5: "nir",
6: "swir1",
7: "swir2",
+ 8: "pan",
10: "thermal",
}
BAND_TITLES = {
@@ -345,6 +403,7 @@ Source code for xlandsat._io
5: "near-infrared",
6: "short-wave infrared 1",
7: "short-wave infrared 2",
+ 8: "panchromatic",
10: "thermal",
}
BAND_UNITS = {
@@ -355,6 +414,7 @@ Source code for xlandsat._io
5: "reflectance",
6: "reflectance",
7: "reflectance",
+ 8: "reflectance",
10: "Kelvin",
}
@@ -424,69 +484,187 @@ Source code for xlandsat._io
"""
path = pathlib.Path(path)
if bands is None:
- bands = BAND_NAMES.values()
- if path.is_file() and ".tar" in path.suffixes:
- reader_class = TarReader
- else:
- reader_class = FolderReader
- with reader_class(path) as reader:
+ bands = [
+ "coastal_aerosol",
+ "blue",
+ "green",
+ "red",
+ "nir",
+ "swir1",
+ "swir2",
+ "thermal",
+ ]
+ with choose_reader(path)(path) as reader:
metadata = reader.read_metadata()
- available_bands = [int(str(f).split("_B")[-1][:-4]) for f in reader.band_files]
- scene_region = (
- metadata["corner_ll_projection_x_product"],
- metadata["corner_lr_projection_x_product"],
- metadata["corner_ll_projection_y_product"],
- metadata["corner_ul_projection_y_product"],
- )
- shape = (metadata["reflective_lines"], metadata["reflective_samples"])
- coords = {
- "easting": np.linspace(*scene_region[:2], shape[1]),
- "northing": np.linspace(*scene_region[2:], shape[0]),
- }
+ coords = coordinates_from_metadata(metadata, "reflective")
data_vars = {}
- dims = ("northing", "easting")
- for number, fname in zip(available_bands, reader.band_files):
+ for fname in reader.band_files:
+ number = int(str(fname).split("_B")[-1][:-4])
if BAND_NAMES[number] not in bands:
continue
- mult, add = None, None
- mult_entries = [f"mult_band_{number}", f"mult_band_st_b{number}"]
- add_entries = [f"add_band_{number}", f"add_band_st_b{number}"]
- for key in metadata:
- if any(key.endswith(entry) for entry in mult_entries):
- mult = metadata[key]
- if any(key.endswith(entry) for entry in add_entries):
- add = metadata[key]
- band = reader.read_band(fname).astype(dtype)[::-1, :]
- band[band == 0] = np.nan
- band *= mult
- band += add
- band_attrs = {
- "long_name": BAND_TITLES[number],
- "units": BAND_UNITS[number],
- "number": number,
- "filename": pathlib.Path(fname).name,
- "scaling_mult": mult,
- "scaling_add": add,
- }
- data_vars[BAND_NAMES[number]] = xr.DataArray(
- data=band,
- dims=dims,
- coords=coords,
- attrs=band_attrs,
- name=BAND_NAMES[number],
+ data_vars[BAND_NAMES[number]] = read_and_scale_band(
+ fname, reader, dtype, number, coords, metadata, region
)
- if region is not None:
- data_vars[BAND_NAMES[number]] = data_vars[BAND_NAMES[number]].sel(
- easting=slice(*region[:2]), northing=slice(*region[2:])
- )
- attrs = {
- "Conventions": "CF-1.8",
- "title": (
- f"{metadata['spacecraft_id'].replace('_', ' ').title()} scene from "
- f"{metadata['date_acquired']} "
- f"(path/row={metadata['wrs_path']}/{metadata['wrs_row']})"
- ),
+ if data_vars:
+ scene = xr.Dataset(data_vars, attrs=attrs_from_metadata(metadata))
+ scene.easting.attrs = {
+ "long_name": "UTM easting",
+ "standard_name": "projection_x_coordinate",
+ "units": "m",
+ }
+ scene.northing.attrs = {
+ "long_name": "UTM northing",
+ "standard_name": "projection_y_coordinate",
+ "units": "m",
+ }
+ return scene
+ else:
+ raise ValueError(
+ f"No Landsat Collection 2 Level 2 band files found in '{str(path)}'."
+ )[docs]def load_panchromatic(path, region=None, dtype="float32"):
+
"""
+
Load the panchromatic band from a USGS EarthExplorer Level 1 Landsat scene.
+
+
Can read from a folder with the ``*.TIF`` file and an ``*_MTL.txt`` file or
+
directly from a tar archive (compressed or not) without the need to first
+
unpack the archive. The band is converted to reflectance units using
+
appropriate scaling parameters and UTM coordinates are set in the returned
+
:class:`xarray.DataArray`.
+
+
.. important::
+
+
Do not rename the TIF or MTL files. The folder/archive can have any
+
name but TIF and MTL files need their original names.
+
+
.. note::
+
+
Only supports Landsat 8 and 9 Collection 2 Level 1 scenes containing
+
the panchromatic band.
+
+
Parameters
+
----------
+
path : str or :class:`pathlib.Path`
+
The path to a folder or tar archive containing the TIF file for the
+
panchromatic band. **Must** include the ``*_MTL.txt`` metadata file.
+
Other band files may be present but will be ignored.
+
region : None or list
+
Crop the band to this bounding box given as a list of West, East,
+
South, and North coordinate values (UTM in meters). If None, no
+
cropping is performed on the band. Default is None.
+
dtype : str or numpy dtype object
+
The type used for the band array. Integer types will result in rounding
+
so floating point is recommended. Default is float16.
+
+
Returns
+
-------
+
panchromatic : :class:`xarray.DataArray`
+
The loaded band including UTM easting and northing as dimensional
+
coordinates and metadata read from the MTL file and other CF compliant
+
fields in the ``attrs`` attribute.
+
"""
+
path = pathlib.Path(path)
+
with choose_reader(path)(path) as reader:
+
metadata = reader.read_metadata()
+
coords = coordinates_from_metadata(metadata, "panchromatic")
+
available_bands = {
+
int(str(fname).split("_B")[-1][:-4]): fname for fname in reader.band_files
+
}
+
if 8 not in available_bands:
+
raise ValueError(
+
f"Could not find the panchromatic band (8) in '{str(path)}'."
+
)
+
band = read_and_scale_band(
+
available_bands[8], reader, dtype, 8, coords, metadata, region
+
)
+
attrs = dict(band.attrs)
+
attrs.update(attrs_from_metadata(metadata))
+
attrs["title"] = (
+
f"{metadata['spacecraft_id'].replace('_', ' ').title()} panchromatic band from "
+
f"{metadata['date_acquired']} "
+
f"(path/row={metadata['wrs_path']}/{metadata['wrs_row']})"
+
)
+
band.attrs = attrs
+
band.easting.attrs = {
+
"long_name": "UTM easting",
+
"standard_name": "projection_x_coordinate",
+
"units": "m",
+
}
+
band.northing.attrs = {
+
"long_name": "UTM northing",
+
"standard_name": "projection_y_coordinate",
+
"units": "m",
}
+
return band
+
+
+def read_and_scale_band(fname, reader, dtype, number, coords, metadata, region):
+ """
+ Read the band and return a DataArray with the scaled values.
+ """
+ band_data = reader.read_band(fname).astype(dtype)[::-1, :]
+ band_data[band_data == 0] = np.nan
+ mult, add = scaling_parameters(metadata, number)
+ band_data *= mult
+ band_data += add
+ band = xr.DataArray(
+ data=band_data,
+ dims=("northing", "easting"),
+ name=BAND_NAMES[number],
+ coords=coords,
+ attrs={
+ "long_name": BAND_TITLES[number],
+ "units": BAND_UNITS[number],
+ "number": number,
+ "filename": pathlib.Path(fname).name,
+ "scaling_mult": mult,
+ "scaling_add": add,
+ },
+ )
+ if region is not None:
+ band = band.sel(easting=slice(*region[:2]), northing=slice(*region[2:]))
+ return band
+
+
+def scaling_parameters(metadata, number):
+ """
+ Get the scaling parameters for the band of the given number.
+ """
+ mult, add = None, None
+ mult_entries = [f"mult_band_{number}", f"mult_band_st_b{number}"]
+ add_entries = [f"add_band_{number}", f"add_band_st_b{number}"]
+ for key in metadata:
+ if any(key.endswith(entry) for entry in mult_entries):
+ mult = metadata[key]
+ if any(key.endswith(entry) for entry in add_entries):
+ add = metadata[key]
+ return mult, add
+
+
+def coordinates_from_metadata(metadata, band_type):
+ """
+ Generate the UTM pixel coordinate arrays from the metadata.
+ """
+ shape = (metadata[f"{band_type}_lines"], metadata[f"{band_type}_samples"])
+ scene_region = (
+ metadata["corner_ll_projection_x_product"],
+ metadata["corner_lr_projection_x_product"],
+ metadata["corner_ll_projection_y_product"],
+ metadata["corner_ul_projection_y_product"],
+ )
+ coords = {
+ "easting": np.linspace(*scene_region[:2], shape[1]),
+ "northing": np.linspace(*scene_region[2:], shape[0]),
+ }
+ return coords
+
+
+def attrs_from_metadata(metadata):
+ """
+ Create the xarray attrs dictionary from the metadata dictionary.
+ """
metadata_to_keep = [
"digital_object_identifier",
"origin",
@@ -506,19 +684,27 @@ Source code for xlandsat._io
"wrs_row",
"mtl_file",
]
- attrs.update({key: metadata[key] for key in metadata_to_keep})
- scene = xr.Dataset(data_vars, attrs=attrs)
- scene.easting.attrs = {
- "long_name": "UTM easting",
- "standard_name": "projection_x_coordinate",
- "units": "m",
- }
- scene.northing.attrs = {
- "long_name": "UTM northing",
- "standard_name": "projection_y_coordinate",
- "units": "m",
+ attrs = {
+ "Conventions": "CF-1.8",
+ "title": (
+ f"{metadata['spacecraft_id'].replace('_', ' ').title()} scene from "
+ f"{metadata['date_acquired']} "
+ f"(path/row={metadata['wrs_path']}/{metadata['wrs_row']})"
+ ),
}
- return sceneSource code for xlandsat._io
"UTM_ZONE",
"REFLECTIVE_LINES",
"REFLECTIVE_SAMPLES",
+ "PANCHROMATIC_LINES",
+ "PANCHROMATIC_SAMPLES",
"THERMAL_LINES",
"THERMAL_SAMPLES",
]
@@ -751,10 +939,16 @@ Source code for xlandsat._io
or "CORNER_UR_PROJECTION_Y_PRODUCT" in line
):
line = line.split(" = ")[0] + f" = {scene.northing.max().values}"
- if "REFLECTIVE_LINES" in line or "THERMAL_LINES" in line:
- line = line.split(" = ")[0] + f" = {scene.dims['northing']}"
- if "REFLECTIVE_SAMPLES" in line or "THERMAL_SAMPLES" in line:
- line = line.split(" = ")[0] + f" = {scene.dims['easting']}"
+ if "pan" in scene:
+ if "PANCHROMATIC_LINES" in line:
+ line = line.split(" = ")[0] + f" = {scene.dims['northing']}"
+ if "PANCHROMATIC_SAMPLES" in line:
+ line = line.split(" = ")[0] + f" = {scene.dims['easting']}"
+ else:
+ if "REFLECTIVE_LINES" in line or "THERMAL_LINES" in line:
+ line = line.split(" = ")[0] + f" = {scene.dims['northing']}"
+ if "REFLECTIVE_SAMPLES" in line or "THERMAL_SAMPLES" in line:
+ line = line.split(" = ")[0] + f" = {scene.dims['easting']}"
mtl_file.append(line)
mtl_file = "\n".join(mtl_file)
# Add the MTL file to the archive
@@ -794,7 +988,7 @@ Source code for xlandsat._io
diff --git a/dev/_modules/xlandsat/_pansharpen.html b/dev/_modules/xlandsat/_pansharpen.html
new file mode 100644
index 0000000..fed38e6
--- /dev/null
+++ b/dev/_modules/xlandsat/_pansharpen.html
@@ -0,0 +1,460 @@
+
+
+
+
+
+
+
+
+
+ xlandsat._pansharpen | xlandsat v0.0.post36
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
+
+
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+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
+
+
+
+
+
+
+
Source code for xlandsat._pansharpen
+# Copyright (c) 2022 The xlandsat developers.
+# Distributed under the terms of the MIT License.
+# SPDX-License-Identifier: MIT
+"""
+Pansharpening methods.
+"""
+
+
+[docs]def pansharpen(scene, panchromatic, weights=(1, 1, 0.2)):
+
"""
+
Pansharpen the red, green, and blue bands of a scene.
+
+
The pansharpened scene will have the same coordinate values of the given
+
panchromatic band. Uses a weighted version of the Brovey Transform
+
[Pohl_and_VanGenderen1998]_ to account for the smaller blue footprint in
+
Landsat 8/9 panchromatic band, following
+
https://github.com/mapbox/rio-pansharpen (MIT license).
+
+
Parameters
+
----------
+
scene : :class:`xarray.Dataset`
+
A Landsat scene, as read with :func:`xlandsat.load_scene`. The scene
+
must contain the red, green, and blue bands. Other bands are ignored.
+
panchromatic : :class:`xarray.DataArray`
+
A Landsat panchromatic band, as read with
+
:func:`xlandsat.load_panchromatic`.
+
weights : tuple
+
The weights applied to the red, green, and blue bands, respectively.
+
+
Returns
+
-------
+
scene_sharpened : :class:`xarray.Dataset`
+
The pandsharpened scene including the red, green, and blue bands only.
+
Metadata from the original scene is copied into the pandsharpened
+
version.
+
"""
+
bands = ["red", "green", "blue"]
+
sharp = scene[bands].interp_like(
+
panchromatic, method="nearest", kwargs={"fill_value": "extrapolate"}
+
)
+
band_average = sum(
+
sharp[band] * weight for band, weight in zip(bands, weights)
+
) / sum(weights)
+
sharp *= panchromatic
+
sharp /= band_average
+
sharp.attrs = {
+
key: value for key, value in scene.attrs.items() if key not in ("mtl_file")
+
}
+
sharp.attrs["title"] = f"Pansharpend {scene.attrs['title']}"
+
sharp.attrs["pansharpening_method"] = "Weighted Brovey Transform"
+
sharp.attrs["pansharpening_rgb_weights"] = weights
+
sharp.attrs["pansharpening_band_filename"] = panchromatic.attrs["filename"]
+
for band in sharp:
+
sharp[band].attrs = scene[band].attrs
+
return sharp
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
Source code for xlandsat.datasets
+# Copyright (c) 2022 The xlandsat developers.
+# Distributed under the terms of the MIT License.
+# SPDX-License-Identifier: MIT
+"""
+Functions for downloading and caching sample datasets.
+"""
+import pathlib
+
+import pooch
+
+
+[docs]def fetch_brumadinho_after(untar=False):
+
"""
+
Download a sample scene from after the Brumadinho tailings dam disaster
+
+
This is a cropped version of a Landsat 8 scene from 2019/01/30. It was
+
taken after the `Brumadinho tailings dam in Brazil
+
<https://en.wikipedia.org/wiki/Brumadinho_dam_disaster>`__ collapsed,
+
flooding a whole region.
+
+
The scene was downloaded from `USGS Earth Explorer
+
<https://earthexplorer.usgs.gov/>`__. Original data are in the public
+
domain and are redistributed here in accordance with the `Landsat Data
+
Distribution Policy
+
<https://www.usgs.gov/media/files/landsat-data-distribution-policy>`__.
+
+
Source: https://doi.org/10.6084/m9.figshare.21665630
+
(`CC0 <https://creativecommons.org/publicdomain/zero/1.0/>`__)
+
+
Parameters
+
----------
+
untar : bool
+
If True, unpack the tar archive after downloading and return a path to
+
the folder containing the unpacked files instead. Default is False.
+
+
Returns
+
-------
+
path : str
+
The path to the downloaded `.tar` file that contains the scene.
+
"""
+
if untar:
+
processor = pooch.Untar()
+
else:
+
processor = None
+
path = pooch.retrieve(
+
"https://figshare.com/ndownloader/files/38902290",
+
fname="LC08_L2SP_218074_20190130_20200829_02_T1-cropped.tar.gz",
+
known_hash="md5:4ae61a2d7a8b853c727c0c433680cece",
+
processor=processor,
+
)
+
if untar:
+
# Get the folder name in case we unpacked the archive
+
path = pathlib.Path(path[0]).parent
+
return path
+
+
+[docs]def fetch_brumadinho_before(untar=False):
+
"""
+
Download a sample scene from before the Brumadinho tailings dam disaster
+
+
This is a cropped version of a Landsat 8 scene from 2019/01/14. It was
+
taken before the `Brumadinho tailings dam in Brazil
+
<https://en.wikipedia.org/wiki/Brumadinho_dam_disaster>`__ collapsed,
+
flooding a whole region.
+
+
The scene was downloaded from `USGS Earth Explorer
+
<https://earthexplorer.usgs.gov/>`__. Original data are in the public
+
domain and are redistributed here in accordance with the `Landsat Data
+
Distribution Policy
+
<https://www.usgs.gov/media/files/landsat-data-distribution-policy>`__.
+
+
Source: https://doi.org/10.6084/m9.figshare.21665630
+
(`CC0 <https://creativecommons.org/publicdomain/zero/1.0/>`__)
+
+
Parameters
+
----------
+
untar : bool
+
If True, unpack the tar archive after downloading and return a path to
+
the folder containing the unpacked files instead. Default is False.
+
+
Returns
+
-------
+
path : str
+
The path to the downloaded `.tar` file that contains the scene.
+
"""
+
if untar:
+
processor = pooch.Untar()
+
else:
+
processor = None
+
path = pooch.retrieve(
+
"https://figshare.com/ndownloader/files/38902284",
+
fname="LC08_L2SP_218074_20190114_20200829_02_T1-cropped.tar.gz",
+
known_hash="md5:d2a503c944bb7ef3b41294d44b77e98c",
+
processor=processor,
+
)
+
if untar:
+
# Get the folder name in case we unpacked the archive
+
path = pathlib.Path(path[0]).parent
+
return path
+
+
+[docs]def fetch_liverpool(untar=False):
+
"""
+
Download a sample scene from the city of Liverpool, UK
+
+
This is a cropped version of a Landsat 8 scene from 2020/09/27. It was
+
taken on a virtually cloud-free day and shows the Mersey river delta and
+
some off-shore wind turbines.
+
+
The scene was downloaded from `USGS Earth Explorer
+
<https://earthexplorer.usgs.gov/>`__. Original data are in the public
+
domain and are redistributed here in accordance with the `Landsat Data
+
Distribution Policy
+
<https://www.usgs.gov/media/files/landsat-data-distribution-policy>`__.
+
+
Source: https://doi.org/10.6084/m9.figshare.22041353
+
(`CC0 <https://creativecommons.org/publicdomain/zero/1.0/>`__)
+
+
Parameters
+
----------
+
untar : bool
+
If True, unpack the tar archive after downloading and return a path to
+
the folder containing the unpacked files instead. Default is False.
+
+
Returns
+
-------
+
path : str
+
The path to the downloaded `.tar` file that contains the scene.
+
"""
+
if untar:
+
processor = pooch.Untar()
+
else:
+
processor = None
+
path = pooch.retrieve(
+
"https://figshare.com/ndownloader/files/39121064",
+
fname="LC08_L2SP_204023_20200927_20201006_02_T1-cropped.tar.gz",
+
known_hash="md5:3c07e343ccf959be4e5dd5c9aca4e0a4",
+
processor=processor,
+
)
+
if untar:
+
# Get the folder name in case we unpacked the archive
+
path = pathlib.Path(path[0]).parent
+
return path
+
+
+[docs]def fetch_liverpool_panchromatic(untar=False):
+
"""
+
Download a sample panchromatic band from the city of Liverpool, UK
+
+
This is a cropped version of the panchromatic band from a Landsat 8 Level 1
+
scene from 2020/09/27. It was taken on a virtually cloud-free day and shows
+
the Mersey river delta and some off-shore wind turbines.
+
+
The scene was downloaded from `USGS Earth Explorer
+
<https://earthexplorer.usgs.gov/>`__. Original data are in the public
+
domain and are redistributed here in accordance with the `Landsat Data
+
Distribution Policy
+
<https://www.usgs.gov/media/files/landsat-data-distribution-policy>`__.
+
+
Source: https://doi.org/10.6084/m9.figshare.22041353
+
(`CC0 <https://creativecommons.org/publicdomain/zero/1.0/>`__)
+
+
Parameters
+
----------
+
untar : bool
+
If True, unpack the tar archive after downloading and return a path to
+
the folder containing the unpacked files instead. Default is False.
+
+
Returns
+
-------
+
path : str
+
The path to the downloaded `.tar` file that contains the scene.
+
"""
+
if untar:
+
processor = pooch.Untar()
+
else:
+
processor = None
+
path = pooch.retrieve(
+
"https://figshare.com/ndownloader/files/39121061",
+
fname="LC08_L1TP_204023_20200927_20201006_02_T1-cropped.tar.gz",
+
known_hash="md5:7d43f8580b8e583d137a93f9ae51a73d",
+
processor=processor,
+
)
+
if untar:
+
# Get the folder name in case we unpacked the archive
+
path = pathlib.Path(path[0]).parent
+
return path
+
+
+
+
+
+
+
+
+
+
+
+
+
xlandsat v0.0.post32
+ xlandsat v0.0.post36
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
xlandsat.datasets.fetch_brumadinho_after
+
+
+
+
+
+
+
+
+xlandsat.datasets.fetch_brumadinho_after
+
+-
+xlandsat.datasets.fetch_brumadinho_after(untar=False)[source]
+Download a sample scene from after the Brumadinho tailings dam disaster
+This is a cropped version of a Landsat 8 scene from 2019/01/30. It was
+taken after the Brumadinho tailings dam in Brazil collapsed,
+flooding a whole region.
+The scene was downloaded from USGS Earth Explorer. Original data are in the public
+domain and are redistributed here in accordance with the Landsat Data
+Distribution Policy.
+Source: https://doi.org/10.6084/m9.figshare.21665630
+(CC0)
+
+- Parameters
+untar (bool) – If True, unpack the tar archive after downloading and return a path to
+the folder containing the unpacked files instead. Default is False.
+- Returns
+path (str) – The path to the downloaded .tar file that contains the scene.
+
+
+
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+
+
xlandsat.datasets.fetch_brumadinho_before
+
+
+
+
+
+
+
+
+xlandsat.datasets.fetch_brumadinho_before
+
+-
+xlandsat.datasets.fetch_brumadinho_before(untar=False)[source]
+Download a sample scene from before the Brumadinho tailings dam disaster
+This is a cropped version of a Landsat 8 scene from 2019/01/14. It was
+taken before the Brumadinho tailings dam in Brazil collapsed,
+flooding a whole region.
+The scene was downloaded from USGS Earth Explorer. Original data are in the public
+domain and are redistributed here in accordance with the Landsat Data
+Distribution Policy.
+Source: https://doi.org/10.6084/m9.figshare.21665630
+(CC0)
+
+- Parameters
+untar (bool) – If True, unpack the tar archive after downloading and return a path to
+the folder containing the unpacked files instead. Default is False.
+- Returns
+path (str) – The path to the downloaded .tar file that contains the scene.
+
+
+
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+
+
+
xlandsat.datasets.fetch_liverpool
+
+
+
+
+
+
+
+
+xlandsat.datasets.fetch_liverpool
+
+-
+xlandsat.datasets.fetch_liverpool(untar=False)[source]
+Download a sample scene from the city of Liverpool, UK
+This is a cropped version of a Landsat 8 scene from 2020/09/27. It was
+taken on a virtually cloud-free day and shows the Mersey river delta and
+some off-shore wind turbines.
+The scene was downloaded from USGS Earth Explorer. Original data are in the public
+domain and are redistributed here in accordance with the Landsat Data
+Distribution Policy.
+Source: https://doi.org/10.6084/m9.figshare.22041353
+(CC0)
+
+- Parameters
+untar (bool) – If True, unpack the tar archive after downloading and return a path to
+the folder containing the unpacked files instead. Default is False.
+- Returns
+path (str) – The path to the downloaded .tar file that contains the scene.
+
+
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+
+
+
xlandsat.datasets.fetch_liverpool_panchromatic
+
+
+
+
+
+
+
+
+xlandsat.datasets.fetch_liverpool_panchromatic
+
+-
+xlandsat.datasets.fetch_liverpool_panchromatic(untar=False)[source]
+Download a sample panchromatic band from the city of Liverpool, UK
+This is a cropped version of the panchromatic band from a Landsat 8 Level 1
+scene from 2020/09/27. It was taken on a virtually cloud-free day and shows
+the Mersey river delta and some off-shore wind turbines.
+The scene was downloaded from USGS Earth Explorer. Original data are in the public
+domain and are redistributed here in accordance with the Landsat Data
+Distribution Policy.
+Source: https://doi.org/10.6084/m9.figshare.22041353
+(CC0)
+
+- Parameters
+untar (bool) – If True, unpack the tar archive after downloading and return a path to
+the folder containing the unpacked files instead. Default is False.
+- Returns
+path (str) – The path to the downloaded .tar file that contains the scene.
+
+
+
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+
+
+
+
+
xlandsat.load_panchromatic
+
+
+
+
+
+
+
+
+xlandsat.load_panchromatic
+
+-
+xlandsat.load_panchromatic(path, region=None, dtype='float32')[source]
+Load the panchromatic band from a USGS EarthExplorer Level 1 Landsat scene.
+Can read from a folder with the *.TIF file and an *_MTL.txt file or
+directly from a tar archive (compressed or not) without the need to first
+unpack the archive. The band is converted to reflectance units using
+appropriate scaling parameters and UTM coordinates are set in the returned
+xarray.DataArray.
+
+
Important
+
Do not rename the TIF or MTL files. The folder/archive can have any
+name but TIF and MTL files need their original names.
+
+
Note
+
Only supports Landsat 8 and 9 Collection 2 Level 1 scenes containing
+the panchromatic band.
+
+- Parameters
+
+path (str or pathlib.Path) – The path to a folder or tar archive containing the TIF file for the
+panchromatic band. Must include the *_MTL.txt metadata file.
+Other band files may be present but will be ignored.
region (None or list) – Crop the band to this bounding box given as a list of West, East,
+South, and North coordinate values (UTM in meters). If None, no
+cropping is performed on the band. Default is None.
dtype (str or numpy dtype object) – The type used for the band array. Integer types will result in rounding
+so floating point is recommended. Default is float16.
+- Returns
+panchromatic (xarray.DataArray) – The loaded band including UTM easting and northing as dimensional
+coordinates and metadata read from the MTL file and other CF compliant
+fields in the attrs attribute.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
xlandsat v0.0.post32
+ xlandsat v0.0.post36