/
example_dask_chunk_OCMs.py
639 lines (568 loc) · 32.3 KB
/
example_dask_chunk_OCMs.py
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import math
from datetime import timedelta as delta
from glob import glob
from os import path
import dask
import numpy as np
import pytest
from parcels import (
AdvectionRK4,
Field,
FieldSet,
JITParticle,
ParticleFile,
ParticleSet,
ScipyParticle,
Variable,
)
from parcels.tools.statuscodes import DaskChunkingError
ptype = {'scipy': ScipyParticle, 'jit': JITParticle}
def fieldset_from_nemo_3D(chunk_mode):
data_path = path.join(path.dirname(__file__), 'NemoNorthSeaORCA025-N006_data/')
ufiles = sorted(glob(data_path + 'ORCA*U.nc'))
vfiles = sorted(glob(data_path + 'ORCA*V.nc'))
wfiles = sorted(glob(data_path + 'ORCA*W.nc'))
mesh_mask = data_path + 'coordinates.nc'
filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': ufiles},
'V': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': vfiles},
'W': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': wfiles}}
variables = {'U': 'uo',
'V': 'vo',
'W': 'wo'}
dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'V': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'W': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'}}
chs = False
if chunk_mode == 'auto':
chs = 'auto'
elif chunk_mode == 'specific':
chs = {'U': {'depth': ('depthu', 75), 'lat': ('y', 16), 'lon': ('x', 16)},
'V': {'depth': ('depthv', 75), 'lat': ('y', 16), 'lon': ('x', 16)},
'W': {'depth': ('depthw', 75), 'lat': ('y', 16), 'lon': ('x', 16)}}
elif chunk_mode == 'failsafe': # chunking time and but not depth
filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': ufiles},
'V': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': vfiles}}
variables = {'U': 'uo',
'V': 'vo'}
dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'V': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'}}
chs = {'U': {'time': ('time_counter', 1), 'depth': ('depthu', 25), 'lat': ('y', -1), 'lon': ('x', -1)},
'V': {'time': ('time_counter', 1), 'depth': ('time_counter', 25), 'lat': ('y', 8), 'lon': ('x', 8)}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions, chunksize=chs)
return fieldset
def fieldset_from_globcurrent(chunk_mode):
filenames = path.join(path.dirname(__file__), 'GlobCurrent_example_data',
'200201*-GLOBCURRENT-L4-CUReul_hs-ALT_SUM-v02.0-fv01.0.nc')
variables = {'U': 'eastward_eulerian_current_velocity', 'V': 'northward_eulerian_current_velocity'}
dimensions = {'lat': 'lat', 'lon': 'lon', 'time': 'time'}
chs = False
if chunk_mode == 'auto':
chs = 'auto'
elif chunk_mode == 'specific':
chs = {'U': {'lat': ('lat', 8), 'lon': ('lon', 8)},
'V': {'lat': ('lat', 8), 'lon': ('lon', 8)}}
elif chunk_mode == 'failsafe': # chunking time but not lat
chs = {'U': {'time': ('time', 1), 'lat': ('lat', 10), 'lon': ('lon', -1)},
'V': {'time': ('time', 1), 'lat': ('lat', -1), 'lon': ('lon', -1)}}
fieldset = FieldSet.from_netcdf(filenames, variables, dimensions, chunksize=chs)
return fieldset
def fieldset_from_pop_1arcs(chunk_mode):
filenames = path.join(path.join(path.dirname(__file__), 'POPSouthernOcean_data'), 't.x1_SAMOC_flux.1690*.nc')
variables = {'U': 'UVEL', 'V': 'VVEL', 'W': 'WVEL'}
timestamps = np.expand_dims(np.array([np.datetime64('2000-%.2d-01' % m) for m in range(1, 7)]), axis=1)
dimensions = {'lon': 'ULON', 'lat': 'ULAT', 'depth': 'w_dep'}
chs = False
if chunk_mode == 'auto':
chs = 'auto'
elif chunk_mode == 'specific':
chs = {'lon': ('i', 8), 'lat': ('j', 8), 'depth': ('k', 3)}
elif chunk_mode == 'failsafe': # here: bad depth entry
chs = {'depth': ('wz', 3), 'lat': ('j', 8), 'lon': ('i', 8)}
fieldset = FieldSet.from_pop(filenames, variables, dimensions, chunksize=chs, timestamps=timestamps)
return fieldset
def fieldset_from_swash(chunk_mode):
filenames = path.join(path.join(path.dirname(__file__), 'SWASH_data'), 'field_*.nc')
variables = {'U': 'cross-shore velocity',
'V': 'along-shore velocity',
'W': 'vertical velocity',
'depth_w': 'time varying depth',
'depth_u': 'time varying depth_u'}
dimensions = {'U': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'},
'V': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'},
'W': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'},
'depth_w': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'},
'depth_u': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'}}
chs = False
if chunk_mode == 'auto':
chs = 'auto'
elif chunk_mode == 'specific':
chs = {'U': {'depth': ('z_u', 6), 'lat': ('y', 4), 'lon': ('x', 4)},
'V': {'depth': ('z_u', 6), 'lat': ('y', 4), 'lon': ('x', 4)},
'W': {'depth': ('z', 7), 'lat': ('y', 4), 'lon': ('x', 4)},
'depth_w': {'depth': ('z', 7), 'lat': ('y', 4), 'lon': ('x', 4)},
'depth_u': {'depth': ('z_u', 6), 'lat': ('y', 4), 'lon': ('x', 4)}
}
elif chunk_mode == 'failsafe': # here: incorrect matching between dimension names and their attachment to the NC-variables
chs = {'U': {'depth': ('depth', 7), 'lat': ('y', 4), 'lon': ('x', 4)},
'V': {'depth': ('z_u', 6), 'lat': ('y', 4), 'lon': ('x', 4)},
'W': {'depth': ('z', 7), 'lat': ('y', 4), 'lon': ('x', 4)},
'depth_w': {'depth': ('z', 7), 'lat': ('y', 4), 'lon': ('x', 4)},
'depth_u': {'depth': ('z_u', 6), 'lat': ('y', 4), 'lon': ('x', 4)}
}
fieldset = FieldSet.from_netcdf(filenames, variables, dimensions, mesh='flat', allow_time_extrapolation=True, chunksize=chs)
fieldset.U.set_depth_from_field(fieldset.depth_u)
fieldset.V.set_depth_from_field(fieldset.depth_u)
fieldset.W.set_depth_from_field(fieldset.depth_w)
return fieldset
def fieldset_from_ofam(chunk_mode):
filenames = {'U': path.join(path.dirname(__file__), 'OFAM_example_data', 'OFAM_simple_U.nc'),
'V': path.join(path.dirname(__file__), 'OFAM_example_data', 'OFAM_simple_V.nc')}
variables = {'U': 'u', 'V': 'v'}
dimensions = {'lat': 'yu_ocean', 'lon': 'xu_ocean', 'depth': 'st_ocean',
'time': 'Time'}
chs = False
if chunk_mode == 'auto':
chs = 'auto'
elif chunk_mode == 'specific':
chs = {'lon': ('xu_ocean', 100), 'lat': ('yu_ocean', 50), 'depth': ('st_edges_ocean', 60), 'time': ('Time', 1)}
return FieldSet.from_netcdf(filenames, variables, dimensions, allow_time_extrapolation=True, chunksize=chs)
def fieldset_from_mitgcm(chunk_mode, using_add_field=True):
data_path = path.join(path.dirname(__file__), "MITgcm_example_data/")
filenames = {"U": data_path + "mitgcm_UV_surface_zonally_reentrant.nc",
"V": data_path + "mitgcm_UV_surface_zonally_reentrant.nc"}
variables = {"U": "UVEL", "V": "VVEL"}
dimensions = {"U": {"lon": "XG", "lat": "YG", "time": "time"},
"V": {"lon": "XG", "lat": "YG", "time": "time"}}
chs = False
if chunk_mode == 'auto':
chs = 'auto'
elif chunk_mode == 'specific_same':
chs = {'U': {'lat': ('YC', 50), 'lon': ('XG', 100)},
'V': {'lat': ('YG', 50), 'lon': ('XC', 100)}}
elif chunk_mode == 'specific_different':
chs = {'U': {'lat': ('YC', 50), 'lon': ('XG', 100)},
'V': {'lat': ('YG', 40), 'lon': ('XC', 100)}}
if using_add_field:
if chs in [False, 'auto']:
chs = {'U': chs, 'V': chs}
fieldset = FieldSet.from_mitgcm(filenames['U'], {'U': variables['U']}, dimensions['U'],
mesh='flat', chunksize=chs['U'])
fieldset2 = FieldSet.from_mitgcm(filenames['V'], {'V': variables['V']}, dimensions['V'],
mesh='flat', chunksize=chs['V'])
fieldset.add_field(fieldset2.V)
else:
fieldset = FieldSet.from_mitgcm(filenames, variables, dimensions, mesh='flat', chunksize=chs)
return fieldset
def compute_nemo_particle_advection(field_set, mode, lonp, latp):
def periodicBC(particle, fieldSet, time):
if particle.lon > 15.0:
particle.lon -= 15.0
if particle.lon < 0:
particle.lon += 15.0
if particle.lat > 60.0:
particle.lat -= 11.0
if particle.lat < 49.0:
particle.lat += 11.0
pset = ParticleSet.from_list(field_set, ptype[mode], lon=lonp, lat=latp)
pfile = ParticleFile("nemo_particles_chunk", pset, outputdt=delta(days=1))
kernels = pset.Kernel(AdvectionRK4) + periodicBC
pset.execute(kernels, runtime=delta(days=4), dt=delta(hours=6), output_file=pfile)
return pset
def compute_globcurrent_particle_advection(field_set, mode, lonp, latp):
pset = ParticleSet(field_set, pclass=ptype[mode], lon=lonp, lat=latp)
pfile = ParticleFile("globcurrent_particles_chunk", pset, outputdt=delta(hours=2))
pset.execute(AdvectionRK4, runtime=delta(days=1), dt=delta(minutes=5), output_file=pfile)
return pset
def compute_pop_particle_advection(field_set, mode, lonp, latp):
pset = ParticleSet.from_list(field_set, ptype[mode], lon=lonp, lat=latp)
pfile = ParticleFile("globcurrent_particles_chunk", pset, outputdt=delta(days=15))
pset.execute(AdvectionRK4, runtime=delta(days=90), dt=delta(days=2), output_file=pfile)
return pset
def compute_swash_particle_advection(field_set, mode, lonp, latp, depthp):
pset = ParticleSet.from_list(field_set, ptype[mode], lon=lonp, lat=latp, depth=depthp)
pfile = ParticleFile("swash_particles_chunk", pset, outputdt=delta(seconds=0.05))
pset.execute(AdvectionRK4, runtime=delta(seconds=0.2), dt=delta(seconds=0.005), output_file=pfile)
return pset
def compute_ofam_particle_advection(field_set, mode, lonp, latp, depthp):
pset = ParticleSet(field_set, pclass=ptype[mode], lon=lonp, lat=latp, depth=depthp)
pfile = ParticleFile("ofam_particles_chunk", pset, outputdt=delta(days=1))
pset.execute(AdvectionRK4, runtime=delta(days=10), dt=delta(minutes=5), output_file=pfile)
return pset
@pytest.mark.parametrize('mode', ['jit'])
@pytest.mark.parametrize('chunk_mode', [False, 'auto', 'specific', 'failsafe'])
def test_nemo_3D(mode, chunk_mode):
if chunk_mode in ['auto', ]:
dask.config.set({'array.chunk-size': '256KiB'})
else:
dask.config.set({'array.chunk-size': '128MiB'})
field_set = fieldset_from_nemo_3D(chunk_mode)
npart = 20
lonp = 2.5 * np.ones(npart)
latp = [i for i in 52.0+(-1e-3+np.random.rand(npart)*2.0*1e-3)]
compute_nemo_particle_advection(field_set, mode, lonp, latp)
# Nemo sample file dimensions: depthu=75, y=201, x=151
if chunk_mode != 'failsafe':
assert (len(field_set.U.grid.load_chunk) == len(field_set.V.grid.load_chunk))
assert (len(field_set.U.grid.load_chunk) == len(field_set.W.grid.load_chunk))
if chunk_mode is False:
assert (len(field_set.U.grid.load_chunk) == 1)
elif chunk_mode == 'auto':
assert field_set.gridset.size == 3 # because three different grids in 'auto' mode
assert (len(field_set.U.grid.load_chunk) != 1)
elif chunk_mode == 'specific':
assert (len(field_set.U.grid.load_chunk) == (1 * int(math.ceil(75.0/75.0)) * int(math.ceil(201.0/16.0)) * int(math.ceil(151.0/16.0))))
elif chunk_mode == 'failsafe': # chunking time and depth but not lat and lon
assert (len(field_set.U.grid.load_chunk) != 1)
assert (len(field_set.U.grid.load_chunk) == (1 * int(math.ceil(75.0/25.0)) * int(math.ceil(201.0/201.0)) * int(math.ceil(151.0/151.0))))
assert (len(field_set.V.grid.load_chunk) != 1)
assert (len(field_set.V.grid.load_chunk) == (1 * int(math.ceil(75.0/75.0)) * int(math.ceil(201.0/8.0)) * int(math.ceil(151.0/8.0))))
@pytest.mark.parametrize('mode', ['jit'])
@pytest.mark.parametrize('chunk_mode', [False, 'auto', 'specific', 'failsafe'])
def test_globcurrent_2D(mode, chunk_mode):
if chunk_mode in ['auto', ]:
dask.config.set({'array.chunk-size': '16KiB'})
else:
dask.config.set({'array.chunk-size': '128MiB'})
field_set = fieldset_from_globcurrent(chunk_mode)
lonp = [25]
latp = [-35]
pset = None
try:
pset = compute_globcurrent_particle_advection(field_set, mode, lonp, latp)
except DaskChunkingError:
# we removed the failsafe, so now if all chunksize dimensions are incorrect, there is nothing left to chunk,
# which raises an error saying so. This is the expected behaviour
if chunk_mode == 'failsafe':
return True
# GlobCurrent sample file dimensions: time=UNLIMITED, lat=41, lon=81
if chunk_mode != 'failsafe': # chunking time but not lat
assert (len(field_set.U.grid.load_chunk) == len(field_set.V.grid.load_chunk))
if chunk_mode is False:
assert (len(field_set.U.grid.load_chunk) == 1)
elif chunk_mode == 'auto':
assert (len(field_set.U.grid.load_chunk) != 1)
elif chunk_mode == 'specific':
assert (len(field_set.U.grid.load_chunk) == (1 * int(math.ceil(41.0/8.0)) * int(math.ceil(81.0/8.0))))
elif chunk_mode == 'failsafe': # chunking time but not lat
assert (len(field_set.U.grid.load_chunk) != 1)
assert (len(field_set.V.grid.load_chunk) != 1)
assert abs(pset[0].lon - 23.8) < 1
assert abs(pset[0].lat - -35.3) < 1
@pytest.mark.parametrize('mode', ['jit'])
@pytest.mark.parametrize('chunk_mode', [False, 'auto', 'specific', 'failsafe'])
def test_pop(mode, chunk_mode):
if chunk_mode in ['auto', ]:
dask.config.set({'array.chunk-size': '256KiB'})
else:
dask.config.set({'array.chunk-size': '128MiB'})
field_set = fieldset_from_pop_1arcs(chunk_mode)
npart = 20
lonp = 70.0 * np.ones(npart)
latp = [i for i in -45.0+(-0.25+np.random.rand(npart)*2.0*0.25)]
compute_pop_particle_advection(field_set, mode, lonp, latp)
# POP sample file dimensions: k=21, j=60, i=60
assert (len(field_set.U.grid.load_chunk) == len(field_set.V.grid.load_chunk))
assert (len(field_set.U.grid.load_chunk) == len(field_set.W.grid.load_chunk))
if chunk_mode is False:
assert field_set.gridset.size == 1
assert (len(field_set.U.grid.load_chunk) == 1)
assert (len(field_set.V.grid.load_chunk) == 1)
assert (len(field_set.W.grid.load_chunk) == 1)
elif chunk_mode == 'auto':
assert field_set.gridset.size == 3 # because three different grids in 'auto' mode
assert (len(field_set.U.grid.load_chunk) != 1)
assert (len(field_set.V.grid.load_chunk) != 1)
assert (len(field_set.W.grid.load_chunk) != 1)
elif chunk_mode == 'specific':
assert field_set.gridset.size == 1
assert (len(field_set.U.grid.load_chunk) == (int(math.ceil(21.0/3.0)) * int(math.ceil(60.0/8.0)) * int(math.ceil(60.0/8.0))))
elif chunk_mode == 'failsafe': # here: done a typo in the netcdf dimname field
assert field_set.gridset.size == 1
assert (len(field_set.U.grid.load_chunk) != 1)
assert (len(field_set.V.grid.load_chunk) != 1)
assert (len(field_set.W.grid.load_chunk) != 1)
assert (len(field_set.U.grid.load_chunk) == (int(math.ceil(21.0/3.0)) * int(math.ceil(60.0/8.0)) * int(math.ceil(60.0/8.0))))
@pytest.mark.parametrize('mode', ['jit'])
@pytest.mark.parametrize('chunk_mode', [False, 'auto', 'specific', 'failsafe'])
def test_swash(mode, chunk_mode):
if chunk_mode in ['auto', ]:
dask.config.set({'array.chunk-size': '32KiB'})
else:
dask.config.set({'array.chunk-size': '128MiB'})
field_set = fieldset_from_swash(chunk_mode)
npart = 20
lonp = [i for i in 9.5 + (-0.2 + np.random.rand(npart) * 2.0 * 0.2)]
latp = [i for i in np.arange(start=12.3, stop=13.1, step=0.04)[0:20]]
depthp = [-0.1, ] * npart
compute_swash_particle_advection(field_set, mode, lonp, latp, depthp)
# SWASH sample file dimensions: t=1, z=7, z_u=6, y=21, x=51
if chunk_mode not in ['failsafe', ]:
assert (len(field_set.U.grid.load_chunk) == len(field_set.V.grid.load_chunk)), "U {} vs V {}".format(field_set.U.grid.chunk_info, field_set.V.grid.chunk_info)
if chunk_mode not in ['failsafe', 'auto']:
assert (len(field_set.U.grid.load_chunk) == len(field_set.W.grid.load_chunk)), "U {} vs W {}".format(field_set.U.grid.chunk_info, field_set.W.grid.chunk_info)
if chunk_mode is False:
assert (len(field_set.U.grid.load_chunk) == 1)
else:
assert (len(field_set.U.grid.load_chunk) != 1)
assert (len(field_set.V.grid.load_chunk) != 1)
assert (len(field_set.W.grid.load_chunk) != 1)
if chunk_mode == 'specific':
assert (len(field_set.U.grid.load_chunk) == (1 * int(math.ceil(6.0 / 6.0)) * int(math.ceil(21.0 / 4.0)) * int(math.ceil(51.0 / 4.0))))
assert (len(field_set.V.grid.load_chunk) == (1 * int(math.ceil(6.0 / 6.0)) * int(math.ceil(21.0 / 4.0)) * int(math.ceil(51.0 / 4.0))))
assert (len(field_set.W.grid.load_chunk) == (1 * int(math.ceil(7.0 / 7.0)) * int(math.ceil(21.0 / 4.0)) * int(math.ceil(51.0 / 4.0))))
@pytest.mark.parametrize('mode', ['jit'])
@pytest.mark.parametrize('chunk_mode', [False, 'auto', 'specific'])
def test_ofam_3D(mode, chunk_mode):
if chunk_mode in ['auto', ]:
dask.config.set({'array.chunk-size': '1024KiB'})
else:
dask.config.set({'array.chunk-size': '128MiB'})
field_set = fieldset_from_ofam(chunk_mode)
lonp = [180]
latp = [10]
depthp = [2.5] # the depth of the first layer in OFAM
pset = compute_ofam_particle_advection(field_set, mode, lonp, latp, depthp)
# OFAM sample file dimensions: time=UNLIMITED, st_ocean=1, st_edges_ocean=52, lat=601, lon=2001
assert (len(field_set.U.grid.load_chunk) == len(field_set.V.grid.load_chunk))
if chunk_mode is False:
assert (len(field_set.U.grid.load_chunk) == 1)
elif chunk_mode == 'auto':
assert (len(field_set.U.grid.load_chunk) != 1)
elif chunk_mode == 'specific':
numblocks = [i for i in field_set.U.grid.chunk_info[1:3]]
dblocks = 1
vblocks = 0
for bsize in field_set.U.grid.chunk_info[3:3+numblocks[0]]:
vblocks += bsize
ublocks = 0
for bsize in field_set.U.grid.chunk_info[3+numblocks[0]:3+numblocks[0]+numblocks[1]]:
ublocks += bsize
matching_numblocks = (ublocks == 2001 and vblocks == 601 and dblocks == 1)
matching_fields = (field_set.U.grid.chunk_info == field_set.V.grid.chunk_info)
matching_uniformblocks = (len(field_set.U.grid.load_chunk) == (1 * int(math.ceil(1.0/60.0)) * int(math.ceil(601.0/50.0)) * int(math.ceil(2001.0/100.0))))
assert (matching_uniformblocks or (matching_fields and matching_numblocks))
assert abs(pset[0].lon - 173) < 1
assert abs(pset[0].lat - 11) < 1
@pytest.mark.parametrize('mode', ['jit'])
@pytest.mark.parametrize('chunk_mode', [False, 'auto', 'specific_same', 'specific_different'])
@pytest.mark.parametrize('using_add_field', [False, True])
def test_mitgcm(mode, chunk_mode, using_add_field):
if chunk_mode in ['auto', ]:
dask.config.set({'array.chunk-size': '256KiB'})
else:
dask.config.set({'array.chunk-size': '128MiB'})
field_set = fieldset_from_mitgcm(chunk_mode, using_add_field)
lons, lats = 5e5, 5e5
pset = ParticleSet.from_list(fieldset=field_set, pclass=ptype[mode], lon=lons, lat=lats)
pset.execute(AdvectionRK4, runtime=delta(days=1), dt=delta(minutes=5))
# MITgcm sample file dimensions: time=10, XG=400, YG=200
if chunk_mode != 'specific_different':
assert (len(field_set.U.grid.load_chunk) == len(field_set.V.grid.load_chunk))
if chunk_mode in [False, ]:
assert (len(field_set.U.grid.load_chunk) == 1)
elif chunk_mode in ['auto', ]:
assert (len(field_set.U.grid.load_chunk) != 1)
elif 'specific' in chunk_mode:
assert (len(field_set.U.grid.load_chunk) == (1 * int(math.ceil(400.0/50.0)) * int(math.ceil(200.0/100.0))))
if chunk_mode == 'specific_same':
assert field_set.gridset.size == 1
elif chunk_mode == 'specific_different':
assert field_set.gridset.size == 2
assert np.allclose(pset[0].lon, 5.27e5, atol=1e3)
@pytest.mark.parametrize('mode', ['jit'])
def test_diff_entry_dimensions_chunks(mode):
data_path = path.join(path.dirname(__file__), 'NemoNorthSeaORCA025-N006_data/')
ufiles = sorted(glob(data_path + 'ORCA*U.nc'))
vfiles = sorted(glob(data_path + 'ORCA*V.nc'))
mesh_mask = data_path + 'coordinates.nc'
filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'data': ufiles},
'V': {'lon': mesh_mask, 'lat': mesh_mask, 'data': vfiles}}
variables = {'U': 'uo',
'V': 'vo'}
dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'time': 'time_counter'},
'V': {'lon': 'glamf', 'lat': 'gphif', 'time': 'time_counter'}}
chs = {'U': {'depth': ('depthu', 75), 'lat': ('y', 16), 'lon': ('x', 16)},
'V': {'depth': ('depthv', 75), 'lat': ('y', 16), 'lon': ('x', 16)}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions, chunksize=chs)
npart = 20
lonp = 5.2 * np.ones(npart)
latp = [i for i in 52.0+(-1e-3+np.random.rand(npart)*2.0*1e-3)]
compute_nemo_particle_advection(fieldset, mode, lonp, latp)
# Nemo sample file dimensions: depthu=75, y=201, x=151
assert (len(fieldset.U.grid.load_chunk) == len(fieldset.V.grid.load_chunk))
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
def test_3d_2dfield_sampling(mode):
data_path = path.join(path.dirname(__file__), 'NemoNorthSeaORCA025-N006_data/')
ufiles = sorted(glob(data_path + 'ORCA*U.nc'))
vfiles = sorted(glob(data_path + 'ORCA*V.nc'))
mesh_mask = data_path + 'coordinates.nc'
filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'data': ufiles},
'V': {'lon': mesh_mask, 'lat': mesh_mask, 'data': vfiles},
'nav_lon': {'lon': mesh_mask, 'lat': mesh_mask, 'data': [ufiles[0], ]}}
variables = {'U': 'uo',
'V': 'vo',
'nav_lon': 'nav_lon'}
dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'time': 'time_counter'},
'V': {'lon': 'glamf', 'lat': 'gphif', 'time': 'time_counter'},
'nav_lon': {'lon': 'glamf', 'lat': 'gphif'}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions, chunksize=False)
fieldset.nav_lon.data = np.ones(fieldset.nav_lon.data.shape, dtype=np.float32)
fieldset.add_field(Field('rectilinear_2D', np.ones((2, 2)),
lon=np.array([-10, 20]), lat=np.array([40, 80]), chunksize=False))
class MyParticle(ptype[mode]):
sample_var_curvilinear = Variable('sample_var_curvilinear')
sample_var_rectilinear = Variable('sample_var_rectilinear')
pset = ParticleSet(fieldset, pclass=MyParticle, lon=2.5, lat=52)
def Sample2D(particle, fieldset, time):
particle.sample_var_curvilinear += fieldset.nav_lon[time, particle.depth, particle.lat, particle.lon]
particle.sample_var_rectilinear += fieldset.rectilinear_2D[time, particle.depth, particle.lat, particle.lon]
runtime, dt = 86400*4, 6*3600
pset.execute(pset.Kernel(AdvectionRK4) + Sample2D, runtime=runtime, dt=dt)
assert pset.sample_var_rectilinear == runtime/dt
assert pset.sample_var_curvilinear == runtime/dt
@pytest.mark.parametrize('mode', ['jit'])
def test_diff_entry_chunksize_error_nemo_simple(mode):
data_path = path.join(path.dirname(__file__), 'NemoNorthSeaORCA025-N006_data/')
ufiles = sorted(glob(data_path + 'ORCA*U.nc'))
vfiles = sorted(glob(data_path + 'ORCA*V.nc'))
wfiles = sorted(glob(data_path + 'ORCA*W.nc'))
mesh_mask = data_path + 'coordinates.nc'
filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': ufiles},
'V': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': vfiles},
'W': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': wfiles}}
variables = {'U': 'uo',
'V': 'vo',
'W': 'wo'}
dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'V': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'W': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'}}
chs = {'U': {'depth': ('depthu', 75), 'lat': ('y', 16), 'lon': ('x', 16)},
'V': {'depth': ('depthv', 20), 'lat': ('y', 4), 'lon': ('x', 16)},
'W': {'depth': ('depthw', 16), 'lat': ('y', 16), 'lon': ('x', 4)}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions, chunksize=chs)
npart = 20
lonp = 5.2 * np.ones(npart)
latp = [i for i in 52.0+(-1e-3+np.random.rand(npart)*2.0*1e-3)]
compute_nemo_particle_advection(fieldset, mode, lonp, latp)
@pytest.mark.parametrize('mode', ['jit'])
def test_diff_entry_chunksize_error_nemo_complex_conform_depth(mode):
# ==== this test is expected to fall-back to a pre-defined minimal chunk as ==== #
# ==== the requested chunks don't match, or throw a value error. ==== #
data_path = path.join(path.dirname(__file__), 'NemoNorthSeaORCA025-N006_data/')
ufiles = sorted(glob(data_path + 'ORCA*U.nc'))
vfiles = sorted(glob(data_path + 'ORCA*V.nc'))
wfiles = sorted(glob(data_path + 'ORCA*W.nc'))
mesh_mask = data_path + 'coordinates.nc'
filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': ufiles},
'V': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': vfiles},
'W': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': wfiles}}
variables = {'U': 'uo',
'V': 'vo',
'W': 'wo'}
dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'V': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'W': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'}}
chs = {'U': {'depth': ('depthu', 75), 'lat': ('y', 16), 'lon': ('x', 16)},
'V': {'depth': ('depthv', 75), 'lat': ('y', 4), 'lon': ('x', 16)},
'W': {'depth': ('depthw', 75), 'lat': ('y', 16), 'lon': ('x', 4)}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions, chunksize=chs)
npart = 20
lonp = 5.2 * np.ones(npart)
latp = [i for i in 52.0+(-1e-3+np.random.rand(npart)*2.0*1e-3)]
compute_nemo_particle_advection(fieldset, mode, lonp, latp)
# Nemo sample file dimensions: depthu=75, y=201, x=151
npart_U = 1
npart_U = [npart_U * k for k in fieldset.U.nchunks[1:]]
npart_V = 1
npart_V = [npart_V * k for k in fieldset.V.nchunks[1:]]
npart_W = 1
npart_W = [npart_W * k for k in fieldset.V.nchunks[1:]]
chn = {'U': {'lat': int(math.ceil(201.0/chs['U']['lat'][1])),
'lon': int(math.ceil(151.0/chs['U']['lon'][1])),
'depth': int(math.ceil(75.0/chs['U']['depth'][1]))},
'V': {'lat': int(math.ceil(201.0/chs['V']['lat'][1])),
'lon': int(math.ceil(151.0/chs['V']['lon'][1])),
'depth': int(math.ceil(75.0/chs['V']['depth'][1]))},
'W': {'lat': int(math.ceil(201.0/chs['W']['lat'][1])),
'lon': int(math.ceil(151.0/chs['W']['lon'][1])),
'depth': int(math.ceil(75.0/chs['W']['depth'][1]))}}
npart_U_request = 1
npart_U_request = [npart_U_request * chn['U'][k] for k in chn['U']]
npart_V_request = 1
npart_V_request = [npart_V_request * chn['V'][k] for k in chn['V']]
npart_W_request = 1
npart_W_request = [npart_W_request * chn['W'][k] for k in chn['W']]
assert (npart_U != npart_U_request)
assert (npart_V != npart_V_request)
assert (npart_W != npart_W_request)
@pytest.mark.parametrize('mode', ['jit'])
def test_diff_entry_chunksize_error_nemo_complex_nonconform_depth(mode):
# ==== this test is expected to fall-back to a pre-defined minimal chunk as the ==== #
# ==== requested chunks don't match, or throw a value error ==== #
data_path = path.join(path.dirname(__file__), 'NemoNorthSeaORCA025-N006_data/')
ufiles = sorted(glob(data_path + 'ORCA*U.nc'))
vfiles = sorted(glob(data_path + 'ORCA*V.nc'))
wfiles = sorted(glob(data_path + 'ORCA*W.nc'))
mesh_mask = data_path + 'coordinates.nc'
filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': ufiles},
'V': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': vfiles}}
variables = {'U': 'uo',
'V': 'vo'}
dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'V': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'}}
chs = {'U': {'depth': ('depthu', 75), 'lat': ('y', 16), 'lon': ('x', 16)},
'V': {'depth': ('depthv', 15), 'lat': ('y', 4), 'lon': ('x', 16)}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions, chunksize=chs)
npart = 20
lonp = 5.2 * np.ones(npart)
latp = [i for i in 52.0+(-1e-3+np.random.rand(npart)*2.0*1e-3)]
compute_nemo_particle_advection(fieldset, mode, lonp, latp)
@pytest.mark.parametrize('mode', ['jit'])
def test_erroneous_fieldset_init(mode):
data_path = path.join(path.dirname(__file__), 'NemoNorthSeaORCA025-N006_data/')
ufiles = sorted(glob(data_path + 'ORCA*U.nc'))
vfiles = sorted(glob(data_path + 'ORCA*V.nc'))
wfiles = sorted(glob(data_path + 'ORCA*W.nc'))
mesh_mask = data_path + 'coordinates.nc'
filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': ufiles},
'V': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': vfiles},
'W': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': wfiles}}
variables = {'U': 'uo',
'V': 'vo',
'W': 'wo'}
dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'V': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},
'W': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'}}
chs = {'U': {'depth': ('depthu', 75), 'lat': ('y', 16), 'lon': ('x', 16)},
'V': {'depth': ('depthv', 75), 'lat': ('y', 16), 'lon': ('x', 16)},
'W': {'depth': ('depthw', 75), 'lat': ('y', 16), 'lon': ('x', 16)}}
try:
FieldSet.from_nemo(filenames, variables, dimensions, chunksize=chs)
except ValueError:
return True
return False
@pytest.mark.parametrize('mode', ['jit'])
def test_diff_entry_chunksize_correction_globcurrent(mode):
filenames = path.join(path.dirname(__file__), 'GlobCurrent_example_data',
'200201*-GLOBCURRENT-L4-CUReul_hs-ALT_SUM-v02.0-fv01.0.nc')
variables = {'U': 'eastward_eulerian_current_velocity', 'V': 'northward_eulerian_current_velocity'}
dimensions = {'lat': 'lat', 'lon': 'lon', 'time': 'time'}
chs = {'U': {'lat': ('lat', 16), 'lon': ('lon', 16)},
'V': {'lat': ('lat', 16), 'lon': ('lon', 4)}}
fieldset = FieldSet.from_netcdf(filenames, variables, dimensions, chunksize=chs)
lonp = [25]
latp = [-35]
compute_globcurrent_particle_advection(fieldset, mode, lonp, latp)
# GlobCurrent sample file dimensions: time=UNLIMITED, lat=41, lon=81
npart_U = 1
npart_U = [npart_U * k for k in fieldset.U.nchunks[1:]]
npart_V = 1
npart_V = [npart_V * k for k in fieldset.V.nchunks[1:]]
npart_V_request = 1
chn = {'U': {'lat': int(math.ceil(41.0/chs['U']['lat'][1])),
'lon': int(math.ceil(81.0/chs['U']['lon'][1]))},
'V': {'lat': int(math.ceil(41.0/chs['V']['lat'][1])),
'lon': int(math.ceil(81.0/chs['V']['lon'][1]))}}
npart_V_request = [npart_V_request * chn['V'][k] for k in chn['V']]
assert (npart_V_request != npart_U)
assert (npart_V_request == npart_V)