def create_custom_grid(ds, filename, var_name, dl, custom_grid):
cgrid = xr.open_dataset(custom_grid)
clon = cgrid['lon']
clat = cgrid['lat']
cgrid_def = geometry.GridDefinition(lons=clon, lats=clat)
ogrid_def = geometry.GridDefinition(lons=ds['lon'], lats=ds['lat'])
# Convert data to custom grid
obs_container = image.ImageContainerNearest(ds[var_name].data[0, :, :],
ogrid_def,
radius_of_influence=20000000)
obs_modelgrid = obs_container.resample(cgrid_def)
data_out = obs_modelgrid.image_data
obs_container = image.ImageContainerNearest(ds['error_std'].data[0, :, :],
ogrid_def,
radius_of_influence=20000000)
obs_modelgrid = obs_container.resample(cgrid_def)
err_out = obs_modelgrid.image_data
ds = xr.Dataset(
{
var_name: (('time', 'x', 'y'), np.expand_dims(data_out, axis=0)),
"error_std": (('time', 'x', 'y'), np.expand_dims(err_out, axis=0)),
"lon": (('x', 'y'), clon),
"lat": (('x', 'y'), clat)
},
coords={
'time': dl[0:1],
},
)
ds.to_netcdf(filename)
def reproject2(data,
lon0,
lat0,
lon1,
lat1,
fill=-9999,
nearest=True,
radius_of_influence=100):
from pyresample import image, geometry
source_def = geometry.SwathDefinition(lons=lon0, lats=lat0)
target_def = geometry.SwathDefinition(lons=lon1, lats=lat1)
if nearest:
source_con = image.ImageContainerNearest(
data, source_def, radius_of_influence=radius_of_influence)
target_con = source_con.resample(target_def)
target_con.fill_value = fill
result = target_con.image_data
else:
from pyresample import kd_tree
result = kd_tree.resample_gauss(
source_def,
data,
target_def,
radius_of_influence=radius_of_influence,
neighbours=1,
sigmas=0,
fill_value=fill)
return result
def resample_it(lat,
lon,
data,
file_str,
channel,
output_path="/home/off/HSAF_SRC/offline/H35/input"):
date_ = datetime.datetime.strptime(file_str.split("_")[4], "%Y%m%d%H%M%S")
date_str = date_.strftime("%Y%m%d")
time_str = date_.strftime("%H%M")
from pyresample import kd_tree, image, geometry
_p_type = 'M01'
area_def = geometry.AreaDefinition('a', 'b', 'c', {'proj': 'longlat'},
35999, 8999, [-180, 0, 180, 90])
swath_def = geometry.SwathDefinition(lons=lon, lats=lat)
swath_con = image.ImageContainerNearest(data,
swath_def,
radius_of_influence=5000)
area_con = swath_con.resample(area_def)
# area_con = kd_tree.resample_nearest(swath_def, data.ravel(), area_def, radius_of_influence = 5000, nprocs = 10)
im_data = area_con.image_data
with h5py.File(
os.path.join(
output_path, "eps_" + _p_type + "_" + date_str + "_" +
time_str + "_" + time_str + '__' + str(channel) + '.hdf'),
'w') as h5file:
h5file.create_dataset('/' + str(channel),
shape=im_data.shape,
dtype=im_data.dtype,
data=im_data)
def test_ref_proj(self):
import h5py
from pyresample import image, geometry
rc_file = r"D:\WorkSpace\20200429\project\data\LUT\ref_065_clear_001.h5"
rc_f = h5py.File(rc_file, 'r')
ds = rc_f['ref_065_clear']
ref_065_clear_gll = np.ma.masked_values(ds[...], ds.attrs['fill_value'])
ref_065_clear_gll = ref_065_clear_gll * ds.attrs['scale_factor']
ref_lon = rc_f['lon'][...]
ref_lat = rc_f['lat'][...]
obj_lat = self.fy4_nav.get_latitude()
obj_lon = self.fy4_nav.get_longitude()
obj_swath_def = geometry.SwathDefinition(
lons=obj_lon,
lats=obj_lat)
ref_lon, ref_lat = np.meshgrid(ref_lon, ref_lat)
ref_swath_def = geometry.SwathDefinition(
lons=ref_lon,
lats=ref_lat)
ref_swath_con = image.ImageContainerNearest(
ref_065_clear_gll,
ref_swath_def,
radius_of_influence=20000,
fill_value=65535)
area_con = ref_swath_con.resample(obj_swath_def)
ref_065_clear_c = area_con.image_data
plt.imshow(ref_065_clear_c)
plt.show()
def goes_2_roi(loaded_goes,
target_extent,
target_rows,
target_cols,
cartopy_target_proj,
data_key='Rad',
radius_of_influence=50000):
"""Function that goes from loaded GOES data to data resampled in a
projection for an extent"""
dat = loaded_goes.metpy.parse_cf(data_key)
geos_crs = dat.metpy.cartopy_crs
cartopy_source_extent = geos_crs.x_limits + geos_crs.y_limits
pyresample_source_extent = (cartopy_source_extent[0],
cartopy_source_extent[2],
cartopy_source_extent[1],
cartopy_source_extent[3])
rad = dat.data
source_area = geometry.AreaDefinition('GOES-1X', 'Full Disk', 'GOES-1X',
geos_crs.proj4_params, rad.shape[1],
rad.shape[0],
pyresample_source_extent)
area_target_def = geometry.AreaDefinition('areaTest', 'Target Region',
'areaTest',
cartopy_target_proj.proj4_params,
target_rows, target_cols,
target_extent)
geos_con_nn = image.ImageContainerNearest(
rad, source_area, radius_of_influence=radius_of_influence)
# Here we are using pyresample for the remapping
area_proj_con_nn = geos_con_nn.resample(area_target_def)
return area_proj_con_nn.image_data
def resample(proj_def, data_def, data, fillValue, resampleRad):
"""Resample the data into the projected area using nearest neighbor.
Input:
proj_def: pyresample projection (grid) definition
data_def: pyresample satellite data geometry definition
(swath for polar or geostationary grid)
data: numpy data array
fillValue: fill value for data grid
resampleRad: radius in meters to look for nearest neighbors from
grid pixel in swath/GOES
Output:
resampled_data: numpy data array resampled to the proj_def grid
"""
log.info("Resampling the data to target projection grid")
data_con = image.ImageContainerNearest(data,
data_def,
radius_of_influence=resampleRad,
fill_value=fillValue,
nprocs=2)
image_con = data_con.resample(proj_def)
resampled_data = image_con.image_data
return resampled_data
def resample(layer, tl, br, samples=256):
"""
Returns a grid, which is resampled.
"""
data_grid = geometry.GridDefinition(lats=layer.lats, lons=layer.lons)
# Form the coordinates for resampling
rlons = np.linspace(tl[0], br[0], 256)
rlats = np.linspace(tl[1], br[1], 256)
resample_grid = geometry.GridDefinition(
lats=np.tile(rlats, (rlons.size, 1)).T,
lons=np.tile(rlons, (rlats.size, 1))
)
# Build a resampler.
resampler = image.ImageContainerNearest(
layer.values,
data_grid,
radius_of_influence=6500,
reduce_data=True
)
# Form the appropriate grid.
grid = np.flipud(resampler.resample(resample_grid).image_data)
grid[grid == 0] = np.nan
return grid
def reproject(dataset, inAreaDefinition, outAreaDefinition):
msg_con_nn = image.ImageContainerNearest(dataset,
inAreaDefinition,
radius_of_influence=2000)
area_con_nn = msg_con_nn.resample(outAreaDefinition)
return (area_con_nn)
def interp_to_obs(var, df, lat, lon, radius=12000.):
"""Short summary.
Parameters
----------
var : type
Description of parameter `var`.
df : type
Description of parameter `df`.
lat : type
Description of parameter `lat`.
lon : type
Description of parameter `lon`.
radius : type
Description of parameter `radius` (the default is 12000.).
Returns
-------
type
Description of returned object.
"""
from numpy import NaN, vstack
from pyresample import geometry, image
from pandas import to_timedelta, DataFrame
# define CMAQ pyresample grid (source)
grid1 = geometry.GridDefinition(lons=lon, lats=lat)
# get unique sites from df
dfn = df.drop_duplicates(subset=['Latitude', 'Longitude'])
# define site grid (target)
lats = dfn.Latitude.values
lons = dfn.Longitude.values
grid2 = geometry.GridDefinition(lons=vstack(lons), lats=vstack(lats))
# Create image container
i = image.ImageContainerNearest(var.transpose('y', 'x', 'time').values,
grid1,
radius_of_influence=radius,
fill_value=NaN)
# resample
ii = i.resample(grid2).image_data.squeeze()
# recombine data
e = DataFrame(ii, index=dfn.SCS, columns=var.time.values)
w = e.stack().reset_index().rename(columns={
'level_1': 'datetime',
0: 'model'
})
w = w.merge(dfn.drop(['datetime', 'datetime_local', 'Obs'], axis=1),
on='SCS',
how='left')
w = w.merge(df[['datetime', 'SCS', 'Obs']],
on=['SCS', 'datetime'],
how='left')
# calculate datetime local
w['datetime_local'] = w.datetime + to_timedelta(w.utcoffset, 'H')
return w
def map_slice(lon_slice, lat_slice, data_slice):
"""
given a slice, return data mapped to a laea projection and the crs dicitonary
for the projection
Parameters
----------
lon_slice,lat_slice,data_slice: 2d arrays (float)
3 2-d arrays of the same shape giving longitudes, latitudes and data values
for each pixel
Returns
-------
mapped_data: 2d array (float)
2-d array with same number of rows and columns as data_slice, projected
to new coordinate system
dst_crs: dict
dictionary with keys needed by pyprojto create the destination projection
"""
llcrnrlat = lat_slice[-1, 0]
llcrnrlon = lon_slice[-1, 0]
urcrnrlat = lat_slice[0, -1]
urcrnrlon = lon_slice[0, -1]
src_crs = dict(units='m', proj='eqc', datum='WGS84')
src_proj = pyproj.Proj(src_crs)
llcrnrx, llcrnry = src_proj(llcrnrlon, llcrnrlat)
urcrnrx, urcrnry = src_proj(urcrnrlon, urcrnrlat)
src_extent = [llcrnrx, llcrnry, urcrnrx, urcrnry]
src_height, src_width = data_slice.shape
from_def = geometry.AreaDefinition('src', 'src image', 'area_src', src_crs,
src_width, src_height, src_extent)
lat_0 = (lat_slice[0, 0] + lat_slice[-1, 0]) / 2.
lon_0 = (lon_slice[0, 0] + lon_slice[0, -1]) / 2.
dst_crs = {
'datum': 'WGS84',
'lat_0': lat_0,
'lon_0': lon_0,
'proj': 'laea'
}
dst_proj = pyproj.Proj(dst_crs)
llcrnrx, llcrnry = dst_proj(llcrnrlon, llcrnrlat)
urcrnrx, urcrnry = dst_proj(urcrnrlon, urcrnrlat)
dst_extent = [llcrnrx, llcrnry, urcrnrx, urcrnry]
to_def = geometry.AreaDefinition('big', 'big image', 'area_big', dst_crs,
src_width, src_height, dst_extent)
from_nn = image.ImageContainerNearest(par_slice,
from_def,
radius_of_influence=50000)
to_nn = from_nn.resample(to_def)
mapped_data = to_nn.image_data
return mapped_data, dst_crs
def test_nearest_resize(self):
data = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
msg_con = image.ImageContainerNearest(
data, self.msg_area, 50000, segments=1)
area_con = msg_con.resample(self.msg_area_resize)
res = area_con.image_data
cross_sum = res.sum()
expected = 2212023.0175830
self.assertAlmostEqual(cross_sum, expected,
msg='ImageContainer resampling nearest neighbour failed')
def test_nearest_neighbour(self):
data = numpy.fromfunction(lambda y, x: y * x * 10**-6, (3712, 3712))
msg_con = image.ImageContainerNearest(data,
self.msg_area,
50000,
segments=1)
area_con = msg_con.resample(self.area_def)
res = area_con.image_data
cross_sum = res.sum()
expected = 399936.70287099993
self.assertAlmostEqual(cross_sum, expected)
def export_l3_mask(self, griddef, nc_filepath=None):
""" Create a gridded mask product in pysiral compliant filenaming.
The argument griddef is needs to be a pysiral.grid.GridDefinition
instance """
# Get the area definition for the grid
if not isinstance(griddef, GridDefinition):
msg = "griddef needs to be of type pysiral.grid.GridDefinition"
self.error.add_error("value-error", msg)
# Resample the mask
if self.cfg.pyresample_method == "ImageContainerNearest":
resample = image.ImageContainerNearest(self.source_mask,
self.source_area_def,
**self.cfg.pyresample_keyw)
resample_result = resample.resample(griddef.pyresample_area_def)
target_mask = resample_result.image_data
elif self.cfg.pyresample_method == "resample_gauss":
result, stddev, count = kd_tree.resample_gauss(
self.source_area_def,
self.source_mask,
griddef.pyresample_area_def,
with_uncert=True,
**self.cfg.pyresample_keyw)
target_mask = result
else:
msg = "Unrecognized opt pyresample_method: %s need to be %s" % (
str(self.cfg.pyresample_method),
"(ImageContainerNearest, resample_gauss)")
self.error.add_error("invalid-pr-method", msg)
self.error.add_error()
# pyresample may use masked arrays -> set nan's to missing data
try:
target_mask[np.where(target_mask.mask)] = np.nan
except AttributeError:
pass
if "post_processing" in self.cfg:
pp_method = getattr(self, self.cfg.post_processing)
target_mask = pp_method(target_mask, griddef)
# Write the mask to a netCDF file
# (the filename will be automatically generated if not specifically
# passed to this method
if nc_filepath is None:
nc_filename = "%s_%s.nc" % (self.mask_name, griddef.grid_id)
nc_filepath = os.path.join(self.mask_dir, nc_filename)
self.log.info("Export mask file: %s" % nc_filepath)
self._write_netcdf(nc_filepath, griddef, target_mask)
def test_nearest_swath(self):
data = numpy.fromfunction(lambda y, x: y * x, (50, 10))
lons = numpy.fromfunction(lambda y, x: 3 + x, (50, 10))
lats = numpy.fromfunction(lambda y, x: 75 - y, (50, 10))
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
swath_con = image.ImageContainerNearest(
data, swath_def, 50000, segments=1)
area_con = swath_con.resample(self.area_def)
res = area_con.image_data
cross_sum = res.sum()
expected = 15874591.0
self.assertEqual(cross_sum, expected,
msg='ImageContainer swath resampling nearest failed')
def resample_sourcegrid_to_targetgrid(self):
""" Use pyresample for nearest neighbour resampling for gridded source
data """
# Resample the image
resample_container = image.ImageContainerNearest(
self.source_thickness,
self.source_areadef,
radius_of_influence=25000,
fill_value=None)
resample_result = resample_container.resample(TARGET_AREA_DEF)
# pyresample uses masked arrays -> set nan's to missing data
data_ease2 = resample_result.image_data
data_ease2[np.where(data_ease2.mask)] = np.nan
self.thickness = data_ease2
def test_nearest_neighbour_multi(self):
data1 = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
data2 = numpy.fromfunction(
lambda y, x: y * x * 10 ** -6, (3712, 3712)) * 2
data = numpy.dstack((data1, data2))
msg_con = image.ImageContainerNearest(
data, self.msg_area, 50000, segments=1)
area_con = msg_con.resample(self.area_def)
res = area_con.image_data
cross_sum1 = res[:, :, 0].sum()
expected1 = 399936.783062
self.assertAlmostEqual(cross_sum1, expected1,
msg='ImageContainer resampling nearest neighbour multi failed')
cross_sum2 = res[:, :, 1].sum()
expected2 = 399936.783062 * 2
self.assertAlmostEqual(cross_sum2, expected2,
msg='ImageContainer resampling nearest neighbour multi failed')
def pre_processGOES(nc_folder, latbox, lonbox, path2save, date, bands):
full_direc = os.listdir(nc_folder) # creating a list of files in the given folder
nc_files = [ii for ii in full_direc if ii.endswith('.nc')] # creating a list with only NetCDF files in the given folder
for band in range(0, len(nc_files)): # iterating over all the bands
g16_data_file = nc_files[band] # selecting the NetCDF file according to the band
g16nc = Dataset(os.path.join(nc_folder, g16_data_file) , 'r') # opening the NetCDF file to read
print('preprocessing band ', bands[band], g16_data_file)
cmiValues = g16nc.variables['CMI'][:] # selecting the CMI variable
latValues, lonValues, newArea, oldArea = reproj(nc_folder, band) #calling the reprojection function and assigning the variables
fileName = 'band'+str(band)+'/'+'goes_'+'{0:02d}'.format(date.hour)+'.nc' # creating the file name
dataset = Dataset(os.path.join(path2save, fileName), 'w', format='NETCDF4', set_fill_off=True) # creating the new netCDF file
# adding dimensions
dataset.createDimension('time', None)
dataset.createDimension('lat', len(latValues))
dataset.createDimension('lon', len(lonValues))
# adding variables
time = dataset.createVariable('time', 'f8', ('time',), zlib=True, shuffle=True, fill_value=0.)
lon = dataset.createVariable('lon', 'f8', ('lon',), zlib=True, shuffle=True, least_significant_digit=4)
lat = dataset.createVariable('lat', 'f8', ('lat',), zlib=True, shuffle=True, least_significant_digit=4)
CMI = dataset.createVariable('CMI', 'f8', ('time', 'lat', 'lon'), fill_value=-1, zlib=True, shuffle=True, least_significant_digit=6)
# adding variables atributtes
lat.long_name = 'Latitude'
lat.units = 'degrees_north'
lat.standard_name = 'latitude'
lon.long_name = 'Longitude'
lon.units = 'degrees_east'
lon.standard_name = 'longitude'
time.long_name = 'Time'
time.units = 'hours since ' + str(date.year) + '-' + str(date.month) + '-' + str(date.day) + ' 00:00:00'
time.standard_name = 'time'
time.axis = 'T'
# assigning the values to the new variables
CC = open_dataset(nc_files[0], mask_and_scale=True, decode_times=False)
lat[:] = latValues[:]
lon[:] = lonValues[:]
time[:] = [date.hour]
# reprojecting the CMI variable to the new domain
CMI[0, :, :] = image.ImageContainerNearest(np.round(cmiValues, decimals=6), oldArea, radius_of_influence=10000, nprocs=8).resample(newArea).image_data
dataset.close() # closing the new NetCDF
def _remap_a_score_on_an_area(self, plot_area_name='npole', vmin=0.0, vmax=1.0,
score='Kuipers'):
from pyresample import image, geometry
area_def = utils.parse_area_file(
'reshaped_files_plotting/region_config_test.cfg',
plot_area_name)[0]
data = getattr(self, score)
data = data.copy()
if np.ma.is_masked(data):
data[np.logical_and(np.equal(data.mask,False),data>vmax)]=vmax
data[np.logical_and(np.equal(data.mask,False),data<vmin)]=vmin #do not wan't low ex hitrates set to nodata!
else:
data[data>vmax]=vmax
data[data<vmin]=vmin
#lons = np.ma.masked_array(self.lons, mask=data.mask)
#lats = np.ma.masked_array(self.lats, mask=data.mask)
lons = self.lons
lats = self.lats
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
swath_con = image.ImageContainerNearest(
data, swath_def,
radius_of_influence=self.radius_km*1000*2.5,
epsilon=1.0)
area_con = swath_con.resample(area_def)
result = area_con.image_data
#pr.plot.show_quicklook(area_def, result,
# vmin=vmin, vmax=vmax, label=score)
if "FAR" in score:
matplotlib.rcParams['image.cmap']= "BrBG"
elif "diff" in score:
matplotlib.rcParams['image.cmap']= "BrBG"
elif "mae" in score:
matplotlib.rcParams['image.cmap']= "Reds"
else:
matplotlib.rcParams['image.cmap']= "BrBG"
plot_label =score
if "mae" in score:
plot_label =""
pr.plot.save_quicklook(self.PLOT_DIR_SCORE + self.PLOT_FILENAME_START+
plot_area_name +'.png',
area_def, result,
vmin=vmin, vmax=vmax, label=plot_label)
def sample_latlon(layer, lat, lon):
"""
Returns a float which is a value grid, which is resampled.
"""
data_grid = geometry.GridDefinition(lats=layer.lats, lons=layer.lons)
resampler = image.ImageContainerNearest(
layer.values,
data_grid,
radius_of_influence=6500,
reduce_data=False
)
resample_grid = geometry.GridDefinition(
lats=np.ones((1, 1)) * lat,
lons=np.ones((1, 1)) * lon)
# Form the appropriate grid.
grid = resampler.resample(resample_grid).image_data
return float(grid[0][0])
def grid_interpolation(src_grid,
tar_grid,
radius_of_influence=500000,
fill_value=None):
src_lat = src_grid['lat']
src_lon = src_grid['lon']
src_data = src_grid['data']
tar_lat = tar_grid['lat']
tar_lon = tar_grid['lon']
src_lon = src_lon % 360.0
tar_lon = tar_lon % 360.0
src_grid_def = geometry.GridDefinition(lons=src_lon, lats=src_lat)
tar_grid_def = geometry.GridDefinition(lons=tar_lon, lats=tar_lat)
src_img_container = image.ImageContainerNearest(
src_data,
src_grid_def,
radius_of_influence=radius_of_influence,
fill_value=fill_value)
tar_data = src_img_container.resample(tar_grid_def)
return tar_data.image_data
area_extent = area_extent)
return ioa1
ioa1 = IOAD1()
ioa_lon, ioa_lat = ioa1.get_lonlats()
ioa_lon = ioa_lon[0,:]
ioa_lat = ioa_lat[:,0]
# Initialize vector coordinates for Dataset
arr = np.empty([ioa_lat.shape[0], ioa_lon.shape[0], len(files)])
tt = np.empty([len(files)], dtype = 'datetime64[s]')
for ii in range(len(files)):
xc = xr.open_dataset(files[ii]).load()
goesproj = goesAD(xc)
arr[:,:,ii] = image.ImageContainerNearest(np.round(xc.CMI.data, decimals=6), goesproj, radius_of_influence=10000, nprocs=8).resample(ioa1).image_data
tt[ii] = xc.t.data
# NEW xarray Dataset
nwnc = xr.Dataset(coords={'lon': ('lon' , ioa_lon), 'lat': ('lat', ioa_lat), 'time': tt})
nwnc['CMI'] = (['lat', 'lon', 'time'], arr)
# Remove variables
del arr, tt, xc
# Basic Attributes for the new NETCDF
nwnc.time.attrs['axis'] = "time"
nwnc.CMI.attrs['units'] = "K"
nwnc.CMI.attrs['axis'] = "lat lon time"
nwnc.CMI.attrs['sensor_band_bit_depth'] = 12
grib3 = pygrib.open(file3)
apcp3 = grib3.read(1)[0]
pcp3 = apcp3.values.data
pcp3[apcp3.values.mask] = 0.
except OSError:
print('File not found: '+file3)
print('Setting nan field')
pcp3 = np.full_like(obs_lat, np.nan)
# sum for 3-hour total
tot_pcp = pcp1 + pcp2 + pcp3
# regrid 3-hour total to forecast grid
print('Regridding 3-hr total precipitation')
resample_quick = image.ImageContainerNearest(tot_pcp, obs_grid, radius_of_influence=12000)
resample_obs = resample_quick.resample(fcst_grid_12km)
new_tot_pcp = resample_obs.image_data
# write the 3-hour total to file
print('Writing to file: '+outname_03h)
total_precipitation[k, :, :] = new_tot_pcp
# write the time for the 3-hour precipitation
time_03h[k,] = date2num(date1, time_unit)
pcp_obj = [pcp1, pcp2, pcp3]
time_obj = [time1, time2, time3]
# regrib and write the hourly totals to file
def test_Ref063Day(self):
lut_file_name = 'Ref_063_Day.nc'
lut_file_path = os.path.join(data_root_dir, 'LUT', lut_file_name)
# 063 ref
import h5py
from pyresample import image, geometry
rc_file = os.path.join(data_root_dir, 'LUT', "ref_065_clear_001.h5")
rc_f = h5py.File(rc_file, 'r')
ds = rc_f['ref_065_clear']
ref_065_clear_gll = np.ma.masked_values(ds[...], ds.attrs['fill_value'])
ref_065_clear_gll = ref_065_clear_gll * ds.attrs['scale_factor']
ref_lon = rc_f['lon'][...]
ref_lat = rc_f['lat'][...]
obj_lat = self.fy4_nav.get_latitude()
obj_lon = self.fy4_nav.get_longitude()
obj_swath_def = geometry.SwathDefinition(
lons=obj_lon,
lats=obj_lat)
ref_lon, ref_lat = np.meshgrid(ref_lon, ref_lat)
ref_swath_def = geometry.SwathDefinition(
lons=ref_lon,
lats=ref_lat)
ref_swath_con = image.ImageContainerNearest(
ref_065_clear_gll,
ref_swath_def,
radius_of_influence=20000,
fill_value=65535)
area_con = ref_swath_con.resample(obj_swath_def)
ref_065_clear_c = area_con.image_data * 100
# obs mask
ref_065 = self.fy4_l1.get_band_by_channel('ref_065') * 100
bt_1080 = self.fy4_l1.get_band_by_channel('bt_1080')
# day mask (covnert to bool)
sun_zen = self.fy4_geo.get_sun_zenith()
sat_zen = self.fy4_geo.get_satellite_zenith()
pix_lat = self.fy4_nav.get_latitude()
pix_lon = self.fy4_nav.get_longitude()
sun_glint = self.fy4_geo.get_sun_glint()
sat_lat = 0
sat_lon = 104.7
# scatter angle
scat_ang = infer_scat_angle_short(pix_lat, pix_lon, sat_lat, sat_lon, sun_zen, sat_zen)
# air mass
air_mass = infer_airmass(sat_zen, sun_zen)
# dem
dem = self.fy4_nav.get_dem()
sft = self.fy4_nav.prepare_surface_type_to_cspp()
# snow mask
snow_mask = self.fy4_nav.get_snow_mask()
snow_mask = snow_mask == 3
# space mask
space_mask = self.fy4_nav.get_space_mask(b=True)
ref063day = Ref063Day(lut_file_path=lut_file_path)
x = ref063day.prepare_feature(ref_065, ref_065_clear_c)
valid_mask = ref063day.prepare_valid_mask(ref_065, ref_065_clear_c, dem, sft, sun_glint, sun_zen, scat_ang,
air_mass, snow_mask, space_mask, bt_1080)
ratio, prob = ref063day.infer(x, sft, valid_mask, space_mask, prob=True)
fig, ax = plt.subplots(1, 3, figsize=(10, 10))
ax[0].imshow(valid_mask, 'plasma')
ax[0].set_title(ref063day.short_name + ' valid mask \n')
pos = ax[1].imshow(ratio, 'plasma')
ax[1].set_title(ref063day.short_name + ' Ratio \n')
fig.colorbar(pos, ax=ax[1])
pos = ax[2].imshow(prob, 'plasma', vmin=0, vmax=1)
ax[2].set_title(ref063day.short_name + ' Prob \n')
fig.colorbar(pos, ax=ax[2])
plt.show()
HH = ABI_time.strftime('%H')
MM = ABI_time.strftime('%M') #Minute
#Loading in the GLM data
GLM_file = 'GLM5-' + YYYY + mm + dd + '-' + HH + MM + '.nc'
GLM_data = nc.Dataset(GLM_loc + GLM_file, 'r')
GLM_x = np.load('/localdata/coordinates/ABI/x2km.npy')
GLM_y = np.load('/localdata/coordinates/ABI/y2km.npy')
GLM_var = GLM_data.variables[v[GLMp][1]][:, :]
GLM_var = np.ma.filled(GLM_var, fill_value=0)
if (ABIp == 'CMIP13') | (ABIp == 'ACTP'):
ABI_new = ABI_var
else:
swath_con = image.ImageContainerNearest(ABI_var,
swath_def10,
radius_of_influence=10000)
swath_resampled = swath_con.resample(swath_def2)
ABI_new = swath_resampled.image_data
#Creating the composites
ABI_new = np.expand_dims(ABI_new, axis=0)
GLM_var = np.expand_dims(GLM_var, axis=0)
if GLM_composite_a.shape[0] < halfway:
ABI_composite_a = np.append(ABI_composite_a,
ABI_new[:, ::10, ::10],
axis=0)
GLM_composite_a = np.append(GLM_composite_a,
GLM_var[:, ::10, ::10],
axis=0)
def write_results(date, enkf_c_dir, ens_out_dir, Nens, save_dir, obs_list):
smnd = str(date.month) if date.month > 9 else '0' + str(date.month)
sday = str(date.day) if date.day > 9 else '0' + str(date.day)
file = save_dir + 'Assim_summary_' + str(date.year) + smnd + sday + '.nc'
# Generate the netcdf, shoudl contain aice, vice, before and after in addition,
# mem1 aicen before and after and sst and vice
# Can add more later on
# Read in temp file and use this as template for the dimensions
print(enkf_c_dir + 'ensemble_6565/mem001_temp.nc')
tt = xr.open_dataset(enkf_c_dir + 'ensemble_6565/mem001_temp.nc')
temp = tt['temp']
print(file)
ds = nc.Dataset(file, 'w', format='NETCDF4')
time = ds.createDimension('time', None)
times = ds.createVariable('time', 'f4', ('time', ))
times[:] = nc.date2num(date,
units='days since 1990-01-01',
calendar='gregorian')
times.units = 'days since 1990-01-01'
times.calendar = 'gregorian'
de = ds.createDimension('de', Nens) # Ens
di = ds.createDimension('di', 5) # Ice categories
dz = ds.createDimension('dz', temp.shape[1]) # Depth levels
dx = ds.createDimension('dx', temp.shape[2])
dy = ds.createDimension('dy', temp.shape[3])
des = ds.createVariable('de', 'f4', ('de', ))
dis = ds.createVariable('di', 'f4', ('di', ))
dzs = ds.createVariable('dz', 'f4', ('dz', ))
dxs = ds.createVariable('dx', 'f4', ('dx', ))
dys = ds.createVariable('dy', 'f4', ('dy', ))
#print(Nens)
#print(np.arange(0, Nens, 1.0))
des[:] = np.arange(0, Nens, 1.0)
dis[:] = np.arange(0, 5, 1.0)
dzs[:] = np.arange(0, temp.shape[1], 1.0)
dxs[:] = np.arange(0, temp.shape[2], 1.0)
dys[:] = np.arange(0, temp.shape[3], 1.0)
tt.close()
aicen_mem1_before = ds.createVariable('aicen1_inn', 'f4', (
'time',
'di',
'dx',
'dy',
))
aicen_mem1_after = ds.createVariable('aicen1_out', 'f4', (
'time',
'di',
'dx',
'dy',
))
vicen_mem1_before = ds.createVariable('vicen1_inn', 'f4', (
'time',
'di',
'dx',
'dy',
))
vicen_mem1_after = ds.createVariable('vicen1_out', 'f4', (
'time',
'di',
'dx',
'dy',
))
aice_before = ds.createVariable('aice_inn', 'f4', (
'time',
'de',
'dx',
'dy',
))
aice_after = ds.createVariable('aice_out', 'f4', (
'time',
'de',
'dx',
'dy',
))
vice_before = ds.createVariable('vice_inn', 'f4', (
'time',
'de',
'dx',
'dy',
))
vice_after = ds.createVariable('vice_out', 'f4', (
'time',
'de',
'dx',
'dy',
))
temp_mem1_before = ds.createVariable('temp1_inn', 'f4', (
'time',
'dz',
'dx',
'dy',
))
temp_mem1_after = ds.createVariable('temp1_out', 'f4', (
'time',
'dz',
'dx',
'dy',
))
sst_before = ds.createVariable('sst_inn', 'f4', (
'time',
'de',
'dx',
'dy',
))
sst_after = ds.createVariable('sst_out', 'f4', (
'time',
'de',
'dx',
'dy',
))
file_ens = open(enkf_c_dir + 'files_in_ensemble', 'r')
Lines = file_ens.readlines()
file_count = 0
for ll in Lines:
file_count += 1
sens = str(file_count) if file_count > 9 else '0' + str(file_count)
file_out_ice = ens_out_dir + 'iced.' + str(
date.year) + smnd + sday + '_' + ll[0:-1] + '.nc'
file_out_ocn = ens_out_dir + 'ocean.' + str(
date.year) + smnd + sday + '_' + ll[0:-1] + '.nc'
############ Write the inn first ###################
# Write aice_inn to res
file_inn = enkf_c_dir + 'ensemble_6565/mem0' + sens + '_aice.nc'
handle = xr.open_dataset(file_inn)
aice_before[0, int(ll[0:-1]) - 1, :, :] = handle['aice'][0, :, :]
handle.close()
# Write vice_inn to res
file_inn = enkf_c_dir + 'ensemble_6565/mem0' + sens + '_vice.nc'
handle = xr.open_dataset(file_inn)
vice_before[0, int(ll[0:-1]) - 1, :, :] = handle['vice'][0, :, :]
handle.close()
# Write sst_inn to res
file_inn = enkf_c_dir + 'ensemble_6565/mem0' + sens + '_sst.nc'
handle = xr.open_dataset(file_inn)
sst_before[0, int(ll[0:-1]) - 1, :, :] = handle['sst'][0, :, :]
handle.close()
# Write the full member 1 states
if file_count == 1:
file_inn = enkf_c_dir + 'ensemble_6565/mem0' + sens + '_aicen.nc'
handle = xr.open_dataset(file_inn)
aicen_mem1_before[0, :, :, :] = handle['aicen'][0, :, :, :]
nx_size = handle['aicen'].shape[2]
handle.close()
file_inn = enkf_c_dir + 'ensemble_6565/mem0' + sens + '_vicen.nc'
handle = xr.open_dataset(file_inn)
vicen_mem1_before[0, :, :, :] = handle['vicen'][0, :, :, :]
handle.close()
file_inn = enkf_c_dir + 'ensemble_6565/mem0' + sens + '_temp.nc'
handle = xr.open_dataset(file_inn)
temp_mem1_before[0, :, :, :] = handle['temp'][0, :, :, :]
handle.close()
###################################################################
##################### Write the out results #####################
handle = xr.open_dataset(file_out_ocn)
sst_after[0, int(ll[0:-1]) - 1, :, :] = handle['temp'][0, 41, :, :]
if file_count == 1:
temp_mem1_after[0, :, :, :] = handle['temp'][0, :, :, :]
handle.close()
handle = xr.open_dataset(file_out_ice)
nx_size2 = handle['aicen'].shape[1]
ice_halo_cells = True if nx_size2 > nx_size else False
if ice_halo_cells:
aice_after[0,
int(ll[0:-1]) - 1, :, :] = np.sum(handle['aicen'][:,
1:-1,
1:-1],
axis=0)
vice_after[0,
int(ll[0:-1]) - 1, :, :] = np.sum(handle['vicen'][:,
1:-1,
1:-1],
axis=0)
if file_count == 1:
aicen_mem1_after[0, :, :, :] = handle['aicen'][:, 1:-1, 1:-1]
vicen_mem1_after[0, :, :, :] = handle['vicen'][:, 1:-1, 1:-1]
else:
aice_after[0, int(ll[0:-1]) - 1, :, :] = np.sum(
handle['aicen'][:, :, :], axis=0)
vice_after[0, int(ll[0:-1]) - 1, :, :] = np.sum(
handle['vicen'][:, :, :], axis=0)
if file_count == 1:
aicen_mem1_after[0, :, :, :] = handle['aicen'][:, :, :]
vicen_mem1_after[0, :, :, :] = handle['vicen'][:, :, :]
####################################################################
##################### Also write the observations for easy reference? #####
# Might convert it first so it might be easier to compare? ################
# With several observations potentially a list of string could be used here,
#OSISAF
file_osisaf = enkf_c_dir + 'obs/OSISAF/this_day.nc'
file_amsr = enkf_c_dir + 'obs/AMSR/this_day.nc'
grid_file = enkf_c_dir + 'conf/new_grid_ice.nc'
Nens = ds.createDimension('Nens', None)
Obs1 = ds.createVariable('Obs1', 'f4', (
'time',
'dx',
'dy',
'Nens',
))
handle2 = xr.open_dataset(grid_file)
lon_mod = handle2['lon']
lat_mod = handle2['lat']
mod_grid_def = geometry.GridDefinition(lons=lon_mod, lats=lat_mod)
i = -1
for obs in obs_list:
fileobs = enkf_c_dir + '/obs/' + obs + '/this_day.nc'
if os.path.exists(fileobs):
i += 1
if obs == 'AMSR' or obs == 'SSMIS':
varname = 'ice_conc'
elif obs == 'SMOS' or obs == 'CRYO':
varname = 'sit'
elif obs == 'MUR':
varname = 'sst'
handle = xr.open_dataset(fileobs)
ice_conc = handle[varname]
lon_obs = handle['lon']
lat_obs = handle['lat']
obs_grid_def = geometry.GridDefinition(lons=lon_obs, lats=lat_obs)
# Fix future warning!
obs_container = image.ImageContainerNearest(
ice_conc[0, :, :].values,
obs_grid_def,
radius_of_influence=20000)
obs_modelgrid = obs_container.resample(mod_grid_def)
res = obs_modelgrid.image_data
Obs1[0, :, :, i] = res[:]
ds.close()
encodedic['time'] = {'units':'seconds since 2000-01-01 12:00:00', 'calendar':'gregorian'}
# Organizing data
for file in range(nfiles):
frame = xr.open_dataset(file_b[file])
arrayshape = frame.data.data.shape
data = frame.data.data.reshape(arrayshape[1], arrayshape[2])
tt = np.array([str2time(frame)])
slat = frame.lat.data
slon = frame.lon.data
data = gvar2bt(data, nband)
outframe = makeframe(area1, tt)
swath = geometry.SwathDefinition(lons=slon, lats=slat)
containr = image.ImageContainerNearest(data,
swath,
radius_of_influence=5000,
nprocs=6).resample(area1).image_data
outframe[var0] = (['lat', 'lon', 'time'], containr.reshape(953, 1347, 1))
outframe[var0].attrs['axis'] = "lat lon time"
outframe[var0].attrs['resample_method'] = "Nearest_Neighbour"
filer = outframe.time.data
for tt in filer:
odate = np.datetime_as_string(tt, unit='s').tolist().split('T')
singlefile = outframe.sel(time=str(tt))
singlefile.to_netcdf(path=join(spath,oname.format(odate[0], odate[-1], nband)),
format='netCDF4',
encoding=encodedic,
unlimited_dims=['time'])
src_width, src_height,
src_extent)
lat_0=(lat_slice[0,0]+lat_slice[-1,0])/2.
lon_0=(lon_slice[0,0]+lon_slice[0,-1])/2.
dst_crs={'datum': 'WGS84','lat_0': lat_0,'lon_0': lon_0,'proj': 'laea'}
dst_proj=pyproj.Proj(dst_crs)
llcrnrx,llcrnry=dst_proj(llcrnrlon,llcrnrlat)
urcrnrx,urcrnry=dst_proj(urcrnrlon,urcrnrlat)
dst_extent=[llcrnrx,llcrnry,urcrnrx,urcrnry]
to_def = geometry.AreaDefinition('big', 'big image','area_big',
dst_crs,
src_width,src_height,
dst_extent)
from_nn = image.ImageContainerNearest(par_slice,from_def, radius_of_influence=50000)
to_nn = from_nn.resample(to_def)
result_data_nn = to_nn.image_data
dst_extent
# ### plot the reprojected image as a raw bitmap
# In[11]:
fig,ax=plt.subplots(1,1,figsize=(12,12))
ax.imshow(result_data_nn,cmap=cmap,norm=the_norm,origin='upper');
# ### Put the coastline on the map
def resample_data(lats, lons, data):
#Grid definition information of existing data
grid_def = geometry.GridDefinition(lons=lons, lats=lats)
#Wanted projection
area_id = 'laps_scan'
description = 'LAPS Scandinavian domain'
proj_id = 'stere'
proj = 'epsg:3995'
lon_0 = 20.0
lat_0 = 90.0
#x_size = 1000
#y_size = 1000
#Corner points to be converted to wanted projection
lon1 = -2.448425
lat1 = 68.79139
lon2 = 29.38635
lat2 = 54.67893
#Calculate coordinate points in projection
p = Proj(init=proj)
x1, y1 = p(lon1, lat1)
x2, y2 = p(lon2, lat2)
print x1, y1, x2, y2
print abs(x1 - x2) / abs(y1 - y2)
print abs(y1 - y2) / abs(x1 - x2)
x_size = 1000
y_size = abs(x1 - x2) / abs(y1 - y2) * 1000
area_extent = (x1, y1, x2, y2)
proj_dict = {
'a': '6371228.0',
'units': 'm',
'lon_0': lon_0,
'proj': proj_id,
'lat_0': lat_0
}
area_def = geometry.AreaDefinition(area_id, description, proj_id,
proj_dict, x_size, y_size, area_extent)
print area_def
#Finland domain
#lon1=16.52893
#lat1=70.34990
#lon2=31.85138
#lat2=58.76623
#Resampling data
laps_con_quick = image.ImageContainerQuick(data, grid_def)
area_con_quick = laps_con_quick.resample(area_def)
result_data_quick = area_con_quick.image_data
laps_con_nn = image.ImageContainerNearest(data,
grid_def,
radius_of_influence=50000)
area_con_nn = laps_con_nn.resample(area_def)
result_data_nn = area_con_nn.image_data
print result_data_nn
'/home/NCMRWFTEMP/vsprasad/EXP_HY2B/validation/diagStat/prodAOH/prodAOHdiagstat2020040200-scat.nc'
)
llon = x.longitude.values
llat = x.latitude.values
ygrid = x.latitude.values.shape[0]
xgrid = x.longitude.values.shape[0]
s = xr.open_dataset(
'/home/NCMRWFTEMP/vsprasad/EXP_HY2B/validation/crr/S_NWC_CRR_MSG1_global-VISIR_20200517T070000Z.nc'
)
data = s.crr.data
lats = s.lat.values
lons = s.lon.values
###############
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
swath_con = image.ImageContainerNearest(data,
swath_def,
radius_of_influence=5000)
area_con = swath_con.resample(area_def)
result = area_con.image_data
variable = 'crr'
######## Create Netcdf
mydata = Dataset('sate.nc', 'w', format='NETCDF3_CLASSIC')
mydata.description = 'CF netcdf regridding '
mydata.history = 'M. Sateesh; NCMRWF; Noida.'
# dimensions
mydata.createDimension("time", 1)
mydata.createDimension('longitude', xgrid)
mydata.createDimension('latitude', ygrid)
lat = mydata.createVariable('latitude', 'f8', ('latitude', ))
lat.long_name = "latitude"
def wrapper(date):
print date
test1 = datetime.datetime.now()
global_data, time_slot = pinkdust.load_channels(date)
print 'Loading data to a scene object:', datetime.datetime.now() - test1
# Reproject it to North Africa - note that this requires a custom
# projection in areas.def config file
test2 = datetime.datetime.now()
#projected_data = global_data.project("NorthAfrica")
msg_area = utils.load_area('/soge-home/projects/seviri_dust/areas.def',
'met09globeFull')
target_area = utils.load_area('/soge-home/projects/seviri_dust/areas.def',
'NorthAfrica')
#print global_data.data
data = global_data[8.7].data
msg_con_nn = image.ImageContainerNearest(data,
msg_area,
radius_of_influence=50000)
area_con_nn = msg_con_nn.resample(target_area)
result_data_nn = area_con_nn.image_data
lons, lats = target_area.get_lonlats()
print np.min(lons)
print np.max(lons)
print np.min(lats)
print np.max(lats)
"""
print 'Constraining the scene to North Africa:', datetime.datetime.now() \
- test2
test3 = datetime.datetime.now()
netcdf4.save(projected_data,
'/soge-home/projects/seviri_dust/raw_seviri_data/bt_nc/'
+ date.strftime(
"%B%Y") + '/North_Africa_SEVIRI_BTs_' + str(
date.strftime("%Y%m%d%H%M")) + '.nc',
compression=True,
dtype=np.int16,
band_axis=2) #area_aggregation=True
# #time_dimension=True
print 'Saving to netCDF:', datetime.datetime.now() - test3
test4 = datetime.datetime.now()
# Pull a netCDF dataset object out so the projection coordinates can
# be obtained
ncdata = Dataset(
'/soge-home/projects/seviri_dust/raw_seviri_data/bt_nc/'
+ date.strftime(
"%B%Y") + '/North_Africa_SEVIRI_BTs_' + str(
date.strftime("%Y%m%d%H%M")) + '.nc')
print 'Loading from netCDF:', datetime.datetime.now() - test4
# Reproject the original geos projection coordinates
test5 = datetime.datetime.now()
lons, lats = pinkdust.reproject_to_latlon(ncdata)
print 'Reprojecting to lat/lon:', datetime.datetime.now() - test5
# Regrid the irregular to regular lat lon
test7 = datetime.datetime.now()
data_array = np.zeros((lons.shape[0], lats.shape[1], 3))
data_array_ordered = np.zeros((lons.shape[0], lats.shape[1], 3))
data_labels = np.array(
[ncdata.variables['band0'][:][0], ncdata.variables['band1'][:][0],
ncdata.variables['band2'][:][0]])
data_array[:, :, 0] = ncdata.variables['Image0'][:]
data_array[:, :, 1] = ncdata.variables['Image1'][:]
data_array[:, :, 2] = ncdata.variables['Image2'][:]
IR_indices = np.array([0, 1, 2])
data_array_ordered[:, :, 0] = data_array[:, :,
IR_indices[data_labels == 'IR_087'][0]]
data_array_ordered[:, :, 1] = data_array[:, :,
IR_indices[data_labels == 'IR_108'][0]]
data_array_ordered[:, :, 2] = data_array[:, :,
IR_indices[data_labels == 'IR_120'][0]]
data_regridded = pinkdust.regrid_data_to_regular(lons, lats,
data_array_ordered)
print 'Regridding to a regular lat/lon:', datetime.datetime.now() - test7
# Pull out cloud mask data to be regridded and added to the nc file
# glob is used for a wildcard
#clouddata = pygrib.open(glob.glob(
# '/soge-home/projects/seviri_dust/raw_seviri_data/cloudmask_grib/MSG*-'
# 'SEVI-MSGCLMK-0100-0100-' + date.strftime(
# "%Y%m%d%H%M%S") + '*')[0])
#grb = clouddata.select()[0]
#cloudmaskarray = grb.values[:, ::-1]
#cloudlats, cloudlons = grb.latlons()
#cloudmaskarray[cloudmaskarray >= 3] = np.nan
#cloudlats[cloudlats > 90] = np.nan
#cloudlons[cloudlons > 180] = np.nan
# Generate a regular lat/lon grid for the cloud mask
regular_lons = np.linspace(np.min(lons), np.max(lons), lons.shape[1])
regular_lats = np.linspace(np.min(lats), np.max(lats), lats.shape[0])
# Regrid the cloud mask to the above regular grid (note a new
# function was defined as it was needed for a previous version of
# the code...)
#cloudmask_regridded = pinkdust.regrid_data(cloudlons, cloudlats,
# regular_lons,
# regular_lats, cloudmaskarray)
time = num2date(ncdata.variables['time'][:],
ncdata.variables['time'].units)
"""
f = tables.open_file(
'/soge-home/projects/seviri_dust/raw_seviri_data'
'/intermediary_files/BT_087_' + date.strftime('%Y%m%d%H%M%S.hdf'), 'w')
atom = tables.Atom.from_dtype(data_regridded[:, :, 0].dtype)
filters = tables.Filters(complib='blosc', complevel=5)
ds = f.create_carray(f.root,
'data',
atom,
data_regridded[:, :, 0].shape,
filters=filters)
ds[:] = data_regridded[:, :, 0]
f.close()
f = tables.open_file(
'/soge-home/projects/seviri_dust/raw_seviri_data'
'/intermediary_files/BT_108_' + date.strftime('%Y%m%d%H%M%S.hdf'), 'w')
atom = tables.Atom.from_dtype(data_regridded[:, :, 1].dtype)
filters = tables.Filters(complib='blosc', complevel=5)
ds = f.create_carray(f.root,
'data',
atom,
data_regridded[:, :, 0].shape,
filters=filters)
ds[:] = data_regridded[:, :, 1]
f.close()
f = tables.open_file(
'/soge-home/projects/seviri_dust/raw_seviri_data'
'/intermediary_files/BT_120_' + date.strftime('%Y%m%d%H%M%S.hdf'), 'w')
atom = tables.Atom.from_dtype(data_regridded[:, :, 2].dtype)
filters = tables.Filters(complib='blosc', complevel=5)
ds = f.create_carray(f.root,
'data',
atom,
data_regridded[:, :, 0].shape,
filters=filters)
ds[:] = data_regridded[:, :, 2]
f.close()
#f = tables.open_file('/soge-home/projects/seviri_dust/raw_seviri_data'
# '/intermediary_files/cloudmask_
# '+date.strftime(
# '%Y%m%d%H%M%S.hdf'), 'w')
#atom = tables.Atom.from_dtype(cloudmask_regridded.dtype)
#filters = tables.Filters(complib='blosc', complevel=5)
#ds = f.create_carray(f.root, 'data', atom,
# data_regridded[:, :, 0].shape,
# filters=filters)
#ds[:] = cloudmask_regridded
f.close()
ncdata.close()
