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test_fieldset.py
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test_fieldset.py
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from parcels import FieldSet, ScipyParticle, JITParticle, Variable, AdvectionRK4, AdvectionRK4_3D, RectilinearZGrid, StateCode, OutOfTimeError
from parcels.field import Field, VectorField
from parcels import ParticleSetSOA, ParticleFileSOA, KernelSOA # noqa
from parcels import ParticleSetAOS, ParticleFileAOS, KernelAOS # noqa
from parcels import ParticleSetNodes, ParticleFileNodes, KernelNodes # noqa
from parcels.tools.converters import TimeConverter, _get_cftime_calendars, _get_cftime_datetimes, UnitConverter, GeographicPolar
import dask.array as da
import dask
from datetime import timedelta as delta
import datetime
import numpy as np
import xarray as xr
import pytest
from os import path
import cftime
import gc
import psutil
import os
import sys
pset_modes = ['soa', 'aos', 'nodes']
ptype = {'scipy': ScipyParticle, 'jit': JITParticle}
pset_type = {'soa': {'pset': ParticleSetSOA, 'pfile': ParticleFileSOA, 'kernel': KernelSOA},
'aos': {'pset': ParticleSetAOS, 'pfile': ParticleFileAOS, 'kernel': KernelAOS},
'nodes': {'pset': ParticleSetNodes, 'pfile': ParticleFileNodes, 'kernel': KernelNodes}}
def generate_fieldset(xdim, ydim, zdim=1, tdim=1):
lon = np.linspace(0., 10., xdim, dtype=np.float32)
lat = np.linspace(0., 10., ydim, dtype=np.float32)
depth = np.zeros(zdim, dtype=np.float32)
time = np.zeros(tdim, dtype=np.float64)
if zdim == 1 and tdim == 1:
U, V = np.meshgrid(lon, lat)
dimensions = {'lat': lat, 'lon': lon}
else:
U = np.ones((tdim, zdim, ydim, xdim))
V = np.ones((tdim, zdim, ydim, xdim))
dimensions = {'lat': lat, 'lon': lon, 'depth': depth, 'time': time}
data = {'U': np.array(U, dtype=np.float32), 'V': np.array(V, dtype=np.float32)}
return (data, dimensions)
@pytest.mark.parametrize('xdim', [100, 200])
@pytest.mark.parametrize('ydim', [100, 200])
def test_fieldset_from_data(xdim, ydim):
""" Simple test for fieldset initialisation from data. """
data, dimensions = generate_fieldset(xdim, ydim)
fieldset = FieldSet.from_data(data, dimensions)
assert len(fieldset.U.data.shape) == 3
assert len(fieldset.V.data.shape) == 3
assert np.allclose(fieldset.U.data[0, :], data['U'], rtol=1e-12)
assert np.allclose(fieldset.V.data[0, :], data['V'], rtol=1e-12)
def test_fieldset_extra_syntax():
""" Simple test for fieldset initialisation from data. """
data, dimensions = generate_fieldset(10, 10)
failed = False
try:
FieldSet.from_data(data, dimensions, unknown_keyword=5)
except SyntaxError:
failed = True
assert failed
@pytest.mark.parametrize('ttype', ['float', 'datetime64'])
@pytest.mark.parametrize('tdim', [1, 20])
def test_fieldset_from_data_timedims(ttype, tdim):
data, dimensions = generate_fieldset(10, 10, tdim=tdim)
if ttype == 'float':
dimensions['time'] = np.linspace(0, 5, tdim)
else:
dimensions['time'] = [np.datetime64('2018-01-01') + np.timedelta64(t, 'D') for t in range(tdim)]
fieldset = FieldSet.from_data(data, dimensions)
for i, dtime in enumerate(dimensions['time']):
assert fieldset.U.grid.time_origin.fulltime(fieldset.U.grid.time[i]) == dtime
@pytest.mark.parametrize('xdim', [100, 200])
@pytest.mark.parametrize('ydim', [100, 50])
def test_fieldset_from_data_different_dimensions(xdim, ydim, zdim=4, tdim=2):
""" Test for fieldset initialisation from data using
dict-of-dict for dimensions. """
lon = np.linspace(0., 1., xdim, dtype=np.float32)
lat = np.linspace(0., 1., ydim, dtype=np.float32)
depth = np.zeros(zdim, dtype=np.float32)
time = np.zeros(tdim, dtype=np.float64)
U = np.zeros((xdim, ydim), dtype=np.float32)
V = np.ones((xdim, ydim), dtype=np.float32)
P = 2 * np.ones((int(xdim/2), int(ydim/2), zdim, tdim), dtype=np.float32)
data = {'U': U, 'V': V, 'P': P}
dimensions = {'U': {'lat': lat, 'lon': lon},
'V': {'lat': lat, 'lon': lon},
'P': {'lat': lat[0::2], 'lon': lon[0::2], 'depth': depth, 'time': time}}
fieldset = FieldSet.from_data(data, dimensions, transpose=True)
assert len(fieldset.U.data.shape) == 3
assert len(fieldset.V.data.shape) == 3
assert len(fieldset.P.data.shape) == 4
assert fieldset.P.data.shape == (tdim, zdim, ydim/2, xdim/2)
assert np.allclose(fieldset.U.data, 0., rtol=1e-12)
assert np.allclose(fieldset.V.data, 1., rtol=1e-12)
assert np.allclose(fieldset.P.data, 2., rtol=1e-12)
@pytest.mark.parametrize('xdim', [100, 200])
@pytest.mark.parametrize('ydim', [100, 200])
def test_fieldset_from_parcels(xdim, ydim, tmpdir, filename='test_parcels'):
""" Simple test for fieldset initialisation from Parcels FieldSet file format. """
filepath = tmpdir.join(filename)
data, dimensions = generate_fieldset(xdim, ydim)
fieldset_out = FieldSet.from_data(data, dimensions)
fieldset_out.write(filepath)
fieldset = FieldSet.from_parcels(filepath)
assert len(fieldset.U.data.shape) == 3 # Will be 4 once we use depth
assert len(fieldset.V.data.shape) == 3
assert np.allclose(fieldset.U.data[0, :], data['U'], rtol=1e-12)
assert np.allclose(fieldset.V.data[0, :], data['V'], rtol=1e-12)
@pytest.mark.parametrize('calendar, cftime_datetime',
zip(_get_cftime_calendars(),
_get_cftime_datetimes()))
def test_fieldset_nonstandardtime(calendar, cftime_datetime, tmpdir, filename='test_nonstandardtime.nc', xdim=4, ydim=6):
filepath = tmpdir.join(filename)
dates = [getattr(cftime, cftime_datetime)(1, m, 1) for m in range(1, 13)]
da = xr.DataArray(np.random.rand(12, xdim, ydim),
coords=[dates, range(xdim), range(ydim)],
dims=['time', 'lon', 'lat'], name='U')
da.to_netcdf(str(filepath))
dims = {'lon': 'lon', 'lat': 'lat', 'time': 'time'}
try:
field = Field.from_netcdf(filepath, 'U', dims)
except NotImplementedError:
field = None
if field is not None:
assert field.grid.time_origin.calendar == calendar
def test_field_from_netcdf():
data_path = path.join(path.dirname(__file__), 'test_data/')
filenames = {'lon': data_path + 'mask_nemo_cross_180lon.nc',
'lat': data_path + 'mask_nemo_cross_180lon.nc',
'data': data_path + 'Uu_eastward_nemo_cross_180lon.nc'}
variable = 'U'
dimensions = {'lon': 'glamf', 'lat': 'gphif'}
Field.from_netcdf(filenames, variable, dimensions, interp_method='cgrid_velocity')
def test_field_from_netcdf_fieldtypes():
data_path = path.join(path.dirname(__file__), 'test_data/')
filenames = {'varU': {'lon': data_path + 'mask_nemo_cross_180lon.nc',
'lat': data_path + 'mask_nemo_cross_180lon.nc',
'data': data_path + 'Uu_eastward_nemo_cross_180lon.nc'},
'varV': {'lon': data_path + 'mask_nemo_cross_180lon.nc',
'lat': data_path + 'mask_nemo_cross_180lon.nc',
'data': data_path + 'Vv_eastward_nemo_cross_180lon.nc'}}
variables = {'varU': 'U', 'varV': 'V'}
dimensions = {'lon': 'glamf', 'lat': 'gphif'}
# first try without setting fieldtype
fset = FieldSet.from_nemo(filenames, variables, dimensions)
assert isinstance(fset.varU.units, UnitConverter)
# now try with setting fieldtype
fset = FieldSet.from_nemo(filenames, variables, dimensions, fieldtype={'varU': 'U', 'varV': 'V'})
assert isinstance(fset.varU.units, GeographicPolar)
def test_fieldset_from_cgrid_interpmethod():
data_path = path.join(path.dirname(__file__), 'test_data/')
filenames = {'lon': data_path + 'mask_nemo_cross_180lon.nc',
'lat': data_path + 'mask_nemo_cross_180lon.nc',
'data': data_path + 'Uu_eastward_nemo_cross_180lon.nc'}
variable = 'U'
dimensions = {'lon': 'glamf', 'lat': 'gphif'}
failed = False
try:
# should fail because FieldSet.from_c_grid_dataset does not support interp_method
FieldSet.from_c_grid_dataset(filenames, variable, dimensions, interp_method='partialslip')
except TypeError:
failed = True
assert failed
@pytest.mark.parametrize('cast_data_dtype', ['float32', 'float64'])
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
def test_fieldset_float64(cast_data_dtype, mode, tmpdir, xdim=10, ydim=5):
lon = np.linspace(0., 10., xdim, dtype=np.float64)
lat = np.linspace(0., 10., ydim, dtype=np.float64)
U, V = np.meshgrid(lon, lat)
dimensions = {'lat': lat, 'lon': lon}
data = {'U': np.array(U, dtype=np.float64), 'V': np.array(V, dtype=np.float64)}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat', cast_data_dtype=cast_data_dtype)
if cast_data_dtype == 'float32':
assert fieldset.U.data.dtype == np.float32
else:
assert fieldset.U.data.dtype == np.float64
pset = ParticleSetAOS(fieldset, ptype[mode], lon=1, lat=2)
failed = False
try:
pset.execute(AdvectionRK4, runtime=1)
except RuntimeError:
failed = True
if mode == 'jit' and cast_data_dtype == 'float64':
assert failed
else:
assert np.isclose(pset[0].lon, 2.70833)
assert np.isclose(pset[0].lat, 5.41667)
filepath = tmpdir.join('test_fieldset_float64')
fieldset.U.write(filepath)
da = xr.open_dataset(str(filepath)+'U.nc')
if cast_data_dtype == 'float32':
assert da['U'].dtype == np.float32
else:
assert da['U'].dtype == np.float64
@pytest.mark.parametrize('indslon', [range(10, 20), [1]])
@pytest.mark.parametrize('indslat', [range(30, 60), [22]])
def test_fieldset_from_file_subsets(indslon, indslat, tmpdir, filename='test_subsets'):
""" Test for subsetting fieldset from file using indices dict. """
data, dimensions = generate_fieldset(100, 100)
filepath = tmpdir.join(filename)
fieldsetfull = FieldSet.from_data(data, dimensions)
fieldsetfull.write(filepath)
indices = {'lon': indslon, 'lat': indslat}
indices_back = indices.copy()
fieldsetsub = FieldSet.from_parcels(filepath, indices=indices, chunksize=None)
assert indices == indices_back
assert np.allclose(fieldsetsub.U.lon, fieldsetfull.U.grid.lon[indices['lon']])
assert np.allclose(fieldsetsub.U.lat, fieldsetfull.U.grid.lat[indices['lat']])
assert np.allclose(fieldsetsub.V.lon, fieldsetfull.V.grid.lon[indices['lon']])
assert np.allclose(fieldsetsub.V.lat, fieldsetfull.V.grid.lat[indices['lat']])
ixgrid = np.ix_([0], indices['lat'], indices['lon'])
assert np.allclose(fieldsetsub.U.data, fieldsetfull.U.data[ixgrid])
assert np.allclose(fieldsetsub.V.data, fieldsetfull.V.data[ixgrid])
def test_empty_indices(tmpdir, filename='test_subsets'):
data, dimensions = generate_fieldset(100, 100)
filepath = tmpdir.join(filename)
FieldSet.from_data(data, dimensions).write(filepath)
error_thrown = False
try:
FieldSet.from_parcels(filepath, indices={'lon': []})
except RuntimeError:
error_thrown = True
assert error_thrown
@pytest.mark.parametrize('calltype', ['from_data', 'from_nemo'])
def test_illegal_dimensionsdict(calltype):
error_thrown = False
try:
if calltype == 'from_data':
data, dimensions = generate_fieldset(10, 10)
dimensions['test'] = None
FieldSet.from_data(data, dimensions)
elif calltype == 'from_nemo':
fname = path.join(path.dirname(__file__), 'test_data', 'mask_nemo_cross_180lon.nc')
filenames = {'dx': fname, 'mesh_mask': fname}
variables = {'dx': 'e1u'}
dimensions = {'lon': 'glamu', 'lat': 'gphiu', 'test': 'test'}
error_thrown = False
FieldSet.from_nemo(filenames, variables, dimensions)
except NameError:
error_thrown = True
assert error_thrown
@pytest.mark.parametrize('xdim', [100, 200])
@pytest.mark.parametrize('ydim', [100, 200])
def test_add_field(xdim, ydim, tmpdir, filename='test_add'):
filepath = tmpdir.join(filename)
data, dimensions = generate_fieldset(xdim, ydim)
fieldset = FieldSet.from_data(data, dimensions)
field = Field('newfld', fieldset.U.data, lon=fieldset.U.lon, lat=fieldset.U.lat)
fieldset.add_field(field)
assert fieldset.newfld.data.shape == fieldset.U.data.shape
fieldset.write(filepath)
@pytest.mark.parametrize('dupobject', ['same', 'new'])
def test_add_duplicate_field(dupobject):
data, dimensions = generate_fieldset(100, 100)
fieldset = FieldSet.from_data(data, dimensions)
field = Field('newfld', fieldset.U.data, lon=fieldset.U.lon, lat=fieldset.U.lat)
fieldset.add_field(field)
error_thrown = False
try:
if dupobject == 'same':
fieldset.add_field(field)
elif dupobject == 'new':
field2 = Field('newfld', np.ones((2, 2)), lon=np.array([0, 1]), lat=np.array([0, 2]))
fieldset.add_field(field2)
except RuntimeError:
error_thrown = True
assert error_thrown
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('fieldtype', ['normal', 'vector'])
def test_add_field_after_pset(pset_mode, fieldtype):
data, dimensions = generate_fieldset(100, 100)
fieldset = FieldSet.from_data(data, dimensions)
pset = pset_type[pset_mode]['pset'](fieldset, ScipyParticle, lon=0, lat=0) # noqa ; to trigger fieldset.check_complete
field1 = Field('field1', fieldset.U.data, lon=fieldset.U.lon, lat=fieldset.U.lat)
field2 = Field('field2', fieldset.U.data, lon=fieldset.U.lon, lat=fieldset.U.lat)
vfield = VectorField('vfield', field1, field2)
error_thrown = False
try:
if fieldtype == 'normal':
fieldset.add_field(field1)
elif fieldtype == 'vector':
fieldset.add_vector_field(vfield)
except RuntimeError:
error_thrown = True
assert error_thrown
@pytest.mark.parametrize('chunksize', ['auto', None])
def test_fieldset_samegrids_from_file(tmpdir, chunksize, filename='test_subsets'):
""" Test for subsetting fieldset from file using indices dict. """
data, dimensions = generate_fieldset(100, 100)
filepath1 = tmpdir.join(filename+'_1')
fieldset1 = FieldSet.from_data(data, dimensions)
fieldset1.write(filepath1)
ufiles = [filepath1+'U.nc', ] * 4
vfiles = [filepath1+'V.nc', ] * 4
timestamps = np.arange(0, 4, 1) * 86400.0
timestamps = np.expand_dims(timestamps, 1)
files = {'U': ufiles, 'V': vfiles}
variables = {'U': 'vozocrtx', 'V': 'vomecrty'}
dimensions = {'lon': 'nav_lon', 'lat': 'nav_lat'}
fieldset = FieldSet.from_netcdf(files, variables, dimensions, timestamps=timestamps, allow_time_extrapolation=True, chunksize=chunksize)
if chunksize == 'auto':
assert fieldset.gridset.size == 2
assert fieldset.U.grid != fieldset.V.grid
else:
assert fieldset.gridset.size == 1
assert fieldset.U.grid == fieldset.V.grid
assert fieldset.U.chunksize == fieldset.V.chunksize
@pytest.mark.parametrize('gridtype', ['A', 'C'])
def test_fieldset_dimlength1_cgrid(gridtype):
fieldset = FieldSet.from_data({'U': 0, 'V': 0}, {'lon': 0, 'lat': 0})
if gridtype == 'C':
fieldset.U.interp_method = 'cgrid_velocity'
fieldset.V.interp_method = 'cgrid_velocity'
try:
fieldset.check_complete()
success = True if gridtype == 'A' else False
except NotImplementedError:
success = True if gridtype == 'C' else False
assert success
@pytest.mark.parametrize('chunksize', ['auto', None])
def test_fieldset_diffgrids_from_file(tmpdir, chunksize, filename='test_subsets'):
""" Test for subsetting fieldset from file using indices dict. """
data, dimensions = generate_fieldset(100, 100)
filepath1 = tmpdir.join(filename+'_1')
fieldset1 = FieldSet.from_data(data, dimensions)
fieldset1.write(filepath1)
data, dimensions = generate_fieldset(50, 50)
filepath2 = tmpdir.join(filename + '_2')
fieldset2 = FieldSet.from_data(data, dimensions)
fieldset2.write(filepath2)
ufiles = [filepath1+'U.nc', ] * 4
vfiles = [filepath2+'V.nc', ] * 4
timestamps = np.arange(0, 4, 1) * 86400.0
timestamps = np.expand_dims(timestamps, 1)
files = {'U': ufiles, 'V': vfiles}
variables = {'U': 'vozocrtx', 'V': 'vomecrty'}
dimensions = {'lon': 'nav_lon', 'lat': 'nav_lat'}
fieldset = FieldSet.from_netcdf(files, variables, dimensions, timestamps=timestamps, allow_time_extrapolation=True, chunksize=chunksize)
assert fieldset.gridset.size == 2
assert fieldset.U.grid != fieldset.V.grid
@pytest.mark.parametrize('chunksize', ['auto', None])
def test_fieldset_diffgrids_from_file_data(tmpdir, chunksize, filename='test_subsets'):
""" Test for subsetting fieldset from file using indices dict. """
data, dimensions = generate_fieldset(100, 100)
filepath = tmpdir.join(filename)
fieldset_data = FieldSet.from_data(data, dimensions)
fieldset_data.write(filepath)
field_data = fieldset_data.U
field_data.name = "B"
ufiles = [filepath+'U.nc', ] * 4
vfiles = [filepath+'V.nc', ] * 4
timestamps = np.arange(0, 4, 1) * 86400.0
timestamps = np.expand_dims(timestamps, 1)
files = {'U': ufiles, 'V': vfiles}
variables = {'U': 'vozocrtx', 'V': 'vomecrty'}
dimensions = {'lon': 'nav_lon', 'lat': 'nav_lat'}
fieldset_file = FieldSet.from_netcdf(files, variables, dimensions, timestamps=timestamps, allow_time_extrapolation=True, chunksize=chunksize)
fieldset_file.add_field(field_data, "B")
assert len(fieldset_file.get_fields()) == 3
if chunksize == 'auto':
assert fieldset_file.gridset.size == 3
else:
assert fieldset_file.gridset.size == 2
assert fieldset_file.U.grid != fieldset_file.B.grid
def test_fieldset_samegrids_from_data(tmpdir, filename='test_subsets'):
""" Test for subsetting fieldset from file using indices dict. """
data, dimensions = generate_fieldset(100, 100)
fieldset1 = FieldSet.from_data(data, dimensions)
field_data = fieldset1.U
field_data.name = "B"
fieldset1.add_field(field_data, "B")
assert fieldset1.gridset.size == 1
assert fieldset1.U.grid == fieldset1.B.grid
@pytest.mark.parametrize('mesh', ['flat', 'spherical'])
def test_fieldset_celledgesizes(mesh):
data, dimensions = generate_fieldset(10, 7)
fieldset = FieldSet.from_data(data, dimensions, mesh=mesh)
fieldset.U.calc_cell_edge_sizes()
D_meridional = fieldset.U.cell_edge_sizes['y']
D_zonal = fieldset.U.cell_edge_sizes['x']
assert np.allclose(D_meridional.flatten(), D_meridional[0, 0]) # all meridional distances should be the same in either mesh
if mesh == 'flat':
assert np.allclose(D_zonal.flatten(), D_zonal[0, 0]) # all zonal distances should be the same in flat mesh
else:
assert all((np.gradient(D_zonal, axis=0) < 0).flatten()) # zonal distances should decrease in spherical mesh
@pytest.mark.parametrize('dx, dy', [('e1u', 'e2u'), ('e1v', 'e2v')])
def test_fieldset_celledgesizes_curvilinear(dx, dy):
fname = path.join(path.dirname(__file__), 'test_data', 'mask_nemo_cross_180lon.nc')
filenames = {'dx': fname, 'dy': fname, 'mesh_mask': fname}
variables = {'dx': dx, 'dy': dy}
dimensions = {'dx': {'lon': 'glamu', 'lat': 'gphiu'},
'dy': {'lon': 'glamu', 'lat': 'gphiu'}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions)
# explicitly setting cell_edge_sizes from e1u and e2u etc
fieldset.dx.grid.cell_edge_sizes['x'] = fieldset.dx.data
fieldset.dx.grid.cell_edge_sizes['y'] = fieldset.dy.data
A = fieldset.dx.cell_areas()
assert np.allclose(A, fieldset.dx.data * fieldset.dy.data)
def test_fieldset_write_curvilinear(tmpdir):
fname = path.join(path.dirname(__file__), 'test_data', 'mask_nemo_cross_180lon.nc')
filenames = {'dx': fname, 'mesh_mask': fname}
variables = {'dx': 'e1u'}
dimensions = {'lon': 'glamu', 'lat': 'gphiu'}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions)
assert fieldset.dx.creation_log == 'from_nemo'
newfile = tmpdir.join('curv_field')
fieldset.write(newfile)
fieldset2 = FieldSet.from_netcdf(filenames=newfile+'dx.nc', variables={'dx': 'dx'},
dimensions={'time': 'time_counter', 'depth': 'depthdx',
'lon': 'nav_lon', 'lat': 'nav_lat'})
assert fieldset2.dx.creation_log == 'from_netcdf'
for var in ['lon', 'lat', 'data']:
assert np.allclose(getattr(fieldset2.dx, var), getattr(fieldset.dx, var))
@pytest.mark.parametrize('mesh', ['flat', 'spherical'])
def test_fieldset_cellareas(mesh):
data, dimensions = generate_fieldset(10, 7)
fieldset = FieldSet.from_data(data, dimensions, mesh=mesh)
cell_areas = fieldset.V.cell_areas()
if mesh == 'flat':
assert np.allclose(cell_areas.flatten(), cell_areas[0, 0], rtol=1e-3)
else:
assert all((np.gradient(cell_areas, axis=0) < 0).flatten()) # areas should decrease with latitude in spherical mesh
for y in range(cell_areas.shape[0]):
assert np.allclose(cell_areas[y, :], cell_areas[y, 0], rtol=1e-3)
def addConst(particle, fieldset, time):
particle.lon = particle.lon + fieldset.movewest + fieldset.moveeast
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
def test_fieldset_constant(pset_mode, mode):
data, dimensions = generate_fieldset(100, 100)
fieldset = FieldSet.from_data(data, dimensions)
westval = -0.2
eastval = 0.3
fieldset.add_constant('movewest', westval)
fieldset.add_constant('moveeast', eastval)
assert fieldset.movewest == westval
pset = pset_type[pset_mode]['pset'].from_line(fieldset, size=1, pclass=ptype[mode], start=(0.5, 0.5), finish=(0.5, 0.5))
pset.execute(pset.Kernel(addConst), dt=1, runtime=1)
assert abs(pset.lon[0] - (0.5 + westval + eastval)) < 1e-4
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('swapUV', [False, True])
def test_vector_fields(pset_mode, mode, swapUV):
lon = np.linspace(0., 10., 12, dtype=np.float32)
lat = np.linspace(0., 10., 10, dtype=np.float32)
U = np.ones((10, 12), dtype=np.float32)
V = np.zeros((10, 12), dtype=np.float32)
data = {'U': U, 'V': V}
dimensions = {'U': {'lat': lat, 'lon': lon},
'V': {'lat': lat, 'lon': lon}}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat')
if swapUV: # we test that we can freely edit whatever UV field
UV = VectorField('UV', fieldset.V, fieldset.U)
fieldset.add_vector_field(UV)
pset = pset_type[pset_mode]['pset'].from_line(fieldset, size=1, pclass=ptype[mode], start=(0.5, 0.5), finish=(0.5, 0.5))
pset.execute(AdvectionRK4, dt=1, runtime=1)
if swapUV:
assert abs(pset.lon[0] - .5) < 1e-9
assert abs(pset.lat[0] - 1.5) < 1e-9
else:
assert abs(pset.lon[0] - 1.5) < 1e-9
assert abs(pset.lat[0] - .5) < 1e-9
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
def test_add_second_vector_field(pset_mode, mode):
lon = np.linspace(0., 10., 12, dtype=np.float32)
lat = np.linspace(0., 10., 10, dtype=np.float32)
U = np.ones((10, 12), dtype=np.float32)
V = np.zeros((10, 12), dtype=np.float32)
data = {'U': U, 'V': V}
dimensions = {'U': {'lat': lat, 'lon': lon},
'V': {'lat': lat, 'lon': lon}}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat')
data2 = {'U2': U, 'V2': V}
dimensions2 = {'lon': [ln + 0.1 for ln in lon], 'lat': [lt - 0.1 for lt in lat]}
fieldset2 = FieldSet.from_data(data2, dimensions2, mesh='flat')
UV2 = VectorField('UV2', fieldset2.U2, fieldset2.V2)
fieldset.add_vector_field(UV2)
def SampleUV2(particle, fieldset, time):
u, v = fieldset.UV2[time, particle.depth, particle.lat, particle.lon]
particle.lon += u * particle.dt
particle.lat += v * particle.dt
pset = pset_type[pset_mode]['pset'](fieldset, pclass=ptype[mode], lon=0.5, lat=0.5)
pset.execute(pset.Kernel(AdvectionRK4)+pset.Kernel(SampleUV2), dt=1, runtime=1)
assert abs(pset.lon[0] - 2.5) < 1e-9
assert abs(pset.lat[0] - .5) < 1e-9
@pytest.mark.parametrize('pset_mode', pset_modes)
def test_fieldset_write(pset_mode, tmpdir):
filepath = tmpdir.join("fieldset_write.nc")
xdim, ydim = 3, 4
lon = np.linspace(0., 10., xdim, dtype=np.float32)
lat = np.linspace(0., 10., ydim, dtype=np.float32)
U = np.ones((ydim, xdim), dtype=np.float32)
V = np.zeros((ydim, xdim), dtype=np.float32)
data = {'U': U, 'V': V}
dimensions = {'U': {'lat': lat, 'lon': lon},
'V': {'lat': lat, 'lon': lon}}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat')
fieldset.U.to_write = True
def UpdateU(particle, fieldset, time):
tmp = fieldset.U[particle] # noqa
fieldset.U.data[particle.ti, particle.yi, particle.xi] += 1
fieldset.U.grid.time[0] = time
pset = pset_type[pset_mode]['pset'](fieldset, pclass=ScipyParticle, lon=5, lat=5)
ofile = pset.ParticleFile(name=filepath, outputdt=2.)
pset.execute(UpdateU, dt=1, runtime=10, output_file=ofile)
assert fieldset.U.data[0, 1, 0] == 11
da = xr.open_dataset(str(filepath).replace('.nc', '_0005U.nc'))
assert np.allclose(fieldset.U.data, da['U'].values)
@pytest.mark.parametrize('pset_mode', ['aos', 'soa'])
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('time_periodic', [4*86400.0, False])
@pytest.mark.parametrize('chunksize', [False, 'auto', {'time': ('time_counter', 1), 'lat': ('y', 32), 'lon': ('x', 32)}])
@pytest.mark.parametrize('with_GC', [False, True])
@pytest.mark.skipif(sys.platform.startswith("win"), reason="skipping windows test as windows memory leaks (#787)")
def test_from_netcdf_memory_containment(pset_mode, mode, time_periodic, chunksize, with_GC):
"""
The fixed max. memory cap in 'field_step_max' is directly calculated and shall not be changed anymore.
It's based on the following values:
# x-entries: 512
# y values: 128
# time allocations: 4
# fields loaded: 2
# buffered timesteps: 2
SciPy-Buffers 1; JIT-Buffers: 2
"""
dt = 3600.0
if time_periodic and dt < 0:
return True # time_periodic does not work in backward-time mode
if chunksize == 'auto':
dask.config.set({'array.chunk-size': '128KiB'})
else:
dask.config.set({'array.chunk-size': '128MiB'})
class PerformanceLog():
samples = []
memory_steps = []
pset = None
pfile = None
_iter = 0
_process = None
def __init__(self):
self._process = psutil.Process(os.getpid())
def advance(self):
offset = 0
if self.pset is not None:
offset -= sys.getsizeof(self.pset)
if self.pfile is not None:
offset -= sys.getsizeof(self.pfile)
self.memory_steps.append(self._process.memory_info().rss - offset)
self.samples.append(self._iter)
self._iter += 1
def perIterGC():
gc.collect()
def periodicBoundaryConditions(particle, fieldset, time):
while particle.lon > 180.:
particle.lon -= 360.
while particle.lon < -180.:
particle.lon += 360.
while particle.lat > 90.:
particle.lat -= 180.
while particle.lat < -90.:
particle.lat += 180.
fnameU = path.join(path.dirname(__file__), 'test_data', 'perlinfieldsU.nc')
fnameV = path.join(path.dirname(__file__), 'test_data', 'perlinfieldsV.nc')
ufiles = [fnameU, ] * 4
vfiles = [fnameV, ] * 4
timestamps = np.arange(0, 4, 1) * 86400.0
timestamps = np.expand_dims(timestamps, 1)
files = {'U': ufiles, 'V': vfiles}
variables = {'U': 'vozocrtx', 'V': 'vomecrty'}
dimensions = {'lon': 'nav_lon', 'lat': 'nav_lat'}
fieldset = FieldSet.from_netcdf(files, variables, dimensions, timestamps=timestamps, time_periodic=time_periodic, allow_time_extrapolation=True if time_periodic in [False, None] else False, chunksize=chunksize)
perflog = PerformanceLog()
postProcessFuncs = [perflog.advance, ]
if with_GC:
postProcessFuncs.append(perIterGC)
perflog.advance()
pset = pset_type[pset_mode]['pset'](fieldset=fieldset, pclass=ptype[mode], lon=[0.5, ], lat=[0.5, ])
perflog.pset = pset
mem_exhausted = False
try:
pset.execute(pset.Kernel(AdvectionRK4)+periodicBoundaryConditions, dt=dt, runtime=delta(days=7), postIterationCallbacks=postProcessFuncs, callbackdt=delta(hours=10))
except MemoryError:
mem_exhausted = True
mem_steps_np = np.array(perflog.memory_steps)
field_step_max = ((4*8+512*128*4+512*128*4+(512*128*4))*2*2)
if mode != 'scipy':
field_step_max *= 2
if with_GC:
if chunksize is not False:
N = mem_steps_np.shape[0]
mem0 = mem_steps_np[0]
# flat-lining - here, the line shall not exceed the theoretical max.
if np.allclose(mem_steps_np[N-8:], perflog.memory_steps[-1], rtol=1e-6):
assert np.alltrue((mem_steps_np-mem0) <= field_step_max)
else: # otherwise, we have a peaky mem usage - make sure exceeding peaks are uncommon (<5% of measurements), and that peaks are less than 2*field_step_max
excessive_occurence = np.count_nonzero((mem_steps_np-mem0) > field_step_max)
limit = max(1, int(0.05*N))
assert excessive_occurence <= limit
increases = np.array([(mem_steps_np[i] - mem_steps_np[i - 1]) for i in range(1, mem_steps_np.shape[0])])
assert np.alltrue(increases < 2.0*field_step_max)
assert not mem_exhausted
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('time_periodic', [4*86400.0, False])
@pytest.mark.parametrize('chunksize', [False, 'auto', {'lat': ('y', 32), 'lon': ('x', 32)}, {'time': ('time_counter', 1), 'lat': ('y', 32), 'lon': ('x', 32)}])
@pytest.mark.parametrize('deferLoad', [True, False])
def test_from_netcdf_chunking(pset_mode, mode, time_periodic, chunksize, deferLoad):
fnameU = path.join(path.dirname(__file__), 'test_data', 'perlinfieldsU.nc')
fnameV = path.join(path.dirname(__file__), 'test_data', 'perlinfieldsV.nc')
ufiles = [fnameU, ] * 4
vfiles = [fnameV, ] * 4
timestamps = np.arange(0, 4, 1) * 86400.0
timestamps = np.expand_dims(timestamps, 1)
files = {'U': ufiles, 'V': vfiles}
variables = {'U': 'vozocrtx', 'V': 'vomecrty'}
dimensions = {'lon': 'nav_lon', 'lat': 'nav_lat'}
fieldset = FieldSet.from_netcdf(files, variables, dimensions, timestamps=timestamps, time_periodic=time_periodic, deferred_load=deferLoad, allow_time_extrapolation=True if time_periodic in [False, None] else False, chunksize=chunksize)
pset = pset_type[pset_mode]['pset'].from_line(fieldset, size=1, pclass=ptype[mode], start=(0.5, 0.5), finish=(0.5, 0.5))
pset.execute(AdvectionRK4, dt=1, runtime=1)
@pytest.mark.parametrize('datetype', ['float', 'datetime64'])
def test_timestamps(datetype, tmpdir):
data1, dims1 = generate_fieldset(10, 10, 1, 10)
data2, dims2 = generate_fieldset(10, 10, 1, 4)
if datetype == 'float':
dims1['time'] = np.arange(0, 10, 1) * 86400
dims2['time'] = np.arange(10, 14, 1) * 86400
else:
dims1['time'] = np.arange('2005-02-01', '2005-02-11', dtype='datetime64[D]')
dims2['time'] = np.arange('2005-02-11', '2005-02-15', dtype='datetime64[D]')
fieldset1 = FieldSet.from_data(data1, dims1)
fieldset1.U.data[0, :, :] = 2.
fieldset1.write(tmpdir.join('file1'))
fieldset2 = FieldSet.from_data(data2, dims2)
fieldset2.U.data[0, :, :] = 0.
fieldset2.write(tmpdir.join('file2'))
fieldset3 = FieldSet.from_parcels(tmpdir.join('file*'), time_periodic=delta(days=14))
timestamps = [dims1['time'], dims2['time']]
fieldset4 = FieldSet.from_parcels(tmpdir.join('file*'), timestamps=timestamps, time_periodic=delta(days=14))
assert np.allclose(fieldset3.U.grid.time_full, fieldset4.U.grid.time_full)
for d in [0, 8, 10, 13]:
fieldset3.computeTimeChunk(d*86400., 1.)
fieldset4.computeTimeChunk(d*86400., 1.)
assert np.allclose(fieldset3.U.data, fieldset4.U.data)
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('time_periodic', [86400., False])
@pytest.mark.parametrize('dt_sign', [-1, 1])
def test_periodic(pset_mode, mode, time_periodic, dt_sign):
lon = np.array([0, 1], dtype=np.float32)
lat = np.array([0, 1], dtype=np.float32)
depth = np.array([0, 1], dtype=np.float32)
tsize = 24*60+1
period = 86400
time = np.linspace(0, period, tsize, dtype=np.float64)
def temp_func(time):
return 20 + 2 * np.sin(time*2*np.pi/period)
temp_vec = temp_func(time)
U = np.zeros((2, 2, 2, tsize), dtype=np.float32)
V = np.zeros((2, 2, 2, tsize), dtype=np.float32)
V[0, 0, 0, :] = 1e-5
W = np.zeros((2, 2, 2, tsize), dtype=np.float32)
temp = np.zeros((2, 2, 2, tsize), dtype=np.float32)
temp[:, :, :, :] = temp_vec
data = {'U': U, 'V': V, 'W': W, 'temp': temp}
dimensions = {'lon': lon, 'lat': lat, 'depth': depth, 'time': time}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat', time_periodic=time_periodic, transpose=True, allow_time_extrapolation=True)
def sampleTemp(particle, fieldset, time):
# Note that fieldset.temp is interpolated at time=time+dt.
# Indeed, sampleTemp is called at time=time, but the result is written
# at time=time+dt, after the Kernel update
particle.temp = fieldset.temp[time+particle.dt, particle.depth, particle.lat, particle.lon]
# test if we can interpolate UV and UVW together
(particle.u1, particle.v1) = fieldset.UV[time+particle.dt, particle.depth, particle.lat, particle.lon]
(particle.u2, particle.v2, w_) = fieldset.UVW[time+particle.dt, particle.depth, particle.lat, particle.lon]
class MyParticle(ptype[mode]):
temp = Variable('temp', dtype=np.float32, initial=20.)
u1 = Variable('u1', dtype=np.float32, initial=0.)
u2 = Variable('u2', dtype=np.float32, initial=0.)
v1 = Variable('v1', dtype=np.float32, initial=0.)
v2 = Variable('v2', dtype=np.float32, initial=0.)
pset = pset_type[pset_mode]['pset'].from_list(fieldset, pclass=MyParticle, lon=[0.5], lat=[0.5], depth=[0.5])
pset.execute(AdvectionRK4_3D + pset.Kernel(sampleTemp), runtime=delta(hours=51), dt=delta(hours=dt_sign*1))
p0 = None
if pset_mode in ['soa', 'aos']:
p0 = pset[0]
elif pset_mode == 'nodes':
p0 = pset.begin().data
if time_periodic is not False:
t = p0.time
temp_theo = temp_func(t)
elif dt_sign == 1:
temp_theo = temp_vec[-1]
elif dt_sign == -1:
temp_theo = temp_vec[0]
assert np.allclose(temp_theo, p0.temp, atol=1e-5)
assert np.allclose(p0.u1, p0.u2)
assert np.allclose(p0.v1, p0.v2)
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('fail', [False, pytest.param(True, marks=pytest.mark.xfail(strict=True))])
def test_fieldset_defer_loading_with_diff_time_origin(pset_mode, tmpdir, fail, filename='test_parcels_defer_loading'):
filepath = tmpdir.join(filename)
data0, dims0 = generate_fieldset(10, 10, 1, 10)
dims0['time'] = np.arange(0, 10, 1) * 3600
fieldset_out = FieldSet.from_data(data0, dims0)
fieldset_out.U.grid.time_origin = TimeConverter(np.datetime64('2018-04-20'))
fieldset_out.V.grid.time_origin = TimeConverter(np.datetime64('2018-04-20'))
data1, dims1 = generate_fieldset(10, 10, 1, 10)
if fail:
dims1['time'] = np.arange(0, 10, 1) * 3600
else:
dims1['time'] = np.arange(0, 10, 1) * 1800 + (24+25)*3600
if fail:
Wtime_origin = TimeConverter(np.datetime64('2018-04-22'))
else:
Wtime_origin = TimeConverter(np.datetime64('2018-04-18'))
gridW = RectilinearZGrid(dims1['lon'], dims1['lat'], dims1['depth'], dims1['time'], time_origin=Wtime_origin)
fieldW = Field('W', np.zeros(data1['U'].shape), grid=gridW)
fieldset_out.add_field(fieldW)
fieldset_out.write(filepath)
fieldset = FieldSet.from_parcels(filepath, extra_fields={'W': 'W'})
assert fieldset.U.creation_log == 'from_parcels'
pset = pset_type[pset_mode]['pset'].from_list(fieldset, pclass=JITParticle, lon=[0.5], lat=[0.5], depth=[0.5],
time=[datetime.datetime(2018, 4, 20, 1)])
pset.execute(AdvectionRK4_3D, runtime=delta(hours=4), dt=delta(hours=1))
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('zdim', [2, 8])
@pytest.mark.parametrize('scale_fac', [0.2, 4, 1])
def test_fieldset_defer_loading_function(pset_mode, zdim, scale_fac, tmpdir, filename='test_parcels_defer_loading'):
filepath = tmpdir.join(filename)
data0, dims0 = generate_fieldset(3, 3, zdim, 10)
data0['U'][:, 0, :, :] = np.nan # setting first layer to nan, which will be changed to zero (and all other layers to 1)
dims0['time'] = np.arange(0, 10, 1) * 3600
dims0['depth'] = np.arange(0, zdim, 1)
fieldset_out = FieldSet.from_data(data0, dims0)
fieldset_out.write(filepath)
fieldset = FieldSet.from_parcels(filepath, chunksize={'time': ('time_counter', 1), 'depth': ('depthu', 1), 'lat': ('y', 2), 'lon': ('x', 2)})
# testing for combination of deferred-loaded and numpy Fields
with pytest.raises(ValueError):
fieldset.add_field(Field('numpyfield', np.zeros((10, zdim, 3, 3)), grid=fieldset.U.grid))
# testing for scaling factors
fieldset.U.set_scaling_factor(scale_fac)
dz = np.gradient(fieldset.U.depth)
DZ = np.moveaxis(np.tile(dz, (fieldset.U.grid.ydim, fieldset.U.grid.xdim, 1)), [0, 1, 2], [1, 2, 0])
def compute(fieldset):
# Calculating vertical weighted average
for f in [fieldset.U, fieldset.V]:
for tind in f.loaded_time_indices:
data = da.sum(f.data[tind, :] * DZ, axis=0) / sum(dz)
data = da.broadcast_to(data, (1, f.grid.zdim, f.grid.ydim, f.grid.xdim))
f.data = f.data_concatenate(f.data, data, tind)
fieldset.compute_on_defer = compute
fieldset.computeTimeChunk(1, 1)
assert isinstance(fieldset.U.data, da.core.Array)
assert np.allclose(fieldset.U.data, scale_fac*(zdim-1.)/zdim)
pset = pset_type[pset_mode]['pset'](fieldset, JITParticle, 0, 0)
def DoNothing(particle, fieldset, time):
return StateCode.Success
pset.execute(DoNothing, dt=3600)
assert np.allclose(fieldset.U.data, scale_fac*(zdim-1.)/zdim)
@pytest.mark.parametrize('time2', [1, 7])
def test_fieldset_initialisation_kernel_dask(time2, tmpdir, filename='test_parcels_defer_loading'):
filepath = tmpdir.join(filename)
data0, dims0 = generate_fieldset(3, 3, 4, 10)
data0['U'] = np.random.rand(10, 4, 3, 3)
dims0['time'] = np.arange(0, 10, 1)
dims0['depth'] = np.arange(0, 4, 1)
fieldset_out = FieldSet.from_data(data0, dims0)
fieldset_out.write(filepath)
fieldset = FieldSet.from_parcels(filepath, chunksize={'time': ('time_counter', 1), 'depth': ('depthu', 1), 'lat': ('y', 2), 'lon': ('x', 2)})
def SampleField(particle, fieldset, time):
particle.u_kernel = fieldset.U[time, particle.depth, particle.lat, particle.lon]
class SampleParticle(JITParticle):
u_kernel = Variable('u_kernel', dtype=np.float32, initial=0.)
u_scipy = Variable('u_scipy', dtype=np.float32, initial=fieldset.U)
pset = pset_type['soa']['pset'](fieldset, pclass=SampleParticle, time=[0, time2], lon=[0.5, 0.5], lat=[0.5, 0.5], depth=[0.5, 0.5])
if time2 > 1:
failed = False
try:
pset.execute(SampleField, dt=0.)
except OutOfTimeError:
failed = True
assert failed
else:
pset.execute(SampleField, dt=0.)
assert np.allclose([p.u_kernel for p in pset], [p.u_scipy for p in pset])
assert isinstance(fieldset.U.data, da.core.Array)
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('tdim', [10, None])
def test_fieldset_from_xarray(pset_mode, tdim):
def generate_dataset(xdim, ydim, zdim=1, tdim=1):
lon = np.linspace(0., 12, xdim, dtype=np.float32)
lat = np.linspace(0., 12, ydim, dtype=np.float32)
depth = np.linspace(0., 20., zdim, dtype=np.float32)
if tdim:
time = np.linspace(0., 10, tdim, dtype=np.float64)
Uxr = np.ones((tdim, zdim, ydim, xdim), dtype=np.float32)
Vxr = np.ones((tdim, zdim, ydim, xdim), dtype=np.float32)
for t in range(Uxr.shape[0]):
Uxr[t, :, :, :] = t/10.
coords = {'lat': lat, 'lon': lon, 'depth': depth, 'time': time}
dims = ('time', 'depth', 'lat', 'lon')
else:
Uxr = np.ones((zdim, ydim, xdim), dtype=np.float32)
Vxr = np.ones((zdim, ydim, xdim), dtype=np.float32)
for z in range(Uxr.shape[0]):
Uxr[z, :, :] = z/2.
coords = {'lat': lat, 'lon': lon, 'depth': depth}
dims = ('depth', 'lat', 'lon')
return xr.Dataset({'Uxr': xr.DataArray(Uxr, coords=coords, dims=dims),
'Vxr': xr.DataArray(Vxr, coords=coords, dims=dims)})
ds = generate_dataset(3, 3, 2, tdim)
variables = {'U': 'Uxr', 'V': 'Vxr'}
if tdim:
dimensions = {'lat': 'lat', 'lon': 'lon', 'depth': 'depth', 'time': 'time'}
else:
dimensions = {'lat': 'lat', 'lon': 'lon', 'depth': 'depth'}
fieldset = FieldSet.from_xarray_dataset(ds, variables, dimensions, mesh='flat')
assert fieldset.U.creation_log == 'from_xarray_dataset'
pset = pset_type[pset_mode]['pset'](fieldset, JITParticle, 0, 0, depth=20)
pset.execute(AdvectionRK4, dt=1, runtime=10)
p0 = None
if pset_mode in ['soa', 'aos']:
p0 = pset[0]
elif pset_mode == 'nodes':
p0 = pset.begin().data
if tdim == 10:
assert np.allclose(p0.lon, 4.5) and np.allclose(p0.lat, 10)
else:
assert np.allclose(p0.lon, 5.0) and np.allclose(p0.lat, 10)
@pytest.mark.parametrize('pset_mode', pset_modes)
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
def test_fieldset_frompop(pset_mode, mode):
filenames = path.join(path.join(path.dirname(__file__), 'test_data'), 'POPtestdata_time.nc')
variables = {'U': 'U', 'V': 'V', 'W': 'W', 'T': 'T'}
dimensions = {'lon': 'lon', 'lat': 'lat', 'time': 'time'}
fieldset = FieldSet.from_pop(filenames, variables, dimensions, mesh='flat')
pset = pset_type[pset_mode]['pset'].from_list(fieldset, ptype[mode], lon=[3, 5, 1], lat=[3, 5, 1])
pset.execute(AdvectionRK4, runtime=3, dt=1)
@pytest.mark.parametrize('pset_mode', pset_modes)
def test_fieldset_from_data_gridtypes(pset_mode, xdim=20, ydim=10, zdim=4):
""" Simple test for fieldset initialisation from data. """
lon = np.linspace(0., 10., xdim, dtype=np.float32)
lat = np.linspace(0., 10., ydim, dtype=np.float32)
depth = np.linspace(0., 1., zdim, dtype=np.float32)
depth_s = np.ones((zdim, ydim, xdim))