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_read_array():
meta = load_dir_of_tifs_meta(TIF_DIR, layer_specs)
dset = load_dir_of_tifs_array(TIF_DIR, meta, layer_specs)
for var in dset.data_vars:
sample = getattr(dset, var)
mean_y = np.mean(sample.y)
mean_x = np.mean(sample.x)
layer_names = np.array([b.name for b in layer_specs])
assertions_on_layer_metadata(sample.attrs)
def test_reader_kwargs():
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_dir_of_tifs_meta(TIF_DIR, layer_specs_kwargs)
dset = load_dir_of_tifs_array(TIF_DIR, meta, layer_specs_kwargs)
for b in dset.layer_order:
assert getattr(dset, b).values.shape == (300, 200)
def test_reader_kwargs():
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_dir_of_tifs_meta(TIF_DIR, band_specs_kwargs)
es = load_dir_of_tifs_array(TIF_DIR, meta, band_specs_kwargs)
for b in es.band_order:
assert getattr(es, b).values.shape == (300, 200)
def test_read_array():
meta = load_dir_of_tifs_meta(TIF_DIR, band_specs)
es = load_dir_of_tifs_array(TIF_DIR, meta, band_specs)
for var in es.data_vars:
sample = getattr(es, var)
mean_y = np.mean(sample.y)
mean_x = np.mean(sample.x)
band_names = np.array([b.name for b in band_specs])
assert sorted((mean_x, sample.canvas.bounds.left,
sample.canvas.bounds.right))[1] == mean_x
assert sorted((mean_y, sample.canvas.bounds.top,
sample.canvas.bounds.bottom))[1] == mean_y
assert np.all(band_names == es.band_order)
assertions_on_band_metadata(sample.attrs)