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| name: docker | ||
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| on: | ||
| push: | ||
| branches: | ||
| - 'master' | ||
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| jobs: | ||
| docker: | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - | ||
| name: Set up Docker Buildx | ||
| uses: docker/setup-buildx-action@v2 | ||
| - | ||
| name: Login to Docker Hub | ||
| uses: docker/login-action@v2 | ||
| with: | ||
| username: ${{ secrets.DOCKERHUB_USERNAME }} | ||
| password: ${{ secrets.DOCKERHUB_TOKEN }} | ||
| - | ||
| name: Build and push | ||
| uses: docker/build-push-action@v3 | ||
| with: | ||
| push: true | ||
| tags: opendrift/opendrift:latest |
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| #!/usr/bin/env python | ||
| """ | ||
| Leeway backtracking | ||
| =================== | ||
| """ | ||
|
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| import os | ||
| from datetime import timedelta | ||
| import cmocean | ||
| import pyproj | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| import xarray as xr | ||
| import opendrift | ||
| from opendrift.readers import reader_netCDF_CF_generic | ||
| from opendrift.models.leeway import Leeway | ||
|
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| #%% | ||
| # We try to find the likelihood of the origin of a found object by two different methods: | ||
| # 1. backwards simulation from position where object is found ('Observation') | ||
| # 2. forwards simulation from a uniform grid of possible initial locations, selecting the origins of particles actually hitting the observed target | ||
| # | ||
| # We use 24 hours from the NorKyst ocean model (800m pixel size) and Arome atmospheric model (2.5km pixel size) | ||
| o = Leeway(loglevel=50) | ||
| reader_arome = reader_netCDF_CF_generic.Reader(o.test_data_folder() + | ||
| '16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc') | ||
| reader_norkyst = reader_netCDF_CF_generic.Reader(o.test_data_folder() + | ||
| '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc') | ||
| o.add_reader([reader_norkyst, reader_arome]) | ||
|
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| duration = timedelta(hours=24) | ||
| start_time = reader_norkyst.start_time | ||
| end_time = start_time + duration | ||
|
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| object_type = 26 # 26 = Life-raft, no ballast | ||
| outfile = 'leeway.nc' | ||
| ilon = 4.3 # Incident position | ||
| ilat = 60.6 | ||
| text = [{'s': 'Observation', 'x': ilon, 'y': ilat, 'fontsize': 20, 'color': 'g', 'zorder': 1000}] | ||
| # Define domain of possible origin | ||
| lons = np.arange(3.4, 5, .1/20) | ||
| lats = np.arange(59.7, 60.8, .05/20) | ||
| corners = [lons[0], lons[-1], lats[0], lats[-1]] | ||
| lons, lats = np.meshgrid(lons, lats) | ||
|
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||
| #%% | ||
| # Simulating first backwards for 24 hours: | ||
| o.seed_elements(lon=ilon, lat=ilat, radius=5000, radius_type='uniform', number=30000, | ||
| time=end_time, object_type=object_type) | ||
| o.run(duration=duration, time_step=-900, time_step_output=3600, outfile=outfile) | ||
| od = opendrift.open_xarray(outfile) | ||
| density_backwards = od.get_histogram(pixelsize_m=5000).isel(time=-1).isel(origin_marker=0) | ||
| density_backwards = density_backwards.where(density_backwards>0) | ||
| density_backwards = density_backwards/density_backwards.sum()*100 | ||
| vmax = density_backwards.max() | ||
| o.plot(background=density_backwards, clabel='Probability of origin [%]', text=text, corners=corners, fast=True, markersize=.5, lalpha=.02, vmin=0, vmax=vmax) | ||
| os.remove(outfile) | ||
|
|
||
| #%% | ||
| # Simulating then forwards, starting at a uniform grid 24 hours earlier (440 x 320 = 140800 elements at ~500m separation) | ||
| o = Leeway(loglevel=50) | ||
| o.add_reader([reader_norkyst, reader_arome]) | ||
| o.seed_elements(lon=lons, lat=lats, radius=0, | ||
| time=start_time, object_type=object_type) | ||
| o.run(duration=duration, time_step=900, time_step_output=3600, outfile=outfile) | ||
| print(o) | ||
|
|
||
| #%% | ||
| # Finding the elements actually hitting the target (within 5 km) after 24 hours: | ||
| lon, lat = o.get_lonlats() | ||
| lonend = lon[:, -1] | ||
| latend = lat[:, -1] | ||
| geod = pyproj.Geod(ellps='WGS84') | ||
| on = np.ones(lonend.shape) | ||
| dummy1, dummy2, dist2incident = geod.inv(lonend, latend, ilon*on, ilat*on) | ||
| hits = np.where(dist2incident<5000)[0] | ||
| hit_start_lons = lon[hits, 0] | ||
| hit_start_lats = lat[hits, 0] | ||
| o_hit = opendrift.open(outfile, elements=hits) | ||
|
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| o.animation(compare=o_hit, legend=['Elements not hitting target', 'Elements hitting target'], fast=True, corners=corners, text=text) | ||
|
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| #%% | ||
| # .. image:: /gallery/animations/example_leeway_backtrack_0.gif | ||
|
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| o.plot(compare=o_hit, legend=['Elements not hitting target', 'Elements hitting target'], show_elements=False, fast=True, corners=corners, text=text) | ||
|
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| #%% | ||
| # Plot the initial density of elements that actually hit the target after 24 hours. To be compared with the density figure from backwards simulation (see top) | ||
| of = opendrift.open_xarray(outfile, elements=hits) | ||
| density_forwards = of.get_histogram(pixelsize_m=5000).isel(time=0).isel(origin_marker=0) | ||
| density_forwards = density_forwards.where(density_forwards>0) | ||
| o_hit.plot(background=density_forwards/density_forwards.sum()*100, clabel='Probability of origin [%]', text=text, corners=corners, fast=True, markersize=.5, lalpha=.02, vmin=0, vmax=vmax) |
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