From dccd4a515c46bc5e56452c1898dcbb694e949fa5 Mon Sep 17 00:00:00 2001 From: Martin Schobben Date: Wed, 18 Sep 2024 14:46:45 +0200 Subject: [PATCH] Built site for gh-pages --- .gitignore | 57 - .gitlab-ci.yml | 42 - .nojekyll | 1 + 01_local_dask.html | 5155 +++++++++++++++++ .../figure-html/cell-34-output-1.png | Bin 0 -> 109650 bytes .../libs/bootstrap/bootstrap-icons.css | 2078 +++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 176200 bytes .../libs/bootstrap/bootstrap.min.css | 12 + .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 203 + .../libs/quarto-html/quarto.js | 899 +++ .../libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + Makefile | 42 - README.md | 11 - index.html | 5155 +++++++++++++++++ pyproject.toml | 3 - requirements.txt | 172 - setup.cfg | 47 - setup.py | 3 - src/dask_flood_mapper/__init__.py | 4 - src/dask_flood_mapper/_version.py | 2 - src/dask_flood_mapper/utils.py | 17 - tests/test_meta.py | 2 - 27 files changed, 13535 insertions(+), 402 deletions(-) delete mode 100644 .gitignore delete mode 100644 .gitlab-ci.yml create mode 100644 .nojekyll create mode 100644 01_local_dask.html create mode 100644 01_local_dask_files/figure-html/cell-34-output-1.png create mode 100644 01_local_dask_files/libs/bootstrap/bootstrap-icons.css create mode 100644 01_local_dask_files/libs/bootstrap/bootstrap-icons.woff create mode 100644 01_local_dask_files/libs/bootstrap/bootstrap.min.css create mode 100644 01_local_dask_files/libs/bootstrap/bootstrap.min.js create mode 100644 01_local_dask_files/libs/clipboard/clipboard.min.js create mode 100644 01_local_dask_files/libs/quarto-html/anchor.min.js create mode 100644 01_local_dask_files/libs/quarto-html/popper.min.js create mode 100644 01_local_dask_files/libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 01_local_dask_files/libs/quarto-html/quarto.js create mode 100644 01_local_dask_files/libs/quarto-html/tippy.css create mode 100644 01_local_dask_files/libs/quarto-html/tippy.umd.min.js delete mode 100644 Makefile delete mode 100644 README.md create mode 100644 index.html delete mode 100644 pyproject.toml delete mode 100644 requirements.txt delete mode 100644 setup.cfg delete mode 100644 setup.py delete mode 100644 src/dask_flood_mapper/__init__.py delete mode 100644 src/dask_flood_mapper/_version.py delete mode 100644 src/dask_flood_mapper/utils.py delete mode 100644 tests/test_meta.py diff --git a/.gitignore b/.gitignore deleted file mode 100644 index af8b345..0000000 --- a/.gitignore +++ /dev/null @@ -1,57 +0,0 @@ -# Temporary and binary files -*~ -*.py[cod] -*.so -*.cfg -!.isort.cfg -!setup.cfg -*.orig -*.log -*.pot -__pycache__/* -.cache/* -.*.swp -*/.ipynb_checkpoints/* -.DS_Store - -# Project files -.ropeproject -.project -.pydevproject -.settings -.idea -.vscode -tags - -# Package files -*.egg -*.eggs/ -.installed.cfg -*.egg-info - -# Unittest and coverage -htmlcov/* -.coverage -.tox -junit.xml -coverage.xml -.pytest_cache/ - -# Build and docs folder/files -build/* -dist/* -sdist/* -docs/api/* -docs/_rst/* -docs/_build/* -cover/* -MANIFEST - -# Per-project virtualenvs -.venv*/ -venv - -# make file markers -.install* - -data/ \ No newline at end of file diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml deleted file mode 100644 index fc6bf00..0000000 --- a/.gitlab-ci.yml +++ /dev/null @@ -1,42 +0,0 @@ -stages: - - test - - deploy - -.test-template: - stage: test - image: swamydev/geo-write:latest - coverage: '/^TOTAL.+?(\d+\%)$/' - tags: - - python - -test-main: - extends: .test-template - script: - - pip install -e .[test] - - make test - rules: - - if: '$CI_COMMIT_TAG == null' - when: always - -test-release: - extends: .test-template - script: - - pip install -e .[test] - - make test - rules: - - if: '$CI_COMMIT_TAG' - when: always - -deploy-pypi: - stage: deploy - image: python:latest - needs: - - test-release - script: - - make dist - - TWINE_PASSWORD=${CI_JOB_TOKEN} TWINE_USERNAME=gitlab-ci-token python -m twine upload --repository-url ${CI_API_V4_URL}/projects/${CI_PROJECT_ID}/packages/pypi dist/* - rules: - - if: $CI_COMMIT_TAG - when: on_success - tags: - - python \ No newline at end of file diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..52fb45d --- /dev/null +++ b/.nojekyll @@ -0,0 +1 @@ +717f249d \ No newline at end of file diff --git a/01_local_dask.html b/01_local_dask.html new file mode 100644 index 0000000..8f8a6f7 --- /dev/null +++ b/01_local_dask.html @@ -0,0 +1,5155 @@ + + + + + + + + + +Dask Client + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+
+

Dask Client

+
+ + + +
+ + + + +
+ + + +
+ + +
+
import pystac_client
+from odc import stac as odc_stac
+import xarray as xr
+import rioxarray
+import numpy as np
+import hvplot.xarray
+
+

Dask makes parallel computing easy by providing a familiar API common libraries, such as Pandas and Numpy. This allow efficient scaling of the here presented workflow for this adaptation of the TU Wien Bayesian flood mapping algorithm. The data size will be a main limiting factor as the data grows larger than RAM. For this reason we will partition our data in chunks which will presented to the machine workers by Dasks task scheduler in a most efficient manner. Although many of Dask’ settings can be handled automatically, we can also modify some parameters for optimal performance of the workflow for the desired processing environment. So, note, that this highly depends on your own machine’s specifications.

+

I will, furthermore, set the temporary directory for when Dask spills data from the workers memory to disk.

+
+
import dask
+dask.config.set(temporary_directory='/tmp')
+
+
<dask.config.set at 0x7f6a9048d0f0>
+
+
+

We can then set the Dask Client, where we avoid inter-worker communication which is common for working with numpy and xarray in this case.

+
+
from dask.distributed import Client, progress, wait
+client = Client(processes=False, threads_per_worker=2,
+                n_workers=3, memory_limit='28GB')
+client
+
+
+
+
+
+

Client

+

Client-9d87ace9-75bb-11ef-9597-f780c6f503ec

+ + + + + + + + + + + + +
Connection method: Cluster objectCluster type: distributed.LocalCluster
Dashboard: http://128.131.72.130:8787/status
+ +
+ +
+

Cluster Info

+
+ +
+
+ +
+
+
+ +
+

Worker: 1

+
+ + + + + + + + + + + + + + + + + + +
Comm: inproc://128.131.72.130/284055/28Total threads: 2
Dashboard: http://128.131.72.130:37339/statusMemory: 26.08 GiB
Nanny: None
Local directory: /tmp/dask-scratch-space/worker-xpcpyg9n
+
+ +
+
+
+
+ +
+
+
+ +
+

Worker: 2

+
+ + + + + + + + + + + + + + + + + + +
Comm: inproc://128.131.72.130/284055/29Total threads: 2
Dashboard: http://128.131.72.130:44919/statusMemory: 26.08 GiB
Nanny: None
Local directory: /tmp/dask-scratch-space/worker-6si_y6fh
+ +
+
+ +
+ +
+
+ + +
+
+ + + +
+ + + + + + + + + + + + + +

In conjunction with setting up the Dask client I will chunk my arrays along three dimensions according to the following specifications for maximum performance on my setup.

+
+
chunks = {'time':1, "latitude": 1300, "longitude": 1300}
+
+
+

Cube Definitions

+

The following generic specifications are used for presenting the data.

+
+
crs = "EPSG:4326" # Coordinate Reference System - World Geodetic System 1984 (WGS84) in this case 
+res = 0.00018 # 20 meter in degree
+
+
+
+

Northern Germany Flood

+

Storm Babet hit the Denmark and Northern coast at the 20th of October 2023 Wikipedia. Here an area around Zingst at the Baltic coast of Northern Germany is selected as the study area.

+
+
time_range = "2022-10-11/2022-10-25"
+minlon, maxlon = 12.3, 13.1
+minlat, maxlat = 54.3, 54.6
+bounding_box = [minlon, minlat, maxlon, maxlat]
+
+
+
+

EODC STAC Catalog

+

The pystac_client establishes a connection to the EODC STAC Catalog. This results in a catalog object that can be used to discover collections and items hosted at EODC.

+
+
eodc_catalog = pystac_client.Client.open("https://stac.eodc.eu/api/v1")
+
+
+
+

Microwave Backscatter Measurements

+

The basic premise of microwave-based backscattering can be seen in the sketch below, the characteristics of backscattering over land and water differ considerably. With this knowledge we can detect whenever a pixel with a predominant land like signature changes to a water like signature in the event of flooding.

+

+

Schematic backscattering over land and water. Image from Geological Survey Ireland

+

We discover Sentinel-1 microwave backscatter (\(\sigma_0\) [1]) at a 20 meter resolution, like so:

+
+
search = eodc_catalog.search(
+    collections="SENTINEL1_SIG0_20M",
+    bbox=bounding_box,
+    datetime=time_range,
+)
+
+items_sig0 = search.item_collection()
+
+

The state of the orbit and relative orbit number is also saved, as the water and land likelihoods (which are calculated later on) dependent on the orbital configuration. These variables will be added as additional coordinates to the data cube. For this purpose a small helper function is defined, like so:

+
+
def extract_orbit_names(items):
+    return np.array([items[i].properties["sat:orbit_state"][0].upper() + \
+                     str(items[i].properties["sat:relative_orbit"]) \
+                     for i in range(len(items))])
+
+

We will also save the scaling factor and nodata values of STAC items to correct the loaded data accordingly. Again a helper function will be used to correctly scale and fill no data values of the cube.

+
+
def post_process_eodc_cube(dc: xr.Dataset, items, bands):
+    if not isinstance(bands, tuple):
+        bands = tuple([bands])
+    for i in bands:
+        dc[i] = post_process_eodc_cube_(dc[i], items, i)
+    return dc
+
+def post_process_eodc_cube_(dc: xr.Dataset, items, band):
+    scale = items[0].assets[band].extra_fields.get('raster:bands')[0]['scale']
+    nodata = items[0].assets[band].extra_fields.get('raster:bands')[0]['nodata']
+    return dc.where(dc != nodata) / scale
+
+

The VV polarization of the discover items can be loaded with odc-stac and cast in the desired projection and resolution. The data is at this point only lazily loaded, meaning that we only make an instance of the outlines of the datacube with the proper shape and resolution, but without actually loading all the data. This is done by providing the chunks as defined before, which partitions the data in portions which are more easily handled by the setup used for processing the data (in this case my own pc).

+
+
bands = "VV"
+sig0_dc = odc_stac.load(items_sig0,
+                        bands=bands,
+                        crs=crs,
+                        chunks=chunks,
+                        resolution=res,
+                        bbox=bounding_box,
+                        groupby=None,
+                        )
+
+

Now we can rescale our variable, fill the no data values with np.nan values, and add the orbit names, with the previous defined functions.

+
+
sig0_dc = post_process_eodc_cube(sig0_dc, items_sig0, bands).\
+    rename_vars({ "VV": "sig0"}).\
+    assign_coords(orbit=("time", extract_orbit_names(items_sig0))).\
+    dropna(dim="time", how="all").\
+    sortby("time")
+
+

Then we remove duplicate time dimensions from the data cube and extract the orbit names as we will need those for obtaining the correct harmonic parameters and local incidence angles,as explained in the next section. Also, note, that we call dask.persist to materialize the object but retain it as a delayed object in the workers memory.

+
+
__, indices = np.unique(sig0_dc.time, return_index=True)
+indices.sort()
+orbit_sig0 = sig0_dc.orbit[indices].data
+sig0_dc = sig0_dc.groupby("time").mean(skipna=True)
+sig0_dc = sig0_dc.assign_coords(orbit=("time", orbit_sig0))
+sig0_dc = sig0_dc.persist()
+wait(sig0_dc)
+sig0_dc
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset> Size: 237MB
+Dimensions:      (time: 8, latitude: 1668, longitude: 4445)
+Coordinates:
+  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
+  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
+    spatial_ref  int32 4B 4326
+  * time         (time) datetime64[ns] 64B 2022-10-11T05:25:01 ... 2022-10-23...
+    orbit        (time) <U4 128B 'D168' 'A44' 'A44' ... 'A146' 'A146' 'D168'
+Data variables:
+    sig0         (time, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
+
+
+
+
+
+

Harmonic Parameters

+

The so-called likelihoods of \(P(\sigma^0|flood)\) and \(P(\sigma^0|nonflood)\) can be calculated from past backscattering information. To be able to this we load the harmonic parameters we can model the expected variations in land back scattering based on seasonal changes in vegetation. The procedure is similar to the backscattering routine.

+

We discover items.

+
+
search = eodc_catalog.search(
+    collections="SENTINEL1_HPAR",
+    bbox=bounding_box
+)
+
+items_hpar = search.item_collection()
+
+

Load the data as a lazy object.

+
+
bands = ("C1", "C2", "C3", "M0", "S1", "S2", "S3", "STD")
+hpar_dc = odc_stac.load(items_hpar,
+                        bands=bands,
+                        crs=crs,
+                        chunks=chunks,
+                        resolution=res,
+                        bbox=bounding_box,
+                        groupby=None,
+                        )
+
+hpar_dc = post_process_eodc_cube(hpar_dc, items_hpar, bands).\
+    rename({"time": "orbit"})
+hpar_dc["orbit"] = extract_orbit_names(items_hpar)
+hpar_dc = hpar_dc.groupby("orbit").mean(skipna=True)
+
+

We expand the variables along the orbits of sigma naught to be able to calculate the correct land reference backscatter signatures.

+
+
hpar_dc = hpar_dc.sel(orbit = orbit_sig0)
+hpar_dc = hpar_dc.persist()
+wait(hpar_dc)
+hpar_dc
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset> Size: 2GB
+Dimensions:      (orbit: 8, latitude: 1668, longitude: 4445)
+Coordinates:
+  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
+  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
+    spatial_ref  int32 4B 4326
+  * orbit        (orbit) object 64B 'D168' 'A44' 'A44' ... 'A146' 'A146' 'D168'
+Data variables:
+    C1           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
+    C2           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
+    C3           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
+    M0           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
+    S1           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
+    S2           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
+    S3           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
+    STD          (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    • latitude
      PandasIndex
      PandasIndex(Index([54.600030000000004,           54.59985,           54.59967,
      +                 54.59949,           54.59931,           54.59913,
      +                 54.59895,           54.59877,           54.59859,
      +                 54.59841,
      +       ...
      +       54.301590000000004, 54.301410000000004, 54.301230000000004,
      +       54.301050000000004,           54.30087,           54.30069,
      +                 54.30051,           54.30033,           54.30015,
      +                 54.29997],
      +      dtype='float64', name='latitude', length=1668))
    • longitude
      PandasIndex
      PandasIndex(Index([12.300030000000001, 12.300210000000002, 12.300390000000002,
      +       12.300570000000002,           12.30075, 12.300930000000001,
      +       12.301110000000001, 12.301290000000002, 12.301470000000002,
      +       12.301650000000002,
      +       ...
      +                 13.09833, 13.098510000000001, 13.098690000000001,
      +       13.098870000000002, 13.099050000000002, 13.099230000000002,
      +                 13.09941, 13.099590000000001, 13.099770000000001,
      +       13.099950000000002],
      +      dtype='float64', name='longitude', length=4445))
    • orbit
      PandasIndex
      PandasIndex(Index(['D168', 'A44', 'A44', 'D66', 'D95', 'A146', 'A146', 'D168'], dtype='object', name='orbit'))
  • +
    +
    + +
    +
    +

    Local Incidence Angles

    +

    Local incidence angles of measured microwave backscattering is as well important for calculating reference backscatter values, but now for water bodies. The procedure is much the same as for the harmonic parameters.

    +
    +
    search = eodc_catalog.search(
    +    collections="SENTINEL1_MPLIA",
    +    bbox=bounding_box
    +)
    +
    +items_plia = search.item_collection()
    +
    +

    Load the lazy object and preprocess.

    +
    +
    bands = "MPLIA"
    +plia_dc = odc_stac.load(items_plia,
    +                        bands=bands,
    +                        crs=crs,
    +                        chunks=chunks,
    +                        resolution=res,
    +                        bbox=bounding_box,
    +                        groupby=None,
    +                        )
    +
    +plia_dc = post_process_eodc_cube(plia_dc, items_plia, bands).\
    +    rename({"time": "orbit"})
    +plia_dc["orbit"] = extract_orbit_names(items_plia)
    +plia_dc = plia_dc.groupby("orbit").mean(skipna=True)
    +
    +

    We expand the variables along the orbits of sigma naught to be able to calculate the correct land reference backscatter signatures.

    +
    +
    plia_dc = plia_dc.sel(orbit = orbit_sig0)
    +plia_dc = plia_dc.persist()
    +wait(plia_dc)
    +plia_dc
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset> Size: 237MB
    +Dimensions:      (orbit: 8, latitude: 1668, longitude: 4445)
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +  * orbit        (orbit) object 64B 'D168' 'A44' 'A44' ... 'A146' 'A146' 'D168'
    +Data variables:
    +    MPLIA        (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +
    +
    +
    +
    +
    +

    ESA World Cover from Terrascope

    +

    For flood mapping we are only interested in microwave backscattering over what used to be land, as such, we need a way to mask water bodies. For this we use the ESA World Cover data from the following STAC catalog.

    +
    +
    import os
    +os.environ['AWS_NO_SIGN_REQUEST'] = 'YES'
    +wcover_catalog = pystac_client.Client.open('https://services.terrascope.be/stac/')
    +
    +

    Similarly, we discover the required items.

    +
    +
    search = wcover_catalog.search(
    +    collections= "urn:eop:VITO:ESA_WorldCover_10m_2021_AWS_V2",
    +    bbox=bounding_box
    +)
    +
    +items_wcover = search.item_collection()
    +
    +

    Load the data lazily.

    +
    +
    wcover_dc = odc_stac.load(items_wcover,
    +                        crs=crs,
    +                        chunks=chunks,
    +                        resolution=res,
    +                        bbox=bounding_box,
    +                       ).\
    +    squeeze("time").\
    +    drop_vars("time").\
    +    rename_vars({"ESA_WORLDCOVER_10M_MAP": "wcover"})
    +wcover_dc = wcover_dc.persist()
    +wait(wcover_dc)
    +wcover_dc
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset> Size: 30MB
    +Dimensions:      (latitude: 1668, longitude: 4445)
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +Data variables:
    +    wcover       (latitude, longitude) float32 30MB dask.array<chunksize=(1300, 1300), meta=np.ndarray>
    +
    +
    +
    +
    +
    +

    Fuse cube

    +

    Here we fuse the four data cubes together and filter for the values that have a HAND value of above zero. We can now drop the obit coordinates as well as time slices which contain no land backscattering data.

    +
    +
    flood_dc = xr.merge([sig0_dc, plia_dc, hpar_dc, wcover_dc])
    +flood_dc = flood_dc.where(flood_dc.wcover != 80)
    +flood_dc = flood_dc.\
    +    reset_index("orbit", drop=True).\
    +    rename({"orbit": "time"}).\
    +    dropna(dim="time", how="all", subset=["sig0"])
    +flood_dc = flood_dc.persist()
    +wait(flood_dc)
    +flood_dc
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset> Size: 2GB
    +Dimensions:      (time: 6, latitude: 1668, longitude: 4445)
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +  * time         (time) datetime64[ns] 48B 2022-10-11T05:25:01 ... 2022-10-23...
    +Data variables:
    +    sig0         (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    MPLIA        (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    C1           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    C2           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    C3           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    M0           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S1           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S2           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S3           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    STD          (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    wcover       (latitude, longitude) float32 30MB dask.array<chunksize=(1300, 1300), meta=np.ndarray>
    • latitude
      PandasIndex
      PandasIndex(Index([54.600030000000004,           54.59985,           54.59967,
      +                 54.59949,           54.59931,           54.59913,
      +                 54.59895,           54.59877,           54.59859,
      +                 54.59841,
      +       ...
      +       54.301590000000004, 54.301410000000004, 54.301230000000004,
      +       54.301050000000004,           54.30087,           54.30069,
      +                 54.30051,           54.30033,           54.30015,
      +                 54.29997],
      +      dtype='float64', name='latitude', length=1668))
    • longitude
      PandasIndex
      PandasIndex(Index([12.300030000000001, 12.300210000000002, 12.300390000000002,
      +       12.300570000000002,           12.30075, 12.300930000000001,
      +       12.301110000000001, 12.301290000000002, 12.301470000000002,
      +       12.301650000000002,
      +       ...
      +                 13.09833, 13.098510000000001, 13.098690000000001,
      +       13.098870000000002, 13.099050000000002, 13.099230000000002,
      +                 13.09941, 13.099590000000001, 13.099770000000001,
      +       13.099950000000002],
      +      dtype='float64', name='longitude', length=4445))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2022-10-11 05:25:01', '2022-10-14 17:01:09',
      +               '2022-10-16 05:33:14', '2022-10-18 05:16:52',
      +               '2022-10-21 16:52:59', '2022-10-23 05:25:01'],
      +              dtype='datetime64[ns]', name='time', freq=None))
  • +
    +
    + +
    +
    +

    Likelihoods

    +

    Now we are ready to calculate the likelihoods of micorwave backscattering given flooding (or non flooding).

    +
    +

    Water

    +

    We start with water which is the simplest calculation, where the hard coded values are coefficients of a regression model fitted to global water backscattering values.

    +
    +
    def calc_water_likelihood(dc):
    +    return  dc.MPLIA * -0.394181 + -4.142015
    +
    +
    +
    flood_dc["wbsc"] = calc_water_likelihood(flood_dc)
    +
    +
    +
    +

    Land

    +

    For land backscattering we construct the harmonic model from the parameters as contained in the fused data cube. By doing so, we obtain a reference land backscattering value to which to compare our actual observed sigma naught values.

    +
    +
    def harmonic_expected_backscatter(dc):
    +    w = np.pi * 2 / 365
    +    
    +    t = dc.time.dt.dayofyear
    +    wt = w * t
    +    
    +    M0 = dc.M0
    +    S1 = dc.S1
    +    S2 = dc.S2
    +    S3 = dc.S3
    +    C1 = dc.C1
    +    C2 = dc.C2
    +    C3 = dc.C3
    +    hm_c1 = (M0 + S1 * np.sin(wt)) + (C1 * np.cos(wt))
    +    hm_c2 = ((hm_c1 + S2 * np.sin(2 * wt)) + C2 * np.cos(2 * wt))
    +    hm_c3 = ((hm_c2 + S3 * np.sin(3 * wt)) + C3 * np.cos(3 * wt))
    +    return hm_c3
    +
    +
    +
    flood_dc["hbsc"] = harmonic_expected_backscatter(flood_dc)
    +
    +
    +
    +
    +

    Flood mapping

    +

    Having calculated the likelihoods, we can now move on to calculate the probability of (non-)flooding given a pixel’s \(\sigma^0\). These so-called posteriors need one more piece of information, as can be seen in the equation above. We need the probability that a pixel is flooded \(P(F)\) or not flooded \(P(NF)\). Of course, these are the figures we’ve been trying to find this whole time. We don’t actually have them yet, so what can we do? In Bayesian statistics, we can just start with our best guess. These guesses are called our “priors”, because they are the beliefs we hold prior to looking at the data. This subjective prior belief is the foundation Bayesian statistics, and we use the likelihoods we just calculated to update our belief in this particular hypothesis. This updated belief is called the “posterior”.

    +

    Let’s say that our best estimate for the chance of flooding versus non-flooding of a pixel is 50-50: a coin flip. We now can also calculate the probability of backscattering \(P(\sigma^0)\), as the weighted average of the water and land likelihoods, ensuring that our posteriors range between 0 to 1.

    +

    The following code block shows how we calculate the priors which allow use to predict whether it is likely if a land pixel became flooded.

    +
    +
    def bayesian_flood_decision(dc):
    +    
    +    nf_std = 2.754041
    +    sig0 = dc.sig0
    +    std = dc.STD
    +    wbsc = dc.wbsc
    +    hbsc = dc.hbsc
    +
    +    f_prob = (1.0 / (std * np.sqrt(2 * np.pi))) * np.exp(-0.5 * \
    +        (((sig0 - wbsc) / nf_std) ** 2))
    +    nf_prob = (1.0 / (nf_std * np.sqrt(2 * np.pi))) * np.exp(-0.5 * \
    +        (((sig0 - hbsc) / nf_std) ** 2))
    +    
    +    evidence = (nf_prob * 0.5) + (f_prob * 0.5)
    +    nf_post_prob = (nf_prob * 0.5) / evidence
    +    f_post_prob = (f_prob * 0.5) / evidence
    +    decision = xr.where(np.isnan(f_post_prob) | np.isnan(nf_post_prob), np.nan, np.greater(f_post_prob, nf_post_prob))
    +    return nf_post_prob, f_post_prob, decision
    +
    +
    +
    flood_dc[["nf_post_prob", "f_post_prob", "decision"]] = bayesian_flood_decision(flood_dc)
    +
    +
    +
    +

    Postprocessing

    +

    We continue by improving our flood map by filtering out observations that we expect to have low sensitivity to flooding based on a predefined set of criteria.

    +

    These criteria include: * Masking of Exceeding Incidence Angles * Identification of Conflicting Distributions * Removal of Measurement Outliers * Denial of High Uncertainty on Decision

    +
    +
    def post_processing(dc):
    +    dc = dc * np.logical_and(dc.MPLIA >= 27, dc.MPLIA <= 48)
    +    dc = dc * (dc.hbsc > (dc.wbsc + 0.5 * 2.754041))
    +    land_bsc_lower = dc.hbsc - 3 * dc.STD
    +    land_bsc_upper = dc.hbsc + 3 * dc.STD
    +    water_bsc_upper = dc.wbsc + 3 * 2.754041
    +    mask_land_outliers = np.logical_and(dc.sig0 > land_bsc_lower, dc.sig0 < land_bsc_upper)
    +    mask_water_outliers = dc.sig0 < water_bsc_upper
    +    dc = dc * (mask_land_outliers | mask_water_outliers)
    +    return  (dc * (dc.f_post_prob > 0.8)).decision
    +
    +
    +
    flood_output = post_processing(flood_dc)
    +
    +
    +
    +

    Removal of Speckles

    +

    The following step is designed to further improve the clarity of the floodmaps. These filters do not directly relate to prior knowledge on backscattering, but consists of contextual evidence that supports, or oppose, a flood classification. This mainly targets so-called speckles. These speckles are areas of one or a few pixels, and which are likely the result of the diversity of scattering surfaces at a sub-pixel level. In this approach it is argued that small, solitary flood surfaces are unlikely. Hence speckles are removed by applying a smoothing filter which consists of a rolling window median along the x and y-axis simultaneously.

    +
    +
    flood_output = flood_output.rolling({"longitude": 5, "latitude": 5}, center=True).median(skipna=True).persist()
    +wait(flood_output)
    +flood_output
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.DataArray 'decision' (time: 6, latitude: 1668, longitude: 4445)> Size: 356MB
    +dask.array<where, shape=(6, 1668, 4445), dtype=float64, chunksize=(1, 1302, 1302), chunktype=numpy.ndarray>
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +  * time         (time) datetime64[ns] 48B 2022-10-11T05:25:01 ... 2022-10-23...
    +
    +
    +
    +
    +
    +

    Results

    +

    We are now ready to visualize our results.

    +
    +
    flood_output.plot(col="time", col_wrap=3)
    +
    +
    +
    +

    +
    +
    +
    +
    +

    In the following graphic we superimpose the data on a map and we can move the slider to see which areas become flooded over time.

    +
    +
    flood_output.hvplot.quadmesh(x='longitude', y='latitude', geo=True, widget_location='bottom', rasterize=True, \
    +                            project=True, clim=(0,1), cmap=["rgba(0, 0, 1, 0.1)","darkred"], tiles=True, \
    +                            clabel="        non-flood                                        flood")
    +
    + +
    +
    + +
    +
    +
    +
    +
    + +
    +
    +
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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

    "},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return 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0000000..d9fd98f --- /dev/null +++ b/01_local_dask_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/01_local_dask_files/libs/quarto-html/tippy.css b/01_local_dask_files/libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/01_local_dask_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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a/Makefile +++ /dev/null @@ -1,42 +0,0 @@ -SHELL = /bin/bash -.PHONY: help clean install test version dist - -help: - @echo "make clean" - @echo " clean all python build/compilation files and directories" - @echo "make install" - @echo " install dependencies in local venv environment and creates a new one if it doesn't exist yet" - @echo "make test" - @echo " run test with coverage" - @echo "make version" - @echo " update _version.py with current version tag" - @echo "make dist" - @echo " build the package ready for distribution and update the version tag" - -clean: - find . -name '*.pyc' -exec rm --force {} + - find . -name '*.pyo' -exec rm --force {} + - find . -name '*~' -exec rm --force {} + - rm --force .coverage - rm --force --recursive .pytest_cache - rm --force --recursive build/ - rm --force --recursive dist/ - rm --force --recursive *.egg-info - -venv/bin/activate: - python3 -m venv .venv - source .venv/bin/activate && pip install --upgrade pip setuptools - source .venv/bin/activate && pip install pygdal=="$(shell gdal-config --version).*" - -install: venv/bin/activate - source .venv/bin/activate && pip install -e .[test] - -test: - pytest tests/ -rsx --verbose --color=yes --cov=dask_flood_mapper --cov-report term-missing - -version: - echo -e "__version__ = \"$(shell git describe --always --tags --abbrev=0)\"\n__commit__ = \"$(shell git rev-parse --short HEAD)\"" > src/dask_flood_mapper/_version.py - -dist: version - pip3 install build twine - python3 -m build diff --git a/README.md b/README.md deleted file mode 100644 index 85741d5..0000000 --- a/README.md +++ /dev/null @@ -1,11 +0,0 @@ -## Dask based Flood Mapping - -This repository contains notebooks that explains how microwave backscattering can be used to map the extent of a flood. We replicate in this exercise the work of Bauer-Marschallinger et al. (2022) on the TU Wien Bayesian-based flood mapping algorithm. This workflow is entirely based on `Dask` and data access via [STAC](https://stacspec.org/en). - -To run the workflow - -``` -git clone git@git.geo.tuwien.ac.at:mschobbe/dask-flood-mapper.git -cd dask-flood-mapper -pip install . -``` \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..8f8a6f7 --- /dev/null +++ b/index.html @@ -0,0 +1,5155 @@ + + + + + + + + + +Dask Client + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    In conjunction with setting up the Dask client I will chunk my arrays along three dimensions according to the following specifications for maximum performance on my setup.

    +
    +
    chunks = {'time':1, "latitude": 1300, "longitude": 1300}
    +
    +
    +

    Cube Definitions

    +

    The following generic specifications are used for presenting the data.

    +
    +
    crs = "EPSG:4326" # Coordinate Reference System - World Geodetic System 1984 (WGS84) in this case 
    +res = 0.00018 # 20 meter in degree
    +
    +
    +
    +

    Northern Germany Flood

    +

    Storm Babet hit the Denmark and Northern coast at the 20th of October 2023 Wikipedia. Here an area around Zingst at the Baltic coast of Northern Germany is selected as the study area.

    +
    +
    time_range = "2022-10-11/2022-10-25"
    +minlon, maxlon = 12.3, 13.1
    +minlat, maxlat = 54.3, 54.6
    +bounding_box = [minlon, minlat, maxlon, maxlat]
    +
    +
    +
    +

    EODC STAC Catalog

    +

    The pystac_client establishes a connection to the EODC STAC Catalog. This results in a catalog object that can be used to discover collections and items hosted at EODC.

    +
    +
    eodc_catalog = pystac_client.Client.open("https://stac.eodc.eu/api/v1")
    +
    +
    +
    +

    Microwave Backscatter Measurements

    +

    The basic premise of microwave-based backscattering can be seen in the sketch below, the characteristics of backscattering over land and water differ considerably. With this knowledge we can detect whenever a pixel with a predominant land like signature changes to a water like signature in the event of flooding.

    +

    +

    Schematic backscattering over land and water. Image from Geological Survey Ireland

    +

    We discover Sentinel-1 microwave backscatter (\(\sigma_0\) [1]) at a 20 meter resolution, like so:

    +
    +
    search = eodc_catalog.search(
    +    collections="SENTINEL1_SIG0_20M",
    +    bbox=bounding_box,
    +    datetime=time_range,
    +)
    +
    +items_sig0 = search.item_collection()
    +
    +

    The state of the orbit and relative orbit number is also saved, as the water and land likelihoods (which are calculated later on) dependent on the orbital configuration. These variables will be added as additional coordinates to the data cube. For this purpose a small helper function is defined, like so:

    +
    +
    def extract_orbit_names(items):
    +    return np.array([items[i].properties["sat:orbit_state"][0].upper() + \
    +                     str(items[i].properties["sat:relative_orbit"]) \
    +                     for i in range(len(items))])
    +
    +

    We will also save the scaling factor and nodata values of STAC items to correct the loaded data accordingly. Again a helper function will be used to correctly scale and fill no data values of the cube.

    +
    +
    def post_process_eodc_cube(dc: xr.Dataset, items, bands):
    +    if not isinstance(bands, tuple):
    +        bands = tuple([bands])
    +    for i in bands:
    +        dc[i] = post_process_eodc_cube_(dc[i], items, i)
    +    return dc
    +
    +def post_process_eodc_cube_(dc: xr.Dataset, items, band):
    +    scale = items[0].assets[band].extra_fields.get('raster:bands')[0]['scale']
    +    nodata = items[0].assets[band].extra_fields.get('raster:bands')[0]['nodata']
    +    return dc.where(dc != nodata) / scale
    +
    +

    The VV polarization of the discover items can be loaded with odc-stac and cast in the desired projection and resolution. The data is at this point only lazily loaded, meaning that we only make an instance of the outlines of the datacube with the proper shape and resolution, but without actually loading all the data. This is done by providing the chunks as defined before, which partitions the data in portions which are more easily handled by the setup used for processing the data (in this case my own pc).

    +
    +
    bands = "VV"
    +sig0_dc = odc_stac.load(items_sig0,
    +                        bands=bands,
    +                        crs=crs,
    +                        chunks=chunks,
    +                        resolution=res,
    +                        bbox=bounding_box,
    +                        groupby=None,
    +                        )
    +
    +

    Now we can rescale our variable, fill the no data values with np.nan values, and add the orbit names, with the previous defined functions.

    +
    +
    sig0_dc = post_process_eodc_cube(sig0_dc, items_sig0, bands).\
    +    rename_vars({ "VV": "sig0"}).\
    +    assign_coords(orbit=("time", extract_orbit_names(items_sig0))).\
    +    dropna(dim="time", how="all").\
    +    sortby("time")
    +
    +

    Then we remove duplicate time dimensions from the data cube and extract the orbit names as we will need those for obtaining the correct harmonic parameters and local incidence angles,as explained in the next section. Also, note, that we call dask.persist to materialize the object but retain it as a delayed object in the workers memory.

    +
    +
    __, indices = np.unique(sig0_dc.time, return_index=True)
    +indices.sort()
    +orbit_sig0 = sig0_dc.orbit[indices].data
    +sig0_dc = sig0_dc.groupby("time").mean(skipna=True)
    +sig0_dc = sig0_dc.assign_coords(orbit=("time", orbit_sig0))
    +sig0_dc = sig0_dc.persist()
    +wait(sig0_dc)
    +sig0_dc
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset> Size: 237MB
    +Dimensions:      (time: 8, latitude: 1668, longitude: 4445)
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +  * time         (time) datetime64[ns] 64B 2022-10-11T05:25:01 ... 2022-10-23...
    +    orbit        (time) <U4 128B 'D168' 'A44' 'A44' ... 'A146' 'A146' 'D168'
    +Data variables:
    +    sig0         (time, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +
    +
    +
    +
    +
    +

    Harmonic Parameters

    +

    The so-called likelihoods of \(P(\sigma^0|flood)\) and \(P(\sigma^0|nonflood)\) can be calculated from past backscattering information. To be able to this we load the harmonic parameters we can model the expected variations in land back scattering based on seasonal changes in vegetation. The procedure is similar to the backscattering routine.

    +

    We discover items.

    +
    +
    search = eodc_catalog.search(
    +    collections="SENTINEL1_HPAR",
    +    bbox=bounding_box
    +)
    +
    +items_hpar = search.item_collection()
    +
    +

    Load the data as a lazy object.

    +
    +
    bands = ("C1", "C2", "C3", "M0", "S1", "S2", "S3", "STD")
    +hpar_dc = odc_stac.load(items_hpar,
    +                        bands=bands,
    +                        crs=crs,
    +                        chunks=chunks,
    +                        resolution=res,
    +                        bbox=bounding_box,
    +                        groupby=None,
    +                        )
    +
    +hpar_dc = post_process_eodc_cube(hpar_dc, items_hpar, bands).\
    +    rename({"time": "orbit"})
    +hpar_dc["orbit"] = extract_orbit_names(items_hpar)
    +hpar_dc = hpar_dc.groupby("orbit").mean(skipna=True)
    +
    +

    We expand the variables along the orbits of sigma naught to be able to calculate the correct land reference backscatter signatures.

    +
    +
    hpar_dc = hpar_dc.sel(orbit = orbit_sig0)
    +hpar_dc = hpar_dc.persist()
    +wait(hpar_dc)
    +hpar_dc
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset> Size: 2GB
    +Dimensions:      (orbit: 8, latitude: 1668, longitude: 4445)
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +  * orbit        (orbit) object 64B 'D168' 'A44' 'A44' ... 'A146' 'A146' 'D168'
    +Data variables:
    +    C1           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    C2           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    C3           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    M0           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S1           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S2           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S3           (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    STD          (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    • latitude
      PandasIndex
      PandasIndex(Index([54.600030000000004,           54.59985,           54.59967,
      +                 54.59949,           54.59931,           54.59913,
      +                 54.59895,           54.59877,           54.59859,
      +                 54.59841,
      +       ...
      +       54.301590000000004, 54.301410000000004, 54.301230000000004,
      +       54.301050000000004,           54.30087,           54.30069,
      +                 54.30051,           54.30033,           54.30015,
      +                 54.29997],
      +      dtype='float64', name='latitude', length=1668))
    • longitude
      PandasIndex
      PandasIndex(Index([12.300030000000001, 12.300210000000002, 12.300390000000002,
      +       12.300570000000002,           12.30075, 12.300930000000001,
      +       12.301110000000001, 12.301290000000002, 12.301470000000002,
      +       12.301650000000002,
      +       ...
      +                 13.09833, 13.098510000000001, 13.098690000000001,
      +       13.098870000000002, 13.099050000000002, 13.099230000000002,
      +                 13.09941, 13.099590000000001, 13.099770000000001,
      +       13.099950000000002],
      +      dtype='float64', name='longitude', length=4445))
    • orbit
      PandasIndex
      PandasIndex(Index(['D168', 'A44', 'A44', 'D66', 'D95', 'A146', 'A146', 'D168'], dtype='object', name='orbit'))
  • +
    +
    + +
    +
    +

    Local Incidence Angles

    +

    Local incidence angles of measured microwave backscattering is as well important for calculating reference backscatter values, but now for water bodies. The procedure is much the same as for the harmonic parameters.

    +
    +
    search = eodc_catalog.search(
    +    collections="SENTINEL1_MPLIA",
    +    bbox=bounding_box
    +)
    +
    +items_plia = search.item_collection()
    +
    +

    Load the lazy object and preprocess.

    +
    +
    bands = "MPLIA"
    +plia_dc = odc_stac.load(items_plia,
    +                        bands=bands,
    +                        crs=crs,
    +                        chunks=chunks,
    +                        resolution=res,
    +                        bbox=bounding_box,
    +                        groupby=None,
    +                        )
    +
    +plia_dc = post_process_eodc_cube(plia_dc, items_plia, bands).\
    +    rename({"time": "orbit"})
    +plia_dc["orbit"] = extract_orbit_names(items_plia)
    +plia_dc = plia_dc.groupby("orbit").mean(skipna=True)
    +
    +

    We expand the variables along the orbits of sigma naught to be able to calculate the correct land reference backscatter signatures.

    +
    +
    plia_dc = plia_dc.sel(orbit = orbit_sig0)
    +plia_dc = plia_dc.persist()
    +wait(plia_dc)
    +plia_dc
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset> Size: 237MB
    +Dimensions:      (orbit: 8, latitude: 1668, longitude: 4445)
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +  * orbit        (orbit) object 64B 'D168' 'A44' 'A44' ... 'A146' 'A146' 'D168'
    +Data variables:
    +    MPLIA        (orbit, latitude, longitude) float32 237MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +
    +
    +
    +
    +
    +

    ESA World Cover from Terrascope

    +

    For flood mapping we are only interested in microwave backscattering over what used to be land, as such, we need a way to mask water bodies. For this we use the ESA World Cover data from the following STAC catalog.

    +
    +
    import os
    +os.environ['AWS_NO_SIGN_REQUEST'] = 'YES'
    +wcover_catalog = pystac_client.Client.open('https://services.terrascope.be/stac/')
    +
    +

    Similarly, we discover the required items.

    +
    +
    search = wcover_catalog.search(
    +    collections= "urn:eop:VITO:ESA_WorldCover_10m_2021_AWS_V2",
    +    bbox=bounding_box
    +)
    +
    +items_wcover = search.item_collection()
    +
    +

    Load the data lazily.

    +
    +
    wcover_dc = odc_stac.load(items_wcover,
    +                        crs=crs,
    +                        chunks=chunks,
    +                        resolution=res,
    +                        bbox=bounding_box,
    +                       ).\
    +    squeeze("time").\
    +    drop_vars("time").\
    +    rename_vars({"ESA_WORLDCOVER_10M_MAP": "wcover"})
    +wcover_dc = wcover_dc.persist()
    +wait(wcover_dc)
    +wcover_dc
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset> Size: 30MB
    +Dimensions:      (latitude: 1668, longitude: 4445)
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +Data variables:
    +    wcover       (latitude, longitude) float32 30MB dask.array<chunksize=(1300, 1300), meta=np.ndarray>
    +
    +
    +
    +
    +
    +

    Fuse cube

    +

    Here we fuse the four data cubes together and filter for the values that have a HAND value of above zero. We can now drop the obit coordinates as well as time slices which contain no land backscattering data.

    +
    +
    flood_dc = xr.merge([sig0_dc, plia_dc, hpar_dc, wcover_dc])
    +flood_dc = flood_dc.where(flood_dc.wcover != 80)
    +flood_dc = flood_dc.\
    +    reset_index("orbit", drop=True).\
    +    rename({"orbit": "time"}).\
    +    dropna(dim="time", how="all", subset=["sig0"])
    +flood_dc = flood_dc.persist()
    +wait(flood_dc)
    +flood_dc
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset> Size: 2GB
    +Dimensions:      (time: 6, latitude: 1668, longitude: 4445)
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +  * time         (time) datetime64[ns] 48B 2022-10-11T05:25:01 ... 2022-10-23...
    +Data variables:
    +    sig0         (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    MPLIA        (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    C1           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    C2           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    C3           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    M0           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S1           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S2           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    S3           (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    STD          (time, latitude, longitude) float32 178MB dask.array<chunksize=(1, 1300, 1300), meta=np.ndarray>
    +    wcover       (latitude, longitude) float32 30MB dask.array<chunksize=(1300, 1300), meta=np.ndarray>
    • latitude
      PandasIndex
      PandasIndex(Index([54.600030000000004,           54.59985,           54.59967,
      +                 54.59949,           54.59931,           54.59913,
      +                 54.59895,           54.59877,           54.59859,
      +                 54.59841,
      +       ...
      +       54.301590000000004, 54.301410000000004, 54.301230000000004,
      +       54.301050000000004,           54.30087,           54.30069,
      +                 54.30051,           54.30033,           54.30015,
      +                 54.29997],
      +      dtype='float64', name='latitude', length=1668))
    • longitude
      PandasIndex
      PandasIndex(Index([12.300030000000001, 12.300210000000002, 12.300390000000002,
      +       12.300570000000002,           12.30075, 12.300930000000001,
      +       12.301110000000001, 12.301290000000002, 12.301470000000002,
      +       12.301650000000002,
      +       ...
      +                 13.09833, 13.098510000000001, 13.098690000000001,
      +       13.098870000000002, 13.099050000000002, 13.099230000000002,
      +                 13.09941, 13.099590000000001, 13.099770000000001,
      +       13.099950000000002],
      +      dtype='float64', name='longitude', length=4445))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2022-10-11 05:25:01', '2022-10-14 17:01:09',
      +               '2022-10-16 05:33:14', '2022-10-18 05:16:52',
      +               '2022-10-21 16:52:59', '2022-10-23 05:25:01'],
      +              dtype='datetime64[ns]', name='time', freq=None))
  • +
    +
    + +
    +
    +

    Likelihoods

    +

    Now we are ready to calculate the likelihoods of micorwave backscattering given flooding (or non flooding).

    +
    +

    Water

    +

    We start with water which is the simplest calculation, where the hard coded values are coefficients of a regression model fitted to global water backscattering values.

    +
    +
    def calc_water_likelihood(dc):
    +    return  dc.MPLIA * -0.394181 + -4.142015
    +
    +
    +
    flood_dc["wbsc"] = calc_water_likelihood(flood_dc)
    +
    +
    +
    +

    Land

    +

    For land backscattering we construct the harmonic model from the parameters as contained in the fused data cube. By doing so, we obtain a reference land backscattering value to which to compare our actual observed sigma naught values.

    +
    +
    def harmonic_expected_backscatter(dc):
    +    w = np.pi * 2 / 365
    +    
    +    t = dc.time.dt.dayofyear
    +    wt = w * t
    +    
    +    M0 = dc.M0
    +    S1 = dc.S1
    +    S2 = dc.S2
    +    S3 = dc.S3
    +    C1 = dc.C1
    +    C2 = dc.C2
    +    C3 = dc.C3
    +    hm_c1 = (M0 + S1 * np.sin(wt)) + (C1 * np.cos(wt))
    +    hm_c2 = ((hm_c1 + S2 * np.sin(2 * wt)) + C2 * np.cos(2 * wt))
    +    hm_c3 = ((hm_c2 + S3 * np.sin(3 * wt)) + C3 * np.cos(3 * wt))
    +    return hm_c3
    +
    +
    +
    flood_dc["hbsc"] = harmonic_expected_backscatter(flood_dc)
    +
    +
    +
    +
    +

    Flood mapping

    +

    Having calculated the likelihoods, we can now move on to calculate the probability of (non-)flooding given a pixel’s \(\sigma^0\). These so-called posteriors need one more piece of information, as can be seen in the equation above. We need the probability that a pixel is flooded \(P(F)\) or not flooded \(P(NF)\). Of course, these are the figures we’ve been trying to find this whole time. We don’t actually have them yet, so what can we do? In Bayesian statistics, we can just start with our best guess. These guesses are called our “priors”, because they are the beliefs we hold prior to looking at the data. This subjective prior belief is the foundation Bayesian statistics, and we use the likelihoods we just calculated to update our belief in this particular hypothesis. This updated belief is called the “posterior”.

    +

    Let’s say that our best estimate for the chance of flooding versus non-flooding of a pixel is 50-50: a coin flip. We now can also calculate the probability of backscattering \(P(\sigma^0)\), as the weighted average of the water and land likelihoods, ensuring that our posteriors range between 0 to 1.

    +

    The following code block shows how we calculate the priors which allow use to predict whether it is likely if a land pixel became flooded.

    +
    +
    def bayesian_flood_decision(dc):
    +    
    +    nf_std = 2.754041
    +    sig0 = dc.sig0
    +    std = dc.STD
    +    wbsc = dc.wbsc
    +    hbsc = dc.hbsc
    +
    +    f_prob = (1.0 / (std * np.sqrt(2 * np.pi))) * np.exp(-0.5 * \
    +        (((sig0 - wbsc) / nf_std) ** 2))
    +    nf_prob = (1.0 / (nf_std * np.sqrt(2 * np.pi))) * np.exp(-0.5 * \
    +        (((sig0 - hbsc) / nf_std) ** 2))
    +    
    +    evidence = (nf_prob * 0.5) + (f_prob * 0.5)
    +    nf_post_prob = (nf_prob * 0.5) / evidence
    +    f_post_prob = (f_prob * 0.5) / evidence
    +    decision = xr.where(np.isnan(f_post_prob) | np.isnan(nf_post_prob), np.nan, np.greater(f_post_prob, nf_post_prob))
    +    return nf_post_prob, f_post_prob, decision
    +
    +
    +
    flood_dc[["nf_post_prob", "f_post_prob", "decision"]] = bayesian_flood_decision(flood_dc)
    +
    +
    +
    +

    Postprocessing

    +

    We continue by improving our flood map by filtering out observations that we expect to have low sensitivity to flooding based on a predefined set of criteria.

    +

    These criteria include: * Masking of Exceeding Incidence Angles * Identification of Conflicting Distributions * Removal of Measurement Outliers * Denial of High Uncertainty on Decision

    +
    +
    def post_processing(dc):
    +    dc = dc * np.logical_and(dc.MPLIA >= 27, dc.MPLIA <= 48)
    +    dc = dc * (dc.hbsc > (dc.wbsc + 0.5 * 2.754041))
    +    land_bsc_lower = dc.hbsc - 3 * dc.STD
    +    land_bsc_upper = dc.hbsc + 3 * dc.STD
    +    water_bsc_upper = dc.wbsc + 3 * 2.754041
    +    mask_land_outliers = np.logical_and(dc.sig0 > land_bsc_lower, dc.sig0 < land_bsc_upper)
    +    mask_water_outliers = dc.sig0 < water_bsc_upper
    +    dc = dc * (mask_land_outliers | mask_water_outliers)
    +    return  (dc * (dc.f_post_prob > 0.8)).decision
    +
    +
    +
    flood_output = post_processing(flood_dc)
    +
    +
    +
    +

    Removal of Speckles

    +

    The following step is designed to further improve the clarity of the floodmaps. These filters do not directly relate to prior knowledge on backscattering, but consists of contextual evidence that supports, or oppose, a flood classification. This mainly targets so-called speckles. These speckles are areas of one or a few pixels, and which are likely the result of the diversity of scattering surfaces at a sub-pixel level. In this approach it is argued that small, solitary flood surfaces are unlikely. Hence speckles are removed by applying a smoothing filter which consists of a rolling window median along the x and y-axis simultaneously.

    +
    +
    flood_output = flood_output.rolling({"longitude": 5, "latitude": 5}, center=True).median(skipna=True).persist()
    +wait(flood_output)
    +flood_output
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.DataArray 'decision' (time: 6, latitude: 1668, longitude: 4445)> Size: 356MB
    +dask.array<where, shape=(6, 1668, 4445), dtype=float64, chunksize=(1, 1302, 1302), chunktype=numpy.ndarray>
    +Coordinates:
    +  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    +  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    +    spatial_ref  int32 4B 4326
    +  * time         (time) datetime64[ns] 48B 2022-10-11T05:25:01 ... 2022-10-23...
    +
    +
    +
    +
    +
    +

    Results

    +

    We are now ready to visualize our results.

    +
    +
    flood_output.plot(col="time", col_wrap=3)
    +
    +
    +
    +

    +
    +
    +
    +
    +

    In the following graphic we superimpose the data on a map and we can move the slider to see which areas become flooded over time.

    +
    +
    flood_output.hvplot.quadmesh(x='longitude', y='latitude', geo=True, widget_location='bottom', rasterize=True, \
    +                            project=True, clim=(0,1), cmap=["rgba(0, 0, 1, 0.1)","darkred"], tiles=True, \
    +                            clabel="        non-flood                                        flood")
    +
    + +
    +
    + +
    +
    +
    +
    +
    + +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 7fd26b9..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,3 +0,0 @@ -[build-system] -requires = ["setuptools"] -build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 4e54feb..0000000 --- a/requirements.txt +++ /dev/null @@ -1,172 +0,0 @@ -affine==2.4.0 -aiohappyeyeballs==2.4.0 -aiohttp==3.10.5 -aiosignal==1.3.1 -anyio==4.4.0 -argon2-cffi==23.1.0 -argon2-cffi-bindings==21.2.0 -arrow==1.3.0 -asttokens==2.4.1 -async-lru==2.0.4 -async-timeout==4.0.3 -attrs==24.2.0 -babel==2.16.0 -beautifulsoup4==4.12.3 -black==24.8.0 -bleach==6.1.0 -bokeh==3.4.3 -cachetools==5.5.0 -Cartopy==0.23.0 -certifi==2024.8.30 -cffi==1.17.1 -charset-normalizer==3.3.2 -click==8.1.7 -click-plugins==1.1.1 -cligj==0.7.2 -cloudpickle==3.0.0 -colorcet==3.1.0 -comm==0.2.2 -contourpy==1.3.0 -cycler==0.12.1 -dask==2024.9.0 -dask_labextension==7.0.0 -datashader==0.16.3 -debugpy==1.8.5 -decorator==5.1.1 -defusedxml==0.7.1 -distributed==2024.9.0 -exceptiongroup==1.2.2 -executing==2.1.0 -fastjsonschema==2.20.0 -fonttools==4.53.1 -fqdn==1.5.1 -frozenlist==1.4.1 -fsspec==2024.9.0 -geoviews==1.12.0 -h11==0.14.0 -holoviews==1.19.1 -httpcore==1.0.5 -httpx==0.27.2 -hvplot==0.10.0 -idna==3.10 -importlib_metadata==8.5.0 -ipykernel==6.29.5 -ipython==8.27.0 -ipywidgets==8.1.5 -isoduration==20.11.0 -jedi==0.19.1 -Jinja2==3.1.4 -json5==0.9.25 -jsonpointer==3.0.0 -jsonschema==4.23.0 -jsonschema-specifications==2023.12.1 -jupyter-events==0.10.0 -jupyter-lsp==2.2.5 -jupyter_client==8.6.2 -jupyter_core==5.7.2 -jupyter_server==2.14.2 -jupyter_server_proxy==4.4.0 -jupyter_server_terminals==0.5.3 -jupyterlab==4.2.5 -jupyterlab_pygments==0.3.0 -jupyterlab_server==2.27.3 -jupyterlab_widgets==3.0.13 -kiwisolver==1.4.7 -linkify-it-py==2.0.3 -llvmlite==0.43.0 -locket==1.0.0 -Markdown==3.7 -markdown-it-py==3.0.0 -MarkupSafe==2.1.5 -matplotlib==3.9.2 -matplotlib-inline==0.1.7 -mdit-py-plugins==0.4.2 -mdurl==0.1.2 -mistune==3.0.2 -msgpack==1.1.0 -multidict==6.1.0 -multipledispatch==1.0.0 -mypy-extensions==1.0.0 -nbclient==0.10.0 -nbconvert==7.16.4 -nbformat==5.10.4 -nest-asyncio==1.6.0 -nodejs==0.1.1 -notebook_shim==0.2.4 -numba==0.60.0 -numpy==2.0.2 -odc-geo==0.4.8 -odc-stac==0.3.10 -optional-django==0.1.0 -overrides==7.7.0 -packaging==24.1 -pandas==2.2.2 -pandocfilters==1.5.1 -panel==1.4.5 -param==2.1.1 -parso==0.8.4 -partd==1.4.2 -pathspec==0.12.1 -pexpect==4.9.0 -pillow==10.4.0 -platformdirs==4.3.3 -prometheus_client==0.20.0 -prompt_toolkit==3.0.47 -psutil==6.0.0 -ptyprocess==0.7.0 -pure_eval==0.2.3 -pycparser==2.22 -pyct==0.5.0 -Pygments==2.18.0 -pyparsing==3.1.4 -pyproj==3.6.1 -pyshp==2.3.1 -pystac==1.10.1 -pystac-client==0.8.3 -python-dateutil==2.9.0.post0 -python-json-logger==2.0.7 -pytz==2024.2 -pyviz_comms==3.0.3 -PyYAML==6.0.2 -pyzmq==26.2.0 -rasterio==1.3.11 -referencing==0.35.1 -requests==2.32.3 -rfc3339-validator==0.1.4 -rfc3986-validator==0.1.1 -rioxarray==0.17.0 -rpds-py==0.20.0 -scipy==1.14.1 -Send2Trash==1.8.3 -shapely==2.0.6 -simpervisor==1.0.0 -six==1.16.0 -sniffio==1.3.1 -snuggs==1.4.7 -sortedcontainers==2.4.0 -soupsieve==2.6 -stack-data==0.6.3 -tblib==3.0.0 -terminado==0.18.1 -tinycss2==1.3.0 -tomli==2.0.1 -toolz==0.12.1 -tornado==6.4.1 -tqdm==4.66.5 -traitlets==5.14.3 -types-python-dateutil==2.9.0.20240906 -typing_extensions==4.12.2 -tzdata==2024.1 -uc-micro-py==1.0.3 -uri-template==1.3.0 -urllib3==2.2.3 -wcwidth==0.2.13 -webcolors==24.8.0 -webencodings==0.5.1 -websocket-client==1.8.0 -widgetsnbextension==4.0.13 -xarray==2024.9.0 -xyzservices==2024.9.0 -yarl==1.11.1 -zict==3.0.0 -zipp==3.20.2 diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 79d34e4..0000000 --- a/setup.cfg +++ /dev/null @@ -1,47 +0,0 @@ -[metadata] -name = dask_flood_mapper -version = attr: dask_flood_mapper.__version__ -description = my project description -author = Martin Schobben -author_email = martin.schobben@tuwien.ac.at -long_description = file: README.md -platforms = any -classifiers = - Intended Audience :: Science/Research - Topic :: Scientific/Engineering - Topic :: Scientific/Engineering :: GIS - Topic :: Software Development :: Libraries - Programming Language :: Python :: 3 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Operating System :: POSIX - Natural Language :: English - -[options] -package_dir = - = src -packages = find: -install_requires = - jupyter - folium - pystac-client - odc-stac - dask[complete] - matplotlib - numpy - rioxarray - geoviews - datashader - cartopy - hvplot - dask-labextension - ipywidgets - jupyter-bokeh - -[options.packages.find] -where = src - -[options.extras_require] -test = - pytest - pytest-cov diff --git a/setup.py b/setup.py deleted file mode 100644 index 6068493..0000000 --- a/setup.py +++ /dev/null @@ -1,3 +0,0 @@ -from setuptools import setup - -setup() diff --git a/src/dask_flood_mapper/__init__.py b/src/dask_flood_mapper/__init__.py deleted file mode 100644 index 0681c4c..0000000 --- a/src/dask_flood_mapper/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from dask_flood_mapper._version import __commit__ -from dask_flood_mapper._version import __version__ - -name = "dask_flood_mapper" diff --git a/src/dask_flood_mapper/_version.py b/src/dask_flood_mapper/_version.py deleted file mode 100644 index c55373b..0000000 --- a/src/dask_flood_mapper/_version.py +++ /dev/null @@ -1,2 +0,0 @@ -__version__ = "v0.0.0" -__commit__ = "0000000" diff --git a/src/dask_flood_mapper/utils.py b/src/dask_flood_mapper/utils.py deleted file mode 100644 index 8af474a..0000000 --- a/src/dask_flood_mapper/utils.py +++ /dev/null @@ -1,17 +0,0 @@ -import xarray as xr -import numpy as np - -def post_process_eodc_cube(dc: xr.Dataset, items, bands): - if not isinstance(bands, tuple): - bands = tuple([bands]) - for i in bands: - dc[i] = post_process_eodc_cube_(dc[i], items, i) - return dc - -def post_process_eodc_cube_(dc: xr.Dataset, items, band): - scale = items[0].assets[band].extra_fields.get('raster:bands')[0]['scale'] - nodata = items[0].assets[band].extra_fields.get('raster:bands')[0]['nodata'] - return dc.where(dc != nodata) / scale - -def extract_orbit_names(items): - return np.array([items[i].properties["sat:orbit_state"][0].upper() + str(items[i].properties["sat:relative_orbit"]) for i in range(len(items))]) \ No newline at end of file diff --git a/tests/test_meta.py b/tests/test_meta.py deleted file mode 100644 index 9f09fc7..0000000 --- a/tests/test_meta.py +++ /dev/null @@ -1,2 +0,0 @@ -def test_that_testing_package_is_working(): - assert True \ No newline at end of file
    + +
    + +
    +
    +

    Dask Client

    +
    + + + +
    + + + + +
    + + + +
    + + +
    +
    import pystac_client
    +from odc import stac as odc_stac
    +import xarray as xr
    +import rioxarray
    +import numpy as np
    +import hvplot.xarray
    +
    +

    Dask makes parallel computing easy by providing a familiar API common libraries, such as Pandas and Numpy. This allow efficient scaling of the here presented workflow for this adaptation of the TU Wien Bayesian flood mapping algorithm. The data size will be a main limiting factor as the data grows larger than RAM. For this reason we will partition our data in chunks which will presented to the machine workers by Dasks task scheduler in a most efficient manner. Although many of Dask’ settings can be handled automatically, we can also modify some parameters for optimal performance of the workflow for the desired processing environment. So, note, that this highly depends on your own machine’s specifications.

    +

    I will, furthermore, set the temporary directory for when Dask spills data from the workers memory to disk.

    +
    +
    import dask
    +dask.config.set(temporary_directory='/tmp')
    +
    +
    <dask.config.set at 0x7f6a9048d0f0>
    +
    +
    +

    We can then set the Dask Client, where we avoid inter-worker communication which is common for working with numpy and xarray in this case.

    +
    +
    from dask.distributed import Client, progress, wait
    +client = Client(processes=False, threads_per_worker=2,
    +                n_workers=3, memory_limit='28GB')
    +client
    +
    +
    +
    +
    +
    +

    Client

    +

    Client-9d87ace9-75bb-11ef-9597-f780c6f503ec

    + + + + + + + + + + + + +
    Connection method: Cluster objectCluster type: distributed.LocalCluster
    Dashboard: http://128.131.72.130:8787/status
    + +
    + +
    +

    Cluster Info

    +
    + +
    +
    + +
    +
    +
    + +
    +

    Worker: 1

    +
    + + + + + + + + + + + + + + + + + + +
    Comm: inproc://128.131.72.130/284055/28Total threads: 2
    Dashboard: http://128.131.72.130:37339/statusMemory: 26.08 GiB
    Nanny: None
    Local directory: /tmp/dask-scratch-space/worker-xpcpyg9n
    +
    + +
    +
    +
    +
    + +
    +
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    Worker: 2

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    Comm: inproc://128.131.72.130/284055/29Total threads: 2
    Dashboard: http://128.131.72.130:44919/statusMemory: 26.08 GiB
    Nanny: None
    Local directory: /tmp/dask-scratch-space/worker-6si_y6fh
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