From 0ec4ce7011827439f098fb99d7117e4574bdaa02 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Wed, 20 Sep 2023 15:38:34 -0300 Subject: [PATCH] Add sample data for Manaus, Brazil The scene shows the merger of the black Negro river and the brown Amazonas river. Use it on the homepage and the README. --- README.md | 36 +++++++++++++++++++------------- doc/_static/readme-example.jpg | Bin 110471 -> 44786 bytes doc/api/index.rst | 1 + doc/index.rst | 32 ++++++++++++++++------------ xlandsat/datasets.py | 35 +++++++++++++++++++++++++++++++ xlandsat/tests/test_datasets.py | 2 ++ 6 files changed, 78 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index 3f2e6c4..ead391c 100644 --- a/README.md +++ b/README.md @@ -25,29 +25,33 @@ manipulation, processing, and visualization. ## Example -Here's a quick example of loading this [Landsat 8 scene from the Brumadinho -tailings dam disaster in Brazil](https://doi.org/10.6084/m9.figshare.21665630.v1): +Here's a quick example of loading this +[Landsat 9 scene from the city of Manaus, Brazil](https://doi.org/10.6084/m9.figshare.24167235.v1), +which is where the Amazonas and Negro river merge: ```python import xlandsat as xls +import matplotlib.pyplot as plt -# Download a cropped Landsat 8 scene from the Brumadinho dam disaster -# (Brazil). The function downloads it and returns the path to the .tar file -# containing the scene. -path = xls.datasets.fetch_brumadinho_after() +# Download a sample Landsat 9 scene in EarthExplorer format +path_to_scene_file = xls.datasets.fetch_manaus() -# Load the scene directly from the archive (no need to unpack it) -scene = xls.load_scene(path) +# Load the data from the file into an xarray.Dataset +scene = xls.load_scene(path_to_scene_file) -# Make an RGB composite and stretch the contrast -rgb = xls.composite(scene, rescale_to=[0.03, 0.2]) +# Make an RGB composite as an xarray.DataArray +rgb = xls.composite(scene, rescale_to=[0.02, 0.2]) -# Plot the composite +# Plot the composite using xarray's plotting machinery rgb.plot.imshow() + +# Annotate the plot with the rich metadata xlandsat adds to the scene +plt.title(f"{rgb.attrs['title']}\n{rgb.attrs['long_name']}") +plt.axis("scaled") +plt.show() ``` -RGB map showing the flooded plain after the dam collapse as light brown +RGB image showing the city on the left and the black waters of the Negro river merging with the brown waters of the Amazonas river ## Project goals @@ -56,8 +60,10 @@ rgb.plot.imshow() user and xarray. Our goal is **not** to provide a solution for large-scale data processing. -Instead, our target is smaller scale analysis done on individual computers. For -cloud-based data processing, see the [Pangeo Project](https://pangeo.io). +Instead, our target is smaller scale analysis done on individual computers. + +* For cloud-based data processing, see the [Pangeo Project](https://pangeo.io). +* For other satellites and more powerful features, use [Satpy](https://github.com/pytroll/satpy). ## Project status diff --git a/doc/_static/readme-example.jpg b/doc/_static/readme-example.jpg index fcd52f56b6d4bea98c58c3bd3a2a276b701883e0..9adf9b82b0d82239f4b36b23e539ff4f75e9f4ed 100644 GIT binary patch literal 44786 zcmdqIWmKHO)-Kq%Lm)tKZGyWygy8P(?(PsQ5Fm|fkj4T8YuqJhH;~}&?(PH#T+aFK zy0gx?Gxyw?H9uy}+iU;oTJNr>YS*r3%lk6_vJQBoAR{jWfP;erSiSxLFG~O^04g#v z3NjKZ3JMAu8Y(&_J{Beh1|~7yTO53H5=shk5;8I>AR9du4GS$983R8f3kN3;4-X~1 zpr`K?g~IzFz|jEUap4ef;a>Uy zlmGx6((7#hG2#FIz`-LRA|a!oqM>8F4rqJ>fQLgsfJa0?LPA7*9Uc7oKL8OI36Gjn z68WvVISP$CK37;$0V?f>+8zRpiE|*gg-19VI^jDaViLOd^bCwlJiL7T0)j$PAEjkv 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zm`sx}ZpWFH?Uyz_4AEEcD#YOMrM({(~Z09LVP%B=-+Ud?Ygp|KvO7+E;; zi>dNY4B-BhyAH%puF=$&KfCU2D*dN+`r?Tud6RkFK@yOD5iJ)aO3vMb07|{>XH*tL N_|QaW{{#HD^gl=|PD=m) diff --git a/doc/api/index.rst b/doc/api/index.rst index d42f35f..c89f923 100644 --- a/doc/api/index.rst +++ b/doc/api/index.rst @@ -42,5 +42,6 @@ Sample datasets datasets.fetch_brumadinho_before datasets.fetch_liverpool datasets.fetch_liverpool_panchromatic + datasets.fetch_manaus datasets.fetch_momotombo datasets.fetch_roraima diff --git a/doc/index.rst b/doc/index.rst index 63c8ef7..58b573a 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -30,21 +30,25 @@ Here's a quick example: .. jupyter-execute:: import xlandsat as xls + import matplotlib.pyplot as plt - # Download a cropped Landsat 8 scene from the Brumadinho dam disaster - # (Brazil). The function downloads it and returns the path to the .tar file - # containing the scene. - path = xls.datasets.fetch_brumadinho_after() + # Download a sample Landsat 9 scene in EarthExplorer format + path_to_scene_file = xls.datasets.fetch_manaus() - # Load the scene directly from the archive (no need to unpack it) - scene = xls.load_scene(path) + # Load the data from the file into an xarray.Dataset + scene = xls.load_scene(path_to_scene_file) - # Make an RGB composite and stretch the contrast - rgb = xls.composite(scene, rescale_to=[0.03, 0.2]) + # Make an RGB composite as an xarray.DataArray + rgb = xls.composite(scene, rescale_to=[0.02, 0.2]) - # Plot the composite + # Plot the composite using xarray's plotting machinery rgb.plot.imshow() + # Annotate the plot with the rich metadata xlandsat adds to the scene + plt.title(f"{rgb.attrs['title']}\n{rgb.attrs['long_name']}") + plt.axis("scaled") + plt.show() + ---- @@ -130,10 +134,12 @@ Here's a quick example: .. admonition:: Looking for large-scale cloud-based processing? :class: seealso - Our goal is not to provide a solution for large-scale data processing. The - target is smaller scale analysis done on individual computers (which is - probably the main way EarthExplorer is used). For cloud-based data - processing, see the `Pangeo Project `__. + Our goal is **not** to provide a solution for large-scale data processing. + Our target is smaller scale analysis done on individual computers (which is + probably the main way EarthExplorer is used). + + * For cloud-based data processing, see the `Pangeo Project `__. + * For other satellites and more powerful features, use `Satpy `__. .. toctree:: diff --git a/xlandsat/datasets.py b/xlandsat/datasets.py index c631cf8..7494c26 100644 --- a/xlandsat/datasets.py +++ b/xlandsat/datasets.py @@ -28,6 +28,8 @@ "LC08_L2SP_017051_20151205_20200908_02_T1-cropped.tar.gz": "md5:8cc2e4c15e65940a7152fc1c8b412aa9", # Roraima "LC08_L2SP_232056_20151004_20200908_02_T1-cropped.tar.gz": "md5:f236a8b024aa4a4c62bee294d3bd737f", + # Manaus + "LC09_L2SP_231062_20230723_20230802_02_T1-cropped.tar.gz": "md5:ffe2003e665dc7a1a3155011f700a61d", }, ) @@ -250,3 +252,36 @@ def fetch_roraima(untar=False): The path to the downloaded `.tar` file that contains the scene. """ return _fetch("LC08_L2SP_232056_20151004_20200908_02_T1-cropped.tar.gz", untar) + + +def fetch_manaus(untar=False): + """ + Download a sample scene from Manaus, Brazil + + Manaus is located in the Brazilian Amazon. The scene shows a part of the + city and the meeting of the Amazon and Negro rivers. + + This is a cropped version of a Landsat 9 scene from 2023/07/23, during + the annual Amazon river floods. + + 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.24167235.v1 + (`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. + """ + return _fetch("LC09_L2SP_231062_20230723_20230802_02_T1-cropped.tar.gz", untar) diff --git a/xlandsat/tests/test_datasets.py b/xlandsat/tests/test_datasets.py index c444edf..3aa386f 100644 --- a/xlandsat/tests/test_datasets.py +++ b/xlandsat/tests/test_datasets.py @@ -13,6 +13,7 @@ fetch_brumadinho_before, fetch_liverpool, fetch_liverpool_panchromatic, + fetch_manaus, fetch_momotombo, fetch_roraima, ) @@ -26,6 +27,7 @@ def test_fetching_functions(untar): fetch_brumadinho_before, fetch_liverpool, fetch_liverpool_panchromatic, + fetch_manaus, fetch_momotombo, fetch_roraima, ]