def test_get_area_def_from_raster(self):
from rasterio.crs import CRS
from affine import Affine
from pyresample import utils
x_size = 791
y_size = 718
transform = Affine(300.0379266750948, 0.0, 101985.0, 0.0,
-300.041782729805, 2826915.0)
crs = CRS(init='epsg:3857')
source = tmptiff(x_size, y_size, transform, crs=crs)
area_id = 'area_id'
proj_id = 'proj_id'
name = 'name'
area_def = utils.get_area_def_from_raster(source,
area_id=area_id,
name=name,
proj_id=proj_id)
self.assertEqual(area_def.area_id, area_id)
self.assertEqual(area_def.proj_id, proj_id)
self.assertEqual(area_def.name, name)
self.assertEqual(area_def.x_size, x_size)
self.assertEqual(area_def.y_size, y_size)
self.assertDictEqual(crs.to_dict(), area_def.proj_dict)
self.assertTupleEqual(
area_def.area_extent,
(transform.c, transform.f + transform.e * y_size,
transform.c + transform.a * x_size, transform.f))
def test_get_area_def_from_raster(self):
from pyresample import utils
from rasterio.crs import CRS
from affine import Affine
x_size = 791
y_size = 718
transform = Affine(300.0379266750948, 0.0, 101985.0, 0.0,
-300.041782729805, 2826915.0)
crs = CRS(init='epsg:3857')
if utils.is_pyproj2():
# pyproj 2.0+ expands CRS parameters
from pyproj import CRS
proj_dict = CRS(3857).to_dict()
else:
proj_dict = crs.to_dict()
source = tmptiff(x_size, y_size, transform, crs=crs)
area_id = 'area_id'
proj_id = 'proj_id'
description = 'name'
area_def = utils._rasterio.get_area_def_from_raster(source,
area_id=area_id,
name=description,
proj_id=proj_id)
self.assertEqual(area_def.area_id, area_id)
self.assertEqual(area_def.proj_id, proj_id)
self.assertEqual(area_def.description, description)
self.assertEqual(area_def.width, x_size)
self.assertEqual(area_def.height, y_size)
self.assertDictEqual(proj_dict, area_def.proj_dict)
self.assertTupleEqual(
area_def.area_extent,
(transform.c, transform.f + transform.e * y_size,
transform.c + transform.a * x_size, transform.f))
def test_get_area_def_from_raster(self):
from pyresample import utils
from rasterio.crs import CRS
from affine import Affine
x_size = 791
y_size = 718
transform = Affine(300.0379266750948, 0.0, 101985.0,
0.0, -300.041782729805, 2826915.0)
crs = CRS(init='epsg:3857')
source = tmptiff(x_size, y_size, transform, crs=crs)
area_id = 'area_id'
proj_id = 'proj_id'
name = 'name'
area_def = utils._rasterio.get_area_def_from_raster(
source, area_id=area_id, name=name, proj_id=proj_id)
self.assertEqual(area_def.area_id, area_id)
self.assertEqual(area_def.proj_id, proj_id)
self.assertEqual(area_def.name, name)
self.assertEqual(area_def.width, x_size)
self.assertEqual(area_def.height, y_size)
self.assertDictEqual(crs.to_dict(), area_def.proj_dict)
self.assertTupleEqual(area_def.area_extent, (transform.c, transform.f + transform.e * y_size,
transform.c + transform.a * x_size, transform.f))
def test_crs_proj_json__from_string():
aeqd_crs = CRS(proj="aeqd", lon_0=-80, lat_0=40.5)
assert CRS.from_string(json.dumps(aeqd_crs.to_dict(projjson=True))) == aeqd_crs
def test_crs_from_json_dict__init():
aeqd_crs = CRS(proj="aeqd", lon_0=-80, lat_0=40.5)
assert CRS(aeqd_crs.to_dict(projjson=True)) == aeqd_crs
def test_crs_to_json_dict__empty():
crs = CRS()
assert crs.to_dict(projjson=True) == {}
def test_crs_to_json_dict():
aeqd_crs = CRS(proj="aeqd", lon_0=-80, lat_0=40.5)
json_dict = aeqd_crs.to_dict(projjson=True)
assert json_dict["type"] == "ProjectedCRS"
def zones(
input,
output,
variable,
attribute,
like,
netcdf3,
zip):
"""
Create zones in a NetCDF from features in a shapefile. This is intended
to be used as input to zonal statistics functions; it is not intended
as a direct replacement for rasterizing geometries into NetCDF.
Only handles < 65,535 features for now.
If --attribute is provided, any features that do not have this will not be
assigned to zones.
A values lookup will be used to store values. The zones are indices of
the unique values encountered when extracting features.
The original values are stored in an additional variable with the name of
the zones variable plus '_values'.
Template NetCDF dataset must have a valid projection defined or be inferred
from dimensions (e.g., lat / long).
"""
with Dataset(like) as template_ds:
template_varname = list(data_variables(template_ds).keys())[0]
template_variable = template_ds.variables[template_varname]
template_crs = get_crs(template_ds, template_varname)
if template_crs:
template_crs = CRS.from_string(template_crs)
elif is_geographic(template_ds, template_varname):
template_crs = CRS({'init': 'EPSG:4326'})
else:
raise click.UsageError('template dataset must have a valid projection defined')
spatial_dimensions = template_variable.dimensions[-2:]
out_shape = template_variable.shape[-2:]
template_y_name, template_x_name = spatial_dimensions
coords = SpatialCoordinateVariables.from_dataset(
template_ds,
x_name=template_x_name,
y_name=template_y_name,
projection=Proj(**template_crs.to_dict())
)
with fiona.open(input, 'r') as shp:
if attribute:
if not attribute in shp.meta['schema']['properties']:
raise click.BadParameter('{0} not found in dataset'.format(attribute),
param='--attribute', param_hint='--attribute')
att_dtype = shp.meta['schema']['properties'][attribute].split(':')[0]
if not att_dtype in ('int', 'str'):
raise click.BadParameter('integer or string attribute required'.format(attribute),
param='--attribute', param_hint='--attribute')
transform_required = CRS(shp.crs) != template_crs
geometries = []
values = set()
values_lookup = {}
# Project bbox for filtering
bbox = coords.bbox
if transform_required:
bbox = bbox.project(Proj(**shp.crs), edge_points=21)
index = 0
for f in shp.filter(bbox=bbox.as_list()):
value = f['properties'].get(attribute) if attribute else int(f['id'])
if value is not None:
geom = f['geometry']
if transform_required:
geom = transform_geom(shp.crs, template_crs, geom)
geometries.append((geom, index))
if not value in values:
values.add(value)
values_lookup[index] = value
index += 1
# Otherwise, these will not be rasterized
num_geometries = len(geometries)
# Save a slot at the end for nodata
if num_geometries < 255:
dtype = numpy.dtype('uint8')
elif num_geometries < 65535:
dtype = numpy.dtype('uint16')
else:
raise click.UsageError('Too many features to rasterize: {0}, Exceptioning...'.format(num_geometries))
fill_value = get_fill_value(dtype)
click.echo('Rasterizing {0} features into zones'.format(num_geometries))
with rasterio.Env():
zones = rasterize(
geometries,
out_shape=out_shape,
transform=coords.affine,
all_touched=False, # True produces undesirable results for adjacent polygons
fill=fill_value,
dtype=dtype
)
format = 'NETCDF4'
out_dtype = dtype
if netcdf3:
format = 'NETCDF3_CLASSIC'
if dtype == numpy.uint8:
out_dtype = numpy.dtype('int16')
elif dtype == numpy.uint16:
out_dtype = numpy.dtype('int32')
# Have to convert fill_value to mask since we changed data type
zones = numpy.ma.masked_array(zones, mask=(zones == fill_value))
with Dataset(output, 'w', format=format) as out:
values_varname = '{0}_values'.format(variable)
coords.add_to_dataset(out, template_x_name, template_y_name)
out_var = out.createVariable(variable, out_dtype,
dimensions=spatial_dimensions,
zlib=zip,
fill_value=get_fill_value(out_dtype))
out_var.setncattr('values', values_varname)
out_var[:] = zones
out_values = numpy.array([values_lookup[k] for k in range(0, len(values_lookup))])
if netcdf3 and out_values.dtype == numpy.int64:
out_values = out_values.astype('int32')
out.createDimension(values_varname, len(out_values))
values_var = out.createVariable(values_varname, out_values.dtype,
dimensions=(values_varname, ),
zlib=zip)
values_var[:] = out_values
def mask(input, output, variable, like, netcdf3, all_touched, invert, zip):
"""
Create a NetCDF mask from a shapefile.
Values are equivalent to a numpy mask: 0 for unmasked areas, and 1 for masked areas.
Template NetCDF dataset must have a valid projection defined or be inferred from dimensions (e.g., lat / long)
"""
with Dataset(like) as template_ds:
template_varname = data_variables(template_ds).keys()[0]
template_variable = template_ds.variables[template_varname]
template_crs = get_crs(template_ds, template_varname)
if template_crs:
template_crs = CRS.from_string(template_crs)
elif is_geographic(template_ds, template_varname):
template_crs = CRS({'init': 'EPSG:4326'})
else:
raise click.UsageError(
'template dataset must have a valid projection defined')
spatial_dimensions = template_variable.dimensions[-2:]
mask_shape = template_variable.shape[-2:]
template_y_name, template_x_name = spatial_dimensions
coords = SpatialCoordinateVariables.from_dataset(
template_ds,
x_name=template_x_name,
y_name=template_y_name,
projection=Proj(**template_crs.to_dict()))
with fiona.open(input, 'r') as shp:
transform_required = CRS(shp.crs) != template_crs
# Project bbox for filtering
bbox = coords.bbox
if transform_required:
bbox = bbox.project(Proj(**shp.crs), edge_points=21)
geometries = []
for f in shp.filter(bbox=bbox.as_list()):
geom = f['geometry']
if transform_required:
geom = transform_geom(shp.crs, template_crs, geom)
geometries.append(geom)
click.echo('Converting {0} features to mask'.format(len(geometries)))
if invert:
fill_value = 0
default_value = 1
else:
fill_value = 1
default_value = 0
with rasterio.Env():
# Rasterize features to 0, leaving background as 1
mask = rasterize(geometries,
out_shape=mask_shape,
transform=coords.affine,
all_touched=all_touched,
fill=fill_value,
default_value=default_value,
dtype=numpy.uint8)
format = 'NETCDF3_CLASSIC' if netcdf3 else 'NETCDF4'
dtype = 'int8' if netcdf3 else 'uint8'
with Dataset(output, 'w', format=format) as out:
coords.add_to_dataset(out, template_x_name, template_y_name)
out_var = out.createVariable(variable,
dtype,
dimensions=spatial_dimensions,
zlib=zip,
fill_value=get_fill_value(dtype))
out_var[:] = mask
def mask(
input,
output,
variable,
like,
netcdf3,
all_touched,
invert,
zip):
"""
Create a NetCDF mask from a shapefile.
Values are equivalent to a numpy mask: 0 for unmasked areas, and 1 for masked areas.
Template NetCDF dataset must have a valid projection defined or be inferred from dimensions (e.g., lat / long)
"""
with Dataset(like) as template_ds:
template_varname = data_variables(template_ds).keys()[0]
template_variable = template_ds.variables[template_varname]
template_crs = get_crs(template_ds, template_varname)
if template_crs:
template_crs = CRS.from_string(template_crs)
elif is_geographic(template_ds, template_varname):
template_crs = CRS({'init': 'EPSG:4326'})
else:
raise click.UsageError('template dataset must have a valid projection defined')
spatial_dimensions = template_variable.dimensions[-2:]
mask_shape = template_variable.shape[-2:]
template_y_name, template_x_name = spatial_dimensions
coords = SpatialCoordinateVariables.from_dataset(
template_ds,
x_name=template_x_name,
y_name=template_y_name,
projection=Proj(**template_crs.to_dict())
)
with fiona.open(input, 'r') as shp:
transform_required = CRS(shp.crs) != template_crs
# Project bbox for filtering
bbox = coords.bbox
if transform_required:
bbox = bbox.project(Proj(**shp.crs), edge_points=21)
geometries = []
for f in shp.filter(bbox=bbox.as_list()):
geom = f['geometry']
if transform_required:
geom = transform_geom(shp.crs, template_crs, geom)
geometries.append(geom)
click.echo('Converting {0} features to mask'.format(len(geometries)))
if invert:
fill_value = 0
default_value = 1
else:
fill_value = 1
default_value = 0
with rasterio.Env():
# Rasterize features to 0, leaving background as 1
mask = rasterize(
geometries,
out_shape=mask_shape,
transform=coords.affine,
all_touched=all_touched,
fill=fill_value,
default_value=default_value,
dtype=numpy.uint8
)
format = 'NETCDF3_CLASSIC' if netcdf3 else 'NETCDF4'
dtype = 'int8' if netcdf3 else 'uint8'
with Dataset(output, 'w', format=format) as out:
coords.add_to_dataset(out, template_x_name, template_y_name)
out_var = out.createVariable(variable, dtype, dimensions=spatial_dimensions, zlib=zip,
fill_value=get_fill_value(dtype))
out_var[:] = mask
