def load_subdataset(subdataset, attrs, band_spec, **reader_kwargs):
'''Load a single subdataset'''
data_file = gdal.Open(subdataset)
raster = raster_as_2d(data_file.ReadAsArray(**reader_kwargs))
#raster = raster.T
if getattr(band_spec, 'stored_coords_order', ['y', 'x'])[0] == 'y':
rows, cols = raster.shape
dims = ('y', 'x')
else:
rows, cols = raster.T.shape
dims = ('x', 'y')
geo_transform = take_geo_transform_from_meta(band_spec, **attrs)
if geo_transform is None:
geo_transform = data_file.GetGeoTransform()
coord_x, coord_y = geotransform_to_coords(cols, rows, geo_transform)
canvas = Canvas(geo_transform=geo_transform,
buf_xsize=cols,
buf_ysize=rows,
dims=dims,
bounds=geotransform_to_bounds(cols, rows, geo_transform),
ravel_order='C')
attrs['canvas'] = canvas
attrs['geo_transform'] = geo_transform
if dims == ('y', 'x'):
coords = [('y', coord_y), ('x', coord_x)]
else:
coords = [('x', coord_x), ('y', coord_y)]
return xr.DataArray(data=raster, coords=coords, dims=dims, attrs=attrs)
def load_dir_of_tifs_array(dir_of_tiffs, meta, band_specs=None):
'''Return an ElmStore where each subdataset is a DataArray
Parameters:
:dir_of_tiffs: directory of GeoTiff files where each is a
single band raster
:meta: meta from earthio.load_dir_of_tifs_meta
:band_specs: list of earthio.BandSpec objects,
defaulting to reading all subdatasets
as bands
Returns:
:X: ElmStore
'''
logger.debug('load_dir_of_tifs_array: {}'.format(dir_of_tiffs))
band_order_info = meta['band_order_info']
tifs = ls_tif_files(dir_of_tiffs)
logger.info('Load tif files from {}'.format(dir_of_tiffs))
if not len(band_order_info):
raise ValueError('No matching bands with '
'band_specs {}'.format(band_specs))
native_dims = ('y', 'x')
elm_store_dict = OrderedDict()
attrs = {'meta': meta}
attrs['band_order'] = []
for (idx, filename, band_spec), band_meta in zip(band_order_info, meta['band_meta']):
band_name = getattr(band_spec, 'name', band_spec)
if not isinstance(band_spec, str):
reader_kwargs = {k: getattr(band_spec, k)
for k in READ_ARRAY_KWARGS
if getattr(band_spec, k)}
else:
reader_kwargs = {}
if 'buf_xsize' in reader_kwargs:
reader_kwargs['width'] = reader_kwargs.pop('buf_xsize')
if 'buf_ysize' in reader_kwargs:
reader_kwargs['height'] = reader_kwargs.pop('buf_ysize')
if 'window' in reader_kwargs:
reader_kwargs['window'] = tuple(map(tuple, reader_kwargs['window']))
# TODO multx, multy should be handled here as well?
if reader_kwargs:
multy = band_meta['height'] / reader_kwargs.get('height', band_meta['height'])
multx = band_meta['width'] / reader_kwargs.get('width', band_meta['width'])
else:
multx = multy = 1.
band_meta.update(reader_kwargs)
geo_transform = take_geo_transform_from_meta(band_spec, **attrs)
handle, raster = open_prefilter(filename, band_meta, **reader_kwargs)
raster = raster_as_2d(raster)
if getattr(band_spec, 'stored_coords_order', ['y', 'x'])[0] == 'y':
rows, cols = raster.shape
else:
rows, cols = raster.T.shape
if geo_transform is None:
band_meta['geo_transform'] = handle.get_transform()
else:
band_meta['geo_transform'] = geo_transform
band_meta['geo_transform'][1] *= multx
band_meta['geo_transform'][-1] *= multy
coords_x, coords_y = geotransform_to_coords(cols,
rows,
band_meta['geo_transform'])
elm_store_dict[band_name] = xr.DataArray(raster,
coords=[('y', coords_y),
('x', coords_x),],
dims=native_dims,
attrs=band_meta)
attrs['band_order'].append(band_name)
gc.collect()
return ElmStore(elm_store_dict, attrs=attrs)
def load_hdf4_array(datafile, meta, band_specs=None):
'''Return an ElmStore where each subdataset is a DataArray
Parameters:
:datafile: filename
:meta: meta from earthio.load_hdf4_meta
:band_specs: list of earthio.BandSpec objects,
defaulting to reading all subdatasets
as bands
Returns:
:Elmstore: Elmstore of teh hdf4 data
'''
from earthio import ElmStore
from earthio.metadata_selection import match_meta
logger.debug('load_hdf4_array: {}'.format(datafile))
f = gdal.Open(datafile, GA_ReadOnly)
sds = meta['sub_datasets']
band_metas = meta['band_meta']
band_order_info = []
if band_specs:
for band_meta, s in zip(band_metas, sds):
for idx, band_spec in enumerate(band_specs):
if match_meta(band_meta, band_spec):
band_order_info.append((idx, band_meta, s, band_spec))
break
band_order_info.sort(key=lambda x: x[0])
if not len(band_order_info):
raise ValueError('No matching bands with '
'band_specs {}'.format(band_specs))
else:
band_order_info = [(idx, band_meta, s, 'band_{}'.format(idx))
for idx, (band_meta,
s) in enumerate(zip(band_metas, sds))]
native_dims = ('y', 'x')
elm_store_data = OrderedDict()
band_order = []
for _, band_meta, s, band_spec in band_order_info:
attrs = copy.deepcopy(meta)
attrs.update(copy.deepcopy(band_meta))
if isinstance(band_spec, BandSpec):
name = band_spec.name
reader_kwargs = {
k: getattr(band_spec, k)
for k in READ_ARRAY_KWARGS if getattr(band_spec, k)
}
geo_transform = take_geo_transform_from_meta(band_spec, **attrs)
else:
reader_kwargs = {}
name = band_spec
geo_transform = None
reader_kwargs = window_to_gdal_read_kwargs(**reader_kwargs)
dat0 = gdal.Open(s[0], GA_ReadOnly)
band_meta.update(reader_kwargs)
raster = raster_as_2d(dat0.ReadAsArray(**reader_kwargs))
if geo_transform is None:
geo_transform = dat0.GetGeoTransform()
attrs['geo_transform'] = geo_transform
if hasattr(band_spec, 'store_coords_order'):
if band_spec.stored_coords_order[0] == 'y':
rows, cols = raster.shape
else:
rows, cols = raster.T.shape
else:
rows, cols = raster.shape
coord_x, coord_y = geotransform_to_coords(cols, rows, geo_transform)
canvas = Canvas(geo_transform=geo_transform,
buf_xsize=cols,
buf_ysize=rows,
dims=native_dims,
ravel_order='C',
bounds=geotransform_to_bounds(cols, rows,
geo_transform))
attrs['canvas'] = canvas
elm_store_data[name] = xr.DataArray(raster,
coords=[('y', coord_y),
('x', coord_x)],
dims=native_dims,
attrs=attrs)
band_order.append(name)
del dat0
attrs = copy.deepcopy(attrs)
attrs['band_order'] = band_order
gc.collect()
return ElmStore(elm_store_data, attrs=attrs)