如何將TXT數據插入python中的netcdf

Question

我是 python 的新手，所以如果我犯了任何初學者的錯誤，我很抱歉。 我正在嘗試將我的文本文件插入 netcdf。

I'm using the netcdf4 package and follow the example in this website: https://pyhogs.github.io/intro_netcdf4.html and I managed to reproduce the example (the example uses random data):

問題：我的文本文件包含：Lon、Lat、SST，當我嘗試插入這些值時，會創建 netcdf 文件，但是它不正確：

在我的代碼中，我嘗試應用 Barnes 插值（var）或 griddata 插值（interp）。 我認為這是必須在我的變量 netcdf 中輸入的內容（也許我錯了）。

到目前為止，這是我的代碼：

import os
import numpy as np
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
import numpy.ma as ma
import netCDF4 as nc4
from numpy.random import uniform, seed
from metpy.interpolate import (interpolate_to_grid, remove_nan_observations, inverse_distance_to_grid, remove_repeat_coordinates)

# Open file
arq_sst = np.loadtxt(fname = "C:\\Users\\Rodrigo\\XYZ.txt", skiprows=0, delimiter=",")

# Getting the Arrays
lonf = arq_sst[:, 0]
latf = arq_sst[:, 1]
sstf = arq_sst[:, 2]

# Atmosphere level
z = [1]   

#shapping grid
x_1, y_1 = np.meshgrid(lonf, latf)

#Barnes Interpolation
var = inverse_distance_to_grid(lonf, latf, sstf, x_1, y_1, r=100000, gamma=0.25, kappa=5.052, min_neighbors=3, kind='barnes')

#Or

#Another interpolation
interp = griddata((lonf, latf), sstf, (lonf[None,:], latf[:,None]), method='nearest')

#Open netcdf to write
f = nc4.Dataset('file_created.nc','w', format='NETCDF4') 

#Creating group in netcdf file
tempgrp = f.createGroup('SAT_DATA')

#Specifying dimensions
tempgrp.createDimension('lon', len(lonf))
tempgrp.createDimension('lat', len(latf))
tempgrp.createDimension('z', len(z))
tempgrp.createDimension('time', None)

#Building variables
longitude = tempgrp.createVariable('Longitude', 'f4', 'lon')
latitude = tempgrp.createVariable('Latitude', 'f4', 'lat')  
levels = tempgrp.createVariable('Levels', 'i4', 'z')
sst = tempgrp.createVariable('sst', 'f4', ('time', 'lon', 'lat', 'z'))
time = tempgrp.createVariable('Time', 'i4', 'time')


#Passing data into variables
longitude[:] = lonf 
latitude[:] = latf
levels[:] = z
sst[0,:,:,:] = var

#get time in days since Jan 01,01
from datetime import datetime
today = datetime.today()
time_num = today.toordinal()
time[0] = time_num

#Add global attributes
f.description = "XYZ dataset containing one group"
f.history = "Created " + today.strftime("%d/%m/%y")

#Add local attributes to variable instances
longitude.units = 'degrees east'
latitude.units = 'degrees north'
time.units = 'days since Jan 01, 0001'
sst.units = 'degrees'
levels.units = 'meters'
sst.warning = 'This data is not real!'

#Closing the dataset
f.close()

這是我的文本數據（標題：經度，緯度，SST）。 我減少了適合這里的行數：

-42.1870,-22.9940,22.4844
-37.4000,-29.9700,20.2000
-37.4200,-29.9600,20.1000
-39.1800,-30.0000,20.5000
-39.2100,-30.0000,20.4000
-39.2300,-30.0000,20.4000
-39.2200,-29.9800,20.4000
-39.2300,-29.9900,20.4000
-39.2000,-29.9800,20.4000
-39.1900,-30.0000,20.5000
-39.2800,-29.9900,20.5000
-39.2700,-29.9900,20.4000
-39.3400,-29.9700,20.5000
-39.3300,-29.9600,20.4000
-39.3100,-29.9600,20.4000
-39.3600,-29.9700,20.6000
-39.3500,-29.9900,20.4000
-39.3900,-29.9900,20.4000
-38.4600,-30.0000,20.3000
-38.4900,-29.9800,20.7000
-37.4800,-29.8800,20.4000
-37.5000,-29.8600,20.3000
-37.4600,-29.8900,20.3000
-41.3800,-29.9900,20.0000
-41.4000,-29.9900,20.1000
-41.0400,-29.9300,20.1000
-41.0200,-29.9200,20.2000
-41.0600,-29.9300,20.1000
-41.1000,-29.9400,19.9000
-41.0900,-29.9600,19.9000
-41.1100,-29.9800,19.9000
-41.1100,-29.9600,20.0000
-41.1200,-29.9400,20.0000
-41.1400,-29.9400,20.0000
-41.1600,-29.9500,20.1000
-41.1700,-29.9500,20.1000
-41.1900,-29.9700,20.0000
-41.1900,-29.9500,20.1000
-40.6800,-29.9900,20.1000
-40.7400,-29.9600,20.1000
-40.7700,-29.9700,20.1000
-40.7800,-29.9700,20.1000
-40.7100,-29.9000,20.1000
-40.7600,-29.9100,20.1000
-40.7400,-29.9000,20.1000
-40.7200,-29.9000,20.2000
-40.7600,-29.9200,20.1000
-40.7500,-29.9400,20.1000
-40.7800,-29.9100,20.2000
-40.8000,-29.9100,20.2000
-40.8100,-29.9300,20.1000
-40.8200,-29.9200,20.2000
-40.7900,-29.9300,20.2000
-40.7900,-29.9500,20.1000
-40.7700,-29.9300,20.1000
-40.8400,-29.9600,20.2000
-40.8600,-29.9600,20.3000
-40.9000,-29.9100,20.1000
-40.9100,-29.9100,20.0000
-40.3900,-29.9400,20.0000
-40.3900,-29.9200,20.0000
-40.4100,-29.9200,20.0000
-40.4100,-29.9400,20.0000
-40.3800,-29.9000,20.0000
-40.3800,-29.9200,20.0000
-40.4000,-29.9000,20.1000
-40.3700,-29.9600,20.0000
-40.3600,-29.9700,20.0000
-40.3800,-29.9800,20.0000
-40.4200,-29.9000,20.0000
-40.4300,-29.9300,20.1000
-40.4500,-29.9300,20.1000
-40.4700,-29.9300,20.0000
-40.4400,-29.9100,20.0000
-40.4500,-29.9100,20.0000
-40.4700,-29.9100,20.0000
-40.5000,-29.9400,19.9000
-40.5300,-29.9200,20.1000
-40.5100,-29.9200,20.1000
-40.4900,-29.9400,19.9000
-40.4900,-29.9200,20.0000
-40.6200,-30.0000,20.2000
-40.6000,-30.0000,20.1000
-40.6800,-29.9900,20.1000
-40.4000,-29.8400,20.1000
-40.4800,-29.8700,20.1000
-40.4500,-29.8300,20.3000
-40.4600,-29.8900,20.1000
-40.4600,-29.8700,20.0000
-40.5000,-29.8800,20.3000
-40.4900,-29.9000,20.1000
-40.5100,-29.9000,20.3000
-40.5300,-29.9000,20.2000
-40.5600,-29.8500,20.3000
-40.5800,-29.8500,20.3000
-40.6300,-29.9000,19.9000
-40.7100,-29.9000,20.1000
-40.0500,-29.9600,20.3000
-40.1100,-29.9800,20.2000
-40.1100,-30.0000,20.2000

有誰能夠幫我？

Answer 1

所以有幾件事。 首先，您沒有為插值和生成的 netCDF 文件提供正確的等距尺寸。 這就是我為網格網格創建空間的方式，（我選擇了 100 的線性空間，但根據您想要的數據分辨率，您可能希望將其更改為適合您目的的任何內容）：

spacing_x = np.linspace(np.min(lonf),np.max(lonf),100)
spacing_y = np.linspace(np.min(latf),np.max(latf),100)
x_1, y_1 = np.meshgrid(spacing_x, spacing_y)

然后進行如下插值：

#Barnes Interpolation
var = inverse_distance_to_grid(lonf, latf, sstf, x_1, y_1, r=100000, gamma=0.25, kappa=5.052, min_neighbors=3, kind='barnes')

#Or

#Another interpolation
interp = griddata((lonf, latf), sstf, (x_1, y_1), method='nearest')

最后，您需要將線性空間添加為緯度和經度維度，因為插值數據正在廣播給它們：

#Passing data into variables
longitude[:] = x_1[0]
latitude[:] = y_1[:,0]

另一個注意事項是，對於 Panoply 或其他軟件以 Geo2D 格式顯示數據，您需要將緯度維度命名為與變量相同。 完整代碼如下：

import os
import numpy as np
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
import numpy.ma as ma
import netCDF4 as nc4
from numpy.random import uniform, seed
from metpy.interpolate import (interpolate_to_grid, remove_nan_observations, inverse_distance_to_grid, remove_repeat_coordinates)

# Open file
arq_sst = np.loadtxt(fname = r"C:\Users\Rodrigo\XYZ.txt", skiprows=0, delimiter=",")

# Getting the Arrays
lonf = arq_sst[:, 0]
latf = arq_sst[:, 1]
sstf = arq_sst[:, 2]

# Atmosphere level
z = [1]

#shapping grid
spacing_x = np.linspace(np.min(lonf),np.max(lonf),100)
spacing_y = np.linspace(np.min(latf),np.max(latf),100)
x_1, y_1 = np.meshgrid(spacing_x, spacing_y)

#Barnes Interpolation
var = inverse_distance_to_grid(lonf, latf, sstf, x_1, y_1, r=100000, gamma=0.25, kappa=5.052, min_neighbors=3, kind='barnes')

#Or

#Another interpolation
interp = griddata((lonf, latf), sstf, (x_1, y_1), method='nearest')

#Open netcdf to write
f = nc4.Dataset('file_created.nc','w', format='NETCDF4')

#Creating group in netcdf file
tempgrp = f.createGroup('SAT_DATA')

#Specifying dimensions
tempgrp.createDimension('longitude', len(spacing_x))
tempgrp.createDimension('latitude', len(spacing_y))
tempgrp.createDimension('z', len(z))
tempgrp.createDimension('time', None)

#Building variables
longitude = tempgrp.createVariable('longitude', 'f8', 'longitude', fill_value=np.nan)
latitude = tempgrp.createVariable('latitude', 'f8', 'latitude', fill_value=np.nan)
levels = tempgrp.createVariable('z', 'i4', 'z')
sst = tempgrp.createVariable('sst', 'f8', ('time','longitude','latitude','z'), fill_value=np.nan)
time = tempgrp.createVariable('time', 'f8', 'time', fill_value=np.nan)

#Passing data into variables
longitude[:] = x_1[0]
latitude[:] = y_1[:,0]
levels[:] = z
sst[0,:,:,:] = var

#get time in days since Jan 01,01
from datetime import datetime
today = datetime.today()
time_num = today.toordinal()
time[0] = time_num

#Add global attributes
f.description = "XYZ dataset containing one group"
f.history = "Created " + today.strftime("%d/%m/%y")

#Add local attributes to variable instances
longitude.units = 'degrees_east'
longitude.point_spacing = "even";
longitude._CoordinateAxisType = "Lon";
latitude.units = 'degrees_north'
latitude.point_spacing = "even";
latitude._CoordinateAxisType = "Lat";
time.units = "days since Jan 01, 0001";
time._ChunkSizes = [1]
sst.long_name = "SEA SURFACE TEMPERATURE"
sst.history = "From coads_climatology"
sst.units = "Deg C";
sst.missing_value = -1.0
sst._ChunkSizes = [1, 100, 100]
levels.units = 'meters'
sst.warning = 'This data is not real!'

#Closing the dataset
f.close()

如果您有任何問題，請告訴我。