Python - Исключение: данные должны быть одномерными при запуске pd.Series - PullRequest
0 голосов
/ 16 июня 2020

Я хочу запустить следующий код. Моя цель - создать фрейм данных .csv из файла netcdf, в котором у меня есть изолированные переменные. Я также скопировал print (my_file) ниже, чтобы вы могли увидеть структуру моего файла netcdf. Ошибка возникает, когда я запускаю pd.Series, как показано ниже:

temp_ts = pd.Series(temp, index=dtime)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/gpfs/apps/miniconda3/lib/python3.7/site-packages/pandas/core/series.py", line 305, in __init__
    data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
  File "/gpfs/apps/miniconda3/lib/python3.7/site-packages/pandas/core/construction.py", line 482, in sanitize_array
    raise Exception("Data must be 1-dimensional")
Exception: Data must be 1-dimensional

Ниже весь мой код:

#netcdf to .csv 

import netCDF4
import pandas as pd
import numpy as np

temp_nc_file = '/gpfs/home/UDCPP/barrier_c/Test_NCO/temp_Corse_10m_201704.nc'
nc = netCDF4.Dataset(temp_nc_file, mode='r')

nc.variables.keys()

lat = nc.variables['latitude'][:]
lon = nc.variables['longitude'][:]
time_var = nc.variables['time']
dtime = netCDF4.num2date(time_var[:],time_var.units)
temp = nc.variables['TEMP'][:]

# a pandas.Series designed for time series of a 2D lat,lon grid
temp_ts = pd.Series(temp, index=dtime) 

temp_ts.to_csv('temp.csv',index=True, header=True)

И, наконец, результат печати (my_file), если он полезен:

print(nc)
<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF4_CLASSIC data model, file format HDF5):
    limi: 0
    lima: 1100
    pasi: 1
    ljmi: 0
    ljma: 462
    pasj: 1
    lkmi: 1
    lkma: 60
    pask: 1
    global_imin: 0
    global_imax: 1100
    global_jmin: 0
    global_jmax: 462
    data_type: OCO oriented grid
    format_version: 1.3.1
    Conventions: CF-1.6 OCO-1.3.1 COMODO-1.0
    netcdf_version: 4.1.2
    product_version: 1.0
    references: http://www.previmer.org/
    easting: longitude
    northing: latitude
    grid_projection: n/a
    distribution_statement: Data restrictions: for registered users only
    institution: IFREMER
    institution_references: http://www.ifremer.fr/
    data_centre: IFREMER OCO DATA CENTER
    data_centre_references: http://www.previmer.org/
    title: PREVIMER MENOR 1200 forecast
    creation_date: 2017-01-03T20:21:27Z
    run_time: 2017-01-03T20:21:27Z
    history: Tue Jun  9 18:24:38 2020: nces -O -d level,10,10 temp_Corse_201704.nc temp_Corse_10m_201704.nc
Tue Jun  9 15:41:33 2020: nces -O -d ni,565,700 -d nj,150,350 temp_201704.nc temp_Corse_201704.nc
Tue Jun  9 15:32:46 2020: ncks -O -v TEMP champs_meno_BE201704.nc temp_201704.nc
Mon Nov  6 10:27:14 2017: /appli/nco/nco-4.6.4__gcc-6.3.0/bin/ncrcat /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170101T0000Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170101T0300Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170101T0600Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170101T0900Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170101T1200Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170101T1500Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170101T1800Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170101T2100Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170102T0000Z.nc /home1/datawork/scoudray/DATARESU/MENOR/201701BE/MARC_F2-MARS3D-MENOR1200_20170102T0300Z.nc 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2017-01-03T20:21:27Z: creation
    model_name: MARS
    arakawa_grid_type: C1
    source: MARS3D V10.10
    area: North Western Mediterranean Sea
    southernmost_latitude: 39.5000
    northernmost_latitude: 44.5000
    latitude_resolution: 1.082250000000000E-002
    westernmost_longitude: 0.0000
    easternmost_longitude: 15.9999
    longitude_resolution: 1.454540000000000E-002
    minimum_depth: 5.000000
    maximum_depth: 3500.000
    depth_resolution: n/a
    forecast_range: 4-days forecast
    forecast_type: forecast
    operational_status: experimental
    NCO: netCDF Operators version 4.9.3 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
    start_date: 2017-01-01T00:00:00Z
    stop_date: 2017-01-01T00:00:00Z
    software_version: PREVIMER forecasting system v2
    product_name: PREVIMER_F2-MARS3D-MENOR1200_20170101T0000Z.nc
    field_type: 3-hourly
    comment: Use of Meteo-France ARPEGEHR meteorological data
    contact: cdoco-exploit@ifremer.fr
    quality_index: 0
    nco_openmp_thread_number: 1
    dimensions(sizes): nj(201), ni(136), time(248), level(1)
    variables(dimensions): float32 H0(nj,ni), float32 TEMP(time,level,nj,ni), float32 XE(time,nj,ni), float32 b(), float32 hc(nj,ni), float64 latitude(nj,ni), float32 level(level), float64 longitude(nj,ni), float32 ni(ni), float32 nj(nj), float32 theta(), float64 time(time)
    groups:

ЗАРАНЕЕ СПАСИБО ЗА ПОМОЩЬ!

1 Ответ

0 голосов
/ 20 июня 2020

Я не уверен, что это то, что вы ищете, но вы можете попробовать это.

import xarray as xr

file = xr.open_dataset('/gpfs/home/UDCPP/barrier_c/Test_NCO/temp_Corse_10m_201704.nc')

temp_ts = file['TEMP'].to_series()
temp_ts.to_csv('temp.csv',index=True, header=True)
...