Module sertit.rasters_rio
Raster tools
You can use this only if you have installed sertit[full] or sertit[rasters_rio]
Expand source code
# -*- coding: utf-8 -*-
# Copyright 2021, SERTIT-ICube - France, https://sertit.unistra.fr/
# This file is part of sertit-utils project
# https://github.com/sertit/sertit-utils
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Raster tools
You can use this only if you have installed sertit[full] or sertit[rasters_rio]
"""
import os
from functools import wraps
from typing import Any, Callable, Optional, Union
import numpy as np
try:
import geopandas as gpd
import rasterio
from rasterio import MemoryFile, features
from rasterio import mask as rmask
from rasterio import merge, warp
from rasterio.enums import Resampling
from shapely.geometry import Polygon
except ModuleNotFoundError as ex:
raise ModuleNotFoundError(
"Please install 'rasterio' and 'geopandas' to use the 'rasters_rio' package."
) from ex
from sertit import files, misc, strings, vectors
MAX_CORES = os.cpu_count() - 2
PATH_ARR_DS = Union[str, tuple, rasterio.DatasetReader]
"""
Types:
- Path
- Rasterio open data: (array, meta)
- rasterio Dataset
- `xarray`
""" # fmt:skip
def path_arr_dst(function: Callable) -> Callable:
"""
Path, `xarray`, (array, metadata) or dataset decorator.
Allows a function to ingest:
- a path
- a `xarray`
- a `rasterio` dataset
- `rasterio` open data: (array, meta)
```python
>>> # Create mock function
>>> @path_or_dst
>>> def fct(dst):
>>> read(dst)
>>>
>>> # Test the two ways
>>> read1 = fct("path\\to\\raster.tif")
>>> with rasterio.open("path\\to\\raster.tif") as dst:
>>> read2 = fct(dst)
>>>
>>> # Test
>>> read1 == read2
True
```
Args:
function (Callable): Function to decorate
Returns:
Callable: decorated function
"""
@wraps(function)
def path_or_arr_or_dst_wrapper(
path_or_arr_or_ds: Union[str, rasterio.DatasetReader], *args, **kwargs
) -> Any:
"""
Path or dataset wrapper
Args:
path_or_arr_or_ds (Union[str, rasterio.DatasetReader]): Raster path or its dataset
*args: args
**kwargs: kwargs
Returns:
Any: regular output
"""
if isinstance(path_or_arr_or_ds, str):
with rasterio.open(path_or_arr_or_ds) as dst:
out = function(dst, *args, **kwargs)
elif isinstance(path_or_arr_or_ds, tuple):
arr, meta = path_or_arr_or_ds
with MemoryFile() as memfile:
with memfile.open(**meta) as dst:
dst.write(arr)
out = function(dst, *args, **kwargs)
else:
out = None
# Try if xarray is importable
try:
import xarray as xr
if isinstance(path_or_arr_or_ds, (xr.DataArray, xr.Dataset)):
meta = {
"driver": "GTiff",
"dtype": path_or_arr_or_ds.dtype,
"nodata": path_or_arr_or_ds.rio.encoded_nodata,
"width": path_or_arr_or_ds.rio.width,
"height": path_or_arr_or_ds.rio.height,
"count": path_or_arr_or_ds.rio.count,
"crs": path_or_arr_or_ds.rio.crs,
"transform": path_or_arr_or_ds.rio.transform(),
}
with MemoryFile() as memfile:
with memfile.open(**meta) as dst:
xds = path_or_arr_or_ds.copy()
if xds.rio.encoded_nodata is not None:
xds = xds.fillna(xds.rio.encoded_nodata)
dst.write(xds.data)
out = function(dst, *args, **kwargs)
except ModuleNotFoundError:
pass
if not out:
out = function(path_or_arr_or_ds, *args, **kwargs)
return out
return path_or_arr_or_dst_wrapper
@path_arr_dst
def get_new_shape(
dst: PATH_ARR_DS, resolution: Union[tuple, list, float], size: Union[tuple, list]
) -> (int, int):
"""
Get the new shape (height, width) of a resampled raster.
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
resolution (Union[tuple, list, float]): Resolution of the wanted band, in dataset resolution unit (X, Y)
size (Union[tuple, list]): Size of the array (width, height). Not used if resolution is provided.
Returns:
(int, int): Height, width
"""
def _get_new_dim(dim: int, res_old: float, res_new: float) -> int:
"""
Get the new dimension in pixels
Args:
dim (int): Old dimension
res_old (float): Old resolution
res_new (float): New resolution
Returns:
int: New dimension
"""
return int(np.round(dim * res_old / res_new))
# By default keep original shape
new_height = dst.height
new_width = dst.width
# Compute new shape
if resolution is not None:
if isinstance(resolution, (int, float)):
new_height = _get_new_dim(dst.height, dst.res[1], resolution)
new_width = _get_new_dim(dst.width, dst.res[0], resolution)
elif resolution is None:
pass
else:
try:
if len(resolution) != 2:
raise ValueError(
"We should have a resolution for X and Y dimensions"
)
if resolution[0] is not None:
new_width = _get_new_dim(dst.width, dst.res[0], resolution[0])
if resolution[1] is not None:
new_height = _get_new_dim(dst.height, dst.res[1], resolution[1])
except (TypeError, KeyError):
raise ValueError(
f"Resolution should be None, 2 floats or a castable to a list: {resolution}"
)
elif size is not None:
try:
new_height = size[1]
new_width = size[0]
except (TypeError, KeyError):
raise ValueError(f"Size should be None or a castable to a list: {size}")
return new_height, new_width
def update_meta(arr: Union[np.ndarray, np.ma.masked_array], meta: dict) -> dict:
"""
Basic metadata update from a numpy array. Updates everything that we can find in the array:
- `dtype`: array dtype,
- `count`: first dimension of the array if the array is in 3D, else 1
- `height`: second dimension of the array
- `width`: third dimension of the array
- `nodata`: if a masked array is given, nodata is its fill_value
.. WARNING::
The array's shape is interpreted in rasterio's way (count, height, width) !
```python
>>> raster_path = "path\\to\\raster.tif"
>>> with rasterio.open(raster_path) as dst:
>>> meta = dst.meta
>>> arr = dst.read()
>>> meta
{
'driver': 'GTiff',
'dtype': 'float32',
'nodata': None,
'width': 300,
'height': 300,
'count': 4,
'crs': CRS.from_epsg(32630),
'transform': Affine(20.0, 0.0, 630000.0,0.0, -20.0, 4870020.0)
}
>>> new_arr = np.ma.masked_array(arr[:, ::2, ::2].astype(np.uint8), fill_value=0)
>>> new_arr.shape
(4, 150, 150)
>>> new_arr.dtype
dtype('uint8')
>>> new_arr.fill_value
0
>>> update_meta(new_arr, meta)
{
'driver': 'GTiff',
'dtype': dtype('uint8'),
'nodata': 0,
'width': 150,
'height': 150,
'count': 4,
'crs': CRS.from_epsg(32630),
'transform': Affine(20.0, 0.0, 630000.0, 0.0, -20.0, 4870020.0)
}
```
Args:
arr (Union[np.ndarray, np.ma.masked_array]): Array from which to update the metadata
meta (dict): Metadata to update
Returns:
dict: Update metadata
"""
# Manage raster shape (Stored in rasterio's way)
shape = arr.shape
count = 1 if len(shape) == 2 else shape[0]
width = shape[-1]
height = shape[-2]
# Update metadata that can be derived from raster
out_meta = meta.copy()
out_meta.update(
{"dtype": arr.dtype, "count": count, "height": height, "width": width}
)
# Nodata
if isinstance(arr, np.ma.masked_array):
out_meta["nodata"] = arr.fill_value
return out_meta
def get_nodata_mask(
array: Union[np.ma.masked_array, np.ndarray],
has_nodata: bool,
default_nodata: int = 0,
) -> np.ma.masked_array:
"""
Get nodata mask from a masked array.
The nodata may not be set before, then pass a nodata value that will be evaluated on the array.
```python
>>> diag_arr = np.diag([1,2,3])
array([[1, 0, 0],
[0, 2, 0],
[0, 0, 3]])
>>> get_nodata_mask(diag_arr, has_nodata=False)
array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]], dtype=uint8)
>>> get_nodata_mask(diag_arr, has_nodata=False, default_nodata=1)
array([[0, 1, 1],
[1, 1, 1],
[1, 1, 1]], dtype=uint8)
```
Args:
array (np.ma.masked_array): Array to evaluate
has_nodata (bool): If the array as its nodata specified. If not, using default_nodata.
default_nodata (int): Default nodata used if the array's nodata is not set
Returns:
np.ma.masked_array: Pixelwise nodata array
"""
# Nodata mask
if not has_nodata or not isinstance(
array, np.ma.masked_array
): # Unspecified nodata is set to None by rasterio
nodata_mask = np.where(array != default_nodata, 1, 0).astype(np.uint8)
else:
nodata_mask = np.where(array.mask, 0, 1).astype(np.uint8)
return nodata_mask
@path_arr_dst
def _vectorize(
dst: PATH_ARR_DS,
values: Union[None, int, list] = None,
get_nodata: bool = False,
default_nodata: int = 0,
) -> gpd.GeoDataFrame:
"""
Vectorize a raster, both to get classes or nodata.
.. WARNING::
If `get_nodata` is set to False:
- Please only use this function on a classified raster.
- This could take a while as the computing time directly depends on the number of polygons to vectorize.
Please be careful.
Else:
- You will get a classified polygon with data (value=0)/nodata pixels. To
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
values (Union[None, int, list]): Get only the polygons concerning this/these particular values
get_nodata (bool): Get nodata vector (raster values are set to 0, nodata values are the other ones)
default_nodata (int): Default values for nodata in case of non existing in file
Returns:
gpd.GeoDataFrame: Vector
"""
# Get the shapes
array = dst.read(masked=True)
# Manage nodata value
has_nodata = dst.nodata is not None
nodata = dst.nodata if has_nodata else default_nodata
# Manage values
if values is not None:
if not isinstance(values, list):
values = [values]
data = np.where(np.isin(array, values), array, nodata).astype(array.dtype)
else:
data = array.data
# Get nodata mask
nodata_mask = get_nodata_mask(data, has_nodata=False, default_nodata=nodata)
# Get shapes (on array or on mask to get nodata vector)
shapes = features.shapes(
nodata_mask if get_nodata else data,
mask=None if get_nodata else nodata_mask,
transform=dst.transform,
)
return vectors.shapes_to_gdf(shapes, dst.crs)
@path_arr_dst
def vectorize(
dst: PATH_ARR_DS, values: Union[None, int, list] = None, default_nodata: int = 0
) -> gpd.GeoDataFrame:
"""
Vectorize a raster to get the class vectors.
.. WARNING::
- Please only use this function on a classified raster.
- This could take a while as the computing time directly depends on the number of polygons to vectorize.
Please be careful.
```python
>>> raster_path = "path\\to\\raster.tif" # Classified raster, with no data set to 255
>>> vec1 = vectorize(raster_path)
>>> # or
>>> with rasterio.open(raster_path) as dst:
>>> vec2 = vectorize(dst)
>>> vec1 == vec2
True
```
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
values (Union[None, int, list]): Get only the polygons concerning this/these particular values
default_nodata (int): Default values for nodata in case of non existing in file
Returns:
gpd.GeoDataFrame: Classes Vector
"""
return _vectorize(
dst, values=values, get_nodata=False, default_nodata=default_nodata
)
@path_arr_dst
def get_valid_vector(dst: PATH_ARR_DS, default_nodata: int = 0) -> gpd.GeoDataFrame:
"""
Get the valid data of a raster as a vector.
Pay attention that every nodata pixel will appear too.
If you want only the footprint of the raster, please use `get_footprint`.
```python
>>> raster_path = "path\\to\\raster.tif" # Classified raster, with no data set to 255
>>> nodata1 = get_nodata_vec(raster_path)
>>> # or
>>> with rasterio.open(raster_path) as dst:
>>> nodata2 = get_nodata_vec(dst)
>>> nodata1 == nodata2
True
```
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
default_nodata (int): Default values for nodata in case of non existing in file
Returns:
gpd.GeoDataFrame: Nodata Vector
"""
nodata = _vectorize(
dst, values=None, get_nodata=True, default_nodata=default_nodata
)
return nodata[
nodata.raster_val != 0
] # 0 is the values of not nodata put there by rasterio
@path_arr_dst
def get_nodata_vector(dst: PATH_ARR_DS, default_nodata: int = 0) -> gpd.GeoDataFrame:
"""
Get the nodata vector of a raster as a vector.
Pay attention that every nodata pixel will appear too.
If you want only the footprint of the raster, please use `get_footprint`.
```python
>>> raster_path = "path\\to\\raster.tif" # Classified raster, with no data set to 255
>>> nodata1 = get_nodata_vec(raster_path)
>>> # or
>>> with rasterio.open(raster_path) as dst:
>>> nodata2 = get_nodata_vec(dst)
>>> nodata1 == nodata2
True
```
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
default_nodata (int): Default values for nodata in case of non existing in file
Returns:
gpd.GeoDataFrame: Nodata Vector
"""
nodata = _vectorize(
dst, values=None, get_nodata=True, default_nodata=default_nodata
)
return nodata[
nodata.raster_val == 0
] # 0 is the values of not nodata put there by rasterio
@path_arr_dst
def _mask(
dst: PATH_ARR_DS,
shapes: Union[Polygon, list],
nodata: Optional[int] = None,
do_crop: bool = False,
**kwargs,
) -> (np.ma.masked_array, dict):
"""
Overload of rasterio mask function in order to create a masked_array.
The `mask` function docs can be seen [here](https://rasterio.readthedocs.io/en/latest/api/rasterio.mask.html).
It basically masks a raster with a vector mask, with the possibility to crop the raster to the vector's extent.
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
shapes (Union[Polygon, list]): Shapes
nodata (int): Nodata value. If not set, uses the ds.nodata. If doesnt exist, set to 0.
do_crop (bool): Whether to crop the raster to the extent of the shapes. Default is False.
**kwargs: Other rasterio.mask options
Returns:
(np.ma.masked_array, dict): Cropped array as a masked array and its metadata
"""
if isinstance(shapes, Polygon):
shapes = [Polygon]
# Set nodata
if not nodata:
if dst.nodata:
nodata = dst.nodata
else:
nodata = 0
# Crop dataset
msk, trf = rmask.mask(dst, shapes, nodata=nodata, crop=do_crop, **kwargs)
# Create masked array
nodata_mask = np.where(msk == nodata, 1, 0).astype(np.uint8)
mask_array = np.ma.masked_array(msk, nodata_mask, fill_value=nodata)
# Update meta
out_meta = update_meta(mask_array, dst.meta)
out_meta["transform"] = trf
return mask_array, out_meta
@path_arr_dst
def mask(
dst: PATH_ARR_DS,
shapes: Union[Polygon, list],
nodata: Optional[int] = None,
**kwargs,
) -> (np.ma.masked_array, dict):
"""
Masking a dataset:
setting nodata outside of the given shapes, but without cropping the raster to the shapes extent.
Overload of rasterio mask function in order to create a masked_array.
The `mask` function docs can be seen [here](https://rasterio.readthedocs.io/en/latest/api/rasterio.mask.html).
It basically masks a raster with a vector mask, with the possibility to crop the raster to the vector's extent.
```python
>>> raster_path = "path\\to\\raster.tif"
>>> shape_path = "path\\to\\shapes.geojson" # Any vector that geopandas can read
>>> shapes = gpd.read_file(shape_path)
>>> masked_raster1, meta1 = mask(raster_path, shapes)
>>> # or
>>> with rasterio.open(raster_path) as dst:
>>> masked_raster2, meta2 = mask(dst, shapes)
>>> masked_raster1 == masked_raster2
True
>>> meta1 == meta2
True
```
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
shapes (Union[Polygon, list]): Shapes
nodata (int): Nodata value. If not set, uses the ds.nodata. If doesnt exist, set to 0.
**kwargs: Other rasterio.mask options
Returns:
(np.ma.masked_array, dict): Masked array as a masked array and its metadata
"""
return _mask(dst, shapes=shapes, nodata=nodata, do_crop=False, **kwargs)
@path_arr_dst
def crop(
dst: PATH_ARR_DS,
shapes: Union[Polygon, list],
nodata: Optional[int] = None,
**kwargs,
) -> (np.ma.masked_array, dict):
"""
Cropping a dataset:
setting nodata outside of the given shapes AND cropping the raster to the shapes extent.
**HOW:**
Overload of rasterio mask function in order to create a masked_array.
The `mask` function docs can be seen [here](https://rasterio.readthedocs.io/en/latest/api/rasterio.mask.html).
It basically masks a raster with a vector mask, with the possibility to crop the raster to the vector's extent.
```python
>>> raster_path = "path\\to\\raster.tif"
>>> shape_path = "path\\to\\shapes.geojson" # Any vector that geopandas can read
>>> shapes = gpd.read_file(shape_path)
>>> cropped_raster1, meta1 = crop(raster_path, shapes)
>>> # or
>>> with rasterio.open(raster_path) as dst:
>>> cropped_raster2, meta2 = crop(dst, shapes)
>>> cropped_raster1 == cropped_raster2
True
>>> meta1 == meta2
True
```
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
shapes (Union[Polygon, list]): Shapes
nodata (int): Nodata value. If not set, uses the ds.nodata. If doesnt exist, set to 0.
**kwargs: Other rasterio.mask options
Returns:
(np.ma.masked_array, dict): Cropped array as a masked array and its metadata
"""
return _mask(dst, shapes=shapes, nodata=nodata, do_crop=True, **kwargs)
@path_arr_dst
def read(
dst: PATH_ARR_DS,
resolution: Union[tuple, list, float] = None,
size: Union[tuple, list] = None,
resampling: Resampling = Resampling.nearest,
masked: bool = True,
) -> (np.ma.masked_array, dict):
"""
Read a raster dataset from a `rasterio.Dataset` or a path.
The resolution can be provided (in dataset unit) as:
- a tuple or a list of (X, Y) resolutions
- a float, in which case X resolution = Y resolution
- None, in which case the dataset resolution will be used
```python
>>> raster_path = "path\\to\\raster.tif"
>>> raster1, meta1 = read(raster_path)
>>> # or
>>> with rasterio.open(raster_path) as dst:
>>> raster2, meta2 = read(dst)
>>> raster1 == raster2
True
>>> meta1 == meta2
True
```
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
resolution (Union[tuple, list, float]): Resolution of the wanted band, in dataset resolution unit (X, Y)
size (Union[tuple, list]): Size of the array (width, height). Not used if resolution is provided.
resampling (Resampling): Resampling method
masked (bool): Get a masked array
Returns:
np.ma.masked_array, dict: Masked array corresponding to the raster data and its meta data
"""
# Get new height and width
new_height, new_width = get_new_shape(dst, resolution, size)
# Read data
array = dst.read(
out_shape=(dst.count, new_height, new_width),
resampling=resampling,
masked=masked,
)
# Update meta
dst_transform = dst.transform * dst.transform.scale(
(dst.width / new_width), (dst.height / new_height)
)
dst_meta = dst.meta.copy()
dst_meta.update(
{
"height": new_height,
"width": new_width,
"transform": dst_transform,
"dtype": array.dtype,
"nodata": dst.nodata,
}
)
return array, dst_meta
def write(
raster: Union[np.ma.masked_array, np.ndarray], path: str, meta: dict, **kwargs
) -> None:
"""
Write raster to disk (encapsulation of rasterio's function)
Metadata will be copied and updated with raster's information (ie. width, height, count, type...)
The driver is GTiff by default, and no nodata value is provided.
The file will be compressed if the raster is a mask (saved as uint8)
```python
>>> raster_path = "path\\to\\raster.tif"
>>> raster_out = "path\\to\\out.tif"
>>> # Read raster
>>> raster, meta = read(raster_path)
>>> # Rewrite it
>>> write(raster, raster_out, meta)
```
Args:
raster (Union[np.ma.masked_array, np.ndarray]): Raster to save on disk
path (str): Path where to save it (directories should be existing)
meta (dict): Basic metadata that will be copied and updated with raster's information
**kwargs: Overloading metadata, ie `nodata=255`
"""
# Manage raster type (impossible to write boolean arrays)
if raster.dtype == bool:
raster = raster.astype(np.uint8)
# Update metadata
out_meta = meta.copy()
# Update raster to be sure to write down correct nodata pixels
if isinstance(raster, np.ma.masked_array):
raster[raster.mask] = raster.fill_value
# Force compression and driver (but can be overwritten by kwargs)
out_meta["driver"] = "GTiff"
# Compress only if uint8 data
if raster.dtype == np.uint8:
out_meta["compress"] = "lzw"
# Update metadata with array data
out_meta = update_meta(raster, out_meta)
# Update metadata with additional params
for key, val in kwargs.items():
out_meta[key] = val
# Manage raster shape
if len(raster.shape) == 2:
raster = np.expand_dims(raster, axis=0)
# Write product
with rasterio.open(path, "w", **out_meta) as dst:
dst.write(raster)
def collocate(
master_meta: dict,
slave_arr: Union[np.ma.masked_array, np.ndarray],
slave_meta: dict,
resampling: Resampling = Resampling.nearest,
) -> (Union[np.ma.masked_array, np.ndarray], dict):
"""
Collocate two georeferenced arrays:
forces the *slave* raster to be exactly georeferenced onto the *master* raster by reprojection.
Use it like `OTB SuperImpose`.
```python
>>> master_path = "path\\to\\master.tif"
>>> slave_path = "path\\to\\slave.tif"
>>> col_path = "path\\to\\collocated.tif"
>>> # Just open the master data
>>> with rasterio.open(master_path) as master_dst:
>>> # Read slave
>>> slave, slave_meta = read(slave_path)
>>> # Collocate the slave to the master
>>> col_arr, col_meta = collocate(master_dst.meta,
>>> slave,
>>> slave_meta,
>>> Resampling.bilinear)
>>> # Write it
>>> write(col_arr, col_path, col_meta)
```
Args:
master_meta (dict): Master metadata
slave_arr (np.ma.masked_array): Slave array to be collocated
slave_meta (dict): Slave metadata
resampling (Resampling): Resampling method
Returns:
np.ma.masked_array, dict: Collocated array and its metadata
"""
collocated_arr = np.zeros(
(master_meta["count"], master_meta["height"], master_meta["width"]),
dtype=master_meta["dtype"],
)
warp.reproject(
source=slave_arr,
destination=collocated_arr,
src_transform=slave_meta["transform"],
src_crs=slave_meta["crs"],
dst_transform=master_meta["transform"],
dst_crs=master_meta["crs"],
src_nodata=slave_meta["nodata"],
dst_nodata=slave_meta["nodata"],
resampling=resampling,
num_threads=MAX_CORES,
)
meta = master_meta.copy()
meta.update(
{
"dtype": slave_meta["dtype"],
"driver": slave_meta["driver"],
"nodata": slave_meta["nodata"],
}
)
return collocated_arr, meta
def sieve(
array: Union[np.ma.masked_array, np.ndarray],
out_meta: dict,
sieve_thresh: int,
connectivity: int = 4,
) -> (Union[np.ma.masked_array, np.ndarray], dict):
"""
Sieving, overloads rasterio function with raster shaped like (1, h, w).
Forces the output to `np.uint8` (as only classified rasters should be sieved)
```python
>>> raster_path = "path\\to\\raster.tif" # classified raster
>>> # Read raster
>>> raster, meta = read(raster_path)
>>> # Rewrite it
>>> sieved, sieved_meta = sieve(raster, meta, sieve_thresh=20)
>>> # Write it
>>> raster_out = "path\\to\\raster_sieved.tif"
>>> write(sieved, raster_out, sieved_meta)
```
Args:
array (Union[np.ma.masked_array, np.ndarray]): Array to sieve
out_meta (dict): Metadata to update
sieve_thresh (int): Sieving threshold in pixels
connectivity (int): Connectivity, either 4 or 8
Returns:
(Union[np.ma.masked_array, np.ndarray], dict): Sieved array and updated meta
"""
assert connectivity in [4, 8]
# Read extraction array
expand = False
if len(array.shape) == 3 and array.shape[0] == 1:
array = np.squeeze(array) # Use this trick to make the sieve work
expand = True
# Convert to np.uint8 if needed
dtype = np.uint8
meta = out_meta.copy()
if meta["dtype"] != dtype:
array = array.astype(dtype)
meta["dtype"] = dtype
# Sieve
result_array = np.empty(array.shape, dtype=array.dtype)
features.sieve(
array, size=sieve_thresh, out=result_array, connectivity=connectivity
)
# Use this trick to get the array back to 'normal'
if expand:
result_array = np.expand_dims(result_array, axis=0)
return result_array, meta
def get_dim_img_path(dim_path: str, img_name: str = "*") -> str:
"""
Get the image path from a *BEAM-DIMAP* data.
A *BEAM-DIMAP* file cannot be opened by rasterio, although its .img file can.
```python
>>> dim_path = "path\\to\\dimap.dim" # BEAM-DIMAP image
>>> img_path = get_dim_img_path(dim_path)
>>> # Read raster
>>> raster, meta = read(img_path)
```
Args:
dim_path (str): DIM path (.dim or .data)
img_name (str): .img file name (or regex), in case there are multiple .img files (ie. for S3 data)
Returns:
str: .img file
"""
if dim_path.endswith(".dim"):
dim_path = dim_path.replace(".dim", ".data")
assert dim_path.endswith(".data") and os.path.isdir(dim_path)
return files.get_file_in_dir(dim_path, img_name, extension="img")
@path_arr_dst
def get_extent(dst: PATH_ARR_DS) -> gpd.GeoDataFrame:
"""
Get the extent of a raster as a `geopandas.Geodataframe`.
```python
>>> raster_path = "path\\to\\raster.tif"
>>> extent1 = get_extent(raster_path)
>>> # or
>>> with rasterio.open(raster_path) as dst:
>>> extent2 = get_extent(dst)
>>> extent1 == extent2
True
```
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
Returns:
gpd.GeoDataFrame: Extent as a `geopandas.Geodataframe`
"""
return vectors.get_geodf(geometry=[*dst.bounds], crs=dst.crs)
@path_arr_dst
def get_footprint(dst: PATH_ARR_DS) -> gpd.GeoDataFrame:
"""
Get real footprint of the product (without nodata, in french == emprise utile)
```python
>>> raster_path = "path\\to\\raster.tif"
>>> footprint1 = get_footprint(raster_path)
>>> # or
>>> with rasterio.open(raster_path) as dst:
>>> footprint2 = get_footprint(dst)
>>> footprint1 == footprint2
```
Args:
dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata
Returns:
gpd.GeoDataFrame: Footprint as a GeoDataFrame
"""
footprint = get_valid_vector(dst)
return vectors.get_wider_exterior(footprint)
def merge_vrt(crs_paths: list, crs_merged_path: str, **kwargs) -> None:
"""
Merge rasters as a VRT. Uses `gdalbuildvrt`.
See here: https://gdal.org/programs/gdalbuildvrt.html
Creates VRT with relative paths !
.. WARNING::
They should have the same CRS otherwise the mosaic will be false !
```python
>>> paths_utm32630 = ["path\\to\\raster1.tif", "path\\to\\raster2.tif", "path\\to\\raster3.tif"]
>>> paths_utm32631 = ["path\\to\\raster4.tif", "path\\to\\raster5.tif"]
>>> mosaic_32630 = "path\\to\\mosaic_32630.vrt"
>>> mosaic_32631 = "path\\to\\mosaic_32631.vrt"
>>> # Create mosaic, one by CRS !
>>> merge_vrt(paths_utm32630, mosaic_32630)
>>> merge_vrt(paths_utm32631, mosaic_32631, {"-srcnodata":255, "-vrtnodata":0})
```
Args:
crs_paths (list): Path of the rasters to be merged with the same CRS)
crs_merged_path (str): Path to the merged raster
kwargs: Other gdlabuildvrt arguments
"""
# Create relative paths
vrt_root = os.path.dirname(crs_merged_path)
rel_paths = [
strings.to_cmd_string(files.real_rel_path(path, vrt_root)) for path in crs_paths
]
rel_vrt = strings.to_cmd_string(files.real_rel_path(crs_merged_path, vrt_root))
# Run cmd
arg_list = [val for item in kwargs.items() for val in item]
vrt_cmd = ["gdalbuildvrt", rel_vrt, *rel_paths, *arg_list]
misc.run_cli(vrt_cmd, cwd=vrt_root)
def merge_gtiff(crs_paths: list, crs_merged_path: str, **kwargs) -> None:
"""
Merge rasters as a GeoTiff.
.. WARNING::
They should have the same CRS otherwise the mosaic will be false !
```python
>>> paths_utm32630 = ["path\\to\\raster1.tif", "path\\to\\raster2.tif", "path\\to\\raster3.tif"]
>>> paths_utm32631 = ["path\\to\\raster4.tif", "path\\to\\raster5.tif"]
>>> mosaic_32630 = "path\\to\\mosaic_32630.tif"
>>> mosaic_32631 = "path\\to\\mosaic_32631.tif"
# Create mosaic, one by CRS !
>>> merge_gtiff(paths_utm32630, mosaic_32630)
>>> merge_gtiff(paths_utm32631, mosaic_32631)
```
Args:
crs_paths (list): Path of the rasters to be merged with the same CRS)
crs_merged_path (str): Path to the merged raster
kwargs: Other rasterio.merge arguments
More info [here](https://rasterio.readthedocs.io/en/latest/api/rasterio.merge.html#rasterio.merge.merge)
"""
# Open datasets for merging
crs_datasets = []
try:
for path in crs_paths:
crs_datasets.append(rasterio.open(path))
# Merge all datasets
merged_array, merged_transform = merge.merge(crs_datasets, **kwargs)
merged_meta = crs_datasets[0].meta.copy()
merged_meta.update(
{
"driver": "GTiff",
"height": merged_array.shape[1],
"width": merged_array.shape[2],
"transform": merged_transform,
}
)
finally:
# Close all datasets
for dataset in crs_datasets:
dataset.close()
# Save merge datasets
write(
merged_array, crs_merged_path, crs_datasets[0].meta, transform=merged_transform
)
def unpackbits(array: np.ndarray, nof_bits: int) -> np.ndarray:
"""
Function found here:
https://stackoverflow.com/questions/18296035/how-to-extract-the-bits-of-larger-numeric-numpy-data-types
```python
>>> bit_array = np.random.randint(5, size=[3,3])
array([[1, 1, 3],
[4, 2, 0],
[4, 3, 2]], dtype=uint8)
# Unpack 8 bits (8*1, as itemsize of uint8 is 1)
>>> unpackbits(bit_array, 8)
array([[[1, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0]]], dtype=uint8)
```
Args:
array (np.ndarray): Array to unpack
nof_bits (int): Number of bits to unpack
Returns:
np.ndarray: Unpacked array
"""
xshape = list(array.shape)
array = array.reshape([-1, 1])
msk = 2 ** np.arange(nof_bits, dtype=array.dtype).reshape([1, nof_bits])
return (array & msk).astype(bool).astype(np.uint8).reshape(xshape + [nof_bits])
def read_bit_array(
bit_mask: np.ndarray, bit_id: Union[list, int]
) -> Union[np.ndarray, list]:
"""
Read bit arrays as a succession of binary masks (sort of read a slice of the bit mask, slice number bit_id)
```python
>>> bit_array = np.random.randint(5, size=[3,3])
array([[1, 1, 3],
[4, 2, 0],
[4, 3, 2]], dtype=uint8)
# Get the 2nd bit array
>>> read_bit_array(bit_array, 2)
array([[0, 0, 0],
[1, 0, 0],
[1, 0, 0]], dtype=uint8)
```
Args:
bit_mask (np.ndarray): Bit array to read
bit_id (int): Bit ID of the slice to be read
Example: read the bit 0 of the mask as a cloud mask (Theia)
Returns:
Union[np.ndarray, list]: Binary mask or list of binary masks if a list of bit_id is given
"""
# Get the number of bits
nof_bits = 8 * bit_mask.dtype.itemsize
# Read cloud mask as bits
msk = unpackbits(bit_mask, nof_bits)
# Only keep the bit number bit_id and reshape the vector
if isinstance(bit_id, list):
bit_arr = [msk[..., bid] for bid in bit_id]
else:
bit_arr = msk[..., bit_id]
return bit_arr
Global variables
var PATH_ARR_DS
-
Types:
- Path
- Rasterio open data: (array, meta)
- rasterio Dataset
xarray
Functions
def path_arr_dst(
function)
-
Path,
xarray
, (array, metadata) or dataset decorator. Allows a function to ingest:- a path
- a
xarray
- a
rasterio
dataset rasterio
open data: (array, meta)
>>> # Create mock function >>> @path_or_dst >>> def fct(dst): >>> read(dst) >>> >>> # Test the two ways >>> read1 = fct("path\to\raster.tif") >>> with rasterio.open("path\to\raster.tif") as dst: >>> read2 = fct(dst) >>> >>> # Test >>> read1 == read2 True
Args
function
:Callable
- Function to decorate
Returns
Callable
- decorated function
Expand source code
def path_arr_dst(function: Callable) -> Callable: """ Path, `xarray`, (array, metadata) or dataset decorator. Allows a function to ingest: - a path - a `xarray` - a `rasterio` dataset - `rasterio` open data: (array, meta) ```python >>> # Create mock function >>> @path_or_dst >>> def fct(dst): >>> read(dst) >>> >>> # Test the two ways >>> read1 = fct("path\\to\\raster.tif") >>> with rasterio.open("path\\to\\raster.tif") as dst: >>> read2 = fct(dst) >>> >>> # Test >>> read1 == read2 True ``` Args: function (Callable): Function to decorate Returns: Callable: decorated function """ @wraps(function) def path_or_arr_or_dst_wrapper( path_or_arr_or_ds: Union[str, rasterio.DatasetReader], *args, **kwargs ) -> Any: """ Path or dataset wrapper Args: path_or_arr_or_ds (Union[str, rasterio.DatasetReader]): Raster path or its dataset *args: args **kwargs: kwargs Returns: Any: regular output """ if isinstance(path_or_arr_or_ds, str): with rasterio.open(path_or_arr_or_ds) as dst: out = function(dst, *args, **kwargs) elif isinstance(path_or_arr_or_ds, tuple): arr, meta = path_or_arr_or_ds with MemoryFile() as memfile: with memfile.open(**meta) as dst: dst.write(arr) out = function(dst, *args, **kwargs) else: out = None # Try if xarray is importable try: import xarray as xr if isinstance(path_or_arr_or_ds, (xr.DataArray, xr.Dataset)): meta = { "driver": "GTiff", "dtype": path_or_arr_or_ds.dtype, "nodata": path_or_arr_or_ds.rio.encoded_nodata, "width": path_or_arr_or_ds.rio.width, "height": path_or_arr_or_ds.rio.height, "count": path_or_arr_or_ds.rio.count, "crs": path_or_arr_or_ds.rio.crs, "transform": path_or_arr_or_ds.rio.transform(), } with MemoryFile() as memfile: with memfile.open(**meta) as dst: xds = path_or_arr_or_ds.copy() if xds.rio.encoded_nodata is not None: xds = xds.fillna(xds.rio.encoded_nodata) dst.write(xds.data) out = function(dst, *args, **kwargs) except ModuleNotFoundError: pass if not out: out = function(path_or_arr_or_ds, *args, **kwargs) return out return path_or_arr_or_dst_wrapper
def get_new_shape(
dst,
resolution,
size)-
Get the new shape (height, width) of a resampled raster.
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata resolution
:Union[tuple, list, float]
- Resolution of the wanted band, in dataset resolution unit (X, Y)
size
:Union[tuple, list]
- Size of the array (width, height). Not used if resolution is provided.
Returns
(int, int): Height, width
Expand source code
@path_arr_dst def get_new_shape( dst: PATH_ARR_DS, resolution: Union[tuple, list, float], size: Union[tuple, list] ) -> (int, int): """ Get the new shape (height, width) of a resampled raster. Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata resolution (Union[tuple, list, float]): Resolution of the wanted band, in dataset resolution unit (X, Y) size (Union[tuple, list]): Size of the array (width, height). Not used if resolution is provided. Returns: (int, int): Height, width """ def _get_new_dim(dim: int, res_old: float, res_new: float) -> int: """ Get the new dimension in pixels Args: dim (int): Old dimension res_old (float): Old resolution res_new (float): New resolution Returns: int: New dimension """ return int(np.round(dim * res_old / res_new)) # By default keep original shape new_height = dst.height new_width = dst.width # Compute new shape if resolution is not None: if isinstance(resolution, (int, float)): new_height = _get_new_dim(dst.height, dst.res[1], resolution) new_width = _get_new_dim(dst.width, dst.res[0], resolution) elif resolution is None: pass else: try: if len(resolution) != 2: raise ValueError( "We should have a resolution for X and Y dimensions" ) if resolution[0] is not None: new_width = _get_new_dim(dst.width, dst.res[0], resolution[0]) if resolution[1] is not None: new_height = _get_new_dim(dst.height, dst.res[1], resolution[1]) except (TypeError, KeyError): raise ValueError( f"Resolution should be None, 2 floats or a castable to a list: {resolution}" ) elif size is not None: try: new_height = size[1] new_width = size[0] except (TypeError, KeyError): raise ValueError(f"Size should be None or a castable to a list: {size}") return new_height, new_width
def update_meta(
arr,
meta)-
Basic metadata update from a numpy array. Updates everything that we can find in the array:
dtype
: array dtype,count
: first dimension of the array if the array is in 3D, else 1height
: second dimension of the arraywidth
: third dimension of the arraynodata
: if a masked array is given, nodata is its fill_value
Warning
The array's shape is interpreted in rasterio's way (count, height, width) !
>>> raster_path = "path\to\raster.tif" >>> with rasterio.open(raster_path) as dst: >>> meta = dst.meta >>> arr = dst.read() >>> meta { 'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 300, 'height': 300, 'count': 4, 'crs': CRS.from_epsg(32630), 'transform': Affine(20.0, 0.0, 630000.0,0.0, -20.0, 4870020.0) } >>> new_arr = np.ma.masked_array(arr[:, ::2, ::2].astype(np.uint8), fill_value=0) >>> new_arr.shape (4, 150, 150) >>> new_arr.dtype dtype('uint8') >>> new_arr.fill_value 0 >>> update_meta(new_arr, meta) { 'driver': 'GTiff', 'dtype': dtype('uint8'), 'nodata': 0, 'width': 150, 'height': 150, 'count': 4, 'crs': CRS.from_epsg(32630), 'transform': Affine(20.0, 0.0, 630000.0, 0.0, -20.0, 4870020.0) }
Args
arr
:Union[np.ndarray, np.ma.masked_array]
- Array from which to update the metadata
meta
:dict
- Metadata to update
Returns
dict
- Update metadata
Expand source code
def update_meta(arr: Union[np.ndarray, np.ma.masked_array], meta: dict) -> dict: """ Basic metadata update from a numpy array. Updates everything that we can find in the array: - `dtype`: array dtype, - `count`: first dimension of the array if the array is in 3D, else 1 - `height`: second dimension of the array - `width`: third dimension of the array - `nodata`: if a masked array is given, nodata is its fill_value .. WARNING:: The array's shape is interpreted in rasterio's way (count, height, width) ! ```python >>> raster_path = "path\\to\\raster.tif" >>> with rasterio.open(raster_path) as dst: >>> meta = dst.meta >>> arr = dst.read() >>> meta { 'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 300, 'height': 300, 'count': 4, 'crs': CRS.from_epsg(32630), 'transform': Affine(20.0, 0.0, 630000.0,0.0, -20.0, 4870020.0) } >>> new_arr = np.ma.masked_array(arr[:, ::2, ::2].astype(np.uint8), fill_value=0) >>> new_arr.shape (4, 150, 150) >>> new_arr.dtype dtype('uint8') >>> new_arr.fill_value 0 >>> update_meta(new_arr, meta) { 'driver': 'GTiff', 'dtype': dtype('uint8'), 'nodata': 0, 'width': 150, 'height': 150, 'count': 4, 'crs': CRS.from_epsg(32630), 'transform': Affine(20.0, 0.0, 630000.0, 0.0, -20.0, 4870020.0) } ``` Args: arr (Union[np.ndarray, np.ma.masked_array]): Array from which to update the metadata meta (dict): Metadata to update Returns: dict: Update metadata """ # Manage raster shape (Stored in rasterio's way) shape = arr.shape count = 1 if len(shape) == 2 else shape[0] width = shape[-1] height = shape[-2] # Update metadata that can be derived from raster out_meta = meta.copy() out_meta.update( {"dtype": arr.dtype, "count": count, "height": height, "width": width} ) # Nodata if isinstance(arr, np.ma.masked_array): out_meta["nodata"] = arr.fill_value return out_meta
def get_nodata_mask(
array,
has_nodata,
default_nodata=0)-
Get nodata mask from a masked array.
The nodata may not be set before, then pass a nodata value that will be evaluated on the array.
>>> diag_arr = np.diag([1,2,3]) array([[1, 0, 0], [0, 2, 0], [0, 0, 3]]) >>> get_nodata_mask(diag_arr, has_nodata=False) array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=uint8) >>> get_nodata_mask(diag_arr, has_nodata=False, default_nodata=1) array([[0, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=uint8)
Args
array
:np.ma.masked_array
- Array to evaluate
has_nodata
:bool
- If the array as its nodata specified. If not, using default_nodata.
default_nodata
:int
- Default nodata used if the array's nodata is not set
Returns
np.ma.masked_array
- Pixelwise nodata array
Expand source code
def get_nodata_mask( array: Union[np.ma.masked_array, np.ndarray], has_nodata: bool, default_nodata: int = 0, ) -> np.ma.masked_array: """ Get nodata mask from a masked array. The nodata may not be set before, then pass a nodata value that will be evaluated on the array. ```python >>> diag_arr = np.diag([1,2,3]) array([[1, 0, 0], [0, 2, 0], [0, 0, 3]]) >>> get_nodata_mask(diag_arr, has_nodata=False) array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=uint8) >>> get_nodata_mask(diag_arr, has_nodata=False, default_nodata=1) array([[0, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=uint8) ``` Args: array (np.ma.masked_array): Array to evaluate has_nodata (bool): If the array as its nodata specified. If not, using default_nodata. default_nodata (int): Default nodata used if the array's nodata is not set Returns: np.ma.masked_array: Pixelwise nodata array """ # Nodata mask if not has_nodata or not isinstance( array, np.ma.masked_array ): # Unspecified nodata is set to None by rasterio nodata_mask = np.where(array != default_nodata, 1, 0).astype(np.uint8) else: nodata_mask = np.where(array.mask, 0, 1).astype(np.uint8) return nodata_mask
def vectorize(
dst,
values=None,
default_nodata=0)-
Vectorize a raster to get the class vectors.
Warning
- Please only use this function on a classified raster.
- This could take a while as the computing time directly depends on the number of polygons to vectorize. Please be careful.
>>> raster_path = "path\to\raster.tif" # Classified raster, with no data set to 255 >>> vec1 = vectorize(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> vec2 = vectorize(dst) >>> vec1 == vec2 True
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata values
:Union[None, int, list]
- Get only the polygons concerning this/these particular values
default_nodata
:int
- Default values for nodata in case of non existing in file
Returns
gpd.GeoDataFrame
- Classes Vector
Expand source code
@path_arr_dst def vectorize( dst: PATH_ARR_DS, values: Union[None, int, list] = None, default_nodata: int = 0 ) -> gpd.GeoDataFrame: """ Vectorize a raster to get the class vectors. .. WARNING:: - Please only use this function on a classified raster. - This could take a while as the computing time directly depends on the number of polygons to vectorize. Please be careful. ```python >>> raster_path = "path\\to\\raster.tif" # Classified raster, with no data set to 255 >>> vec1 = vectorize(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> vec2 = vectorize(dst) >>> vec1 == vec2 True ``` Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata values (Union[None, int, list]): Get only the polygons concerning this/these particular values default_nodata (int): Default values for nodata in case of non existing in file Returns: gpd.GeoDataFrame: Classes Vector """ return _vectorize( dst, values=values, get_nodata=False, default_nodata=default_nodata )
def get_valid_vector(
dst,
default_nodata=0)-
Get the valid data of a raster as a vector.
Pay attention that every nodata pixel will appear too. If you want only the footprint of the raster, please use
get_footprint()
.>>> raster_path = "path\to\raster.tif" # Classified raster, with no data set to 255 >>> nodata1 = get_nodata_vec(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> nodata2 = get_nodata_vec(dst) >>> nodata1 == nodata2 True
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata default_nodata
:int
- Default values for nodata in case of non existing in file
Returns
gpd.GeoDataFrame
- Nodata Vector
Expand source code
@path_arr_dst def get_valid_vector(dst: PATH_ARR_DS, default_nodata: int = 0) -> gpd.GeoDataFrame: """ Get the valid data of a raster as a vector. Pay attention that every nodata pixel will appear too. If you want only the footprint of the raster, please use `get_footprint`. ```python >>> raster_path = "path\\to\\raster.tif" # Classified raster, with no data set to 255 >>> nodata1 = get_nodata_vec(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> nodata2 = get_nodata_vec(dst) >>> nodata1 == nodata2 True ``` Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata default_nodata (int): Default values for nodata in case of non existing in file Returns: gpd.GeoDataFrame: Nodata Vector """ nodata = _vectorize( dst, values=None, get_nodata=True, default_nodata=default_nodata ) return nodata[ nodata.raster_val != 0 ] # 0 is the values of not nodata put there by rasterio
def get_nodata_vector(
dst,
default_nodata=0)-
Get the nodata vector of a raster as a vector.
Pay attention that every nodata pixel will appear too. If you want only the footprint of the raster, please use
get_footprint()
.>>> raster_path = "path\to\raster.tif" # Classified raster, with no data set to 255 >>> nodata1 = get_nodata_vec(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> nodata2 = get_nodata_vec(dst) >>> nodata1 == nodata2 True
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata default_nodata
:int
- Default values for nodata in case of non existing in file
Returns
gpd.GeoDataFrame
- Nodata Vector
Expand source code
@path_arr_dst def get_nodata_vector(dst: PATH_ARR_DS, default_nodata: int = 0) -> gpd.GeoDataFrame: """ Get the nodata vector of a raster as a vector. Pay attention that every nodata pixel will appear too. If you want only the footprint of the raster, please use `get_footprint`. ```python >>> raster_path = "path\\to\\raster.tif" # Classified raster, with no data set to 255 >>> nodata1 = get_nodata_vec(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> nodata2 = get_nodata_vec(dst) >>> nodata1 == nodata2 True ``` Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata default_nodata (int): Default values for nodata in case of non existing in file Returns: gpd.GeoDataFrame: Nodata Vector """ nodata = _vectorize( dst, values=None, get_nodata=True, default_nodata=default_nodata ) return nodata[ nodata.raster_val == 0 ] # 0 is the values of not nodata put there by rasterio
def mask(
dst,
shapes,
nodata=None,
**kwargs)-
Masking a dataset: setting nodata outside of the given shapes, but without cropping the raster to the shapes extent.
Overload of rasterio mask function in order to create a masked_array.
The
mask()
function docs can be seen here. It basically masks a raster with a vector mask, with the possibility to crop the raster to the vector's extent.>>> raster_path = "path\to\raster.tif" >>> shape_path = "path\to\shapes.geojson" # Any vector that geopandas can read >>> shapes = gpd.read_file(shape_path) >>> masked_raster1, meta1 = mask(raster_path, shapes) >>> # or >>> with rasterio.open(raster_path) as dst: >>> masked_raster2, meta2 = mask(dst, shapes) >>> masked_raster1 == masked_raster2 True >>> meta1 == meta2 True
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata shapes
:Union[Polygon, list]
- Shapes
nodata
:int
- Nodata value. If not set, uses the ds.nodata. If doesnt exist, set to 0.
**kwargs
- Other rasterio.mask options
Returns
(np.ma.masked_array, dict): Masked array as a masked array and its metadata
Expand source code
@path_arr_dst def mask( dst: PATH_ARR_DS, shapes: Union[Polygon, list], nodata: Optional[int] = None, **kwargs, ) -> (np.ma.masked_array, dict): """ Masking a dataset: setting nodata outside of the given shapes, but without cropping the raster to the shapes extent. Overload of rasterio mask function in order to create a masked_array. The `mask` function docs can be seen [here](https://rasterio.readthedocs.io/en/latest/api/rasterio.mask.html). It basically masks a raster with a vector mask, with the possibility to crop the raster to the vector's extent. ```python >>> raster_path = "path\\to\\raster.tif" >>> shape_path = "path\\to\\shapes.geojson" # Any vector that geopandas can read >>> shapes = gpd.read_file(shape_path) >>> masked_raster1, meta1 = mask(raster_path, shapes) >>> # or >>> with rasterio.open(raster_path) as dst: >>> masked_raster2, meta2 = mask(dst, shapes) >>> masked_raster1 == masked_raster2 True >>> meta1 == meta2 True ``` Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata shapes (Union[Polygon, list]): Shapes nodata (int): Nodata value. If not set, uses the ds.nodata. If doesnt exist, set to 0. **kwargs: Other rasterio.mask options Returns: (np.ma.masked_array, dict): Masked array as a masked array and its metadata """ return _mask(dst, shapes=shapes, nodata=nodata, do_crop=False, **kwargs)
def crop(
dst,
shapes,
nodata=None,
**kwargs)-
Cropping a dataset: setting nodata outside of the given shapes AND cropping the raster to the shapes extent.
HOW:
Overload of rasterio mask function in order to create a masked_array.
The
mask()
function docs can be seen here. It basically masks a raster with a vector mask, with the possibility to crop the raster to the vector's extent.>>> raster_path = "path\to\raster.tif" >>> shape_path = "path\to\shapes.geojson" # Any vector that geopandas can read >>> shapes = gpd.read_file(shape_path) >>> cropped_raster1, meta1 = crop(raster_path, shapes) >>> # or >>> with rasterio.open(raster_path) as dst: >>> cropped_raster2, meta2 = crop(dst, shapes) >>> cropped_raster1 == cropped_raster2 True >>> meta1 == meta2 True
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata shapes
:Union[Polygon, list]
- Shapes
nodata
:int
- Nodata value. If not set, uses the ds.nodata. If doesnt exist, set to 0.
**kwargs
- Other rasterio.mask options
Returns
(np.ma.masked_array, dict): Cropped array as a masked array and its metadata
Expand source code
@path_arr_dst def crop( dst: PATH_ARR_DS, shapes: Union[Polygon, list], nodata: Optional[int] = None, **kwargs, ) -> (np.ma.masked_array, dict): """ Cropping a dataset: setting nodata outside of the given shapes AND cropping the raster to the shapes extent. **HOW:** Overload of rasterio mask function in order to create a masked_array. The `mask` function docs can be seen [here](https://rasterio.readthedocs.io/en/latest/api/rasterio.mask.html). It basically masks a raster with a vector mask, with the possibility to crop the raster to the vector's extent. ```python >>> raster_path = "path\\to\\raster.tif" >>> shape_path = "path\\to\\shapes.geojson" # Any vector that geopandas can read >>> shapes = gpd.read_file(shape_path) >>> cropped_raster1, meta1 = crop(raster_path, shapes) >>> # or >>> with rasterio.open(raster_path) as dst: >>> cropped_raster2, meta2 = crop(dst, shapes) >>> cropped_raster1 == cropped_raster2 True >>> meta1 == meta2 True ``` Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata shapes (Union[Polygon, list]): Shapes nodata (int): Nodata value. If not set, uses the ds.nodata. If doesnt exist, set to 0. **kwargs: Other rasterio.mask options Returns: (np.ma.masked_array, dict): Cropped array as a masked array and its metadata """ return _mask(dst, shapes=shapes, nodata=nodata, do_crop=True, **kwargs)
def read(
dst,
resolution=None,
size=None,
resampling=Resampling.nearest,
masked=True)-
Read a raster dataset from a
rasterio.Dataset
or a path.The resolution can be provided (in dataset unit) as:
- a tuple or a list of (X, Y) resolutions
- a float, in which case X resolution = Y resolution
- None, in which case the dataset resolution will be used
>>> raster_path = "path\to\raster.tif" >>> raster1, meta1 = read(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> raster2, meta2 = read(dst) >>> raster1 == raster2 True >>> meta1 == meta2 True
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata resolution
:Union[tuple, list, float]
- Resolution of the wanted band, in dataset resolution unit (X, Y)
size
:Union[tuple, list]
- Size of the array (width, height). Not used if resolution is provided.
resampling
:Resampling
- Resampling method
masked
:bool
- Get a masked array
Returns
np.ma.masked_array, dict
- Masked array corresponding to the raster data and its meta data
Expand source code
@path_arr_dst def read( dst: PATH_ARR_DS, resolution: Union[tuple, list, float] = None, size: Union[tuple, list] = None, resampling: Resampling = Resampling.nearest, masked: bool = True, ) -> (np.ma.masked_array, dict): """ Read a raster dataset from a `rasterio.Dataset` or a path. The resolution can be provided (in dataset unit) as: - a tuple or a list of (X, Y) resolutions - a float, in which case X resolution = Y resolution - None, in which case the dataset resolution will be used ```python >>> raster_path = "path\\to\\raster.tif" >>> raster1, meta1 = read(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> raster2, meta2 = read(dst) >>> raster1 == raster2 True >>> meta1 == meta2 True ``` Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata resolution (Union[tuple, list, float]): Resolution of the wanted band, in dataset resolution unit (X, Y) size (Union[tuple, list]): Size of the array (width, height). Not used if resolution is provided. resampling (Resampling): Resampling method masked (bool): Get a masked array Returns: np.ma.masked_array, dict: Masked array corresponding to the raster data and its meta data """ # Get new height and width new_height, new_width = get_new_shape(dst, resolution, size) # Read data array = dst.read( out_shape=(dst.count, new_height, new_width), resampling=resampling, masked=masked, ) # Update meta dst_transform = dst.transform * dst.transform.scale( (dst.width / new_width), (dst.height / new_height) ) dst_meta = dst.meta.copy() dst_meta.update( { "height": new_height, "width": new_width, "transform": dst_transform, "dtype": array.dtype, "nodata": dst.nodata, } ) return array, dst_meta
def write(
raster,
path,
meta,
**kwargs)-
Write raster to disk (encapsulation of rasterio's function)
Metadata will be copied and updated with raster's information (ie. width, height, count, type…) The driver is GTiff by default, and no nodata value is provided. The file will be compressed if the raster is a mask (saved as uint8)
>>> raster_path = "path\to\raster.tif" >>> raster_out = "path\to\out.tif" >>> # Read raster >>> raster, meta = read(raster_path) >>> # Rewrite it >>> write(raster, raster_out, meta)
Args
raster
:Union[np.ma.masked_array, np.ndarray]
- Raster to save on disk
path
:str
- Path where to save it (directories should be existing)
meta
:dict
- Basic metadata that will be copied and updated with raster's information
**kwargs
- Overloading metadata, ie
nodata=255
Expand source code
def write( raster: Union[np.ma.masked_array, np.ndarray], path: str, meta: dict, **kwargs ) -> None: """ Write raster to disk (encapsulation of rasterio's function) Metadata will be copied and updated with raster's information (ie. width, height, count, type...) The driver is GTiff by default, and no nodata value is provided. The file will be compressed if the raster is a mask (saved as uint8) ```python >>> raster_path = "path\\to\\raster.tif" >>> raster_out = "path\\to\\out.tif" >>> # Read raster >>> raster, meta = read(raster_path) >>> # Rewrite it >>> write(raster, raster_out, meta) ``` Args: raster (Union[np.ma.masked_array, np.ndarray]): Raster to save on disk path (str): Path where to save it (directories should be existing) meta (dict): Basic metadata that will be copied and updated with raster's information **kwargs: Overloading metadata, ie `nodata=255` """ # Manage raster type (impossible to write boolean arrays) if raster.dtype == bool: raster = raster.astype(np.uint8) # Update metadata out_meta = meta.copy() # Update raster to be sure to write down correct nodata pixels if isinstance(raster, np.ma.masked_array): raster[raster.mask] = raster.fill_value # Force compression and driver (but can be overwritten by kwargs) out_meta["driver"] = "GTiff" # Compress only if uint8 data if raster.dtype == np.uint8: out_meta["compress"] = "lzw" # Update metadata with array data out_meta = update_meta(raster, out_meta) # Update metadata with additional params for key, val in kwargs.items(): out_meta[key] = val # Manage raster shape if len(raster.shape) == 2: raster = np.expand_dims(raster, axis=0) # Write product with rasterio.open(path, "w", **out_meta) as dst: dst.write(raster)
def collocate(
master_meta,
slave_arr,
slave_meta,
resampling=Resampling.nearest)-
Collocate two georeferenced arrays: forces the slave raster to be exactly georeferenced onto the master raster by reprojection.
Use it like
OTB SuperImpose
.>>> master_path = "path\to\master.tif" >>> slave_path = "path\to\slave.tif" >>> col_path = "path\to\collocated.tif" >>> # Just open the master data >>> with rasterio.open(master_path) as master_dst: >>> # Read slave >>> slave, slave_meta = read(slave_path) >>> # Collocate the slave to the master >>> col_arr, col_meta = collocate(master_dst.meta, >>> slave, >>> slave_meta, >>> Resampling.bilinear) >>> # Write it >>> write(col_arr, col_path, col_meta)
Args
master_meta
:dict
- Master metadata
slave_arr
:np.ma.masked_array
- Slave array to be collocated
slave_meta
:dict
- Slave metadata
resampling
:Resampling
- Resampling method
Returns
np.ma.masked_array, dict
- Collocated array and its metadata
Expand source code
def collocate( master_meta: dict, slave_arr: Union[np.ma.masked_array, np.ndarray], slave_meta: dict, resampling: Resampling = Resampling.nearest, ) -> (Union[np.ma.masked_array, np.ndarray], dict): """ Collocate two georeferenced arrays: forces the *slave* raster to be exactly georeferenced onto the *master* raster by reprojection. Use it like `OTB SuperImpose`. ```python >>> master_path = "path\\to\\master.tif" >>> slave_path = "path\\to\\slave.tif" >>> col_path = "path\\to\\collocated.tif" >>> # Just open the master data >>> with rasterio.open(master_path) as master_dst: >>> # Read slave >>> slave, slave_meta = read(slave_path) >>> # Collocate the slave to the master >>> col_arr, col_meta = collocate(master_dst.meta, >>> slave, >>> slave_meta, >>> Resampling.bilinear) >>> # Write it >>> write(col_arr, col_path, col_meta) ``` Args: master_meta (dict): Master metadata slave_arr (np.ma.masked_array): Slave array to be collocated slave_meta (dict): Slave metadata resampling (Resampling): Resampling method Returns: np.ma.masked_array, dict: Collocated array and its metadata """ collocated_arr = np.zeros( (master_meta["count"], master_meta["height"], master_meta["width"]), dtype=master_meta["dtype"], ) warp.reproject( source=slave_arr, destination=collocated_arr, src_transform=slave_meta["transform"], src_crs=slave_meta["crs"], dst_transform=master_meta["transform"], dst_crs=master_meta["crs"], src_nodata=slave_meta["nodata"], dst_nodata=slave_meta["nodata"], resampling=resampling, num_threads=MAX_CORES, ) meta = master_meta.copy() meta.update( { "dtype": slave_meta["dtype"], "driver": slave_meta["driver"], "nodata": slave_meta["nodata"], } ) return collocated_arr, meta
def sieve(
array,
out_meta,
sieve_thresh,
connectivity=4)-
Sieving, overloads rasterio function with raster shaped like (1, h, w).
Forces the output to
np.uint8
(as only classified rasters should be sieved)>>> raster_path = "path\to\raster.tif" # classified raster >>> # Read raster >>> raster, meta = read(raster_path) >>> # Rewrite it >>> sieved, sieved_meta = sieve(raster, meta, sieve_thresh=20) >>> # Write it >>> raster_out = "path\to\raster_sieved.tif" >>> write(sieved, raster_out, sieved_meta)
Args
array
:Union[np.ma.masked_array, np.ndarray]
- Array to sieve
out_meta
:dict
- Metadata to update
sieve_thresh
:int
- Sieving threshold in pixels
connectivity
:int
- Connectivity, either 4 or 8
Returns
(Union[np.ma.masked_array, np.ndarray], dict): Sieved array and updated meta
Expand source code
def sieve( array: Union[np.ma.masked_array, np.ndarray], out_meta: dict, sieve_thresh: int, connectivity: int = 4, ) -> (Union[np.ma.masked_array, np.ndarray], dict): """ Sieving, overloads rasterio function with raster shaped like (1, h, w). Forces the output to `np.uint8` (as only classified rasters should be sieved) ```python >>> raster_path = "path\\to\\raster.tif" # classified raster >>> # Read raster >>> raster, meta = read(raster_path) >>> # Rewrite it >>> sieved, sieved_meta = sieve(raster, meta, sieve_thresh=20) >>> # Write it >>> raster_out = "path\\to\\raster_sieved.tif" >>> write(sieved, raster_out, sieved_meta) ``` Args: array (Union[np.ma.masked_array, np.ndarray]): Array to sieve out_meta (dict): Metadata to update sieve_thresh (int): Sieving threshold in pixels connectivity (int): Connectivity, either 4 or 8 Returns: (Union[np.ma.masked_array, np.ndarray], dict): Sieved array and updated meta """ assert connectivity in [4, 8] # Read extraction array expand = False if len(array.shape) == 3 and array.shape[0] == 1: array = np.squeeze(array) # Use this trick to make the sieve work expand = True # Convert to np.uint8 if needed dtype = np.uint8 meta = out_meta.copy() if meta["dtype"] != dtype: array = array.astype(dtype) meta["dtype"] = dtype # Sieve result_array = np.empty(array.shape, dtype=array.dtype) features.sieve( array, size=sieve_thresh, out=result_array, connectivity=connectivity ) # Use this trick to get the array back to 'normal' if expand: result_array = np.expand_dims(result_array, axis=0) return result_array, meta
def get_dim_img_path(
dim_path,
img_name='*')-
Get the image path from a BEAM-DIMAP data.
A BEAM-DIMAP file cannot be opened by rasterio, although its .img file can.
>>> dim_path = "path\to\dimap.dim" # BEAM-DIMAP image >>> img_path = get_dim_img_path(dim_path) >>> # Read raster >>> raster, meta = read(img_path)
Args
dim_path
:str
- DIM path (.dim or .data)
img_name
:str
- .img file name (or regex), in case there are multiple .img files (ie. for S3 data)
Returns
str
- .img file
Expand source code
def get_dim_img_path(dim_path: str, img_name: str = "*") -> str: """ Get the image path from a *BEAM-DIMAP* data. A *BEAM-DIMAP* file cannot be opened by rasterio, although its .img file can. ```python >>> dim_path = "path\\to\\dimap.dim" # BEAM-DIMAP image >>> img_path = get_dim_img_path(dim_path) >>> # Read raster >>> raster, meta = read(img_path) ``` Args: dim_path (str): DIM path (.dim or .data) img_name (str): .img file name (or regex), in case there are multiple .img files (ie. for S3 data) Returns: str: .img file """ if dim_path.endswith(".dim"): dim_path = dim_path.replace(".dim", ".data") assert dim_path.endswith(".data") and os.path.isdir(dim_path) return files.get_file_in_dir(dim_path, img_name, extension="img")
def get_extent(
dst)
-
Get the extent of a raster as a
geopandas.Geodataframe
.>>> raster_path = "path\to\raster.tif" >>> extent1 = get_extent(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> extent2 = get_extent(dst) >>> extent1 == extent2 True
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata
Returns
gpd.GeoDataFrame
- Extent as a
geopandas.Geodataframe
Expand source code
@path_arr_dst def get_extent(dst: PATH_ARR_DS) -> gpd.GeoDataFrame: """ Get the extent of a raster as a `geopandas.Geodataframe`. ```python >>> raster_path = "path\\to\\raster.tif" >>> extent1 = get_extent(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> extent2 = get_extent(dst) >>> extent1 == extent2 True ``` Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata Returns: gpd.GeoDataFrame: Extent as a `geopandas.Geodataframe` """ return vectors.get_geodf(geometry=[*dst.bounds], crs=dst.crs)
def get_footprint(
dst)
-
Get real footprint of the product (without nodata, in french == emprise utile)
>>> raster_path = "path\to\raster.tif" >>> footprint1 = get_footprint(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> footprint2 = get_footprint(dst) >>> footprint1 == footprint2
Args
dst
:PATH_ARR_DS
- Path to the raster, its dataset, its
xarray
or a tuple containing its array and metadata
Returns
gpd.GeoDataFrame
- Footprint as a GeoDataFrame
Expand source code
@path_arr_dst def get_footprint(dst: PATH_ARR_DS) -> gpd.GeoDataFrame: """ Get real footprint of the product (without nodata, in french == emprise utile) ```python >>> raster_path = "path\\to\\raster.tif" >>> footprint1 = get_footprint(raster_path) >>> # or >>> with rasterio.open(raster_path) as dst: >>> footprint2 = get_footprint(dst) >>> footprint1 == footprint2 ``` Args: dst (PATH_ARR_DS): Path to the raster, its dataset, its `xarray` or a tuple containing its array and metadata Returns: gpd.GeoDataFrame: Footprint as a GeoDataFrame """ footprint = get_valid_vector(dst) return vectors.get_wider_exterior(footprint)
def merge_vrt(
crs_paths,
crs_merged_path,
**kwargs)-
Merge rasters as a VRT. Uses
gdalbuildvrt
.See here: https://gdal.org/programs/gdalbuildvrt.html
Creates VRT with relative paths !
Warning
They should have the same CRS otherwise the mosaic will be false !
>>> paths_utm32630 = ["path\to\raster1.tif", "path\to\raster2.tif", "path\to\raster3.tif"] >>> paths_utm32631 = ["path\to\raster4.tif", "path\to\raster5.tif"] >>> mosaic_32630 = "path\to\mosaic_32630.vrt" >>> mosaic_32631 = "path\to\mosaic_32631.vrt" >>> # Create mosaic, one by CRS ! >>> merge_vrt(paths_utm32630, mosaic_32630) >>> merge_vrt(paths_utm32631, mosaic_32631, {"-srcnodata":255, "-vrtnodata":0})
Args
crs_paths
:list
- Path of the rasters to be merged with the same CRS)
crs_merged_path
:str
- Path to the merged raster
kwargs
- Other gdlabuildvrt arguments
Expand source code
def merge_vrt(crs_paths: list, crs_merged_path: str, **kwargs) -> None: """ Merge rasters as a VRT. Uses `gdalbuildvrt`. See here: https://gdal.org/programs/gdalbuildvrt.html Creates VRT with relative paths ! .. WARNING:: They should have the same CRS otherwise the mosaic will be false ! ```python >>> paths_utm32630 = ["path\\to\\raster1.tif", "path\\to\\raster2.tif", "path\\to\\raster3.tif"] >>> paths_utm32631 = ["path\\to\\raster4.tif", "path\\to\\raster5.tif"] >>> mosaic_32630 = "path\\to\\mosaic_32630.vrt" >>> mosaic_32631 = "path\\to\\mosaic_32631.vrt" >>> # Create mosaic, one by CRS ! >>> merge_vrt(paths_utm32630, mosaic_32630) >>> merge_vrt(paths_utm32631, mosaic_32631, {"-srcnodata":255, "-vrtnodata":0}) ``` Args: crs_paths (list): Path of the rasters to be merged with the same CRS) crs_merged_path (str): Path to the merged raster kwargs: Other gdlabuildvrt arguments """ # Create relative paths vrt_root = os.path.dirname(crs_merged_path) rel_paths = [ strings.to_cmd_string(files.real_rel_path(path, vrt_root)) for path in crs_paths ] rel_vrt = strings.to_cmd_string(files.real_rel_path(crs_merged_path, vrt_root)) # Run cmd arg_list = [val for item in kwargs.items() for val in item] vrt_cmd = ["gdalbuildvrt", rel_vrt, *rel_paths, *arg_list] misc.run_cli(vrt_cmd, cwd=vrt_root)
def merge_gtiff(
crs_paths,
crs_merged_path,
**kwargs)-
Merge rasters as a GeoTiff.
Warning
They should have the same CRS otherwise the mosaic will be false !
>>> paths_utm32630 = ["path\to\raster1.tif", "path\to\raster2.tif", "path\to\raster3.tif"] >>> paths_utm32631 = ["path\to\raster4.tif", "path\to\raster5.tif"] >>> mosaic_32630 = "path\to\mosaic_32630.tif" >>> mosaic_32631 = "path\to\mosaic_32631.tif" # Create mosaic, one by CRS ! >>> merge_gtiff(paths_utm32630, mosaic_32630) >>> merge_gtiff(paths_utm32631, mosaic_32631)
Args
crs_paths
:list
- Path of the rasters to be merged with the same CRS)
crs_merged_path
:str
- Path to the merged raster
kwargs
- Other rasterio.merge arguments More info here
Expand source code
def merge_gtiff(crs_paths: list, crs_merged_path: str, **kwargs) -> None: """ Merge rasters as a GeoTiff. .. WARNING:: They should have the same CRS otherwise the mosaic will be false ! ```python >>> paths_utm32630 = ["path\\to\\raster1.tif", "path\\to\\raster2.tif", "path\\to\\raster3.tif"] >>> paths_utm32631 = ["path\\to\\raster4.tif", "path\\to\\raster5.tif"] >>> mosaic_32630 = "path\\to\\mosaic_32630.tif" >>> mosaic_32631 = "path\\to\\mosaic_32631.tif" # Create mosaic, one by CRS ! >>> merge_gtiff(paths_utm32630, mosaic_32630) >>> merge_gtiff(paths_utm32631, mosaic_32631) ``` Args: crs_paths (list): Path of the rasters to be merged with the same CRS) crs_merged_path (str): Path to the merged raster kwargs: Other rasterio.merge arguments More info [here](https://rasterio.readthedocs.io/en/latest/api/rasterio.merge.html#rasterio.merge.merge) """ # Open datasets for merging crs_datasets = [] try: for path in crs_paths: crs_datasets.append(rasterio.open(path)) # Merge all datasets merged_array, merged_transform = merge.merge(crs_datasets, **kwargs) merged_meta = crs_datasets[0].meta.copy() merged_meta.update( { "driver": "GTiff", "height": merged_array.shape[1], "width": merged_array.shape[2], "transform": merged_transform, } ) finally: # Close all datasets for dataset in crs_datasets: dataset.close() # Save merge datasets write( merged_array, crs_merged_path, crs_datasets[0].meta, transform=merged_transform )
def unpackbits(
array,
nof_bits)-
Function found here: https://stackoverflow.com/questions/18296035/how-to-extract-the-bits-of-larger-numeric-numpy-data-types
>>> bit_array = np.random.randint(5, size=[3,3]) array([[1, 1, 3], [4, 2, 0], [4, 3, 2]], dtype=uint8) # Unpack 8 bits (8*1, as itemsize of uint8 is 1) >>> unpackbits(bit_array, 8) array([[[1, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0]]], dtype=uint8)
Args
array
:np.ndarray
- Array to unpack
nof_bits
:int
- Number of bits to unpack
Returns
np.ndarray
- Unpacked array
Expand source code
def unpackbits(array: np.ndarray, nof_bits: int) -> np.ndarray: """ Function found here: https://stackoverflow.com/questions/18296035/how-to-extract-the-bits-of-larger-numeric-numpy-data-types ```python >>> bit_array = np.random.randint(5, size=[3,3]) array([[1, 1, 3], [4, 2, 0], [4, 3, 2]], dtype=uint8) # Unpack 8 bits (8*1, as itemsize of uint8 is 1) >>> unpackbits(bit_array, 8) array([[[1, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0]]], dtype=uint8) ``` Args: array (np.ndarray): Array to unpack nof_bits (int): Number of bits to unpack Returns: np.ndarray: Unpacked array """ xshape = list(array.shape) array = array.reshape([-1, 1]) msk = 2 ** np.arange(nof_bits, dtype=array.dtype).reshape([1, nof_bits]) return (array & msk).astype(bool).astype(np.uint8).reshape(xshape + [nof_bits])
def read_bit_array(
bit_mask,
bit_id)-
Read bit arrays as a succession of binary masks (sort of read a slice of the bit mask, slice number bit_id)
>>> bit_array = np.random.randint(5, size=[3,3]) array([[1, 1, 3], [4, 2, 0], [4, 3, 2]], dtype=uint8) # Get the 2nd bit array >>> read_bit_array(bit_array, 2) array([[0, 0, 0], [1, 0, 0], [1, 0, 0]], dtype=uint8)
Args
bit_mask
:np.ndarray
- Bit array to read
bit_id
:int
- Bit ID of the slice to be read Example: read the bit 0 of the mask as a cloud mask (Theia)
Returns
Union[np.ndarray, list]
- Binary mask or list of binary masks if a list of bit_id is given
Expand source code
def read_bit_array( bit_mask: np.ndarray, bit_id: Union[list, int] ) -> Union[np.ndarray, list]: """ Read bit arrays as a succession of binary masks (sort of read a slice of the bit mask, slice number bit_id) ```python >>> bit_array = np.random.randint(5, size=[3,3]) array([[1, 1, 3], [4, 2, 0], [4, 3, 2]], dtype=uint8) # Get the 2nd bit array >>> read_bit_array(bit_array, 2) array([[0, 0, 0], [1, 0, 0], [1, 0, 0]], dtype=uint8) ``` Args: bit_mask (np.ndarray): Bit array to read bit_id (int): Bit ID of the slice to be read Example: read the bit 0 of the mask as a cloud mask (Theia) Returns: Union[np.ndarray, list]: Binary mask or list of binary masks if a list of bit_id is given """ # Get the number of bits nof_bits = 8 * bit_mask.dtype.itemsize # Read cloud mask as bits msk = unpackbits(bit_mask, nof_bits) # Only keep the bit number bit_id and reshape the vector if isinstance(bit_id, list): bit_arr = [msk[..., bid] for bid in bit_id] else: bit_arr = msk[..., bit_id] return bit_arr