def _validate_band_specs(self, band_specs, name):
'''Validate "band_specs"'''
if all(isinstance(bs, BandSpec) for bs in band_specs):
return band_specs
if not band_specs or not isinstance(band_specs, (tuple, list)):
raise ElmConfigError(
'data_sources:{} gave band_specs which are not a '
'list {}'.format(name, band_specs))
if not all(isinstance(bs, (dict, string_types)) for bs in band_specs):
raise ElmConfigError(
'Expected "band_specs" to be a list of dicts or list of strings'
)
new_band_specs = []
field_names = [x.name for x in attr.fields(BandSpec)]
for band_spec in band_specs:
if isinstance(band_spec, string_types):
new_band_specs.append(
BandSpec(
**{
'search_key': 'sub_dataset_name',
'search_value': band_spec,
'name': band_spec
}))
elif not all(k in field_names for k in band_spec):
raise ElmConfigError(
"band_spec {} did not have keys: {}".format(
band_spec, field_names))
else:
new_band_specs.append(BandSpec(**band_spec))
return new_band_specs
def test_read_using_list_of_variables():
for nc_file in NETCDF_FILES:
meta = load_netcdf_meta(nc_file)
ds = load_netcdf_array(nc_file, meta, variables_list)
_validate_array_test_result(ds)
variables_list2 = [BandSpec('', '', v) for v in variables_list]
ds = load_netcdf_array(nc_file, meta, variables_list2)
_validate_array_test_result(ds)
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_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_hdf4_meta(HDF4_FILES[0])
es = load_hdf4_array(HDF4_FILES[0], meta, band_specs_kwargs)
for b in es.band_order:
assert getattr(es, b).values.shape == (300, 200)
def test_read_meta():
TIF_DIR = os.path.dirname(TIF_FILES[0])
for tif in TIF_FILES:
raster, meta = load_tif_meta(tif)
assert hasattr(raster, 'read')
assert hasattr(raster, 'width')
band_specs_with_band_8 = band_specs + [BandSpec('name', '_B8.TIF', 'band_8')]
meta = load_dir_of_tifs_meta(TIF_DIR, band_specs_with_band_8)
band_meta = meta['band_meta']
heights_names = [(m['height'], m['name']) for m in band_meta]
# band 8 is panchromatic with 15 m resolution
# other bands have 30 m resolution. They
# have the same bounds, so band 8 has 4 times as many pixels
heights_names.sort(key=lambda x:x[0])
assert heights_names[-1][-1].endswith('_B8.TIF')
def get_band_specs(filename):
if os.path.basename(filename).startswith('3B-MO'):
sub_dataset_names = ('/precipitation',)
else:
sub_dataset_names = ('HQobservationTime',
'HQprecipSource',
'HQprecipitation',
'IRkalmanFilterWeight',
'IRprecipitation',
'precipitationCal',
'precipitationUncal',
'probabilityLiquidPrecipitation',)
band_specs = []
for sub in sub_dataset_names:
band_specs.append(BandSpec(search_key='sub_dataset_name',
search_value=sub + '$', # line ender regex
name=sub,
meta_to_geotransform="earthio.util:grid_header_to_geo_transform",
stored_coords_order=("x", "y")))
return sub_dataset_names, band_specs
import attr
import numpy as np
import pytest
from earthio.tif import (load_dir_of_tifs_meta, load_dir_of_tifs_array,
load_tif_meta, ls_tif_files)
from earthio.tests.util import (ELM_HAS_EXAMPLES, ELM_EXAMPLE_DATA_PATH,
TIF_FILES, assertions_on_metadata,
assertions_on_band_metadata)
from earthio.util import BandSpec
TIF_DIR = os.path.dirname(TIF_FILES[0])
band_specs = [
BandSpec('name', '_B1.TIF', 'band_1'),
BandSpec('name', '_B2.TIF', 'band_2'),
BandSpec('name', '_B3.TIF', 'band_3'),
BandSpec('name', '_B4.TIF', 'band_4'),
BandSpec('name', '_B5.TIF', 'band_5'),
BandSpec('name', '_B6.TIF', 'band_6'),
BandSpec('name', '_B7.TIF', 'band_7'),
BandSpec('name', '_B9.TIF', 'band_9'),
BandSpec('name', '_B10.TIF', 'band_10'),
BandSpec('name', '_B11.TIF', 'band_11'),
]
@pytest.mark.skipif(not ELM_HAS_EXAMPLES,
reason='elm-data repo has not been cloned')
def test_read_meta():
import pytest
from earthio.hdf4 import (load_hdf4_meta,
load_hdf4_array)
from earthio.util import BandSpec
from earthio.tests.util import (ELM_HAS_EXAMPLES,
ELM_EXAMPLE_DATA_PATH,
HDF4_FILES,
assertions_on_metadata,
assertions_on_band_metadata)
kwargs = {}
band_specs = [
BandSpec('long_name', 'Band 1 ', 'band_1', **kwargs),
BandSpec('long_name', 'Band 2 ', 'band_2', **kwargs),
BandSpec('long_name', 'Band 3 ', 'band_3', **kwargs),
BandSpec('long_name', 'Band 4 ', 'band_4', **kwargs),
BandSpec('long_name', 'Band 5 ', 'band_5', **kwargs),
BandSpec('long_name', 'Band 7 ', 'band_7', **kwargs),
BandSpec('long_name', 'Band 8 ', 'band_8', **kwargs),
BandSpec('long_name', 'Band 10 ', 'band_10', **kwargs),
BandSpec('long_name', 'Band 11 ', 'band_11', **kwargs),
]
@pytest.mark.parametrize('hdf', HDF4_FILES or [])
@pytest.mark.skipif(not ELM_HAS_EXAMPLES,
reason='elm-data repo has not been cloned')
def test_read_meta(hdf):
meta = load_hdf4_meta(hdf)