def load_array(filename, meta=None, band_specs=None, reader=None):
'''Create ElmStore from HDF4 / 5 or NetCDF files or TIF directories
Parameters:
:filename: filename (HDF4 / 5 or NetCDF) or directory name (TIF)
:meta: meta data from "filename" already loaded
:band_specs: list of strings or earthio.BandSpec objects
:reader: named reader from earthio - one of: ('tif', 'hdf4', 'hdf5', 'netcdf')
Returns:
:es: ElmStore (xarray.Dataset) with bands specified by band_specs as DataArrays in "data_vars" attribute
'''
ftype = reader or _find_file_type(filename)
if meta is None:
if ftype == 'tif':
meta = _load_meta(filename, ftype, band_specs=band_specs)
else:
meta = _load_meta(filename, ftype)
if ftype == 'netcdf':
return load_netcdf_array(filename, meta, band_specs=band_specs)
elif ftype == 'hdf5':
return load_hdf5_array(filename, meta, band_specs=band_specs)
elif ftype == 'hdf4':
return load_hdf4_array(filename, meta, band_specs=band_specs)
elif ftype == 'tif':
return load_dir_of_tifs_array(filename, meta, band_specs=band_specs)
elif ftype == 'hdf':
try:
es = load_hdf4_array(filename, meta, band_specs=band_specs)
except Exception as e:
logger.info(
'NOTE: guessed HDF4 type. Failed: {}. \nTrying HDF5'.format(
repr(e)))
es = load_hdf5_array(filename, meta, band_specs=band_specs)
return es
def test_reader_kwargs():
sub_dataset_names, band_specs = get_band_specs(HDF5_FILES[0])
band_specs_kwargs = []
for b in band_specs:
b = attr.asdict(b)
b['buf_xsize'], b['buf_ysize'] = 200, 300
band_specs_kwargs.append(BandSpec(**b))
meta = load_hdf5_meta(HDF5_FILES[0])
es = load_hdf5_array(HDF5_FILES[0], meta, band_specs_kwargs)
for b in es.band_order:
assert getattr(es, b).values.shape == (300, 200)
def test_read_array(filename):
sub_dataset_names, band_specs = get_band_specs(filename)
meta = load_hdf5_meta(filename)
es = load_hdf5_array(filename, meta, band_specs)
assert len(es.data_vars) == len(sub_dataset_names)
for band in es.data_vars:
sample = getattr(es, band)
assert sample.y.size == 1800
assert sample.x.size == 3600
assert len(es.data_vars) == len(band_specs)
assertions_on_band_metadata(sample.attrs)
def test_reader_kwargs():
sub_dataset_names, layer_specs = get_layer_specs(HDF5_FILES[0])
layer_specs_kwargs = []
for b in layer_specs:
b = b.get_params()
b['buf_xsize'], b['buf_ysize'] = 200, 300
layer_specs_kwargs.append(LayerSpec(**b))
meta = load_hdf5_meta(HDF5_FILES[0])
dset = load_hdf5_array(HDF5_FILES[0], meta, layer_specs_kwargs)
for b in dset.layer_order:
assert getattr(dset, b).values.shape == (300, 200)
def test_read_array(filename):
sub_dataset_names, layer_specs = get_layer_specs(filename)
meta = load_hdf5_meta(filename)
dset = load_hdf5_array(filename, meta, layer_specs)
assert len(dset.data_vars) == len(sub_dataset_names)
for layer in dset.data_vars:
sample = getattr(dset, layer)
assert sample.y.size == 1800
assert sample.x.size == 3600
assert len(dset.data_vars) == len(layer_specs)
assertions_on_layer_metadata(sample.attrs)