Python读取NetCDF4文件触发NameError：lat_array未定义

Looks like you hit a simple variable name typo here—your script throws a NameError because you’re referencing lat_array and lon_array, but those variables were never defined. Looking at your code’s logic, you clearly intended to use the target coordinates you already set up: lat_target and lon_target.

Fixed Full Code #

#!/usr/bin/python
#=========================================================
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
#=========================================================
# SET TARGET DATA
#=========================================================
day=1
lat_target=45.0
lon_target=360-117.0
#=========================================================
# SET OPENDAP PATH
#=========================================================
pathname = 'http://thredds.northwestknowledge.net:8080/thredds/dodsC/agg_macav2metdata_huss_BNU-ESM_r1i1p1_historical_1950_2005_CONUS_daily.nc'
#=========================================================
# GET DATA HANDLES
#=========================================================
filehandle=Dataset(pathname,'r',format="NETCDF4")
lathandle=filehandle.variables['lat']
lonhandle=filehandle.variables['lon']
timehandle=filehandle.variables['time']
datahandle=filehandle.variables['specific_humidity']
#=========================================================
# GET DATA
#=========================================================
#get data
time_num=len(timehandle)
timeindex=range(day-1,time_num,365) #python starts arrays at 0
time=timehandle[timeindex]
lat = lathandle[:]
lon = lonhandle[:]
#=========================================================
#find indices of target lat/lon/day
# Fix: Replace lat_array with lat_target, lon_array with lon_target
lat_index = (np.abs(lat - lat_target)).argmin()
lon_index = (np.abs(lon - lon_target)).argmin()
#check final is in right bounds
if(lat[lat_index]>lat_target):
    if(lat_index!=0):
        lat_index = lat_index - 1
if(lat[lat_index]<lat_target):
    if(lat_index!=len(lat)-1):  # Fix: Avoid index out of bounds (max index is len(lat)-1)
        lat_index =lat_index +1
if(lon[lon_index]>lon_target):
    if(lon_index!=0):
        lon_index = lon_index - 1
if(lon[lon_index]<lon_target):
    if(lon_index!=len(lon)-1):  # Same fix for longitude index
        lon_index = lon_index + 1
# Rename variables to avoid overwriting the original arrays
lat_selected = lat[lat_index]
lon_selected = lon[lon_index]
#=========================================================
#get data
data = datahandle[timeindex,lat_index,lon_index]
#=========================================================
# MAKE A PLOT
#=========================================================
yearref=1950
years = np.arange(yearref,yearref+len(time))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(u'Year')
ax.set_ylabel(u'Specific Humidity')
ax.set_title(u'Specific Humidity on Day %d ,\n %4.2f°N, %4.2f°W' % (day, lat_selected, abs(360 - lon_selected)))
#ax.plot_date(x=time,y=data,fmt="b-")
ax.ticklabel_format(style='plain')
ax.plot(years,data,'b-')
plt.savefig("myPythonGraph.png")
plt.show()

Key Fixes & Improvements #

1. Fix the Typo (Root Cause)：The NameError happens because you wrote lat_array/lon_array instead of lat_target/lon_target. The line (np.abs(lat - lat_target)).argmin() calculates the absolute difference between every latitude in the NetCDF file and your target, then finds the index of the closest match—exactly what you need to pull data for your desired location.
2. Prevent Index Out of Bounds：Your original code had if(lat_index!=len(lat)) which would cause an error if lat_index reached the last position (since array indices go from 0 to len(lat)-1). Changing this to len(lat)-1 keeps things safe.
3. Avoid Variable Overwriting：You originally overwrote the lat and lon arrays with single values, which could cause confusion if you wanted to reuse the full coordinate arrays later. Renaming the selected coordinates to lat_selected/lon_selected keeps things clear.

内容的提问来源于stack exchange，提问作者Markus Haas