def __init__(self, uri, **kwds):
self.uri = uri
self.dataset = self.connect(uri)
## construct interfaces
self.spatial = SpatialInterface(self.dataset, **kwds)
self.temporal = TemporalInterface(self.dataset, **kwds)
try:
self.level = LevelInterface(self.dataset, **kwds)
except PolyElementNotFound:
warn('No "level" variable found. Assuming NoneType.')
self.level = None
## extract other keyword arguments -------------------------------------
self.verbose = kwds.get('verbose')
self.time_units = kwds.get(
'time_units') or 'days since 1950-01-01 00:00:00'
self.calendar = kwds.get('calendar') or 'proleptic_gregorian'
self.level_name = kwds.get('level_name') or 'levels'
## extract the row and column bounds from the dataset
self.row_bnds = self.spatial.rowbnds.value[:]
self.col_bnds = self.spatial.colbnds.value[:]
## convert the time vector to datetime objects
self.timevec = nc.netcdftime.num2date(self.temporal.time.value[:],
self.time_units, self.calendar)
self.timeidx = np.arange(0, len(self.timevec))
self.tids = np.arange(1, len(self.timevec) + 1)
## pull levels if possible
if self.level is not None:
self.levelvec = np.arange(1, len(self.level.level.value[:]) + 1)
self.levelidx = np.arange(0, len(self.levelvec))
else:
self.levelvec = np.array([1])
self.levelidx = np.array([0])
## these are base numpy arrays used by spatial operations. -------------
## four numpy arrays one for each bounding coordinate of a polygon
self.min_col, self.min_row = self.spatial.get_min_bounds()
self.max_col, self.max_row = self.spatial.get_max_bounds()
## these are the original indices of the row and columns. they are
## referenced after the spatial subset to retrieve data from the dataset
self.real_col, self.real_row = np.meshgrid(
np.arange(0, len(self.col_bnds)), np.arange(0, len(self.row_bnds)))
## calculate approximate data resolution
self.res = approx_resolution(self.min_col[0, :])
## generate unique id for each grid cell
self.gids = np.arange(
1, self.real_col.shape[0] * self.real_col.shape[1] + 1)
self.gids = self.gids.reshape(self.real_col.shape)
# self.gids = np.empty(self.real_col.shape,dtype=int)
# curr_id = 1
# for i,j in itr_array(self.gids):
# self.gids[i,j] = curr_id
# curr_id += 1
## set the array shape.
self.shape = self.real_col.shape
def __init__(self,uri,**kwds):
self.uri = uri
self.dataset = self.connect(uri)
## construct interfaces
self.spatial = SpatialInterface(self.dataset,**kwds)
self.temporal = TemporalInterface(self.dataset,**kwds)
try:
self.level = LevelInterface(self.dataset,**kwds)
except PolyElementNotFound:
warn('No "level" variable found. Assuming NoneType.')
self.level = None
## extract other keyword arguments -------------------------------------
self.verbose = kwds.get('verbose')
self.time_units = kwds.get('time_units') or 'days since 1950-01-01 00:00:00'
self.calendar = kwds.get('calendar') or 'proleptic_gregorian'
self.level_name = kwds.get('level_name') or 'levels'
## extract the row and column bounds from the dataset
self.row_bnds = self.spatial.rowbnds.value[:]
self.col_bnds = self.spatial.colbnds.value[:]
## convert the time vector to datetime objects
self.timevec = nc.netcdftime.num2date(self.temporal.time.value[:],
self.time_units,
self.calendar)
self.timeidx = np.arange(0,len(self.timevec))
self.tids = np.arange(1,len(self.timevec)+1)
## pull levels if possible
if self.level is not None:
self.levelvec = np.arange(1,len(self.level.level.value[:])+1)
self.levelidx = np.arange(0,len(self.levelvec))
else:
self.levelvec = np.array([1])
self.levelidx = np.array([0])
## these are base numpy arrays used by spatial operations. -------------
## four numpy arrays one for each bounding coordinate of a polygon
self.min_col,self.min_row = self.spatial.get_min_bounds()
self.max_col,self.max_row = self.spatial.get_max_bounds()
## these are the original indices of the row and columns. they are
## referenced after the spatial subset to retrieve data from the dataset
self.real_col,self.real_row = np.meshgrid(np.arange(0,len(self.col_bnds)),
np.arange(0,len(self.row_bnds)))
## calculate approximate data resolution
self.res = approx_resolution(self.min_col[0,:])
## generate unique id for each grid cell
self.gids = np.arange(1,self.real_col.shape[0]*self.real_col.shape[1]+1)
self.gids = self.gids.reshape(self.real_col.shape)
# self.gids = np.empty(self.real_col.shape,dtype=int)
# curr_id = 1
# for i,j in itr_array(self.gids):
# self.gids[i,j] = curr_id
# curr_id += 1
## set the array shape.
self.shape = self.real_col.shape
class OcgDataset(object):
"""
Wraps and netCDF4-python Dataset object providing extraction methods by
spatial and temporal queries.
uri -- location of the dataset object.
**kwds -- arguments for the names of multiple configuration parameters:
rowbnds_name
colbnds_name
time_name
time_units
level_name
calendar
verbose
"""
@timing
def __init__(self, uri, **kwds):
self.uri = uri
self.dataset = self.connect(uri)
## construct interfaces
self.spatial = SpatialInterface(self.dataset, **kwds)
self.temporal = TemporalInterface(self.dataset, **kwds)
try:
self.level = LevelInterface(self.dataset, **kwds)
except PolyElementNotFound:
warn('No "level" variable found. Assuming NoneType.')
self.level = None
## extract other keyword arguments -------------------------------------
self.verbose = kwds.get('verbose')
self.time_units = kwds.get(
'time_units') or 'days since 1950-01-01 00:00:00'
self.calendar = kwds.get('calendar') or 'proleptic_gregorian'
self.level_name = kwds.get('level_name') or 'levels'
## extract the row and column bounds from the dataset
self.row_bnds = self.spatial.rowbnds.value[:]
self.col_bnds = self.spatial.colbnds.value[:]
## convert the time vector to datetime objects
self.timevec = nc.netcdftime.num2date(self.temporal.time.value[:],
self.time_units, self.calendar)
self.timeidx = np.arange(0, len(self.timevec))
self.tids = np.arange(1, len(self.timevec) + 1)
## pull levels if possible
if self.level is not None:
self.levelvec = np.arange(1, len(self.level.level.value[:]) + 1)
self.levelidx = np.arange(0, len(self.levelvec))
else:
self.levelvec = np.array([1])
self.levelidx = np.array([0])
## these are base numpy arrays used by spatial operations. -------------
## four numpy arrays one for each bounding coordinate of a polygon
self.min_col, self.min_row = self.spatial.get_min_bounds()
self.max_col, self.max_row = self.spatial.get_max_bounds()
## these are the original indices of the row and columns. they are
## referenced after the spatial subset to retrieve data from the dataset
self.real_col, self.real_row = np.meshgrid(
np.arange(0, len(self.col_bnds)), np.arange(0, len(self.row_bnds)))
## calculate approximate data resolution
self.res = approx_resolution(self.min_col[0, :])
## generate unique id for each grid cell
self.gids = np.arange(
1, self.real_col.shape[0] * self.real_col.shape[1] + 1)
self.gids = self.gids.reshape(self.real_col.shape)
# self.gids = np.empty(self.real_col.shape,dtype=int)
# curr_id = 1
# for i,j in itr_array(self.gids):
# self.gids[i,j] = curr_id
# curr_id += 1
## set the array shape.
self.shape = self.real_col.shape
def __del__(self):
try:
self.dataset.close()
finally:
pass
@timing
def connect(self, uri):
return (nc.Dataset(uri, 'r'))
def extent(self):
minx = self.min_col.min()
maxx = self.max_col.max()
miny = self.min_row.min()
maxy = self.max_row.max()
poly = Polygon(
((minx, miny), (maxx, miny), (maxx, maxy), (minx, maxy)))
return (poly)
def check_extent(self, target):
extent = self.extent()
return (keep(prepared.prep(extent), extent, target))
def check_masked(self, var_name, polygon):
try:
self.subset(var_name,
polygon=polygon,
time_range=[self.timevec[0], self.timevec[1]])
ret = True
except MaskedDataError:
ret = False
return (ret)
def display(self, show=True, overlays=None):
import matplotlib.pyplot as plt
from descartes.patch import PolygonPatch
ax = plt.axes()
if overlays is not None:
for geom in overlays:
ax.add_patch(PolygonPatch(geom, alpha=0.5, fc='#999999'))
ax.scatter(self.min_col, self.min_row)
ax.scatter(self.max_col, self.max_row)
if show: plt.show()
@timing
def get_numpy_data(self, var, args):
if len(args) == 3:
npd = var[args[0], args[1], args[2]]
if len(args) == 4:
npd = var[args[0], args[1], args[2], args[3]]
return (npd)
@timing
def subset(self,
var_name,
polygon=None,
time_range=None,
level_range=None): ## intersects + touches
"""
polygon -- shapely Polygon object
return -- SubOcgDataset
"""
## do a quick extent check if a polygon is passed
if polygon is not None:
if not self.check_extent(polygon):
raise (ExtentError)
## the base cell selection. does basic find operation to identify
## cells to keep.
if polygon is not None:
prep_polygon = prepared.prep(polygon)
emin_col, emin_row, emax_col, emax_row = polygon.envelope.bounds
smin_col = contains(self.min_col, emin_col, emax_col, self.res)
smax_col = contains(self.max_col, emin_col, emax_col, self.res)
smin_row = contains(self.min_row, emin_row, emax_row, self.res)
smax_row = contains(self.max_row, emin_row, emax_row, self.res)
include = np.any((smin_col, smax_col), axis=0) * np.any(
(smin_row, smax_row), axis=0)
else:
include = np.empty(self.min_row.shape, dtype=bool)
include[:, :] = True
## construct the reference matrices
geometry = []
row = []
col = []
gids = []
idx = []
## fill the matrices if value is included
def _append(ii, jj, geom):
geometry.append(geom)
row.append(self.real_row[ii, jj])
col.append(self.real_col[ii, jj])
gids.append(self.gids[ii, jj])
idx.append([self.real_row[ii, jj], self.real_col[ii, jj]])
for ii, jj in itr_array(include):
if include[ii, jj]:
test_geom = make_poly(
(self.min_row[ii, jj], self.max_row[ii, jj]),
(self.min_col[ii, jj], self.max_col[ii, jj]))
if polygon is not None and keep(prep_polygon, polygon,
test_geom):
_append(ii, jj, test_geom)
elif polygon is None:
_append(ii, jj, test_geom)
## get the number of dimensions of the target variable
ndim = len(self.dataset.variables[var_name].dimensions)
## get the time indices
if time_range is not None:
timeidx = self.timeidx[(self.timevec >= time_range[0]) *
(self.timevec <= time_range[1])]
else:
timeidx = self.timeidx
## convert the level indices
levelidx = self.levelidx
if ndim == 4:
if level_range is not None:
level_range = np.array([ii - 1 for ii in level_range])
levelidx = sub_range(level_range)
else:
if level_range is not None:
raise ValueError('Target variable has no levels.')
## extract the data
var = self.dataset.variables[var_name]
rowidx = sub_range(row)
colidx = sub_range(col)
# ## extract the global gids
# gids = self.gids[min(rowidx):max(rowidx)+1,min(colidx):max(colidx)+1]
if ndim == 3:
args = [timeidx, rowidx, colidx]
if ndim == 4:
args = [timeidx, levelidx, rowidx, colidx]
npd = self.get_numpy_data(var, args)
## ensure we have four-dimensional data.
len_sh = len(npd.shape)
if ndim == 3:
if len_sh == 3 and len(timeidx) == 1 and level_range is None:
npd = npd.reshape(1, 1, npd.shape[1], npd.shape[2])
elif len_sh == 3 and len(timeidx) > 1 and level_range is None:
npd = npd.reshape(npd.shape[0], 1, npd.shape[1], npd.shape[2])
elif len_sh == 2 and len(timeidx) > 1 and level_range is None:
npd = npd.reshape(npd.shape[0], 1, npd.shape[1], npd.shape[1])
else:
raise (NotImplementedError)
if ndim == 4:
if len_sh == 3:
npd = npd.reshape(1, 1, npd.shape[1], npd.shape[2])
## we need to remove the unwanted data and reshape in the process. first,
## construct the relative indices.
rel_mask = np.repeat(False, npd.shape[2] * npd.shape[3]).reshape(
(npd.shape[2], npd.shape[3]))
## now iterate and remove the data
min_row = min(row)
min_col = min(col)
for ii in idx:
rel_mask[ii[0] - min_row, ii[1] - min_col] = True
## reshape the data
npd = npd[:, :, rel_mask]
## test for masked data
if hasattr(npd, 'mask'):
mask = npd.mask
## if all the data values are masked, raise an error.
if mask.all():
raise (MaskedDataError)
else:
mask = None
return (SubOcgDataset(geometry,
npd,
self.timevec[timeidx],
gid=gids,
levelvec=self.levelvec[levelidx],
mask=mask,
tid=self.tids[timeidx]))
def split_subset(self, var_name, max_proc=1, subset_opts={}):
"""
returns -- list of SubOcgDatasets
"""
## the initial subset
ref = self.subset(var_name, **subset_opts)
## make base process map
ref_idx_array = np.arange(0, len(ref.geometry))
splits = np.array_split(ref_idx_array, max_proc)
## for the case of a single value, truncate the last split if it is
## empty
if len(splits[-1]) == 0:
splits = splits[0:-1]
## will hold the subsets
subs = []
## create the subsets
for ii, split in enumerate(splits):
geometry = ref.geometry[split]
value = ref.value[:, :, split]
gid = ref.gid[split]
sub = SubOcgDataset(geometry,
value,
ref.timevec,
gid=gid,
levelvec=ref.levelvec,
id=ii,
tid=ref.tid)
subs.append(sub)
return (subs)
def parallel_process_subsets(self,
subs,
polygon=None,
clip=False,
union=False,
debug=False):
def f(out, sub, polygon, clip, union):
if clip:
sub.clip(polygon)
if union:
sub.union_nosum()
if not clip:
sub.select_values(clip=True, igeom=polygon)
else:
sub.select_values(clip=False)
out.append(sub)
if not debug:
import multiprocessing as mp
out = mp.Manager().list()
pps = [
mp.Process(target=f, args=(out, sub, polygon, clip, union))
for sub in subs
]
for pp in pps:
pp.start()
for pp in pps:
pp.join()
else:
out = []
for sub in subs:
f(out, sub, polygon, clip, union)
return (list(out))
def combine_subsets(self, subs, union=False):
## collect data from subsets
for ii, sub in enumerate(subs):
if ii == 0:
base = sub
else:
base = base.merge(sub, union=union)
## if union is true, sum the values, add new gid, and union the
## geometries.
if union:
base.geometry = np.array([cascaded_union(base.geometry)],
dtype=object)
base.value = union_sum(base.weight, base.value, normalize=True)
base.gid = np.array([1])
return (base)
class OcgDataset(object):
"""
Wraps and netCDF4-python Dataset object providing extraction methods by
spatial and temporal queries.
uri -- location of the dataset object.
**kwds -- arguments for the names of multiple configuration parameters:
rowbnds_name
colbnds_name
time_name
time_units
level_name
calendar
verbose
"""
@timing
def __init__(self,uri,**kwds):
self.uri = uri
self.dataset = self.connect(uri)
## construct interfaces
self.spatial = SpatialInterface(self.dataset,**kwds)
self.temporal = TemporalInterface(self.dataset,**kwds)
try:
self.level = LevelInterface(self.dataset,**kwds)
except PolyElementNotFound:
warn('No "level" variable found. Assuming NoneType.')
self.level = None
## extract other keyword arguments -------------------------------------
self.verbose = kwds.get('verbose')
self.time_units = kwds.get('time_units') or 'days since 1950-01-01 00:00:00'
self.calendar = kwds.get('calendar') or 'proleptic_gregorian'
self.level_name = kwds.get('level_name') or 'levels'
## extract the row and column bounds from the dataset
self.row_bnds = self.spatial.rowbnds.value[:]
self.col_bnds = self.spatial.colbnds.value[:]
## convert the time vector to datetime objects
self.timevec = nc.netcdftime.num2date(self.temporal.time.value[:],
self.time_units,
self.calendar)
self.timeidx = np.arange(0,len(self.timevec))
self.tids = np.arange(1,len(self.timevec)+1)
## pull levels if possible
if self.level is not None:
self.levelvec = np.arange(1,len(self.level.level.value[:])+1)
self.levelidx = np.arange(0,len(self.levelvec))
else:
self.levelvec = np.array([1])
self.levelidx = np.array([0])
## these are base numpy arrays used by spatial operations. -------------
## four numpy arrays one for each bounding coordinate of a polygon
self.min_col,self.min_row = self.spatial.get_min_bounds()
self.max_col,self.max_row = self.spatial.get_max_bounds()
## these are the original indices of the row and columns. they are
## referenced after the spatial subset to retrieve data from the dataset
self.real_col,self.real_row = np.meshgrid(np.arange(0,len(self.col_bnds)),
np.arange(0,len(self.row_bnds)))
## calculate approximate data resolution
self.res = approx_resolution(self.min_col[0,:])
## generate unique id for each grid cell
self.gids = np.arange(1,self.real_col.shape[0]*self.real_col.shape[1]+1)
self.gids = self.gids.reshape(self.real_col.shape)
# self.gids = np.empty(self.real_col.shape,dtype=int)
# curr_id = 1
# for i,j in itr_array(self.gids):
# self.gids[i,j] = curr_id
# curr_id += 1
## set the array shape.
self.shape = self.real_col.shape
def __del__(self):
try:
self.dataset.close()
finally:
pass
@timing
def connect(self,uri):
return(nc.Dataset(uri,'r'))
def extent(self):
minx = self.min_col.min()
maxx = self.max_col.max()
miny = self.min_row.min()
maxy = self.max_row.max()
poly = Polygon(((minx,miny),(maxx,miny),(maxx,maxy),(minx,maxy)))
return(poly)
def check_extent(self,target):
extent = self.extent()
return(keep(prepared.prep(extent),extent,target))
def check_masked(self,var_name,polygon):
try:
self.subset(var_name,
polygon=polygon,
time_range=[self.timevec[0],self.timevec[1]])
ret = True
except MaskedDataError:
ret = False
return(ret)
def display(self,show=True,overlays=None):
import matplotlib.pyplot as plt
from descartes.patch import PolygonPatch
ax = plt.axes()
if overlays is not None:
for geom in overlays:
ax.add_patch(PolygonPatch(geom,alpha=0.5,fc='#999999'))
ax.scatter(self.min_col,self.min_row)
ax.scatter(self.max_col,self.max_row)
if show: plt.show()
@timing
def get_numpy_data(self,var,args):
if len(args) == 3:
npd = var[args[0],args[1],args[2]]
if len(args) == 4:
npd = var[args[0],args[1],args[2],args[3]]
return(npd)
@timing
def subset(self,var_name,polygon=None,time_range=None,level_range=None): ## intersects + touches
"""
polygon -- shapely Polygon object
return -- SubOcgDataset
"""
## do a quick extent check if a polygon is passed
if polygon is not None:
if not self.check_extent(polygon):
raise(ExtentError)
## the base cell selection. does basic find operation to identify
## cells to keep.
if polygon is not None:
prep_polygon = prepared.prep(polygon)
emin_col,emin_row,emax_col,emax_row = polygon.envelope.bounds
smin_col = contains(self.min_col,emin_col,emax_col,self.res)
smax_col = contains(self.max_col,emin_col,emax_col,self.res)
smin_row = contains(self.min_row,emin_row,emax_row,self.res)
smax_row = contains(self.max_row,emin_row,emax_row,self.res)
include = np.any((smin_col,smax_col),axis=0)*np.any((smin_row,smax_row),axis=0)
else:
include = np.empty(self.min_row.shape,dtype=bool)
include[:,:] = True
## construct the reference matrices
geometry = []
row = []
col = []
gids = []
idx = []
## fill the matrices if value is included
def _append(ii,jj,geom):
geometry.append(geom)
row.append(self.real_row[ii,jj])
col.append(self.real_col[ii,jj])
gids.append(self.gids[ii,jj])
idx.append([self.real_row[ii,jj],self.real_col[ii,jj]])
for ii,jj in itr_array(include):
if include[ii,jj]:
test_geom = make_poly((self.min_row[ii,jj],self.max_row[ii,jj]),
(self.min_col[ii,jj],self.max_col[ii,jj]))
if polygon is not None and keep(prep_polygon,polygon,test_geom):
_append(ii,jj,test_geom)
elif polygon is None:
_append(ii,jj,test_geom)
## get the number of dimensions of the target variable
ndim = len(self.dataset.variables[var_name].dimensions)
## get the time indices
if time_range is not None:
timeidx = self.timeidx[(self.timevec>=time_range[0])*
(self.timevec<=time_range[1])]
else:
timeidx = self.timeidx
## convert the level indices
levelidx = self.levelidx
if ndim == 4:
if level_range is not None:
level_range = np.array([ii-1 for ii in level_range])
levelidx = sub_range(level_range)
else:
if level_range is not None:
raise ValueError('Target variable has no levels.')
## extract the data
var = self.dataset.variables[var_name]
rowidx = sub_range(row)
colidx = sub_range(col)
# ## extract the global gids
# gids = self.gids[min(rowidx):max(rowidx)+1,min(colidx):max(colidx)+1]
if ndim == 3:
args = [timeidx,rowidx,colidx]
if ndim == 4:
args = [timeidx,levelidx,rowidx,colidx]
npd = self.get_numpy_data(var,args)
## ensure we have four-dimensional data.
len_sh = len(npd.shape)
if ndim == 3:
if len_sh == 3 and len(timeidx) == 1 and level_range is None:
npd = npd.reshape(1,1,npd.shape[1],npd.shape[2])
elif len_sh == 3 and len(timeidx) > 1 and level_range is None:
npd = npd.reshape(npd.shape[0],1,npd.shape[1],npd.shape[2])
elif len_sh == 2 and len(timeidx) > 1 and level_range is None:
npd = npd.reshape(npd.shape[0],1,npd.shape[1],npd.shape[1])
else:
raise(NotImplementedError)
if ndim == 4:
if len_sh == 3:
npd = npd.reshape(1,1,npd.shape[1],npd.shape[2])
## we need to remove the unwanted data and reshape in the process. first,
## construct the relative indices.
rel_mask = np.repeat(False,npd.shape[2]*npd.shape[3]).reshape((npd.shape[2],npd.shape[3]))
## now iterate and remove the data
min_row = min(row)
min_col = min(col)
for ii in idx:
rel_mask[ii[0]-min_row,ii[1]-min_col] = True
## reshape the data
npd = npd[:,:,rel_mask]
## test for masked data
if hasattr(npd,'mask'):
mask = npd.mask
## if all the data values are masked, raise an error.
if mask.all():
raise(MaskedDataError)
else:
mask = None
return(SubOcgDataset(geometry,
npd,
self.timevec[timeidx],
gid=gids,
levelvec=self.levelvec[levelidx],
mask=mask,
tid=self.tids[timeidx]))
def split_subset(self,var_name,
max_proc=1,
subset_opts={}):
"""
returns -- list of SubOcgDatasets
"""
## the initial subset
ref = self.subset(var_name,**subset_opts)
## make base process map
ref_idx_array = np.arange(0,len(ref.geometry))
splits = np.array_split(ref_idx_array,max_proc)
## for the case of a single value, truncate the last split if it is
## empty
if len(splits[-1]) == 0:
splits = splits[0:-1]
## will hold the subsets
subs = []
## create the subsets
for ii,split in enumerate(splits):
geometry = ref.geometry[split]
value = ref.value[:,:,split]
gid = ref.gid[split]
sub = SubOcgDataset(geometry,
value,
ref.timevec,
gid=gid,
levelvec=ref.levelvec,
id=ii,
tid=ref.tid)
subs.append(sub)
return(subs)
def parallel_process_subsets(self,subs,polygon=None,clip=False,union=False,debug=False):
def f(out,sub,polygon,clip,union):
if clip:
sub.clip(polygon)
if union:
sub.union_nosum()
if not clip:
sub.select_values(clip=True,igeom=polygon)
else:
sub.select_values(clip=False)
out.append(sub)
if not debug:
import multiprocessing as mp
out = mp.Manager().list()
pps = [mp.Process(target=f,args=(out,sub,polygon,clip,union)) for sub in subs]
for pp in pps: pp.start()
for pp in pps: pp.join()
else:
out = []
for sub in subs:
f(out,sub,polygon,clip,union)
return(list(out))
def combine_subsets(self,subs,union=False):
## collect data from subsets
for ii,sub in enumerate(subs):
if ii == 0:
base = sub
else:
base = base.merge(sub,union=union)
## if union is true, sum the values, add new gid, and union the
## geometries.
if union:
base.geometry = np.array([cascaded_union(base.geometry)],dtype=object)
base.value = union_sum(base.weight,base.value,normalize=True)
base.gid = np.array([1])
return(base)
