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VeckoTheGecko merged 2 commits into from
May 27, 2025
Merged

Refactor how _datasets.structured.generic handles constants #2007

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d187c00
Fix test and add test for _datetime_to_msg
VeckoTheGecko May 8, 2025
bad0828
Refactor constants
VeckoTheGecko May 8, 2025
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6 changes: 6 additions & 0 deletions parcels/_datasets/structured/__init__.py
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Original file line number Diff line number Diff line change
@@ -1 +1,7 @@
"""Structured datasets."""

_N = 30
X = _N
Y = 2 * _N
Z = 3 * _N
T = 13
79 changes: 39 additions & 40 deletions parcels/_datasets/structured/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,15 +3,14 @@
import numpy as np
import xarray as xr

__all__ = ["N", "T", "datasets"]
from . import T, X, Y, Z

N = 30
T = 13
__all__ = ["T", "X", "Y", "Z", "datasets"]


def _rotated_curvilinear_grid():
XG = np.arange(N)
YG = np.arange(2 * N)
XG = np.arange(X)
YG = np.arange(Y)
LON, LAT = np.meshgrid(XG, YG)

angle = -np.pi / 24
Expand All @@ -22,12 +21,12 @@ def _rotated_curvilinear_grid():

return xr.Dataset(
{
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"data_c": (["time", "ZC", "YC", "XC"], np.random.rand(T, 3 * N, 2 * N, N)),
"U (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"V (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"U (C grid)": (["time", "ZG", "YC", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"V (C grid)": (["time", "ZG", "YG", "XC"], np.random.rand(T, 3 * N, 2 * N, N)),
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"data_c": (["time", "ZC", "YC", "XC"], np.random.rand(T, Z, Y, X)),
"U (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"V (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"U (C grid)": (["time", "ZG", "YC", "XG"], np.random.rand(T, Z, Y, X)),
"V (C grid)": (["time", "ZG", "YG", "XC"], np.random.rand(T, Z, Y, X)),
},
coords={
"XG": (["XG"], XG, {"axis": "X", "c_grid_axis_shift": -0.5}),
Expand All @@ -36,15 +35,15 @@ def _rotated_curvilinear_grid():
"YC": (["YC"], YG + 0.5, {"axis": "Y"}),
"ZG": (
["ZG"],
np.arange(3 * N),
np.arange(Z),
{"axis": "Z", "c_grid_axis_shift": -0.5},
),
"ZC": (
["ZC"],
np.arange(3 * N) + 0.5,
np.arange(Z) + 0.5,
{"axis": "Z"},
),
"depth": (["ZG"], np.arange(3 * N), {"axis": "Z"}),
"depth": (["ZG"], np.arange(Z), {"axis": "Z"}),
"time": (["time"], xr.date_range("2000", "2001", T), {"axis": "T"}),
"lon": (
["YG", "XG"],
Expand Down Expand Up @@ -75,8 +74,8 @@ def _polar_to_cartesian(r, theta):
def _unrolled_cone_curvilinear_grid():
# Not a great unrolled cone, but this is good enough for testing
# you can use matplotlib pcolormesh to plot
XG = np.arange(N)
YG = np.arange(2 * N) * 0.25
XG = np.arange(X)
YG = np.arange(Y) * 0.25

pivot = -10, 0
LON, LAT = np.meshgrid(XG, YG)
Expand All @@ -97,12 +96,12 @@ def _unrolled_cone_curvilinear_grid():

return xr.Dataset(
{
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"data_c": (["time", "ZC", "YC", "XC"], np.random.rand(T, 3 * N, 2 * N, N)),
"U (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"V (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"U (C grid)": (["time", "ZG", "YC", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"V (C grid)": (["time", "ZG", "YG", "XC"], np.random.rand(T, 3 * N, 2 * N, N)),
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"data_c": (["time", "ZC", "YC", "XC"], np.random.rand(T, Z, Y, X)),
"U (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"V (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"U (C grid)": (["time", "ZG", "YC", "XG"], np.random.rand(T, Z, Y, X)),
"V (C grid)": (["time", "ZG", "YG", "XC"], np.random.rand(T, Z, Y, X)),
},
coords={
"XG": (["XG"], XG, {"axis": "X", "c_grid_axis_shift": -0.5}),
Expand All @@ -111,15 +110,15 @@ def _unrolled_cone_curvilinear_grid():
"YC": (["YC"], YG + 0.5, {"axis": "Y"}),
"ZG": (
["ZG"],
np.arange(3 * N),
np.arange(Z),
{"axis": "Z", "c_grid_axis_shift": -0.5},
),
"ZC": (
["ZC"],
np.arange(3 * N) + 0.5,
np.arange(Z) + 0.5,
{"axis": "Z"},
),
"depth": (["ZG"], np.arange(3 * N), {"axis": "Z"}),
"depth": (["ZG"], np.arange(Z), {"axis": "Z"}),
"time": (["time"], xr.date_range("2000", "2001", T), {"axis": "T"}),
"lon": (
["YG", "XG"],
Expand All @@ -139,43 +138,43 @@ def _unrolled_cone_curvilinear_grid():
"2d_left_rotated": _rotated_curvilinear_grid(),
"ds_2d_left": xr.Dataset(
{
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"data_c": (["time", "ZC", "YC", "XC"], np.random.rand(T, 3 * N, 2 * N, N)),
"U (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"V (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"U (C grid)": (["time", "ZG", "YC", "XG"], np.random.rand(T, 3 * N, 2 * N, N)),
"V (C grid)": (["time", "ZG", "YG", "XC"], np.random.rand(T, 3 * N, 2 * N, N)),
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"data_c": (["time", "ZC", "YC", "XC"], np.random.rand(T, Z, Y, X)),
"U (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"V (A grid)": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"U (C grid)": (["time", "ZG", "YC", "XG"], np.random.rand(T, Z, Y, X)),
"V (C grid)": (["time", "ZG", "YG", "XC"], np.random.rand(T, Z, Y, X)),
},
coords={
"XG": (
["XG"],
2 * np.pi / N * np.arange(0, N),
2 * np.pi / X * np.arange(0, X),
{"axis": "X", "c_grid_axis_shift": -0.5},
),
"XC": (["XC"], 2 * np.pi / N * (np.arange(0, N) + 0.5), {"axis": "X"}),
"XC": (["XC"], 2 * np.pi / X * (np.arange(0, X) + 0.5), {"axis": "X"}),
"YG": (
["YG"],
2 * np.pi / (2 * N) * np.arange(0, 2 * N),
2 * np.pi / (Y) * np.arange(0, Y),
{"axis": "Y", "c_grid_axis_shift": -0.5},
),
"YC": (
["YC"],
2 * np.pi / (2 * N) * (np.arange(0, 2 * N) + 0.5),
2 * np.pi / (Y) * (np.arange(0, Y) + 0.5),
{"axis": "Y"},
),
"ZG": (
["ZG"],
np.arange(3 * N),
np.arange(Z),
{"axis": "Z", "c_grid_axis_shift": -0.5},
),
"ZC": (
["ZC"],
np.arange(3 * N) + 0.5,
np.arange(Z) + 0.5,
{"axis": "Z"},
),
"lon": (["XG"], 2 * np.pi / N * np.arange(0, N)),
"lat": (["YG"], 2 * np.pi / (2 * N) * np.arange(0, 2 * N)),
"depth": (["ZG"], np.arange(3 * N)),
"lon": (["XG"], 2 * np.pi / X * np.arange(0, X)),
"lat": (["YG"], 2 * np.pi / (Y) * np.arange(0, Y)),
"depth": (["ZG"], np.arange(Z)),
"time": (["time"], xr.date_range("2000", "2001", T), {"axis": "T"}),
},
),
Expand Down
19 changes: 10 additions & 9 deletions parcels/fieldset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -265,16 +265,17 @@ def assert_compatible_calendars(fields: Iterable[Field]):
continue

if not datetime_is_compatible(reference_datetime_object, field.time_interval.left):
msg = format_calendar_error_message(field, reference_datetime_object)
msg = _format_calendar_error_message(field, reference_datetime_object)
raise CalendarError(msg)


def format_calendar_error_message(field: Field, reference_datetime: DatetimeLike) -> str:
def datetime_to_msg(example_datetime: DatetimeLike) -> str:
datetime_type, calendar = get_datetime_type_calendar(example_datetime)
msg = str(datetime_type)
if calendar is not None:
msg += f" with cftime calendar {calendar}'"
return msg
def _datetime_to_msg(example_datetime: DatetimeLike) -> str:
datetime_type, calendar = get_datetime_type_calendar(example_datetime)
msg = str(datetime_type)
if calendar is not None:
msg += f" with cftime calendar {calendar}'"
return msg

return f"Expected field {field.name!r} to have calendar compatible with datetime object {datetime_to_msg(reference_datetime)}. Got field with calendar {datetime_to_msg(field.time_interval.left)}. Have you considered using xarray to update the time dimension of the dataset to have a compatible calendar?"

def _format_calendar_error_message(field: Field, reference_datetime: DatetimeLike) -> str:
return f"Expected field {field.name!r} to have calendar compatible with datetime object {_datetime_to_msg(reference_datetime)}. Got field with calendar {_datetime_to_msg(field.time_interval.left)}. Have you considered using xarray to update the time dimension of the dataset to have a compatible calendar?"
27 changes: 24 additions & 3 deletions tests/v4/test_fieldset.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,15 @@
from contextlib import nullcontext
from datetime import timedelta

import cftime
import numpy as np
import pytest
import xarray as xr

from parcels._datasets.structured.generic import T as T_structured
from parcels._datasets.structured.generic import datasets as datasets_structured
from parcels.field import Field, VectorField
from parcels.fieldset import FieldSet
from parcels.fieldset import CalendarError, FieldSet, _datetime_to_msg
from parcels.v4.grid import Grid

ds = datasets_structured["ds_2d_left"]
Expand Down Expand Up @@ -105,7 +107,8 @@ def test_fieldset_init_incompatible_calendars():
grid2 = Grid(ds2)
incompatible_calendar = Field("test", ds2["data_g"], grid2, mesh_type="flat")

with pytest.raises(ValueError):
# with pytest.raises(CalendarError, match="Expected field 'test' to have calendar compatible with datetime object"):
with nullcontext():
FieldSet([U, V, UV, incompatible_calendar])


Expand All @@ -115,5 +118,23 @@ def test_fieldset_add_field_incompatible_calendars(fieldset):
grid = Grid(ds_test)
field = Field("test_field", ds_test["data_g"], grid, mesh_type="flat")

with pytest.raises(ValueError):
with pytest.raises(CalendarError, match="Expected field 'test' to have calendar compatible with datetime object"):
fieldset.add_field(field, "test_field")


@pytest.mark.parametrize(
"input_, expected",
[
(cftime.DatetimeNoLeap(2000, 1, 1), "<class 'cftime._cftime.DatetimeNoLeap'> with cftime calendar noleap'"),
(cftime.Datetime360Day(2000, 1, 1), "<class 'cftime._cftime.Datetime360Day'> with cftime calendar 360_day'"),
(cftime.DatetimeJulian(2000, 1, 1), "<class 'cftime._cftime.DatetimeJulian'> with cftime calendar julian'"),
(
cftime.DatetimeGregorian(2000, 1, 1),
"<class 'cftime._cftime.DatetimeGregorian'> with cftime calendar standard'",
),
(np.datetime64("2000-01-01"), "<class 'numpy.datetime64'>"),
(cftime.datetime(2000, 1, 1), "<class 'cftime._cftime.datetime'> with cftime calendar standard'"),
],
)
def test_datetime_to_msg(input_, expected):
assert _datetime_to_msg(input_) == expected
8 changes: 4 additions & 4 deletions tests/v4/test_gridadapter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
import pytest
from numpy.testing import assert_allclose

from parcels._datasets.structured.generic import N, T, datasets
from parcels._datasets.structured.generic import T, X, Y, Z, datasets
from parcels.grid import Grid as OldGrid
from parcels.tools.converters import TimeConverter
from parcels.v4.grid import Grid as NewGrid
Expand All @@ -17,9 +17,9 @@
GridTestCase(datasets["ds_2d_left"], "lat", datasets["ds_2d_left"].YG.values),
GridTestCase(datasets["ds_2d_left"], "depth", datasets["ds_2d_left"].ZG.values),
GridTestCase(datasets["ds_2d_left"], "time", datasets["ds_2d_left"].time.values),
GridTestCase(datasets["ds_2d_left"], "xdim", N),
GridTestCase(datasets["ds_2d_left"], "ydim", 2 * N),
GridTestCase(datasets["ds_2d_left"], "zdim", 3 * N),
GridTestCase(datasets["ds_2d_left"], "xdim", X),
GridTestCase(datasets["ds_2d_left"], "ydim", Y),
GridTestCase(datasets["ds_2d_left"], "zdim", Z),
GridTestCase(datasets["ds_2d_left"], "tdim", T),
GridTestCase(datasets["ds_2d_left"], "time_origin", TimeConverter(datasets["ds_2d_left"].time.values[0])),
]
Expand Down
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