def extract_band(job, image, band_num):
"""
Extract the temperature data from the processed ISIS cube.
Parameters
----------
job : dict
Job specification dictionary
image : str
PATH to an ISIS cube to extract bands from
band_num : int
Band number that needs to be extracted
Returns
----------
: ndarray
Array representation of the extracted band
"""
header = pvl.load(job['images'])
bands = find_in_dict(header, 'BAND_BIN_BAND_NUMBER')
for i, band in enumerate(bands):
if band_num == band:
geo_image = io_gdal.GeoDataset(image)
return geo_image.read_array(band=i + 1)
def test_with_projection(self):
wktsrs = """PROJCS["Moon2000_Mercator180",
GEOGCS["GCS_Moon_2000",
DATUM["Moon_2000",
SPHEROID["Moon_2000_IAU_IAG",1737400.0,0.0]],
PRIMEM["Reference_Meridian",0.0],
UNIT["Degree",0.017453292519943295]],
PROJECTION["Mercator_1SP"],
PARAMETER["False_Easting",0.0],
PARAMETER["False_Northing",0.0],
PARAMETER["Central_Meridian",180.0],
PARAMETER["latitude_of_origin",0.0],
UNIT["Meter",1.0]]"""
io_gdal.array_to_raster(self.arr, 'test.tif', projection=wktsrs)
expected_srs = """PROJCS["Moon2000_Mercator180",
GEOGCS["GCS_Moon_2000",
DATUM["Moon_2000",
SPHEROID["Moon_2000_IAU_IAG",1737400,0]],
PRIMEM["Reference_Meridian",0],
UNIT["Degree",0.017453292519943295]],
PROJECTION["Mercator_2SP"],
PARAMETER["central_meridian",180],
PARAMETER["false_easting",0],
PARAMETER["false_northing",0],
PARAMETER["standard_parallel_1",0],
UNIT["Meter",1]]"""
dataset = io_gdal.GeoDataset('test.tif')
test_srs = dataset.spatial_reference.__str__()
self.assertEqual(test_srs.split(), expected_srs.split())
def setUpClass(cls):
cls.dataset = io_gdal.GeoDataset(get_path('AS15-M-0295_SML.png'))
cls.data_array = cls.dataset.read_array(dtype='uint8')
cls.parameters = {
"nfeatures": 10,
"nOctaveLayers": 3,
"contrastThreshold": 0.02,
"edgeThreshold": 10,
"sigma": 1.6
}
def extract_temperature(isiscube, reference_dataset=None):
"""
Extract the temperature data from the processed ISIS cube.
Parameters
----------
isiscube : str
PATH to an ISIS cube to extract
"""
temperature = io_gdal.GeoDataset(isiscube)
processing_resolution = temperature.pixel_width
tempshape = list(temperature.raster_size)[::-1]
logger.info('Themis temperature data has {} lines and {} samples'.format(
tempshape[0], tempshape[1]))
srs = temperature.spatial_reference.ExportToWkt()
logger.info('The input temperature image projection is: {}'.format(srs))
return temperature
def extract_latlon_transform(isiscube, job):
"""
Given an ISIS cube, extract the upper left (x, y) coords and the height and width
Parameters
----------
isiscube : str
PATH to an ISIS cube to use for latlon to pixel translation
job : dict
Job specification dictionary
Returns
----------
xoff : int
x coordinate of the upper left pixel
yoff : int
y coordinate of the upper left pixel
width : int
Width of the new area
height : int
Height of the new area
"""
isiscube_geodata = io_gdal.GeoDataset(isiscube)
lry, uly = job["lat_extent"]
ulx, lrx = job["lon_extent"]
ul_coords = isiscube_geodata.latlon_to_pixel(uly, ulx)
lr_coords = isiscube_geodata.latlon_to_pixel(lry, lrx)
xoff = ul_coords[0]
yoff = ul_coords[1]
width = abs(lr_coords[0] - xoff)
height = abs(lr_coords[1] - yoff)
return xoff, yoff, width, height
def extract_ancillary_data(job, temperature, parameters, workingpath, shape,
reference_dataset):
"""
For all ancillary data sets, extract the requested spatial extent
Parameters
----------
job : dict
Job specification dictionary
temperature : object
Plio GeoDataset object
parameters : dict
of extent and time parameters
"""
ancillarydata = job['ancillarydata']
for k, v in ancillarydata.items():
ancillarydata[k] = v
# Flag to punt on the image if some ancillary data load fails.
ancillary_failure = False
#Iterate through the ancillary data. Clip and resample to the input image
for k, v in ancillarydata.items():
if isinstance(v, int) or isinstance(v, float):
#The user wants a constant value to be used
arr = np.empty(shape, dtype=np.float32)
logger.debug('{} set to a constant value, {}'.format(k, v))
del arr
else:
basename = os.path.basename(v)
root, extension = os.path.splitext(basename)
tif = os.path.join(workingpath, root + '.tif')
if v == 'montone': # Custom dust opacity
startls = parameters['startlsubs'][0]
startmartianyear = int(parameters['startmartianyear'][0])
files = glob.glob(
'/scratch/jlaura/KRC/basemaps/tau_geotiff/MY{}*'.format(
startmartianyear))
if len(files) == 0:
logger.error(
'Requested an image with Mars Year {}. No Montabone data available for that Mars Year'
.format(startmartianyear))
ancillary_failure = True
continue
ls = {}
for t, f in enumerate(files):
base, ext = os.path.splitext(f)
ls[float(base.split('_')[2])] = t
keys = []
for key in ls.keys():
try:
keys.append(float(key))
except:
pass
key = min(keys, key=lambda x: abs(x - startls))
v = files[ls[key]]
if v == 'tes':
startls = startlsubs[0]
files = glob.glob('/scratch/jlaura/KRC/basemaps/tes_opac/*')
ls = {}
for t, f in enumerate(files):
base, ext = os.path.splitext(f)
ls[float(base.split('_')[-1][2:])] = t
keys = []
for key in ls.keys():
try:
keys.append(float(key))
except:
pass
key = min(keys, key=lambda x: abs(x - startls))
v = files[ls[key]]
#Clip and resample the image to the correct resolution
v = io_gdal.GeoDataset(v)
io_gdal.match_rasters(reference_dataset, v, tif)
#Read the resampled tif and extract the array
ancillarydata[k] = io_gdal.GeoDataset(tif)
logger.debug('Dataset {} extract.'.format(v))
if ancillary_failure:
print('FAILED TO EXTRACT ANCILLARY DATA')
MPI.COMM_WORLD.Abort(1)
sys.exit()
return ancillarydata
def test_with_no_data_value(self):
no_data_value = 0.0
io_gdal.array_to_raster(self.arr, 'test.tif', ndv=no_data_value)
dataset = io_gdal.GeoDataset('test.tif')
self.assertEqual(dataset.no_data_value, no_data_value)
def test_with_geotrasform(self):
gt = (-464400.0, 3870.0, 0.0, -506970.0, 0.0, -3870.0)
io_gdal.array_to_raster(self.arr, 'test.tif', geotransform=gt)
dataset = io_gdal.GeoDataset('test.tif')
self.assertEqual(gt, dataset.geotransform)
def setUp(self):
self.dataset = io_gdal.GeoDataset(
get_path('Mars_MGS_MOLA_ClrShade_MAP2_90.0N0.0_POLA.tif'))
def setUp(self):
self.dataset = io_gdal.GeoDataset(
get_path('Lunar_LRO_LOLA_Shade_MAP2_90.0N20.0_LAMB.tif'))
def test_without_gdal(self):
io_gdal.has_gdal = False
with self.assertRaises(ImportError):
io_gdal.GeoDataset('foo')
