Repository: digitalfabrik/opendrift-leeway-webgui (language: python)

Issue:
improve simulation visualization results
- [x] Zoom in more to have less blank space at the image borders to have more detail view / autozoom to fit content
- [x] Show hours or minutes instead of seconds in the colorbar
- [ ] Add discrete semitransparent points for positions every 1/4 of the simulation time 
- [ ] TBC

Of course we need to have access to the simualtion result data for this to implement this.
But it was already suggested that we might add those improvements upstream, but its good to have it already here documented.
We can also implement this already on our side, and later up stream this.


Discussion / hints:
- [ ] Show grid in DD MM SS format instead of DD.dd
We do have access to the data with :

lw.get_lonlats()
> We do have access to the data with :
> 
> lw.get_lonlats()

Does it gives us timeseriies data?
I created a new map rendering for the results. First edtion:

![output](https://github.com/digitalfabrik/opendrift-leeway-webgui/assets/83226481/ecdcd07a-98f2-432d-9af9-595f31d31135)

opendrift version for refrence:
![image](https://github.com/digitalfabrik/opendrift-leeway-webgui/assets/83226481/e8b5f357-9857-4d28-ad5f-8915a6b25581)


comments are welcome
Even more pretty and complete:

![output](https://github.com/digitalfabrik/opendrift-leeway-webgui/assets/83226481/8c012bff-08bf-412f-8244-47aba307f399)

Wow! Looking very nice! Good job!

Where did you implement this?
> Wow! Looking very nice! Good job!

Thanks

> Where did you implement this?

Python on my local machine. I will branch it here


Fix patch:
diff --git a/.github/leeway-simulation-output.png b/.github/leeway-simulation-output.png
index f0cee98..21f0ee8 100644
Binary files a/.github/leeway-simulation-output.png and b/.github/leeway-simulation-output.png differ
diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml
index 96c6f98..d284c1e 100644
--- a/.github/workflows/linting.yml
+++ b/.github/workflows/linting.yml
@@ -23,7 +23,7 @@ jobs:
       - uses: actions/setup-python@v4
       - uses: jamescurtin/isort-action@master
         with:
-          configuration: --check-only
+          configuration: --check-only -n
   pylint:
     runs-on: ubuntu-latest
     steps:
diff --git a/README.md b/README.md
index 9106b76..827124b 100644
--- a/README.md
+++ b/README.md
@@ -57,6 +57,11 @@ Authentication can be provided in two ways:
    python3 manage.py migrate
    python3 manage.py createsuperuser
    ```
+6. Build the enhanced Docker container:
+   ```bash
+   cd opendrift
+   docker build -t opendrift-leeway-custom .
+   ```
 
 # Development Server
 
diff --git a/opendrift/Dockerfile b/opendrift/Dockerfile
new file mode 100644
index 0000000..d7acb78
--- /dev/null
+++ b/opendrift/Dockerfile
@@ -0,0 +1,2 @@
+FROM opendrift/opendrift
+RUN pip3 install OWSLib
diff --git a/opendrift_leeway_webgui/api/urls.py b/opendrift_leeway_webgui/api/urls.py
index 1e5bdac..2f5c698 100644
--- a/opendrift_leeway_webgui/api/urls.py
+++ b/opendrift_leeway_webgui/api/urls.py
@@ -1,6 +1,7 @@
 """
 URL patterns for the Opendrift Leeway Webgui API
 """
+
 from django.urls import include, path
 
 #: The namespace for this URL config (see :attr:`django.urls.ResolverMatch.app_name`)
diff --git a/opendrift_leeway_webgui/api/v1/urls.py b/opendrift_leeway_webgui/api/v1/urls.py
index 5e9ad3a..5e510d0 100644
--- a/opendrift_leeway_webgui/api/v1/urls.py
+++ b/opendrift_leeway_webgui/api/v1/urls.py
@@ -1,6 +1,7 @@
 """
 URL patterns for the first version of the Opendrift Leeway Webgui API
 """
+
 from django.urls import include, path
 from drf_spectacular.views import SpectacularAPIView, SpectacularSwaggerView
 from rest_framework.routers import DefaultRouter
diff --git a/opendrift_leeway_webgui/core/settings.py b/opendrift_leeway_webgui/core/settings.py
index 06f6d7f..7aeb889 100644
--- a/opendrift_leeway_webgui/core/settings.py
+++ b/opendrift_leeway_webgui/core/settings.py
@@ -7,6 +7,7 @@
 For the full list of settings and their values, see
 https://docs.djangoproject.com/en/3.2/ref/settings/
 """
+
 import os
 from pathlib import Path
 
diff --git a/opendrift_leeway_webgui/core/urls.py b/opendrift_leeway_webgui/core/urls.py
index 76593e1..eeea555 100644
--- a/opendrift_leeway_webgui/core/urls.py
+++ b/opendrift_leeway_webgui/core/urls.py
@@ -13,6 +13,7 @@
     1. Import the include() function: from django.urls import include, path
     2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
 """
+
 from django.contrib import admin
 from django.urls import include, path
 
diff --git a/opendrift_leeway_webgui/leeway/celery.py b/opendrift_leeway_webgui/leeway/celery.py
index 3046ba7..e6d69ff 100644
--- a/opendrift_leeway_webgui/leeway/celery.py
+++ b/opendrift_leeway_webgui/leeway/celery.py
@@ -1,6 +1,7 @@
 """
 Configure Celery workers
 """
+
 import configparser
 import email
 import os
diff --git a/opendrift_leeway_webgui/leeway/forms.py b/opendrift_leeway_webgui/leeway/forms.py
index 9e04f9e..8e5c6c7 100644
--- a/opendrift_leeway_webgui/leeway/forms.py
+++ b/opendrift_leeway_webgui/leeway/forms.py
@@ -1,6 +1,7 @@
 """
 Forms for the web GUI
 """
+
 from django.forms import CharField, ModelForm, TextInput
 
 from .models import LeewaySimulation
diff --git a/opendrift_leeway_webgui/leeway/static/image/84eed031-ed5b-4802-a0e9-580b5fd9b93f.png b/opendrift_leeway_webgui/leeway/static/image/84eed031-ed5b-4802-a0e9-580b5fd9b93f.png
new file mode 100644
index 0000000..6bf384c
Binary files /dev/null and b/opendrift_leeway_webgui/leeway/static/image/84eed031-ed5b-4802-a0e9-580b5fd9b93f.png differ
diff --git a/opendrift_leeway_webgui/leeway/tasks.py b/opendrift_leeway_webgui/leeway/tasks.py
index 4d8d67b..9bf2e5c 100644
--- a/opendrift_leeway_webgui/leeway/tasks.py
+++ b/opendrift_leeway_webgui/leeway/tasks.py
@@ -31,7 +31,7 @@ def run_leeway_simulation(request_id):
         f"{settings.SIMULATION_ROOT}:/code/leeway",
         "--volume",
         f"{settings.SIMULATION_SCRIPT_PATH}:/code/leeway/simulation.py",
-        "opendrift/opendrift",
+        "opendrift-leeway-custom:latest",
         "python3",
         "leeway/simulation.py",
         "--longitude",
diff --git a/opendrift_leeway_webgui/leeway/templates/leeway/555af619-11b3-462e-b560-78764290d2f7.png b/opendrift_leeway_webgui/leeway/templates/leeway/555af619-11b3-462e-b560-78764290d2f7.png
deleted file mode 100644
index f581ca2..0000000
Binary files a/opendrift_leeway_webgui/leeway/templates/leeway/555af619-11b3-462e-b560-78764290d2f7.png and /dev/null differ
diff --git a/opendrift_leeway_webgui/leeway/templates/leeway/leewaysimulation_documentation.html b/opendrift_leeway_webgui/leeway/templates/leeway/leewaysimulation_documentation.html
index 9052c3c..a3c44c3 100644
--- a/opendrift_leeway_webgui/leeway/templates/leeway/leewaysimulation_documentation.html
+++ b/opendrift_leeway_webgui/leeway/templates/leeway/leewaysimulation_documentation.html
@@ -145,10 +145,16 @@ <h3>Example</h3>
         </table>
     </p>
     <p>
-        You recive an e-mail with the simulation result. It is clear the the boat is likely to drift North to South-East-East so you set your course to 171 to position 37.5°N 18,68°. From that position you should be able to spot the drifting vessel if the weather conditions are good.
-        <img src="data:image/png;base64,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"
-             alt="Simulation result example"
-             width="1100"
-             height="803">
+        You recive an e-mail with the simulation result. It is clear the the boat is likely to drift Sout to South-West. As the points are spread across about 8 nautical miles, it is unlikely that you will find the boat from one position.
     </p>
+    <p>
+        You need to define a search pattern. First, you navigate to the nearest and largest cluster of points, which in this case is in position 37° 28' N 18° 35' E. To reach that position, you set your course to 178.
+    </p>
+    <p>
+        When you reach that position, you need to define a course to the next largest cluster that at the same time covers as many drift positions as possible. You choose position 39° 29' N 18° 39' E as you next and final waypoint. To reach that last waypoint, you set course to 070.
+    </p>
+    <img src="{% static 'leeway/static/image/84eed031-ed5b-4802-a0e9-580b5fd9b93f.png' %}"
+         alt="Simulation result example"
+         width="1100"
+         height="803">
 {% endblock content %}
diff --git a/opendrift_leeway_webgui/leeway/urls.py b/opendrift_leeway_webgui/leeway/urls.py
index 8c16ff8..84a543c 100644
--- a/opendrift_leeway_webgui/leeway/urls.py
+++ b/opendrift_leeway_webgui/leeway/urls.py
@@ -4,6 +4,7 @@
 
 For more information on this file, see :doc:`django:topics/http/urls`.
 """
+
 from django.conf import settings
 from django.conf.urls.static import static
 from django.urls import include, path
diff --git a/opendrift_leeway_webgui/leeway/utils.py b/opendrift_leeway_webgui/leeway/utils.py
index 88a46c1..7e1c328 100644
--- a/opendrift_leeway_webgui/leeway/utils.py
+++ b/opendrift_leeway_webgui/leeway/utils.py
@@ -1,6 +1,7 @@
 """
 Utilities
 """
+
 from django.apps import apps
 from django.contrib.auth import get_user_model
 from django.core.mail import EmailMessage, send_mail
diff --git a/opendrift_leeway_webgui/simulation.py b/opendrift_leeway_webgui/simulation.py
index 91060fc..5674cd3 100644
--- a/opendrift_leeway_webgui/simulation.py
+++ b/opendrift_leeway_webgui/simulation.py
@@ -1,3 +1,4 @@
+# pylint: disable=too-many-function-args
 """
 Wrapper/Helper script for Leeway simulations with OpenDrift.
 
@@ -13,12 +14,24 @@
 from datetime import datetime, timedelta
 
 # pylint: disable=import-error
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import numpy as np
+
+# pylint: disable=import-error
+from cartopy.mpl import gridliner
+from matplotlib import ticker
+from matplotlib.collections import LineCollection
+from matplotlib.colors import ListedColormap
+
+# pylint: disable=import-error, disable=no-name-in-module
 from opendrift.models.leeway import Leeway
 from opendrift.readers import reader_global_landmask
 
 INPUTDIR = "/code/leeway/input"
 
 
+# pylint: disable=too-many-locals, disable=too-many-statements
 def main():
     """Run opendrift leeway simulation"""
     parser = argparse.ArgumentParser(description="Simulate drift of object")
@@ -107,11 +120,229 @@ def main():
     simulation.run(
         duration=timedelta(hours=args.duration), time_step=600, outfile=f"{outfile}.nc"
     )
-    simulation.plot(
-        fast=True, legend=True, filename=f"{outfile}.png", linecolor="age_seconds"
+
+    # Plotting results
+    lon, lat = np.array(simulation.get_lonlats())
+    lon[lon == 0] = np.nan
+    lat[lat == 0] = np.nan
+
+    crs = ccrs.Mercator()  # Mercator projection to have angle true projection
+    gcrs = ccrs.PlateCarree(globe=crs.globe)  # PlateCarree for straight lines
+
+    fig = plt.figure(figsize=(8, 8))  # figsize set low to get small files
+    ax = plt.axes(projection=crs)
+
+    # base map layer
+    ax.add_wms(wms="https://sgx.geodatenzentrum.de/wms_topplus_open", layers=["web"])
+    # quote source: Kartendarstellung: © Bundesamt für Kartographie und Geodäsie
+    # (2021), Datenquellen:
+    # https://gdz.bkg.bund.de/index.php/default/wms-topplusopen-wms-topplus-open.html
+
+    stranded = None
+    active = None
+    for i in range(lon.shape[0]):
+        lon_max_idx = (
+            np.where(np.isnan(lon[i, ...]))[0][0] - 1 if np.isnan(lon[i, -1]) else -1
+        )
+        lat_max_idx = (
+            np.where(np.isnan(lat[i, ...]))[0][0] - 1 if np.isnan(lat[i, -1]) else -1
+        )
+
+        points = np.array([lon[i, ...], lat[i, ...]]).T.reshape(-1, 1, 2)
+        segments = np.concatenate([points[:-1], points[1:]], axis=1)
+
+        lc = LineCollection(
+            segments, cmap="jet", norm=plt.Normalize(0, args.duration), transform=gcrs
+        )
+        lc.set_array(np.linspace(0, args.duration, len(segments)))
+        lc.set_linewidth(2)
+        line = ax.add_collection(lc)
+
+        initial = ax.scatter(
+            lon[i, 0],
+            lat[i, 0],
+            transform=gcrs,
+            color="green",
+            zorder=100 + i,
+            s=15,
+            edgecolor="black",
+            linewidth=0.5,
+        )
+        if lon_max_idx > 0:
+            stranded = ax.scatter(
+                lon[i, lon_max_idx],
+                lat[i, lat_max_idx],
+                transform=gcrs,
+                color="red",
+                zorder=100 + i + 1,
+                s=15,
+                edgecolor="black",
+                linewidth=0.5,
+            )
+        else:
+            active = ax.scatter(
+                lon[i, lon_max_idx],
+                lat[i, lat_max_idx],
+                transform=gcrs,
+                color="blue",
+                zorder=100 + i + 1,
+                s=15,
+                edgecolor="black",
+                linewidth=0.5,
+            )
+
+    # print legend for points
+    initial.set_label("Initial")
+    if stranded is not None:
+        stranded.set_label("Stranded")
+    if active is not None:
+        active.set_label("Active")
+
+    ax.legend(loc="center right", bbox_to_anchor=(0, 0.5))
+
+    # add colorbar with hours
+    cbar = fig.colorbar(line, location="bottom")
+    cbar.set_label("Hours")
+
+    # set map extent
+    x_max = np.nanmax(lon)
+    x_min = np.nanmin(lon)
+    y_max = np.nanmax(lat)
+    y_min = np.nanmin(lat)
+
+    dis_x = (x_max - x_min) * 0.02
+    dis_y = (y_max - y_min) * 0.02
+    extent = [
+        floor_min(x_min - dis_x),
+        ceil_min(x_max + dis_x),
+        floor_min(y_min - dis_y),
+        ceil_min(y_max + dis_y),
+    ]
+    ax.set_extent(extent)
+
+    # prepare gridlines on good position
+    x_step = 1 / 60  # step size for grid lines a line every minute
+    x_step_div = 10  # num of zebra stripes between grid lines
+    y_step = 1 / 60
+    y_step_div = 10
+
+    x_ticks = np.arange(extent[0], extent[1], x_step)
+    y_ticks = np.arange(extent[2], extent[3], y_step)
+
+    # check if too many grid lines:
+    if len(x_ticks) > 100:  # a line every 5 minutes
+        x_step = 1 / 12
+        x_step_div = 5
+        x_ticks = np.arange(extent[0], extent[1], x_step)
+    if len(y_ticks) > 100:
+        y_step = 1 / 12
+        y_step_div = 5
+        y_ticks = np.arange(extent[2], extent[3], y_step)
+    if len(x_ticks) > 100:  # a line every 10 minutes
+        x_step = 1 / 6
+        x_step_div = 10
+        x_ticks = np.arange(extent[0], extent[1], x_step)
+    if len(y_ticks) > 100:
+        y_step = 1 / 6
+        y_step_div = 10
+        y_ticks = np.arange(extent[2], extent[3], y_step)
+
+    xloc = ticker.FixedLocator(x_ticks)
+    yloc = ticker.FixedLocator(y_ticks)
+
+    longitude_formatter = gridliner.LongitudeFormatter(dms=True, auto_hide=False)
+    latitude_formatter = gridliner.LatitudeFormatter(dms=True, auto_hide=False)
+
+    gl = ax.gridlines(
+        draw_labels=True, linewidth=1, color="gray", alpha=0.5, rotate_labels=True
+    )
+    gl.ylocator = yloc
+    gl.xlocator = xloc
+    gl.xlabels_top = False
+    gl.ylabels_left = False
+    gl.xformatter = longitude_formatter
+    gl.yformatter = latitude_formatter
+
+    # add zebra frame
+    zebra_x = np.arange(extent[0], extent[1] + x_step / x_step_div, x_step / x_step_div)
+    zebra_y = np.arange(extent[2], extent[3] + y_step / y_step_div, y_step / y_step_div)
+
+    if len(zebra_x) > 200 or len(zebra_y) > 200:
+        x_step_div = 2
+        zebra_x = np.arange(
+            extent[0], extent[1] + x_step / x_step_div, x_step / x_step_div
+        )
+        y_step_div = 2
+        zebra_y = np.arange(
+            extent[2], extent[3] + y_step / y_step_div, y_step / y_step_div
+        )
+
+    points = np.array([zebra_x, np.zeros_like(zebra_x) + extent[2]]).T.reshape(-1, 1, 2)
+    lc = get_zebra_line(points, gcrs)
+    line = ax.add_collection(lc)
+    points = np.array([zebra_x, np.zeros_like(zebra_x) + extent[3]]).T.reshape(-1, 1, 2)
+    lc = get_zebra_line(points, gcrs)
+    line = ax.add_collection(lc)
+    points = np.array([np.zeros_like(zebra_y) + extent[0], zebra_y]).T.reshape(-1, 1, 2)
+    lc = get_zebra_line(points, gcrs)
+    line = ax.add_collection(lc)
+    points = np.array([np.zeros_like(zebra_y) + extent[1], zebra_y]).T.reshape(-1, 1, 2)
+    lc = get_zebra_line(points, gcrs)
+    line = ax.add_collection(lc)
+
+    start = datetime.strptime(args.start_time, "%Y-%m-%d %H:%M")
+    end = start + timedelta(hours=args.duration)
+
+    plt.title(
+        f"Leeway Simulation Object Type: {simulation.get_config('seed:object_type')}\n"
+        f" From {start.strftime('%d-%m-%Y %H:%M')} to {end.strftime('%d-%m-%Y %H:%M')} UTC"
+    )
+    fig.text(
+        0,
+        0,
+        "Kartendarstellung: © Bundesamt für Kartographie und Geodäsie (2021),\nDatenquellen:"
+        " https://gdz.bkg.bund.de/index.php/default/wms-topplusopen-wms-topplus-open.html",
+        fontsize=8,
     )
+
+    fig.savefig(f"{outfile}.png")
+
     print(f"Success: {outfile}.png written.")
 
 
+def floor_min(decimal):
+    """
+    floor funtion for arc minutes
+    """
+    deg = np.floor(decimal)
+    minutes = (decimal - deg) * 60
+    minutes = np.floor(minutes)
+    return deg + (minutes / 60)
+
+
+def ceil_min(decimal):
+    """
+    ceil fnction for arc minutes
+    """
+    deg = np.floor(decimal)
+    minutes = (decimal - deg) * 60
+    minutes = np.ceil(minutes)
+    return deg + (minutes / 60)
+
+
+def get_zebra_line(points, gcrs):
+    """
+    Define a blak/white dashed line for a map frame
+    """
+    segments = np.concatenate([points[:-1], points[1:]], axis=1)
+    cmap = ListedColormap(["k", "w"])
+    lc = LineCollection(segments, cmap=cmap, norm=plt.Normalize(0, 1), transform=gcrs)
+    array = np.zeros(points.shape[0])
+    array[::2] = 1
+    lc.set_array(array)
+    lc.set_linewidth(4)
+    return lc
+
+
 if __name__ == "__main__":
     main()
diff --git a/pyproject.toml b/pyproject.toml
index c4e09dd..1a8e32c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,6 +36,8 @@ dependencies = [
 dev = [
     "bumpver",
     "pre-commit",
+    "numpy",
+    "matplotlib",
     "pylint",
     "pylint-django",
     "pytest",


Test patch:
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 9721035..3cb5161 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -19,5 +19,16 @@ jobs:
         uses: supercharge/redis-github-action@1.4.0
       - name: Install dependencies
         run: pip install -e .[dev]
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Build custom container with OWSLib
+        uses: docker/build-push-action@v5
+        with:
+          file: opendrift/Dockerfile
+          push: false
+          tags: opendrift-leeway-custom:latest
       - name: Run tests
         run: pytest
diff --git a/tests/conftest.py b/tests/conftest.py
index 3d904e2..b1f8eb5 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,6 +1,7 @@
 """
 This module contains shared fixtures for pytest
 """
+
 import pytest
 
 from .leeway.test_simulation import _teardown_test_simulation