def test_read_raw_data(tmpdir):
"""Check our utility for reading raw data."""
from xmitgcm.utils import read_raw_data
shape = (2, 4)
for dtype in [np.dtype('f8'), np.dtype('f4'), np.dtype('i4')]:
# create some test data
testdata = np.zeros(shape, dtype)
# write to a file
datafile = tmpdir.join("tmp.data")
datafile.write_binary(testdata.tobytes())
fname = str(datafile)
# now test the function
data = read_raw_data(fname, dtype, shape)
np.testing.assert_allclose(data, testdata)
# interestingly, memmaps are also ndarrays, but not vice versa
assert isinstance(data, np.ndarray) and not isinstance(data, np.memmap)
# check memmap
mdata = read_raw_data(fname, dtype, shape, use_mmap=True)
assert isinstance(mdata, np.memmap)
# make sure errors are correct
wrongshape = (2, 5)
with pytest.raises(IOError):
_ = read_raw_data(fname, dtype, wrongshape)
def test_llc_facets_2d_to_compact(llc_mds_datadirs):
from xmitgcm.utils import llc_facets_2d_to_compact, get_extra_metadata
from xmitgcm.utils import rebuild_llc_facets, read_raw_data
from xmitgcm.utils import write_to_binary
from xmitgcm import open_mdsdataset
dirname, expected = llc_mds_datadirs
# open dataset
ds = open_mdsdataset(dirname,
iters=expected['test_iternum'],
geometry=expected['geometry'])
nt, nfaces, ny, nx = expected['shape']
md = get_extra_metadata(domain=expected['geometry'], nx=nx)
# split in facets
facets = rebuild_llc_facets(ds['XC'], md)
flatdata = llc_facets_2d_to_compact(facets, md)
# compare with raw data
raw = read_raw_data(dirname + '/XC.data', np.dtype('>f'), (nfaces, ny, nx))
flatraw = raw.flatten()
assert len(flatdata) == len(flatraw)
assert flatdata.min() == flatraw.min()
assert flatdata.max() == flatraw.max()
# write new file
write_to_binary(flatdata, 'tmp.bin', dtype=np.dtype('f'))
md5new = file_md5_checksum('tmp.bin')
md5old = file_md5_checksum(dirname + '/XC.data')
assert md5new == md5old
os.remove('tmp.bin')
def read_pigbin_xy(fname, ds):
"""Read a binary file with dimensions (YC,XC)
and return dataarray
Parameters
----------
fname : str
full path filename to binary file
ds : xarray Dataset
with coordinates to create the DataArray
Returns
-------
xda : xarray DataArray
"""
arr = read_raw_data(fname, np.float64(), ds.Depth.shape).byteswap()
return xr.DataArray(arr, ds.Depth.coords, ds.Depth.dims)
def read_pigbin_yz(fname, ds):
"""Read a binary file with dimensions (Z,YC)
and return dataarray
Parameters
----------
fname : str
full path filename to binary file
ds : xarray Dataset
with coordinates to create the DataArray
Returns
-------
xda : xarray DataArray
"""
arr = read_raw_data(fname, np.float64(),
[len(ds.Z), len(ds.YC)]).byteswap()
return xr.DataArray(arr, {'Z': ds.Z, 'YC': ds.YC}, ('Z', 'YC'))
def test_read_raw_data(tmpdir, dtype):
"""Check our utility for reading raw data."""
from xmitgcm.utils import read_raw_data
shape = (2, 4)
# create some test data
testdata = np.zeros(shape, dtype)
# write to a file
datafile = tmpdir.join("tmp.data")
datafile.write_binary(testdata.tobytes())
fname = str(datafile)
# now test the function
data = read_raw_data(fname, dtype, shape)
np.testing.assert_allclose(data, testdata)
# interestingly, memmaps are also ndarrays, but not vice versa
assert isinstance(data, np.ndarray) and not isinstance(data, np.memmap)
# check memmap
mdata = read_raw_data(fname, dtype, shape, use_mmap=True)
assert isinstance(mdata, np.memmap)
# make sure errors are correct
wrongshape = (2, 5)
with pytest.raises(IOError):
read_raw_data(fname, dtype, wrongshape)
# test optional functionalities
shape = (5, 15, 10)
shape_subset = (15, 10)
testdata = np.zeros(shape, dtype)
# create some test data
x = np.arange(shape[0], dtype=dtype)
for k in np.arange(shape[0]):
testdata[k, :, :] = x[k]
# write to a file
datafile = tmpdir.join("tmp.data")
datafile.write_binary(testdata.tobytes())
fname = str(datafile)
# now test the function
for k in np.arange(shape[0]):
offset = (k * shape[1] * shape[2] * dtype.itemsize)
data = read_raw_data(fname,
dtype,
shape_subset,
offset=offset,
partial_read=True)
np.testing.assert_allclose(data, testdata[k, :, :])
assert isinstance(data, np.ndarray) and not isinstance(data, np.memmap)
# check memmap
mdata = read_raw_data(fname,
dtype,
shape_subset,
offset=offset,
partial_read=True,
use_mmap=True)
assert isinstance(mdata, np.memmap)
# test it breaks when it should
with pytest.raises(IOError):
# read with wrong shape
read_raw_data(fname,
dtype,
shape_subset,
offset=0,
partial_read=False)
with pytest.raises(IOError):
read_raw_data(fname,
dtype,
shape_subset,
offset=0,
partial_read=False,
use_mmap=True)
with pytest.raises(ValueError):
# use offset when trying to read global file
read_raw_data(fname,
dtype,
shape_subset,
offset=4,
partial_read=False)
with pytest.raises(ValueError):
read_raw_data(fname,
dtype,
shape_subset,
offset=4,
partial_read=False,
use_mmap=True)
# offset is too big
with pytest.raises(ValueError):
read_raw_data(fname,
dtype,
shape,
offset=(shape[0] * shape[1] * shape[2] *
dtype.itemsize),
partial_read=True)
with pytest.raises(ValueError):
read_raw_data(fname,
dtype,
shape,
offset=(shape[0] * shape[1] * shape[2] *
dtype.itemsize),
partial_read=True,
use_mmap=True)
def test_get_grid_from_input(all_grid_datadirs, usedask):
from xmitgcm.utils import get_grid_from_input, get_extra_metadata
from xmitgcm.utils import read_raw_data
dirname, expected = all_grid_datadirs
md = get_extra_metadata(domain=expected['domain'], nx=expected['nx'])
ds = get_grid_from_input(dirname + '/' + expected['gridfile'],
geometry=expected['geometry'],
dtype=np.dtype('d'),
endian='>',
use_dask=usedask,
extra_metadata=md)
# test types
assert type(ds) == xarray.Dataset
assert type(ds['XC']) == xarray.core.dataarray.DataArray
if usedask:
ds.load()
# check all variables are in
expected_variables = [
'XC', 'YC', 'DXF', 'DYF', 'RAC', 'XG', 'YG', 'DXV', 'DYU', 'RAZ',
'DXC', 'DYC', 'RAW', 'RAS', 'DXG', 'DYG'
]
for var in expected_variables:
assert type(ds[var]) == xarray.core.dataarray.DataArray
assert ds[var].values.shape == expected['shape']
# check we don't leave points behind
if expected['geometry'] == 'llc':
nx = expected['nx'] + 1
nvars = len(expected_variables)
sizeofd = 8
grid = expected['gridfile']
grid1 = dirname + '/' + grid.replace('<NFACET>', '001')
grid2 = dirname + '/' + grid.replace('<NFACET>', '002')
grid3 = dirname + '/' + grid.replace('<NFACET>', '003')
grid4 = dirname + '/' + grid.replace('<NFACET>', '004')
grid5 = dirname + '/' + grid.replace('<NFACET>', '005')
size1 = os.path.getsize(grid1)
size2 = os.path.getsize(grid2)
size3 = os.path.getsize(grid3)
size4 = os.path.getsize(grid4)
size5 = os.path.getsize(grid5)
ny1 = int(size1 / sizeofd / nvars / nx)
ny2 = int(size2 / sizeofd / nvars / nx)
ny3 = int(size3 / sizeofd / nvars / nx)
ny4 = int(size4 / sizeofd / nvars / nx)
ny5 = int(size5 / sizeofd / nvars / nx)
xc1 = read_raw_data(grid1,
dtype=np.dtype('>d'),
shape=(ny1, nx),
partial_read=True)
xc2 = read_raw_data(grid2,
dtype=np.dtype('>d'),
shape=(ny2, nx),
partial_read=True)
xc3 = read_raw_data(grid3,
dtype=np.dtype('>d'),
shape=(ny3, nx),
partial_read=True)
xc4 = read_raw_data(grid4,
dtype=np.dtype('>d'),
shape=(ny4, nx),
order='F',
partial_read=True)
xc5 = read_raw_data(grid5,
dtype=np.dtype('>d'),
shape=(ny5, nx),
order='F',
partial_read=True)
yc1 = read_raw_data(grid1,
dtype=np.dtype('>d'),
shape=(ny1, nx),
partial_read=True,
offset=nx * ny1 * sizeofd)
yc2 = read_raw_data(grid2,
dtype=np.dtype('>d'),
shape=(ny2, nx),
partial_read=True,
offset=nx * ny2 * sizeofd)
yc3 = read_raw_data(grid3,
dtype=np.dtype('>d'),
shape=(ny3, nx),
partial_read=True,
offset=nx * ny3 * sizeofd)
yc4 = read_raw_data(grid4,
dtype=np.dtype('>d'),
shape=(ny4, nx),
order='F',
partial_read=True,
offset=nx * ny4 * sizeofd)
yc5 = read_raw_data(grid5,
dtype=np.dtype('>d'),
shape=(ny5, nx),
order='F',
partial_read=True,
offset=nx * ny5 * sizeofd)
xc = np.concatenate([
xc1[:-1, :-1].flatten(), xc2[:-1, :-1].flatten(),
xc3[:-1, :-1].flatten(), xc4[:-1, :-1].flatten(),
xc5[:-1, :-1].flatten()
])
yc = np.concatenate([
yc1[:-1, :-1].flatten(), yc2[:-1, :-1].flatten(),
yc3[:-1, :-1].flatten(), yc4[:-1, :-1].flatten(),
yc5[:-1, :-1].flatten()
])
xc_from_ds = ds['XC'].values.flatten()
yc_from_ds = ds['YC'].values.flatten()
assert xc.min() == xc_from_ds.min()
assert xc.max() == xc_from_ds.max()
assert yc.min() == yc_from_ds.min()
assert yc.max() == yc_from_ds.max()
# passing llc without metadata should fail
if expected['geometry'] == 'llc':
with pytest.raises(ValueError):
ds = get_grid_from_input(dirname + '/' + expected['gridfile'],
geometry=expected['geometry'],
dtype=np.dtype('d'),
endian='>',
use_dask=False,
extra_metadata=None)
