def basic_test_7():
old_use_exceptions_status = gdal.GetUseExceptions()
gdal.UseExceptions()
ret = basic_test_7_internal()
if old_use_exceptions_status == 0:
gdal.DontUseExceptions()
return ret
def setGdalException(e=True):
old = gdal.GetUseExceptions()
if e:
gdal.PushErrorHandler(GdalErrorHandler().handler)
gdal.UseExceptions()
else:
gdal.DontUseExceptions()
gdal.PopErrorHandler()
return old == 1
def get_user_dst_dataset(dst_img,
cols,
rows,
layers,
dtype,
proj,
gt,
driver_name='ENVI'):
"""
Create a GDAL dataset using specified driver, default
is a generic binary with ENVI header.
:param dst_img: Output filenane full path
:param cols: Number of columns
:param rows: Number of rows
:param layers: Number of layers
:param dtype: GDAL type code
:param proj: Projection information in WKT format
:param gt: GeoTransform tupple
:param driver_name: GDAL driver
:return dst_ds: GDAL destination dataset object
"""
gdal.UseExceptions()
try:
# Default driver options to create a COG
driver = gdal.GetDriverByName(driver_name)
# Create driver
dst_ds = driver.Create(dst_img, cols, rows, layers, dtype)
# Set cartographic projection
dst_ds.SetProjection(proj)
dst_ds.SetGeoTransform(gt)
except Exception as err:
if err.err_level >= gdal.CE_Warning:
print('Cannot write dataset: %s' % self.input.value)
# Stop using GDAL exceptions
gdal.DontUseExceptions()
raise RuntimeError(err.err_level, err.err_no, err.err_msg)
gdal.DontUseExceptions()
return dst_ds
def get_dst_dataset(dst_img, cols, rows, layers, dtype, proj, gt):
"""
Create a GDAL data set in TATSSI default format
Cloud Optimized GeoTIFF (COG)
:param dst_img: Output filenane full path
:param cols: Number of columns
:param rows: Number of rows
:param layers: Number of layers
:param dtype: GDAL type code
:param proj: Projection information in WKT format
:param gt: GeoTransform tupple
:return dst_ds: GDAL destination dataset object
"""
gdal.UseExceptions()
try:
# Default driver options to create a COG
driver = gdal.GetDriverByName('GTiff')
driver_options = ['COMPRESS=DEFLATE',
'BIGTIFF=YES',
'PREDICTOR=1',
'TILED=YES',
'COPY_SRC_OVERVIEWS=YES']
# Create driver
dst_ds = driver.Create(dst_img, cols, rows, layers,
dtype, driver_options)
# Set cartographic projection
dst_ds.SetProjection(proj)
dst_ds.SetGeoTransform(gt)
except Exception as err:
if err.err_level >= gdal.CE_Warning:
print('Cannot write dataset: %s' % self.input.value)
# Stop using GDAL exceptions
gdal.DontUseExceptions()
raise RuntimeError(err.err_level, err.err_no, err.err_msg)
gdal.DontUseExceptions()
return dst_ds
def __on_translate_button_clicked(self, b):
"""
Performs the translation into an output file with selected format
"""
# Checks...
try:
# Use GDAL exceptions
gdal.UseExceptions()
tmp_d = gdal.Open(self.input.value)
except Exception as err:
if err.err_level >= gdal.CE_Warning:
print('Cannot read input dataset: %s' % self.input.value)
# Stop using GDAL exceptions
gdal.DontUseExceptions()
raise RuntimeError(err.err_level, err.err_no, err.err_msg)
if not os.path.isdir(os.path.dirname(self.output.value)):
print("Output directory %s does not exist" % \
os.path.dirname(self.output.value))
# Selected GDAL driver
driver = self.format.value.split('|')[1].strip()
# Translate
Translate(self.input.value, self.output.value, driver)
def clearGdalExceptions():
gdal.DontUseExceptions()
def save_outputs(self):
"""Save outputs as Numpy arrays (backwards compatible) and a netcdf file.
The dimensions of each output NumPy array are [Number of time steps, Ny, Nx]
For the netcdf file, the initial conditions are prepended to the output
and all grids have the dimenstions Nt, Ny, Nx.
"""
out_file = os.path.join(self.c.output_dir, '{}_{}m_{}yr.npy')
# save time series of land cover coverage
np.save(out_file.format('landcover', self.c.scale, self.c.Nt), self.crop_id_all)
# save time series of profits
np.save(out_file.format('profits', self.c.scale, self.c.Nt), self.profits_actual)
# save domain, can be used for initialization
np.save(out_file.format('domain', self.c.scale, self.c.Nt), self.agent_domain)
out_file = self.c.output_file
if out_file == "":
return
years = [self.c.target_year + n for n in range(self.c.Nt)]
# TODO(kyle): handle appending/assumed restart
nc = netcdf.Dataset(out_file, 'w', format='NETCDF4')
jmd = nc.createVariable('janus', 'i4')
jmd.version = self.version_info()
# TODO(kyle): determine what other metadata to write to the output, and
# which are required to enable a restart. This may include data files,
# config files/options, etc.
now = datetime.now().strftime('%Y%m%dT%H%M%S')
jmd.history = 'created {}'.format(now)
# Grab metadata from the original landcover file for geo referencing
lc = gdal.Open(self.c.f_init_lc_file)
gt = lc.GetGeoTransform()
# GetProjectionRef() is safe for GDAL 2.x and 3.x, use GetSpatialRef() +
# export as needed for GDAL 3.0+.
srs = lc.GetProjectionRef()
lc = None
# set gdal based crs
sr = nc.createVariable('spatial_ref', 'i4')
sr.spatial_ref = srs
# store number of pixel/lines in a more convenient variable
# TODO(kyle): check axis ordering
nx = self.crop_id_all.shape[0]
ny = self.crop_id_all.shape[1]
# setup the dimensions for the output file.
# TODO(kyle): check on axis ordering
# time is unlimited for appending
dt = nc.createDimension('time', None)
dx = nc.createDimension('x', nx)
dy = nc.createDimension('y', ny)
time = nc.createVariable('time', 'u2', ('time',))
time.units = 'years'
time.long_name = 'time'
time[:] = years[:]
# TODO(kyle): more axis ordering (-dy, dy, etc.), as well as half pixel
# offsets.
x = nc.createVariable('x', 'f8', ('x',))
x.long_name = 'x coordinate'
x[:] = [gt[0] + (0.5 * gt[1]) + i * gt[1] for i in range(nx)]
y = nc.createVariable('y', 'f8', ('y',))
y.long_name = 'y coordinate'
y[:] = [gt[3] + (0.5 * gt[5]) + i * gt[5] for i in range(ny)]
lon = nc.createVariable('lon', 'f8', ('y', 'x'))
lon.long_name = 'longitude'
lon.units = 'degrees'
lat = nc.createVariable('lat', 'f8', ('y', 'x'))
lat.long_name = 'latitude'
lat.units = 'degrees'
# TODO(kyle): discuss how to handle errors. If ImportFromWkt fails,
# should we write the netcdf file without the geo-referencing? For
# now, just let gdal raise an exception.
gdal.UseExceptions()
s_srs = osr.SpatialReference()
s_srs.ImportFromWkt(srs)
t_srs = osr.SpatialReference()
t_srs.ImportFromEPSG(4326)
if gdal.VersionInfo() >= '3000000':
t_srs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
ct = osr.CoordinateTransformation(s_srs, t_srs)
for i in range(ny):
for j in range(nx):
#TODO(kyle): do we need 1/2 pixel offsets?
y = gt[3] + (0.5 * gt[5]) + i * gt[5]
x = gt[0] + (0.5 * gt[1]) + j * gt[1]
lon[i, j], lat[i, j], z = ct.TransformPoint(x, y)
# TODO(kyle): check on range of crop values, cursory inspection showed
# a max of 254, but it could have been the wrong file.
crop = nc.createVariable('crop', 'i2', ('time', 'y', 'x'))
crop.units = 'gcam(?) crop classification'
crop.long_name = 'crop landcover'
# prepend the initial conditions
crop[:] = np.insert(self.crop_id_all, 0, self.lc, axis=0)
crop.grid_mapping = 'spatial_ref'
profits = nc.createVariable('profits', 'f8', ('time', 'y', 'x'))
profits.units = 'total dollars (per unit area?)'
profits.long_name = 'profit'
# prepend zeros for the initial crop conditions
profits[:] = np.insert(self.profits_actual, 0, np.zeros(self.lc.shape), axis=0)
profits.grid_mapping = 'spatial_ref'
# TODO(kyle): write the domain data
nc.close()
# This is a broader issue, discussion needed.
gdal.DontUseExceptions()
