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netcdf2json.py
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netcdf2json.py
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"""
Read in a netCDF file and output the u and v data to json. Write one file per
time step for compatibility with the earth package:
https://github.com/cambecc/earth
"""
import os
import sys
import glob
import json
import time
import multiprocessing
import numpy as np
from netCDF4 import Dataset, num2date
from datetime import datetime
from scipy.interpolate import griddata
def main(f):
"""
Function to run the main logic. This is mapped with the multiprocessing
tools to run in parallel.
"""
def interpolate(var, r, config, extranan=np.inf):
"""
Interpolate the data in u.data and v.data onto a grid from 0 to 360 and
-80 to 80.
Parameters
----------
var : Process
Process class objects with the relevant data to interpolate.
r : float
Grid resolution for the interpolation.
config : Config
Config class with the various configuration parameters for the data
in var.
extranan : float
An extra check for nonsense data. Set to np.inf by default (i.e.
ignored).
Returns
-------
vari : Process
Updated class with the interpolated data.
"""
# Move longitudes to 0-360 instead of -180 to 180.
var.x[var.x < 0] = var.x[var.x < 0] + 360
lon, lat = np.arange(0, 360, r), np.arange(-80, 80 + r, r)
LON, LAT = np.meshgrid(lon, lat)
X, Y, VAR = var.x.flatten(), var.y.flatten(), var.data.flatten()
var.data = griddata((X, Y), VAR, (LON.flatten(), LAT.flatten()))
var.data = np.reshape(var.data, (len(lat), len(lon)))
var.data[np.isnan(var.data)] = config.nanvalue
# Fix unrealistic values from the interpolation to the nanvalue.
var.data[var.data > extranan] = config.nanvalue
var.data[var.data < -extranan] = config.nanvalue
# Update the metadata
var.x, var.y = LON, LAT
var.dx, var.dy = r, r
var.nx, var.ny = len(lon), len(lat)
del(lon, lat, LON, LAT, X, Y, VAR, r)
return var
print('File {} of {}'.format(f + 1, len(files['u'])))
uconfig = Config(file=files['u'][f],
calendar='noleap',
clip={'depthu':(0, 1), 'time_counter':(0, 1)})
vconfig = Config(file=files['v'][f],
calendar='noleap',
clip={'depthv':(0, 1), 'time_counter':(0, 1)})
pconfig = Config(file=files['chl1'][f],
uname='Chl1',
calendar='noleap',
clip={'deptht':(0, 1), 'time_counter':(0, 1)})
try:
u = Process(files['u'][f], uconfig.uname, config=uconfig)
except:
print('Warning: interpolation for {} failed.'.format(files['u'][f]))
return
try:
v = Process(files['v'][f], vconfig.vname, config=vconfig)
except:
print('Warning: interpolation for {} failed.'.format(files['v'][f]))
return
try:
p = Process(files['chl1'][f], pconfig.uname, config=pconfig)
except:
print('Warning: interpolation for {} failed.'.format(files['chl1'][f]))
return
r = 1 # native resolution, but on a sensible grid.
u = interpolate(u, r, uconfig, extranan=100)
v = interpolate(v, r, uconfig, extranan=100)
p = interpolate(p, r, uconfig, extranan=100)
uvstem = os.path.join(out, 'nemo', '{}-{}_{:04d}'.format(
os.path.split(os.path.splitext(uconfig.file)[0])[-1],
os.path.split(os.path.splitext(vconfig.file)[0])[-1],
f + 1
))
pstem = os.path.join(out, 'ersem', '{}-{:04d}'.format(
os.path.split(os.path.splitext(pconfig.file)[0])[-1],
f + 1
))
# Write out the JSON of the UV data and the chlorophyll data.
W = WriteJSON(u, v, uconf=uconfig, vconf=vconfig, fstem=uvstem)
W = WriteJSON(p, uconf=pconfig, fstem=pstem)
class Config():
"""
Class for storing netCDF configuration options.
Parameters
----------
file : str, optional
Full paths to the netCDF file.
uname, vname, xname, yname, tname : str
Names of the u and v velocity component variable names, and the x,
y and time variable names.
basedate : str, optional
The time to which the time variable refers (assumes time is stored as
units since some date). Format is "%Y-%m-%d %H:%M:%S".
calendar : str, optional
netCDF calendar to use. One of `standard', `gregorian',
`proleptic_gregorian' `noleap', `365_day', `360_day', `julian',
`all_leap' or `366_day'. Defaults to `standard'.
xdim, ydim, tdim : str, optional
Names of the x, y and time dimensions in the netCDF files.
clip : dict, optional
Dictionary of the index and dimension name to extract from the
netCDF variable. Can be multiple dimensions (e.g. {'time_counter':(0,
100), 'depthu':0}).
nanvalue : float, optional
Specify a value to replace with null values when exporting to JSON.
Author
------
Pierre Cazenave (Plymouth Marine Laboratory)
"""
def __init__(self, file=None, uname=None, vname=None, xname=None, yname=None, tname=None, basedate=None, calendar=None, xdim=None, ydim=None, tdim=None, clip=None, nanvalue=None):
self.__dict = {}
self.__set(file, 'file', str)
self.__set(uname, 'uname', str)
self.__set(vname, 'vname', str)
self.__set(xname, 'xname', str)
self.__set(yname, 'yname', str)
self.__set(tname, 'tname', str)
self.__set(basedate, 'basedate', str)
self.__set(calendar, 'calendar', str)
self.__set(xdim, 'xdim', str)
self.__set(ydim, 'ydim', str)
self.__set(tdim, 'tdim', str)
self.__set(clip, 'clip', dict)
self.__set(nanvalue, 'nanvalue', float)
def __set(self, value, target_name, value_type):
if value:
actual = value
else:
actual = self.__default[target_name]
self.__dict[target_name] = value_type(actual)
def __file(self):
return self.__dict['file']
def __uname(self):
return self.__dict['uname']
def __vname(self):
return self.__dict['vname']
def __xname(self):
return self.__dict['xname']
def __yname(self):
return self.__dict['yname']
def __tname(self):
return self.__dict['tname']
def __basedate(self):
return self.__dict['basedate']
def __calendar(self):
return self.__dict['calendar']
def __xdim(self):
return self.__dict['xdim']
def __ydim(self):
return self.__dict['ydim']
def __tdim(self):
return self.__dict['tdim']
def __clip(self):
return self.__dict['clip']
def __nanvalue(self):
return self.__dict['nanvalue']
file = property(__file)
uname = property(__uname)
vname = property(__vname)
xname = property(__xname)
yname = property(__yname)
tname = property(__tname)
basedate = property(__basedate)
calendar = property(__calendar)
xdim = property(__xdim)
ydim = property(__ydim)
tdim = property(__tdim)
clip = property(__clip)
nanvalue = property(__nanvalue)
# Set some sensible defaults. These are based on my concatenated netCDFs of
# Lee's global model run (so NEMO, I guess).
__default = {}
__default['file'] = 'test_u.nc'
__default['uname'] = 'vozocrtx'
__default['vname'] = 'vomecrty'
__default['xname'] = 'nav_lon'
__default['yname'] = 'nav_lat'
__default['tname'] = 'time_counter'
__default['basedate'] = '1890-01-01 00:00:00'
__default['calendar'] = 'standard'
__default['xdim'] = 'x'
__default['ydim'] = 'y'
__default['tdim'] = 'time_counter'
__default['clip'] = {'depth':(0, 1)}
__default['nanvalue'] = 9.969209968386869e+36
class Process():
"""
Class for loading data from the netCDFs and preprocessing ready for writing
out to JSON.
"""
def __init__(self, file, var, config=None):
if config:
self.config = config
else:
self.config = Config()
self.__read_var(file, var)
def __read_var(self, file, var):
ds = Dataset(file, 'r')
self.nx = len(ds.dimensions[self.config.xdim])
self.ny = len(ds.dimensions[self.config.ydim])
self.nt = len(ds.dimensions[self.config.tdim])
self.x = ds.variables[self.config.xname][:]
self.y = ds.variables[self.config.yname][:]
# Sort out the dimensions.
if self.config.clip:
alldims = {}
for key, val in list(ds.dimensions.items()):
alldims[key] = (0, len(val))
vardims = ds.variables[var].dimensions
for clipname in self.config.clip:
clipdims = self.config.clip[clipname]
common = set(alldims.keys()).intersection([clipname])
for k in common:
alldims[k] = clipdims
dims = [alldims[d] for d in vardims]
self.data = np.flipud(np.squeeze(ds.variables[var][
dims[0][0]:dims[0][1],
dims[1][0]:dims[1][1],
dims[2][0]:dims[2][1],
dims[3][0]:dims[3][1]
]))
self.time = ds.variables[self.config.tname][:]
self.Times = []
for t in self.time:
self.Times.append(num2date(
t,
'seconds since {}'.format(self.config.basedate),
calendar=self.config.calendar
))
ds.close()
class WriteJSON():
"""
Write the Process object data to JSON in the earth format.
Parameters
----------
u, v : Process
Process classes of data to export. Each time step will be exported to
a new file. If v is None, only write u.
uconf, vconf : Config
Config classes containing the relevant information. If omitted, assumes
default options (see `Config.__doc__' for more information).
"""
def __init__(self, u, v=None, uconf=None, vconf=None, fstem=None):
self.data = {}
if uconf:
self.uconf = uconf
else:
self.uconf = Config()
if vconf:
self.vconf = vconf
else:
self.vconf = Config()
if fstem:
self.fstem = fstem
else:
self.fstem = '{}-{}'.format(
os.path.split(os.path.splitext(self.uconf.file)[0])[-1],
os.path.split(os.path.splitext(self.vconf.file)[0])[-1]
)
self.header = {}
# The y data is complicated because NEMO has twin poles.
self.header['template'] = {
'discipline':10,
'disciplineName':'Oceanographic_products',
'center':-3,
'centerName':'Plymouth Marine Laboratory',
'significanceOfRT':0,
'significanceOfRTName':'Analysis',
'parameterCategory':1,
'parameterCategoryName':'Currents',
'parameterNumber':2,
'parameterNumberName':'U_component_of_current',
'parameterUnit':'m.s-1',
'forecastTime':0,
'surface1Type':160,
'surface1TypeName':'Depth below sea level',
'surface1Value':15,
'numberPoints':u.nx * u.ny,
'shape':0,
'shapeName':'Earth spherical with radius = 6,367,470 m',
'scanMode':0,
'nx':u.nx,
'ny':u.ny,
'lo1':u.x.min().astype(float),
'la1':u.y.max().astype(float),
'lo2':u.x.max().astype(float),
'la2':u.y.min().astype(float),
'dx':u.dx,
'dy':u.dy
}
if v:
self.write_json(u, uconf, v=v, vconf=vconf)
else:
self.write_json(u, uconf)
def write_json(self, u, uconf, v=None, vconf=None, fstem=None):
self.data['u'], self.data['v'] = {}, {}
# Template is based on u data.
self.data['u']['header'] = self.header['template'].copy()
# Can't use datetime.strftime because the model starts before 1900.
date = datetime.strptime(str(u.Times[uconf.clip[uconf.tname][0]]), '%Y-%m-%d %H:%M:%S')
self.data['u']['header']['refTime'] = '{:04d}-{:02d}-{:02d}T{:02d}:{:02d}:{:06.3f}Z'.format(
date.year, date.month, date.day, date.hour, date.minute, date.second
)
# Add the flattened data.
self.data['u']['data'] = u.data.flatten().tolist()
self.data['u']['data'] = [None if i == uconf.nanvalue else i for i in self.data['u']['data']]
# Do the same for v if we have it.
if v:
self.data['v']['header'] = self.header['template'].copy()
self.data['v']['header']['parameterNumber'] = 3
self.data['v']['header']['parameterNumberName'] = 'V_component_of_current'
date = datetime.strptime(str(v.Times[vconf.clip[vconf.tname][0]]), '%Y-%m-%d %H:%M:%S')
self.data['v']['header']['refTime'] = '{:04d}-{:02d}-{:02d}T{:02d}:{:02d}:{:06.3f}Z'.format(
date.year, date.month, date.day, date.hour, date.minute, date.second
)
self.data['v']['data'] = v.data.flatten().tolist()
self.data['v']['data'] = [None if i == vconf.nanvalue else i for i in self.data['v']['data']]
with open('{}.json'.format(self.fstem), 'w') as f:
f.write('[')
for count, var in enumerate(np.sort(self.data.keys())):
s = json.dumps(self.data[var])
f.write(s)
if count < len(self.data.keys()) - 1: f.write(',')
f.write(']')
if __name__ == '__main__':
serial = False
base = os.path.join(os.path.sep,
'data',
'euryale7',
'scratch',
'ledm',
'iMarNet',
'xhonc',
'MEANS')
out = os.path.join(os.path.sep,
'users',
'modellers',
'pica',
'Software',
'src',
'ocean',
'public',
'data')
files = {}
files['u'] = glob.glob(os.path.join(base, 'xhonco_???????????U.nc'))
files['v'] = glob.glob(os.path.join(base, 'xhonco_???????????V.nc'))
files['chl1'] = glob.glob(os.path.join(base, 'xhonco_???????????P.nc'))
idx = range(len(files['u']))
if serial:
for f in idx:
main(f)
else:
pool = multiprocessing.Pool(multiprocessing.cpu_count() - 1)
pool.map(main, idx)
pool.close()
# Make the catalog.json file with all the files we've just made in it.
files = glob.glob(os.path.join(out, 'nemo', 'xhonco_*.json'))
files = [os.path.split(i)[-1] for i in files]
with open(os.path.join(out, 'nemo', 'catalog.json'), 'w') as f:
json.dump(np.sort(files).tolist(), f)
files = glob.glob(os.path.join(out, 'ersem', 'xhonco_*.json'))
files = [os.path.split(i)[-1] for i in files]
with open(os.path.join(out, 'ersem', 'catalog.json'), 'w') as f:
json.dump(np.sort(files).tolist(), f)