TopoVar.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 12 16:01:09 2018

@author: deborahkhider

Open and manipulate a netCDF file that contains all the needed variables for
initializing TOPOFLOW

"""

from netCDF4 import Dataset 
import numpy as np

file = "/Volumes/Data HD/Documents/MINT/Climate/netCDFTutorial/Example.nc"

nc_fid = Dataset(file)
nc_vars = [var for var in nc_fid.variables]

# Get the needed keys
keys=[]
for vars in nc_vars:
    keys.append(nc_fid.variables[vars].getncattr('long_name'))
    
#Get a dictionary that contains all the extraacted variables
def getNcVar(nc_file, keys):
    ''' Extract variables from a netCDF file.
    
    This function gets the variable contained in a netCDF file 
    and return them into Python nested dictionaries. The first
    dictionary's key contains the longname, while the
    second dictionary contains values, standard name (CF),
    units and the missing data flag.
    
    Args:
        nc_file (str): A name (path) of a netCDF file
        keys (list): A list of keys to fetch the variables according
            to the CF standard
    
    Returns:
        dict_out (dict): A dictionary containing the standard names as keys and
            the associated data as values.
    '''
    from netCDF4 import Dataset
    #Open the netCDF file
    nc_fid = Dataset(nc_file)
    # Get the variable names
    nc_vars = [var for var in nc_fid.variables]
    # Get the longnames for each variables
    nc_vars_longname = []
    #Get the units
    nc_vars_units =[]
    # Get the standard name
    nc_vars_standardname=[]
    #Add corrections if needed
    nc_vars_scale_factor=[]
    nc_vars_add_offset=[]
    # Check the missing value tags
    nc_vars_missing_value=[]
    
    for vars in nc_vars:
        if 'long_name' in nc_fid.variables[vars].ncattrs():
            nc_vars_longname.append(nc_fid.variables[vars].getncattr('long_name'))
        else:
            nc_vars_longname.append(vars)
        if 'units' in nc_fid.variables[vars].ncattrs():
            nc_vars_units.append(nc_fid.variables[vars].getncattr('units'))
        else:
            nc_vars_units.append('NA')
        if 'standard_name' in nc_fid.variables[vars].ncattrs():
            nc_vars_standardname.append(nc_fid.variables[vars].getncattr('standard_name'))
        else:
            nc_vars_standardname.append('NA')    
        if 'scale_factor' in nc_fid.variables[vars].ncattrs():
            nc_vars_scale_factor.append(nc_fid.variables[vars].getncattr('scale_factor'))
        else:
            nc_vars_scale_factor.append(1)
        if 'add_offset' in nc_fid.variables[vars].ncattrs():
            nc_vars_add_offset.append(nc_fid.variables[vars].getncattr('add_offset'))
        else:
            nc_vars_add_offset.append(0) 
        if 'missing_value' in nc_fid.variables[vars].ncattrs(): 
            nc_vars_missing_value.append(nc_fid.variables[vars].getncattr('missing_value'))
        else:
            nc_vars_missing_value.append('NA')
    # Check for the list against the desired variables and output.
    dict_out ={}
    for name in nc_vars_longname:
        if name in keys:
            f = {'values':[],'units':[],'missing_value':[],'standard_name':{}}
            idx = nc_vars_longname.index(name)
            f['values']=(nc_fid.variables[nc_vars[idx]][:]*nc_vars_scale_factor[idx])\
                +nc_vars_add_offset[idx]
            f['units']=nc_vars_units[idx]
            f['missing_value'] = nc_vars_missing_value[idx]
            f['standard_name'] = nc_vars_standardname[idx]
            dict_out[name] = f
               
    return dict_out

dict_out =  getNcVar(file,keys)   

# Data manipulation
# 1. Relative humidity calculation
TD = np.array(dict_out['2 metre dewpoint temperature']['values'])
T = np.array(dict_out['2 metre temperature']['values'])
RH = 100*(np.exp((17.625*TD)/(243.04+TD))/np.exp((17.625*T)/(243.04+T)))

# put in dict_out
dict_out['relative humidity']={'values':RH,'units':'NA'} 

#2. wind speed
U = np.array(dict_out['10 metre U wind component']['values'])
V = np.array(dict_out['10 metre V wind component']['values'])

W = np.sqrt(U**2+V**2)

dict_out['wind speed']={'values':W,'units':dict_out['10 metre V wind component']['units']}