初探netCDF4——ECMWF学习笔记 

from netCDF4 import Dataset

nc_obj = Dataset ( "2.nc" )

# 查看nc文件有些啥东西
print ( nc_obj )

<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF3_64BIT_OFFSET data model, file format NETCDF3):
    Conventions: CF-1.6
    history: 2019-12-01 10:46:48 GMT by grib_to_netcdf-2.14.1: grib_to_netcdf /data/data03/scratch/a4/47/_mars-atls05-a562cefde8a29a7288fa0b8b7f9413f7-H8cgHC.grib -o /data/data01/scratch/77/2a/_grib2netcdf-atls12-95e2cf679cd58ee9b4db4dd119a05a8d-xCPWwr.nc -utime
    dimensions(sizes): longitude(411), latitude(181), time(249)
    variables(dimensions): float32 longitude(longitude), float32 latitude(latitude), int32 time(time), int16 u10(time,latitude,longitude), int16 v10(time,latitude,longitude), int16 t2m(time,latitude,longitude), int16 d2m(time,latitude,longitude), int16 al(time,latitude,longitude), int16 fg10(time,latitude,longitude), int16 bld(time,latitude,longitude), int16 blh(time,latitude,longitude)
    groups: 

# 查看nc文件中的变量
print ( nc_obj.variables.keys() )

odict_keys(['longitude', 'latitude', 'time', 'u10', 'v10', 't2m', 'd2m', 'al', 'fg10', 'bld', 'blh'])

这里我们就发现nc_obj.variables类似于python中的字典结构，所以选取变量就简单多了

# 查看每个变量的信息
for i in nc_obj.variables.keys ():
    print("---------------------------------")
    print ( nc_obj.variables[i] )

---------------------------------
<class 'netCDF4._netCDF4.Variable'>
float32 longitude(longitude)
    units: degrees_east
    long_name: longitude
unlimited dimensions: 
current shape = (411,)
filling on, default _FillValue of 9.969209968386869e+36 used

---------------------------------
<class 'netCDF4._netCDF4.Variable'>
float32 latitude(latitude)
    units: degrees_north
    long_name: latitude
unlimited dimensions: 
current shape = (181,)
filling on, default _FillValue of 9.969209968386869e+36 used

---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int32 time(time)
    units: hours since 1900-01-01 00:00:00.0
    long_name: time
    calendar: gregorian
unlimited dimensions: time
current shape = (249,)
filling on, default _FillValue of -2147483647 used

---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int16 u10(time, latitude, longitude)
    scale_factor: 0.0009272951431673736
    add_offset: -1.7788449952278338
    _FillValue: -32767
    missing_value: -32767
    units: m s**-1
    long_name: 10 metre U wind component
unlimited dimensions: time
current shape = (249, 181, 411)
filling on
---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int16 v10(time, latitude, longitude)
    scale_factor: 0.0010392589789417878
    add_offset: 0.7994910516628728
    _FillValue: -32767
    missing_value: -32767
    units: m s**-1
    long_name: 10 metre V wind component
unlimited dimensions: time
current shape = (249, 181, 411)
filling on
---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int16 t2m(time, latitude, longitude)
    scale_factor: 0.001968216609007461
    add_offset: 259.7936554790978
    _FillValue: -32767
    missing_value: -32767
    units: K
    long_name: 2 metre temperature
unlimited dimensions: time
current shape = (249, 181, 411)
filling on
---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int16 d2m(time, latitude, longitude)
    scale_factor: 0.002020472251113585
    add_offset: 239.82672566475335
    _FillValue: -32767
    missing_value: -32767
    units: K
    long_name: 2 metre dewpoint temperature
unlimited dimensions: time
current shape = (249, 181, 411)
filling on
---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int16 al(time, latitude, longitude)
    scale_factor: 6.4676328535098725e-06
    add_offset: 0.28191839132090235
    _FillValue: -32767
    missing_value: -32767
    units: (0 - 1)
    long_name: Albedo
    standard_name: surface_albedo
unlimited dimensions: time
current shape = (249, 181, 411)
filling on
---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int16 fg10(time, latitude, longitude)
    scale_factor: 0.0006845262936044095
    add_offset: 22.73808793886049
    _FillValue: -32767
    missing_value: -32767
    units: m s**-1
    long_name: 10 metre wind gust since previous post-processing
unlimited dimensions: time
current shape = (249, 181, 411)
filling on
---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int16 bld(time, latitude, longitude)
    scale_factor: 87.75322623464827
    add_offset: 2875361.3594556083
    _FillValue: -32767
    missing_value: -32767
    units: J m**-2
    long_name: Boundary layer dissipation
    standard_name: kinetic_energy_dissipation_in_atmosphere_boundary_layer
unlimited dimensions: time
current shape = (249, 181, 411)
filling on
---------------------------------
<class 'netCDF4._netCDF4.Variable'>
int16 blh(time, latitude, longitude)
    scale_factor: 0.1570381308592232
    add_offset: 5152.502007763336
    _FillValue: -32767
    missing_value: -32767
    units: m
    long_name: Boundary layer height
unlimited dimensions: time
current shape = (249, 181, 411)
filling on

# 查看每个变量的所有属性
for i in nc_obj.variables.keys ():
    print ( nc_obj.variables[i].ncattrs () )
print ( '---------------------------------------' )

['units', 'long_name']
['units', 'long_name']
['units', 'long_name', 'calendar']
['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'units', 'long_name']
['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'units', 'long_name']
['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'units', 'long_name']
['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'units', 'long_name']
['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'units', 'long_name', 'standard_name']
['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'units', 'long_name']
['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'units', 'long_name', 'standard_name']
['scale_factor', 'add_offset', '_FillValue', 'missing_value', 'units', 'long_name']
---------------------------------------

# 查看每个变量的单位
for i in nc_obj.variables.keys ():
    print ( nc_obj.variables[i].units )

degrees_east
degrees_north
hours since 1900-01-01 00:00:00.0
m s**-1
m s**-1
K
K
(0 - 1)
m s**-1
J m**-2
m

# 查看每个变量的维数
for i in nc_obj.variables.keys ():
    print ( nc_obj.variables[i].ndim )

1
1
1
3
3
3
3
3
3
3
3

# 查看每个变量的形状（维度）
for i in nc_obj.variables.keys ():
    print ( nc_obj.variables[i].shape )

(411,)
(181,)
(249,)
(249, 181, 411)
(249, 181, 411)
(249, 181, 411)
(249, 181, 411)
(249, 181, 411)
(249, 181, 411)
(249, 181, 411)
(249, 181, 411)

#取出变量blh  10 metre U wind component(10米U风分量)
u10=nc_obj['u10'][:]

#print(u10)

import numpy as np
np.max(u10),np.min(u10)

(23.986977852820807, -20.62426418981837)

# 读取数据值
print ( "读取数据值" )
for i in nc_obj.variables.keys ():
    print ( '--------------------------------------------------------------------------------------------------------------' )
    print ( nc_obj.variables[i][:5] )

读取数据值
--------------------------------------------------------------------------------------------------------------
[-180.   -179.25 -178.5  -177.75 -177.  ]
--------------------------------------------------------------------------------------------------------------
[45.   44.25 43.5  42.75 42.  ]
--------------------------------------------------------------------------------------------------------------
[1048224       0       0       0       0]
--------------------------------------------------------------------------------------------------------------
[[[ 1.43330538  1.71705769  2.04624747 ...  4.2355913   4.50636149
    4.75673117]
  [ 1.15326225  1.4286689   1.80515073 ...  4.35706697  4.17068064
    4.23466401]
  [ 0.90011067  1.11802503  1.46576071 ...  4.15306204  3.70703307
    3.77101644]
  ...
  [ 3.95740276  3.97873055  4.00005834 ...  3.73763381  3.71537873
    3.69219635]
  [ 5.48002139  5.48929434  5.50042188 ...  3.63934053  3.68570528
    3.73207004]
  [ 1.57425424  1.57425424  1.57425424 ...  1.57425424  1.57425424
    1.57425424]]

 [[-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  ...
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]]

 [[-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  ...
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]]

 [[-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  ...
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]]

 [[-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  ...
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]
  [-1.778845   -1.778845   -1.778845   ... -1.778845   -1.778845
   -1.778845  ]]]
--------------------------------------------------------------------------------------------------------------
[[[ 7.71368104  7.07141899  6.28989624 ...  0.47836003  1.65999749
    1.97489296]
  [ 7.42996334  6.8178398   6.22857996 ...  1.25260797  1.61738787
    1.50203012]
  [ 7.1192249   6.54971098  5.99994298 ...  1.65064416  1.75560931
    1.65687971]
  ...
  [ 1.71715673  1.81276856  1.90630186 ... -2.03352892 -1.97636968
   -1.91817118]
  [-0.29173088 -0.24496422 -0.19715831 ... -4.00708173 -3.95511878
   -3.90107731]
  [-5.88190492 -5.88190492 -5.88190492 ... -5.88190492 -5.88190492
   -5.88190492]]

 [[ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  ...
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]]

 [[ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  ...
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]]

 [[ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  ...
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]]

 [[ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  ...
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]
  [ 0.79949105  0.79949105  0.79949105 ...  0.79949105  0.79949105
    0.79949105]]]
--------------------------------------------------------------------------------------------------------------
[[[287.71674451 287.55338253 287.31719654 ... 298.4513979  298.93164275
   298.32936847]
  [288.24619478 288.04346847 287.99229484 ... 299.40598295 298.54587229
   296.91815716]
  [289.0787504  288.74809001 288.76383575 ... 298.57539554 297.1012013
   295.42231254]
  ...
  [210.20640623 210.29300776 210.37960929 ... 206.41758926 206.42546213
   206.43530321]
  [209.65530558 209.69663813 209.7399389  ... 209.16521965 209.14160105
   209.11995066]
  [211.01927969 211.01927969 211.01927969 ... 211.01927969 211.01927969
   211.01927969]]

 [[259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  ...
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]]

 [[259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  ...
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]]

 [[259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  ...
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]]

 [[259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  ...
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]
  [259.79365548 259.79365548 259.79365548 ... 259.79365548 259.79365548
   259.79365548]]]
--------------------------------------------------------------------------------------------------------------
[[[286.98656848 286.97646612 286.80674645 ... 292.86412226 293.18739782
   293.72282296]
  [287.56644401 287.44319521 287.50785032 ... 294.20571583 294.82802128
   294.75528428]
  [288.46757464 288.13419672 288.25542505 ... 296.15345108 295.95948575
   295.05835512]
  ...
  [208.36797271 208.44070972 208.51142624 ... 203.09454014 203.07231494
   203.04604881]
  [206.20606741 206.24445638 206.27880441 ... 205.11097145 205.08874625
   205.06854153]
  [206.927376   206.927376   206.927376   ... 206.927376   206.927376
   206.927376  ]]

 [[239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  ...
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]]

 [[239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  ...
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]]

 [[239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  ...
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
  [239.82672566 239.82672566 239.82672566 ... 239.82672566 239.82672566
   239.82672566]
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--------------------------------------------------------------------------------------------------------------
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--------------------------------------------------------------------------------------------------------------
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--------------------------------------------------------------------------------------------------------------
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posted @ 2019-12-05 23:15  爱做梦的子浩  阅读(1410)  评论(0)  编辑  收藏  举报