氣象海洋預測-Task1 氣象數據分析常用工具

氣象科學中的數據通常包含多個維度，例如本賽題中給出的數據就包含年、月、經度、緯度四個維度，為了便于數據的讀取和操作，氣象數據通常采用netCDF文件來存儲，文件后綴為.nc。

對于以netCDF文件存儲的氣象數據，有兩個常用的數據分析庫，即NetCDF4和Xarray。在此次任務中，我們將學習這兩個庫的基本對象和基本操作，掌握用這兩個庫讀取和處理氣象數據的基本方法。

學習目標

1.了解和學習NetCDF4和Xarray的基本對象和基本操作，掌握用這兩個庫讀取和處理氣象數據的基本方法。

內容介紹

NetCDF4

• 創建、打開和關閉netCDF文件
• 組（Groups）
• 維度（Dimensions）
• 變量（Variables）
• 屬性（Attributes）
• 寫入或讀取變量數據
• 應用

Xarray

• 創建DataArray
• 索引
• 屬性
• 計算
• GroupBy
• 繪圖
• 與Pandas對象相互轉換
• Dataset
• 讀/寫netCDF文件
• 應用

NetCDF4

官方文檔

NetCDF4是NetCDF C庫的Python模塊，支持Groups、Dimensions、Variables和Attributes等對象類型及其相關操作。

安裝NetCDF4

!pip install netCDF4 import netCDF4 as nc

創建、打開和關閉netCDF文件

NetCDF4可以通過調用Dataset創建netCDF文件或打開已存在的文件，并通過查看data_model屬性確定文件的格式。需要注意創建或打開文件后要先關閉文件才能再次調用Dataset打開文件。

• 創建netCDF文件

from netCDF4 import Dataset# Dataset包含三個輸入參數：文件名，模式（其中'w', 'r+', 'a'為可寫入模式），文件格式 test = Dataset('test.nc', 'w', 'NETCDF4') test <class 'netCDF4._netCDF4.Dataset'> root group (NETCDF4 data model, file format HDF5):dimensions(sizes): variables(dimensions): groups:

• 打開已存在的netCDF文件

# 打開訓練樣本中的SODA數據 soda = Dataset('test.nc') soda <class 'netCDF4._netCDF4.Dataset'> root group (NETCDF4 data model, file format HDF5):dimensions(sizes): variables(dimensions): groups:

• 查看文件格式

print(soda.data_model) NETCDF4

• 關閉netCDF文件

soda.close()

Groups

NetCDF4支持按層級的組（Groups）來組織數據，類似于文件系統中的目錄，Groups中可以包含Variables、Dimenions、Attributes對象以及其他Groups對象，Dataset會創建一個特殊的Groups，稱為根組（Root Group），類似于根目錄，使用Dataset.createGroup方法創建的組都包含在根組中。

• 創建Groups

# 接受一個字符串參數作為Group名稱 group1 = test.createGroup('group1') group2 = test.createGroup('group2') group1 <class 'netCDF4._netCDF4.Group'> group /group1:dimensions(sizes): variables(dimensions): groups:

• 查看文件中的所有Groups

# 返回一個Group字典 test.groups OrderedDict([('group1',<class 'netCDF4._netCDF4.Group'>group /group1:dimensions(sizes): variables(dimensions): groups: ),('group2',<class 'netCDF4._netCDF4.Group'>group /group2:dimensions(sizes): variables(dimensions): groups: )])

• Groups嵌套

# 在group1和group2下分別再創建一個Group group1_1 = test.createGroup('group1/group11') group2_1 = test.createGroup('group2/group21') test.groups OrderedDict([('group1',<class 'netCDF4._netCDF4.Group'>group /group1:dimensions(sizes): variables(dimensions): groups: group11),('group2',<class 'netCDF4._netCDF4.Group'>group /group2:dimensions(sizes): variables(dimensions): groups: group21)]) test.groups.values() odict_values([<class 'netCDF4._netCDF4.Group'> group /group1:dimensions(sizes): variables(dimensions): groups: group11, <class 'netCDF4._netCDF4.Group'> group /group2:dimensions(sizes): variables(dimensions): groups: group21])

• 遍歷查看所有Groups

# 定義一個生成器函數用來遍歷所有目錄樹 def walktree(top):values = top.groups.values()yield valuesfor value in top.groups.values():for children in walktree(value):yield children for groups in walktree(test):for group in groups:print(group) <class 'netCDF4._netCDF4.Group'> group /group1:dimensions(sizes): variables(dimensions): groups: group11 <class 'netCDF4._netCDF4.Group'> group /group2:dimensions(sizes): variables(dimensions): groups: group21 <class 'netCDF4._netCDF4.Group'> group /group1/group11:dimensions(sizes): variables(dimensions): groups: <class 'netCDF4._netCDF4.Group'> group /group2/group21:dimensions(sizes): variables(dimensions): groups:

Dimensions

NetCDF4用維度來定義各個變量的大小，例如本賽題中訓練樣本的第二維度month就是一個維度對象，每個樣本包含36個月的數據，因此month維度內的變量的大小就是36。變量是包含在維度中的，因此在創建每個變量時要先創建其所在的維度。

• 創建Dimensions

Dataset.createDimension方法接受兩個參數：維度名稱，維度大小。維度大小設置為None或0時表示無窮維度。

# 創建無窮維度 level = test.createDimension('level', None) time = test.createDimension('time', None) # 創建有限維度 lat = test.createDimension('lat', 180) lon = test.createDimension('lon', 360)

• 查看Dimensions

test.dimensions OrderedDict([('level',<class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'level', size = 0),('time',<class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time', size = 0),('lat',<class 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 180),('lon',<class 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 360)])

• 查看維度大小

# 查看維度大小 print(len(lon)) 360 # Dimension對象存儲在字典中 print(level) <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'level', size = 0 # 判斷維度是否是無窮 print(time.isunlimited()) print(lat.isunlimited()) True False

Variables

NetCDF4的Variables對象類似于Numpy中的多維數組，不同的是，NetCDF4的Variables變量可以存儲在無窮維度中。

• 創建Variables

Dataset.createVariable方法接受的參數為：變量名，變量的數據類型，變量所在的維度。

變量的有效數據類型包括：‘f4’(32位浮點數)、‘f8’(64位浮點數)、‘i1’(8位有符號整型)、‘i2’(16位有符號整型)、‘i4’(32位有符號整型)、‘i8’(64位有符號整型)、‘u1’(8位無符號整型)、‘u2’(16位無符號整型)、‘u4’(32位無符號整型)、‘u8’(64位無符號整型)、‘s1’(單個字符)。

# 創建單個維度上的變量 times = test.createVariable('time', 'f8', ('time',)) levels = test.createVariable('level', 'i4', ('level',)) lats = test.createVariable('lat', 'f4', ('lat',)) lons = test.createVariable('lon', 'f4', ('lon',))# 創建多個維度上的變量 temp = test.createVariable('temp', 'f4', ('time', 'level', 'lat', 'lon')) times <class 'netCDF4._netCDF4.Variable'> float64 time(time) unlimited dimensions: time current shape = (0,) filling on, default _FillValue of 9.969209968386869e+36 used levels <class 'netCDF4._netCDF4.Variable'> int32 level(level) unlimited dimensions: level current shape = (0,) filling on, default _FillValue of -2147483647 used

• 查看Variables

print(temp) <class 'netCDF4._netCDF4.Variable'> float32 temp(time, level, lat, lon) unlimited dimensions: time, level current shape = (0, 0, 180, 360) filling on, default _FillValue of 9.969209968386869e+36 used # 通過路徑的方式在Group中創建變量 ftemp = test.createVariable('/group1/group11/ftemp', 'f8', ('time', 'level', 'lat', 'lon')) ftemp <class 'netCDF4._netCDF4.Variable'> float64 ftemp(time, level, lat, lon) path = /group1/group11 unlimited dimensions: time, level current shape = (0, 0, 180, 360) filling on, default _FillValue of 9.969209968386869e+36 used # 可以通過路徑查看變量 print(test['/group1/group11/ftemp']) <class 'netCDF4._netCDF4.Variable'> float64 ftemp(time, level, lat, lon) path = /group1/group11 unlimited dimensions: time, level current shape = (0, 0, 180, 360) filling on, default _FillValue of 9.969209968386869e+36 used print(test['/group1/group11']) <class 'netCDF4._netCDF4.Group'> group /group1/group11:dimensions(sizes): variables(dimensions): float64 ftemp(time, level, lat, lon)groups: # 查看文件中的所有變量 print(test.variables) OrderedDict([('time', <class 'netCDF4._netCDF4.Variable'> float64 time(time) unlimited dimensions: time current shape = (0,) filling on, default _FillValue of 9.969209968386869e+36 used), ('level', <class 'netCDF4._netCDF4.Variable'> int32 level(level) unlimited dimensions: level current shape = (0,) filling on, default _FillValue of -2147483647 used), ('lat', <class 'netCDF4._netCDF4.Variable'> float32 lat(lat) unlimited dimensions: current shape = (180,) filling on, default _FillValue of 9.969209968386869e+36 used), ('lon', <class 'netCDF4._netCDF4.Variable'> float32 lon(lon) unlimited dimensions: current shape = (360,) filling on, default _FillValue of 9.969209968386869e+36 used), ('temp', <class 'netCDF4._netCDF4.Variable'> float32 temp(time, level, lat, lon) unlimited dimensions: time, level current shape = (0, 0, 180, 360) filling on, default _FillValue of 9.969209968386869e+36 used)])

Attributes

Attributes對象用于存儲對文件或維變量的描述信息，netcdf文件中包含兩種屬性：全局屬性和變量屬性。全局屬性提供Groups或整個文件對象的信息，變量屬性提供Variables對象的信息，屬性的名稱可以自己設置，下面例子中的description和history等都是自定義的屬性名稱。

import time# 設置對文件的描述 test.description = 'bogus example script' # 設置文件的歷史信息 test.history = 'Created' + time.ctime(time.time()) # 設置文件的來源信息 test.source = 'netCDF4 python module tutorial' # 設置變量屬性 lats.units = 'degrees north' lons.units = 'degrees east' levels.units = 'hPa' temp.units = 'K' times.units = 'hours since 0001-01-01 00:00:00.0' times.calendar = 'gregorian' # 查看文件屬性名稱 print(test.ncattrs()) # 查看變量屬性名稱 print(test['lat'].ncattrs()) print(test['time'].ncattrs()) ['description', 'history', 'source'] ['units'] ['units', 'calendar'] # 查看文件屬性 for name in test.ncattrs():print('Global attr {} = {}'.format(name, getattr(test, name))) Global attr description = bogus example script Global attr history = CreatedTue Jan 11 19:47:20 2022 Global attr source = netCDF4 python module tutorial

寫入或讀取變量數據

類似于數組，可以通過切片的方式向變量中寫入或讀取數據。

• 向變量中寫入數據

from numpy.random import uniformnlats = len(test.dimensions['lat']) nlons = len(test.dimensions['lon']) print('temp shape before adding data = {}'.format(temp.shape)) temp shape before adding data = (0, 0, 180, 360) # 無窮維度的大小會隨著寫入的數據的大小自動擴展 temp[0:5, 0:10, :, :] = uniform(size=(5, 10, nlats, nlons)) print('temp shape after adding data = {}'.format(temp.shape)) temp shape after adding data = (5, 10, 180, 360) print('levels shape after adding pressure data = {}'.format(levels.shape)) levels shape after adding pressure data = (10,)

• 讀取變量中的數據

print(temp[1, 5, 100, 200]) 0.13303915 print(temp[1, 5, 10:20, 100:110].shape) (10, 10) # 可以用start:stop:step的形式進行切片 print(temp[1, 5, 10, 100:110:2]) [0.08290233 0.44888723 0.11997929 0.7889917 0.17327116]

應用

我們嘗試用NetCDF4來操作一下訓練樣本中的SODA數據。

# 打開SODA文件 soda = Dataset('test.nc') # 查看文件格式 print('SODA文件格式：', soda.data_model) # 查看文件中包含的對象 print(soda) SODA文件格式： NETCDF4 <class 'netCDF4._netCDF4.Dataset'> root group (NETCDF4 data model, file format HDF5):description: bogus example scripthistory: CreatedTue Jan 11 19:47:20 2022source: netCDF4 python module tutorialdimensions(sizes): level(10), time(5), lat(180), lon(360)variables(dimensions): float64 time(time), int32 level(level), float32 lat(lat), float32 lon(lon), float32 temp(time, level, lat, lon)groups: group1, group2 # 查看維度和變量 print(soda.dimensions) print(soda.variables) OrderedDict([('level', <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'level', size = 10), ('time', <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time', size = 5), ('lat', <class 'netCDF4._netCDF4.Dimension'>: name = 'lat', size = 180), ('lon', <class 'netCDF4._netCDF4.Dimension'>: name = 'lon', size = 360)]) OrderedDict([('time', <class 'netCDF4._netCDF4.Variable'> float64 time(time)units: hours since 0001-01-01 00:00:00.0calendar: gregorian unlimited dimensions: time current shape = (5,) filling on, default _FillValue of 9.969209968386869e+36 used), ('level', <class 'netCDF4._netCDF4.Variable'> int32 level(level)units: hPa unlimited dimensions: level current shape = (10,) filling on, default _FillValue of -2147483647 used), ('lat', <class 'netCDF4._netCDF4.Variable'> float32 lat(lat)units: degrees north unlimited dimensions: current shape = (180,) filling on, default _FillValue of 9.969209968386869e+36 used), ('lon', <class 'netCDF4._netCDF4.Variable'> float32 lon(lon)units: degrees east unlimited dimensions: current shape = (360,) filling on, default _FillValue of 9.969209968386869e+36 used), ('temp', <class 'netCDF4._netCDF4.Variable'> float32 temp(time, level, lat, lon)units: K unlimited dimensions: time, level current shape = (5, 10, 180, 360) filling on, default _FillValue of 9.969209968386869e+36 used)])

可以看到，SODA文件中包含year、month、lat、lon四個維度，維度大小分別是100、36、24和72，包含sst、t300、ua、va四個變量，每個變量都定義在(year, month, lat, lon)維度上。

# 讀取每個變量中的數據 soda_sst = soda['level'][:] print(soda_sst[1])soda_t300 = soda['temp'][:] print(soda_t300[1, 2, 12:24, 36])# soda_ua = soda['ua'][:] # print(soda_ua[1, 2, 12:24:2, 36:38])# soda_va = soda['va'][:] # print(soda_va[5:10, 0:12, 12, 36])# 關閉文件 soda.close() -- [0.31591734 0.59989274 0.44380635 0.7643542 0.5319885 0.788631860.97203755 0.4588462 0.19999161 0.9740341 0.65341175 0.9087504 ]

Xarray

官方文檔

Xarray是一個開源的Python庫，支持在類似Numpy的多維數組上引入維度、坐標和屬性標記并可以直接使用標記的名稱進行相關操作，能夠讀寫netcdf文件并進行進一步的數據分析和可視化。

Xarray有兩個基本的數據結構：DataArray和Dataset，這兩個數據結構都是在多維數組上建立的，其中DataArray用于標記的實現，Dataset則是一個類似于字典的DataArray容器。

安裝Xarray要求滿足以下依賴包：

• Python(3.7+)
• setuptools(40.4+)
• Numpy(1.17+)
• Pandas(1.0+)

!pip install xarray Defaulting to user installation because normal site-packages is not writeable Looking in indexes: https://mirrors.aliyun.com/pypi/simple Collecting xarrayDownloading https://mirrors.aliyun.com/pypi/packages/10/6f/9aa15b1f9001593d51a0e417a8ad2127ef384d08129a0720b3599133c1ed/xarray-0.16.2-py3-none-any.whl (736 kB) [K |████████████████████████████████| 736 kB 198 kB/s eta 0:00:01 [?25hRequirement already satisfied: setuptools>=38.4 in /opt/conda/lib/python3.6/site-packages (from xarray) (51.1.1) Requirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.6/site-packages (from xarray) (1.19.4) Requirement already satisfied: pandas>=0.25 in /opt/conda/lib/python3.6/site-packages (from xarray) (1.1.5) Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.25->xarray) (2020.5) Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.25->xarray) (2.8.1) Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.6/site-packages (from python-dateutil>=2.7.3->pandas>=0.25->xarray) (1.15.0) Installing collected packages: xarray Successfully installed xarray-0.16.2 [33mWARNING: You are using pip version 21.0.1; however, version 21.1.2 is available. You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.[0m import numpy as np import pandas as pd import xarray as xr

創建DataArray

xr.DataArray接受三個輸入參數：數組，維度，坐標。其中維度為數組的維度名稱，坐標以字典的形式給維度賦予坐標標簽。

# 創建一個2x3的數組，將維度命名為'x'和'y'，并賦予'x'維度10和20兩個坐標標簽 data = xr.DataArray(np.random.randn(2, 3), dims=('x', 'y'), coords={'x': [10, 20]}) data # 查看數據 print(data.values)# 查看維度 print(data.dims)# 查看坐標 print(data.coords)# 可以用data.attrs字典來存儲任意元數據 print(data.attrs) [[-1.89477837 -0.58997363 -1.77758946][-0.21793173 0.77616912 0.45868184]] ('x', 'y') Coordinates:* x (x) int64 10 20 {}

索引

Xarray支持四種索引方式。

# 通過位置索引，類似于numpy print(data[0, :], '\n')# 通過坐標標簽索引 print(data.loc[10], '\n')# 通過維度名稱和位置索引，isel表示"integer select" print(data.isel(x=0), '\n')# 通過維度名稱和坐標標簽索引，sel表示"select" print(data.sel(x=10), '\n') <xarray.DataArray (y: 3)> array([-1.89477837, -0.58997363, -1.77758946]) Coordinates:x int64 10 Dimensions without coordinates: y <xarray.DataArray (y: 3)> array([-1.89477837, -0.58997363, -1.77758946]) Coordinates:x int64 10 Dimensions without coordinates: y <xarray.DataArray (y: 3)> array([-1.89477837, -0.58997363, -1.77758946]) Coordinates:x int64 10 Dimensions without coordinates: y <xarray.DataArray (y: 3)> array([-1.89477837, -0.58997363, -1.77758946]) Coordinates:x int64 10 Dimensions without coordinates: y

屬性

和NetCDF4一樣，Xarray也支持自定義DataArray或標記的屬性描述。

# 設置DataArray的屬性 data.attrs['long_name'] = 'random velocity' data.attrs['units'] = 'metres/sec' data.attrs['description'] ='A random variable created as an example' data.attrs['ramdom_attribute'] = 123 # 查看屬性 print(data.attrs) {'long_name': 'random velocity', 'units': 'metres/sec', 'description': 'A random variable created as an example', 'ramdom_attribute': 123} # 設置維度標記的屬性描述 data.x.attrs['units'] ='x units' print('Attributes of x dimension:', data.x.attrs, '\n') Attributes of x dimension: {'units': 'x units'}

計算

DataArray的計算方式類似于numpy ndarray。

data + 10 data.T data.sum()

可以直接使用維度名稱進行聚合操作。

data.mean(dim='x')

DataArray之間的計算操作可以根據維度名稱進行廣播。

a = xr.DataArray(np.random.randn(3), [data.coords['y']]) b = xr.DataArray(np.random.randn(4), dims='z') print(a, '\n') print(b, '\n') print(a+b, '\n') <xarray.DataArray (y: 3)> array([0.04405523, 0.36823828, 0.38351121]) Coordinates:* y (y) int64 0 1 2 <xarray.DataArray (z: 4)> array([ 0.62771044, -0.41870179, -1.38038185, -0.19742089]) Dimensions without coordinates: z <xarray.DataArray (y: 3, z: 4)> array([[ 0.67176567, -0.37464656, -1.33632661, -0.15336566],[ 0.99594872, -0.05046351, -1.01214356, 0.17081739],[ 1.01122165, -0.03519058, -0.99687063, 0.18609032]]) Coordinates:* y (y) int64 0 1 2 Dimensions without coordinates: z data - data.T data[:-1] - data[:1]

GroupBy

Xarray支持使用類似于Pandas的API進行分組操作。

labels = xr.DataArray(['E', 'F', 'E'], [data.coords['y']], name='labels') labels # 將data的y坐標對齊labels后按labels的值分組求均值 data.groupby(labels).mean('y') # 將data的y坐標按labels分組后減去組內的最小值 data.groupby(labels).map(lambda x: x - x.min())

繪圖

Xarray支持簡單方便的可視化操作，這里只做簡單的介紹，更多的繪圖方法感興趣的同學們可以自行去探索。

%matplotlib inline data.plot() <matplotlib.collections.QuadMesh at 0x7f7ea4327828>

[外鏈圖片轉存失敗,源站可能有防盜鏈機制,建議將圖片保存下來直接上傳(img-mEPsd2AX-1645665921672)(Task1_files/Task1_116_1.png)]

與Pandas對象互相轉換

Xarray可以方便地轉換成Pandas的Series或DataFrame，也可以由Pandas對象轉換回Xarray。

# 轉換成Pandas的Series series = data.to_series() series x y 10 0 -1.8947781 -0.5899742 -1.777589 20 0 -0.2179321 0.7761692 0.458682 dtype: float64 # Series轉換成Xarray series.to_xarray() # 轉換成Pandas的DataFrame df = data.to_dataframe(name='colname') df colnamexy1001220012

-1.894778

-0.589974

-1.777589

-0.217932

0.776169

0.458682

# DataFrame轉換成Xarray xr.Dataset.from_dataframe(df)

Dataset

Dataset是一個類似于字典的DataArray的容器，可以看作是一個具有多為結構的DataFrame。對比NetCDF4庫中的Dataset，我們可以發現兩者的作用是相似的，都是作為容器用來存儲其他的對象。

# 創建一個Dataset，其中包含三個DataArray ds = xr.Dataset({'foo': data, 'bar': ('x', [1, 2]), 'baz': np.pi}) ds

可以通過字典的方式或者點索引的方式來查看DataArray，但是只有采用字典方式時才可以進行賦值。

# 通過字典方式查看DataArray print(ds['foo'], '\n')# 通過點索引的方式查看DataArray print(ds.foo)

讀/寫netCDF文件

# 寫入到netcdf文件 ds.to_netcdf('xarray_test.nc')# 讀取已存在的netcdf文件 xr.open_dataset('xarray_test.nc')

應用

嘗試用Xarray來操作一下訓練樣本中的SODA數據。

# 打開SODA文件 soda = xr.open_dataset('SODA_train.nc') # 查看文件屬性 print(soda.attrs) # 查看文件中包含的對象 print(soda) {} <xarray.Dataset> Dimensions: (lat: 24, lon: 72, month: 36, year: 100) Coordinates:* year (year) int32 1 2 3 4 5 6 7 8 9 10 ... 92 93 94 95 96 97 98 99 100* month (month) int32 1 2 3 4 5 6 7 8 9 10 ... 28 29 30 31 32 33 34 35 36* lat (lat) float64 -55.0 -50.0 -45.0 -40.0 -35.0 ... 45.0 50.0 55.0 60.0* lon (lon) float64 0.0 5.0 10.0 15.0 20.0 ... 340.0 345.0 350.0 355.0 Data variables:sst (year, month, lat, lon) float32 ...t300 (year, month, lat, lon) float32 ...ua (year, month, lat, lon) float64 ...va (year, month, lat, lon) float64 ... # 查看維度和坐標 print(soda.dims) print(soda.coords) Frozen(SortedKeysDict({'year': 100, 'month': 36, 'lat': 24, 'lon': 72})) Coordinates:* year (year) int32 1 2 3 4 5 6 7 8 9 10 ... 92 93 94 95 96 97 98 99 100* month (month) int32 1 2 3 4 5 6 7 8 9 10 ... 28 29 30 31 32 33 34 35 36* lat (lat) float64 -55.0 -50.0 -45.0 -40.0 -35.0 ... 45.0 50.0 55.0 60.0* lon (lon) float64 0.0 5.0 10.0 15.0 20.0 ... 340.0 345.0 350.0 355.0 # 讀取數據 soda_sst = soda['sst'] print(soda_sst[1, 1, 1, 1], '\n')soda_t300 = soda['t300'] print(soda_t300[1, 2, 12:24, 36], '\n')soda_ua = soda['ua'] print(soda_ua[1, 2, 12:24:2, 36:38], '\n')soda_va = soda['va'] print(soda_va[5:10, 0:12, 12, 36]) <xarray.DataArray 'sst' ()> array(0.549156, dtype=float32) Coordinates:year int32 2month int32 2lat float64 -50.0lon float64 5.0 <xarray.DataArray 't300' (lat: 12)> array([ 0.350308, -0.271906, -0.394029, 0.534374, 0.378115, 0.371367,0.082296, 0.754251, 0.682577, 0.147856, 0.220678, 0.574088],dtype=float32) Coordinates:year int32 2month int32 3* lat (lat) float64 5.0 10.0 15.0 20.0 25.0 ... 40.0 45.0 50.0 55.0 60.0lon float64 180.0 <xarray.DataArray 'ua' (lat: 6, lon: 2)> array([[ 1.222841, 1.084187],[-0.106073, -0.286916],[-0.983318, -0.892802],[-1.157512, -1.04381 ],[ 1.443658, 1.275039],[ 2.179182, 1.776857]]) Coordinates:year int32 2month int32 3* lat (lat) float64 5.0 15.0 25.0 35.0 45.0 55.0* lon (lon) float64 180.0 185.0 <xarray.DataArray 'va' (year: 5, month: 12)> array([[ 0.875687, 0.640397, 1.346922, 0.532989, 0.985298, 1.02812 ,0.853269, 0.746913, 0.289339, -0.401898, -0.832116, -0.432147],[ 0.040508, 0.157661, -0.734164, -0.706849, -0.567588, 0.104219,0.588996, 0.224966, -0.252701, -0.519716, -1.152297, -1.315635],[-1.742571, -2.09365 , -3.080663, -2.863212, -1.135314, 0.053631,0.513007, 1.139938, 1.030276, 1.018402, 0.882338, 2.161939],[ 1.876133, 1.298197, 0.912559, 0.072299, -0.547984, 0.95893 ,1.205327, 0.956807, 0.993742, 0.75878 , 0.690233, 0.910672],[ 0.564618, -0.047889, 0.537964, 0.341526, -0.142936, -0.160385,0.36168 , 0.315495, 0.51516 , 0.513514, 0.066542, 0.423261]]) Coordinates:* year (year) int32 6 7 8 9 10* month (month) int32 1 2 3 4 5 6 7 8 9 10 11 12lat float64 5.0lon float64 180.0

作業

基礎作業：

1.嘗試用NetCDF4和Xarray來操作賽題數據，對數據有基本的了解。

進階作業：

2.嘗試用Xarray對訓練數據進行數據探索和數據可視化。

總結

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