本文整理汇总了Python中netCDF4.Dataset.standard_name_vocabulary方法的典型用法代码示例。如果您正苦于以下问题:Python Dataset.standard_name_vocabulary方法的具体用法?Python Dataset.standard_name_vocabulary怎么用?Python Dataset.standard_name_vocabulary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.standard_name_vocabulary方法的4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: generate_nc
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import standard_name_vocabulary [as 别名]
def generate_nc(parser_context):
parser = XLSParser()
with open(parser_context.filepath, 'r') as f:
doc = f.read()
info = parser.extract_worksheets(doc)
nccl = info[parser_context.worksheet]
#header_line = 3
#columns = nccl[header_line]
#data_range = (4, 66)
data_rows = nccl[parser_context.data_range[0]:parser_context.data_range[1]]
print 'Generating',parser_context.output_file
nc = Dataset(parser_context.output_file, 'w')
nc.createDimension('time', len(data_rows)*12)
nc.GDAL = "GDAL 1.9.2, released 2012/10/08"
nc.history = "Created dynamically in IPython Notebook 2013-11-14"
nc.title = nccl[0][0]
nc.summary = nccl[1][0]
nc.naming_authority = 'GLOS'
nc.source = 'GLERL'
nc.standard_name_vocabulary = "http://www.cgd.ucar.edu/cms/eaton/cf-metadata/standard_name.html"
nc.project = 'GLOS'
nc.Conventions = "CF-1.6"
time = nc.createVariable('time', 'f8', ('time',))
time.standard_name = 'time'
time.units = 'seconds since 1970-01-01'
time.long_name = 'Time'
time.axis = 'T'
precip = nc.createVariable(parser_context.variable, 'f8', ('time',), fill_value=parser_context.fill_value)
#precip.standard_name = 'precipitation_amount'
precip.standard_name = parser_context.standard_name
precip.units = parser_context.units
for i,row in enumerate(data_rows):
for j in xrange(12):
the_date = datetime(row[0], j+1, 1)
timestamp = calendar.timegm(the_date.utctimetuple())
time[i*12 + j] = timestamp
try:
value = float(row[j+1])
except ValueError:
continue
except TypeError:
continue
precip[i*12 + j] = value
nc.close()
示例2: Table
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import standard_name_vocabulary [as 别名]
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"
fid.keywords = "EARTH SCIENCE > SPECTRAL/ENGINEERING > MICROWAVE > BRIGHTNESS TEMPERATURE"
fid.keywords_vocabulary = "NASA Global Change Master Directory (GCMD) Earth Science Keywords, Version 8.1"
fid.platform = "TBD"
fid.sensor = "TBD"
fid.naming_authority = "org.doi.dx"
fid.id = "10.5067/MEASURES/CRYOSPHERE/nsidc-0630.001"
fid.date_created = "TBD"
fid.acknowledgement = ["This data set was created with funding from NASA MEaSUREs Grant #NNX13AI23A.\n",
"Data archiving and distribution is supported by the NASA NSIDC Distributed Active Archive Center (DAAC)."]
fid.license = "No constraints on data access or use"
fid.processing_level = "Level 3"
fid.creator_name = "Mary J. Brodzik"
fid.creator_email = "[email protected]"
fid.creator_url = "http://nsidc.org/charis"
示例3: Dataset
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import standard_name_vocabulary [as 别名]
# In[120]:
###################
### CREATE NETCDF #
###################
from netCDF4 import Dataset
import time
# Create HDF5 *format*, classic *model*
dataset = Dataset('o3_interp_press.nc', 'w', format='NETCDF3_CLASSIC')
# Global Attributes
dataset.description = 'TEST NETCDF-CF COMPLIANT SCRIPT'
dataset.history = 'Created ' + time.ctime(time.time())
dataset.source = ''
dataset.Conventions = 'CF-1.0'
dataset.standard_name_vocabulary='CF-1.0'
pressure = dataset.createDimension('pressure', nplevs)
pressure = dataset.createVariable('pressure', np.int32, ('pressure',))
time = dataset.createDimension('time', None)
time = dataset.createVariable('time', 'f8', ('time',)) # or can use np as below
lat = dataset.createDimension('latitude', ny2)
lat = dataset.createVariable('latitude', np.float32, ('latitude',))
lon = dataset.createDimension('longitude', nx2)
lon = dataset.createVariable('longitude', np.float32, ('longitude',))
o3interp = dataset.createVariable('o3_interp', np.float32, ('time','pressure','latitude', 'longitude',))
示例4: initialize_output
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import standard_name_vocabulary [as 别名]
def initialize_output(filename, id_dim_name, time_len,
id_len, time_step_seconds):
"""Creates netCDF file with CF dimensions and variables, but no data.
Arguments:
filename -- full path and filename for output netCDF file
id_dim_name -- name of Id dimension and variable, e.g., COMID
time_len -- (integer) length of time dimension (number of time steps)
id_len -- (integer) length of Id dimension (number of time series)
time_step_seconds -- (integer) number of seconds per time step
"""
cf_nc = Dataset(filename, 'w', format='NETCDF3_CLASSIC')
# Create global attributes
log(' globals', 'DEBUG')
cf_nc.featureType = 'timeSeries'
cf_nc.Metadata_Conventions = 'Unidata Dataset Discovery v1.0'
cf_nc.Conventions = 'CF-1.6'
cf_nc.cdm_data_type = 'Station'
cf_nc.nodc_template_version = (
'NODC_NetCDF_TimeSeries_Orthogonal_Template_v1.1')
cf_nc.standard_name_vocabulary = ('NetCDF Climate and Forecast (CF) ' +
'Metadata Convention Standard Name ' +
'Table v28')
cf_nc.title = 'RAPID Result'
cf_nc.summary = ("Results of RAPID river routing simulation. Each river " +
"reach (i.e., feature) is represented by a point " +
"feature at its midpoint, and is identified by the " +
"reach's unique NHDPlus COMID identifier.")
cf_nc.time_coverage_resolution = 'point'
cf_nc.geospatial_lat_min = 0.0
cf_nc.geospatial_lat_max = 0.0
cf_nc.geospatial_lat_units = 'degrees_north'
cf_nc.geospatial_lat_resolution = 'midpoint of stream feature'
cf_nc.geospatial_lon_min = 0.0
cf_nc.geospatial_lon_max = 0.0
cf_nc.geospatial_lon_units = 'degrees_east'
cf_nc.geospatial_lon_resolution = 'midpoint of stream feature'
cf_nc.geospatial_vertical_min = 0.0
cf_nc.geospatial_vertical_max = 0.0
cf_nc.geospatial_vertical_units = 'm'
cf_nc.geospatial_vertical_resolution = 'midpoint of stream feature'
cf_nc.geospatial_vertical_positive = 'up'
cf_nc.project = 'National Flood Interoperability Experiment'
cf_nc.processing_level = 'Raw simulation result'
cf_nc.keywords_vocabulary = ('NASA/Global Change Master Directory ' +
'(GCMD) Earth Science Keywords. Version ' +
'8.0.0.0.0')
cf_nc.keywords = 'DISCHARGE/FLOW'
cf_nc.comment = 'Result time step (seconds): ' + str(time_step_seconds)
timestamp = datetime.utcnow().isoformat() + 'Z'
cf_nc.date_created = timestamp
cf_nc.history = (timestamp + '; added time, lat, lon, z, crs variables; ' +
'added metadata to conform to NODC_NetCDF_TimeSeries_' +
'Orthogonal_Template_v1.1')
# Create dimensions
log(' dimming', 'DEBUG')
cf_nc.createDimension('time', time_len)
cf_nc.createDimension(id_dim_name, id_len)
# Create variables
log(' timeSeries_var', 'DEBUG')
timeSeries_var = cf_nc.createVariable(id_dim_name, 'i4', (id_dim_name,))
timeSeries_var.long_name = (
'Unique NHDPlus COMID identifier for each river reach feature')
timeSeries_var.cf_role = 'timeseries_id'
log(' time_var', 'DEBUG')
time_var = cf_nc.createVariable('time', 'i4', ('time',))
time_var.long_name = 'time'
time_var.standard_name = 'time'
time_var.units = 'seconds since 1970-01-01 00:00:00 0:00'
time_var.axis = 'T'
log(' lat_var', 'DEBUG')
lat_var = cf_nc.createVariable('lat', 'f8', (id_dim_name,),
fill_value=-9999.0)
lat_var.long_name = 'latitude'
lat_var.standard_name = 'latitude'
lat_var.units = 'degrees_north'
lat_var.axis = 'Y'
log(' lon_var', 'DEBUG')
lon_var = cf_nc.createVariable('lon', 'f8', (id_dim_name,),
fill_value=-9999.0)
lon_var.long_name = 'longitude'
lon_var.standard_name = 'longitude'
lon_var.units = 'degrees_east'
lon_var.axis = 'X'
log(' z_var', 'DEBUG')
z_var = cf_nc.createVariable('z', 'f8', (id_dim_name,),
fill_value=-9999.0)
z_var.long_name = ('Elevation referenced to the North American ' +
'Vertical Datum of 1988 (NAVD88)')
z_var.standard_name = 'surface_altitude'
z_var.units = 'm'
#.........这里部分代码省略.........