In [1]:

    

import datetime
import matplotlib.pyplot as plt
import netCDF4
import os
import pandas as pd


    

In [2]:

    

def read_netcdf(nc_full_name,variable_list=[]):
    # open the netCDF file
    nc_file = netCDF4.Dataset(nc_full_name,"r")
    # creat the attribute dictionary
    attr = {}
    attr["global"] = {}
    attr["variable"] = {}
    # get the global attributes
    gattrlist = nc_file.ncattrs()
    if len(gattrlist)!=0:
        for item in gattrlist:
            attr["global"][item] = getattr(nc_file,item)
    # get a list of Python datetimes
    time = time = nc_file.variables["time"][:]
    time_units = getattr(nc_file.variables["time"],"units")
    dt = list(netCDF4.num2date(time,time_units))
    # get a list of variables to read from the netCDF file
    # was a variable list passed in as variable_list?
    if len(variable_list)==0:
        # if not, get the variable list from the netCDF file contents
        variable_list = nc_file.variables.keys()
    else:
        # if so, add the QC flags to the list entered as an argument
        flag_list = []
        for item in variable_list: flag_list.append(item+"_QCFlag")
        variable_list = variable_list+flag_list
    # create the data dictionary
    data = {}
    # loop over the variables to be read
    for item in variable_list:
        # get the number of dimensions
        # variables in OzFlux netCDF files can have 1 (time) or 3 dimensions (time,latitude,longitude)
        ndims = len(nc_file.variables[item].shape)
        if ndims==1:
            data[item] = ncFile.variables[item][:]
        elif ndims==3:
            data[item] = nc_file.variables[item][:,0,0]
        else:
            raise Exception("unrecognised number of dimensions for variable"+str(item))
        # get the variable attributes
        vattrlist = nc_file.variables[item].ncattrs()
        if len(vattrlist)!=0:
            attr["variable"][item] = {}
            for vattr in vattrlist:
                attr["variable"][item][vattr] = getattr(nc_file.variables[item],vattr)
    # close the netCDF file
    nc_file.close()
    # convert the dictionary to a Pandas data frame
    df = pd.DataFrame(data,index=dt)
    return df,attr


    

In [3]:

    

nc_full_name = "../../Sites/Whroo/Data/Processed/all/Whroo_2011_to_2014_L6.nc"
variable_list = ['Fsd','Ta','VPD','NEE_SOLO']


    

In [4]:

    

df,attr = read_netcdf(nc_full_name,variable_list=variable_list)


    

In [5]:

    

fig=plt.figure()
ax1 = plt.subplot(411)
ax1.plot(df['Fsd'])
ax2 = plt.subplot(412)
ax2.plot(df['Ta'])
ax3 = plt.subplot(413)
ax3.plot(df['VPD'])
ax4 = plt.subplot(414)
ax4.plot(df['NEE_SOLO'])
plt.show()


    

In [6]:

    

attr.keys()


    

    
Out[6]:





['variable', 'global']

In [8]:

    

attr["variable"].keys()


    

    
Out[8]:





['Fsd',
 'Fsd_QCFlag',
 'NEE_SOLO_QCFlag',
 'VPD',
 'NEE_SOLO',
 'Ta_QCFlag',
 'VPD_QCFlag',
 'Ta']

In [ ]: