def fp(size1, scale1, offset1, offset_factor2):
tl = TIF_FP.tl + [offset1, offset1] * np.asarray(offset_factor2)
size = [size1, size1]
rsize = (size / np.asarray(scale1)).astype(int)
assert (size == rsize * scale1).all()
fp = Footprint(tl=tl, size=size, rsize=np.abs(rsize))
return fp
def rast(ds, driver, band_count, dtype, src_nodata):
"""Fixture for the datasource creation"""
fp = Footprint(
tl=(100, 110), size=(10, 10), rsize=(10, 10)
)
if driver == 'numpy':
rast = ds.awrap_numpy_raster(
fp,
np.empty(np.r_[fp.shape, band_count], dtype=dtype),
band_schema=dict(nodata=src_nodata),
sr=None,
mode='w',
)
elif driver == 'MEM':
rast = ds.acreate_raster(
'', fp, dtype, band_count, band_schema=dict(nodata=src_nodata), driver='MEM',
)
else:
path = '{}/{}.tif'.format(tempfile.gettempdir(), uuid.uuid4())
rast = ds.acreate_raster(
path, fp, dtype, band_count, band_schema=dict(nodata=src_nodata), driver=driver
)
yield rast
if driver in {'numpy', 'MEM'}:
rast.close()
else:
rast.delete()
def _footprint_of_letters(letters):
tl = tl_of_letter[letters[0]]
br = br_of_letter[letters[-1]]
diagvec = br - tl
fp = Footprint(
tl=tl, size=np.abs(diagvec), rsize=(diagvec / reso)
)
return fp
'cv_nearest': 0,
'cv_cubic': 1, # Massive spikes near borders. Nodata holes near topleft borders.
'cv_lanczos4': 3, # Nodata holes near topleft borders.
}
INTERPOLATIONS_OUTSIDE_UNCERTAINTY_BORDER = {
'cv_area': 1,
'cv_linear': 1,
'cv_nearest': 2,
'cv_cubic': 2,
'cv_lanczos4': 2,
}
# CONSTANTS - TIF GENERATION ******************************************************************** **
TIF_NODATA = 200
TIF_FP = Footprint(
tl=(100, 110), size=(10, 10), rsize=(10, 10)
)
TIF_VALUES = np.add(*TIF_FP.meshgrid_raster).astype('float32')
TIF_VALUES[:1] = TIF_NODATA
TIF_VALUES[:, :2] = TIF_NODATA
XS, YS = TIF_FP.meshgrid_spatial
TIF_DATAMASK = TIF_VALUES != TIF_NODATA
TIF_DATA_MAXY = YS[TIF_DATAMASK].max()
TIF_DATA_MINY = YS[TIF_DATAMASK].min()
TIF_DATA_MAXX = XS[TIF_DATAMASK].max()
TIF_DATA_MINX = XS[TIF_DATAMASK].min()
TIF_MAXY = YS.max()
TIF_MINY = YS.min()
TIF_MAXX = XS.max()
TIF_MINX = XS.min()
'MEM': True,
}
TESTS = [
dict( # Classic dsm
subtests=[
dict(driver='GTiff', ext='.tif', options=[]),
dict(driver='MEM', options=[]),
dict(driver='numpy'),
],
dtype='float32',
band_count=1,
band_schema= {'nodata': [-32767]},
sr=SR1['wkt'],
fp=Footprint(tl=(0, 10), size=(10, 10), rsize=(30, 30)),
),
dict( # Classic rgb
subtests=[
dict(driver='GTiff', ext='.tif', options=[]),
dict(driver='MEM', options=[]),
dict(driver='numpy'),
],
dtype='uint8',
band_count=3,
band_schema= {},
sr=SR1['wkt'],
fp=Footprint(tl=(0, 10), size=(10, 10), rsize=(30, 30)),
),
def queryfp(tl, size, reso):
rsize = (size / reso).astype(int)
assert (size == rsize * reso).all()
fp = Footprint(tl=tl, size=size, rsize=np.abs(rsize))
return fp
from buzzard.test import tools
LOGGER = logging.getLogger('buzzard')
# CONSTANTS - TEST ********************************************************** **
READ_TOL = 0.032 # Tolerance in adjacent differences differences
WRITE_TOL = 0.7 # Tolerance in adjacent differences differences
PRECISION = 6
# CONSTANTS - TIF GENERATION ************************************************ **
TIF_NODATA = -99
TIF_SIDE = 10
TIF_NODATA_BORDER_SIZE = (2, 0, 1, 0)
TIF_FP = (Footprint(tl=(100 + 0, 100 + TIF_SIDE),
size=(TIF_SIDE, TIF_SIDE),
rsize=(TIF_SIDE, TIF_SIDE)))
TIF_DATA_START_LEFT = np.around(
TIF_FP.lx + TIF_NODATA_BORDER_SIZE[0] * TIF_FP.pxvec[0], PRECISION)
TIF_DATA_LAST_RIGHT = (np.around(
TIF_FP.rx - (TIF_NODATA_BORDER_SIZE[1] + 1.) * TIF_FP.pxvec[0], PRECISION))
TIF_DATA_START_TOP = (np.around(
TIF_FP.ty + TIF_NODATA_BORDER_SIZE[2] * TIF_FP.pxvec[1], PRECISION))
TIF_DATA_LAST_BOTTOM = (np.around(
TIF_FP.by - (TIF_NODATA_BORDER_SIZE[3] + 1.) * TIF_FP.pxvec[1], PRECISION))
# CONSTANTS - CORNERS DEFINITION ******************************************** **
Corner = collections.namedtuple('Corner', ['name', 'x', 'y', 'offset_sign'])
TL_CORNER = Corner('tl', TIF_FP.tlx, TIF_FP.tly, np.array([1., -1.]))
ALL_CORNERS = [
TL_CORNER,