110,10)
environmentalFlowPercent= 10
if environmentalFlowPercent not in percents:
percents.append(environmentalFlowPercent)
percents.sort()
#-start
# obtain attributes
pcr.setclone(cloneMapFileName)
cloneSpatialAttributes= spatialAttributes(cloneMapFileName)
years= range(startYear,endYear+1)
# output path
if not os.path.isdir(outputPath):
os.makedirs(outputPath)
os.chdir(outputPath)
# compute catchments
ldd= pcr.readmap(lddFileName)
cellArea= pcr.readmap(cellAreaFileName)
catchments= pcr.catchment(ldd,pcr.pit(ldd))
fractionWater= pcr.scalar(0.0) # temporary!
lakeMask= pcr.boolean(0) # temporary!
pcr.report(catchments,os.path.join(outputPath,'catchments.map'))
maximumCatchmentID= int(pcr.cellvalue(pcr.mapmaximum(pcr.scalar(catchments)),1)[0])
# iterate over years
weight= float(len(years))**-1
for year in years:
#-echo year
print ' - processing year %d' % year
#-process data
startDate= datetime.datetime(year,1,1)
endDate= datetime.datetime(year,12,31)
timeSteps= endDate.toordinal()-startDate.toordinal()+1
dynamicIncrement= 1
for variableName in ncData.keys():
print ' extracting %s' % variableName,
ncFileIn= ncData[variableName]['fileName']
#-process data
pcrDataSet= pcrObject(variableName, ncData[variableName]['fileRoot'],\
ncFileIn,cloneSpatialAttributes, pcrVALUESCALE= pcr.Scalar, resamplingAllowed= True,\
dynamic= True, dynamicStart= startDate, dynamicEnd= endDate, dynamicIncrement= dynamicIncrement, ncDynamicDimension= 'time')
pcrDataSet.initializeFileInfo()
pcrDataSet.processFileInfo()
for fileInfo in pcrDataSet.fileProcessInfo.values()[0]:
tempFileName= fileInfo[1]
variableField= pcr.readmap(tempFileName)
variableField= pcr.ifthen(pcr.defined(ldd),pcr.cover(variableField,0))
if variableName == 'discharge':
dayNumber= int(os.path.splitext(tempFileName)[1].strip('.'))
date= datetime.date(year,1,1)+datetime.timedelta(dayNumber-1)
numberDays= calendar.monthrange(year,date.month)[1]
variableField= pcr.max(0,pcr.catchmenttotal(variableField*cellArea,ldd)/(numberDays*24*3600))
ncData[variableName]['annualAverage']+= weight*variableField
if 'mapStack' in ncData[variableName].keys():
tempArray= pcr2numpy(variableField,MV)
mask= tempArray != MV
if ncData[variableName]['mapStack'].size != 0:
ncData[variableName]['mapStack']= np.vstack((ncData[variableName]['mapStack'],tempArray[mask]))
else:
ncData[variableName]['mapStack']= tempArray[mask]
coordinates= np.zeros((ncData[variableName]['mapStack'].size,2))
pcr.setglobaloption('unitcell')
tempArray= pcr2numpy(pcr.ycoordinate(pcr.boolean(1))+0.5,MV)
coordinates[:,0]= tempArray[mask]
tempArray= pcr2numpy(pcr.xcoordinate(pcr.boolean(1))+0.5,MV)
coordinates[:,1]= tempArray[mask]
os.remove(tempFileName)
# delete object
pcrDataSet= None
del pcrDataSet
# close line on screen
print
# report annual averages
key= 'annualAverage'
ncData['discharge'][key]/= 12
for variableName in ncData.keys():
ncData[variableName][key]= pcr.max(0,ncData[variableName][key])
pcr.report(ncData[variableName][key],\
os.path.join(outputPath,'%s_%s.map' % (variableName,key)))
# remove aux.xml
for tempFileName in os.listdir(outputPath):
if 'aux.xml' in tempFileName:
os.remove(tempFileName)
# sort data
print 'sorting discharge data'
variableName= 'discharge'
key= 'mapStack'
indices= np.zeros((ncData[variableName][key].shape),np.uint)
for iCnt in xrange(ncData[variableName][key].shape[1]):
indices[:,iCnt]= ncData[variableName][key][:,iCnt].argsort(kind= 'mergesort')
ncData[variableName][key][:,iCnt]= ncData[variableName][key][:,iCnt][indices[:,iCnt]]
# extract values for percentiles
print 'returning maps'
for percent in percents:
percentile= 0.01*percent
index0= min(ncData[variableName][key].shape[0]-1,int(percentile*ncData[variableName][key].shape[0]))
index1= min(ncData[variableName][key].shape[0]-1,int(percentile*ncData[variableName][key].shape[0])+1)
x0= float(index0)/ncData[variableName][key].shape[0]
x1= float(index1)/ncData[variableName][key].shape[0]
if x0 <> x1:
y= ncData[variableName][key][index0,:]+(percentile-x0)*\
(ncData[variableName][key][index1,:]-ncData[variableName][key][index0,:])/(x1-x0)
else:
y= ncData[variableName][key][index0,:]
# convert a slice of the stack into an array
tempArray= np.ones((cloneSpatialAttributes.numberRows,cloneSpatialAttributes.numberCols))*MV
for iCnt in xrange(coordinates.shape[0]):
row= coordinates[iCnt,0]-1
col= coordinates[iCnt,1]-1
tempArray[row,col]= y[iCnt]
variableField= numpy2pcr(pcr.Scalar,tempArray,MV)
pcr.report(variableField,percentileMapFileName % percent)
if percent == environmentalFlowPercent:
ncData[variableName]['environmentalFlow']= variableField
tempArray= None; variableField= None
del tempArray, variableField
# process environmental flow
# initialize map of reserved recharge fraction
fractionReservedRechargeMap= pcr.ifthen(ncData[variableName]['environmentalFlow'] < 0,pcr.scalar(0))
fractionMinimumReservedRechargeMap= pcr.ifthen(ncData[variableName]['environmentalFlow'] < 0,pcr.scalar(0))
textFile= open(textFileName % environmentalFlowPercent,'w')
hStr= 'Environmental flow analysis per basin, resulting in a map of renewable, exploitable recharge, for the %d%s quantile of discharge\n' % (environmentalFlowPercent,'%')
hStr+= 'Returns Q_%d/R, the fraction of reserved recharge needed to sustain fully the environental flow requirement defined as the %d percentile,\n' % (environmentalFlowPercent, environmentalFlowPercent)
hStr+= 'and Q*_%d/R, a reduced fraction that takes the availability of surface water into account\n' % environmentalFlowPercent
textFile.write(hStr)
print hStr
# create header to display on screen and write to file
# reported are: 1: ID, 2: Area, 3: average discharge, 4: environmental flow, 5: average recharge,
# 6: Q_%d/Q, 7: Q_%d/R_Avg, 8: R_Avg/Q_Avg, 9: Q*_%d/R_Avg
hStr= '%6s,%15s,%15s,%15s,%15s,%15s,%15s,%15s,%15s\n' % \
('ID','Area [km2]','Q_Avg [m3]','Q_%d [m3]' % environmentalFlowPercent ,'R_Avg [m3]','Q_%d/Q_Avg [-]' % environmentalFlowPercent,\
'Q_%d/Q_Avg [-]' % environmentalFlowPercent,'R_Avg/Q_Avg [-]','Q*_%d/Q_Avg [-]' % environmentalFlowPercent)
textFile.write(hStr)
print hStr
for catchment in xrange(1,maximumCatchmentID+1):
# create catchment mask and check whether it does not coincide with a lake
catchmentMask= catchments == catchment
catchmentSize= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,cellArea*1.e-6)),1)[0]
#~ ##~ if pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,pcr.scalar(lakeMask))),1) <> \
#~ ##~ pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,pcr.scalar(catchmentMask))),1)[0] and \
#~ ##~ catchmentSize > catchmentSizeLimit:
key= 'annualAverage'
variableName= 'discharge'
if bool(pcr.cellvalue(pcr.maptotal(pcr.ifthen((ldd == 5) & catchmentMask,\
pcr.scalar(ncData[variableName][key] > 0))),1)[0]) and catchmentSize >= catchmentSizeLimit:
# valid catchment, process
# all volumes are in m3 per year
key= 'annualAverage'
catchmentAverageDischarge= pcr.cellvalue(pcr.mapmaximum(pcr.ifthen(catchmentMask & (ldd == 5),\
ncData[variableName][key])),1)[0]*365.25*3600*24
variableName= 'gwRecharge'
catchmentRecharge= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,ncData[variableName][key]*\
(1.-fractionWater)*cellArea)),1)[0]
variableName= 'totalRunoff'
catchmentRunoff= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,ncData[variableName][key]*\
cellArea)),1)[0]
key= 'environmentalFlow'
variableName= 'discharge'
catchmentEnvironmentalFlow= pcr.cellvalue(pcr.mapmaximum(pcr.ifthen(catchmentMask & (ldd == 5),\
ncData[variableName][key])),1)[0]*365.25*3600*24
catchmentRunoff= max(catchmentRunoff,catchmentEnvironmentalFlow)
if catchmentAverageDischarge > 0.:
fractionEnvironmentalFlow= catchmentEnvironmentalFlow/catchmentAverageDischarge
fractionGroundWaterContribution= catchmentRecharge/catchmentAverageDischarge
else:
fractionEnvironmentalFlow= 0.
fractionGroundWaterContribution= 0.
if catchmentRecharge > 0:
fractionReservedRecharge= min(1,catchmentEnvironmentalFlow/catchmentRecharge)
else:
fractionReservedRecharge= 1.0
fractionMinimumReservedRecharge= (fractionReservedRecharge+fractionGroundWaterContribution-\
fractionReservedRecharge*fractionGroundWaterContribution)*fractionReservedRecharge
#~ # echo to screen, and write to file and map
wStr= '%6s,%15.1f,%15.6g,%15.6g,%15.6g,%15.6f,%15.6f,%15.6f,%15.6f\n' % \
(catchment,catchmentSize,catchmentAverageDischarge,catchmentEnvironmentalFlow,catchmentRecharge,\
fractionEnvironmentalFlow,fractionReservedRecharge,fractionGroundWaterContribution,fractionMinimumReservedRecharge)
print wStr
textFile.write(wStr)
# update maps
fractionReservedRechargeMap= pcr.ifthenelse(catchmentMask,\
pcr.scalar(fractionReservedRecharge),fractionReservedRechargeMap)
fractionMinimumReservedRechargeMap= pcr.ifthenelse(catchmentMask,\
pcr.scalar(fractionMinimumReservedRecharge),fractionMinimumReservedRechargeMap)
#-report map and close text file
pcr.report(fractionReservedRechargeMap,fractionReservedRechargeMapFileName % environmentalFlowPercent)
pcr.report(fractionMinimumReservedRechargeMap,fractionMinimumReservedRechargeMapFileName % environmentalFlowPercent)
# close text file
textFile.close()
# finished
print 'all done!'
def __init__(
self,
netcdffile,
logger,
starttime,
timesteps,
EPSG="EPSG:4326",
timestepsecs=86400,
metadata={},
zlib=True,
Format="NETCDF4",
maxbuf=25,
least_significant_digit=None,
):
"""
Under construction
"""
self.EPSG = EPSG
self.zlib = zlib
self.Format = Format
self.least_significant_digit = least_significant_digit
def date_range(start, end, timestepsecs):
r = int((end + dt.timedelta(seconds=timestepsecs) -
start).total_seconds() / timestepsecs)
return [
start + dt.timedelta(seconds=(timestepsecs * i))
for i in range(r)
]
self.logger = logger
# Do not allow a max buffer larger than the number of timesteps
self.maxbuf = maxbuf if timesteps >= maxbuf else timesteps
self.ncfile = netcdffile
self.timesteps = timesteps
rows = pcr.clone().nrRows()
cols = pcr.clone().nrCols()
cellsize = pcr.clone().cellSize()
yupper = pcr.clone().north()
xupper = pcr.clone().west()
x = pcr.pcr2numpy(pcr.xcoordinate(pcr.boolean(pcr.cover(1.0))),
np.nan)[0, :]
y = pcr.pcr2numpy(pcr.ycoordinate(pcr.boolean(pcr.cover(1.0))),
np.nan)[:, 0]
# Shift one timestep as we output at the end
# starttime = starttime + dt.timedelta(seconds=timestepsecs)
end = starttime + dt.timedelta(seconds=timestepsecs *
(self.timesteps - 1))
timeList = date_range(starttime, end, timestepsecs)
self.timestepbuffer = np.zeros((self.maxbuf, len(y), len(x)))
self.bufflst = {}
self.buffdirty = False
globmetadata.update(metadata)
prepare_nc(
self.ncfile,
timeList,
x,
y,
globmetadata,
logger,
Format=self.Format,
EPSG=EPSG,
zlib=self.zlib,
least_significant_digit=self.least_significant_digit,
)
def __init__(self, iniItems, landmask, spinUp):
object.__init__(self)
self.cloneMap = iniItems.cloneMap
self.tmpDir = iniItems.tmpDir
self.inputDir = iniItems.globalOptions['inputDir']
# landmask/area of interest
self.landmask = landmask
if iniItems.globalOptions['landmask'] != "None":
self.landmask = vos.readPCRmapClone(\
iniItems.globalOptions['landmask'],
self.cloneMap,self.tmpDir,self.inputDir)
# option to ignore snow (temperature will be set to 25 deg C if this option is activated)
self.ignore_snow = False
if 'ignoreSnow' in list(iniItems.meteoOptions.keys(
)) and iniItems.meteoOptions['ignoreSnow'] == "True":
self.ignore_snow = True
self.preFileNC = iniItems.meteoOptions[
'precipitationNC'] # starting from 19 Feb 2014, we only support netcdf input files
self.tmpFileNC = iniItems.meteoOptions['temperatureNC']
self.refETPotMethod = iniItems.meteoOptions['referenceETPotMethod']
if self.refETPotMethod == 'Hamon': self.latitudes = \
pcr.ycoordinate(self.cloneMap) # needed to calculate 'referenceETPot'
if self.refETPotMethod == 'Input': self.etpFileNC = \
iniItems.meteoOptions['refETPotFileNC']
#-----------------------------------------------------------------------
# NOTE: RvB 13/07/2016 Added correction constant and factor and variable name
# to allow for easier use of netCDF climate inpute files
# EHS 20/08/2016 modified for more flexibilities.
# - meteo conversion factors
self.preConst = 0.0
self.preFactor = 1.0
self.tmpConst = 0.0
self.tmpFactor = 1.0
self.refETPotConst = 0.0
self.refETPotFactor = 1.0
self.read_meteo_conversion_factors(iniItems.meteoOptions)
# - variable names
self.preVarName = 'precipitation'
self.tmpVarName = 'temperature'
self.refETPotVarName = 'evapotranspiration'
self.read_meteo_variable_names(iniItems.meteoOptions)
# daily time step
self.usingDailyTimeStepForcingData = False
if iniItems.timeStep == 1.0 and iniItems.timeStepUnit == "day":
self.usingDailyTimeStepForcingData = True
# forcing downscaling options:
self.forcingDownscalingOptions(iniItems)
# option to use netcdf files that are defined per year (one file for each year)
self.precipitation_set_per_year = iniItems.meteoOptions[
'precipitation_set_per_year'] == "True"
self.temperature_set_per_year = iniItems.meteoOptions[
'temperature_set_per_year'] == "True"
self.refETPotFileNC_set_per_year = iniItems.meteoOptions[
'refETPotFileNC_set_per_year'] == "True"
# make the iniItems available for the other modules:
self.iniItems = iniItems
self.report = True
try:
self.outDailyTotNC = iniItems.meteoOptions['outDailyTotNC'].split(
",")
self.outMonthTotNC = iniItems.meteoOptions['outMonthTotNC'].split(
",")
self.outMonthAvgNC = iniItems.meteoOptions['outMonthAvgNC'].split(
",")
self.outMonthEndNC = iniItems.meteoOptions['outMonthEndNC'].split(
",")
self.outAnnuaTotNC = iniItems.meteoOptions['outAnnuaTotNC'].split(
",")
self.outAnnuaAvgNC = iniItems.meteoOptions['outAnnuaAvgNC'].split(
",")
self.outAnnuaEndNC = iniItems.meteoOptions['outAnnuaEndNC'].split(
",")
except:
self.report = False
if self.report == True:
# daily output in netCDF files:
self.outNCDir = iniItems.outNCDir
self.netcdfObj = PCR2netCDF(iniItems)
#
if self.outDailyTotNC[0] != "None":
for var in self.outDailyTotNC:
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_dailyTot.nc",\
var,"undefined")
# MONTHly output in netCDF files:
# - cummulative
if self.outMonthTotNC[0] != "None":
for var in self.outMonthTotNC:
# initiating monthlyVarTot (accumulator variable):
vars(self)[var + 'MonthTot'] = None
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthTot.nc",\
var,"undefined")
# - average
if self.outMonthAvgNC[0] != "None":
for var in self.outMonthAvgNC:
# initiating monthlyTotAvg (accumulator variable)
vars(self)[var + 'MonthTot'] = None
# initiating monthlyVarAvg:
vars(self)[var + 'MonthAvg'] = None
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthAvg.nc",\
var,"undefined")
# - last day of the month
if self.outMonthEndNC[0] != "None":
for var in self.outMonthEndNC:
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthEnd.nc",\
var,"undefined")
# YEARly output in netCDF files:
# - cummulative
if self.outAnnuaTotNC[0] != "None":
for var in self.outAnnuaTotNC:
# initiating yearly accumulator variable:
vars(self)[var + 'AnnuaTot'] = None
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaTot.nc",\
var,"undefined")
# - average
if self.outAnnuaAvgNC[0] != "None":
for var in self.outAnnuaAvgNC:
# initiating annualyVarAvg:
vars(self)[var + 'AnnuaAvg'] = None
# initiating annualyTotAvg (accumulator variable)
vars(self)[var + 'AnnuaTot'] = None
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaAvg.nc",\
var,"undefined")
# - last day of the year
if self.outAnnuaEndNC[0] != "None":
for var in self.outAnnuaEndNC:
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaEnd.nc",\
var,"undefined")
def getPCRcoords(PCRmap, missing_value_pcr=-999):
"""
Get all vertices coordinates of a PCRaster map.
Input:
-----
pcraster map (preferrably landmask)
value for MV (optional, default at -999)
Output:
------
list of (x,y) coordinates of each polygon
"""
# Get coordinates as numpy array:
# upper left coordinates
pcr.setglobaloption("coorul")
xcoord_pcr_ul_map = pcr.xcoordinate(PCRmap)
xcoord_pcr_ul_np = pcr.pcr2numpy(xcoord_pcr_ul_map, missing_value_pcr)
ycoord_pcr_ul_map = pcr.ycoordinate(PCRmap)
ycoord_pcr_ul_np = pcr.pcr2numpy(ycoord_pcr_ul_map, missing_value_pcr)
# lower right coordinates
pcr.setglobaloption("coorlr")
xcoord_pcr_lr_map = pcr.xcoordinate(PCRmap)
xcoord_pcr_lr_np = pcr.pcr2numpy(xcoord_pcr_lr_map, missing_value_pcr)
ycoord_pcr_lr_map = pcr.ycoordinate(PCRmap)
ycoord_pcr_lr_np = pcr.pcr2numpy(ycoord_pcr_lr_map, missing_value_pcr)
# centroid coordinates
pcr.setglobaloption("coorcentre")
xcoord_pcr_centr_map = pcr.xcoordinate(PCRmap)
xcoord_pcr_centr_np = pcr.pcr2numpy(xcoord_pcr_centr_map,
missing_value_pcr)
ycoord_pcr_centr_map = pcr.ycoordinate(PCRmap)
ycoord_pcr_centr_np = pcr.pcr2numpy(ycoord_pcr_centr_map,
missing_value_pcr)
# Construct collection of polygon vertices:
# number of arrays/elements to loop over and/or construct new arrays
array_count_pcr = len(ycoord_pcr_lr_np)
elements_per_array_pcr = np.size(ycoord_pcr_lr_np) / array_count_pcr
nonmiss_val_per_array_pcr = np.sum(ycoord_pcr_lr_np != missing_value_pcr)
# filling empty arrays while looping over data
i, j = np.where(xcoord_pcr_lr_np != missing_value_pcr)
xcoord_pcr_lr_np_nonmiss = xcoord_pcr_lr_np[i, j]
xcoord_pcr_ul_np_nonmiss = xcoord_pcr_ul_np[i, j]
xcoord_pcr_ll_np_nonmiss = xcoord_pcr_ul_np[i, j]
xcoord_pcr_ur_np_nonmiss = xcoord_pcr_lr_np[i, j]
ycoord_pcr_lr_np_nonmiss = ycoord_pcr_lr_np[i, j]
ycoord_pcr_ul_np_nonmiss = ycoord_pcr_ul_np[i, j]
ycoord_pcr_ll_np_nonmiss = ycoord_pcr_lr_np[i, j]
ycoord_pcr_ur_np_nonmiss = ycoord_pcr_ul_np[i, j]
xcoord_pcr_centr_np_nonmiss = xcoord_pcr_centr_np[i, j]
ycoord_pcr_centr_np_nonmiss = ycoord_pcr_centr_np[i, j]
# empty collection for polygons
ll = zip(xcoord_pcr_ll_np_nonmiss, ycoord_pcr_ll_np_nonmiss)
lr = zip(xcoord_pcr_lr_np_nonmiss, ycoord_pcr_lr_np_nonmiss)
ur = zip(xcoord_pcr_ur_np_nonmiss, ycoord_pcr_ur_np_nonmiss)
ul = zip(xcoord_pcr_ul_np_nonmiss, ycoord_pcr_ul_np_nonmiss)
# wrap all cell coordinates into a list of lists (one list per cell, with multiple tuples per cell corner)
all_cell_coords_pcr = [[ll[i], lr[i], ur[i], ul[i]]
for i in range(len(ll))]
return all_cell_coords_pcr
def evaluateAllModelResults(self,globalCloneMapFileName,\
catchmentClassFileName,\
lddMapFileName,\
cellAreaMapFileName,\
pcrglobwb_output,\
analysisOutputDir="",\
tmpDir = "/dev/shm/edwin_grdc_"):
# output directory for all analyses for all stations
analysisOutputDir = str(analysisOutputDir)
self.chartOutputDir = analysisOutputDir+"/chart/"
self.tableOutputDir = analysisOutputDir+"/table/"
#
if analysisOutputDir == "": self.chartOutputDir = "chart/"
if analysisOutputDir == "": self.tableOutputDir = "table/"
#
# make the chart and table directories:
os.system('rm -r '+self.chartOutputDir+"*")
os.system('rm -r '+self.tableOutputDir+"*")
os.makedirs(self.chartOutputDir)
os.makedirs(self.tableOutputDir)
# cloneMap for all pcraster operations
pcr.setclone(globalCloneMapFileName)
cloneMap = pcr.boolean(1)
self.cell_size_in_arc_degree = vos.getMapAttributesALL(globalCloneMapFileName)['cellsize']
lddMap = pcr.lddrepair(pcr.readmap(lddMapFileName))
cellArea = pcr.scalar(pcr.readmap(cellAreaMapFileName))
# The landMaskClass map contains the nominal classes for all landmask regions.
landMaskClass = pcr.nominal(cloneMap) # default: if catchmentClassFileName is not given
if catchmentClassFileName != None:
landMaskClass = pcr.nominal(pcr.readmap(catchmentClassFileName))
# model catchment areas and cordinates
catchmentAreaAll = pcr.catchmenttotal(cellArea, lddMap) / (1000*1000) # unit: km2
xCoordinate = pcr.xcoordinate(cloneMap)
yCoordinate = pcr.ycoordinate(cloneMap)
print "Jaaaaaa"
for id in self.list_of_grdc_ids:
logger.info("Evaluating simulated discharge to the grdc observation at "+str(self.attributeGRDC["id_from_grdc"][str(id)])+".")
# identify model pixel
self.identifyModelPixel(tmpDir,catchmentAreaAll,landMaskClass,xCoordinate,yCoordinate,str(id))
# evaluate model results to GRDC data
self.evaluateModelResultsToGRDC(str(id),pcrglobwb_output,catchmentClassFileName,tmpDir)
# write the summary to a table
summary_file = analysisOutputDir+"summary.txt"
#
logger.info("Writing the summary for all stations to the file: "+str(summary_file)+".")
#
# prepare the file:
summary_file_handle = open(summary_file,"w")
#
# write the header
summary_file_handle.write( ";".join(self.grdc_dict_keys)+"\n")
#
# write the content
for id in self.list_of_grdc_ids:
rowLine = ""
for key in self.grdc_dict_keys: rowLine += str(self.attributeGRDC[key][str(id)]) + ";"
rowLine = rowLine[0:-1] + "\n"
summary_file_handle.write(rowLine)
summary_file_handle.close()
def evaluateAllModelResults(self,globalCloneMapFileName,\
catchmentClassFileName,\
lddMapFileName,\
cellAreaMapFileName,\
pcrglobwb_output,\
analysisOutputDir="",\
tmpDir = None):
# temporary directory
if tmpDir == None: tmpDir = self.tmpDir+"/edwin_grdc_"
# output directory for all analyses for all stations
analysisOutputDir = str(analysisOutputDir)
self.chartOutputDir = analysisOutputDir+"/chart/"
self.tableOutputDir = analysisOutputDir+"/table/"
#
if analysisOutputDir == "": self.chartOutputDir = "chart/"
if analysisOutputDir == "": self.tableOutputDir = "table/"
#
# make the chart and table directories:
os.system('rm -r '+self.chartOutputDir+"*")
os.system('rm -r '+self.tableOutputDir+"*")
os.makedirs(self.chartOutputDir)
os.makedirs(self.tableOutputDir)
# cloneMap for all pcraster operations
pcr.setclone(globalCloneMapFileName)
cloneMap = pcr.boolean(1)
self.cell_size_in_arc_degree = vos.getMapAttributesALL(globalCloneMapFileName)['cellsize']
lddMap = pcr.lddrepair(pcr.readmap(lddMapFileName))
cellArea = pcr.scalar(pcr.readmap(cellAreaMapFileName))
# The landMaskClass map contains the nominal classes for all landmask regions.
landMaskClass = pcr.nominal(cloneMap) # default: if catchmentClassFileName is not given
if catchmentClassFileName != None:
landMaskClass = pcr.nominal(pcr.readmap(catchmentClassFileName))
# model catchment areas and cordinates
catchmentAreaAll = pcr.catchmenttotal(cellArea, lddMap) / (1000*1000) # unit: km2
xCoordinate = pcr.xcoordinate(cloneMap)
yCoordinate = pcr.ycoordinate(cloneMap)
for id in self.list_of_grdc_ids:
logger.info("Evaluating simulated discharge to the grdc observation at "+str(self.attributeGRDC["id_from_grdc"][str(id)])+".")
# identify model pixel
self.identifyModelPixel(tmpDir,catchmentAreaAll,landMaskClass,xCoordinate,yCoordinate,str(id))
# evaluate model results to GRDC data
self.evaluateModelResultsToGRDC(str(id),pcrglobwb_output,catchmentClassFileName,tmpDir)
# write the summary to a table
summary_file = analysisOutputDir+"summary.txt"
#
logger.info("Writing the summary for all stations to the file: "+str(summary_file)+".")
#
# prepare the file:
summary_file_handle = open(summary_file,"w")
#
# write the header
summary_file_handle.write( ";".join(self.grdc_dict_keys)+"\n")
#
# write the content
for id in self.list_of_grdc_ids:
rowLine = ""
for key in self.grdc_dict_keys: rowLine += str(self.attributeGRDC[key][str(id)]) + ";"
rowLine = rowLine[0:-1] + "\n"
summary_file_handle.write(rowLine)
summary_file_handle.close()
def __init__(self, iniItems, landmask, spinUp):
object.__init__(self)
self.cloneMap = iniItems.cloneMap
self.tmpDir = iniItems.tmpDir
self.inputDir = iniItems.globalOptions['inputDir']
# landmask/area of interest
self.landmask = landmask
if iniItems.globalOptions['landmask'] != "None":
self.landmask = vos.readPCRmapClone(\
iniItems.globalOptions['landmask'],
self.cloneMap,self.tmpDir,self.inputDir)
self.preFileNC = iniItems.meteoOptions[
'precipitationNC'] # starting from 19 Feb 2014, we only support netcdf input files
self.tmpFileNC = iniItems.meteoOptions['temperatureNC']
self.refETPotMethod = iniItems.meteoOptions['referenceETPotMethod']
if self.refETPotMethod == 'Hamon': self.latitudes = \
pcr.ycoordinate(self.cloneMap) # needed to calculate 'referenceETPot'
if self.refETPotMethod == 'Input': self.etpFileNC = \
iniItems.meteoOptions['refETPotFileNC']
# daily time step
self.usingDailyTimeStepForcingData = False
if iniItems.timeStep == 1.0 and iniItems.timeStepUnit == "day":
self.usingDailyTimeStepForcingData = True
# forcing downscaling options:
self.forcingDownscalingOptions(iniItems)
self.report = True
try:
self.outDailyTotNC = iniItems.meteoOptions['outDailyTotNC'].split(
",")
self.outMonthTotNC = iniItems.meteoOptions['outMonthTotNC'].split(
",")
self.outMonthAvgNC = iniItems.meteoOptions['outMonthAvgNC'].split(
",")
self.outMonthEndNC = iniItems.meteoOptions['outMonthEndNC'].split(
",")
self.outAnnuaTotNC = iniItems.meteoOptions['outAnnuaTotNC'].split(
",")
self.outAnnuaAvgNC = iniItems.meteoOptions['outAnnuaAvgNC'].split(
",")
self.outAnnuaEndNC = iniItems.meteoOptions['outAnnuaEndNC'].split(
",")
except:
self.report = False
if self.report == True:
# daily output in netCDF files:
self.outNCDir = iniItems.outNCDir
self.netcdfObj = PCR2netCDF(iniItems)
#
if self.outDailyTotNC[0] != "None":
for var in self.outDailyTotNC:
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_dailyTot.nc",\
var,"undefined")
# MONTHly output in netCDF files:
# - cummulative
if self.outMonthTotNC[0] != "None":
for var in self.outMonthTotNC:
# initiating monthlyVarTot (accumulator variable):
vars(self)[var + 'MonthTot'] = None
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthTot.nc",\
var,"undefined")
# - average
if self.outMonthAvgNC[0] != "None":
for var in self.outMonthAvgNC:
# initiating monthlyTotAvg (accumulator variable)
vars(self)[var + 'MonthTot'] = None
# initiating monthlyVarAvg:
vars(self)[var + 'MonthAvg'] = None
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthAvg.nc",\
var,"undefined")
# - last day of the month
if self.outMonthEndNC[0] != "None":
for var in self.outMonthEndNC:
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthEnd.nc",\
var,"undefined")
# YEARly output in netCDF files:
# - cummulative
if self.outAnnuaTotNC[0] != "None":
for var in self.outAnnuaTotNC:
# initiating yearly accumulator variable:
vars(self)[var + 'AnnuaTot'] = None
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaTot.nc",\
var,"undefined")
# - average
if self.outAnnuaAvgNC[0] != "None":
for var in self.outAnnuaAvgNC:
# initiating annualyVarAvg:
vars(self)[var + 'AnnuaAvg'] = None
# initiating annualyTotAvg (accumulator variable)
vars(self)[var + 'AnnuaTot'] = None
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaAvg.nc",\
var,"undefined")
# - last day of the year
if self.outAnnuaEndNC[0] != "None":
for var in self.outAnnuaEndNC:
# creating the netCDF files:
self.netcdfObj.createNetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaEnd.nc",\
var,"undefined")
