def from_bounds(left,
bottom,
right,
top,
transform,
height=None,
width=None,
boundless=False,
precision=6):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left : float
Left (west) bounding coordinate
bottom : float
Bottom (south) bounding coordinate
right : float
Right (east) bounding coordinate
top : float
Top (north) bounding coordinate
transform : Affine
Affine transform matrix
height : int
Number of rows
width : int
Number of columns
boundless : boolean, optional
If boundless is False, window is limited
to extent of this dataset.
precision : int, optional
float precision
Returns
-------
window: tuple
((row_start, row_stop), (col_start, col_stop))
corresponding to the bounding coordinates
"""
window_start = rowcol(transform,
left,
top,
op=math.floor,
precision=precision)
window_stop = rowcol(transform,
right,
bottom,
op=math.ceil,
precision=precision)
window = tuple(zip(window_start, window_stop))
if boundless:
return window
else:
if None in (height, width):
raise ValueError("Must supply height and width unless boundless")
return crop(window, height, width)
def from_bounds(left, bottom, right, top, transform=None,
height=None, width=None, precision=None):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left, bottom, right, top: float
Left (west), bottom (south), right (east), and top (north)
bounding coordinates.
transform: Affine
Affine transform matrix.
height, width: int
Number of rows and columns of the window.
precision: int, optional
Number of decimal points of precision when computing inverse
transform.
Returns
-------
Window
A new Window
"""
row_start, col_start = rowcol(
transform, left, top, op=float, precision=precision)
row_stop, col_stop = rowcol(
transform, right, bottom, op=float, precision=precision)
return Window.from_slices(
(row_start, row_stop), (col_start, col_stop), height=height,
width=width, boundless=True)
def from_bounds(left, bottom, right, top, transform=None,
height=None, width=None, precision=None):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left, bottom, right, top: float
Left (west), bottom (south), right (east), and top (north)
bounding coordinates.
transform: Affine
Affine transform matrix.
height, width: int
Number of rows and columns of the window.
precision: int, optional
Number of decimal points of precision when computing inverse
transform.
Returns
-------
Window
A new Window
"""
row_start, col_start = rowcol(
transform, left, top, op=float, precision=precision)
row_stop, col_stop = rowcol(
transform, right, bottom, op=float, precision=precision)
return Window.from_slices(
(row_start, row_stop), (col_start, col_stop), height=height,
width=width, boundless=True)
def test_rowcol():
with rasterio.open("tests/data/RGB.byte.tif", 'r') as src:
aff = src.transform
left, bottom, right, top = src.bounds
assert rowcol(aff, left, top) == (0, 0)
assert rowcol(aff, right, top) == (0, src.width)
assert rowcol(aff, right, bottom) == (src.height, src.width)
assert rowcol(aff, left, bottom) == (src.height, 0)
assert rowcol(aff, 101985.0, 2826915.0) == (0, 0)
def from_bounds(left,
bottom,
right,
top,
transform=None,
height=None,
width=None,
precision=None):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left, bottom, right, top: float
Left (west), bottom (south), right (east), and top (north)
bounding coordinates.
transform: Affine, required
Affine transform matrix.
height, width: int, required
Number of rows and columns of the window.
precision: int, optional
Number of decimal points of precision when computing inverse
transform.
Returns
-------
Window
A new Window.
Raises
------
WindowError
If a window can't be calculated.
"""
if not isinstance(transform, Affine): # TODO: RPCs?
raise WindowError(
"A transform object is required to calculate the window")
row_start, col_start = rowcol(transform,
left,
top,
op=float,
precision=precision)
row_stop, col_stop = rowcol(transform,
right,
bottom,
op=float,
precision=precision)
return Window.from_slices((row_start, row_stop), (col_start, col_stop),
height=height,
width=width,
boundless=True)
def discretize(p, t):
if type(p) is Polygon:
x, y = p.exterior.xy
rc = rowcol(t, x, y)
return Polygon(list(zip(rc[0], rc[1])))
if type(p) is MultiPolygon:
polygons = []
for pp in p:
x, y = pp.exterior.xy
rc = rowcol(t, x, y)
polygons.append(Polygon(list(zip(rc[0], rc[1]))))
return MultiPolygon(polygons)
def from_bounds(left,
bottom,
right,
top,
transform,
height=None,
width=None,
boundless=False,
precision=6):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left, bottom, right, top : float
Left (west), bottom (south), right (east), and top (north)
bounding coordinates.
transform : Affine
Affine transform matrix.
height, width : int
Number of rows and columns of the window.
boundless : boolean, optional
If True, the output window's size may exceed the given height
and width.
precision : int, optional
Number of decimal points of precision when computing inverse
transform.
Returns
-------
Window
A new Window
"""
window_start = rowcol(transform,
left,
top,
op=math.floor,
precision=precision)
window_stop = rowcol(transform,
right,
bottom,
op=math.ceil,
precision=precision)
window = Window.from_ranges(*tuple(zip(window_start, window_stop)))
if boundless:
return window
else:
if None in (height, width):
raise ValueError("Must supply height and width unless boundless")
return crop(window, height, width)
def from_bounds(left,
bottom,
right,
top,
transform=None,
height=None,
width=None,
precision=6,
**kwargs):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left, bottom, right, top: float
Left (west), bottom (south), right (east), and top (north)
bounding coordinates.
transform: Affine
Affine transform matrix.
height, width: int
Number of rows and columns of the window.
precision: int, optional
Number of decimal points of precision when computing inverse
transform.
kwargs: mapping
Absorbs deprecated keyword args
Returns
-------
Window
A new Window
"""
if 'boundless' in kwargs:
warnings.warn("boundless keyword should not be used",
RasterioDeprecationWarning)
row_start, col_start = rowcol(transform,
left,
top,
op=float,
precision=precision)
row_stop, col_stop = rowcol(transform,
right,
bottom,
op=float,
precision=precision)
return Window.from_slices((row_start, row_stop), (col_start, col_stop),
height=height,
width=width,
boundless=True)
def test_rowcol_gcps_rpcs(dataset, transform_attr, coords, expected):
with rasterio.open(dataset, 'r') as src:
transform = getattr(src, transform_attr)
if transform_attr == 'gcps':
transform = transform[0]
for coord, truth in zip(coords, expected):
assert rowcol(transform, *coord) == truth
def extract_training_data_over_centroids(centroid_shapefiles,
image_stack,
class_labels,
image_meta,
label_meta,
save_directory,
tile_size=224):
assert (image_stack.shape[1] == class_labels.shape[0])
assert (image_stack.shape[2] == class_labels.shape[1])
x = []
y = []
for shapefile in centroid_shapefiles:
shp = gpd.read_file(shapefile)
shp = shp[shp.geometry.notnull()]
crs = CRS(image_meta['crs'])
shp = shp.to_crs(crs)
features = get_features(shp)
xs = [f['coordinates'][0] for f in features]
ys = [f['coordinates'][1] for f in features]
x.extend(xs)
y.extend(ys)
rows, cols = rowcol(image_meta['transform'], x, y)
ts = tile_size // 2
for x, y in zip(rows, cols):
image_tile = image_stack[:, x - ts:x + ts, y - ts:y + ts]
class_label_tile = class_labels[x - ts:x + ts, y - ts:y + ts]
save_image_tile_and_mask(save_directory, image_tile, class_label_tile,
image_meta, label_meta)
def index(self, x, y, op=math.floor, precision=6):
"""
Returns the (row, col) index of the pixel containing (x, y) given a
coordinate reference system.
Use an epsilon, magnitude determined by the precision parameter
and sign determined by the op function:
positive for floor, negative for ceil.
Parameters
----------
x : float
x value in coordinate reference system
y : float
y value in coordinate reference system
op : function, optional (default: math.floor)
Function to convert fractional pixels to whole numbers (floor,
ceiling, round)
precision : int, optional (default: 6)
Decimal places of precision in indexing, as in `round()`.
Returns
-------
tuple
(row index, col index)
"""
return rowcol(self.transform, x, y, op=op, precision=precision)
def load_image_data(self):
all_files = os.listdir(self.base_dir)
all_files = [fn for fn in all_files if fn.endswith(self.file_suffix)]
gdf = gps.read_file(self.annotation_file)
frame_infos = []
total_annotations = 0
for i, fn in enumerate(all_files):
annotations = []
image = rasterio.open(os.path.join(self.base_dir, fn))
for _, row in gdf[gdf.image_idx == fn].iterrows():
p = row['geometry']
coord = list(p.coords[0])
ann = rowcol(image.transform, coord[0], coord[1])
if not np.isnan(row['KRONE_DM']):
tree_size = int(row['KRONE_DM'])
else:
tree_size = 4
# Convert from metres to pixels and convert to radius from diameter
tree_size *= 2.5
tree_size = min(tree_size, 60)
# Is the annotation correctly initialized???
annotations.append((ann[0], ann[1], int(tree_size)))
#print((ann[0], ann[1], tree_size))
frame_info = FrameInfo(self.base_dir, fn,
(0, 0, image.shape[0], image.shape[1]),
annotations)
frame_infos.append(frame_info)
total_annotations += len(annotations)
return frame_infos
def coords2Array(a, x, y): """ * convert between coords and array position * returns row,col (y,x) as expected by rasterio """ r, c = rowcol(a, x, y) return int(r), int(c)
def index(self, x, y, op=math.floor, precision=6):
"""
Returns the (row, col) index of the pixel containing (x, y) given a
coordinate reference system.
Use an epsilon, magnitude determined by the precision parameter
and sign determined by the op function:
positive for floor, negative for ceil.
Parameters
----------
x : float
x value in coordinate reference system
y : float
y value in coordinate reference system
op : function, optional (default: math.floor)
Function to convert fractional pixels to whole numbers (floor,
ceiling, round)
precision : int, optional (default: 6)
Decimal places of precision in indexing, as in `round()`.
Returns
-------
tuple
(row index, col index)
"""
return rowcol(self.transform, x, y, op=op, precision=precision)
def load_image_data(self):
all_files = os.listdir(self.base_dir)
all_files = [fn for fn in all_files if fn.endswith(self.file_suffix)]
gdf = gps.read_file(self.annotation_file)
frame_infos = []
total_annotations = 0
for i, fn in enumerate(all_files):
annotations = []
image = rasterio.open(os.path.join(self.base_dir, fn))
for _, row in gdf[gdf.image_idx == fn].iterrows():
p = row['geometry']
coord = list(p.coords[0])
ann = rowcol(image.transform, coord[0], coord[1])
if not np.isnan(row['KRONE_DM']):
tree_size = int(row['KRONE_DM'])
else:
tree_size = 10
tree_size = min(20, max(tree_size, 60))
annotations.append((ann[1], ann[0], tree_size))
frame_info = FrameInfo(self.base_dir, fn,
(0, 0, image.shape[0], image.shape[1]),
annotations)
frame_infos.append(frame_info)
total_annotations += len(annotations)
return frame_infos
def test_xy_rowcol_inverse():
# TODO this is an ideal candiate for
# property-based testing with hypothesis
aff = Affine.identity()
rows_cols = ([0, 0, 10, 10],
[0, 10, 0, 10])
assert rows_cols == rowcol(aff, *xy(aff, *rows_cols))
def pixel_position(x: float, y: float, transform: Affine) -> list:
"""
CONVERT SPATIAL COORDINATES TO PIXEL X AND Y
:param transform:
:param x:
:param y:
:return:
"""
return rowcol(transform, x, y)
def _get_window(self, idx: int, transform: Affine = None) -> Window:
"""Returns a Window in the given transformation with sides of
self.feature_chip_size and with an upper left corner self.chip_xs[idx],
self.chip_ys[idx]"""
if transform is None:
transform = self.input_transform
row_off, col_off = rowcol(transform, self.chip_xs[idx],
self.chip_ys[idx])
return Window(col_off, row_off, self.feature_chip_size,
self.feature_chip_size)
def sample_gen(dataset, xy, indexes=None, masked=False):
"""Sample pixels from a dataset
Parameters
----------
dataset : rasterio Dataset
Opened in "r" mode.
xy : iterable
Pairs of x, y coordinates in the dataset's reference system.
indexes : int or list of int
Indexes of dataset bands to sample.
masked : bool, default: False
Whether to mask samples that fall outside the extent of the
dataset.
Yields
------
array
A array of length equal to the number of specified indexes
containing the dataset values for the bands corresponding to
those indexes.
"""
dt = dataset.transform
read = dataset.read
height = dataset.height
width = dataset.width
if indexes is None:
indexes = dataset.indexes
elif isinstance(indexes, int):
indexes = [indexes]
nodata = np.full(len(indexes), (dataset.nodata or 0),
dtype=dataset.dtypes[0])
if masked:
# Masks for masked arrays are inverted (False means valid)
mask = [
MaskFlags.all_valid not in dataset.mask_flag_enums[i - 1]
for i in indexes
]
nodata = np.ma.array(nodata, mask=mask)
for pts in _grouper(xy, 256):
pts = zip(*filter(None, pts))
for row_off, col_off in zip(*rowcol(dt, *pts)):
if row_off < 0 or col_off < 0 or row_off >= height or col_off >= width:
yield nodata
else:
window = Window(col_off, row_off, 1, 1)
data = read(indexes, window=window, masked=masked)
yield data[:, 0, 0]
def create_cutline(
src_dst: Union[DatasetReader, DatasetWriter, WarpedVRT],
geometry: Dict,
geometry_crs: CRS = None,
) -> str:
"""
Create WKT Polygon Cutline for GDALWarpOptions.
Ref: https://gdal.org/api/gdalwarp_cpp.html?highlight=vrt#_CPPv415GDALWarpOptions
Args:
src_dst (rasterio.io.DatasetReader or rasterio.io.DatasetWriter or rasterio.vrt.WarpedVRT): Rasterio dataset.
geometry (dict): GeoJSON feature or GeoJSON geometry. By default the cordinates are considered to be in the dataset CRS. Use `geometry_crs` to set a specific CRS.
geometry_crs (rasterio.crs.CRS, optional): Input geometry Coordinate Reference System
Returns:
str: WKT geometry in form of `POLYGON ((x y, x y, ...)))
"""
if "geometry" in geometry:
geometry = geometry["geometry"]
if not is_valid_geom(geometry):
raise RioTilerError("Invalid geometry")
geom_type = geometry["type"]
if geom_type not in ["Polygon", "MultiPolygon"]:
raise RioTilerError(
"Invalid geometry type: {geom_type}. Should be Polygon or MultiPolygon"
)
if geometry_crs:
geometry = transform_geom(geometry_crs, src_dst.crs, geometry)
polys = []
geom = (
[geometry["coordinates"]] if geom_type == "Polygon" else geometry["coordinates"]
)
for p in geom:
xs, ys = zip(*coords(p))
src_y, src_x = rowcol(src_dst.transform, xs, ys)
src_x = [max(0, min(src_dst.width, x)) for x in src_x]
src_y = [max(0, min(src_dst.height, y)) for y in src_y]
poly = ", ".join([f"{x} {y}" for x, y in list(zip(src_x, src_y))])
polys.append(f"(({poly}))")
str_poly = ",".join(polys)
return (
f"POLYGON {str_poly}"
if geom_type == "Polygon"
else f"MULTIPOLYGON ({str_poly})"
)
def map_to_pixel(self, map_point):
"""Transform point from map to pixel-based coordinates.
Args:
map_point: (x, y) tuple in map coordinates
Returns:
(x, y) tuple in pixel coordinates
"""
image_point = self.map2image.transform(*map_point)
pixel_point = rowcol(self.transform, image_point[0], image_point[1])
pixel_point = (pixel_point[1], pixel_point[0])
return pixel_point
def from_bounds(left, bottom, right, top, transform,
height=None, width=None, boundless=False, precision=6):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left, bottom, right, top : float
Left (west), bottom (south), right (east), and top (north)
bounding coordinates.
transform : Affine
Affine transform matrix.
height, width : int
Number of rows and columns of the window.
boundless : boolean, optional
If True, the output window's size may exceed the given height
and width.
precision : int, optional
Number of decimal points of precision when computing inverse
transform.
Returns
-------
Window
A new Window
"""
window_start = rowcol(
transform, left, top, op=math.floor, precision=precision)
window_stop = rowcol(
transform, right, bottom, op=math.ceil, precision=precision)
window = Window.from_ranges(*tuple(zip(window_start, window_stop)))
if boundless:
return window
else:
if None in (height, width):
raise ValueError("Must supply height and width unless boundless")
return crop(window, height, width)
def elevation_adjustment(coords, elevation_array, transformation_matrix):
''''
Function to return adjustment time
coords - Must be the coordinates in [x, y]
elevation_array - Must be a numpy array containing the elevation data
transformation_matrix - The transformation matrix output.
Code adapted from a function by Mahmoud Abdelrazek, 2019 - https://github.com/razekmh
''''
rise = 0 # Initialise rise to zero
try:
for i, point in enumerate(coords): # Extract coordinates
x_coord, y_coord = point
if i == 0: # First value
back_height = elevation_array[rowcol(transformation_matrix, x_coord, y_coord)] # Get elevation
else:
fore_height = elevation_array[rowcol(transformation_matrix, x_coord, y_coord)] # Get elevation
if fore_height > back_height: # Ignore negative elevation changes
rise += fore_height - back_height # Add each posotive changes to the rise
back_height = fore_height
elevation_adjustment = rise * 0.1 # To get the adjustment value in minutes.
return elevation_adjustment
except IOError:
print("Unable to perform this operation")
def get_window_from_xy(self, xy):
"""Get the window index given a coordinate (raster CRS)."""
a_transform = self._get_template_for_given_resolution(res=self.dst_res, return_="meta")["transform"]
row, col = transform.rowcol(a_transform, xy[0], xy[1])
ij_containing_xy = None
for ji, win in enumerate(self.windows):
(row_start, row_end), (col_start, col_end) = rasterio.windows.toranges(win)
# print(row, col, row_start, row_end, col_start, col_end)
if ((col >= col_start) & (col < col_end)) & ((row >= row_start) & (row < row_end)):
ij_containing_xy = ji
break
if ij_containing_xy is None:
raise ValueError("The given 'xy' value is not contained in any window.")
return ij_containing_xy
def from_bounds(left, bottom, right, top, transform=None,
height=None, width=None, precision=6, **kwargs):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left, bottom, right, top: float
Left (west), bottom (south), right (east), and top (north)
bounding coordinates.
transform: Affine
Affine transform matrix.
height, width: int
Number of rows and columns of the window.
precision: int, optional
Number of decimal points of precision when computing inverse
transform.
kwargs: mapping
Absorbs deprecated keyword args
Returns
-------
Window
A new Window
"""
if 'boundless' in kwargs:
warnings.warn("boundless keyword should not be used",
RasterioDeprecationWarning)
row_start, col_start = rowcol(
transform, left, top, op=float, precision=precision)
row_stop, col_stop = rowcol(
transform, right, bottom, op=float, precision=precision)
return Window.from_slices(
(row_start, row_stop), (col_start, col_stop), height=height,
width=width, boundless=True)
def test_rowcol():
with rasterio.open("tests/data/RGB.byte.tif", 'r') as src:
aff = src.transform
left, bottom, right, top = src.bounds
assert rowcol(aff, left, top) == (0, 0)
assert rowcol(aff, right, top) == (0, src.width)
assert rowcol(aff, right, bottom) == (src.height, src.width)
assert rowcol(aff, left, bottom) == (src.height, 0)
assert rowcol(aff, 101985.0, 2826915.0) == (0, 0)
assert rowcol(aff, 101985.0 + 400.0, 2826915.0) == (0, 1)
def create_cutline(src_dst: DataSet,
geometry: Dict,
geometry_crs: CRS = None) -> str:
"""
Create WKT Polygon Cutline for GDALWarpOptions.
Ref: https://gdal.org/api/gdalwarp_cpp.html?highlight=vrt#_CPPv415GDALWarpOptions
Attributes
----------
src_dst: rasterio.io.DatasetReader
rasterio.io.DatasetReader object
geometry: dict
GeoJSON feature or GeoJSON geometry
geometry_crs: CRS or str, optional
Specify bounds coordinate reference system, default is same as input dataset.
Returns
-------
wkt: str
Cutline WKT geometry in form of `POLYGON ((x y, x y, ...)))
"""
if "geometry" in geometry:
geometry = geometry["geometry"]
geom_type = geometry["type"]
if not geom_type == "Polygon":
raise RioTilerError(
"Invalid geometry type: {geom_type}. Should be Polygon")
if geometry_crs:
geometry = transform_geom(geometry_crs, src_dst.crs, geometry)
xs, ys = zip(*coords(geometry))
src_y, src_x = rowcol(src_dst.transform, xs, ys)
src_x = [max(0, min(src_dst.width, x)) for x in src_x]
src_y = [max(0, min(src_dst.height, y)) for y in src_y]
poly = ", ".join([f"{x} {y}" for x, y in list(zip(src_x, src_y))])
return f"POLYGON (({poly}))"
def cell_series(rast_dir, lon, lat):
"""
get the values of a particular latitude longitude in a time series of rasters
"""
cell_series = []
with rasterio.open(rast_dir + '/' + os.listdir(rast_dir)[0]) as data:
aff = data.transform
row, col = rowcol(aff, lon, lat)
for fname in os.listdir(rast_dir):
with rasterio.open(rast_dir + '/' + fname) as rast:
vals = rast.read()
val_at_coord = vals[0, row, col]
date_str = fname[9:-4]
date = dt.datetime.strptime(date_str, '%Y%m%d_%H%M%S')
cell_series.append((date, val_at_coord))
return (cell_series)
def geometry_window(
dataset,
shapes,
pad_x=0,
pad_y=0,
north_up=None,
rotated=None,
pixel_precision=None,
boundless=False,
):
"""Calculate the window within the raster that fits the bounds of
the geometry plus optional padding. The window is the outermost
pixel indices that contain the geometry (floor of offsets, ceiling
of width and height).
If shapes do not overlap raster, a WindowError is raised.
Parameters
----------
dataset : dataset object opened in 'r' mode
Raster for which the mask will be created.
shapes : iterable over geometries.
A geometry is a GeoJSON-like object or implements the geo
interface. Must be in same coordinate system as dataset.
pad_x : float
Amount of padding (as fraction of raster's x pixel size) to add
to left and right side of bounds.
pad_y : float
Amount of padding (as fraction of raster's y pixel size) to add
to top and bottom of bounds.
north_up : optional
This parameter is ignored since version 1.2.1. A deprecation
warning will be emitted in 1.3.0.
rotated : optional
This parameter is ignored since version 1.2.1. A deprecation
warning will be emitted in 1.3.0.
pixel_precision : int or float, optional
Number of places of rounding precision or absolute precision for
evaluating bounds of shapes.
boundless : bool, optional
Whether to allow a boundless window or not.
Returns
-------
rasterio.windows.Window
"""
if pad_x:
pad_x = abs(pad_x * dataset.res[0])
if pad_y:
pad_y = abs(pad_y * dataset.res[1])
all_bounds = [bounds(shape) for shape in shapes]
xs = [
x for (left, bottom, right, top) in all_bounds
for x in (left - pad_x, right + pad_x, right + pad_x, left - pad_x)
]
ys = [
y for (left, bottom, right, top) in all_bounds
for y in (top + pad_y, top + pad_y, bottom - pad_x, bottom - pad_x)
]
rows1, cols1 = rowcol(dataset.transform,
xs,
ys,
op=math.floor,
precision=pixel_precision)
if isinstance(rows1, (int, float)):
rows1 = [rows1]
if isinstance(cols1, (int, float)):
cols1 = [cols1]
rows2, cols2 = rowcol(dataset.transform,
xs,
ys,
op=math.ceil,
precision=pixel_precision)
if isinstance(rows2, (int, float)):
rows2 = [rows2]
if isinstance(cols2, (int, float)):
cols2 = [cols2]
rows = rows1 + rows2
cols = cols1 + cols2
row_start, row_stop = min(rows), max(rows)
col_start, col_stop = min(cols), max(cols)
window = Window(
col_off=col_start,
row_off=row_start,
width=max(col_stop - col_start, 0.0),
height=max(row_stop - row_start, 0.0),
)
# Make sure that window overlaps raster
raster_window = Window(0, 0, dataset.width, dataset.height)
if not boundless:
window = window.intersection(raster_window)
return window
def test_xy_rowcol_inverse():
# TODO this is an ideal candiate for
# property-based testing with hypothesis
aff = Affine.identity()
rows_cols = ([0, 0, 10, 10], [0, 10, 0, 10])
assert rows_cols == rowcol(aff, *xy(aff, *rows_cols))
def from_bounds(left,
bottom,
right,
top,
transform=None,
height=None,
width=None,
precision=None):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left: float, required
Left (west) bounding coordinates
bottom: float, required
Bottom (south) bounding coordinates
right: float, required
Right (east) bounding coordinates
top: float, required
Top (north) bounding coordinates
transform: Affine, required
Affine transform matrix.
precision, height, width: int, optional
These parameters are unused, deprecated in rasterio 1.3.0, and
will be removed in version 2.0.0.
Returns
-------
Window
A new Window.
Raises
------
WindowError
If a window can't be calculated.
"""
if height is not None or width is not None or precision is not None:
warnings.warn(
"The height, width, and precision parameters are unused, deprecated, and will be removed in 2.0.0.",
RasterioDeprecationWarning,
)
if not isinstance(transform, Affine): # TODO: RPCs?
raise WindowError(
"A transform object is required to calculate the window")
if (right - left) / transform.a < 0:
raise WindowError("Bounds and transform are inconsistent")
if (bottom - top) / transform.e < 0:
raise WindowError("Bounds and transform are inconsistent")
rows, cols = rowcol(
transform,
[left, right, right, left],
[top, top, bottom, bottom],
op=float,
)
row_start, row_stop = min(rows), max(rows)
col_start, col_stop = min(cols), max(cols)
return Window(
col_off=col_start,
row_off=row_start,
width=max(col_stop - col_start, 0.0),
height=max(row_stop - row_start, 0.0),
)
def merge_rgba_tool(sources, outtif, bounds=None, res=None, precision=7,
creation_options={}):
"""A windowed, top-down approach to merging.
For each block window, it loops through the sources,
reads the corresponding source window until the block
is filled with data or we run out of sources.
Uses more disk IO but is faster* and
consumes significantly less memory
* The read efficiencies comes from using
RGBA tifs where we can assume band 4 is the sole
determinant of nodata. This avoids the use of
expensive masked reads but, of course, limits
what data can used. Hence merge_rgba.
"""
first = sources[0]
first_res = first.res
dtype = first.dtypes[0]
profile = first.profile
# Extent from option or extent of all inputs.
if bounds:
dst_w, dst_s, dst_e, dst_n = bounds
else:
# scan input files.
# while we're at it, validate assumptions about inputs
xs = []
ys = []
for src in sources:
left, bottom, right, top = src.bounds
xs.extend([left, right])
ys.extend([bottom, top])
if src.profile['count'] != 4: # TODO, how to test for alpha?
raise ValueError("Inputs must be 4-band RGBA rasters")
dst_w, dst_s, dst_e, dst_n = min(xs), min(ys), max(xs), max(ys)
logger.debug("Output bounds: %r", (dst_w, dst_s, dst_e, dst_n))
output_transform = Affine.translation(dst_w, dst_n)
logger.debug("Output transform, before scaling: %r", output_transform)
# Resolution/pixel size.
if not res:
res = first_res
elif not np.iterable(res):
res = (res, res)
elif len(res) == 1:
res = (res[0], res[0])
output_transform *= Affine.scale(res[0], -res[1])
logger.debug("Output transform, after scaling: %r", output_transform)
# Compute output array shape. We guarantee it will cover the output
# bounds completely.
output_width = int(math.ceil((dst_e - dst_w) / res[0]))
output_height = int(math.ceil((dst_n - dst_s) / res[1]))
# Adjust bounds to fit.
dst_e, dst_s = output_transform * (output_width, output_height)
logger.debug("Output width: %d, height: %d", output_width, output_height)
logger.debug("Adjusted bounds: %r", (dst_w, dst_s, dst_e, dst_n))
profile['transform'] = output_transform
profile['height'] = output_height
profile['width'] = output_width
profile['nodata'] = None # rely on alpha mask
# Creation opts
profile.update(creation_options)
# create destination file
with rasterio.open(outtif, 'w', **profile) as dstrast:
for idx, dst_window in dstrast.block_windows():
left, bottom, right, top = dstrast.window_bounds(dst_window)
blocksize = ((dst_window[0][1] - dst_window[0][0]) *
(dst_window[1][1] - dst_window[1][0]))
# initialize array destined for the block
dst_count = first.count
dst_rows, dst_cols = tuple(b - a for a, b in dst_window)
dst_shape = (dst_count, dst_rows, dst_cols)
logger.debug("Temp shape: %r", dst_shape)
dstarr = np.zeros(dst_shape, dtype=dtype)
# Read up srcs until
# a. everything is data; i.e. no nodata
# b. no sources left
for src in sources:
# The full_cover behavior is problematic here as it includes
# extra pixels along the bottom right when the sources are
# slightly misaligned
#
# src_window = get_window(left, bottom, right, top,
# src.transform, precision=precision)
#
# With rio merge this just adds an extra row, but when the
# imprecision occurs at each block, you get artifacts
# Alternative, custom get_window using rounding
window_start = rowcol(
src.transform, left, top, op=round, precision=precision)
window_stop = rowcol(
src.transform, right, bottom, op=round, precision=precision)
src_window = tuple(zip(window_start, window_stop))
temp = np.zeros(dst_shape, dtype=dtype)
temp = src.read(out=temp, window=src_window,
boundless=True, masked=False)
# pixels without data yet are available to write
write_region = np.logical_and(
(dstarr[3] == 0), # 0 is nodata
(temp[3] != 0))
np.copyto(dstarr, temp, where=write_region)
# check if dest has any nodata pixels available
if np.count_nonzero(dstarr[3]) == blocksize:
break
dstrast.write(dstarr, window=dst_window)
return output_transform
def merge(input_ortho_and_ortho_cuts, output_orthophoto, orthophoto_vars={}):
"""
Based on https://github.com/mapbox/rio-merge-rgba/
Merge orthophotos around cutlines using a blend buffer.
"""
inputs = []
bounds = None
precision = 7
for o, c in input_ortho_and_ortho_cuts:
if not io.file_exists(o):
log.ODM_WARNING(
"%s does not exist. Will skip from merged orthophoto." % o)
continue
if not io.file_exists(c):
log.ODM_WARNING(
"%s does not exist. Will skip from merged orthophoto." % c)
continue
inputs.append((o, c))
if len(inputs) == 0:
log.ODM_WARNING("No input orthophotos, skipping merge.")
return
with rasterio.open(inputs[0][0]) as first:
res = first.res
dtype = first.dtypes[0]
profile = first.profile
num_bands = first.meta['count'] - 1 # minus alpha
colorinterp = first.colorinterp
log.ODM_INFO("%s valid orthophoto rasters to merge" % len(inputs))
sources = [(rasterio.open(o), rasterio.open(c)) for o, c in inputs]
# scan input files.
# while we're at it, validate assumptions about inputs
xs = []
ys = []
for src, _ in sources:
left, bottom, right, top = src.bounds
xs.extend([left, right])
ys.extend([bottom, top])
if src.profile["count"] < 4:
raise ValueError("Inputs must be at least 4-band rasters")
dst_w, dst_s, dst_e, dst_n = min(xs), min(ys), max(xs), max(ys)
log.ODM_INFO("Output bounds: %r %r %r %r" % (dst_w, dst_s, dst_e, dst_n))
output_transform = Affine.translation(dst_w, dst_n)
output_transform *= Affine.scale(res[0], -res[1])
# Compute output array shape. We guarantee it will cover the output
# bounds completely.
output_width = int(math.ceil((dst_e - dst_w) / res[0]))
output_height = int(math.ceil((dst_n - dst_s) / res[1]))
# Adjust bounds to fit.
dst_e, dst_s = output_transform * (output_width, output_height)
log.ODM_INFO("Output width: %d, height: %d" %
(output_width, output_height))
log.ODM_INFO("Adjusted bounds: %r %r %r %r" % (dst_w, dst_s, dst_e, dst_n))
profile["transform"] = output_transform
profile["height"] = output_height
profile["width"] = output_width
profile["tiled"] = orthophoto_vars.get('TILED', 'YES') == 'YES'
profile["blockxsize"] = orthophoto_vars.get('BLOCKXSIZE', 512)
profile["blockysize"] = orthophoto_vars.get('BLOCKYSIZE', 512)
profile["compress"] = orthophoto_vars.get('COMPRESS', 'LZW')
profile["predictor"] = orthophoto_vars.get('PREDICTOR', '2')
profile["bigtiff"] = orthophoto_vars.get('BIGTIFF', 'IF_SAFER')
profile.update()
# create destination file
with rasterio.open(output_orthophoto, "w", **profile) as dstrast:
dstrast.colorinterp = colorinterp
for idx, dst_window in dstrast.block_windows():
left, bottom, right, top = dstrast.window_bounds(dst_window)
blocksize = dst_window.width
dst_rows, dst_cols = (dst_window.height, dst_window.width)
# initialize array destined for the block
dst_count = first.count
dst_shape = (dst_count, dst_rows, dst_cols)
dstarr = np.zeros(dst_shape, dtype=dtype)
# First pass, write all rasters naively without blending
for src, _ in sources:
src_window = tuple(
zip(
rowcol(src.transform,
left,
top,
op=round,
precision=precision),
rowcol(src.transform,
right,
bottom,
op=round,
precision=precision)))
temp = np.zeros(dst_shape, dtype=dtype)
temp = src.read(out=temp,
window=src_window,
boundless=True,
masked=False)
# pixels without data yet are available to write
write_region = np.logical_and(
(dstarr[-1] == 0),
(temp[-1] != 0) # 0 is nodata
)
np.copyto(dstarr, temp, where=write_region)
# check if dest has any nodata pixels available
if np.count_nonzero(dstarr[-1]) == blocksize:
break
# Second pass, write all feathered rasters
# blending the edges
for src, _ in sources:
src_window = tuple(
zip(
rowcol(src.transform,
left,
top,
op=round,
precision=precision),
rowcol(src.transform,
right,
bottom,
op=round,
precision=precision)))
temp = np.zeros(dst_shape, dtype=dtype)
temp = src.read(out=temp,
window=src_window,
boundless=True,
masked=False)
where = temp[-1] != 0
for b in range(0, num_bands):
blended = temp[-1] / 255.0 * temp[b] + (
1 - temp[-1] / 255.0) * dstarr[b]
np.copyto(dstarr[b],
blended,
casting='unsafe',
where=where)
dstarr[-1][where] = 255.0
# check if dest has any nodata pixels available
if np.count_nonzero(dstarr[-1]) == blocksize:
break
# Third pass, write cut rasters
# blending the cutlines
for _, cut in sources:
src_window = tuple(
zip(
rowcol(cut.transform,
left,
top,
op=round,
precision=precision),
rowcol(cut.transform,
right,
bottom,
op=round,
precision=precision)))
temp = np.zeros(dst_shape, dtype=dtype)
temp = cut.read(out=temp,
window=src_window,
boundless=True,
masked=False)
# For each band, average alpha values between
# destination raster and cut raster
for b in range(0, num_bands):
blended = temp[-1] / 255.0 * temp[b] + (
1 - temp[-1] / 255.0) * dstarr[b]
np.copyto(dstarr[b],
blended,
casting='unsafe',
where=temp[-1] != 0)
dstrast.write(dstarr, window=dst_window)
return output_orthophoto
def extent_to_windows(self, extent, actual_transform):
window_min = transform.rowcol(actual_transform, extent['xmin'],
extent['ymin'])
window_max = transform.rowcol(actual_transform, extent['xmax'],
extent['ymax'])
return self.window_transform(window_min, window_max)
def from_bounds(
left, bottom, right, top, transform=None, height=None, width=None, precision=None
):
"""Get the window corresponding to the bounding coordinates.
Parameters
----------
left: float, required
Left (west) bounding coordinates
bottom: float, required
Bottom (south) bounding coordinates
right: float, required
Right (east) bounding coordinates
top: float, required
Top (north) bounding coordinates
transform: Affine, required
Affine transform matrix.
height: int, required
Number of rows of the window.
width: int, required
Number of columns of the window.
precision: int or float, optional
An integer number of decimal points of precision when computing
inverse transform, or an absolute float precision.
Returns
-------
Window
A new Window.
Raises
------
WindowError
If a window can't be calculated.
"""
if not isinstance(transform, Affine): # TODO: RPCs?
raise WindowError("A transform object is required to calculate the window")
if (right - left) / transform.a < 0:
raise WindowError("Bounds and transform are inconsistent")
if (bottom - top) / transform.e < 0:
raise WindowError("Bounds and transform are inconsistent")
rows, cols = rowcol(
transform,
[left, right, right, left],
[top, top, bottom, bottom],
op=float,
precision=precision,
)
row_start, row_stop = min(rows), max(rows)
col_start, col_stop = min(cols), max(cols)
return Window(
col_off=col_start,
row_off=row_start,
width=max(col_stop - col_start, 0.0),
height=max(row_stop - row_start, 0.0),
)
def cmip(
store='az',
df=None,
tlim=None,
model=None,
scenario=None,
coarsen=None,
variables=['ppt', 'tmean'],
mask=None,
member=None,
method='bias-corrected',
sampling='annual',
historical=False,
remove_nans=False,
):
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=ResourceWarning)
warnings.simplefilter('ignore', category=FutureWarning)
warnings.simplefilter('ignore', category=RuntimeWarning)
if scenario is None:
raise ValueError('must specify scenario')
if model is None:
raise ValueError('must specify model')
path = setup.loading(store)
prefix = f'cmip6/{method}/conus/4000m/{sampling}/{model}.{scenario}.{member}.zarr'
if store == 'az':
mapper = zarr.storage.ABSStore('carbonplan-downscaling',
prefix=prefix,
account_name='carbonplan')
else:
mapper = fsspec.get_mapper(
(path / 'carbonplan-downscaling' / prefix).as_uri())
ds = xr.open_zarr(mapper, consolidated=True)
if historical:
prefix = f'cmip6/{method}/conus/4000m/{sampling}/{model}.historical.{member}.zarr'
if store == 'az':
mapper = zarr.storage.ABSStore('carbonplan-downscaling',
prefix=prefix,
account_name='carbonplan')
else:
mapper = fsspec.get_mapper(
(path / 'carbonplan-downscaling' / prefix).as_uri())
ds_historical = xr.open_zarr(mapper, consolidated=True)
ds = xr.concat([ds_historical, ds], 'time')
ds['cwd'] = ds['def']
ds['pdsi'] = ds['pdsi'].clip(-16, 16)
X = xr.Dataset()
keys = variables
for key in keys:
X[key] = ds[key]
if tlim is not None:
tlim = list(map(str, tlim))
X = X.sel(time=slice(*tlim))
if mask is not None:
vals = mask.values
vals[vals == 0] = np.NaN
X = X * vals
if coarsen:
X_coarse = xr.Dataset()
for key in keys:
X_coarse[key] = X[key].coarsen(x=coarsen,
y=coarsen,
boundary='trim').mean()
X = X_coarse
if df is not None:
t = Affine(*utils.albers_conus_transform(4000))
p1 = Proj(utils.albers_conus_crs())
p2 = Proj(proj='latlong', datum='WGS84')
x, y = transform(p2, p1, df['lon'].values, df['lat'].values)
rc = rowcol(t, x, y)
ind_r = xr.DataArray(rc[0], dims=['c'])
ind_c = xr.DataArray(rc[1], dims=['c'])
base = X[keys].isel(y=ind_r, x=ind_c).load()
for key in keys:
df[key + '_mean'] = base[key].mean('time').values
df[key + '_min'] = base[key].min('time').values
df[key + '_max'] = base[key].max('time').values
if remove_nans:
for key in keys:
df = df[~np.isnan(df[key + '_mean'])]
df = df.reset_index(drop=True)
return df
X = X.drop(['x', 'y'])
X.load(retries=10)
return X
def get_window(extent: tuple, transform: Affine) -> (tuple, tuple):
row_start, col_start = rowcol(transform, extent[0], extent[-1], op=int)
row_stop, col_stop = rowcol(transform, extent[2], extent[1], op=int)
return (row_start, row_stop), (col_start, col_stop)
