def reshuffle(input_root, outputpath,
startdate, enddate,
parameters=None, land_points=True, ignore_meta=False,
imgbuffer=200):
"""
Reshuffle method applied to ESA CCI SM images.
Parameters
----------
input_root: string
input path where era interim data was downloaded
outputpath : string
Output path.
startdate : datetime
Start date.
enddate : datetime
End date.
parameters: list, optional (default: None)
parameters to read and convert
If none are passed, we read an image in the root path and use vars from
the image.
land_points : bool, optional (default: True)
Use the land grid to calculate time series on.
Leads to faster processing and smaller files.
imgbuffer: int, optional
How many images to read at once before writing time series.
"""
if land_points:
grid = CCILandGrid()
else:
grid = CCICellGrid()
if not os.path.exists(outputpath):
os.makedirs(outputpath)
file_args, file_vars = parse_filename(input_root)
if parameters is None:
parameters = [p for p in file_vars if p not in ['lat', 'lon', 'time']]
input_dataset = CCI_SM_025Ds(data_path=input_root, parameter=parameters,
subgrid=grid, array_1D=True)
if not ignore_meta:
global_attr, ts_attributes = read_metadata(sensortype=file_args['sensor_type'],
version=int(file_args['version']),
varnames=parameters)
global_attr['time_coverage_start'] = str(startdate)
global_attr['time_coverage_end'] = str(enddate)
else:
global_attr = {'product' : 'ESA CCI SM'}
ts_attributes = None
reshuffler = Img2Ts(input_dataset=input_dataset, outputpath=outputpath,
startdate=startdate, enddate=enddate, input_grid=grid,
imgbuffer=imgbuffer, cellsize_lat=5.0, cellsize_lon=5.0,
global_attr=global_attr, zlib=True,
unlim_chunksize=1000, ts_attributes=ts_attributes)
reshuffler.calc()
def __init__(self,
filename,
mode='r',
parameter=None,
subgrid=None,
array_1D=False):
super(CCI_SM_025Img, self).__init__(filename, mode=mode)
self.parameters = [parameter] if isinstance(parameter,
str) else parameter
self.grid = CCICellGrid() if not subgrid else subgrid
self.array_1D = array_1D
def __init__(self,
filename,
mode='r',
parameter='sm',
subgrid=None,
array_1D=False):
super(CCI_SM_025Img, self).__init__(filename, mode=mode)
if type(parameter) != list:
parameter = [parameter]
self.parameters = parameter
self.grid = CCICellGrid() if not subgrid else subgrid
self.array_1D = array_1D
def test_CCICellGrid(self):
grid = CCICellGrid()
gp, dist = grid.find_nearest_gpi(75.625, 14.625)
assert gp == 602942
lon, lat = grid.gpi2lonlat(602942)
assert lon == 75.625
assert lat == 14.625
assert np.where(
grid.get_grid_points()[0] == 602942)[0][0] == 434462 # index
assert grid.get_grid_points()[1][434462] == lon
assert grid.get_grid_points()[2][434462] == lat
assert grid.gpi2cell(602942) == 1856
assert grid.gpis.size == 1036800
assert grid.gpis[0] == 1035360
assert np.unique(grid.get_grid_points()[3]).size == 2592
lon, lat = grid.gpi2lonlat(642933)
assert lon == -6.625
assert lat == 21.625
assert np.where(
grid.get_grid_points()[0] == 642933)[0][0] == 393813 # index
assert grid.get_grid_points()[1][393813] == lon
assert grid.get_grid_points()[2][393813] == lat
assert grid.gpi2cell(642933) == 1246
def reshuffle(input_root,
outputpath,
startdate,
enddate,
parameters=None,
land_points=True,
ignore_meta=False,
imgbuffer=200):
"""
Reshuffle method applied to ESA CCI SM images.
Parameters
----------
input_root: string
input path where era interim data was downloaded
outputpath : string
Output path.
startdate : datetime
Start date.
enddate : datetime
End date.
parameters: list, optional (default: None)
parameters to read and convert
If none are passed, we read an image in the root path and use vars from
the image.
land_points : bool, optional (default: True)
Use the land grid to calculate time series on.
Leads to faster processing and smaller files.
imgbuffer: int, optional
How many images to read at once before writing time series.
"""
if land_points:
grid = CCILandGrid()
else:
grid = CCICellGrid()
gpis, lons, lats, cells = grid.get_grid_points()
grid_vars = {'gpis': gpis, 'lons': lons, 'lats': lats}
# repurpose cannot handle masked arrays
for k, v in grid_vars.items(): # type v: np.ma.MaskedArray
if isinstance(v, np.ma.MaskedArray):
grid_vars[k] = v.filled()
grid = BasicGrid(lon=grid_vars['lons'],
lat=grid_vars['lats'],
gpis=grid_vars['gpis']).to_cell_grid(5.)
if not os.path.exists(outputpath):
os.makedirs(outputpath)
file_args, file_vars = parse_filename(input_root)
if parameters is None:
parameters = [p for p in file_vars if p not in ['lat', 'lon', 'time']]
input_dataset = CCI_SM_025Ds(data_path=input_root,
parameter=parameters,
subgrid=grid,
array_1D=True)
if not ignore_meta:
global_attr, ts_attributes = read_metadata(
sensortype=file_args['sensor_type'],
version=int(file_args['version']),
varnames=parameters,
subversion=file_args['sub_version'])
else:
global_attr = {'product': 'ESA CCI SM'}
ts_attributes = None
reshuffler = Img2Ts(input_dataset=input_dataset,
outputpath=outputpath,
startdate=startdate,
enddate=enddate,
input_grid=grid,
imgbuffer=imgbuffer,
cellsize_lat=5.0,
cellsize_lon=5.0,
global_attr=global_attr,
zlib=True,
unlim_chunksize=1000,
ts_attributes=ts_attributes)
reshuffler.calc()