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RODIER Quentin authoredQuentin 07/07/2021: Python : ZTSERIES and XTSERIES need to be transposed to use psection functions without specifying .T each time + bugfix
RODIER Quentin authoredQuentin 07/07/2021: Python : ZTSERIES and XTSERIES need to be transposed to use psection functions without specifying .T each time + bugfix
read_MNHfile.py 14.83 KiB
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
MNH_LIC Copyright 1994-2021 CNRS, Meteo-France and Universite Paul Sabatier
MNH_LIC This is part of the Meso-NH software governed by the CeCILL-C licence
MNH_LIC version 1. See LICENSE, CeCILL-C_V1-en.txt and CeCILL-C_V1-fr.txt
MNH_LIC for details. version 1.
@author: 07/2021 Quentin Rodier
"""
import netCDF4 as nc
import numpy as np
def read_netcdf(LnameFiles, Dvar_input, path='.', get_data_only=True, del_empty_dim=True, removeHALO=True):
"""Read a netCDF4 Meso-NH file
For each file, call functions to read diachronic or synchronous file
Parameters
----------
LnameFiles : list of str
list of Meso-NH netCDF4 file (diachronic or synchronous)
Dvar_input : Dict{'fileNumber' : 'var_name',('group_name','var_name')}
where
'fileNumber' is a str corresponding to 'f' + the file number in LnameFiles (by order)
'var_name' is the exact str of the netCDF4 variable name
('group_name','var_name') is the exact tuple of the (sub-)groups name and the netCDF4 variable name
e.g. : {'f1':['ZS', 'WT','ni', 'level'],
'f2':[('/LES_budgets/Cartesian/Not_time_averaged/Not_normalized/cart/',MEAN_TH'),('/Budgets/RI','AVEF')]
}
path : str
unique path of the files
get_data_only : bool, default: True
if True, the function returns Dvar as masked_array (only data)
if False, the function returns Dvar as netCDF4._netCDF4.Variable
del_empty_dim : bool, default: True
if get_data_only=True and del_empty_dim=True, returns Dvar as an array without dimensions with size 1 and 0
e.g. : an array of dimensions (time_budget, cart_level, cart_nj, cart_ni) with shape (180,1,50,1) is returned (180,50)
removeHALO : bool, default: True
if True, remove first and last (NHALO=1) point [1:-1] if get_data_only=True on each
level, level_w, ni, ni_u, ni_v, nj, nj_u, nj_v dimensions
Returns
-------
Dvar : Dict
Dvar[ifile]['var_name'] if the group contains only one variable
Dvar[ifile][('group_name','var_name')] if the group contains more than one variable
"""
Dvar = {}
for i,nameFiles in enumerate(LnameFiles):
f_nb = 'f' + str(i+1)
print('Reading file ' + f_nb)
print(path + nameFiles)
theFile = nc.Dataset(path + nameFiles,'r')
Dvar[f_nb] = {}
if '000' in nameFiles[-6:-3]:
if theFile['MASDEV'][0] <= 54:
raise TypeError('The python lib is available for MNH >= 5.5')
else:
Dvar[f_nb] = read_TIMESfiles_55(theFile, Dvar_input[f_nb], Dvar[f_nb], get_data_only, del_empty_dim, removeHALO)
else:
Dvar[f_nb]= read_BACKUPfile(theFile, Dvar_input[f_nb], Dvar[f_nb], get_data_only, del_empty_dim, removeHALO)
theFile.close()
return Dvar
def read_var(theFile, Dvar, var_name, get_data_only=True, del_empty_dim=True, removeHALO=True):
"""Read a netCDF4 variable
Parameters
----------
theFile : netCDF4._netCDF4.Dataset
a Meso-NH diachronic netCDF4 file
var_name : str
a Meso-NH netCDF4 variable name
get_data_only : bool, default: True
if True, the function returns Dvar as masked_array (only data)
if False, the function returns Dvar as netCDF4._netCDF4.Variable
del_empty_dim : bool, default: True
if get_data_only=True and del_empty_dim=True, returns Dvar as an array without dimensions with size 1 and 0
e.g. : an array of dimensions (time_budget, cart_level, cart_nj, cart_ni) with shape (180,1,50,1) is returned (180,50)
removeHALO : bool, default: True
if True, remove first and last (NHALO=1) point [1:-1] if get_data_only=True on each
level, level_w, ni, ni_u, ni_v, nj, nj_u, nj_v dimensions
Returns
-------
Dvar : Dict
Dvar['var_name'] if the group contains only one variable
Dvar[('group_name','var_name')] if the group contains more than one variable
"""
try:
var_dim = theFile.variables[var_name].ndim
var_dim_name = theFile.variables[var_name].dimensions
except:
raise KeyError("Group and variable name not found in the file, check the group/variable name with ncdump -h YourMNHFile.000.nc. You asked for variable : " + var_name)
if not get_data_only:
Dvar[var_name] = theFile.variables[var_name]
else:
if var_dim == 0:
Dvar[var_name] = theFile.variables[var_name][0].data
elif var_dim == 1:
Dvar[var_name] = theFile.variables[var_name][:]
elif var_dim == 2:
Dvar[var_name] = theFile.variables[var_name][:,:]
elif var_dim == 3:
Dvar[var_name] = theFile.variables[var_name][:,:,:]
elif var_dim == 4:
Dvar[var_name] = theFile.variables[var_name][:,:,:,:]
elif var_dim == 5:
Dvar[var_name] = theFile.variables[var_name][:,:,:,:,:]
elif var_dim == 6:
Dvar[var_name] = theFile.variables[var_name][:,:,:,:,:,:]
elif var_dim == 7:
Dvar[var_name] = theFile.variables[var_name][:,:,:,:,:,:,:]
if removeHALO:
for i in range(8):
try:
if var_dim_name[i]=='level' or var_dim_name[i]=='level_w' or \
var_dim_name[i]=='ni' or var_dim_name[i]=='ni_u' or var_dim_name[i]=='ni_v' or \
var_dim_name[i]=='nj' or var_dim_name[i]=='nj_u' or var_dim_name[i]=='nj_v':
if var_dim != 0:
Dvar[var_name] = removetheHALO(i+1, Dvar[var_name])
except:
break
if del_empty_dim:
Ldimtosqueeze=[]
var_shape = theFile.variables[var_name].shape
for i in range(8):
try:
if var_shape[i]==1: Ldimtosqueeze.append(i)
except IndexError:
break
Ldimtosqueeze=tuple(Ldimtosqueeze)
Dvar[var_name] = np.squeeze(Dvar[var_name], axis=Ldimtosqueeze)
return Dvar
def read_from_group(theFile, Dvar, group_name, var_name, get_data_only=True, del_empty_dim=True,removeHALO=True):
"""Read a variable from a netCDF4 group
Parameters
----------
theFile : netCDF4._netCDF4.Dataset
a Meso-NH diachronic netCDF4 file
group_name : str
a Meso-NH netCDF4 group name
var_name : str
a Meso-NH netCDF4 variable name
get_data_only : bool, default: True
if True, the function returns Dvar as masked_array (only data)
if False, the function returns Dvar as netCDF4._netCDF4.Variable
del_empty_dim : bool, default: True
if get_data_only=True and del_empty_dim=True, returns Dvar as an array without dimensions with size 1 and 0
e.g. : an array of dimensions (time_budget, cart_level, cart_nj, cart_ni) with shape (180,1,50,1) is returned (180,50)
removeHALO : bool, default: True
if True, remove first and last (NHALO=1) point [1:-1] if get_data_only=True on each
level, level_w, ni, ni_u, ni_v, nj, nj_u, nj_v dimensions
Returns
-------
Dvar : Dict
Dvar['var_name'] if the group contains only one variable
Dvar[('group_name','var_name')] if the group contains more than one variable
"""
try:
var_dim = theFile[group_name].variables[var_name].ndim
var_dim_name = theFile[group_name].variables[var_name].dimensions
except:
raise KeyError("Group and variable name not found in the file, check the group/variable name with ncdump -h YourMNHFile.000.nc. You asked for group/variable : " + group_name + var_name)
if not get_data_only:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name]
else:
if var_dim == 0:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name][0].data
if var_dim == 1:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name][:]
elif var_dim == 2:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name][:,:]
elif var_dim == 3:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name][:,:,:]
elif var_dim == 4:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name][:,:,:,:]
elif var_dim == 5:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name][:,:,:,:,:]
elif var_dim == 6:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name][:,:,:,:,:,:]
elif var_dim == 7:
Dvar[(group_name,var_name)] = theFile[group_name].variables[var_name][:,:,:,:,:,:,:]
if removeHALO:
for i in range(8):
try:
if var_dim_name[i]=='level' or var_dim_name[i]=='level_w' or \
var_dim_name[i]=='ni' or var_dim_name[i]=='ni_u' or var_dim_name[i]=='ni_v' or \
var_dim_name[i]=='nj' or var_dim_name[i]=='nj_u' or var_dim_name[i]=='nj_v':
if var_dim != 0:
Dvar[(group_name,var_name)] = removetheHALO(i+1, Dvar[(group_name,var_name)])
except:
break
if del_empty_dim:
Ldimtosqueeze=[]
var_shape = Dvar[(group_name,var_name)].shape
for i in range(8):
try:
if var_shape[i]==1: Ldimtosqueeze.append(i)
except IndexError:
break
Ldimtosqueeze=tuple(Ldimtosqueeze)
Dvar[(group_name,var_name)] = np.squeeze(Dvar[(group_name,var_name)], axis=Ldimtosqueeze)
# LES budget, ZTSERIES needs to be transposed to use psection functions without specifying .T each time
if 'LES_budgets' or 'ZTSERIES' or 'XTSERIES' in group_name:
Dvar[(group_name,var_name)] = Dvar[(group_name,var_name)].T
return Dvar
def read_BACKUPfile(theFile, Dvar_input, Dvar, get_data_only=True, del_empty_dim=True, removeHALO=True):
"""Read variables from Meso-NH MASDEV >= 5.5.0 synchronous file
For all variables in Dvar_input of one file, call functions to read the variable of the group+variable
Parameters
----------
theFile : netCDF4._netCDF4.Dataset
a Meso-NH diachronic netCDF4 file
Dvar_input : Dict{'var_name',('group_name','var_name')}
with
'var_name' is the exact str of the netCDF4 variable name
('group_name','var_name') is the exact tuple of the (sub-)groups name and the netCDF4 variable name
e.g. : {'f1':['ZS', 'WT','ni', 'level'],
'f2':[('/LES_budgets/Cartesian/Not_time_averaged/Not_normalized/cart/',MEAN_TH'),('/Budgets/RI','AVEF')]
}
get_data_only: bool, default: True
if True, the function returns Dvar as masked_array (only data)
if False, the function returns Dvar as netCDF4._netCDF4.Variable
del_empty_dim: bool, default: True
if get_data_only=True and del_empty_dim=True, returns Dvar as masked_array without dimensions with size 1 and 0
e.g. : an array of dimensions (time_budget, cart_level, cart_nj, cart_ni) with shape (180,1,50,1) is returned (180,50)
Returns
-------
Dvar : Dict
Dvar['var_name'] if the group contains only one variable
Dvar[('group_name','var_name')] if the group contains more than one variable
"""
# Reading date since beginning of the model run
Dvar['time'] = theFile.variables['time'][0]
Dvar['date'] = nc.num2date(Dvar['time'],units=theFile.variables['time'].units, calendar = theFile.variables['time'].calendar)
for var in Dvar_input:
if type(var) == tuple:
Dvar = read_from_group(theFile, Dvar, var[0], var[1], get_data_only, del_empty_dim, removeHALO)
else:
Dvar = read_var(theFile, Dvar, var, get_data_only, del_empty_dim, removeHALO)
# For all variables except scalars, change Fill_Value to NaN
Dvar[var]= np.where(Dvar[var] != -99999.0, Dvar[var], np.nan)
Dvar[var]= np.where(Dvar[var] != 999.0, Dvar[var], np.nan)
return Dvar
def read_TIMESfiles_55(theFile, Dvar_input, Dvar, get_data_only=True, del_empty_dim=True, removeHALO=True):
"""Read variables from Meso-NH MASDEV >= 5.5.0 diachronic file
For all variables in Dvar_input of one file, call functions to read the variable of the group+variable
Parameters
----------
theFile : netCDF4._netCDF4.Dataset
a Meso-NH diachronic netCDF4 file
Dvar_input : Dict{'var_name',('group_name','var_name')}
with
'var_name' is the exact str of the netCDF4 variable name
('group_name','var_name') is the exact tuple of the (sub-)groups name and the netCDF4 variable name
e.g. : {'f1':['ZS', 'WT','ni', 'level'],
'f2':[('/LES_budgets/Cartesian/Not_time_averaged/Not_normalized/cart/',MEAN_TH'),('/Budgets/RI','AVEF')]
}
get_data_only: bool, default: True
if True, the function returns Dvar as masked_array (only data)
if False, the function returns Dvar as netCDF4._netCDF4.Variable
del_empty_dim: bool, default: True
if get_data_only=True and del_empty_dim=True, returns Dvar as masked_array without dimensions with size 1 and 0
e.g. : an array of dimensions (time_budget, cart_level, cart_nj, cart_ni) with shape (180,1,50,1) is returned (180,50)
Returns
-------
Dvar : Dict
Dvar[ifile]['var_name'] if the group contains only one variable
Dvar[ifile][('group_name','var_name')] if the group contains more than one variable
"""
for var in Dvar_input:
print(var)
if type(var) == tuple:
Dvar = read_from_group(theFile, Dvar, var[0], var[1], get_data_only, del_empty_dim, removeHALO)
else:
Dvar = read_var(theFile, Dvar, var, get_data_only, del_empty_dim, removeHALO)
return Dvar
def removetheHALO(idim, var):
"""Remove a NHALO=1 point [1:-1] at a given dimension idim of a variable var
Parameters
----------
idim: int
the dimension over which remove the first and last point
var: array
a Meso-NH netCDF4 variable name
Returns
-------
var : array
"""
if idim == 1:
var = var[1:-1]
elif idim == 2:
var = var[:,1:-1]
elif idim == 3:
var = var[:,:,1:-1]
elif idim == 4:
var = var[:,:,:,1:-1]
elif idim == 5:
var = var[:,:,:,:,1:-1]
elif idim == 6:
var = var[:,:,:,:,:,1:-1]
elif idim == 7:
var = var[:,:,:,:,:,:,1:-1]
return var