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test_against_collect.py
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test_against_collect.py
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import pytest
import numpy.testing as npt
from xbout import open_boutdataset, collect as new_collect
from .utils_for_tests import create_bout_ds, create_bout_ds_list, METADATA_VARS
boutdata = pytest.importorskip("boutdata", reason="boutdata is not available")
old_collect = boutdata.collect
class TestAccuracyAgainstOldCollect:
def test_single_file(self, tmp_path_factory):
# Create temp directory for files
test_dir = tmp_path_factory.mktemp("test_data")
# Generate some test data
generated_ds = create_bout_ds(syn_data_type="linear")
generated_ds.to_netcdf(test_dir.joinpath("BOUT.dmp.0.nc"))
var = "n"
expected = old_collect(var, path=test_dir, xguards=True, yguards=False)
# Test against new standard - open_boutdataset
with pytest.warns(UserWarning):
ds = open_boutdataset(test_dir.joinpath("BOUT.dmp.0.nc"))
actual = ds[var].values
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
# Test against backwards compatible collect function
actual = new_collect(var, path=test_dir, xguards=True, yguards=False)
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
def test_multiple_files_along_x(self, tmp_path_factory):
# Create temp directory for files
test_dir = tmp_path_factory.mktemp("test_data")
# Generate some test data
ds_list, file_list = create_bout_ds_list(
"BOUT.dmp", nxpe=3, nype=1, syn_data_type="linear"
)
for temp_ds, file_name in zip(ds_list, file_list):
temp_ds.to_netcdf(test_dir.joinpath(file_name))
var = "n"
expected = old_collect(var, path=test_dir, prefix="BOUT.dmp", xguards=True)
# Test against new standard - open_boutdataset
with pytest.warns(UserWarning):
ds = open_boutdataset(test_dir.joinpath("BOUT.dmp.*.nc"))
actual = ds[var].values
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
# Test against backwards compatible collect function
actual = new_collect(var, path=test_dir, prefix="BOUT.dmp", xguards=True)
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
def test_multiple_files_along_y(self, tmp_path_factory):
# Create temp directory for files
test_dir = tmp_path_factory.mktemp("test_data")
# Generate some test data
ds_list, file_list = create_bout_ds_list(
"BOUT.dmp", nxpe=1, nype=3, syn_data_type="linear"
)
for temp_ds, file_name in zip(ds_list, file_list):
temp_ds.to_netcdf(test_dir.joinpath(file_name))
var = "n"
expected = old_collect(var, path=test_dir, prefix="BOUT.dmp", xguards=True)
# Test against new standard - .open_boutdataset
with pytest.warns(UserWarning):
ds = open_boutdataset(test_dir.joinpath("BOUT.dmp.*.nc"))
actual = ds[var].values
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
# Test against backwards compatible collect function
actual = new_collect(var, path=test_dir, prefix="BOUT.dmp", xguards=True)
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
def test_multiple_files_along_xy(self, tmp_path_factory):
# Create temp directory for files
test_dir = tmp_path_factory.mktemp("test_data")
# Generate some test data
ds_list, file_list = create_bout_ds_list(
"BOUT.dmp", nxpe=3, nype=3, syn_data_type="linear"
)
for temp_ds, file_name in zip(ds_list, file_list):
temp_ds.to_netcdf(test_dir.joinpath(file_name))
var = "n"
expected = old_collect(var, path=test_dir, prefix="BOUT.dmp", xguards=True)
# Test against new standard - .open_boutdataset
with pytest.warns(UserWarning):
ds = open_boutdataset(test_dir.joinpath("BOUT.dmp.*.nc"))
actual = ds[var].values
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
# Test against backwards compatible collect function
actual = new_collect(var, path=test_dir, prefix="BOUT.dmp", xguards=True)
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
def test_metadata(self, tmp_path_factory):
# Create temp directory for files
test_dir = tmp_path_factory.mktemp("test_data")
# Generate some test data
generated_ds = create_bout_ds(syn_data_type="linear")
generated_ds.to_netcdf(test_dir.joinpath("BOUT.dmp.0.nc"))
with pytest.warns(UserWarning):
ds = open_boutdataset(test_dir.joinpath("BOUT.dmp.*.nc"))
for v in METADATA_VARS:
expected = old_collect(v, path=test_dir)
# Check metadata against new standard - open_boutdataset
actual = ds.bout.metadata[v]
npt.assert_equal(actual, expected)
# Check against backwards compatible collect function
actual = new_collect(v, path=test_dir)
npt.assert_equal(actual, expected)
def test_new_collect_indexing_int(self, tmp_path_factory):
# Create temp directory for files
test_dir = tmp_path_factory.mktemp("test_data")
# Generate some test data
ds_list, file_list = create_bout_ds_list(
"BOUT.dmp", nxpe=3, nype=3, syn_data_type="linear"
)
for temp_ds, file_name in zip(ds_list, file_list):
temp_ds.to_netcdf(test_dir.joinpath(file_name))
var = "n"
indexers = ["tind", "xind", "yind", "zind"]
ind_arg = 0
for kwarg in indexers:
# Extracting a the first index of each dimension for comparison
expected = old_collect(var, path=test_dir, **{kwarg: ind_arg})
# Test against backwards compatible collect function
actual = new_collect(var, path=test_dir, **{kwarg: ind_arg})
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
def test_new_collect_indexing_list(self, tmp_path_factory):
# Create temp directory for files
test_dir = tmp_path_factory.mktemp("test_data")
# Generate some test data
ds_list, file_list = create_bout_ds_list(
"BOUT.dmp", nxpe=3, nype=3, syn_data_type="linear"
)
for temp_ds, file_name in zip(ds_list, file_list):
temp_ds.to_netcdf(test_dir.joinpath(file_name))
var = "n"
indexers = ["tind", "xind", "yind", "zind"]
ind_arg = [0, 4]
for kwarg in indexers:
# Extracting a the first index of each dimension for comparison
expected = old_collect(var, path=test_dir, **{kwarg: ind_arg})
# Test against backwards compatible collect function
actual = new_collect(var, path=test_dir, **{kwarg: ind_arg})
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
def test_new_collect_indexing_slice(self, tmp_path_factory):
# Create temp directory for files
test_dir = tmp_path_factory.mktemp("test_data")
# Generate some test data
ds_list, file_list = create_bout_ds_list(
"BOUT.dmp", nxpe=3, nype=3, syn_data_type="linear"
)
for temp_ds, file_name in zip(ds_list, file_list):
temp_ds.to_netcdf(test_dir.joinpath(file_name))
var = "n"
indexers = ["tind", "xind", "yind", "zind"]
ind_list = [slice(0, 4, 2), slice(0, 4)]
for kwarg in indexers:
for ind_arg in ind_list:
# Extracting a the first index of each dimension for comparison
expected = old_collect(var, path=test_dir, **{kwarg: ind_arg})
# Test against backwards compatible collect function
actual = new_collect(var, path=test_dir, **{kwarg: ind_arg})
assert expected.shape == actual.shape
npt.assert_equal(actual, expected)
@pytest.mark.skip
class test_speed_against_old_collect: ...