def Testing(GCPs, img, outpImg):
from skimage.transform import AffineTransform, warp
from skimage.measure import ransac
from affine import Affine
img_info = rt.GetRasterInfo(inputRaster=img)
img_array = np.array(img_info["raster"].GetRasterBand(1).ReadAsArray())
map = GCPs[:, 0:2]
pixel = GCPs[:, 2:4]
model = AffineTransform()
model.estimate(src=pixel, dst=map)
print(model.params)
model_robust, inliers = ransac((pixel, map),
AffineTransform,
min_samples=3,
residual_threshold=1,
max_trials=1000)
print(model_robust.params)
geoTransform = Affine.from_gdal(*img_info["raster"].GetGeoTransform())
# print(geoTransform)
geoTransform = Affine.from_gdal(model_robust.params[0, 2],
model_robust.params[0, 0],
model_robust.params[0, 1],
model_robust.params[1, 2],
model_robust.params[1, 0],
model_robust.params[1, 1])
print(geoTransform)
point_A = []
point_B = []
for i, val in enumerate(GCPs):
point_A.append(tuple([val[0], val[1]]))
point_B.append(tuple([val[2], val[3]]))
point_A = tuple(point_A)
point_B = tuple(point_B)
trn = Affine_Fit(from_pts=point_B, to_pts=point_A)
res, tr = trn.To_Str()
geoTransform_ = Affine.from_gdal(tr[2][3], tr[0][3], tr[1][3], tr[2][4],
tr[0][4], tr[1][4])
print(geoTransform_)
# geoTransform = Affine.from_gdal((1,2,3,5))
nrows, ncols = np.shape(img_array)
ras_name = gdal.GetDriverByName("GTiff").Create(outpImg, ncols, nrows, 1,
gdal.GDT_Float64)
ras_name.SetGeoTransform(geoTransform_.to_gdal())
# important part ends
wkt = img_info["raster"].GetProjection()
ras_name.SetProjection(wkt)
ras_name.GetRasterBand(1).WriteArray(img_array,
resample_alg=gdal.GRA_Lanczos)
resampling_method = gdal.GRA_Lanczos
ras_name = None
def open(path, mode='r', width=None, height=None, count=None, transform=None,
crs=None, no_data=None, dtype=None, chunks=(256, 256),
blocksize=256, compression=1, band_names=None):
# should we pass to different read write classes based on the mode?
if mode == 'r':
fid = h5py.File(path, mode)
ds = KeaH5RDOnly(fid)
elif mode == 'r+':
fid = h5py.File(path, mode)
ds = KeaH5RW(fid)
elif mode =='w':
# Check we have all the necessary creation options
if (width is None) or (height is None):
msg = "Error. Both width and height must be specified."
raise ValueError(msg)
if dtype is None:
msg = "Error. The dtype must be specifified."
raise ValueError(msg)
if count is None:
msg = "Error. The count must be specified."
raise ValueError(msg)
# If we have no transform, default to image co-ordinates
if (transform is None) or (crs is None):
ul = (0, 0)
rot = (0, 0)
res = (1, -1)
transform = Affine.from_gdal(*[0.0, 1.0, 0.0, 0.0, 0.0, -1.0])
crs = ""
if (chunks[0] > height) or (chunks[1] > width):
msg = "The chunks must not exceed the width or height."
raise ValueError(msg)
# we'll use rasterio's proj4 dict mapping
if not isinstance(crs, dict):
msg = "Error. The crs is not a valid proj4 dict style mapping."
raise ValueError(msg)
# we'll follow rasterio in using an affine
if not isinstance(transform, Affine):
msg = "Error. The transform is not an Affine instance."
transform = Affine.from_gdal(*transform)
fid = h5py.File(path, mode)
create_kea_image(fid, width, height, count, transform, crs, no_data,
dtype, chunks, blocksize, compression, band_names)
ds = KeaH5RW(fid)
return ds
def test_to_raster__preserve_profile__none_nodata(request, tmpdir):
tmp_raster = tmpdir.join("output_profile.tif")
input_raster = tmpdir.join("input_profile.tif")
transform = Affine.from_gdal(0, 512, 0, 0, 0, 512)
with rasterio.open(
str(input_raster),
"w",
driver="GTiff",
height=512,
width=512,
count=1,
crs="+init=epsg:4326",
transform=transform,
dtype=rasterio.float32,
tiled=True,
tilexsize=256,
tileysize=256,
) as rds:
rds.write(numpy.empty((1, 512, 512), dtype=numpy.float32))
with request.param(str(input_raster)) as mda:
mda.rio.to_raster(str(tmp_raster))
with rasterio.open(str(tmp_raster)) as rds, rasterio.open(
str(input_raster)) as rdc:
assert rds.count == rdc.count
assert rds.crs == rdc.crs
assert_array_equal(rds.transform, rdc.transform)
assert_array_equal(rds.nodata, rdc.nodata)
assert_array_equal(rds.read(), rdc.read())
assert rds.profile == rdc.profile
assert rds.nodata is None
def get_geo_transform(raster_src):
"""Get the geotransform for a raster image source.
Arguments
---------
raster_src : str, :class:`rasterio.DatasetReader`, or `osgeo.gdal.Dataset`
Path to a raster image with georeferencing data to apply to `geom`.
Alternatively, an opened :class:`rasterio.Band` object or
:class:`osgeo.gdal.Dataset` object can be provided. Required if not
using `affine_obj`.
Returns
-------
transform : :class:`affine.Affine`
An affine transformation object to the image's location in its CRS.
"""
if isinstance(raster_src, str):
affine_obj = rasterio.open(raster_src).transform
elif isinstance(raster_src, rasterio.DatasetReader):
affine_obj = raster_src.transform
elif isinstance(raster_src, gdal.Dataset):
affine_obj = Affine.from_gdal(*raster_src.GetGeoTransform())
return affine_obj
def write_grid(grid, fname, origin, spacing):
"""
Write an epipolar resampling grid to file
:param grid: the grid to write
:type grid: 3D numpy array
:param fname: the filename to which the grid will be written
:type fname: string
:param origin: origin of the grid
:type origin: (float, float)
:param spacing: spacing of the grid
:type spacing: (float, float)
"""
geotransform = (origin[0] - 0.5 * spacing[0], spacing[0], 0.0,
origin[1] - 0.5 * spacing[1], 0.0, spacing[1])
transform = Affine.from_gdal(*geotransform)
with rio.open(fname,
'w',
height=grid.shape[0],
width=grid.shape[1],
count=2,
driver='GTiff',
dtype=grid.dtype,
transform=transform) as dst:
dst.write_band(1, grid[:, :, 0])
dst.write_band(2, grid[:, :, 1])
def coord2pixel(x_coord, y_coord, dataset):
"""Returns base-0 raster index using global coordinates to pixel center
Parameters
----------
x_coord: float
The projected x coordinate of the cell center.
y_coord: float
The projected y coordinate of the cell center.
Returns
-------
:obj:`tuple`
(col, row) - The 0-based column and row index of the pixel.
"""
affine = Affine.from_gdal(*dataset.GetGeoTransform())
col, row = ~affine * (x_coord, y_coord)
if col > dataset.RasterXSize or col < 0:
raise IndexError("Longitude {0} is out of bounds ..."
.format(x_coord))
if row > dataset.RasterYSize or row < 0:
raise IndexError("Latitude {0} is out of bounds ..."
.format(y_coord))
return int(col), int(row)
def pointExtract(self, geopnts):
'''
Inputs:
------------------
:raster - gdal object
:geopnts - geopandas vector object
'''
self._validateType(self.raster, 'raster')
self._validateType(geopnts, 'vector')
geotrans= self.raster.geotransform
forward_transform= Affine.from_gdal(*geotrans)
values= {'lats': [],
'lons': [],
'samples': []}
for i in range(len(geopnts.layer)):
# print(np.array(geopnts.layer.geometry[i]).squeeze())
geometry= np.array(geopnts.layer.geometry[i]).squeeze()
res = ~forward_transform* geometry
x,y = int(res[0]), int(res[1])
values['lats'].append(geometry[0])
values['lons'].append(geometry[1])
values['samples'].append(self._pointsampling(self.raster, x, y))
return values
def extract_point_from_raster(point, data_source, band_number=1):
"""Return floating-point value that corresponds to given point."""
pppoint = PassepartoutPoint(point)
# Convert point co-ordinates so that they are in same projection as raster
target_srs_wkt = data_source.GetProjection()
try:
pppoint.transform_to(target_srs_wkt)
except GDALException:
raise RuntimeError("Couldn't convert point to raster's CRS")
infinities = (float("inf"), float("-inf"))
if pppoint.x in infinities or pppoint.y in infinities:
raise RuntimeError("Couldn't convert point to raster's CRS")
# Convert geographic co-ordinates to pixel co-ordinates
forward_transform = Affine.from_gdal(*data_source.GetGeoTransform())
reverse_transform = ~forward_transform
px, py = reverse_transform * (pppoint.x, pppoint.y)
px, py = int(px + 0.5), int(py + 0.5)
# Extract pixel value
band = data_source.GetRasterBand(band_number)
structval = band.ReadRaster(px, py, 1, 1, buf_type=gdal.GDT_Float32)
result = struct.unpack("f", structval)[0]
if result == band.GetNoDataValue():
result = float("nan")
return result
def test_data_dir_2(tmpdir):
kwargs = {
"crs": {
'init': 'epsg:4326'
},
"transform": Affine.from_gdal(-114, 0.2, 0, 46, 0, -0.2),
"count": 4,
"dtype": rasterio.uint8,
"driver": "GTiff",
"width": 10,
"height": 10
}
with rasterio.Env():
with rasterio.open(str(tmpdir.join('b.tif')), 'w', **kwargs) as dst:
data = numpy.zeros((4, 10, 10), dtype=rasterio.uint8)
data[0:3, 0:6, 0:6] = 255
data[3, 0:6, 0:6] = 255
dst.write(data)
with rasterio.open(str(tmpdir.join('a.tif')), 'w', **kwargs) as dst:
data = numpy.ones((4, 10, 10), dtype=rasterio.uint8)
data[3, :, :] = 255 # no nodata
dst.write(data)
return tmpdir
def get_cropped_profile(profile: dict, slice_x: slice, slice_y: slice):
"""
slice_x and slice_y are numpy slices
"""
x_start = slice_x.start or 0
y_start = slice_y.start or 0
x_stop = slice_x.stop or profile['width']
y_stop = slice_y.stop or profile['height']
width = x_stop - x_start
height = y_stop - y_start
profile_cropped = profile.copy()
trans = profile['transform']
x_cropped, y_cropped = xy(trans, y_start, x_start, offset='ul')
trans_list = list(trans.to_gdal())
trans_list[0] = x_cropped
trans_list[3] = y_cropped
tranform_cropped = Affine.from_gdal(*trans_list)
profile_cropped['transform'] = tranform_cropped
profile_cropped['height'] = height
profile_cropped['width'] = width
return profile_cropped
def get_mask():
"""
Gets PSN Arctic region mask
"""
src_dir = '/disks/sidads_ftp/DATASETS/NOAA/G02135/seaice_analysis'
src_file = 'Arctic_region_mask_Meier_AnnGlaciol2007.msk'
nrow, ncol = (448, 304)
mask = np.fromfile(os.path.join(src_dir, src_file),
dtype='byte').reshape(nrow, ncol)
# msk = np.rot90(msk,2)
# msk = np.flipud(msk)
geo_transform = [-3850000.000, 25000., 0., 5850000.000, 0.,
-25000.] # GDAL style geotransform
transform = Affine.from_gdal(*geo_transform)
x, _ = (np.arange(ncol) + 0.5, np.zeros(ncol) + 0.5) * transform
_, y = (np.zeros(nrow) + 0.5, np.arange(nrow) + 0.5) * transform
# x = [(a * (ic, 0))[0] for ic in np.arange(ncol)]
# y = [(a * (0, ir))[1] for ir in np.arange(nrow)]
da = xr.DataArray(mask, coords={'x': x, 'y': y}, dims=['y', 'x'])
#da = xr.DataArray(msk, dims=['y','x'])
return da
def point_index_from_grid(gdf, dem_path):
# load geo raster and get pixel centers
da = xr.open_rasterio(dem_path)
transform = Affine.from_gdal(*da.transform)
nx, ny = da.sizes['x'], da.sizes['y']
x, y = transform * np.meshgrid(np.arange(nx) + 0.5, np.arange(ny) + 0.5)
# put point data into projection of gridded data
new = gdf.to_crs(da.crs[6:])
#station index
x_idx = []
y_idx = []
for i in range(len(new)):
minx = abs(new.geometry.x[i] - da.x.values)
x = np.where(minx == min(abs(new.geometry.x[i] - da.x.values)))[0][0]
x_idx.append(x)
# flip y values to align with cartesian coordinates
miny = abs(new.geometry.y[i] - np.flip(da.y.values))
y = np.where(miny == min(abs(new.geometry.y[i] -
np.flip(da.y.values))))[0][0]
y_idx.append(y)
gdf['x_idx'] = x_idx
gdf['y_idx'] = y_idx
return gdf
def write_measurement_h5(
p: DatasetAssembler,
name: str,
g: h5py.Dataset,
overviews=images.DEFAULT_OVERVIEWS,
overview_resampling=Resampling.nearest,
expand_valid_data=True,
file_id: str = None,
):
"""
Write a measurement by copying it from a hdf5 dataset.
"""
if hasattr(g, "chunks"):
data = g[:]
else:
data = g
p.write_measurement_numpy(
name=name,
array=data,
grid_spec=images.GridSpec(
shape=g.shape,
transform=Affine.from_gdal(*g.attrs["geotransform"]),
crs=CRS.from_wkt(g.attrs["crs_wkt"]),
),
nodata=(g.attrs.get("no_data_value")),
overviews=overviews,
overview_resampling=overview_resampling,
expand_valid_data=expand_valid_data,
file_id=file_id,
)
def heads_to_raster(
headsfile,
rasterfolder,
transform,
noflow=1e30,
driver='GTiff',
epsg=28992,
):
# create output folder
rasterfolder.mkdir(exist_ok=True)
hds = flopy.utils.binaryfile.HeadFile(headsfile)
heads = np.ma.masked_equal(hds.get_data(), noflow)
for layer, iheads in enumerate(heads[::2]):
log.debug('exporting heads layer {layer:d}'.format(layer=layer + 1))
rasterfile = rasterfolder / 'heads_l{layer:02d}.tif'.format(
layer=layer + 1, )
width, height = iheads.shape
profile = {
'driver': driver,
'width': width,
'height': height,
'count': 1,
'transform': Affine.from_gdal(*transform),
'dtype': iheads.dtype,
}
write_raster(rasterfile, iheads, profile)
def test_rasterio_handles():
"""
Test to create a file handle(s) depending on whether the output data has a color layer or not.
"""
bounds = (675248.0, 4897075.0, 675460.5, 4897173.0)
resolution = 0.5
geotransform = (bounds[0], resolution, 0.0, bounds[3], 0.0, -resolution)
transform = Affine.from_gdal(*geotransform)
rio_params = dict(
height=196, width=425, driver='GTiff', dtype=np.float32,
transform=transform, crs='EPSG:{}'.format(32631), tiled=True
)
dsm_no_data = -32768
color_no_data = 0
nb_bands = 1
# Create file handles
with tempfile.TemporaryDirectory(dir=temporary_dir()) as directory:
dsm_file = os.path.join(directory, 'dsm.tif')
clr_file = os.path.join(directory, 'clr.tif')
file_handles = readwrite.rasterio_handles(['hgt', 'clr'], [dsm_file, clr_file], [rio_params, rio_params], [dsm_no_data, color_no_data], [1, nb_bands])
with file_handles as rio_handles:
assert isinstance(rio_handles, dict) == True
assert 'hgt' in rio_handles.keys() and 'clr' in rio_handles.keys()
for key in rio_handles.keys():
assert isinstance(rio_handles[key], rio.io.DatasetWriter)
def test_data_dir_1(tmpdir):
kwargs = {
"crs": {
"init": "epsg:4326"
},
"transform": Affine.from_gdal(-114, 0.2, 0, 46, 0, -0.2),
"count": 4,
"dtype": rasterio.uint8,
"driver": "GTiff",
"width": 10,
"height": 10,
}
with rasterio.Env():
with rasterio.open(str(tmpdir.join("b.tif")), "w", **kwargs) as dst:
data = numpy.zeros((4, 10, 10), dtype=rasterio.uint8)
data[0:3, 0:6, 0:6] = 255
data[3, 0:6, 0:6] = 255
dst.write(data)
with rasterio.open(str(tmpdir.join("a.tif")), "w", **kwargs) as dst:
data = numpy.zeros((4, 10, 10), dtype=rasterio.uint8)
data[0:3, 4:8, 4:8] = 254
data[3, 4:8, 4:8] = 255
dst.write(data)
return tmpdir
def compute_stats(image, geoms):
# Get Affine object in order to run zonal_stats
aff = Affine.from_gdal(*image.GetGeoTransform())
# Extract values
values = image.ReadAsArray()
# Get no-data value
nodata = image.GetRasterBand(1).GetNoDataValue()
# Create WKT representation for each polygon
wkts = []
for g in geoms:
wkts.append(g.ExportToWkt())
# Compute stats for each polygon
stats = zonal_stats(wkts,
values,
stats=['min', 'max', 'count', 'std'],
affine=aff,
nodata=nodata,
raster_out=False,
prefix='',
add_stats={'model': model},
geojson_out=True)
return stats
def test_data_dir_1(tmpdir):
kwargs = {
"crs": {'init': 'epsg:4326'},
"transform": Affine.from_gdal(-114, 0.2, 0, 46, 0, -0.2),
"count": 4,
"dtype": rasterio.uint8,
"driver": "GTiff",
"width": 10,
"height": 10
}
with rasterio.Env():
with rasterio.open(str(tmpdir.join('b.tif')), 'w', **kwargs) as dst:
data = numpy.zeros((4, 10, 10), dtype=rasterio.uint8)
data[0:3, 0:6, 0:6] = 255
data[3, 0:6, 0:6] = 255
dst.write(data)
with rasterio.open(str(tmpdir.join('a.tif')), 'w', **kwargs) as dst:
data = numpy.zeros((4, 10, 10), dtype=rasterio.uint8)
data[0:3, 4:8, 4:8] = 254
data[3, 4:8, 4:8] = 255
dst.write(data)
return tmpdir
def convert_coordinates(geotransform, xy, to_map=True, centre=False):
"""
Given a tuple containing an (x, y) co-ordinate pair, convert
the co-ordinate pair to either image/array co-ordinates or
real world (map) co-ordinates.
:param geotransform:
A list or tuple of length 6 containing a valid GDAL style
GeoTransform.
:param xy:
A tuple containing an (x, y) co-ordinate pair. The pair
can be either image/array co-ordinates or map co-ordinates.
If xy is a list of tuple co-ordinate pairs, then each (x, y)
pair will be converted, eg [(x, y), (x, y), (x, y)].
If image co-ordinates are input, then set to_map=True. If map
co-ordinates are input, then set to_map=False.
:param to_map:
A boolean indicating if the conversion should be image to
map or map to image. Default is True (image to map).
:param centre:
A boolean indicating if the returned co-ordinate pair
should be offset by 0.5 indicating the centre of a pixel.
Default is False.
:return:
A tuple containing an (x, y) co-ordinate pair.
The returned type will be int if to_map=False and float
if to_map=True (Default). If xy is a list of tuple
co-ordinate pairs, then a list of (x, y) co-ordinate pairs
will be returned, eg [(x, y), (x, y), (x, y)].
"""
# define the affine transformation
affine = Affine.from_gdal(*geotransform)
# If we have a list of tuples otherwise we'll get an int
if isinstance(xy[0], collections.Sequence):
points = []
if to_map:
if centre:
xy = [(x + 0.5, y + 0.5) for x, y in xy]
for point in xy:
xy = point * affine
points.append(xy)
else:
for point in xy:
x, y = point * ~affine
points.append((int(x), int(y)))
return points
else:
if to_map:
if centre:
xy = tuple(v + 0.5 for v in xy)
x, y = xy * affine
else:
xy = xy * ~affine
x, y = tuple(int(v) for v in xy)
return x, y
def clip_land_use_raster(land_use_raster, region_shapefile, output_file):
with rasterio.open(land_use_raster) as r:
with fiona.open(region_shapefile) as clipper:
(w, s, e, n) = clipper.bounds
a = r.affine
#TODO: need to transform the affine for new clipping
(min_col, min_row) = map(int, ~a * (w, n))
(max_col, max_row) = map(int, ~a * (e, s))
w2, n2 = a * (min_col, min_row)
new_affine = Affine.from_gdal(w2, 100, 0.0, n2, 0.0, -100)
(height,width) = r.read(1, window = ((min_row, max_row), (min_col, max_col))).shape
profile = r.profile
profile.update({
'transform': new_affine,
'affine': new_affine,
'height': height,
'width': width
})
with rasterio.open(output_file, 'w', **profile) as out:
for i in r.indexes:
clipped = r.read(i, window = ((min_row, max_row), (min_col, max_col)))
#print clipped.shape
out.write(clipped, indexes = i)
def to_geojson(self, projection, geotransform):
fwd = Affine.from_gdal(*geotransform)
min_x_min_y = fwd * (self.min_x, self.min_y)
max_x_min_y = fwd * (self.max_x, self.min_y)
max_x_max_y = fwd * (self.max_x, self.max_y)
min_x_max_y = fwd * (self.min_x, self.max_y)
coordinates = [
min_x_min_y,
max_x_min_y,
max_x_max_y,
min_x_max_y,
min_x_min_y
]
src_proj = osr.SpatialReference()
src_proj.ImportFromWkt(projection)
dst_proj = osr.SpatialReference()
dst_proj.ImportFromEPSG(4326)
coord_trans = osr.CoordinateTransformation(src_proj, dst_proj)
coordinates = [
coord_trans.TransformPoint(coord[0], coord[1], 0.0)[:2]
for coord in coordinates
]
return Polygon(coordinates=coordinates)
def _rasterise_gdf(dissolved):
"""Rasterise the GeoDataFrame dissolved.
:param dissolved: A GeoDataFrame in which there is one row for each desired range
map.
"""
with open(SCI_NAME_RASTER_FILENAME_MAPPING_FP, 'a', newline='') as snrfmf:
snrfmf_writer = csv.writer(snrfmf)
for row in dissolved.itertuples():
sisid_str = str(row.SISID)
breeding_str = str(row.BREEDING)
uncompressed_filename = map_sisid_breeding_to_filename(
sisid_str, breeding_str, True)
uncompressed_file_path = os.path.join(RASTER_DIR_PATH,
uncompressed_filename)
least_longitude = row.geometry.bounds[0]
least_latitude = row.geometry.bounds[1]
longitude_range = row.geometry.bounds[2] - row.geometry.bounds[0]
latitude_range = row.geometry.bounds[3] - row.geometry.bounds[1]
pixel_width_float = float(Fraction(PIXEL_WIDTH_STR))
pixel_height_float = float(Fraction(PIXEL_HEIGHT_STR))
width = ceil(longitude_range / pixel_width_float)
height = ceil(latitude_range / pixel_height_float)
geotransform = (least_longitude, pixel_width_float, 0.0,
least_latitude, 0.0, pixel_height_float)
transform = Affine.from_gdal(*geotransform)
print_w_timestamp('Generating %s...' % uncompressed_filename,
end=' ')
_generate_raster(uncompressed_file_path, width, height, transform,
row.geometry)
print('Done.')
compressed_filename = map_sisid_breeding_to_filename(
sisid_str, breeding_str, False)
compressed_file_path = os.path.join(RASTER_DIR_PATH,
compressed_filename)
print_w_timestamp('Compressing...', end=' ')
_compress_raster(uncompressed_file_path, compressed_file_path)
print('Done.')
# Delete uncompressed raster.
os.remove(uncompressed_file_path)
# Delete ".tif.aux.xml" file.
xml_file_path = compressed_file_path + '.aux.xml'
os.remove(xml_file_path)
# At this point, I assert that a GeoTIFF has been generated and compressed
# successfully. Therefore, a mapping is added.
sci_name = str(row.SCINAME)
snrfmf_writer.writerow((sci_name, compressed_filename))
def check_vals(data,affine,points=TEST_POINTS):
inv_a = Affine.from_gdal(*gdal.InvGeoTransform(affine.to_gdal()))
for (coords,val) in points:
[col_f,row_f] = inv_a*coords[::-1]
[col,row] = [int(col_f),int(row_f)]
assert data[row,col]==val
def __init__(self):
self.p = pyproj.Proj(self._p_str)
self.gt = (self._x0, self._res, 0, self._y0, 0, -self._res)
self.fwd = Affine.from_gdal(*self.gt)
self.rev = ~self.fwd
# to get coords
self.x_size = self._res * self._shape[1]
self.y_size = self._res * self._shape[0]
def write_geotiff(self, data_array, file_name):
assert data_array.ndim == 2
_args = dict(driver='GTiff', height=data_array.shape[0], width=data_array.shape[1],
count=1, dtype=data_array.dtype.name, crs=data_array.attrs['crs'],
transform=Affine.from_gdal(*data_array.attrs['gt']))
with rio.open(file_name, 'w', **_args) as geotif:
geotif.write(data_array.values, 1)
def transform(self):
transform = [self.header['TL'][0],
self.header['RES'][0],
self.header['ROT'][0],
self.header['TL'][1],
self.header['ROT'][1],
self.header['RES'][1]]
return Affine.from_gdal(*transform)
def test_wrf_project(wrf):
"""Test project wrf grid"""
with wrf.xd as xd:
pgrid = xd.lsm.to_utm('RAINC')
# make sure coordinates correct
assert 'lat' in pgrid.coords
assert 'lon' in pgrid.coords
assert 'time' in pgrid.coords
# check @property attributes
date_array = [
'2016-08-23 22:00:00', '2016-08-23 23:00:00',
'2016-08-24 00:00:00', '2016-08-24 01:00:00',
'2016-08-24 02:00:00', '2016-08-24 03:00:00',
'2016-08-24 04:00:00', '2016-08-24 05:00:00',
'2016-08-24 06:00:00', '2016-08-24 07:00:00',
'2016-08-24 08:00:00', '2016-08-24 09:00:00',
'2016-08-24 10:00:00', '2016-08-24 11:00:00',
'2016-08-24 12:00:00', '2016-08-24 13:00:00'
]
assert (pgrid.lsm.datetime == pd.to_datetime(date_array)).all()
# check projection
proj4_str = ('+proj=utm +zone=13 +datum=WGS84 +units=m +no_defs ')
compare_proj4(pgrid.lsm.projection.ExportToProj4(), proj4_str)
# check other attrs
assert pgrid.lsm.epsg == '32613'
assert_almost_equal(pgrid.lsm.geotransform, [
-529776.2885911233, 6010.014137057385, 0.0, 4558039.843039687, 0.0,
-6010.014137057385
],
decimal=3)
assert_almost_equal(pgrid.lsm.dx, 6010.014137057385)
assert_almost_equal(pgrid.lsm.dy, 6010.014137057385)
assert pgrid.lsm.affine == Affine.from_gdal(*pgrid.lsm.geotransform)
assert pgrid.lsm.x_size == 291
assert pgrid.lsm.y_size == 230
lat, lon = pgrid.lsm.latlon
assert lat.shape == (230, 291)
assert lon.shape == (230, 291)
assert_almost_equal(lat[20:23, 145:148],
[[40.0494547, 40.0505404, 40.0515833],
[39.9953332, 39.9964168, 39.9974578],
[39.9412112, 39.9422928, 39.9433317]])
assert_almost_equal(
lon[144:147, 15:17],
[[-114.9990177, -114.9356954], [-114.9929548, -114.9296693],
[-114.9869079, -114.9236591]])
y_coords, x_coords = pgrid.lsm.coords
assert y_coords.shape == (230, 291)
assert x_coords.shape == (230, 291)
assert_almost_equal(x_coords[100:102, 220:223],
[[795431.8286, 801441.8428, 807451.8569],
[795431.8286, 801441.8428, 807451.8569]],
decimal=4)
assert_almost_equal(y_coords[100:102, 220:223],
[[3954033.4223, 3954033.4223, 3954033.4223],
[3948023.4081, 3948023.4081, 3948023.4081]],
decimal=4)
assert_almost_equal(pgrid.lsm.center, [-106.6965833, 34.8059311])
def get_affine(src):
aff = None
# See https://github.com/mapbox/rasterio/issues/86
with warnings.catch_warnings():
warnings.simplefilter("ignore")
aff = src.transform
if isinstance(aff,list):
aff = Affine.from_gdal(*aff)
return aff
def get_affine(src):
aff = None
# See https://github.com/mapbox/rasterio/issues/86
with warnings.catch_warnings():
warnings.simplefilter("ignore")
aff = src.transform
if isinstance(aff, list):
aff = Affine.from_gdal(*aff)
return aff
def load_grid(self):
from tools4msp.models import PartracGrid
r = PartracGrid.objects.all()[0]
grid = r.rast.bands[0].data()
grid[:] = 0
gtransform = Affine.from_gdal(*r.rast.geotransform)
proj = r.rast.srs.srid
self.grid = rg.RectifiedGrid(grid, proj, gtransform)
def test_convert():
col, row = 0, 100
affine_params = Affine.from_gdal(*(-237481.5, 425.0, 0.0, 237536.4, 0.0,
-425.0))
assert (-237481.5, 195036.4) == rowcol2xy((row, col), affine_params)
assert (row, col) == xy2rowcol((-237481.5, 195036.4),
affine_params,
round_function=round)
def concatenate_files(
infile_paths: Iterable[str],
outfile: str,
h5_info: Dict,
doy: str,
tile_metadata: Dict,
) -> None:
"""
A function to concatenate multiple h5 files and append metadata information.
"""
assert len(infile_paths) == 7
# set the uuids used in processing average for given day of year
tile_metadata["lineage"]["doy_average"] = [
munge_metadata(h5_info[key]) for key in h5_info
if folder_doy(key) == doy
]
# Set deterministic UUID from lineage
tile_metadata["id"] = str(
uuid.uuid5(
FALLBACK_NAMESPACE,
FALLBACK_PRODUCT_HREF + "&" +
urllib.parse.urlencode(tile_metadata["lineage"]),
))
geom_mask = None
transform = None
with atomic_h5_write(Path(outfile), "w") as out_fid:
# Sorting works since No. of bands < 10
# note that 3 hdf5 datasets will show consecutively per band
for fp in sorted(infile_paths):
with h5py.File(fp, "r") as in_fid:
for ds_band in in_fid:
if "BRDF_Albedo_Parameters_" in ds_band:
_band = in_fid[ds_band]
if not transform:
transform = Affine.from_gdal(
*_band.attrs["geotransform"])
nodata_value = _band.attrs["_FillValue"]
albedo_params = _band[()]
if geom_mask is not None:
geom_mask |= np.logical_or(
*((albedo_params[layer] != nodata_value)
for layer in DTYPE_MAIN.names))
else:
geom_mask = np.logical_or(
*((albedo_params[layer] != nodata_value)
for layer in DTYPE_MAIN.names))
in_fid.copy(source=ds_band, dest=out_fid)
# Calculate valid bounds from source ISO, VOL, GEO params
tile_metadata["geometry"] = _calculate_valid_bounds(
geom_mask, transform)
# Write out metadata
write_h5_md(out_fid, [tile_metadata], ["/"])
def retrieve_pixel_coords(geo_coord, geot_params):
x, y = geo_coord[0], geo_coord[1]
forward_transform = Affine.from_gdal(*geot_params)
reverse_transform = ~forward_transform
px, py = reverse_transform * (x, y)
px = np.around(px).astype(int)
py = np.around(py).astype(int)
pixel_coord = px, py
return pixel_coord
def rasterize_geom(geom, src_offset, new_gt, all_touched):
geoms = [(geom, 1)]
affinetrans = Affine.from_gdal(*new_gt)
rv_array = features.rasterize(
geoms,
out_shape=(src_offset[3], src_offset[2]),
transform=affinetrans,
fill=0,
all_touched=all_touched)
return rv_array
def test_gdal():
t = Affine.from_gdal(-237481.5, 425.0, 0.0, 237536.4, 0.0, -425.0)
assert t.c == t.xoff == -237481.5
assert t.a == 425.0
assert t.b == 0.0
assert t.f == t.yoff == 237536.4
assert t.d == 0.0
assert t.e == -425.0
assert tuple(t) == (425.0, 0.0, -237481.5, 0.0, -425.0, 237536.4, 0, 0, 1.0)
assert t.to_gdal() == (-237481.5, 425.0, 0.0, 237536.4, 0.0, -425.0)
def test_ndarray_affine():
polygons = os.path.join(DATA, 'polygons.shp')
arr, gt = _get_raster_array_gt(raster)
stats1 = zonal_stats(polygons, arr, transform=gt)
from affine import Affine
atrans = Affine.from_gdal(*gt)
stats2 = zonal_stats(polygons, arr, transform=atrans)
assert stats1[0]['count'] == stats2[0]['count']
stats3 = zonal_stats(polygons, arr, affine=gt)
assert stats1[0]['count'] == stats3[0]['count']
def guard_transform(transform):
"""Return an Affine transformation instance"""
if not isinstance(transform, Affine):
if tastes_like_gdal(transform):
warnings.warn(
"GDAL-style transforms are deprecated and will not "
"be supported in Rasterio 1.0.",
FutureWarning,
stacklevel=2)
transform = Affine.from_gdal(*transform)
else:
transform = Affine(*transform)
return transform
def Raster_to_Polygon(input_file):
gdal.UseExceptions()
src_ds = gdal.Open(input_file,GA_ReadOnly)
print(input_file)
#target = osr.SpatialReference()
wkt =src_ds.GetProjection()
print(wkt)
src =osr.SpatialReference()
print(src)
ds=src.ImportFromWkt(wkt)
#srcband = src_ds.GetRasterBand(1)
myarray =(src_ds.GetRasterBand(1).ReadAsArray())
#print(myarray)
T0 = Affine.from_gdal(*src_ds.GetGeoTransform())
tx=[T0[0], T0[1], T0[2], T0[3],T0[4], T0[5]]
print(tx)
epsg_code=[]
#if (src.IsProjected()):
# ds=epsg_code.append(int(src.GetAuthorityCode("PROJCS")))
# print(ds)
# else:
# epsg_code.append(int(src.GetAuthorityCode("GEOGCS")))
target = osr.SpatialReference()
target.ImportFromEPSG(102003)
if src_ds is None:
#print 'Unable to open %s' % src_filename
sys.exit(1)
try:
srcband = src_ds.GetRasterBand(1)
srd=srcband.GetMaskBand()
except RuntimeError as e:
# for example, try GetRasterBand(10)
#print 'Band ( %i ) not found' % band_num
#print e
sys.exit(1)
drv = ogr.GetDriverByName("ESRI Shapefile")
if os.path.exists('temp.shp'):
drv.DeleteDataSource('temp.shp')
dst_layername ='temp'
dst_ds = drv.CreateDataSource('temp'+ ".shp" )
print(dst_ds)
dst_layer = dst_ds.CreateLayer(dst_layername, srs=target)
gdal.Polygonize( srcband,srd, dst_layer, -1, [], callback=None)
src_ds=None
def _geofactory(bounds, proj, res, dtype=np.float64, eea=False):
if eea:
gbounds = calculate_eea_gbounds(bounds, res)
else:
gbounds = calculate_gbounds(bounds, res)
cols = int(round((gbounds[2] - gbounds[0]) / res))
rows = int(round((gbounds[3] - gbounds[1]) / res))
_gtransform = (gbounds[0], res, 0.0, gbounds[3], 0.0, -res)
gtransform = Affine.from_gdal(*_gtransform)
# we use copy=True in order to avoid sharedmask=True
return RectifiedGrid(np.zeros((rows, cols), dtype),
proj,
gtransform)
def makeCoverageGrid(covshp,geodict):
shapes = fiona.open(covshp)
geoms = []
for shape in shapes:
geoms.append(shape['geometry'])
shapes.close()
outshape = (geodict['nrows'],geodict['ncols'])
transform = Affine.from_gdal(geodict['xmin'],geodict['xdim'],0.0,geodict['ymax'],0.0,-geodict['ydim'])
img = features.rasterize(geoms,out_shape=outshape,fill=0,
transform=transform,all_touched=True,
default_value=1)
covgrid = GMTGrid()
covgrid.geodict = geodict
covgrid.griddata = np.int8(img.copy())
return covgrid
def get_metadata( self ):
'''
return a rasterio-style metadata dict for the output file to be
created. Only GeoTiff is currently supported.
'''
from affine import Affine as A
meta = {'affine': A.from_gdal( *self.geotransform ),
'count': 1,
'crs': self.crs,
'driver': u'GTiff',
'dtype': 'uint8',
'height': self.height,
'nodata': self.nodata,
'width': self.width,
'compress': 'lzw'}
return meta
def guard_transform(transform):
"""Return an Affine transformation instance"""
if not isinstance(transform, Affine):
if tastes_like_gdal(transform):
warnings.warn(
"GDAL-style transforms are deprecated and will not "
"be supported in Rasterio 1.0.",
FutureWarning,
stacklevel=2)
transform = Affine.from_gdal(*transform)
else:
transform = Affine(*transform)
a, e = transform.a, transform.e
if a == 0.0 or e == 0.0:
raise ValueError(
"Transform has invalid coefficients a, e: (%f, %f)" % (
transform.a, transform.e))
return transform
def Extract_Soil_Texture2(self):
import numpy as np
from affine import Affine
lon_cell_size = abs(self.lon_grid[0] - self.lon_grid[1])
lat_cell_size = abs(self.lat_grid[0] - self.lat_grid[1])
min_lon = min(self.lon_grid) - lon_cell_size/2.0*0.
min_lat = min(self.lat_grid) - lat_cell_size/2.0*0.
n_lat = np.size(self.lat)
n_lon = np.size(self.lon)
aff = Affine.from_gdal(min_lon, lon_cell_size, 0.0, min_lat, 0.0, lat_cell_size)
lon = np.reshape(np.repeat(self.lon,n_lat), (n_lon,n_lat));
lat = np.transpose(np.reshape(np.repeat(self.lat,n_lon), (n_lat,n_lon)));
x_coords, y_coords = ~aff * (lon, lat)
x_coords = np.round(x_coords).astype(np.int)
y_coords = np.round(y_coords).astype(np.int)
if np.size(x_coords) >= 1 and np.size(y_coords) >= 1:
clay_perc0 = self.Clay_percent[y_coords, x_coords]
sand_perc0 = self.Sand_percent[y_coords, x_coords]
silt_perc0 = self.Silt_percent[y_coords, x_coords]
peat_perc0 = self.Peat_percent[y_coords, x_coords]
clay_perc = np.transpose(np.reshape(clay_perc0, (n_lon, n_lat)))
sand_perc = np.transpose(np.reshape(sand_perc0, (n_lon, n_lat)))
silt_perc = np.transpose(np.reshape(silt_perc0, (n_lon, n_lat)))
peat_perc = np.transpose(np.reshape(peat_perc0, (n_lon, n_lat)))
else:
clay_perc = np.nan;
sand_perc = np.nan;
silt_perc = np.nan;
peat_perc = np.nan;
return clay_perc, sand_perc, silt_perc, peat_perc
def test_data_dir_3(tmpdir):
kwargs = {
"crs": {'init': 'epsg:4326'},
"transform": Affine.from_gdal(-114, 0.1, 0, 46, 0, -0.1),
"count": 4,
"dtype": rasterio.uint8,
"driver": "GTiff",
"width": 32,
"height": 32,
"compress": "JPEG"
}
with rasterio.Env():
with rasterio.open(str(tmpdir.join('a.tif')), 'w', **kwargs) as dst:
data = numpy.ones((4, 32, 32), dtype=rasterio.uint8)
data[3, :, :] = 255
dst.write(data)
return tmpdir
def test_reproject():
from rasterio.warp import reproject
from rasterio.enums import Resampling
with rasterio.Env():
# As source: a 1024 x 1024 raster centered on 0 degrees E and 0
# degrees N, each pixel covering 15".
rows, cols = src_shape = (1024, 1024)
# decimal degrees per pixel
d = 1.0 / 240
# The following is equivalent to
# A(d, 0, -cols*d/2, 0, -d, rows*d/2).
src_transform = rasterio.Affine.translation(
-cols*d/2,
rows*d/2) * rasterio.Affine.scale(d, -d)
src_crs = {'init': 'EPSG:4326'}
source = np.ones(src_shape, np.uint8) * 255
# Destination: a 2048 x 2048 dataset in Web Mercator (EPSG:3857)
# with origin at 0.0, 0.0.
dst_shape = (2048, 2048)
dst_transform = Affine.from_gdal(
-237481.5, 425.0, 0.0, 237536.4, 0.0, -425.0)
dst_crs = {'init': 'EPSG:3857'}
destination = np.zeros(dst_shape, np.uint8)
reproject(
source,
destination,
src_transform=src_transform,
src_crs=src_crs,
dst_transform=dst_transform,
dst_crs=dst_crs,
resampling=Resampling.nearest)
# Assert that the destination is only partly filled.
assert destination.any()
assert not destination.all()
def extract_point_from_raster(point, data_source, band_number=1):
"""Return floating-point value that corresponds to given point."""
# Convert point co-ordinates so that they are in same projection as raster
point_sr = point.GetSpatialReference()
raster_sr = osr.SpatialReference()
raster_sr.ImportFromWkt(data_source.GetProjection())
transform = osr.CoordinateTransformation(point_sr, raster_sr)
point.Transform(transform)
# Convert geographic co-ordinates to pixel co-ordinates
x, y = point.GetX(), point.GetY()
forward_transform = Affine.from_gdal(*data_source.GetGeoTransform())
reverse_transform = ~forward_transform
px, py = reverse_transform * (x, y)
px, py = int(px + 0.5), int(py + 0.5)
# Extract pixel value
band = data_source.GetRasterBand(band_number)
structval = band.ReadRaster(px, py, 1, 1, buf_type=gdal.GDT_Float32)
result = struct.unpack('f', structval)[0]
if result == band.GetNoDataValue():
result = float('nan')
return result
def project(self,projection,method='bilinear'):
"""Project Grid2D data into desired projection.
:param projection:
Valid proj4 projection string.
:param method:
One of the sampling methods described here: https://mapbox.github.io/rasterio/topics/resampling.html#resampling-methods
:raises DataSetException:
If input projection is not a valid Proj4 string.
If method is not a valid resampling method found in above URL.
:returns:
Re-projected Grid2D object.
"""
#check to see if the input projection is valid
srs = osr.SpatialReference()
srs.ImportFromProj4(projection)
if srs.ExportToProj4() == '':
raise DataSetException('%s is not a valid proj4 string.' % geodict['projection'])
#check to see if the input resampling method is valid
int_method = 1 #bi-linear
try:
int_method = getattr(Resampling,method)
except AttributeError as ae:
raise DataSetException('%s is not a valid resampling method.' % method)
with Env():
#get the dimensions of the input data
nrows, ncols = src_shape = self._data.shape
#define the input Affine object
src_transform = Affine.from_gdal(self._geodict.xmin - self._geodict.dx/2.0,
self._geodict.dx,
0.0, #x rotation, not used by us
self._geodict.ymax + self._geodict.dy/2.0,
0.0, #y rotation, not used by us
-1*self._geodict.dy) #their dy is negative
#set the source and destination projections (have to be CRS dictionaries)
src_crs = CRS().from_string(self._geodict.projection).to_dict()
dst_crs = CRS().from_string(projection).to_dict()
#determine the boundaries in src coordinates
if self._geodict.xmin < self._geodict.xmax:
right = self._geodict.xmax - (self._geodict.dx/2.0)
else:
txmax = self._geodict.xmax + 360
right = txmax - (self._geodict.dx/2.0)
left = self._geodict.xmin - (self._geodict.dx/2.0)
top = self._geodict.ymax + (self._geodict.dy/2.0)
bottom = self._geodict.ymin + (self._geodict.dy/2.0)
#use this convenience function to determine optimal output transform and dimensions
dst_transform,width,height = calculate_default_transform(src_crs,dst_crs,
ncols,nrows,
left,bottom,
right,top)
#allocate space for output data (very C-like)
destination = np.zeros((height,width))
#if the input has nan values, then tell reproject about that
#and set the output to that value as well
src_nan = None
dst_nan = None
if np.any(np.isnan(self._data)):
src_nan = np.nan
dst_nan = np.nan
if self._data.dtype in (np.float32,np.float64):
src_nan = np.nan
dst_nan = np.nan
#call the reproject function
reproject(
self._data,
destination,
src_transform=src_transform,
src_crs=src_crs,
dst_transform=dst_transform,
src_nodata=src_nan,
dst_nodata=dst_nan,
dst_crs=projection,
resampling=int_method)
#get the pieces of the output transformation
xmin,dx,xrot,ymax,yrot,mdy = dst_transform.to_gdal()
#affine dy is negative, so we have to flip it back
dy = -1*mdy
#correct for different pixel offsets
xmin = xmin + (dx/2.0)
ymax = ymax - (dy/2.0)
#Construct a new GeoDict
gdict = {'xmin':xmin,
'xmax':xmin+width*dx,
'ymin':ymax-height*dy,
'ymax':ymax,
'dx':dx,
'dy':dy,
'nx':width,
'ny':height,
'projection':projection}
geodict = GeoDict(gdict,adjust='bounds')
#Make a new Grid2D object and return it
newgrid = Grid2D(destination,geodict)
return newgrid
def run():
# TODO - update at each use
# drive_path = os.path.join('/', 'Volumes', 'SeagateExpansionDrive', )
# #tiff_path = os.path.join(drive_path, "jan_metric", 'for_stacking', 'aligned_nlcd_full_warp_near_clip_3336.tif')
# stack_location = os.path.join(drive_path, "jan_metric_PHX_GR", 'green_river_stack', 'stack_20150728_ETrF_NDVI')
drive_path = os.path.join('/', 'Users', 'Gabe', 'Desktop', 'juliet_problem')
# tiff_path = os.path.join(drive_path, "jan_metric", 'for_stacking', 'aligned_nlcd_full_warp_near_clip_3336.tif')
stack_location = os.path.join(drive_path, 'juliet_stack')
#### find the right window to use.
# First get the minimum raster extent.
comparison_list = []
comparison_dict = {}
for directory_path, subdir, file in os.walk(stack_location, topdown=False):
for tf in file:
if tf.endswith(".tif"):
tiff_path = os.path.join(directory_path, tf)
with rasterio.open(tiff_path) as src:
ras = src.read(1)
# raster.shape -> (###,###)
#
# raster.shape[1] raster.shape[0]
comparison_list.append(ras.shape[0]*ras.shape[1])
comparison_dict["{}".format(ras.shape[0]*ras.shape[1])] = tiff_path
# get the minimum dimensions raster.
val = min(comparison_list)
min_raster_path = comparison_dict["{}".format(val)]
print (min_raster_path)
with rasterio.open(min_raster_path) as raster:
ras = raster.read(1)
print 'ras shape 0', ras.shape[0]
print 'ras shape 1', ras.shape[1]
window = ((0, ras.shape[0]), (0, ras.shape[1]))
print "WINDOW", window
bounds = raster.window_bounds(window)
print "BOUNDS", bounds
# Take the bounds from the minimum raster and for each raster in the dir,
# get the correct window to be read in for the dict using the bounds from the min raster.
raster_dict = {}
window_lst = []
for directory_path, subdir, file in os.walk(stack_location, topdown=False):
for tf in file:
if tf.endswith(".tif"):
tiff_path = os.path.join(directory_path, tf)
with rasterio.open(tiff_path) as r:
T0 = r.affine # upper-left pixel corner affine transform
print "Here is T0", T0
window = r.window(*bounds)
print "edited window", window
top_left = [bounds[0], bounds[-1]]
print "Here is top left", top_left
print 'r.window', r.window
A = r.read(1, window=window)
print "A", A
print "A shape", A.shape
# top left x and top left y coord from bounds of window
tlx = bounds[0]
tly = bounds[-1]
geotransform = (tlx, T0[0], 0.0, tly, 0.0, T0[4])
# make an Affine transformation matrix out of the geotransform
fwd = Affine.from_gdal(*geotransform)
# use the affine matrix to shift half a raster over.
T1 = fwd * Affine.translation(0.5, 0.5)
print " The new T1 ", T1
# make a grid to hold the columns and rows based on the shape of the raster you read in.
col, row = np.meshgrid(np.arange(A.shape[1]), np.arange(A.shape[0]))
# convert col and row to UTM centroids using translated affine matrix
x, y = T1 * (col, row)
# add the rasters to a raster dict.
raster_dict['{}'.format(tf.split(".")[0])] = (A, tiff_path)
# make the list of northings y and eastings x
x_list = x.ravel().tolist()
y_list = y.ravel().tolist()
print "Starting the formatter"
# add the raster dict and the list of x and y coordinates separately.
df = data_frame_formatter(x_list, y_list, raster_dict)
#graph ETrF vs NDVI and ideal ETrF vs NDVI
grapher(df)
def rasterizeFromGeometry(cls,shapes,samplegeodict,burnValue=1.0,fillValue=np.nan,allTouched=True,attribute=None):
"""
Create a Grid2D object from vector shapes, where the presence of a shape (point, line, polygon) inside a cell turns that cell "on".
:param shapes:
One of:
- One shapely geometry object (Point, Polygon, etc.) or a sequence of such objects
- One GeoJSON like object or sequence of such objects. (http://geojson.org/)
- A tuple of (geometry,value) or sequence of (geometry,value).
:param samplegeodict:
GeoDict with at least xmin,xmax,ymin,ymax,xdim,ydim values set.
:param burnValue:
Optional value which will be used to set the value of the pixels if there is no value in the geometry field.
:param fillValue:
Optional value which will be used to fill the cells not touched by any geometry.
:param allTouched:
Optional boolean which indicates whether the geometry must touch the center of the cell or merely be inside the cell in order to set the value.
:raises DataSetException:
When geometry input is not a subclass of shapely.geometry.base.BaseGeometry.
:returns:
Grid2D object.
This method is a thin wrapper around rasterio->features->rasterize(), documented here:
https://github.com/mapbox/rasterio/blob/master/docs/features.rst
which is itself a Python wrapper around the functionality found in gdal_rasterize, documented here:
http://www.gdal.org/gdal_rasterize.html
"""
#check the type of shapes
#features.rasterize() documentation says this:
#iterable of (geometry, value) pairs or iterable over
#geometries. `geometry` can either be an object that implements
#the geo interface or GeoJSON-like object.
#figure out whether this is a single shape or a sequence of shapes
isGeoJSON = False
isGeometry = False
isSequence = False
isTuple = False
if hasattr(shapes, '__iter__'):
if isinstance(shapes[0],tuple):
isTuple = True
isOk = False
isShape = False
if isinstance(shapes,shapely.geometry.base.BaseGeometry):
isOk = True
isShape = True
elif len(shapes) and isinstance(shapes[0],shapely.geometry.base.BaseGeometry):
isOk = True
isShape = True
elif isinstance(shapes,dict) and shapes.has_key('geometry') and shapes.has_key('properties'):
isOk = True
elif len(shapes) and isinstance(shapes[0],dict) and shapes[0].has_key('geometry') and shapes[0].has_key('properties'):
isOk = True
else:
pass
if not isOk:
raise DataSetException('shapes must be a single shapely object or sequence of them, or single Geo-JSON like-object')
if not isShape:
shapes2 = []
for shape in shapes:
geometry = shape['geometry']
props = shape['properties']
if attribute is not None:
if not props.has_key(attribute):
raise DataSetException('Input shapes do not have attribute "%s".' % attribute)
value = props[attribute]
if not isinstance(value (int,float,long)):
raise DataSetException('value from input shapes object is not a number')
else:
value = burnValue
shapes2.append((geometry,value))
shapes = shapes2
xmin,xmax,ymin,ymax = (samplegeodict['xmin'],samplegeodict['xmax'],samplegeodict['ymin'],samplegeodict['ymax'])
xdim,ydim = (samplegeodict['xdim'],samplegeodict['ydim'])
xvar = np.arange(xmin,xmax+xdim,xdim)
yvar = np.arange(ymin,ymax+ydim,ydim)
ncols = len(xvar)
nrows = len(yvar)
#the rasterize function assumes a pixel registered data set, where we are grid registered. In order to make this work
#we need to adjust the edges of our grid out by half a cell width in each direction.
txmin = xmin - xdim/2.0
tymax = ymax + ydim/2.0
outshape = (nrows,ncols)
transform = Affine.from_gdal(txmin,xdim,0.0,tymax,0.0,-ydim)
img = features.rasterize(shapes,out_shape=outshape,fill=fillValue,transform=transform,all_touched=allTouched,default_value=burnValue)
geodict = {'xmin':xmin,'xmax':xmax,'ymin':ymin,'ymax':ymax,'xdim':xdim,'ydim':ydim,'nrows':nrows,'ncols':ncols}
return cls(img,geodict)
# Pixel size in the web mercator projection
pixel_width = abs((top_left_web[0] - bottom_right_web[0]) / img_width)
pixel_height = abs((top_left_web[1] - bottom_right_web[1]) / img_height)
# Gdal Transform matrix
geotransform = (
top_left_web[0], # top left corner x
pixel_width, # pixel width,
0.0, # rotation about y-axis
top_left_web[1], # top left corner y
0.0, # rotation about x-axis
-pixel_height, # pixel height.
)
pix2coord = Affine.from_gdal(*geotransform)
coord2pix = ~pix2coord
def wgs2pix(x, y):
"""
Gets WGS84 coordinates and transforms them
to x,y pixel indices in the target image
"""
# Reoject WGS84 coords to web mercator
web_xy = pyproj.transform(wgs, web, x, y)
# Tranform web mercator into pixel indexes
pix_x, pix_y = coord2pix * web_xy
# Round the pixel indexes
return int(pix_x), int(pix_y)
def gen_zonal_stats(
vectors, raster,
layer=0,
band=1,
nodata=None,
affine=None,
stats=None,
all_touched=False,
categorical=False,
category_map=None,
add_stats=None,
zone_func=None,
raster_out=False,
prefix=None,
geojson_out=False, **kwargs):
"""Zonal statistics of raster values aggregated to vector geometries.
Parameters
----------
vectors: path to an vector source or geo-like python objects
raster: ndarray or path to a GDAL raster source
If ndarray is passed, the ``affine`` kwarg is required.
layer: int or string, optional
If `vectors` is a path to an fiona source,
specify the vector layer to use either by name or number.
defaults to 0
band: int, optional
If `raster` is a GDAL source, the band number to use (counting from 1).
defaults to 1.
nodata: float, optional
If `raster` is a GDAL source, this value overrides any NODATA value
specified in the file's metadata.
If `None`, the file's metadata's NODATA value (if any) will be used.
defaults to `None`.
affine: Affine instance
required only for ndarrays, otherwise it is read from src
stats: list of str, or space-delimited str, optional
Which statistics to calculate for each zone.
All possible choices are listed in ``utils.VALID_STATS``.
defaults to ``DEFAULT_STATS``, a subset of these.
all_touched: bool, optional
Whether to include every raster cell touched by a geometry, or only
those having a center point within the polygon.
defaults to `False`
categorical: bool, optional
category_map: dict
A dictionary mapping raster values to human-readable categorical names.
Only applies when categorical is True
add_stats: dict
with names and functions of additional stats to compute, optional
zone_func: callable
function to apply to zone ndarray prior to computing stats
raster_out: boolean
Include the masked numpy array for each feature?, optional
Each feature dictionary will have the following additional keys:
mini_raster_array: The clipped and masked numpy array
mini_raster_affine: Affine transformation
mini_raster_nodata: NoData Value
prefix: string
add a prefix to the keys (default: None)
geojson_out: boolean
Return list of GeoJSON-like features (default: False)
Original feature geometry and properties will be retained
with zonal stats appended as additional properties.
Use with `prefix` to ensure unique and meaningful property names.
Returns
-------
generator of dicts (if geojson_out is False)
Each item corresponds to a single vector feature and
contains keys for each of the specified stats.
generator of geojson features (if geojson_out is True)
GeoJSON-like Feature as python dict
"""
stats, run_count = check_stats(stats, categorical)
# Handle 1.0 deprecations
transform = kwargs.get('transform')
if transform:
warnings.warn("GDAL-style transforms will disappear in 1.0. "
"Use affine=Affine.from_gdal(*transform) instead",
DeprecationWarning)
if not affine:
affine = Affine.from_gdal(*transform)
cp = kwargs.get('copy_properties')
if cp:
warnings.warn("Use `geojson_out` to preserve feature properties",
DeprecationWarning)
bn = kwargs.get('band_num')
if bn:
warnings.warn("Use `band` to specify band number", DeprecationWarning)
band = band_num
with Raster(raster, affine, nodata, band) as rast:
features_iter = read_features(vectors, layer)
for _, feat in enumerate(features_iter):
geom = shape(feat['geometry'])
if 'Point' in geom.type:
geom = boxify_points(geom, rast)
geom_bounds = tuple(geom.bounds)
fsrc = rast.read(bounds=geom_bounds)
# rasterized geometry
rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched)
# nodata mask
isnodata = (fsrc.array == fsrc.nodata)
# add nan mask (if necessary)
if np.issubdtype(fsrc.array.dtype, float) and \
np.isnan(fsrc.array.min()):
isnodata = (isnodata | np.isnan(fsrc.array))
# Mask the source data array
# mask everything that is not a valid value or not within our geom
masked = np.ma.MaskedArray(
fsrc.array,
mask=(isnodata | ~rv_array))
# execute zone_func on masked zone ndarray
if zone_func is not None:
if not callable(zone_func):
raise TypeError(('zone_func must be a callable '
'which accepts function a '
'single `zone_array` arg.'))
zone_func(masked)
if masked.compressed().size == 0:
# nothing here, fill with None and move on
feature_stats = dict([(stat, None) for stat in stats])
if 'count' in stats: # special case, zero makes sense here
feature_stats['count'] = 0
else:
if run_count:
keys, counts = np.unique(masked.compressed(), return_counts=True)
pixel_count = dict(zip([np.asscalar(k) for k in keys],
[np.asscalar(c) for c in counts]))
if categorical:
feature_stats = dict(pixel_count)
if category_map:
feature_stats = remap_categories(category_map, feature_stats)
else:
feature_stats = {}
if 'min' in stats:
feature_stats['min'] = float(masked.min())
if 'max' in stats:
feature_stats['max'] = float(masked.max())
if 'mean' in stats:
feature_stats['mean'] = float(masked.mean())
if 'count' in stats:
feature_stats['count'] = int(masked.count())
# optional
if 'sum' in stats:
feature_stats['sum'] = float(masked.sum())
if 'std' in stats:
feature_stats['std'] = float(masked.std())
if 'median' in stats:
feature_stats['median'] = float(np.median(masked.compressed()))
if 'majority' in stats:
feature_stats['majority'] = float(key_assoc_val(pixel_count, max))
if 'minority' in stats:
feature_stats['minority'] = float(key_assoc_val(pixel_count, min))
if 'unique' in stats:
feature_stats['unique'] = len(list(pixel_count.keys()))
if 'range' in stats:
try:
rmin = feature_stats['min']
except KeyError:
rmin = float(masked.min())
try:
rmax = feature_stats['max']
except KeyError:
rmax = float(masked.max())
feature_stats['range'] = rmax - rmin
for pctile in [s for s in stats if s.startswith('percentile_')]:
q = get_percentile(pctile)
pctarr = masked.compressed()
feature_stats[pctile] = np.percentile(pctarr, q)
if 'nodata' in stats:
featmasked = np.ma.MaskedArray(fsrc.array, mask=np.logical_not(rv_array))
feature_stats['nodata'] = float((featmasked == fsrc.nodata).sum())
if add_stats is not None:
for stat_name, stat_func in add_stats.items():
feature_stats[stat_name] = stat_func(masked)
if raster_out:
feature_stats['mini_raster_array'] = masked
feature_stats['mini_raster_affine'] = fsrc.affine
feature_stats['mini_raster_nodata'] = fsrc.nodata
if prefix is not None:
prefixed_feature_stats = {}
for key, val in feature_stats.items():
newkey = "{}{}".format(prefix, key)
prefixed_feature_stats[newkey] = val
feature_stats = prefixed_feature_stats
if geojson_out:
for key, val in feature_stats.items():
if 'properties' not in feat:
feat['properties'] = {}
feat['properties'][key] = val
yield feat
else:
yield feature_stats
def rasterizeFromGeometry(cls,shapes,geodict,burnValue=1.0,fillValue=np.nan,
mustContainCenter=False,attribute=None):
"""
Create a Grid2D object from vector shapes, where the presence of a shape
(point, line, polygon) inside a cell turns that cell "on".
:param shapes:
One of:
- One shapely geometry object (Point, Polygon, etc.) or a sequence of such objects
- One GeoJSON like object or sequence of such objects. (http://geojson.org/)
- A tuple of (geometry,value) or sequence of (geometry,value).
:param geodict:
GeoDict object which defines the grid onto which the shape values should be "burned".
:param burnValue:
Optional value which will be used to set the value of the pixels if there is no
value in the geometry field.
:param fillValue:
Optional value which will be used to fill the cells not touched by any geometry.
:param mustContainCenter:
Optional boolean which indicates whether the geometry must touch
the center of the cell or merely be inside the cell in order to set the value.
:raises DataSetException:
When geometry input is not a subclass of shapely.geometry.base.BaseGeometry.
:returns:
Grid2D object.
This method is a thin wrapper around rasterio->features->rasterize(), documented here:
https://github.com/mapbox/rasterio/blob/master/docs/features.rst
which is itself a Python wrapper around the functionality found in gdal_rasterize, documented here:
http://www.gdal.org/gdal_rasterize.html
"""
#check the type of shapes
#features.rasterize() documentation says this:
#iterable of (geometry, value) pairs or iterable over
#geometries. `geometry` can either be an object that implements
#the geo interface or GeoJSON-like object.
#create list of allowable types
if sys.version_info.major == 2:
types = (int,float,long)
else:
types = (int,float)
#figure out whether this is a single shape or a sequence of shapes
isGeoJSON = False
isGeometry = False
isSequence = False
isTuple = False
if hasattr(shapes, '__iter__'):
if isinstance(shapes[0],tuple):
isTuple = True
isOk = False
isShape = False
if isinstance(shapes,shapely.geometry.base.BaseGeometry):
isOk = True
isShape = True
elif len(shapes) and isinstance(shapes[0],shapely.geometry.base.BaseGeometry):
isOk = True
isShape = True
elif isinstance(shapes,dict) and 'geometry' in shapes and 'properties' in shapes:
isOk = True
elif len(shapes) and isinstance(shapes[0],dict) and 'geometry' in shapes[0] and 'properties' in shapes[0]:
isOk = True
else:
pass
if not isOk:
raise DataSetException('shapes must be a single shapely object or sequence of them, or single Geo-JSON like-object')
if not isShape:
shapes2 = []
for shape in shapes:
geometry = shape['geometry']
props = shape['properties']
if attribute is not None:
if not attribute in props:
raise DataSetException('Input shapes do not have attribute "%s".' % attribute)
value = props[attribute]
if not isinstance(value,types):
raise DataSetException('value from input shapes object is not a number')
else:
value = burnValue
shapes2.append((geometry,value))
shapes = shapes2
xmin,xmax,ymin,ymax = (geodict.xmin,geodict.xmax,geodict.ymin,geodict.ymax)
dx,dy = (geodict.dx,geodict.dy)
if xmax < xmin:
xmax += 360
xvar = np.arange(xmin,xmax+(dx*0.1),dx)
yvar = np.arange(ymin,ymax+(dy*0.1),dy)
nx = len(xvar)
ny = len(yvar)
#the rasterize function assumes a pixel registered data set, where we are grid registered. In order to make this work
#we need to adjust the edges of our grid out by half a cell width in each direction.
txmin = xmin - dx/2.0
tymax = ymax + dy/2.0
outshape = (ny,nx)
transform = Affine.from_gdal(txmin,dx,0.0,tymax,0.0,-dy)
allTouched = not mustContainCenter
img = features.rasterize(shapes,out_shape=outshape,fill=fillValue,transform=transform,all_touched=allTouched,default_value=burnValue)
#geodict = GeoDict({'xmin':xmin,'xmax':xmax,'ymin':ymin,'ymax':ymax,'dx':dx,'dy':dy,'ny':ny,'nx':nx})
# gd = geodict.asDict()
# ny,nx = img.shape
# gd['nx'] = nx
# gd['ny'] = ny
# geodict = GeoDict(gd,adjust='bounds')
return cls(img,geodict)
from __future__ import absolute_import
import numpy as np
import rectifiedgrid as rg
from affine import Affine
from shapely import geometry
epsg = 3035
gtransform = Affine.from_gdal(4500000.0, 10000.0, 0.0, 1560000.0, 0.0, -10000.0)
arr1 = np.zeros((9, 9), np.float64)
arr1[:3, :3] = np.array([[-1, -2, -3],
[0, -1, -2],
[0, 0, -1]])
arr1[4:8, 4:8] = np.array([[1, 2, 3, 4],
[1, 2, 3, 4],
[1, 2, 3, 4],
[1, 2, 3, 4]])
arr2 = np.zeros((9, 9), np.float64)
arr2[4, 4] = 1
arr3 = np.zeros((9, 9), np.float64)
arr3[0, 0] = 1
lstring = geometry.LineString(((12.0, 36.0), (13.0, 37.0)))
def get_demo_data(name='default'):
import logging
import sys
import pytest
from affine import Affine
import numpy
import rasterio
from rasterio.warp import (
reproject, RESAMPLING, transform_geom, transform, transform_bounds,
calculate_default_transform)
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
DST_TRANSFORM = Affine.from_gdal(-8789636.708, 300.0, 0.0, 2943560.235, 0.0, -300.0)
class ReprojectParams(object):
""" Class to assist testing reprojection by encapsulating parameters """
def __init__(self, left, bottom, right, top, width, height, src_crs,
dst_crs):
self.width = width
self.height = height
src_res = float(right - left) / float(width)
self.src_transform = Affine(src_res, 0, left, 0, -src_res, top)
self.src_crs = src_crs
self.dst_crs = dst_crs
with rasterio.drivers():
dt, dw, dh = calculate_default_transform(
def gen_zonal_stats(
vectors, raster,
layer=0,
band_num=1,
nodata=None,
affine=None,
stats=None,
all_touched=False,
percent_cover_scale=None,
percent_cover_selection=None,
percent_cover_weighting=False,
latitude_correction=False,
categorical=False,
category_map=None,
add_stats=None,
raster_out=False,
prefix=None,
save_properties=False,
geojson_out=False,
**kwargs):
"""Zonal statistics of raster values aggregated to vector geometries.
Parameters
----------
vectors: path to an vector source or geo-like python objects
raster: ndarray or path to a GDAL raster source
If ndarray is passed, the ``affine`` kwarg is required.
layer: int or string, optional
If `vectors` is a path to an fiona source,
specify the vector layer to use either by name or number.
defaults to 0
band_num: int, optional
If `raster` is a GDAL source, the band number to use (counting from 1).
defaults to 1.
nodata: float, optional
If `raster` is a GDAL source, this value overrides any NODATA value
specified in the file's metadata.
If `None`, the file's metadata's NODATA value (if any) will be used.
defaults to `None`.
affine: Affine instance
required only for ndarrays, otherwise it is read from src
stats: list of str, or space-delimited str, optional
Which statistics to calculate for each zone.
All possible choices are listed in ``utils.VALID_STATS``.
defaults to ``DEFAULT_STATS``, a subset of these.
all_touched: bool, optional
Whether to include every raster cell touched by a geometry, or only
those having a center point within the polygon.
defaults to `False`
percent_cover_scale: int, optional
Scale used when generating percent coverage estimates of each
raster cell by vector feature. Percent coverage is generated by
rasterizing the feature at a finer resolution than the raster
(based on percent_cover_scale value) then using a summation to aggregate
to the raster resolution and dividing by the square of percent_cover_scale
to get percent coverage value for each cell. Increasing percent_cover_scale
will increase the accuracy of percent coverage values; three orders
magnitude finer resolution (percent_cover_scale=1000) is usually enough to
get coverage estimates with <1% error in individual edge cells coverage
estimates, though much smaller values (e.g., percent_cover_scale=10) are often
sufficient (<10% error) and require less memory.
percent_cover_selection: float, optional
Include only raster cells that have at least the given percent
covered by the vector feature. Requires percent_cover_scale argument
be used to specify scale at which to generate percent coverage
estimates
percent_cover_weighting: bool, optional
x
latitude_correction: bool, optional
* For use with WGS84 raster data only.
* Only applies to "mean" stat.
Weights cell values when generating statistics based on latitude
(using haversin function) in order to account for actual area
represented by pixel cell.
categorical: bool, optional
category_map: dict
A dictionary mapping raster values to human-readable categorical names.
Only applies when categorical is True
add_stats: dict
with names and functions of additional stats to compute, optional
raster_out: boolean
Include the masked numpy array for each feature?, optional
Each feature dictionary will have the following additional keys:
mini_raster_array: The clipped and masked numpy array
mini_raster_affine: Affine transformation
mini_raster_nodata: NoData Value
prefix: string
add a prefix to the keys (default: None)
save_properties: boolean
Returns original features along with specified stats when
geojson_out is set to False.
geojson_out: boolean
Return list of GeoJSON-like features (default: False)
Original feature geometry and properties will be retained
with zonal stats appended as additional properties.
Use with `prefix` to ensure unique and meaningful property names.
Returns
-------
generator of dicts (if geojson_out is False)
Each item corresponds to a single vector feature and
contains keys for each of the specified stats.
If save_properties is True, also contains original properties
generator of geojson features (if geojson_out is True)
GeoJSON-like Feature as python dict
"""
stats, run_count = check_stats(stats, categorical)
# Handle 1.0 deprecations
transform = kwargs.get('transform')
if transform:
warnings.warn("GDAL-style transforms will disappear in 1.0. "
"Use affine=Affine.from_gdal(*transform) instead",
DeprecationWarning)
if not affine:
affine = Affine.from_gdal(*transform)
ndv = kwargs.get('nodata_value')
if ndv:
warnings.warn("Use `nodata` instead of `nodata_value`", DeprecationWarning)
if not nodata:
nodata = ndv
cp = kwargs.get('copy_properties')
if cp:
warnings.warn("Use `geojson_out` or `save_properties` to preserve feature properties",
DeprecationWarning)
valid_percent_cover_stat = any([i in stats for i in ['mean', 'count', 'sum']])
if percent_cover_weighting and not percent_cover_selection and not valid_percent_cover_stat:
warnings.warn('`percent_cover_weighting` option selected but no stats which can use percent cover were selected')
percent_cover_weighting = False
percent_cover = False
if percent_cover_weighting or percent_cover_selection is not None:
percent_cover = True
if percent_cover_scale is None:
raise Exception('Value for `percent_cover_scale must be provided in order '
'to use percent_cover_weighting or percent_cover_selection')
try:
if percent_cover_scale != int(percent_cover_scale):
warnings.warn('Value for `percent_cover_scale` given ({0}) '
'was converted to int ({1}) but does not '
'match original value'.format(
percent_cover_scale, int(percent_cover_scale)))
percent_cover_scale = int(percent_cover_scale)
if percent_cover_scale <= 1:
raise Exception('Value for `percent_cover_scale` must be greater than one '
'({0})'.format(percent_cover_scale))
except:
raise Exception('Invalid value for `percent_cover_scale` provided '
'({0}). Must be type int.'.format(percent_cover_scale))
if percent_cover_selection is not None:
try:
percent_cover_selection = float(percent_cover_selection)
except:
raise Exception('Invalid value for `percent_cover_selection` provided '
'({0}). Must be type float.'.format(percent_cover_selection))
# if not all_touched:
# warnings.warn('`all_touched` was not enabled but an option requiring '
# 'percent_cover calculations was selected. Automatically '
# 'enabling `all_touched`.')
# all_touched = True
with Raster(raster, affine, nodata, band_num) as rast:
features_iter = read_features(vectors, layer)
for i, feat in enumerate(features_iter):
geom = shape(feat['geometry'])
feature_stats = {}
if 'Point' in geom.type:
percent_cover = False
geom = boxify_points(geom, rast)
geom_bounds = tuple(geom.bounds)
try:
fsrc = rast.read(bounds=geom_bounds)
fsrc_nodata = copy(fsrc.nodata)
fsrc_affine = copy(fsrc.affine)
fsrc_shape = copy(fsrc.shape)
except MemoryError:
print "Memory Error (fsrc): \n"
print feat['properties']
continue
if percent_cover:
try:
rv_array = rasterize_pctcover_geom(
geom, shape=fsrc_shape, affine=fsrc_affine,
scale=percent_cover_scale,
all_touched=all_touched)
assert rv_array.shape == fsrc_shape
except MemoryError:
print "Memory Error (percent_cover rv_array: \n"
print feat['properties']
continue
else:
try:
# create ndarray of rasterized geometry
rv_array = rasterize_geom(
geom, shape=fsrc_shape, affine=fsrc_affine,
all_touched=all_touched)
assert rv_array.shape == fsrc_shape
except MemoryError:
print "Memory Error (standard rv_array): \n"
print feat['properties']
continue
try:
# Mask the source data array with our current feature
# we take the logical_not to flip 0<->1 for the correct mask effect
# we also mask out nodata values explicitly
if percent_cover_selection is not None:
masked = np.ma.MaskedArray(
fsrc.array,
mask=np.logical_or(
fsrc.array == fsrc_nodata,
np.logical_not(rv_array),
percent_cover > percent_cover_selection))
else:
masked = np.ma.MaskedArray(
fsrc.array,
mask=np.logical_or(
fsrc.array == fsrc_nodata,
np.logical_not(rv_array)))
# print masked
# print rv_array
# print masked*rv_array
# print ~masked.mask * rv_array
except MemoryError:
print "Memory Error (masked): \n"
print feat['properties']
continue
###
if latitude_correction and 'mean' in stats:
latitude_scale = [
get_latitude_scale(fsrc_affine[5] - fsrc_affine[0] * (0.5 + i))
for i in range(fsrc_shape[0])
]
# print 'raster all data {0}'.format(fsrc.array.size)
# print 'raster no data vals {0}'.format(np.sum(fsrc.array == fsrc_nodata))
# print 'vector {0}'.format(np.sum(rv_array > 0))
# print 'vector no data {0}'.format(
# np.sum(
# ~np.ma.MaskedArray(
# rv_array,
# mask=(fsrc.array == fsrc_nodata)
# ).mask
# )
# )
# print 'masked not masked {0}'.format(np.sum(~masked.mask))
###
if 'nodata' in stats:
featmasked = np.ma.MaskedArray(fsrc.array, mask=np.logical_not(rv_array))
feature_stats['nodata'] = float((featmasked == fsrc_nodata).sum())
del fsrc
if not percent_cover_weighting:
del rv_array
try:
compressed = masked.compressed()
except MemoryError:
print "Memory Error (compressed): \n"
print feat['properties']
continue
if len(compressed) == 0:
# nothing here, fill with None and move on
feature_stats = dict([(stat, None) for stat in stats])
if 'count' in stats: # special case, zero makes sense here
feature_stats['count'] = 0
else:
if run_count:
keys, counts = np.unique(compressed, return_counts=True)
pixel_count = dict(zip([np.asscalar(k) for k in keys],
[np.asscalar(c) for c in counts]))
if categorical:
feature_stats = dict(pixel_count)
if category_map:
feature_stats = remap_categories(category_map, feature_stats)
# if 'weighted_mean' in stats:
# if latitude_correction:
# feature_stats['weighted_mean'] = float(
# np.sum((masked.T * latitude_scale).T * raster_pctcover) /
# np.sum((~masked.mask.T * latitude_scale).T * raster_pctcover))
# else:
# feature_stats['weighted_mean'] = float(
# np.sum(masked * raster_pctcover) /
# np.sum(~masked.mask * raster_pctcover))
# if 'weighted_count' in stats:
# print masked
# print raster_pctcover
# print masked*raster_pctcover
# print ~masked.mask * raster_pctcover
# feature_stats['weighted_count'] = float(np.sum(~masked.mask * raster_pctcover))
# if 'weighted_sum' in stats:
# feature_stats['weighted_sum'] = float(np.sum(masked * raster_pctcover))
if 'mean' in stats:
if percent_cover_weighting and latitude_correction:
feature_stats['mean'] = float(
np.sum((masked.T * latitude_scale).T * rv_array) /
np.sum((~masked.mask.T * latitude_scale).T * rv_array))
elif percent_cover_weighting:
feature_stats['mean'] = float(
np.sum(masked * rv_array) /
np.sum(~masked.mask * rv_array))
elif latitude_correction:
feature_stats['mean'] = float(
np.sum((masked.T * latitude_scale).T) /
np.sum(latitude_scale * (masked.shape[1] - np.sum(masked.mask, axis=1))))
else:
feature_stats['mean'] = float(compressed.mean())
if 'count' in stats:
if percent_cover_weighting:
feature_stats['count'] = float(np.sum(~masked.mask * rv_array))
else:
feature_stats['count'] = int(len(compressed))
if 'sum' in stats:
if percent_cover_weighting:
feature_stats['sum'] = float(np.sum(masked * rv_array))
else:
feature_stats['sum'] = float(compressed.sum())
if 'min' in stats:
feature_stats['min'] = float(compressed.min())
if 'max' in stats:
feature_stats['max'] = float(compressed.max())
if 'std' in stats:
feature_stats['std'] = float(compressed.std())
if 'median' in stats:
feature_stats['median'] = float(np.median(compressed))
if 'majority' in stats:
feature_stats['majority'] = float(key_assoc_val(pixel_count, max))
if 'minority' in stats:
feature_stats['minority'] = float(key_assoc_val(pixel_count, min))
if 'unique' in stats:
feature_stats['unique'] = len(list(pixel_count.keys()))
if 'range' in stats:
try:
rmin = feature_stats['min']
except KeyError:
rmin = float(compressed.min())
try:
rmax = feature_stats['max']
except KeyError:
rmax = float(compressed.max())
feature_stats['range'] = rmax - rmin
for pctile in [s for s in stats if s.startswith('percentile_')]:
q = get_percentile(pctile)
pctarr = compressed
feature_stats[pctile] = np.percentile(pctarr, q)
if add_stats is not None:
for stat_name, stat_func in add_stats.items():
feature_stats[stat_name] = stat_func(masked)
if raster_out:
feature_stats['mini_raster_array'] = masked
feature_stats['mini_raster_affine'] = fsrc_affine
feature_stats['mini_raster_nodata'] = fsrc_nodata
if prefix is not None:
prefixed_feature_stats = {}
for key, val in feature_stats.items():
newkey = "{}{}".format(prefix, key)
prefixed_feature_stats[newkey] = val
feature_stats = prefixed_feature_stats
if geojson_out or save_properties:
for key, val in feature_stats.items():
if 'properties' not in feat:
feat['properties'] = {}
feat['properties'][key] = val
if geojson_out:
yield feat
else:
yield feat['properties']
else:
yield feature_stats
def warp(self, dem=None, proj="EPSG:4326", **kwargs):
"""Delayed warp across an entire AOI or Image
Creates a new dask image by deferring calls to the warp_geometry on chunks
Args:
dem (ndarray): optional. A DEM for warping to specific elevation planes
proj (str): optional. An EPSG proj string to project the image data into ("EPSG:32612")
Returns:
daskarray: a warped image as deferred image array
"""
try:
img_md = self.rda.metadata["image"]
x_size = img_md["tileXSize"]
y_size = img_md["tileYSize"]
except (AttributeError, KeyError):
x_size = kwargs.get("chunk_size", 256)
y_size = kwargs.get("chunk_size", 256)
# Create an affine transform to convert between real-world and pixels
if self.proj is None:
from_proj = "EPSG:4326"
else:
from_proj = self.proj
try:
# NOTE: this only works on images that have rda rpcs metadata
center = wkt.loads(self.rda.metadata["image"]["imageBoundsWGS84"]).centroid
g = box(*(center.buffer(self.rda.metadata["rpcs"]["gsd"] / 2).bounds))
tfm = partial(pyproj.transform, pyproj.Proj(init="EPSG:4326"), pyproj.Proj(init=proj))
gsd = kwargs.get("gsd", ops.transform(tfm, g).area ** 0.5)
current_bounds = wkt.loads(self.rda.metadata["image"]["imageBoundsWGS84"]).bounds
except (AttributeError, KeyError, TypeError):
tfm = partial(pyproj.transform, pyproj.Proj(init=self.proj), pyproj.Proj(init=proj))
gsd = kwargs.get("gsd", (ops.transform(tfm, shape(self)).area / (self.shape[1] * self.shape[2])) ** 0.5 )
current_bounds = self.bounds
tfm = partial(pyproj.transform, pyproj.Proj(init=from_proj), pyproj.Proj(init=proj))
itfm = partial(pyproj.transform, pyproj.Proj(init=proj), pyproj.Proj(init=from_proj))
output_bounds = ops.transform(tfm, box(*current_bounds)).bounds
gtf = Affine.from_gdal(output_bounds[0], gsd, 0.0, output_bounds[3], 0.0, -1 * gsd)
ll = ~gtf * (output_bounds[:2])
ur = ~gtf * (output_bounds[2:])
x_chunks = int((ur[0] - ll[0]) / x_size) + 1
y_chunks = int((ll[1] - ur[1]) / y_size) + 1
num_bands = self.shape[0]
try:
dtype = RDA_TO_DTYPE[img_md["dataType"]]
except:
dtype = 'uint8'
daskmeta = {
"dask": {},
"chunks": (num_bands, y_size, x_size),
"dtype": dtype,
"name": "warp-{}".format(self.name),
"shape": (num_bands, y_chunks * y_size, x_chunks * x_size)
}
def px_to_geom(xmin, ymin):
xmax = int(xmin + x_size)
ymax = int(ymin + y_size)
bounds = list((gtf * (xmin, ymax)) + (gtf * (xmax, ymin)))
return box(*bounds)
full_bounds = box(*output_bounds)
dasks = []
if isinstance(dem, GeoDaskImage):
if dem.proj != proj:
dem = dem.warp(proj=proj, dem=dem)
dasks.append(dem.dask)
for y in xrange(y_chunks):
for x in xrange(x_chunks):
xmin = x * x_size
ymin = y * y_size
geometry = px_to_geom(xmin, ymin)
daskmeta["dask"][(daskmeta["name"], 0, y, x)] = (self._warp, geometry, gsd, dem, proj, dtype, 5)
daskmeta["dask"], _ = optimization.cull(HighLevelGraph.merge(daskmeta["dask"], *dasks), list(daskmeta["dask"].keys()))
gi = mapping(full_bounds)
gt = AffineTransform(gtf, proj)
image = GeoDaskImage(daskmeta, __geo_interface__ = gi, __geo_transform__ = gt)
return image[box(*output_bounds)]
def from_xarray(da, crs=None, apply_transform=False, nan_nodata=False, **kwargs):
"""
Returns an RGB or Image element given an xarray DataArray
loaded using xr.open_rasterio.
If a crs attribute is present on the loaded data it will
attempt to decode it into a cartopy projection otherwise it
will default to a non-geographic HoloViews element.
Parameters
----------
da: xarray.DataArray
DataArray to convert to element
crs: Cartopy CRS or EPSG string (optional)
Overrides CRS inferred from the data
apply_transform: boolean
Whether to apply affine transform if defined on the data
nan_nodata: boolean
If data contains nodata values convert them to NaNs
**kwargs:
Keyword arguments passed to the HoloViews/GeoViews element
Returns
-------
element: Image/RGB/QuadMesh element
"""
if crs:
kwargs['crs'] = crs
elif hasattr(da, 'crs'):
try:
kwargs['crs'] = process_crs(da.crs)
except:
param.main.warning('Could not decode projection from crs string %r, '
'defaulting to non-geographic element.' % da.crs)
coords = list(da.coords)
if coords not in (['band', 'y', 'x'], ['y', 'x']):
from .element.geo import Dataset, HvDataset
el = Dataset if 'crs' in kwargs else HvDataset
return el(da, **kwargs)
if len(coords) == 2:
y, x = coords
bands = 1
else:
y, x = coords[1:]
bands = len(da.coords[coords[0]])
if apply_transform:
from affine import Affine
transform = Affine.from_gdal(*da.attrs['transform'][:6])
nx, ny = da.sizes[x], da.sizes[y]
xs, ys = np.meshgrid(np.arange(nx)+0.5, np.arange(ny)+0.5) * transform
data = (xs, ys)
else:
xres, yres = da.attrs['res'] if 'res' in da.attrs else (1, 1)
xs = da.coords[x][::-1] if xres < 0 else da.coords[x]
ys = da.coords[y][::-1] if yres < 0 else da.coords[y]
data = (xs, ys)
for b in range(bands):
values = da[b].values
if nan_nodata and da.attrs.get('nodatavals', []):
values = values.astype(float)
for d in da.attrs['nodatavals']:
values[values==d] = np.NaN
data += (values,)
if 'datatype' not in kwargs:
kwargs['datatype'] = ['xarray', 'grid', 'image']
if xs.ndim > 1:
from .element.geo import QuadMesh, HvQuadMesh
el = QuadMesh if 'crs' in kwargs else HvQuadMesh
el = el(data, [x, y], **kwargs)
elif bands < 3:
from .element.geo import Image, HvImage
el = Image if 'crs' in kwargs else HvImage
el = el(data, [x, y], **kwargs)
else:
from .element.geo import RGB, HvRGB
el = RGB if 'crs' in kwargs else HvRGB
vdims = el.vdims[:bands]
el = el(data, [x, y], vdims, **kwargs)
if hasattr(el.data, 'attrs'):
el.data.attrs = da.attrs
return el
def dissolve_polygon(input_raster,output_file,dir):
#os.chdir(dir)
ds = gdal.Open(input_raster)
print(ds)
print(input_raster)
if os.path.exists(output_file):
drv.DeleteDataSource(output_file)
grid_code=[]
band = ds.GetRasterBand(1)
myarray =(ds.GetRasterBand(1).ReadAsArray())
#print(myarray)
T0 = Affine.from_gdal(*ds.GetGeoTransform())
tx=[T0[0], T0[1], T0[2], T0[3],T0[4], T0[5]]
drv = ogr.GetDriverByName("ESRI Shapefile")
for shp, val in rasterio.features.shapes(myarray,transform=tx):
# print('%s: %s' % (val, shape(shp)))
if(val>=0):
grid_code.append(float(val))
#add_filed_existing_shapefile('temp1.shp',np.asarray(grid_code))
pj=[]
with fiona.open('temp.shp') as input:
meta = input.meta
with fiona.open('temp.shp', 'r') as source:
# Copy the source schema and add two new properties.
sink_schema = source.schema.copy()
sink_schema['properties']['ID'] = 'float'
# Create a sink for processed features with the same format and
# coordinate reference system as the source.
with fiona.open(
'temp2.shp', 'w',
crs=source.crs,
driver=source.driver,
schema=sink_schema,
) as sink:
i=0
for f in source:
#print(f)
# Add the signed area of the polygon and a timestamp
# to the feature properties map.
f['properties'].update(
ID=grid_code[i],
)
i+=1
sink.write(f)
pj=[]
with fiona.open('temp2.shp') as input:
meta = input.meta
print('srt')
with fiona.open('final.shp', 'w',**meta) as output:
# groupby clusters consecutive elements of an iterable which have the same key so you must first sort the features by the 'STATEFP' field
e = sorted(input, key=lambda k: k['properties']['ID'])
print(e)
# group by the 'STATEFP' field
for key, group in itertools.groupby(e, key=lambda x:x['properties']['ID']):
properties, geom = zip(*[(feature['properties'],shape(feature['geometry'])) for feature in group])
# write the feature, computing the unary_union of the elements in the group with the properties of the first element in the group
output.write({'geometry': mapping(unary_union(geom)), 'properties': properties[0]})