本文整理汇总了Python中netCDF4.Dataset.references方法的典型用法代码示例。如果您正苦于以下问题:Python Dataset.references方法的具体用法?Python Dataset.references怎么用?Python Dataset.references使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类netCDF4.Dataset
的用法示例。
在下文中一共展示了Dataset.references方法的9个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: netcdfPiomas
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
def netcdfPiomas(lats,lons,var,directory):
name = 'piomas_regrid_March_19792015.nc'
filename = directory + name
ncfile = Dataset(filename,'w',format='NETCDF4')
ncfile.description = 'PIOMAS Sea ice thickness reanalysis from 1979-2015 ' \
'interpolated on a 180x180 grid (latxlon)' \
'of NSIDC EASE100'
### Dimensions
ncfile.createDimension('years',var.shape[0])
ncfile.createDimension('lat',var.shape[1])
ncfile.createDimension('lon',var.shape[2])
### Variables
years = ncfile.createVariable('years','f4',('years'))
latitude = ncfile.createVariable('lat','f4',('lat','lat'))
longitude = ncfile.createVariable('lon','f4',('lon','lon'))
varns = ncfile.createVariable('thick','f4',('years','lat','lon'))
### Metrics
varns.units = 'meters'
ncfile.title = 'PIOMAS March SIT'
ncfile.instituion = 'Dept. ESS at University of California, Irvine'
ncfile.source = 'University of Washington'
ncfile.references = '[Zhang and Rothrock, 2003]'
### Data
years[:] = list(xrange(var.shape[0]))
latitude[:] = lats
longitude[:] = lons
varns[:] = var
ncfile.close()
print 'Completed: Created netCDF4 File!'
示例2: netcdfSIT
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
def netcdfSIT(lats,lons,var):
directory = '/home/zlabe/Surtsey/seaice_obs/Thk/March/'
name = 'icesatG_regrid_March_20032008.nc'
filename = directory + name
ncfile = Dataset(filename,'w',format='NETCDF4')
ncfile.description = 'Sea ice thickness processed by NASA-G and now' \
'regridded on an EASE2.0 100 km grid for the' \
'period of March 2003-2008'
### Dimensions
ncfile.createDimension('years',var.shape[0])
ncfile.createDimension('lat',var.shape[1])
ncfile.createDimension('lon',var.shape[2])
### Variables
years = ncfile.createVariable('years','f4',('years'))
latitude = ncfile.createVariable('lat','f4',('lat','lat'))
longitude = ncfile.createVariable('lon','f4',('lon','lon'))
varns = ncfile.createVariable('sit','f4',('years','lat','lon'))
### Units
varns.units = 'meters'
ncfile.title = 'ICESat-G'
ncfile.instituion = 'Dept. ESS at University of California, Irvine'
ncfile.source = 'NASA-G'
ncfile.references = 'Donghui Yi, H. Zwally'
### Data
years[:] = list(xrange(var.shape[0]))
latitude[:] = lats
longitude[:] = lons
varns[:] = var
ncfile.close()
print 'Completed: Created netCDF4 File!'
示例3: new
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
def new(self, secs):
"""
Creates a new seNorge netCDF file.
Convention: Climate and Forecast (CF) version 1.4
@param secs: in seconds since 1970-01-01 00:00:00
"""
# create new file
rootgrp = Dataset(self.filename, 'w') # create new file using netcdf4
# rootgrp = netcdf_file(self.filename, 'w') # create new file using scipy.IO
# add root dimensions
rootgrp.createDimension('time', size=self.default_senorge_time)
rootgrp.createDimension('x', size=self.default_senorge_width)
rootgrp.createDimension('y', size=self.default_senorge_height)
# add root attributes
rootgrp.Conventions = "CF-1.4"
rootgrp.institution = "Norwegian Water Resources and Energy Directorate (NVE)"
rootgrp.source = ""
rootgrp.history = ""
rootgrp.references = ""
rootgrp.comment = "Data distributed via www.senorge.no"
self.rootgrp = rootgrp
# add coordinates
time = self.rootgrp.createVariable('time', 'f8', ('time',))
time.units = 'seconds since 1970-01-01 00:00:00 +00:00'
time.long_name = 'time'
time.standard_name = 'time'
time[:] = secs
self._set_utm()
self._set_latlon()
示例4: netcdfSatelliteG
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
def netcdfSatelliteG(lats,lons,var,directory):
name = 'satelliteG_regrid_March_20032015.nc'
filename = directory + name
ncfile = Dataset(filename,'w',format='NETCDF4')
ncfile.description = 'Satellite data for ICESat-G (2003-2008) and' \
'CryoSat-2 (2011-2015) that have been regridded' \
'on 180x180 EASE2.0 100km grids. Years 2009-2010 are' \
'available in the data but have been filled with' \
'nan values to be inclusive. Shape is therefore' \
'[13,180,180]'
### Dimensions
ncfile.createDimension('years',var.shape[0])
ncfile.createDimension('lat',var.shape[1])
ncfile.createDimension('lon',var.shape[2])
### Variables
years = ncfile.createVariable('years','f4',('years'))
latitude = ncfile.createVariable('lat','f4',('lat','lat'))
longitude = ncfile.createVariable('lon','f4',('lon','lon'))
varns = ncfile.createVariable('thick','f4',('years','lat','lon'))
### Metrics
varns.units = 'meters'
ncfile.title = 'ICESat_G/CryoSat March SIT'
ncfile.instituion = 'Dept. ESS at University of California, Irvine'
ncfile.source = 'NASA-G/ESA Products'
ncfile.references = 'Donghui Yi, H. Zwally, S. Laxon'
### Data
years[:] = list(xrange(var.shape[0]))
latitude[:] = lats
longitude[:] = lons
varns[:] = var
ncfile.close()
print 'Completed: Created netCDF4 File!'
示例5: len
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
conusmask[:] = conusmask_ccpa
thrnumv[:] = xc[:]
thrvalv[:] = thresh[:]
rootgrp.latcorners = [lats_anal[0,0], lats_anal[0,-1], lats_anal[-1,0], lats_anal[-1,-1]]
rootgrp.loncorners = [lons_anal[0,0], lons_anal[0,-1], lons_anal[-1,0], lons_anal[-1,-1]]
rootgrp.stream = "s4" # ????
rootgrp.title = "Reforecast V2 accum. ensemble-mean precip forecast and analyzed CDF + rank correlation"
rootgrp.Conventions = "CF-1.0" # ????
rootgrp.history = "Revised Mar 2016 by Hamill"
rootgrp.institution = \
"Reforecast from ERSL/PSD using NCEP/EMC GEFS, circa 2012"
rootgrp.platform = "Model"
rootgrp.references = "http://www.esrl.noaa.gov/psd/forecasts/reforecast2/"
# ---- open ensemble data file for each year, read in data, and augment cdf
# information for that year if the sample is within the month of interest
# or the neighboring month
rankcorr_fa = -99.99*np.ones((nja,nia),dtype=np.float)
nyears = len(range(2002,2016))
print 'nsamps = ',92*nyears
precipa = np.zeros((nja,nia),dtype=np.float)
precipf3d = np.zeros((92*nyears,njf,nif),dtype=np.float)
precipa3d = np.zeros((92*nyears,nja,nia),dtype=np.float)
ipktr = 0
infilename = input_data_directory+'/refcstv2_precip_ccpav3_'+\
cleadb+'_to_'+cleade+'.nc'
示例6: create_uGrid_ncdf
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
def create_uGrid_ncdf(filename,
nMesh2_node, nMesh2_edge, nMesh2_face, nMaxMesh2_face_nodes,
Mesh2_edge_nodes, Mesh2_edge_faces, Mesh2_face_nodes, Mesh2_face_edges,
Mesh2_node_x, Mesh2_node_y, Mesh2_edge_x, Mesh2_edge_y,
Mesh2_face_x, Mesh2_face_y, Mesh2_face_center_x, Mesh2_face_center_y,
Mesh2_face_area=None,
Mesh2_edge_x_bnd=None, Mesh2_edge_y_bnd=None,
Mesh2_face_x_bnd=None, Mesh2_face_y_bnd=None,
coord_type='geographic',
dim_nMesh2_layer2d=1, dim_nMesh2_layer3d=1, dim_nMesh2_class_names_strlen=20, dim_nMesh2_suspension_classes=1,
start_index=0,
log=True):
'''
Function creates a NETCDF4 file. Data is stored in accordance with
BAW convention for 2D Unstructured Grid (http://www.baw.de/methoden/index.php5/NetCDF_Unstrukturiertes_Gitter)
input:
filename - string, containing filename of netcdf file to be created.
nMesh2_node, nMesh2_edge, nMesh2_face - integers, indicating number of nodes/edges/faces in a grid
nMaxMesh2_face_nodes - integer, showing maximum number of nodes/edges in a face (could be 3 or 4)
Mesh2_edge_nodes, Mesh2_edge_faces, Mesh2_face_nodes, Mesh2_face_edges, |
Mesh2_node_x, Mesh2_node_y, Mesh2_edge_x, Mesh2_edge_y, | => 1D numpy arrays with data
Mesh2_face_x, Mesh2_face_y, Mesh2_face_center_x, Mesh2_face_center_y, |
Mesh2_face_area |
coord_type - string indicating in which variable to store passed data, in x,y or in lon,lat
by default - 'geographic'
'cartesian' <> 'geographic'
'''
# --------------------------------------------------
# Creating ncdf
# --------------------------------------------------
root_grp = Dataset(filename, mode='w', format='NETCDF4')
root_grp.title = 'mossco >>> uGrid conversion'
root_grp.history = 'Createded on ' + time.ctime(time.time())
#root_grp.comment = 'WARNING! Latitude and longitude valueas are stored as X,Y coordinates. Therefore any calculations that involve length or area in X or Y dimension are wrong'
root_grp.Conventions = 'CF-1.6'
#root_grp.institution = 'Bundesanstalt fuer Wasserbau - Federal Waterways Engineering and Research Institute'
root_grp.references = 'http://www.baw.de/ und http://www.baw.de/methoden/index.php5/NetCDF'
#root_grp.source = ''
# --------------------------------------------------
# Creating dimensions
# --------------------------------------------------
root_grp.createDimension('nMesh2_node', nMesh2_node)
root_grp.createDimension('nMesh2_edge', nMesh2_edge)
root_grp.createDimension('nMesh2_face', nMesh2_face)
root_grp.createDimension('nMaxMesh2_face_nodes', nMaxMesh2_face_nodes)
root_grp.createDimension('two', 2)
root_grp.createDimension('three', 3)
root_grp.createDimension('nMesh2_time', 1)
root_grp.createDimension('nMesh2_data_time', None) # None stands for UNLIMITED
if dim_nMesh2_layer2d is not None:
root_grp.createDimension('nMesh2_layer_2d', dim_nMesh2_layer2d)
if dim_nMesh2_layer3d is not None:
root_grp.createDimension('nMesh2_layer_3d', dim_nMesh2_layer3d)
root_grp.createDimension('nMesh2_class_names_strlen', dim_nMesh2_class_names_strlen)
root_grp.createDimension('nMesh2_suspension_classes', dim_nMesh2_suspension_classes)
if coord_type in ['cartesian', 'both'] :
# **********************************************************************************************************************************************
#
# 1) Local coordinates
#
# **********************************************************************************************************************************************
# --------------------------------------------------
# 1.1) NODES
# --------------------------------------------------
ncVar_Mesh2_node_x = root_grp.createVariable('Mesh2_node_x', 'f8', ('nMesh2_node'), fill_value=False)
ncVar_Mesh2_node_x.long_name = 'x-Koordinate der Knoten eines 2D-Gitters'
ncVar_Mesh2_node_x.units = 'm'
ncVar_Mesh2_node_x.name_id = 1650
ncVar_Mesh2_node_x.standard_name = 'projection_x_coordinate'
ncVar_Mesh2_node_x[:] = Mesh2_node_x[:]
ncVar_Mesh2_node_y = root_grp.createVariable('Mesh2_node_y', 'f8', ('nMesh2_node'), fill_value=False)
ncVar_Mesh2_node_y.long_name = 'y-Koordinate der Knoten eines 2D-Gitters'
ncVar_Mesh2_node_y.units = 'm'
ncVar_Mesh2_node_y.name_id = 1651
ncVar_Mesh2_node_y.standard_name = 'projection_y_coordinate'
ncVar_Mesh2_node_y[:] = Mesh2_node_y[:]
# --------------------------------------------------
# 1.2) EDGES
# --------------------------------------------------
ncVar_Mesh2_edge_x = root_grp.createVariable('Mesh2_edge_x', 'f8', ('nMesh2_edge'), fill_value=False)
ncVar_Mesh2_edge_x.long_name = 'x-Koordinate der Kanten eines 2D-Gitters, Kantenmitte'
ncVar_Mesh2_edge_x.units = 'm'
ncVar_Mesh2_edge_x.name_id = 1650
ncVar_Mesh2_edge_x.bounds = 'Mesh2_edge_x_bnd'
ncVar_Mesh2_edge_x.standard_name = 'projection_x_coordinate'
ncVar_Mesh2_edge_x[:] = Mesh2_edge_x[:]
#.........这里部分代码省略.........
示例7: cloneUM4
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
def cloneUM4(masterfile, newfile, startdate="copy", tn=24, dt=3600.0):
"""
Creates a new UM4 netCDF file based on a master file.
Convention: Climate and Forecast (CF) version 1.4
@param secs: in seconds since 1970-01-01 00:00:00
"""
print "Started cloning %s as %s" % (masterfile, newfile)
# open master file
master = Dataset(masterfile, "r")
Mdimensions = master.dimensions.keys()
# create new file
rootgrp = Dataset(newfile, "w", format="NETCDF3_CLASSIC")
# add root dimensions
rootgrp.createDimension("time", size=tn)
rootgrp.createDimension("rlon", size=default_UM4_width)
rootgrp.createDimension("rlat", size=default_UM4_height)
rootgrp.createDimension("sigma", size=1)
# add root attributes
rootgrp.Conventions = "CF-1.4"
rootgrp.institution = "Norwegian Water Resources and Energy Directorate (NVE)"
rootgrp.source = "Compiled from several +66 hour prognoses by the Norwegian Meteorological Institute (met.no)"
rootgrp.history = "%s created" % time.ctime(time.time())
rootgrp.references = "met.no"
rootgrp.comment = "Progonosis data for www.senorge.no"
# add time variable
Mtime = master.variables["time"]
# determine start date
try:
_end = date2num(iso2datetime(startdate), timeunit)
_start = _end - ((tn - 1) * dt)
except ValueError:
# if the startdate is set to "copy" use the date of the last input file
Mdate = num2date(Mtime[0], timeunit).date()
utc6 = datetime.time(06, 00, 00)
_end = date2num(datetime.datetime.combine(Mdate, utc6), timeunit)
_start = _end - ((tn - 1) * dt)
print (_end - _start) / dt
_time = rootgrp.createVariable("time", "f8", ("time",))
_time[:] = arange(_start, _end + dt, dt) # ensures that _end is included
for attr in Mtime.ncattrs():
_time.setncattr(attr, Mtime.getncattr(attr))
# add rlon variable
Mrlon = master.variables["rlon"]
_rlon = rootgrp.createVariable("rlon", "f4", ("rlon",))
_rlon[:] = Mrlon[:]
for attr in Mrlon.ncattrs():
_rlon.setncattr(attr, Mrlon.getncattr(attr))
# add rlat variable
Mrlat = master.variables["rlat"]
_rlat = rootgrp.createVariable("rlat", "f4", ("rlat",))
_rlat[:] = Mrlat[:]
for attr in Mrlat.ncattrs():
_rlat.setncattr(attr, Mrlat.getncattr(attr))
# add sigma variable
try:
Msigma = master.variables["sigma"]
_sigma = rootgrp.createVariable("sigma", "i2", ("sigma",))
_sigma[:] = Msigma[:]
for attr in Msigma.ncattrs():
_sigma.setncattr(attr, Msigma.getncattr(attr))
except KeyError:
print "No variable called 'sigma'!"
for var in master.variables.keys():
# exclude the variables referring to dimensions
if var not in Mdimensions:
exec ("M%s = master.variables['%s']" % (var, var))
exec ("print 'Cloning %s', master.variables['%s'].dimensions" % (var, var))
exec ("_%s = rootgrp.createVariable('%s', M%s.dtype, M%s.dimensions)" % (var, var, var, var))
exec ("""for attr in M%s.ncattrs():\n\t_%s.setncattr(attr, M%s.getncattr(attr))""" % (var, var, var))
rootgrp.close()
master.close()
print "Cloning completed!"
示例8: Dataset
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
# Assume that /projects/CHARIS is sshfs mounted on this machine, and
# that the user has write permission
fid = Dataset('~/projects/CHARIS/snow_cover/modice.v0.4/min05yr_nc/MODICE.v0.4.1test.nc', 'w', format='NETCDF4')
fid.Conventions = "CF-1.6"
fid = Dataset('/home/vagrant/measures-byu/src/prod/cetb_file/templates/cetb_global_template.nc', 'w', format='NETCDF4')
fid.Conventions = "CF-1.6"
fid.title = "MODICE mask for a minimum number of years"
fid.product_version = "v0.4"
#fid.software_version_id = "TBD"
#fid.software_repository = "[email protected]:nsidc/measures-byu.git"
fid.source = "MODICE"
fid.source_version_id = "v04"
fid.history = ""
fid.comment = "Mask locations with 2 indicate MODICE for >= min_years."
fid.references = "Painter, T. H., Brodzik, M. J., A. Racoviteanu, R. Armstrong. 2012. Automated mapping of Earth's annual minimum exposed snow and ice with MODIS. Geophysical Research Letters, 39(20):L20501, doi:10.1029/2012GL053340."
fid.summary = ["An improved, enhanced-resolution, gridded passive microwave Earth System Data Record \n",
"for monitoring cryospheric and hydrologic time series\n" ]fid.title = "MEaSUREs Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR"
fid.institution = ["National Snow and Ice Data Center\n",
"Cooperative Institute for Research in Environmental Sciences\n",
"University of Colorado at Boulder\n",
"Boulder, CO"]
fid.publisher = ["National Snow and Ice Data Center\n",
"Cooperative Institute for Research in Environmental Sciences\n",
"University of Colorado at Boulder\n",
"Boulder, CO"]
fid.publisher_url = "http://nsidc.org/charis"
fid.publisher_email = "[email protected]"
fid.project = "CHARIS"
fid.standard_name_vocabulary = "CF Standard Name Table (v27, 28 September 2013)"
fid.cdm_data_type = "grid"
示例9: Dataset
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import references [as 别名]
# --------------------------
# Create output NetCDF file
# --------------------------
if os.path.exists(outfilename): os.remove(outfilename)
f1 = Dataset(outfilename, mode='w', format='NETCDF4_CLASSIC')
# Global attributes
f1.Conventions = "CF-1.6"
f1.title = "Monthly-mean (full water column) fields"
f1.history = "Simulations were done using a 8x8 km polar stereographic grid projection, however the final " \
"data are presented using a reference grid. Conversion between grid " \
"projectionsby grid2lonlatZ.py"
f1.source = "IMR, ROMSv3.7, IS4DVAR, NorthSea-8km reanalysis"
f1.institution = "Institute of Marine Research, Norway"
f1.references = "http://www.imr.no"
f1.product_version = "1.0"
f1.contact = "[email protected]"
f1.netcdf_version_id = "netCDF-4 Classic"
# Define dimensions
f1.createDimension('time', None)
f1.createDimension('depth', len(outlevels))
f1.createDimension('longitude', len(lon))
f1.createDimension('latitude', len(lat))
v = f1.createVariable('time', 'd', ('time',))
v0 = f0.variables['time']
v.long_name = 'time'
v.units = "Days since 1948-01-01 00:00:00"
v.calendar = "Gregorian"