Python Dataset.variables['longitude'][:]方法代码示例

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import variables['longitude'][:] [as 别名]
def wave_copy(fname, out_fname, begin,end, stat_dict,mode="storm_surge", step = 1):
    if os.path.exists(out_fname):
        os.remove(out_fname)
    
    #get station id for the station_id dimension
    stn_site_id = get_global_attribute(fname, 'stn_station_number')
    
    alt = get_variable_data(fname, 'altitude')
    lat = get_variable_data(fname, 'latitude')
    long = get_variable_data(fname, 'longitude')
    d = Dataset(fname)
    output = Dataset(out_fname, 'w', format='NETCDF4_CLASSIC')
    output.createDimension('time', len(stat_dict['time']))
    output.createDimension("station_id", len(stn_site_id))

    # copy globals
    for att in d.ncattrs():
        setattr(output, att, d.__dict__[att])
    
    setattr(output, 'uuid', str(uuid.uuid4()))   
    
    has_station_id = False
    # copy variables
    for key in d.variables:
        
        #skip adding pressure qc if the mode is storm surge
        if mode != 'station_id':
            continue
        else:
            has_station_id = True 
        
        name = key
        datatype = d.variables[key].datatype 
          
        dim = d.variables[key].dimensions
        
        if datatype == "int32":
            var = output.createVariable(name, datatype, dim)
        else:
            var = output.createVariable(name, datatype, dim, fill_value=FILL_VALUE)
        
        for att in d.variables[key].ncattrs():
            if att != '_FillValue':
                setattr(var, att, d.variables[key].__dict__[att])
                
    for x in stat_dict:
        append_variable(out_fname, x, stat_dict[x], x, x)
        
    output.variables['altitude'][:] = alt
    output.variables['longitude'][:] = long
    output.variables['latitude'][:] = lat
#     output.variables['sea_pressure'][:] = 0

    if has_station_id == False:
    #the following changes are essential in case the air and sea gui files are processed
    #with older versions of the script
        st_id = output.createVariable('station_id','S1',('station_id'))
        st_id.setncattr('cf_role', 'time_series_id')
        st_id.setncattr('long_name', 'station identifier')
        st_id[:] = list(stn_site_id)
    
    #I have to keep this hunk of garbage until enought time has passed for all
    #data files to be properly formatted,
    deployment_time = unit_conversion.convert_ms_to_datestring(stat_dict['time'][0], pytz.utc)
    retrieval_time = unit_conversion.convert_ms_to_datestring(stat_dict['time'][-1], pytz.utc)
    set_global_attribute(out_fname, 'deployment_time', deployment_time)
    set_global_attribute(out_fname, 'retrieval_time', retrieval_time)
    set_global_attribute(out_fname, 'salinity_ppm', 'unused')
    set_global_attribute(out_fname, 'device_depth', 'unused')
    set_global_attribute(out_fname, 'geospatial_lon_min', np.float64(-180))
    set_global_attribute(out_fname, 'geospatial_lon_max', np.float64(180))
    set_global_attribute(out_fname, 'geospatial_lat_min', np.float64(-90))
    set_global_attribute(out_fname, 'geospatial_lat_max', np.float64(90))
    set_global_attribute(out_fname, 'geospatial_vertical_min', np.float64(0))
    set_global_attribute(out_fname, 'geospatial_vertical_max', np.float64(0))
    
    first, last = get_sensor_orifice_elevation(out_fname)
    set_global_attribute(out_fname, 'sensor_orifice_elevation_at_deployment_time', \
                         np.float64("{0:.4f}".format(first)))
    set_global_attribute(out_fname, 'sensor_orifice_elevation_at_retrieval_time', \
                         np.float64("{0:.4f}".format(last)))
    set_global_attribute(out_fname, 'sensor_orifice_elevation_units', 'meters')
    
    first_land, last_land = get_land_surface_elevation(out_fname)
    set_global_attribute(out_fname, 'initial_land_surface_elevation', \
                         np.float64("{0:.4f}".format(first_land)))
    set_global_attribute(out_fname, 'final_land_surface_elevation', \
                         np.float64("{0:.4f}".format(last_land)))
    set_global_attribute(out_fname, 'land_surface_elevation_units', 'meters')
    
#     first_depth = get_initial_water_depth(out_fname)
#     set_global_attribute(out_fname, 'initial_water_depth', \
#                          np.float64("{0:.4f}".format(first_depth)))
#     final_depth = get_final_water_depth(out_fname)
#     set_global_attribute(out_fname, 'final_water_depth', \
#                          np.float64("{0:.4f}".format(final_depth)))
#     set_global_attribute(out_fname, 'water_depth_units', 'meters')
    set_global_attribute(out_fname, 'featureType', 'timeSeries')
    
    set_var_attribute(out_fname, 'latitude', 'valid_max', np.float64(90))
#.........这里部分代码省略.........

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import variables['longitude'][:] [as 别名]
def chop_netcdf(fname, out_fname, begin, end, air_pressure = False):
    """Truncate the data in a netCDF file between two indices"""
    if os.path.exists(out_fname):
        os.remove(out_fname)
    length = end - begin
    
    if air_pressure == False:
        p = get_pressure(fname)[begin:end]
    else:
        p = get_air_pressure(fname)[begin:end]
        
    #get station id for the station_id dimension
    stn_site_id = get_global_attribute(fname, 'stn_station_number')
    
    t = get_time(fname)[begin:end]
    flags = get_flags(fname)[begin:end]
    alt = get_variable_data(fname, 'altitude')
    lat = get_variable_data(fname, 'latitude')
    long = get_variable_data(fname, 'longitude')
    d = Dataset(fname)
    output = Dataset(out_fname, 'w', format='NETCDF4_CLASSIC')
    output.createDimension('time', length)
    
    # copy globals
    for att in d.ncattrs():
        setattr(output, att, d.__dict__[att])
    
    og_uuid = get_global_attribute(fname, 'uuid') 
    setattr(output, 'uuid', str(uuid.uuid4()))
    # copy variables
    for key in d.variables:
        name = key
        
        if name == 'station_id':
            output.createDimension("station_id", len(stn_site_id))
            
        datatype = d.variables[key].datatype 
        
        dim = d.variables[key].dimensions
        
        if datatype == "int32":
            var = output.createVariable(name, datatype, dim)
        else:
            var = output.createVariable(name, datatype, dim, fill_value=FILL_VALUE)
        
        for att in d.variables[key].ncattrs():
            if att != '_FillValue':
                setattr(var, att, d.variables[key].__dict__[att])
                
        #add uuid of previous netCDF file to pressure variable
        if name == 'sea_pressure':
            setattr(var, 'sea_uuid', og_uuid)
        if name == 'air_pressure':
            setattr(var, 'air_uuid', og_uuid)
            
    output.variables['time'][:] = t
    
    if air_pressure == False:
        output.variables['sea_pressure'][:] = p
    else:
        output.variables['air_pressure'][:] = p
    
    output.variables['pressure_qc'][:] = flags
    output.variables['altitude'][:] = alt
    output.variables['longitude'][:] = long
    output.variables['latitude'][:] = lat
    
    setattr(output,"time_coverage_start", \
    unit_conversion.convert_ms_to_datestring(t[0], pytz.utc))

    setattr(output,"time_coverage_end", \
    unit_conversion.convert_ms_to_datestring(t[-1], pytz.utc))

    setattr(output,"time_coverage_duration",  \
    unit_conversion.get_time_duration(t[-1] - t[0]))
    
    d.close()
    output.close()

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import variables['longitude'][:] [as 别名]
def replace_cfradial_metadata( stdtape_filepath ):

	# open standard tape file for reading
	stdtape_file = Dataset(stdtape_filepath,'r') 

	# get stdtape timestamp
	base_time=stdtape_file.variables['base_time'][:]
	stdtape_secs=stdtape_file.variables['Time'][:]
	stdtape_timestamp=pd.to_datetime(stdtape_secs+base_time,unit='s')
	stdtape_lats=stdtape_file.variables['LAT'][:]
	stdtape_lons=stdtape_file.variables['LON'][:]
	stdtape_geo_alt=stdtape_file.variables['GEOPOT_ALT'][:]
	stdtape_pres_alt=stdtape_file.variables['PRES_ALT'][:]

	# close the file
	stdtape_file.close()

	# creates dictionary
	dict_stdtape={	'lats':stdtape_lats,
			'lons':stdtape_lons,
			'galt': stdtape_geo_alt,
			'palt': stdtape_pres_alt}
	
	# pandas dataframe for standar tape		
	df_stdtape=pd.DataFrame(data=dict_stdtape,index=stdtape_timestamp)

	# get a list cfradial files in the current directory
	# (bash script changes current directory)
	nclist = glob.glob('cfrad.*')
	nlist=len(nclist)
	print "Folder contains ",str(nlist)," cfradial files"

	for f in range(nlist):
		# open cfradial file for reading and writing
		print 'Processing: '+nclist[f]
		cfrad_file = Dataset(nclist[f],'r+') 

		# get cfradial timestamp
		start_datetime_nparray=cfrad_file.variables['time_coverage_start'][0:20]
		strpattern=''
		start_datetime_str=strpattern.join(str(v) for v in start_datetime_nparray)
		time_format="%Y-%m-%dT%H:%M:%SZ"
		cfrad_start_datetime=pd.to_datetime(start_datetime_str,format=time_format)
		cfrad_time = cfrad_file.variables['time'][:]
		cfrad_secs=pd.to_timedelta(cfrad_time.astype(int),unit='s')
		cfrad_timestamp=cfrad_start_datetime+cfrad_secs
		
		# create timestamp2 to miliseconds precision
		foo=[math.modf(x) for x in cfrad_time]
		f=zip(*foo) # unpair list of tuples
		ms = np.asarray(f[0])*1000
		s = f[1]
		cfrad_ms=pd.to_timedelta(ms,unit='ms')
		cfrad_s=pd.to_timedelta(s,unit='s')		
		cfrad_timestamp2=cfrad_start_datetime+cfrad_s+cfrad_ms

		# remove duplicated timestamps (str type)
		unique_timestamp=cfrad_timestamp.drop_duplicates()
		nstamps=unique_timestamp.nunique()

		# cfradial information
		cfrad_lats = cfrad_file.variables['latitude'][:]
		cfrad_lons = cfrad_file.variables['longitude'][:]
		cfrad_altitude = cfrad_file.variables['altitude'][:]
		cfrad_altitude_agl = cfrad_file.variables['altitude_agl'][:]

		# creates dictionary
		dict_cfrad = {	'lats':cfrad_lats,
				'lons':cfrad_lons,
				'alt': cfrad_altitude,
				'alt_agl': cfrad_altitude_agl }

		# pandas dataframe for cfradial file	
		df_cfrad=pd.DataFrame(data=dict_cfrad,index=cfrad_timestamp)
		df_cfrad_new=df_cfrad.copy()

		for t in range(nstamps):
			timestamp=str(unique_timestamp[t])

			# from std_tape
			try:
				new_lats=df_stdtape[timestamp]['lats']
				new_lons=df_stdtape[timestamp]['lons']
				new_galts=df_stdtape[timestamp]['galt']
				new_palts=df_stdtape[timestamp]['palt']
			except:
				print "\nERROR: check STDTAPE file is correct"
				cfrad_file.close()
				sys.exit(1)

			# to cfradial
			# since the 'altitude' field does not exist in dict_cfrad
			# pandas creates a new column (field). See if it's more
			# efficient creating a dict_cfrad with 'altitude' and
			# 'altitude_agl' fields
			df_cfrad_new.loc[timestamp,'lats']=new_lats
			df_cfrad_new.loc[timestamp,'lons']=new_lons
			df_cfrad_new.loc[timestamp,'altitude']=new_palts
			df_cfrad_new.loc[timestamp,'altitude_agl']=new_galts

#.........这里部分代码省略.........

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import variables['longitude'][:] [as 别名]
def replace_cfradial_coords( stdtape_filepath ):

	from netCDF4 import Dataset
	import glob
	import numpy as np 
	import pandas as pd	

	# open standard tape file for reading
	stdtape_file = Dataset(stdtape_filepath,'r') 

	# get stdtape timestamp
	base_time=stdtape_file.variables['base_time'][:]
	stdtape_secs=stdtape_file.variables['Time'][:]
	stdtape_timestamp=pd.to_datetime(stdtape_secs+base_time,unit='s')
	stdtape_lats=stdtape_file.variables['LAT'][:]
	stdtape_lons=stdtape_file.variables['LON'][:]

	# close the file
	stdtape_file.close()

	# pandas dataframe for standar tape
	d={'lats':stdtape_lats,'lons':stdtape_lons}
	df_stdtape=pd.DataFrame(data=d,index=stdtape_timestamp)

	# get a list cfradial files
	nclist = glob.glob('cfrad.*')
	nlist=len(nclist)
	print "Folder contains ",str(nlist)," cfradial files"

	for f in np.arange(nlist):
		# open cfradial file for reading and writing
		print 'Processing: '+nclist[f]
		cfrad_file = Dataset(nclist[f],'r+') 

		# get cfradial timestamp
		start_datetime_nparray=cfrad_file.variables['time_coverage_start'][0:20]
		strpattern=''
		start_datetime_str=strpattern.join(str(v) for v in start_datetime_nparray)
		time_format="%Y-%m-%dT%H:%M:%SZ"
		cfrad_start_datetime=pd.to_datetime(start_datetime_str,format=time_format)
		cfrad_time = cfrad_file.variables['time'][:]
		cfrad_secs=pd.to_timedelta(cfrad_time.astype(int),unit='s')
		cfrad_timestamp=cfrad_start_datetime+cfrad_secs

		# remove duplicated timestamps (str)
		unique_timestamp=cfrad_timestamp.drop_duplicates()
		nstamps=unique_timestamp.nunique()

		# cfradial coordinates
		cfrad_lats = cfrad_file.variables['latitude'][:]
		cfrad_lons = cfrad_file.variables['longitude'][:]

		# pandas dataframe for cfradial file
		d={'lats':cfrad_lats,'lons':cfrad_lons}
		df_cfrad=pd.DataFrame(data=d,index=cfrad_timestamp)
		df_cfrad_new=df_cfrad.copy()

		for t in np.arange(nstamps):
			timestamp=str(unique_timestamp[t])
			new_lats=df_stdtape[timestamp]['lats']
			new_lons=df_stdtape[timestamp]['lons']
			df_cfrad_new.loc[timestamp,'lats']=new_lats
			df_cfrad_new.loc[timestamp,'lons']=new_lons

		cfrad_file.variables['latitude'][:]=df_cfrad_new['lats'].values
		cfrad_file.variables['longitude'][:]=df_cfrad_new['lons'].values

		# print ''
		# print df_cfrad['lats'].values
		# print ''
		# print type(df_cfrad_new['lats'].values)
		# print ''
		# print df_cfrad_new['2001-01-25 18:44:51']['lats']
		# print ''

		# close the file.
		cfrad_file.close()

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import variables['longitude'][:] [as 别名]

#.........这里部分代码省略.........

    moo=np.where(np.array(nc_dims) == 'longitude')
    goo=np.where(np.array(nc_vars) == 'longitude')
    if not(goo[0] >= 0): goo=np.where(np.array(nc_vars) == 'lon')	# Look for mistakes in HadISDH
    if (moo[0] >= 0) & (goo[0] >= 0):
        ncfw.createDimension(nc_dims[moo[0]],ncf.variables[nc_vars[goo[0]]].size)        
    else:
        ncfw.createDimension('longitude',TheCLongs)        

    data['longitude']=ncfw.createVariable('longitude','f8',('longitude',))     
    
    data['longitude'].setncattr('standard_name',u'longitude')
    data['longitude'].setncattr('long_name',u'longitude')
    data['longitude'].setncattr('units',u'degrees_east')
    data['longitude'].setncattr('point_spacing',u'even')
    data['longitude'].setncattr('axis',u'X')

    makemonth=0    
    moo=np.where(np.array(nc_dims) == 'month')
    goo=np.where(np.array(nc_vars) == 'month')
    if not(goo[0] >= 0): goo=np.where(np.array(nc_vars) == 'months')	# Look for mistakes in HadISDH
    if (moo[0] >= 0) & (goo[0] >= 0):
        makemonth=1
        ncfw.createDimension('month',12)        
    
        data['month']=ncfw.createVariable('month','i',('month',))    

        data['month'].setncattr('standard_name',u'month')
        data['month'].setncattr('long_name',u'month')
        data['month'].setncattr('units',u'days since 1973-1-1 00:00:00')
        data['month'].setncattr('calendar',u'gregorian')
        data['month'].setncattr('start_year',u'1973s')
        data['month'].setncattr('end_year',u'1973s')
        data['month'].setncattr('start_month',u'1s')
        data['month'].setncattr('end_month',u'12s')
        data['month'].setncattr('axis',u'T')
    
# Now set up the variables
#    stop()
    for loo in range(nvars):	# miss out time, lat and lon - and month at the end
        print(loo)
	if (nc_vars[loo] != 'time') & (nc_vars[loo] != 'latitude') & (nc_vars[loo] != 'longitude') & (nc_vars[loo] != 'month') & \
	   (nc_vars[loo] != 'times') & (nc_vars[loo] != 'latitudes') & (nc_vars[loo] != 'longitudes') & (nc_vars[loo] != 'months') & \
	   (nc_vars[loo] != 'lat') & (nc_vars[loo] != 'lon'): 
	   
	    print(nc_vars[loo])

	    ncfw_var=ncfw.createVariable(nc_vars[loo],ncf.variables[nc_vars[loo]].dtype,ncf.variables[nc_vars[loo]].dimensions)

            if (any(np.where(np.array(ncf.variables[nc_vars[loo]].ncattrs()) == '_FillValue'))):
	        ncfw_var.setncattr('_FillValue',ncf.variables[nc_vars[loo]].getncattr('_FillValue')) 
            elif (any(np.where(np.array(ncf.variables[nc_vars[loo]].ncattrs()) == 'missing_value'))):
	        ncfw_var.setncattr('_FillValue',ncf.variables[nc_vars[loo]].getncattr('missing_value')) 
	    else:    
		ncfw_var.setncattr('_FillValue',TheMissing) 

            if (any(np.where(np.array(ncf.variables[nc_vars[loo]].ncattrs()) == 'missing_value'))):
	        ncfw_var.setncattr('missing_value',ncf.variables[nc_vars[loo]].getncattr('missing_value')) 
            elif (any(np.where(np.array(ncf.variables[nc_vars[loo]].ncattrs()) == '_FillValue'))):
	        ncfw_var.setncattr('missing_value',ncf.variables[nc_vars[loo]].getncattr('_FillValue')) 
	    else:    
		ncfw_var.setncattr('missing_value',TheMissing) 

            if (any(np.where(np.array(ncf.variables[nc_vars[loo]].ncattrs()) == 'valid_min'))):
	        ncfw_var.setncattr('valid_min',ncf.variables[nc_vars[loo]].getncattr('valid_min')) 
	    else:    
		ncfw_var.setncattr('valid_min',min(ncf.variables[nc_vars[0]][np.where(ncf.variables[nc_vars[0]][:] != TheMissing)])) 

            if (any(np.where(np.array(ncf.variables[nc_vars[loo]].ncattrs()) == 'valid_max'))):
	        ncfw_var.setncattr('valid_max',ncf.variables[nc_vars[loo]].getncattr('valid_max')) 
	    else:    
		ncfw_var.setncattr('valid_max',max(ncf.variables[nc_vars[0]][np.where(ncf.variables[nc_vars[0]][:] != TheMissing)])) 

            if (any(np.where(np.array(ncf.variables[nc_vars[loo]].ncattrs()) == 'reference_period'))):
	        ncfw_var.setncattr('reference_period',ncf.variables[nc_vars[loo]].getncattr('reference_period')) 
	    else:    
		ncfw_var.setncattr('reference_period',ClimPeriod) 
	        
	    ncfw_var.setncatts({'long_name':ncf.variables[nc_vars[loo]].getncattr('long_name'),
                            'units':ncf.variables[nc_vars[loo]].getncattr('units')})
# Now fill the variables
    ncfw.variables['time'][:]=TheDaysArray
    ncfw.variables['latitude'][:]=ncf.variables[nc_vars[1]][:]
    ncfw.variables['longitude'][:]=ncf.variables[nc_vars[2]][:]
    if (makemonth == 1): 
        ncfw.variables['month'][:]=TheDaysArray[0:12]

    for loo in range((nvars)):	# miss out time, lat and lon
        print(loo)
	if (nc_vars[loo] != 'time') & (nc_vars[loo] != 'latitude') & (nc_vars[loo] != 'longitude') & (nc_vars[loo] != 'month') & \
	   (nc_vars[loo] != 'times') & (nc_vars[loo] != 'latitudes') & (nc_vars[loo] != 'longitudes') & (nc_vars[loo] != 'months') & \
	   (nc_vars[loo] != 'lat') & (nc_vars[loo] != 'lon'): 
	   
	    print(nc_vars[loo])
            ncfw.variables[nc_vars[loo]][:]=ncf.variables[nc_vars[loo]][:]
   
    
    ncfw.close()
   
    return # ConvertNCCF

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import variables['longitude'][:] [as 别名]
    for i in range(num_lon):
        for j in range(num_lat):
            for k in range(num_depth):
                temp_3d[k,j,i] = temp[posn]
                salt_3d[k,j,i] = salt[posn]
                posn = posn+1

    # Output to NetCDF file
    print 'Writing NetCDF file'
    id = Dataset(out_file, 'w')
    id.createDimension('longitude', num_lon)
    id.createDimension('latitude', num_lat)
    id.createDimension('depth', num_depth)
    id.createVariable('longitude', 'f8', ('longitude'))
    id.variables['longitude'].units = 'degrees'
    id.variables['longitude'][:] = lon
    id.createVariable('latitude', 'f8', ('latitude'))
    id.variables['latitude'].units = 'degrees'
    id.variables['latitude'][:] = lat
    id.createVariable('depth', 'f8', ('depth'))
    id.variables['depth'].units = 'metres'
    id.variables['depth'][:] = depth
    id.createVariable('temp', 'f8', ('depth', 'latitude', 'longitude'))
    id.variables['temp'].units = 'C'
    id.variables['temp'][:,:,:] = temp_3d
    id.createVariable('salt', 'f8', ('depth', 'latitude', 'longitude'))
    id.variables['salt'].units = 'psu'
    id.variables['salt'][:,:,:] = salt_3d
    id.close()