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test_fieldset.py
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test_fieldset.py
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import datetime
import gc
import os
import sys
from datetime import timedelta as delta
from os import path
import cftime
import dask
import dask.array as da
import numpy as np
import psutil
import pytest
import xarray as xr
from parcels import ( # noqa
AdvectionRK4,
AdvectionRK4_3D,
FieldSet,
JITParticle,
ParticleSet,
RectilinearZGrid,
ScipyParticle,
TimeExtrapolationError,
Variable,
)
from parcels.field import Field, VectorField
from parcels.fieldfilebuffer import DaskFileBuffer
from parcels.tools.converters import (
GeographicPolar,
TimeConverter,
UnitConverter,
_get_cftime_calendars,
_get_cftime_datetimes,
)
ptype = {'scipy': ScipyParticle, 'jit': JITParticle}
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
def test_fieldset_vmin_vmax():
data, dimensions = generate_fieldset(11, 11)
fieldset = FieldSet.from_data(data, dimensions, vmin=3, vmax=7)
assert np.isclose(np.amin(fieldset.U.data[fieldset.U.data > 0.]), 3)
assert np.isclose(np.amax(fieldset.U.data), 7)
@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)
def test_field_from_netcdf_variables():
data_path = path.join(path.dirname(__file__), 'test_data/')
filename = data_path + 'perlinfieldsU.nc'
dims = {'lon': 'x', 'lat': 'y'}
variable = 'vozocrtx'
f1 = Field.from_netcdf(filename, variable, dims)
variable = ('U', 'vozocrtx')
f2 = Field.from_netcdf(filename, variable, dims)
variable = {'U': 'vozocrtx'}
f3 = Field.from_netcdf(filename, variable, dims)
assert np.allclose(f1.data, f2.data, atol=1e-12)
assert np.allclose(f1.data, f3.data, atol=1e-12)
failed = False
try:
variable = {'U': 'vozocrtx', 'nav_lat': 'nav_lat'} # multiple variables will fail
f3 = Field.from_netcdf(filename, variable, dims)
except AssertionError:
failed = True
assert failed
@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
@pytest.mark.parametrize('with_timestamps', [True, False])
def test_field_from_netcdf(with_timestamps):
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'}
if with_timestamps:
timestamp_types = [[[2]], [[np.datetime64('2000-01-01')]]]
for timestamps in timestamp_types:
Field.from_netcdf(filenames, variable, dimensions, interp_method='cgrid_velocity', timestamps=timestamps)
else:
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 = ParticleSet(fieldset, ptype[mode], lon=1, lat=2)
failed = False
try:
pset.execute(AdvectionRK4, runtime=2)
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('fieldtype', ['normal', 'vector'])
def test_add_field_after_pset(fieldtype):
data, dimensions = generate_fieldset(100, 100)
fieldset = FieldSet.from_data(data, dimensions)
pset = ParticleSet(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")
fields = [f for f in fieldset_file.get_fields() if isinstance(f, Field)]
assert len(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))
def test_curv_fieldset_add_periodic_halo():
fname = path.join(path.dirname(__file__), 'test_data', 'mask_nemo_cross_180lon.nc')
filenames = {'dx': fname, 'dy': fname, 'mesh_mask': fname}
variables = {'dx': 'e1u', 'dy': 'e1v'}
dimensions = {'dx': {'lon': 'glamu', 'lat': 'gphiu'},
'dy': {'lon': 'glamu', 'lat': 'gphiu'}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions)
fieldset.add_periodic_halo(zonal=3, meridional=2)
@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('mode', ['scipy', 'jit'])
def test_fieldset_constant(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 = ParticleSet.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('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('swapUV', [False, True])
def test_vector_fields(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 = ParticleSet.from_line(fieldset, size=1, pclass=ptype[mode], start=(0.5, 0.5), finish=(0.5, 0.5))
pset.execute(AdvectionRK4, dt=1, runtime=2)
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('mode', ['scipy', 'jit'])
def test_add_second_vector_field(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_dlon += u * particle.dt # noqa
particle_dlat += v * particle.dt # noqa
pset = ParticleSet(fieldset, pclass=ptype[mode], lon=0.5, lat=0.5)
pset.execute(AdvectionRK4+pset.Kernel(SampleUV2), dt=1, runtime=2)
assert abs(pset.lon[0] - 2.5) < 1e-9
assert abs(pset.lat[0] - .5) < 1e-9
def test_fieldset_write(tmpdir):
filepath = tmpdir.join("fieldset_write.zarr")
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):
tmp1, tmp2 = fieldset.UV[particle] # noqa
fieldset.U.data[particle.ti, particle.yi, particle.xi] += 1
fieldset.U.grid.time[0] = time
pset = ParticleSet(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('.zarr', '_0005U.nc'))
assert np.allclose(fieldset.U.data, da['U'].values, atol=1.)
@pytest.mark.parametrize('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('time_periodic', [4*86400.0, False])
@pytest.mark.parametrize('dt', [-3600, 3600])
@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(mode, time_periodic, dt, chunksize, with_GC):
if time_periodic and dt < 0:
return # time_periodic does not work in backward-time mode
if chunksize == 'auto':
dask.config.set({'array.chunk-size': '2MiB'})
else:
dask.config.set({'array.chunk-size': '128MiB'})
class PerformanceLog():
samples = []
memory_steps = []
_iter = 0
def advance(self):
process = psutil.Process(os.getpid())
self.memory_steps.append(process.memory_info().rss)
self.samples.append(self._iter)
self._iter += 1
def perIterGC():
gc.collect()
def periodicBoundaryConditions(particle, fieldset, time):
while particle.lon > 180.:
particle_dlon -= 360. # noqa
while particle.lon < -180.:
particle_dlon += 360. # noqa
while particle.lat > 90.:
particle_dlat -= 180. # noqa
while particle.lat < -90.:
particle_dlat += 180. # noqa
process = psutil.Process(os.getpid())
mem_0 = process.memory_info().rss
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)
pset = ParticleSet(fieldset=fieldset, pclass=ptype[mode], lon=[0.5, ], lat=[0.5, ])
mem_0 = process.memory_info().rss
mem_exhausted = False
try:
pset.execute(pset.Kernel(AdvectionRK4)+periodicBoundaryConditions, dt=dt, runtime=delta(days=7), postIterationCallbacks=postProcessFuncs, callbackdt=delta(hours=12))
except MemoryError:
mem_exhausted = True
mem_steps_np = np.array(perflog.memory_steps)
if with_GC:
assert np.allclose(mem_steps_np[8:], perflog.memory_steps[-1], rtol=0.01)
if (chunksize is not False or with_GC) and mode != 'scipy':
assert np.all((mem_steps_np-mem_0) <= 5275648) # represents 4 x [U|V] * sizeof(field data) + 562816
assert not mem_exhausted
@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(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 = ParticleSet.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('mode', ['scipy', 'jit'])
@pytest.mark.parametrize('use_xarray', [True, False])
@pytest.mark.parametrize('time_periodic', [86400., False])
@pytest.mark.parametrize('dt_sign', [-1, 1])
def test_periodic(mode, use_xarray, 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
D = np.ones((2, 2), dtype=np.float32) # adding non-timevarying field
full_dims = {'lon': lon, 'lat': lat, 'depth': depth, 'time': time}
dimensions = {'U': full_dims, 'V': full_dims, 'W': full_dims, 'temp': full_dims, 'D': {'lon': lon, 'lat': lat}}
if use_xarray:
coords = {'lat': lat, 'lon': lon, 'depth': depth, 'time': time}
variables = {'U': 'Uxr', 'V': 'Vxr', 'W': 'Wxr', 'temp': 'Txr', 'D': 'Dxr'}
dimnames = {'lon': 'lon', 'lat': 'lat', 'depth': 'depth', 'time': 'time'}
ds = xr.Dataset({'Uxr': xr.DataArray(U, coords=coords, dims=('lon', 'lat', 'depth', 'time')),
'Vxr': xr.DataArray(V, coords=coords, dims=('lon', 'lat', 'depth', 'time')),
'Wxr': xr.DataArray(W, coords=coords, dims=('lon', 'lat', 'depth', 'time')),
'Txr': xr.DataArray(temp, coords=coords, dims=('lon', 'lat', 'depth', 'time')),
'Dxr': xr.DataArray(D, coords={'lat': lat, 'lon': lon}, dims=('lon', 'lat'))})
fieldset = FieldSet.from_xarray_dataset(ds, variables,
{'U': dimnames, 'V': dimnames, 'W': dimnames, 'temp': dimnames,
'D': {'lon': 'lon', 'lat': 'lat'}},
time_periodic=time_periodic, transpose=True, allow_time_extrapolation=True)
else:
data = {'U': U, 'V': V, 'W': W, 'temp': temp, 'D': D}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat', time_periodic=time_periodic, transpose=True, allow_time_extrapolation=True)
def sampleTemp(particle, fieldset, time):
particle.temp = fieldset.temp[time, particle.depth, particle.lat, particle.lon]
# test if we can interpolate UV and UVW together
(particle.u1, particle.v1) = fieldset.UV[time, particle.depth, particle.lat, particle.lon]
(particle.u2, particle.v2, w_) = fieldset.UVW[time, particle.depth, particle.lat, particle.lon]
# test if we can sample a non-timevarying field too
particle.d = fieldset.D[0, 0, particle.lat, particle.lon]
MyParticle = ptype[mode].add_variables([
Variable('temp', dtype=np.float32, initial=20.),
Variable('u1', dtype=np.float32, initial=0.),
Variable('u2', dtype=np.float32, initial=0.),
Variable('v1', dtype=np.float32, initial=0.),
Variable('v2', dtype=np.float32, initial=0.),
Variable('d', dtype=np.float32, initial=0.),
])
pset = ParticleSet.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))
if time_periodic is not False:
t = pset.time[0]
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, pset.temp[0], atol=1e-5)
assert np.allclose(pset.u1[0], pset.u2[0])
assert np.allclose(pset.v1[0], pset.v2[0])
assert np.allclose(pset.d[0], 1.)
@pytest.mark.parametrize('fail', [False, pytest.param(True, marks=pytest.mark.xfail(strict=True))])
def test_fieldset_defer_loading_with_diff_time_origin(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 = ParticleSet.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('zdim', [2, 8])
@pytest.mark.parametrize('scale_fac', [0.2, 4, 1])
def test_fieldset_defer_loading_function(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 = ParticleSet(fieldset, JITParticle, 0, 0)
def DoNothing(particle, fieldset, time):
pass
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, particle.v_kernel = fieldset.UV[time, particle.depth, particle.lat, particle.lon]
SampleParticle = JITParticle.add_variables([
Variable('u_kernel', dtype=np.float32, initial=0.),
Variable('v_kernel', dtype=np.float32, initial=0.),
Variable('u_scipy', dtype=np.float32, initial=0.)])
pset = ParticleSet(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, runtime=10)
except TimeExtrapolationError:
failed = True
assert failed
else:
pset.execute(SampleField, runtime=1)
assert np.allclose([p.u_kernel for p in pset], [p.u_scipy for p in pset], atol=1e-5)
assert isinstance(fieldset.U.data, da.core.Array)
@pytest.mark.parametrize('tdim', [10, None])
def test_fieldset_from_xarray(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'