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util.py
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util.py
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import numpy as np
import re
import datetime as dt
import netCDF4 as nc
def normalise_lons(lons, data=None):
"""
Normalise longitudes to 0-360 deg. Perform the same transformation on data.
"""
# Remove -ves
new_lons = np.copy(lons)
new_lons[lons < 0] = lons[lons < 0] + 360
lons_copy = np.copy(new_lons)
if data is not None:
new_data = np.copy(data)
else:
new_data = None
# Use changes in 2nd derivative to find jumps. Then offset (the +2) to get
# element directly after jump. It just works OK.
jumps = list(np.where(np.diff(new_lons[0, :], 2))[0][::2] + 2)
# Beginning and sizes of continuous segments of lons.
segs = [0] + jumps
sizes = np.diff(segs + [len(new_lons[0, :])])
# Sort according to value of lon at segment begin index.
segs = zip(new_lons[0, segs], segs, sizes)
src_segs = sorted(segs, key=lambda x : x[0])
dest_idx = 0
for i, (_, src_idx, size) in enumerate(src_segs):
new_lons[:, dest_idx:dest_idx+size] = lons_copy[:, src_idx:src_idx+size]
if new_data is not None:
if len(data.shape) == 3:
new_data[:, :, dest_idx:dest_idx+size] = data[:, :, src_idx:src_idx+size]
else:
new_data[:, dest_idx:dest_idx+size] = data[:, src_idx:src_idx+size]
dest_idx += size
if i+1 == len(segs):
break
return new_lons, new_data
def get_time_origin(filename):
"""
Parse time.units to find the start/origin date of the file. Return a
datetime.date object.
"""
date_search_strings = ['\d{4}-\d{2}-\d{2}','\d{4}-\d{1}-\d{2}',
'\d{4}-\d{2}-\d{1}','\d{4}-\d{1}-\d{1}']
with nc.Dataset(filename) as f:
time_var = f.variables['time']
assert 'months since' in time_var.units or \
'days since' in time_var.units or \
'hours since' in time_var.units, \
"Time units doesn't have expected format: {}".format(time_var.units)
for ds in date_search_strings:
m = re.search(ds, time_var.units)
if m is not None:
break
assert m is not None
date = dt.datetime.strptime(m.group(0), '%Y-%m-%d')
return dt.date(date.year, date.month, date.day)
def col_idx_largest_lat(lats):
"""
The col index with the largest lat.
"""
_, c = np.unravel_index(np.argmax(lats), lats.shape)
return c
def create_mom_output(ocean_grid, filename, start_date, history):
f = nc.Dataset(filename, 'w')
f.createDimension('GRID_X_T', ocean_grid.num_lon_points)
f.createDimension('GRID_Y_T', ocean_grid.num_lat_points)
f.createDimension('ZT', ocean_grid.num_levels)
f.createDimension('time')
lons = f.createVariable('GRID_X_T', 'f8', ('GRID_X_T'))
lons.long_name = 'Nominal Longitude of T-cell center'
lons.units = 'degree_east'
lons.modulo = 360.
lons.point_spacing = 'even'
lons.axis = 'X'
# MOM needs this to be a single dimension
lons[:] = ocean_grid.x_t[ocean_grid.x_t.shape[0] // 2, :]
lats = f.createVariable('GRID_Y_T', 'f8', ('GRID_Y_T'))
lats.long_name = 'Nominal Latitude of T-cell center'
lats.units = 'degree_north'
lats.point_spacing = 'uneven'
lats.axis = 'Y'
# MOM needs this to be a single dimension
col = col_idx_largest_lat(ocean_grid.y_t[:])
lats[:] = ocean_grid.y_t[:, col]
zt = f.createVariable('ZT', 'f8', ('ZT'))
zt.long_name = 'zt'
zt.units = 'meters'
zt.positive = 'down'
zt.point_spacing = 'uneven'
zt.axis = 'Z'
zt[:] = ocean_grid.z[:]
time = f.createVariable('time', 'f8', ('time'))
time.long_name = 'time'
time.units = "days since {}-{}-{} 00:00:00".format(str(start_date.year).zfill(4),
str(start_date.month).zfill(2),
str(start_date.day).zfill(2))
time.cartesian_axis = "T"
time.calendar_type = "GREGORIAN"
time.calendar = "GREGORIAN"
f.close()
def write_mom_output_at_time(filename, var_name, var_longname, var_units,
var_data, time_idx, time_pt, write_ic=False):
with nc.Dataset(filename, 'r+') as f:
if not var_name in f.variables:
var = f.createVariable(var_name, 'f8',
('time', 'ZT', 'GRID_Y_T', 'GRID_X_T'),
fill_value=-1.e+34, zlib=True, complevel=5, shuffle=True)
var.missing_value = -1.e+34
var.long_name = var_longname
var.units = var_units
var = f.variables[var_name]
if write_ic:
var[0, :] = var_data[:]
f.variables['time'][0] = time_pt
else:
var[time_idx, :] = var_data[:]
f.variables['time'][time_idx] = time_pt
def create_nemo_output(ocean_grid, filename, start_date, history):
f = nc.Dataset(filename, 'w')
f.createDimension('y', ocean_grid.num_lat_points)
f.createDimension('x', ocean_grid.num_lon_points)
f.createDimension('z', ocean_grid.num_levels)
f.createDimension('time_counter')
lats = f.createVariable('nav_lat', 'f8', ('y', 'x'))
lats[:] = ocean_grid.y_t[:]
lons = f.createVariable('nav_lon', 'f8', ('y', 'x'))
lons[:] = ocean_grid.x_t[:]
depth = f.createVariable('depth', 'f8', ('z'))
depth[:] = ocean_grid.z[:]
time = f.createVariable('time_counter', 'f8', ('time_counter'))
time.long_name = 'time'
time.units = "days since {}-{}-{} 00:00:00".format(str(start_date.year).zfill(4),
str(start_date.month).zfill(2),
str(start_date.day).zfill(2))
time.cartesian_axis = "T"
f.close()
def write_nemo_output_at_time(filename, var_name, var_longname, var_units,
var_data, time_idx, time_pt, write_ic=False):
with nc.Dataset(filename, 'r+') as f:
if not f.variables.has_key(var_name):
var = f.createVariable(var_name, 'f8', ('time_counter', 'z', 'y', 'x'))
var.long_name = var_longname
var.units = var_units
var = f.variables[var_name]
if write_ic:
var[0, :] = var_data[:]
f.variables['time_counter'][0] = time_pt
else:
var[time_idx, :] = var_data[:]
f.variables['time_counter'][time_idx] = time_pt