From aceb0e31e53510a25349066d7f320d3ff4c43a88 Mon Sep 17 00:00:00 2001 From: Nathan Clerkx Date: Thu, 19 Dec 2024 15:21:41 +0100 Subject: [PATCH] gca-sprint report outcome; uv docker config --- .../report-python-cloud-run/.dockerignore | 10 + .../report-python-cloud-run/Dockerfile | 52 +- .../report-python-cloud-run/__init__.py | 0 .../docker-compose.yml | 9 + App/functions/report-python-cloud-run/main.py | 2 +- .../report-python-cloud-run/pyproject.toml | 28 + .../report-python-cloud-run/report/app.html | 39 + .../report/datasets/base_dataset.py | 25 +- .../report/datasets/esl.py | 199 +- .../report/datasets/overview.py | 90 + .../report/datasets/popgpd.py | 79 + .../report/datasets/shoremon.py | 514 ++ .../report/datasets/slr.py | 231 + .../report/datasets/subtreat.py | 218 + .../report/datasets/utils.py | 4 +- .../images/Deltares_logo_D-blauw_RGB.svg | 1 + .../images/Deltares_pay-off_D-blauw_RGB.svg | 1 + .../report/report.html | 305 ++ .../report-python-cloud-run/report/report.py | 192 +- .../report/template.css | 113 +- .../report/template.html.jinja | 117 +- .../report/utils/gentext.py | 274 ++ .../report/utils/stac.py | 2 +- .../report-python-cloud-run/requirements.txt | 13 - App/functions/report-python-cloud-run/uv.lock | 4143 +++++++++++++++++ 25 files changed, 6495 insertions(+), 166 deletions(-) create mode 100644 App/functions/report-python-cloud-run/.dockerignore delete mode 100644 App/functions/report-python-cloud-run/__init__.py create mode 100644 App/functions/report-python-cloud-run/docker-compose.yml create mode 100644 App/functions/report-python-cloud-run/pyproject.toml create mode 100644 App/functions/report-python-cloud-run/report/app.html create mode 100644 App/functions/report-python-cloud-run/report/datasets/overview.py create mode 100644 App/functions/report-python-cloud-run/report/datasets/popgpd.py create mode 100644 App/functions/report-python-cloud-run/report/datasets/shoremon.py create mode 100644 App/functions/report-python-cloud-run/report/datasets/slr.py create mode 100644 App/functions/report-python-cloud-run/report/datasets/subtreat.py create mode 100644 App/functions/report-python-cloud-run/report/images/Deltares_logo_D-blauw_RGB.svg create mode 100644 App/functions/report-python-cloud-run/report/images/Deltares_pay-off_D-blauw_RGB.svg create mode 100644 App/functions/report-python-cloud-run/report/report.html create mode 100644 App/functions/report-python-cloud-run/report/utils/gentext.py delete mode 100644 App/functions/report-python-cloud-run/requirements.txt create mode 100644 App/functions/report-python-cloud-run/uv.lock diff --git a/App/functions/report-python-cloud-run/.dockerignore b/App/functions/report-python-cloud-run/.dockerignore new file mode 100644 index 000000000..8d6b7dc6e --- /dev/null +++ b/App/functions/report-python-cloud-run/.dockerignore @@ -0,0 +1,10 @@ +.venv/ +*__pycache__/ +*.pyc +*.pyo +*.pyd +*.pyw +*.pyz +*.pyj +*.pyx +*.pyd diff --git a/App/functions/report-python-cloud-run/Dockerfile b/App/functions/report-python-cloud-run/Dockerfile index a63dac06b..f5b2ec1a0 100644 --- a/App/functions/report-python-cloud-run/Dockerfile +++ b/App/functions/report-python-cloud-run/Dockerfile @@ -12,23 +12,35 @@ # See the License for the specific language governing permissions and # limitations under the License. -# Use the official lightweight Python image. -# https://hub.docker.com/_/python -FROM python:3.11-slim - -# Allow statements and log messages to immediately appear in the Knative logs -ENV PYTHONUNBUFFERED True - -# Copy local code to the container image. -ENV APP_HOME /app -WORKDIR $APP_HOME -COPY . ./ - -# Install production dependencies. -RUN pip install -r requirements.txt - -# Run the web service on container startup. Here we use the gunicorn -# webserver, with one worker process and 8 threads. -# For environments with multiple CPU cores, increase the number of workers -# to be equal to the cores available. -CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 --timeout 0 main:app \ No newline at end of file +# Use the official uv python image +# Use a Python image with uv pre-installed +FROM ghcr.io/astral-sh/uv:python3.11-bookworm + +# Install the project into `/app` +WORKDIR /app + +# Enable bytecode compilation +ENV UV_COMPILE_BYTECODE=1 + +# Copy from the cache instead of linking since it's a mounted volume +ENV UV_LINK_MODE=copy +RUN apt-get update && apt-get install -y libgdal-dev libgl1 + +# Install the project's dependencies using the lockfile and settings +RUN --mount=type=cache,target=/root/.cache/uv \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + uv sync --frozen --no-install-project --no-dev + +# Then, add the rest of the project source code and install it +# Installing separately from its dependencies allows optimal layer caching +ADD . /app +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-dev + +# Place executables in the environment at the front of the path +ENV PATH="/app/.venv/bin:$PATH" + +# CMD uv run --with waitress waitress-serve main:app +# CMD uv run python main.py +CMD uv run --with gunicorn gunicorn --bind :8080 --workers 1 --threads 8 --timeout 0 main:app diff --git a/App/functions/report-python-cloud-run/__init__.py b/App/functions/report-python-cloud-run/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/App/functions/report-python-cloud-run/docker-compose.yml b/App/functions/report-python-cloud-run/docker-compose.yml new file mode 100644 index 000000000..99feb9e15 --- /dev/null +++ b/App/functions/report-python-cloud-run/docker-compose.yml @@ -0,0 +1,9 @@ +services: + report-service: + build: + context: . + dockerfile: Dockerfile + env_file: + - .env.prod + ports: + - "8080:8080" diff --git a/App/functions/report-python-cloud-run/main.py b/App/functions/report-python-cloud-run/main.py index 2054cf726..7a45f8904 100644 --- a/App/functions/report-python-cloud-run/main.py +++ b/App/functions/report-python-cloud-run/main.py @@ -73,4 +73,4 @@ def return_html(): if __name__ == "__main__": - app.run(debug=True, host="0.0.0.0", port=int(os.environ.get("PORT", 8080))) + app.run(host="0.0.0.0", port=int(os.environ.get("PORT", 8080))) diff --git a/App/functions/report-python-cloud-run/pyproject.toml b/App/functions/report-python-cloud-run/pyproject.toml new file mode 100644 index 000000000..de46f2b3d --- /dev/null +++ b/App/functions/report-python-cloud-run/pyproject.toml @@ -0,0 +1,28 @@ +[project] +name = "gca-report" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "flask>=3.1.0", + "geopandas>=1.0.1", + "ipykernel>=6.29.5", + "jinja2>=3.1.4", + "langchain>=0.3.11", + "matplotlib>=3.9.3", + "numba>=0.60.0", + "openai>=1.57.1", + "opencv-python>=4.10.0.84", + "pymupdf>=1.25.1", + "pystac-client>=0.7", + "resilientplotterclass", + "rioxarray>=0.16.0", + "sentence-transformers>=3.3.1", + "weasyprint>=63.1", + "xarray>=2024.4.0", + "zarr>=2.18.2", +] + +[tool.uv.sources] +resilientplotterclass = { git = "https://github.com/Deltares-research/ResilientPlotterClass" } diff --git a/App/functions/report-python-cloud-run/report/app.html b/App/functions/report-python-cloud-run/report/app.html new file mode 100644 index 000000000..c60c8c384 --- /dev/null +++ b/App/functions/report-python-cloud-run/report/app.html @@ -0,0 +1,39 @@ +
+ The State of the Coast Report, explained +

The data of this report is obtained from various STACs and the content of this report is generated by the Azure AI. + This page explains the approach to process individual data from the STACs and relevant literature regarding the datasets. + The datasets can be found in the following STACs:

+

https://raw.githubusercontent.com/openearth/global-coastal-atlas/subsidence_etienne/STAC/data/current/catalog.json

+

https://raw.githubusercontent.com/openearth/coclicodata/main/current/catalog.json

+ +

Coastal Types: The dataset indicates the sediment composition of a beach at certain transects, + which are globally spaced shore-normal transects in every 500m. The transects can either labelled as sandy, muddy, vegetated, coastal cliff or other materials. + This dataset is derived from satellite images and other parameters and generated by using a supervised random forest classifier. + Details of the methodology can be referred to Breiman et al. (2001).

+ +

The Population: The dataset provides a global population count per pixel at approximately 100m resolution + and it is based on the United Nation Development Programme (UNDP) 2020 estimates for in total 183 countries.

+ +

Historical Shoreline Change: The dataset provides the annual shoreline position over the period from 1984 to 2021 along 1.8 million transects in the world. + The transects are 500m spaced shore-normal transects. The position of shoreline is derived from satellite images. + Details of the methodology can be referred to XXXX.

+ +

SSP: SSP stands for Shared Socioeconomic Pathway, which was introduced in the IPCC 6th Assessment Report (AR6). + This indicator describes different socioeconomic assumptions, such as population, economic growth, technological development and etc. + Details can be referred to the AR6.

+ +

RCP: RCP stands for Representative Concentration Pathway, which was introduced in IPCC 5th Assessment Report (AR5). + RCP4.5 and 8.5 describe scenarios with intermediate or very high greenhouse gas (GHG) emission and other radiative forcings. + RCP4.5 describes a scenario that the CO2 emission will remain around current levels until 2050 and fall but not reach net zero by 2100. + RCP8.5 describes a scenario that the CO2 emission will become triple by 2075.

+ +

Sea Level Rise Projection: This is median projections of regional sea level rise from 2020 to 2150, relative to a 1995-2014 baseline. + This projection data is originally from the AR6. The projections are based on several scenarios, including SSP126, SSP245 and SSP585. + Details of the projection can be found in XXX.

+ +

Future Shoreline Projections: The average shoreline change rate is based on projection of locations of sandy shorelines relative to their reference locations in RCP4.5 and RCP8.5 scenarios. + According to Luijendijk et al. (XXXX), the shoreline locations in 2021 and the projected shoreline locations in 2050 and 2100 are defined. + The average shoreline change rate is calculated based on the spatial distance difference of the shoreline location between two referenced years and divided by the year difference. + The future shoreline location is estimated based on XXXXX. Details can be found in “XXXX” (Luijendijk et al., XXXX ). + The erosion and accretion classification system is the same as the one in the historical shoreline change.

+
\ No newline at end of file diff --git a/App/functions/report-python-cloud-run/report/datasets/base_dataset.py b/App/functions/report-python-cloud-run/report/datasets/base_dataset.py index 39313b39e..ef687feed 100644 --- a/App/functions/report-python-cloud-run/report/datasets/base_dataset.py +++ b/App/functions/report-python-cloud-run/report/datasets/base_dataset.py @@ -1,13 +1,30 @@ from typing import Optional import xarray as xr -from .datasetcontent import DatasetContent -from .esl import get_esl_content +from report.datasets.datasetcontent import DatasetContent + +from report.datasets.shoremon import ( + get_sedclass_content, + get_shoremon_content, + get_shoremon_fut_content, +) +from report.datasets.popgpd import get_world_pop_content +# from .subtreat import get_sub_threat_content def get_dataset_content(dataset_id: str, xarr: xr.Dataset) -> Optional[DatasetContent]: match dataset_id: - case "esl_gwl": - return get_esl_content(xarr) + # case "esl_gwl": + # return get_esl_content(xarr) + case "sed_class": + return get_sedclass_content(xarr) + case "shore_mon": + return get_shoremon_content(xarr) + case "shore_mon_fut": + return get_shoremon_fut_content(xarr) + case "world_pop": + return get_world_pop_content(xarr) + # case "sub_threat": + # return None case _: return None diff --git a/App/functions/report-python-cloud-run/report/datasets/esl.py b/App/functions/report-python-cloud-run/report/datasets/esl.py index 8b3565c41..70247f977 100644 --- a/App/functions/report-python-cloud-run/report/datasets/esl.py +++ b/App/functions/report-python-cloud-run/report/datasets/esl.py @@ -1,27 +1,58 @@ +# Packages for loading data from pathlib import Path + +import geopandas as gpd import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import xarray as xr -matplotlib.use("Agg") -from matplotlib import colors -from matplotlib import pyplot as plt +# Packages for plotting +from resilientplotterclass import rpc +from report.datasets.datasetcontent import DatasetContent +from report.datasets.utils import plot_to_base64 + +matplotlib.use("Agg") plt.rcParams["svg.fonttype"] = "none" -import numpy as np -import xarray as xr +# from matplotlib import colors ##TODO + +world = gpd.read_file( + Path(__file__).parent.parent.parent / "data" / "world_administrative.zip" +) + + +def get_esl_content(xarr: xr.Dataset) -> list[DatasetContent]: + dataset_contents_list = [] + dataset_contents_list.append(get_esl26_content(xarr)) + dataset_contents_list.append(get_esl45_content(xarr)) + dataset_contents_list.append(get_esl85_content(xarr)) + + return dataset_contents_list + -import geopandas as gpd # type: ignore +def get_esl26_content(xarr: xr.Dataset) -> DatasetContent: + """Get content for ESL dataset""" + dataset_id = "esl_RCP26" + title = "Extreme Sea Level" + text = "Here we generate some content based on the ESL dataset" -from .utils import plot_to_base64 -from .datasetcontent import DatasetContent + image_base64 = create_esl_plot(xarr, "RCP26") + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) -def get_esl_content(xarr: xr.Dataset) -> DatasetContent: +def get_esl45_content(xarr: xr.Dataset) -> DatasetContent: """Get content for ESL dataset""" - dataset_id = "esl" + dataset_id = "esl_RCP45" title = "Extreme Sea Level" text = "Here we generate some content based on the ESL dataset" - image_base64 = create_esl_plot(xarr) + image_base64 = create_esl_plot(xarr, "RCP45") return DatasetContent( dataset_id=dataset_id, title=title, @@ -30,74 +61,102 @@ def get_esl_content(xarr: xr.Dataset) -> DatasetContent: ) -def create_esl_plot(xarr): - GWL = 0 # look at ds.gwl.values for options - GWLs = "present-day" +def get_esl85_content(xarr: xr.Dataset) -> DatasetContent: + """Get content for ESL dataset""" + dataset_id = "esl_RCP85" + title = "Extreme Sea Level" + text = "Here we generate some content based on the ESL dataset" + + image_base64 = create_esl_plot(xarr, "RCP85") + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +def create_esl_plot(xarr, scenario): + match scenario: + case "RCP26": + GWL = 1.5 + GWLs = "1.5 ℃" + case "RCP45": + GWL = 3 + GWLs = "3 ℃" + case "RCP85": + GWL = 5 + GWLs = "5 ℃" + # ens = 50 # look at ds.ensemble.values for options rp = 50.0 # look at ds.rp.values for options - world = gpd.read_file( - Path(__file__).parent.parent.parent / "data" / "world_administrative.zip" - ) - cmap = matplotlib.cm.RdYlGn_r - norm = colors.BoundaryNorm(np.arange(0, 7.5, 0.5), cmap.N) - ds_fil = xarr.sel(gwl=GWL, rp=rp) # filter the other params - lonmin = min(ds_fil.lon.values) - lonmax = max(ds_fil.lon.values) - latmin = min(ds_fil.lat.values) - latmax = max(ds_fil.lat.values) - fig, ax = plt.subplots() - fig.set_size_inches(15, 8) # fig.set_size_inches(15, 20) + + xarr = xarr.sel(gwl=GWL, rp=rp) # filter the other params + + lonmin = min(xarr.lon.values) + lonmax = max(xarr.lon.values) + latmin = min(xarr.lat.values) + latmax = max(xarr.lat.values) + + xlim = [lonmin - 0.1, lonmax + 0.1] + ylim = [latmin - 0.1, latmax + 0.1] + + fig, ax = plt.subplots(1, 1, figsize=(10, 10)) + base = world.boundary.plot( ax=ax, edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 ) - im1 = ax.scatter( - ds_fil.lon.values, - ds_fil.lat.values, - 10 * ds_fil.sel(ensemble=5).esl.values, - ds_fil.sel(ensemble=5).esl.values, - cmap=cmap, - norm=norm, - zorder=1, + + rpc.scatter( + xarr.sel(ensemble=5), + data_type="data", + ax=ax, + x="lon", + y="lat", + hue="esl", + cmap="RdYlGn_r", + add_colorbar=False, ) - # plt.set_clim(0,5) - im2 = ax.scatter( - ds_fil.lon.values, - ds_fil.lat.values + 0.1, - 10 * ds_fil.sel(ensemble=50).esl.values, - ds_fil.sel(ensemble=50).esl.values, - cmap=cmap, - norm=norm, - zorder=1, + + xarr["lat"].values = xarr["lat"].values + 0.01 + + rpc.scatter( + xarr.sel(ensemble=50), + data_type="data", + ax=ax, + x="lon", + y="lat", + hue="esl", + cmap="RdYlGn_r", + add_colorbar=False, ) - im3 = ax.scatter( - ds_fil.lon.values, - ds_fil.lat.values + 0.2, - 10 * ds_fil.sel(ensemble=95).esl.values, - ds_fil.sel(ensemble=95).esl.values, - cmap=cmap, - norm=norm, - zorder=1, + + xarr["lat"].values = xarr["lat"].values + 0.01 + + rpc.scatter( + xarr.sel(ensemble=95), + data_type="data", + ax=ax, + x="lon", + y="lat", + vmin=np.nanmin(xarr.sel(ensemble=5).esl.values), + vmax=np.nanmax(xarr.sel(ensemble=95).esl.values), + hue="esl", + cmap="RdYlGn_r", + add_colorbar=True, + cbar_kwargs={"label": "ESL [m]"}, ) + ax.set_title("%s-year extreme sea level for %s global warming level" % (rp, GWLs)) - ax.axis("square") + ax.set( - xlabel="lon", - ylabel="lat", - xlim=[lonmin - 2, lonmax + 2], - ylim=[latmin - 2, latmax + 2], + xlim=xlim, + ylim=ylim, ) - # fig.colorbar(im1, ax=ax) - im1.set_clim(0, 7) - - cax = fig.add_axes( - [ - ax.get_position().x1 + 0.01, - ax.get_position().y0, - 0.02, - ax.get_position().height, - ] - ) # to give colorbar own axes - plt.colorbar(im1, cax=cax) # Similar to fig.colorbar(im, cax = cax) - cax.set_title("ESL in meters") - # + + ax.set_aspect(1 / np.cos(np.mean(ylim) * np.pi / 180)) + ax.grid(False) + + fig.tight_layout() + return plot_to_base64(fig) diff --git a/App/functions/report-python-cloud-run/report/datasets/overview.py b/App/functions/report-python-cloud-run/report/datasets/overview.py new file mode 100644 index 000000000..8048a2535 --- /dev/null +++ b/App/functions/report-python-cloud-run/report/datasets/overview.py @@ -0,0 +1,90 @@ +# Packages for loading data +from pathlib import Path + +import geopandas as gpd +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +from mpl_toolkits.axes_grid1.inset_locator import inset_axes + +# Packages for plotting +from resilientplotterclass import rpc +from shapely import Polygon # type: ignore + +from report.datasets.datasetcontent import DatasetContent +from report.datasets.utils import plot_to_base64 +from report.utils.gentext import describe_overview + +matplotlib.use("Agg") +plt.rcParams["svg.fonttype"] = "none" +world = gpd.read_file( + Path(__file__).parent.parent.parent / "data" / "world_administrative.zip" +) + + +def get_overview(polygon: Polygon, dataset_contents: DatasetContent) -> DatasetContent: + """Get overview""" + dataset_id = "overview" + title = "Overview" + text = "Here we generate some content based on all datasets" + text = describe_overview(polygon, dataset_contents) + + image_base64 = create_overview_img(polygon) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +def cal_xylims(gdf_aoi, buffer): + bounds = gdf_aoi.bounds.values[0] + center = gdf_aoi.centroid + length = max([bounds[2] - bounds[0], bounds[3] - bounds[1]]) + ylims = [center.y[0] - length / 2 - buffer, center.y[0] + length / 2 + buffer] + ylength = (ylims[1] - ylims[0]) / np.cos(np.radians(center.y[0])) + xlims = [center.x[0] - ylength / 2, center.x[0] + ylength / 2] + + return xlims, ylims + + +def create_overview_img(polygon: Polygon): + gdf_aoi = gpd.GeoDataFrame( + {"Name": ["Custom"], "geometry": [polygon]}, crs="EPSG:4326" + ) + center = gdf_aoi.centroid + + fig, ax = plt.subplots(1, 2, figsize=(20, 16), width_ratios=[1, 1]) + + xlims, ylims = cal_xylims(gdf_aoi, 2.5) + + ax[0].scatter(center.x[0], center.y[0], color="r", marker="o") + ax[0].set_xlim(xlims) + ax[0].set_ylim(ylims) + rpc.basemap( + crs="EPSG:4326", map_type="satellite", ax=ax[0], source="CartoDB.Voyager" + ) + + worldax = inset_axes(ax[0], width=2.5, height=2, loc="upper left") + worldax.scatter(center.x[0], center.y[0], color="r", marker="o") + xlims, ylims = cal_xylims(gdf_aoi, 18) + worldax.set_xlim(xlims) + worldax.set_ylim(ylims) + rpc.basemap( + crs=gdf_aoi.crs, ax=worldax, map_type="satellite", source="CartoDB.Positron" + ) + worldax.set_xticklabels([]) + worldax.set_yticklabels([]) + worldax.set_xlabel(None) + worldax.set_ylabel(None) + worldax.tick_params(axis="both", which="both", length=0) + + xlims, ylims = cal_xylims(gdf_aoi, 0.01) + + rpc.geometries(gdf_aoi, ax=ax[1], facecolor="none", edgecolor="white", linewidth=1) + ax[1].set_xlim(xlims) + ax[1].set_ylim(ylims) + rpc.basemap(crs=gdf_aoi.crs, map_type="satellite", ax=ax[1]) + + return plot_to_base64(fig) diff --git a/App/functions/report-python-cloud-run/report/datasets/popgpd.py b/App/functions/report-python-cloud-run/report/datasets/popgpd.py new file mode 100644 index 000000000..091d3d27c --- /dev/null +++ b/App/functions/report-python-cloud-run/report/datasets/popgpd.py @@ -0,0 +1,79 @@ +# Packages for loading data +from pathlib import Path + +import geopandas as gpd +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import xarray as xr + +# Packages for plotting +from resilientplotterclass import rpc + +from report.datasets.datasetcontent import DatasetContent +from report.datasets.utils import plot_to_base64 +from report.utils.gentext import describe_data + +matplotlib.use("Agg") +plt.rcParams["svg.fonttype"] = "none" +world = gpd.read_file( + Path(__file__).parent.parent.parent / "data" / "world_administrative.zip" +) + + +def get_world_pop_content(xarr: xr.Dataset) -> DatasetContent: + """Get content for the dataset""" + dataset_id = "world_pop" + title = "The Population" + text = "Here we generate some content based on the dataset" + text = describe_data(xarr, dataset_id) + + image_base64 = create_world_pop_plot(xarr) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +def create_world_pop_plot(xarr): + fig, ax = plt.subplots(1, 1, figsize=(10, 10)) + + base = world.boundary.plot( + ax=ax, edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 + ) + + p = rpc.scatter( + xarr, + ax=ax, + data_type="data", + x="lon", + y="lat", + s=xarr["pop_tot"].values / 100, + hue="pop_tot", + edgecolor="none", + cmap="RdYlGn", + add_colorbar=True, + cbar_kwargs={"label": "Population"}, + ) + + lonmin = min(xarr.lon.values) + lonmax = max(xarr.lon.values) + latmin = min(xarr.lat.values) + latmax = max(xarr.lat.values) + + xlim = [lonmin - 0.1, lonmax + 0.1] + ylim = [latmin - 0.1, latmax + 0.1] + + ax.set( + xlim=xlim, + ylim=ylim, + ) + + ax.set_aspect(1 / np.cos(np.mean(ylim) * np.pi / 180)) + ax.grid(False) + + fig.tight_layout() + + return plot_to_base64(fig) diff --git a/App/functions/report-python-cloud-run/report/datasets/shoremon.py b/App/functions/report-python-cloud-run/report/datasets/shoremon.py new file mode 100644 index 000000000..ac954bcf9 --- /dev/null +++ b/App/functions/report-python-cloud-run/report/datasets/shoremon.py @@ -0,0 +1,514 @@ +# Packages for loading data +import geopandas as gpd +import matplotlib.pyplot as plt +import xarray as xr + +# Packages for plotting +from resilientplotterclass import rpc +from pathlib import Path +import matplotlib +import numpy as np + +from report.datasets.utils import plot_to_base64 +from report.datasets.datasetcontent import DatasetContent +from report.utils.gentext import describe_data + +matplotlib.use("Agg") +plt.rcParams["svg.fonttype"] = "none" + +world = gpd.read_file( + Path(__file__).parent.parent.parent / "data" / "world_administrative.zip" +) + + +def get_sedclass_content(xarr: xr.Dataset) -> DatasetContent: + """Get content for the dataset""" + dataset_id = "sediment_class" + title = "Coastal Types" + text = "Here we generate some content based on the the dataset" + text = describe_data(xarr, dataset_id) + + image_base64 = create_sedclass_plot(xarr) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +def get_shoremon_content(xarr: xr.Dataset) -> DatasetContent: + """Get content for the dataset""" + dataset_id = "shoreline_change" + title = "Historical Shoreline Change (1984-2021)" + text = "Here we generate some content based on the the dataset" + text = describe_data(xarr, dataset_id) + + image_base64 = create_shoremon_plot(xarr) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +def get_shoremon_fut_content(xarr: xr.Dataset) -> list[DatasetContent]: + dataset_contents_list = [] + dataset_contents_list.append(get_shoremon_fut2050_content(xarr)) + dataset_contents_list.append(get_shoremon_fut2100_content(xarr)) + + return dataset_contents_list + + +def get_shoremon_fut2050_content(xarr: xr.Dataset) -> DatasetContent: + ### remove outlier ### ##TODO## + """Get content for the dataset""" + dataset_id = "future_shoreline_change_2050" + title = "Future Shoreline Projections in 2050" + text = "Here we generate some content based on the the dataset" + text = describe_data(xarr, dataset_id) + + image_base64 = create_shoremon_fut_plot(xarr, 2050) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +def get_shoremon_fut2100_content(xarr: xr.Dataset) -> DatasetContent: + """Get content for the dataset""" + dataset_id = "future_shoreline_change_2100" + title = "Future Shoreline Projections in 2100" + text = "Here we generate some content based on the the dataset" + text = describe_data(xarr, dataset_id) + + image_base64 = create_shoremon_fut_plot(xarr, 2100) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +# def get_shoremon_fut_content(xarr: xr.Dataset) -> list[DatasetContent]: +# dataset_contents_list = [] +# dataset_contents_list.append(get_shoremon_fut45_content(xarr)) +# dataset_contents_list.append(get_shoremon_fut85_content(xarr)) + +# return dataset_contents_list + + +# def get_shoremon_fut45_content(xarr: xr.Dataset) -> DatasetContent: +# """Get content for the dataset""" +# dataset_id = "future_shoreline_change_RCP45" +# title = "Future Shoreline Projections" +# text = "Here we generate some content based on the the dataset" ##TODO: to be filled in by LLM +# text = describe_data(xarr, dataset_id) + +# image_base64 = create_shoremon_fut_plot(xarr, 'RCP45') +# return DatasetContent( +# dataset_id=dataset_id, +# title=title, +# text=text, +# image_base64=image_base64, +# ) + +# def get_shoremon_fut85_content(xarr: xr.Dataset) -> DatasetContent: +# """Get content for the dataset""" +# dataset_id = "future_shoreline_change_RCP85" +# title = "Future Shoreline Projections" +# text = "Here we generate some content based on the the dataset" ##TODO: to be filled in by LLM +# text = describe_data(xarr, dataset_id) + +# image_base64 = create_shoremon_fut_plot(xarr, 'RCP85') +# return DatasetContent( +# dataset_id=dataset_id, +# title=title, +# text=text, +# image_base64=image_base64, +# ) + + +def create_sedclass_plot(xarr): + sediment_classes_dict = { + 0: "sand", + 1: "mud", + 2: "coastal cliff", + 3: "vegetated", + 4: "other", + } + color_dict = {0: "yellow", 1: "brown", 2: "blue", 3: "green", 4: "gray"} + + from matplotlib.colors import ListedColormap, Normalize + import matplotlib as mpl + + existing_values = np.unique(xarr["sediment_label"]) + existing_class = [sediment_classes_dict[val] for val in existing_values] + existing_color = [color_dict[val] for val in existing_values] + + sediment_classes = [sediment_classes_dict[i] for i in xarr["sediment_label"].values] + xarr["sediment_class"] = xr.DataArray(sediment_classes, dims="stations") + + cmap = ListedColormap(existing_color) + norm = Normalize(vmin=0, vmax=cmap.N) + cb = mpl.cm.ScalarMappable(norm=norm, cmap=cmap) + + portion = [] + + for type in existing_class: + port = len(np.where(xarr["sediment_class"] == type)[0]) / len( + xarr["sediment_class"] + ) + portion.append(port) + + # Plot the data + fig, ax = plt.subplots(1, 2, figsize=(10, 5), width_ratios=[1, 1]) + + base = world.boundary.plot( + ax=ax[0], edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 + ) + + aspect = len(existing_class) / 0.8 + p = rpc.scatter( + xarr, + ax=ax[0], + x="lon", + y="lat", + hue="sediment_label", + edgecolor="none", + cmap=cmap, + add_colorbar=False, + ) + + cbar = plt.colorbar( + cb, + ax=ax[0], + **{ + "label": "Sediment classes", + "pad": 0.01, + "fraction": 0.05, + "aspect": aspect, + }, + ) + cbar.set_ticks(ticks=np.arange(0.5, len(existing_color), 1), labels=existing_class) + + ax[1].pie(portion, labels=existing_class, autopct="%1.1f%%", colors=existing_color) + + lonmin = min(xarr.lon.values) + lonmax = max(xarr.lon.values) + latmin = min(xarr.lat.values) + latmax = max(xarr.lat.values) + + xlim = [lonmin - 0.1, lonmax + 0.1] + ylim = [latmin - 0.1, latmax + 0.1] + + ax[0].set( + xlim=xlim, + ylim=ylim, + ) + + ax[0].set_aspect(1 / np.cos(np.mean(ylim) * np.pi / 180)) + ax[0].grid(False) + + fig.tight_layout() + + return plot_to_base64(fig) + + +def create_shoremon_plot(xarr): + lonmin = min(xarr.lon.values) + lonmax = max(xarr.lon.values) + latmin = min(xarr.lat.values) + latmax = max(xarr.lat.values) + + xlim = [lonmin - 0.1, lonmax + 0.1] + ylim = [latmin - 0.1, latmax + 0.1] + + # Get pie chart data + labels = [ + "Extreme\nerosion", + "Severe\nerosion", + "Intense\nerosion", + "Erosion", + "Stable", + "Accretion", + "Intense\naccretion", + "Severe\naccretion", + "Extreme\naccretion", + ] + colors = [matplotlib.cm.RdYlGn(i) for i in np.linspace(0.05, 0.95, len(labels))] + bins = [-np.inf, -5, -3, -1, -0.5, 0.5, 1, 3, 5, np.inf] + data = np.histogram(xarr["changerate"].values, bins=bins)[0] + + # Plot data + fig, axs = plt.subplots(1, 3, figsize=(15, 5), width_ratios=[1.2, 0.8, 0.3]) + base = world.boundary.plot( + ax=axs[0], edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 + ) + rpc.scatter( + xarr, + data_type="data", + ax=axs[0], + x="lon", + y="lat", + hue="changerate", + vmin=-5, + vmax=5, + cmap="RdYlGn", + add_colorbar=True, + cbar_kwargs={"label": "Erosion/Accretion [m/yr]"}, + ) + axs[0].set_xlim(xlim) + axs[0].set_ylim(ylim) + axs[0].set_aspect(1 / np.cos(np.mean(ylim) * np.pi / 180)) + + # Add a pie chart showing the distribution of the classes + axs[1].pie( + data, + labels=labels, + colors=colors, + autopct="%1.0f%%", + startangle=90, + counterclock=False, + ) + axs[1].axis("equal") + + # Add legend + labels2 = [ + "Extreme erosion (<-5 m/yr)", + "Severe erosion (-5 to -3 m/yr)", + "Intense erosion (-3 to -1 m/yr)", + "Erosion (-1 to -0.5 m/yr)", + "Stable (-0.5 to 0.5 m/yr)", + "Accretion (0.5 to 1 m/yr)", + "Intense accretion (1 to 3 m/yr)", + "Severe accretion (3 to 5 m/yr)", + "Extreme accretion (>5 m/yr)", + ] + colors2 = [matplotlib.cm.RdYlGn(i) for i in np.linspace(0.05, 0.95, len(labels2))] + axs[2].legend( + handles=[ + matplotlib.patches.Patch(color=colors2[i], label=labels2[i]) + for i in range(len(labels2)) + ], + loc="center left", + frameon=False, + ) + axs[2].axis("off") + + fig.tight_layout() + + return plot_to_base64(fig) + + +# def create_shoremon_fut_plot(xarr): +# scenariolist = ['sp_rcp45_p50', 'sp_rcp85_p50'] +# yearlist = [2021, 2050, 2100] + + +# fig, ax = plt.subplots(2, 2, figsize=(15, 15)) + + +# for yr in range(0,2): +# for scenario in scenariolist: +# if scenario == 'sp_rcp45_p50': +# scenarioname = 'RCP4.5' +# jj = 0 +# elif scenario == 'sp_rcp85_p50': +# scenarioname = 'RCP8.5' +# jj = 1 + +# diff = xarr.diff('time', 1).sel(time=str(yearlist[yr + 1])) + +# base = world.boundary.plot( +# ax=ax[yr, jj], edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 +# ) + +# p = rpc.scatter(diff / (yearlist[yr + 1] - yearlist[yr]), +# ax=ax[yr, jj], data_type='data', +# x='lon', y='lat', +# vmin=-5, vmax=5, +# hue=scenario, +# edgecolor='none', cmap='RdYlGn', +# add_colorbar=True, cbar_kwargs={'label': 'Average Shoreline Change Rate [m/yr]'}) + +# lonmin = min(xarr.lon.values) +# lonmax = max(xarr.lon.values) +# latmin = min(xarr.lat.values) +# latmax = max(xarr.lat.values) + +# xlim = [lonmin - 0.1, lonmax + 0.1] +# ylim = [latmin - 0.1, latmax + 0.1] + +# ax[yr, jj].set( +# xlim=xlim, +# ylim=ylim, +# ) + +# ax[yr, jj].set_aspect(1/np.cos(np.mean(ylim)*np.pi/180)) +# ax[yr, jj].grid(False) +# ax[yr, jj].set_title('50%ile Future Prediction between Year {}'.format(yearlist[yr+1]) + ' and Year {}'.format(yearlist[yr]) + '\n' + '- Scenario {}'.format(scenarioname)) + +# fig.tight_layout() + +# return plot_to_base64(fig) + + +def create_shoremon_fut_plot(xarr, year): + scenariolist = ["sp_rcp45_p50", "sp_rcp85_p50"] + yearlist = [2021, 2050, 2100] + yr = yearlist.index(year) - 1 + + fig, ax = plt.subplots(2, 2, figsize=(10, 10), width_ratios=[6, 4]) + + for nn in range(len(scenariolist)): + match scenariolist[nn]: + case "sp_rcp45_p50": + var = "sp_rcp45_p50" + scenarioname = "RCP4.5" + case "sp_rcp85_p50": + var = "sp_rcp85_p50" + scenarioname = "RCP8.5" + + diff = xarr.diff("time", 1).sel(time=str(yearlist[yr + 1])) + rate = diff / (yearlist[yr + 1] - yearlist[yr]) + + base = world.boundary.plot( + ax=ax[nn, 0], edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 + ) + + p = rpc.scatter( + rate, + ax=ax[nn, 0], + data_type="data", + x="lon", + y="lat", + vmin=-5, + vmax=5, + hue=var, + edgecolor="none", + cmap="RdYlGn", + add_colorbar=True, + cbar_kwargs={"label": "Average Shoreline Change Rate [m/yr]"}, + ) + + lonmin = min(xarr.lon.values) + lonmax = max(xarr.lon.values) + latmin = min(xarr.lat.values) + latmax = max(xarr.lat.values) + + xlim = [lonmin - 0.1, lonmax + 0.1] + ylim = [latmin - 0.1, latmax + 0.1] + + ax[nn, 0].set( + xlim=xlim, + ylim=ylim, + ) + + ax[nn, 0].set_aspect(1 / np.cos(np.mean(ylim) * np.pi / 180)) + ax[nn, 0].grid(False) + ax[nn, 0].set_title( + "50%ile Future Prediction between Year {}".format(yearlist[yr + 1]) + + " and Year {}".format(yearlist[yr]) + + "\n" + + "- Scenario {}".format(scenarioname) + ) + + # Get pie chart data + labels = [ + "Extreme\nerosion", + "Severe\nerosion", + "Intense\nerosion", + "Erosion", + "Stable", + "Accretion", + "Intense\naccretion", + "Severe\naccretion", + "Extreme\naccretion", + ] + colors = [matplotlib.cm.RdYlGn(i) for i in np.linspace(0.05, 0.95, len(labels))] + bins = [-np.inf, -5, -3, -1, -0.5, 0.5, 1, 3, 5, np.inf] + data = np.histogram(rate[var].values, bins=bins)[0] + + # Add a pie chart showing the distribution of the classes + ax[nn, 1].pie( + data, + labels=labels, + colors=colors, + autopct="%1.0f%%", + startangle=90, + counterclock=False, + ) + ax[nn, 1].axis("equal") + + fig.tight_layout() + + return plot_to_base64(fig) + + +# def create_shoremon_fut_plot(xarr, scenario): + +# yearlist = [2021, 2050, 2100] + +# match scenario: +# case 'RCP45': +# var = 'sp_rcp45_p50' +# scenarioname = 'RCP4.5' + +# case 'RCP85': +# var = 'sp_rcp85_p50' +# scenarioname = 'RCP8.5' + +# fig, ax = plt.subplots(2, 2, figsize=(10, 10), width_ratios=[6,4]) + +# for yr in range(0,len(yearlist) - 1): +# diff = xarr.diff('time', 1).sel(time=str(yearlist[yr + 1])) +# rate = diff / (yearlist[yr + 1] - yearlist[yr]) + +# base = world.boundary.plot( +# ax=ax[yr, 0], edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 +# ) + +# p = rpc.scatter(rate, +# ax=ax[yr, 0], data_type='data', +# x='lon', y='lat', +# vmin=-5, vmax=5, +# hue=var, +# edgecolor='none', cmap='RdYlGn', +# add_colorbar=True, cbar_kwargs={'label': 'Average Shoreline Change Rate [m/yr]'}) + +# lonmin = min(xarr.lon.values) +# lonmax = max(xarr.lon.values) +# latmin = min(xarr.lat.values) +# latmax = max(xarr.lat.values) + +# xlim = [lonmin - 0.1, lonmax + 0.1] +# ylim = [latmin - 0.1, latmax + 0.1] + +# ax[yr, 0].set( +# xlim=xlim, +# ylim=ylim, +# ) + +# ax[yr, 0].set_aspect(1/np.cos(np.mean(ylim)*np.pi/180)) +# ax[yr, 0].grid(False) +# ax[yr, 0].set_title('50%ile Future Prediction between Year {}'.format(yearlist[yr+1]) + ' and Year {}'.format(yearlist[yr]) + '\n' + '- Scenario {}'.format(scenarioname)) + +# # Get pie chart data +# labels = ['Extreme\nerosion', 'Severe\nerosion', 'Intense\nerosion', 'Erosion', 'Stable', 'Accretion', 'Intense\naccretion', 'Severe\naccretion', 'Extreme\naccretion'] +# colors = [matplotlib.cm.RdYlGn(i) for i in np.linspace(0.05, 0.95, len(labels))] +# bins = [-np.inf, -5, -3, -1, -0.5, 0.5 , 1, 3, 5, np.inf] +# data = np.histogram(rate[var].values, bins=bins)[0] + +# # Add a pie chart showing the distribution of the classes +# ax[yr, 1].pie(data, labels=labels, colors=colors, autopct='%1.0f%%', startangle=90, counterclock=False) +# ax[yr, 1].axis('equal') + + +# fig.tight_layout() + +# return plot_to_base64(fig) diff --git a/App/functions/report-python-cloud-run/report/datasets/slr.py b/App/functions/report-python-cloud-run/report/datasets/slr.py new file mode 100644 index 000000000..a0510ae00 --- /dev/null +++ b/App/functions/report-python-cloud-run/report/datasets/slr.py @@ -0,0 +1,231 @@ +# Packages for loading data +import matplotlib.pyplot as plt + +# Packages for plotting +import matplotlib + +from shapely import Polygon # type: ignore +import pystac_client +import rioxarray as rio + +from report.datasets.utils import plot_to_base64 +from report.datasets.datasetcontent import DatasetContent +from report.utils.gentext import describe_data + +matplotlib.use("Agg") +plt.rcParams["svg.fonttype"] = "none" + + +def get_slr_content(polygon: Polygon) -> DatasetContent: + slps = get_slps_data(polygon) + """Get content for the dataset""" + dataset_id = "slr" + title = "Sea Level Rise Projection" + text = "Here we generate some content based on the dataset" + text = describe_data(slps, dataset_id) + + image_base64 = create_slr_plot(slps) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +# def get_slr_content(polygon: Polygon) -> list[DatasetContent]: +# dataset_contents_list = [] +# dataset_contents_list.append(get_slr26_content(polygon)) +# dataset_contents_list.append(get_slr45_content(polygon)) +# dataset_contents_list.append(get_slr85_content(polygon)) + +# return dataset_contents_list + +# def get_slr26_content(polygon: Polygon) -> DatasetContent: +# slps = get_slps_data(polygon, 'RCP26') +# """Get content for the dataset""" +# dataset_id = "slr_RCP26" +# title = "Sea Level Rise Projection" +# text = "Here we generate some content based on the dataset" +# text = describe_data(slps, dataset_id) + +# image_base64 = create_slr_plot(slps, 'RCP26') +# return DatasetContent( +# dataset_id=dataset_id, +# title=title, +# text=text, +# image_base64=image_base64, +# ) + +# def get_slr45_content(polygon: Polygon) -> DatasetContent: +# slps = get_slps_data(polygon, 'RCP45') +# """Get content for the dataset""" +# dataset_id = "slr_RCP45" +# title = "Sea Level Rise Projection" +# text = "Here we generate some content based on the dataset" +# text = describe_data(slps, dataset_id) + +# image_base64 = create_slr_plot(slps, 'RCP45') +# return DatasetContent( +# dataset_id=dataset_id, +# title=title, +# text=text, +# image_base64=image_base64, +# ) + +# def get_slr85_content(polygon: Polygon) -> DatasetContent: +# slps = get_slps_data(polygon, 'RCP85') +# """Get content for the dataset""" +# dataset_id = "slr_RCP85" +# title = "Sea Level Rise Projection" +# text = "Here we generate some content based on the dataset" +# text = describe_data(slps, dataset_id) + +# image_base64 = create_slr_plot(slps, 'RCP85') +# return DatasetContent( +# dataset_id=dataset_id, +# title=title, +# text=text, +# image_base64=image_base64, +# ) + + +def get_slps_data(polygon: Polygon) -> dict: + # match scenario: + # case 'RCP26': + # ssps = ['ssp126'] + # case 'RCP45': + # ssps = ['ssp245'] + # case 'RCP85': + # ssps = ['ssp585'] + + # Set stac URL + STAC_url = "https://raw.githubusercontent.com/openearth/coclicodata/main/current/catalog.json" + + # Open the catalog + catalog = pystac_client.Client.open(STAC_url) + + # Get the AR6 collection + collection = catalog.get_child("slp") + + ssps = ["high_end", "ssp126", "ssp245", "ssp585"] + msls = ["msl_m"] + years = [ + "2031", + "2041", + "2051", + "2061", + "2071", + "2081", + "2091", + "2101", + "2111", + "2121", + "2131", + "2141", + "2151", + ] + + slps = [] + + # Iterate over all ssps, ens and years + for ssp in ssps: + for msl in msls: + for year in years: + # Get the item + item = collection.get_item(f"{ssp}\{msl}\{year}.tif") + + # Get href + href = item.assets["data"].href + + # Load tif into xarray + ds = rio.open_rasterio(href, masked=True) + + # First clip to bounding box of polygon + ds_clip = ds.rio.clip_box( + *polygon.bounds, allow_one_dimensional_raster=True + ) + + ds_point = ds_clip.sel( + x=polygon.centroid.x, y=polygon.centroid.y, method="nearest" + ) + + # Check if nearest pixel to centroid has data, if not use max of ds_clip + if not ds_point.notnull().values: + # Find the maximum value in the dataset + max_value = ds_clip.max() + + # Find the coordinates of the max value + max_coords = ds_clip.where(ds == max_value, drop=True) + + # Retrieve the coordinates (x, y) for the maximum value + max_x = max_coords.coords["x"].values + max_y = max_coords.coords["y"].values + + # Select the dataset at the coordinates of the maximum value + ds_point = ds_clip.sel(x=max_x, y=max_y) + + # Retrieve the point value + value = ds_point.values.item() + + # Append the result as a dictionary + slps.append({"ssp": ssp, "msl": msl, "year": year, "value": value}) + + return slps + + +# def create_slr_plot(slps: dict, scenario: str): + +# match scenario: +# case 'RCP26': +# ssp = ['ssp126'] +# color = 'g' +# case 'RCP45': +# ssp = ['ssp245'] +# color = 'b' +# case 'RCP85': +# ssp = ['ssp585'] +# color = 'r' + +# fig, ax = plt.subplots(1,1, figsize=(5,5)) + +# # Filter the data for the current ssp +# ssp_values_m = [slp['value'] for slp in slps if (slp['msl'] == 'msl_m')] +# ssp_values_l = [slp['value'] for slp in slps if (slp['msl'] == 'msl_l')] +# ssp_values_h = [slp['value'] for slp in slps if (slp['msl'] == 'msl_h')] +# ssp_years = ["2031","2041","2051", "2061", "2071", "2081", "2091", "2101","2111","2121","2131","2141","2151"] + +# # Line plot +# ax.plot(ssp_years, ssp_values_m, label=ssp, marker='o', color=color) +# ax.plot(ssp_years, ssp_values_l, label=ssp, color=color, alpha=0) +# ax.plot(ssp_years, ssp_values_h, label=ssp, color=color, alpha=0) + +# ax.fill_between(ssp_years, ssp_values_l, ssp_values_h, color=color, alpha=0.2) + +# ax.set_xlabel("Year") +# ax.set_ylabel("Sea Level Rise [mm]") +# ax.set_xticks(ssp_years, ssp_years, rotation=45) +# ax.legend(['Medium Confidence', '_', '_', 'Low to High Confidence Range']) + +# return plot_to_base64(fig) + + +def create_slr_plot(slps: dict): + fig, ax = plt.subplots(1, 1, figsize=(5, 5)) + + ssps = ["high_end", "ssp126", "ssp245", "ssp585"] + + for ssp in ssps: + # Filter the data for the current ssp + ssp_values = [slp["value"] for slp in slps if slp["ssp"] == ssp] + ssp_years = [slp["year"] for slp in slps if slp["ssp"] == ssp] + + # Plot the data for this ssp + ax.plot(ssp_years, ssp_values, label=ssp, marker="o") + + ax.set_xlabel("Year") + ax.set_ylabel("Sea Level Rise [mm]") + ax.set_xticks(ssp_years, ssp_years, rotation=45) + ax.legend() + + return plot_to_base64(fig) diff --git a/App/functions/report-python-cloud-run/report/datasets/subtreat.py b/App/functions/report-python-cloud-run/report/datasets/subtreat.py new file mode 100644 index 000000000..68a965c5b --- /dev/null +++ b/App/functions/report-python-cloud-run/report/datasets/subtreat.py @@ -0,0 +1,218 @@ +# Packages for loading data +import geopandas as gpd +import matplotlib.pyplot as plt +import xarray as xr + +# Packages for plotting +from resilientplotterclass import rpc +from pathlib import Path +import matplotlib +import numpy as np +from shapely import Polygon +import rioxarray as rio +from rioxarray.merge import merge_arrays + +from report.datasets.utils import plot_to_base64 +from utils.stac import STACClientGCA +from report.datasets.datasetcontent import DatasetContent + +matplotlib.use("Agg") +plt.rcParams["svg.fonttype"] = "none" + +world = gpd.read_file( + Path(__file__).parent.parent.parent / "data" / "world_administrative.zip" +) + +# def get_sub_threat_content(xarr: xr.Dataset) -> DatasetContent: +# """Get content for the dataset""" +# dataset_id = "land_sub" +# title = "Land Subsidence" +# text = "Here we generate some content based on the dataset" ##TODO + +# image_base64 = create_sub_treat_plot(xarr) +# return DatasetContent( +# dataset_id=dataset_id, +# title=title, +# text=text, +# image_base64=image_base64, +# ) + + +def get_landsub_content(polygon: Polygon) -> list[DatasetContent]: + dataset_contents_list = [] + dataset_contents_list.append(get_landsub2010_content(polygon)) + dataset_contents_list.append(get_landsub2040_content(polygon)) + + return dataset_contents_list + + +def get_landsub2040_content(polygon: Polygon) -> DatasetContent: + rasteridlist = find_extent(polygon) + combinedraster = get_raster("Haz-Land_Sub_2040_COGs", rasteridlist) + clippedraster = clip_raster(polygon, combinedraster) + + """Get content for the dataset""" + dataset_id = "landsub2040" + title = "Land Subsidence in 2040" + text = "Here we generate some content based on the dataset" ##TODO + + image_base64 = create_landsub_plot(polygon, clippedraster) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +def get_landsub2010_content(polygon: Polygon) -> DatasetContent: + rasteridlist = find_extent(polygon) + combinedraster = get_raster("Haz-Land_Sub_2010_COGs", rasteridlist) + clippedraster = clip_raster(polygon, combinedraster) + + """Get content for the dataset""" + dataset_id = "landsub2010" + title = "Land Subsidence in 2010" + text = "Here we generate some content based on the dataset" ##TODO + + image_base64 = create_landsub_plot(polygon, clippedraster) + return DatasetContent( + dataset_id=dataset_id, + title=title, + text=text, + image_base64=image_base64, + ) + + +def create_sub_treat_plot(xarr: xr.Dataset): + fig, ax = plt.subplots(1, 1, figsize=(10, 10)) + + base = world.boundary.plot( + ax=ax, edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 + ) + + p = rpc.scatter( + xarr, + ax=ax, + data_type="data", + x="lon", + y="lat", + hue="epsi", + edgecolor="none", + cmap="RdYlGn", + add_colorbar=True, + cbar_kwargs={"label": "Land Subsidence Index"}, + ) + + from mpl_toolkits.axes_grid1 import make_axes_locatable + + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size=0.25, pad=0.10) + + lonmin = min(xarr.lon.values) + lonmax = max(xarr.lon.values) + latmin = min(xarr.lat.values) + latmax = max(xarr.lat.values) + + xlim = [lonmin - 0.1, lonmax + 0.1] + ylim = [latmin - 0.1, latmax + 0.1] + + ax.set( + xlim=xlim, + ylim=ylim, + ) + + ax.set_aspect(1 / np.cos(np.mean(ylim) * np.pi / 180)) + ax.grid(False) + + fig.tight_layout() + + return plot_to_base64(fig) + + +def find_extent(polygon: Polygon) -> list: + polyx = polygon.exterior.xy[0] + polyy = polygon.exterior.xy[1] + + minx = min(polyx) + miny = min(polyy) + maxx = max(polyx) + maxy = max(polyy) + + extentx = [divmod(minx, 1)[0] // 3 * 3, (divmod(maxx, 1)[0] // 3 + 1) * 3] + extenty = [np.round(miny, 0) // 3 * 3 - 1, (np.round(maxy, 0) // 3 + 1) * 3 - 1] + + xlist = np.arange( + divmod(minx, 1)[0] // 3 * 3, (divmod(maxx, 1)[0] // 3 + 2) * 3, 3 + ) ##TODO: now hard coded to have additional one tile -> to be improved + ylist = np.arange( + divmod(miny, 1)[0] // 3 * 3 - 1, (divmod(maxy, 1)[0] // 3 + 2) * 3 - 1, 3 + ) + + rasteridlist = [] + + for y in ylist: + for x in xlist: + idname = "B01_x" + str(x) + "_" + "y" + str(y) + rasteridlist.append(idname) + + return rasteridlist + + +def get_raster(collectionid: str, rasteridlist: list): + stac = ( + "https://storage.googleapis.com/dgds-data-public/gca/SOTC/gca-sotc/catalog.json" + ) + + rasterlist = [] + + for rasterid in rasteridlist: + gca_client = STACClientGCA.open(stac) + href = ( + gca_client.get_child(collectionid) + .get_item(rasterid) + .assets["band_data"] + .href + ) + raster = rio.open_rasterio(href, masked=True) + rasterlist.append(raster) + + combinedraster = merge_arrays(dataarrays=rasterlist, method="sum") + + return combinedraster + + +def clip_raster(polygon: Polygon, raster): + # clip = raster.rio.clip_box(*polygon.bounds) ##TODO: clip_box can only clip to a box + clip = raster.rio.clip([polygon]) + return clip + + +def create_landsub_plot(polygon: Polygon, clip): + fig, ax = plt.subplots(1, 1, figsize=(10, 10)) + + base = world.boundary.plot( + ax=ax, edgecolor="grey", facecolor="grey", alpha=0.1, zorder=0 + ) + + clip.plot() + + lonmin = min(polygon.exterior.xy[0]) + lonmax = max(polygon.exterior.xy[0]) + latmin = min(polygon.exterior.xy[1]) + latmax = max(polygon.exterior.xy[1]) + + xlim = [lonmin - 0.1, lonmax + 0.1] + ylim = [latmin - 0.1, latmax + 0.1] + + ax.set( + xlim=xlim, + ylim=ylim, + ) + + ax.set_aspect(1 / np.cos(np.mean(ylim) * np.pi / 180)) + ax.grid(False) + + fig.tight_layout() + + return plot_to_base64(fig) diff --git a/App/functions/report-python-cloud-run/report/datasets/utils.py b/App/functions/report-python-cloud-run/report/datasets/utils.py index 6e6017b05..292661d46 100644 --- a/App/functions/report-python-cloud-run/report/datasets/utils.py +++ b/App/functions/report-python-cloud-run/report/datasets/utils.py @@ -6,7 +6,7 @@ def plot_to_base64(fig: plt.Figure) -> str: """Convert a matplotlib figure to base64""" img = BytesIO() - fig.savefig(img, format="png", bbox_inches="tight") + fig.savefig(img, transparent=True, format="png", bbox_inches="tight") return base64.b64encode(img.getbuffer()).decode("ascii") @@ -14,6 +14,6 @@ def plot_to_base64(fig: plt.Figure) -> str: def plot_to_svg(fig: plt.Figure) -> str: """Convert a matplotlib figure to svg""" img = BytesIO() - fig.savefig(img, format="svg", bbox_inches="tight") + fig.savefig(img, transparent=True, format="svg", bbox_inches="tight") return img.getvalue().decode("ascii") diff --git a/App/functions/report-python-cloud-run/report/images/Deltares_logo_D-blauw_RGB.svg b/App/functions/report-python-cloud-run/report/images/Deltares_logo_D-blauw_RGB.svg new file mode 100644 index 000000000..e8af003e4 --- /dev/null +++ b/App/functions/report-python-cloud-run/report/images/Deltares_logo_D-blauw_RGB.svg @@ -0,0 +1 @@ +Artboard 1 \ No newline at end of file diff --git a/App/functions/report-python-cloud-run/report/images/Deltares_pay-off_D-blauw_RGB.svg b/App/functions/report-python-cloud-run/report/images/Deltares_pay-off_D-blauw_RGB.svg new file mode 100644 index 000000000..0c13e0ae5 --- /dev/null +++ b/App/functions/report-python-cloud-run/report/images/Deltares_pay-off_D-blauw_RGB.svg @@ -0,0 +1 @@ +Artboard 1 \ No newline at end of file diff --git a/App/functions/report-python-cloud-run/report/report.html b/App/functions/report-python-cloud-run/report/report.html new file mode 100644 index 000000000..43a42181d --- /dev/null +++ b/App/functions/report-python-cloud-run/report/report.html @@ -0,0 +1,305 @@ + + +
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The State of the Coast Report

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The Present Day

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Beach Sediment Classification

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The coast in this area is characterized by a diverse composition of materials. Sand constitutes the majority of the coastline, making up 38.1% of the total. Mud is nearly as prevalent, accounting for 38.0%. Vegetated areas cover 16.0% of the coast, providing significant ecological value. Coastal cliffs are minimal, representing only 0.3% of the shoreline. Other materials make up the remaining 7.6%. This distribution indicates a predominantly sandy and muddy coastline with notable vegetated regions and minimal cliff presence.

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The Population

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The coast in this area is characterized by a varied human population distribution, with significant concentrations at specific longitudes and latitudes. Notably, high population densities are observed around longitude 3.8999 and latitude 51.5869, with a population of 10,087, and longitude 3.8997 and latitude 51.5862, with a population of 11,282. These areas are critical as they represent substantial potential loss in the event of coastal hazards. Conversely, other regions along the coast exhibit lower population densities, indicating a reduced risk of human and property loss in those areas.

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The Historical Trend

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The Shoreline Monitor

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The coast in this area is characterized by a mix of accretion and erosion. Significant portions exhibit stable conditions, with change rates between -0.5 and 0.5 m/yr. However, there are areas of intense accretion (1 to 3 m/yr) and severe accretion (3 to 5 m/yr), particularly around 4.19°E and 51.43°N. Extreme accretion (>5 m/yr) is also observed in some segments. Conversely, intense erosion (-3 to -1 m/yr) and severe erosion (-5 to -3 m/yr) are noted, especially near 4.18°E and 51.51°N. Extreme erosion (<-5 m/yr) is present in isolated locations, indicating significant coastal dynamics.

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The Future Drivers

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Sea Level Rise Projection

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Here we generate some content based on the dataset

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Extreme Sea Level

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Here we generate some content based on the ESL dataset

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The Shoreline Monitor - Future Projection

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Here we generate some content based on the the dataset

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+ + + + diff --git a/App/functions/report-python-cloud-run/report/report.py b/App/functions/report-python-cloud-run/report/report.py index 4c2451e99..c3717c016 100644 --- a/App/functions/report-python-cloud-run/report/report.py +++ b/App/functions/report-python-cloud-run/report/report.py @@ -1,17 +1,28 @@ +# %% from dataclasses import dataclass from io import BytesIO from pathlib import Path -import fitz # type: ignore -import jinja2 + +# import fitz # type: ignore +from jinja2 import Environment, FileSystemLoader from shapely import Polygon # type: ignore +import weasyprint from report.utils.stac import STACClientGCA, ZarrDataset from report.utils.zarr_slicing import ZarrSlicer from report.datasets.datasetcontent import DatasetContent from report.datasets.base_dataset import get_dataset_content +from report.datasets.overview import get_overview +from report.datasets.slr import get_slr_content + +# from datasets.subtreat import get_landsub_content +from datetime import datetime POLYGON_DEFAULT = """{"coordinates":[[[2.3915028831735015,51.7360381463356],[5.071438932343227,50.89406012060684],[6.955992986278972,51.49577449585874],[7.316959036046541,53.18700330195111],[6.636226617140238,53.961350092621075],[3.8631377106468676,54.14643052276938],[2.1218958391276317,53.490771261555096],[2.3915028831735015,51.7360381463356]]],"type":"Polygon"}""" STAC_ROOT_DEFAULT = "https://raw.githubusercontent.com/openearth/global-coastal-atlas/subsidence_etienne/STAC/data/current/catalog.json" +STAC_COCLICO = ( + "https://raw.githubusercontent.com/openearth/coclicodata/main/current/catalog.json" +) @dataclass @@ -20,68 +31,163 @@ class ReportContent: def create_report_html(polygon: Polygon, stac_root: str) -> str: - htmlpath = Path(__file__).parent / Path("template.html.jinja") + env = Environment(loader=FileSystemLoader(Path(__file__).parent)) + # htmlpath = Path(__file__).parent / Path("template.html.jinja") csspath = Path(__file__).parent / Path("template.css") - with htmlpath.open() as f: - template = jinja2.Template(f.read()) + template = env.get_template("template.html.jinja") - data = generate_report_content(polygon=polygon, stac_root=stac_root) + data = generate_report_content(polygon=polygon) css: str = csspath.read_bytes().decode() html = template.render(data=data, css=css) return html -def create_report_pdf(page_content: str) -> BytesIO: - story = fitz.Story(html=page_content) - - MEDIABOX = fitz.paper_rect("A4") # output page format: Letter - WHERE = MEDIABOX + (36, 36, -36, -36) # leave borders of 0.5 inches +def create_report_pdf(page_content: str) -> BytesIO: ##TODO in_memory_pdf = BytesIO() - writer = fitz.DocumentWriter(in_memory_pdf) - - with fitz.DocumentWriter(in_memory_pdf) as writer: - more = 1 - while more: - device = writer.begin_page(MEDIABOX) - more, _ = story.place(WHERE) - story.draw(device) - writer.end_page() + weasyprint.HTML(string=page_content, base_url=".").write_pdf(in_memory_pdf) return in_memory_pdf -def generate_report_content(polygon: Polygon, stac_root: str) -> ReportContent: - gca_client = STACClientGCA.open(stac_root) - zarr_datasets: list[ZarrDataset] = gca_client.get_all_zarr_uris() # type: ignore +def generate_report_content(polygon: Polygon) -> ReportContent: + start = datetime.now() dataset_contents: list[DatasetContent] = [] + final_dataset_contents: list[DatasetContent] = [] + + ### getting gca datasets ### + time = datetime.now() + print("start retrieving gca dataset {}".format(time - start)) + + gca_client = STACClientGCA.open(STAC_ROOT_DEFAULT) + zarr_datasets: list[ZarrDataset] = gca_client.get_all_zarr_uris() + for zarr_dataset in zarr_datasets: xarr = ZarrSlicer._get_dataset_from_zarr_url(zarr_dataset.zarr_uri) sliced_xarr = ZarrSlicer.slice_xarr_with_polygon(xarr, polygon) + sliced_xarr = sliced_xarr.rio.write_crs("EPSG:4326") if ZarrSlicer.check_xarr_contains_data(sliced_xarr): dataset_content = get_dataset_content(zarr_dataset.dataset_id, sliced_xarr) if dataset_content: - dataset_contents.append(dataset_content) - - return ReportContent(datasets=dataset_contents) - - -if __name__ == "__main__": - polygon = Polygon( + if isinstance(dataset_content, list): + dataset_contents.extend(dataset_content) + else: + dataset_contents.append(dataset_content) + + time = datetime.now() + print("finished retrieving gca dataset {}".format(time - start)) + + ## getting SLR ### + time = datetime.now() + print("start retrieving slr dataset {}".format(time - start)) + dataset_content = get_slr_content(polygon) + if dataset_content: + if isinstance(dataset_content, list): + dataset_contents.extend(dataset_content) + else: + dataset_contents.append(dataset_content) + + time = datetime.now() + print("finished retrieving slr dataset {}".format(time - start)) + + # ### getting land subsidence ### + # time = datetime.now() + # print('start retrieving landsub dataset {}'.format(time - start)) + # dataset_content = get_landsub_content(polygon) + # if dataset_content: + # if isinstance(dataset_content,list): + # dataset_contents.extend(dataset_content) + # else: + # dataset_contents.append(dataset_content) + # time = datetime.now() + # print('finished retrieving landsub dataset {}'.format(time - start)) + + # ### getting DTM ### ##TODO + + ### generating overview ### + print("start making overview {}".format(time - start)) + dataset_content = get_overview(polygon, dataset_contents) + dataset_contents.append(dataset_content) + print("finished making overview {}".format(time - start)) + + ### re-arranging datasets ### + collection_dict = [ + "overview", + "dtm", + "sediment_class", + "world_pop", + "flooding", + "shoreline_change", + "landsub2010", + "landsub2040", + "slr", + "esl", + "future_shoreline_change_2050", + "future_shoreline_change_2100", + ] + + existing_collection = [ + dataset_contents[ind].dataset_id for ind in range(len(dataset_contents)) + ] + + for item in collection_dict: + if item in existing_collection: + final_dataset_contents.append( + dataset_contents[existing_collection.index(item)] + ) + else: + None + + return ReportContent(datasets=final_dataset_contents) + + +# %%for testing only + +### Test Case 1: Terschelling; Case 2: Aveiro, Portugal; Case 3: Po Delta, Italy ### +name = ["Terschelling", "Aveiro", "PoDelta"] +polylist = [ + Polygon( + [ + (5.0713, 53.3602), + (5.2567, 53.44), + (5.5281, 53.4646), + (5.6351, 53.4086), + (5.1669, 53.3079), + (5.0713, 53.3602), + ] + ), + Polygon( + [ + (-8.8754, 40.7728), + (-8.6110, 40.7749), + (-8.6067, 40.5479), + (-8.8719, 40.5444), + (-8.8754, 40.7728), + ] + ), + Polygon( [ - [2.3915028831735015, 51.7360381463356], - [5.071438932343227, 50.89406012060684], - [6.955992986278972, 51.49577449585874], - [7.316959036046541, 53.18700330195111], - [6.636226617140238, 53.961350092621075], - [3.8631377106468676, 54.14643052276938], - [2.1218958391276317, 53.490771261555096], - [2.3915028831735015, 51.7360381463356], + (12.1953, 45.1659), + (12.6634, 45.1680), + (12.6733, 44.7430), + (12.2017, 44.7437), + (12.1953, 45.1659), ] - ) - html = create_report_html(polygon=polygon, stac_root=STAC_ROOT_DEFAULT) - print(html) - pdf = create_report_pdf(html) - print(pdf.getvalue()) + ), +] + +for nn in range(1): + if __name__ == "__main__": + html = create_report_html(polygon=polylist[nn], stac_root=STAC_ROOT_DEFAULT) + print(html) + pdf = create_report_pdf(html) + print(pdf.getvalue()) + + # Write pdf to file + with open("report-" + name[nn] + ".html", "w") as f: + f.write(html) + with open("report-" + name[nn] + ".pdf", "wb") as f: + f.write(pdf.getvalue()) +# %% diff --git a/App/functions/report-python-cloud-run/report/template.css b/App/functions/report-python-cloud-run/report/template.css index f31f35e37..09261a744 100644 --- a/App/functions/report-python-cloud-run/report/template.css +++ b/App/functions/report-python-cloud-run/report/template.css @@ -1,5 +1,112 @@ html, + body { - margin: 0; - padding: 0; -} \ No newline at end of file + font-family: Arial; + text-align: justify; + text-justify: auto; + line-height: 1.2; +} + +h1 { + color: rgb(0, 179, 137); + font-size: 35px; + padding-left: 14mm; +} + +h2 { + color: rgb(0, 204, 150); + margin-top: 0; +} + +h3 { + font-style: bold; + font-size: 26px; + margin-top: 0; +} + +h4 { + font-style: bold; + font-size: 22px; + margin-top: 0; + margin-bottom: 5px; + text-align: left; +} + +div{ + border-width: 0px; + border-style: solid; + border-color: gray; + break-inside: avoid; + margin-top: 0; + margin-bottom: 0; +} + + +p{ + font-size: 12px; + margin-top: 0; + margin-bottom: 5px; +} + + +.image{ + width: 100%; + height: auto; + margin-bottom: 5px; +} + +.section-grid{ + background-color: None; + padding-left: 14mm; + padding-right: 14mm; + padding-top: 25px; +} + +.left-right-grid{ + display: grid; + grid-template-columns: 1fr 1fr; +} + +.top-bottom-grid{ + +} + +.main-text{ + height: 70%; +} + +.catchy-text{ + height: 30%; +} + +@page { + size: A4; + margin-left: 0; + margin-right: 0; + + @bottom-right { + content: 'Page ' counter(page) ' of ' counter(pages); + font-size: 12px; + margin-right: 14mm; + } + + @bottom-left { + content: " "; + background-image: url(./report/images/Deltares_pay-off_D-blauw_RGB.svg); + background-repeat: no-repeat; + width: 200px; + text-align: center; + margin-left: 14mm; + } + + @top-right { + content: " "; + background-image: url(./report/images/Deltares_logo_D-blauw_RGB.svg); + background-repeat: no-repeat; + height: 45px; + width: 100px; + text-align: center; + margin-right: 14mm; + } + +} diff --git a/App/functions/report-python-cloud-run/report/template.html.jinja b/App/functions/report-python-cloud-run/report/template.html.jinja index 62a2edcff..5f5be8b41 100644 --- a/App/functions/report-python-cloud-run/report/template.html.jinja +++ b/App/functions/report-python-cloud-run/report/template.html.jinja @@ -2,22 +2,121 @@ {{ css }} -

Coastal report

-

In this report some coastal characteristics of the selected area are explained.

+
+

The State of the Coast Report

+
{% for dataset in data.datasets %} -

{{ dataset.title }}

-

{{ dataset.text }}

- {% if dataset.image_svg %} - {{ dataset.image_svg }} + {% if dataset.dataset_id == "overview" or dataset.dataset_id == "shoreline_change" or dataset.dataset_id == "land_sub" %} +
+ {% else %} +
{% endif %} - {% if dataset.image_base64 %} - + + {% if dataset.dataset_id == "sediment_class" %} +

The Present Day

+ {% elif dataset.dataset_id == "shoreline_change" %} +

The Historical Trends

+ {% elif dataset.dataset_id == "slr" %} +

The Future Drivers

{% endif %} + + {% if dataset.dataset_id == "slr_RCP26" %} +
+

The Most Optimistic Scenario

+

The most optimistic scenario refers to the most sustainable and green scenario in the future. In particular, this refers to SSP126 or RCP2.6 scenarios.

+
+ {% elif dataset.dataset_id == "slr_RCP45" %} +
+

The Intermediate Scenario

+

Intermediate scenario refers to the medium scenario in the future, which refers to SSP245 or RCP4.5 scenarios.

+
+ {% elif dataset.dataset_id == "slr_RCP85" %} +
+

The Most Pessimistic Scenario

+

The most peesimistic scenario refers to the worst climate change scenario in the future. This refers to SSP585 or RCP8.5 scenarios.

+
+ {% endif %} + +

{{ dataset.title }}

+ + {% if dataset.dataset_id == "world_pop" or dataset.dataset_id == "slr" or dataset.dataset_id == "esl_RCP26" or dataset.dataset_id == "esl_RCP45" or dataset.dataset_id == "esl_RCP85" %} +
+
+

{{ dataset.text }}

+
+ +
+ {% if dataset.image_svg %} + {{ dataset.image_svg }} + {% endif %} + + {% if dataset.image_base64 %} + + {% endif %} +
+
+ + {% elif dataset.dataset_id == "overview" %} +
+
+
+

{{ dataset.text }}

+
+ +
+ {% if dataset.image_svg %} + {{ dataset.image_svg }} + {% endif %} + + {% if dataset.image_base64 %} + + {% endif %} +
+
+ +
+
+

Contents

+
+ +
+

The Present Day

+

Coastal Types

+

The Population

+

The Historical Trends

+

Historical Shoreline Change (1984-2021)

+

The Future Drivers

+

Sea Level Rise Projection

+

Future Shoreline Projections in 2050

+

Future Shoreline Projections in 2100

+
+
+
+ + {% else %} +
+
+

{{ dataset.text }}

+
+ +
+ {% if dataset.image_svg %} + {{ dataset.image_svg }} + {% endif %} + + {% if dataset.image_base64 %} + + {% endif %} +
+
+ {% endif %} +
{% endfor %} + {% if not data.datasets %}

Unfortunately there was no data available in the region you selected.

-{% endif %} \ No newline at end of file +{% endif %} diff --git a/App/functions/report-python-cloud-run/report/utils/gentext.py b/App/functions/report-python-cloud-run/report/utils/gentext.py new file mode 100644 index 000000000..a30ffb55b --- /dev/null +++ b/App/functions/report-python-cloud-run/report/utils/gentext.py @@ -0,0 +1,274 @@ +import os +from openai import AzureOpenAI +import numpy as np +import xarray as xr +from typing import Union + +def describe_data(xarr: xr.Dataset, dataset_id: str) -> str: + # Create prompt + prompt = make_prompt(xarr, dataset_id) + + # Load environment variables + api_version = '2024-03-01-preview' + api_base_url = os.getenv('OPENAI_API_BASE') + api_key = os.getenv('AZURE_OPENAI_API_KEY') + deployment_name = os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME') + + # Initialise large language model + model = AzureOpenAI( + api_key=api_key, + api_version=api_version, + base_url=f'{api_base_url}/deployments/{deployment_name}', + ) + + # Trigger model + response = model.chat.completions.create( + model=os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME'), + messages=[ + {'role': 'system', 'content': prompt}, + ], + max_tokens=1000, + temperature=0.1, + ) + + # Return response + return response.choices[0].message.content + +def describe_overview(polygon, dataset_contents) -> str: + para_list = [dataset_contents[ind].text for ind in range(len(dataset_contents))] + coor_list = list(zip(*polygon.boundary.xy)) + + prompt = """ + You are a coastal scientist tasked with writing for a report describing the state of the coast. This report includes key information about a location, + such as population, sediment characteristic of that location, land subsidence risk, shoreline erosion or accretion, future sea level rise, future extreme sea level, and + future shoreline change. You should write two paragraphs with the provided information. The first paragraph is a factual description about an area of location. + The coordinates (longitude, latitude) of this area are provided at the end. You should only describe the geographical information of this location, such as longitude, latitude, in which countries, or which big cities is in the nearby. + You should describe it in about 100 words. After the first paragraph, you should also write a second paragraph, which contains approximately 250 words, + summarising the provided texts. The text are also provided at the end + Ensure the description is clear, professional, and aligned with the dataset. + + * Coordinates of this area: {} + * texts: {} + """.format(str(coor_list), str(para_list)) + + # Load environment variables + api_version = '2024-03-01-preview' + api_base_url = os.getenv('OPENAI_API_BASE') + api_key = os.getenv('AZURE_OPENAI_API_KEY') + deployment_name = os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME') + + # Initialise large language model + model = AzureOpenAI( + api_key=api_key, + api_version=api_version, + base_url=f'{api_base_url}/deployments/{deployment_name}', + ) + + # Trigger model + response = model.chat.completions.create( + model=os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME'), + messages=[ + {'role': 'system', 'content': prompt}, + ], + max_tokens=1000, + temperature=0.1, + ) + + # Return response + return response.choices[0].message.content + + +def make_prompt(xarr: Union[xr.Dataset, dict], dataset_id: str) -> str: + match dataset_id: + case 'sediment_class': + var = 'sediment_label' + + sand_port = np.round(len(np.where(xarr[var].values == 0)[0]) / len(xarr[var].values) * 100, 1) + mud_port = np.round(len(np.where(xarr[var].values == 1)[0]) / len(xarr[var].values) * 100, 1) + cliff_port = np.round(len(np.where(xarr[var].values == 2)[0]) / len(xarr[var].values) * 100, 1) + veg_port = np.round(len(np.where(xarr[var].values == 3)[0]) / len(xarr[var].values) * 100, 1) + other_port = np.round(len(np.where(xarr[var].values == 4)[0]) / len(xarr[var].values) * 100, 1) + + + prompt = """ + You are a coastal scientist tasked with writing a concise paragraph (maximum 100 words) for a report describing the state of the coast. + The dataset contains the proportion of different materials existing along a coastline. There are 5 different numbers under the dataset here. + The first one is the proportion of sand in percentage. The second one is the propotion of mud in percentage. + The third one is the proportion of coastal cliff in percentage. The fourth one is the proportion of vegetated area in percentage. + The last one is the propotion of other materials in percentage. + You should summarise the dataset and list the proportions of each material. You should describe what material is the majority of the coast and how materials are distributed along the shoreline. + Ensure the description is clear, professional, and aligned with the dataset's trends. Begin your paragraph with: "The coast in this area is characterized by...". + + * Dataset: {} {} {} {} {} + """.format(sand_port, mud_port, cliff_port, veg_port, other_port) + + + case 'world_pop': + var = 'pop_tot' + + # Create data dictionary + data_dict = {} + data_dict['lon'] = {'value': np.round(xarr['lon'].values, 2), 'long_name': xarr['lon'].attrs['long_name'], 'units': xarr['lon'].attrs['units']} + data_dict['lat'] = {'value': np.round(xarr['lat'].values, 2), 'long_name': xarr['lat'].attrs['long_name'], 'units': xarr['lat'].attrs['units']} + data_dict[var] = {'value': np.round(xarr[var].values, 0), 'long_name': xarr[var].attrs['long_name']} + + prompt = """ + You are a coastal scientist tasked with writing a concise paragraph (maximum 100 words) for a report describing the state of the coast. + This paragraph is related to human population living along coastlines. Higher population along a coastline means that there will be higher potential loss, + including property and human loss, if there is any coastal hazard. Smaller loss or impact if ther population is smaller. + You should summarise the population and its distribution along the coast. You should also spot if there is any important location, where you can find a high population. + The dataset is provided below, which contains the longitude, the lattitude and the population at each location. + You should at least specify at which longitude and latitude where we can find a substantial amount of population. + Please explicitly mention that this is the population along the coastline. + Ensure the description is clear, professional, and aligned with the dataset's trends. Begin your paragraph with: "The coast in this area is characterized by...". + + * Dataset: {} + """.format(str(data_dict)) + + + + case 'shoreline_change': + var = 'changerate' + + # Create data dictionary + data_dict = {} + data_dict['lon'] = {'value': np.round(xarr['lon'].values, 2), 'long_name': xarr['lon'].attrs['long_name'], 'units': xarr['lon'].attrs['units']} + data_dict['lat'] = {'value': np.round(xarr['lat'].values, 2), 'long_name': xarr['lat'].attrs['long_name'], 'units': xarr['lat'].attrs['units']} + data_dict[var] = {'value': np.round(xarr[var].values, 2), 'long_name': xarr[var].attrs['long_name'], 'units': xarr[var].attrs['units']} + + # Create changerate classes dictionary + classes_dict = {} + classes_dict['extreme_accretion'] = {'min': 5, 'max': np.inf, 'unit': xarr[var].attrs['units']} + classes_dict['severe_accretion'] = {'min': 3, 'max': 5, 'unit': xarr[var].attrs['units']} + classes_dict['intense_accretion'] = {'min': 1, 'max': 3, 'unit': xarr[var].attrs['units']} + classes_dict['accretion'] = {'min': 0.5, 'max': 1, 'unit': xarr[var].attrs['units']} + classes_dict['stable'] = {'min': -0.5, 'max': 0.5, 'unit': xarr[var].attrs['units']} + classes_dict['erosion'] = {'min': -1, 'max': -0.5, 'unit': xarr[var].attrs['units']} + classes_dict['intense_erosion'] = {'min': -3, 'max': -1, 'unit': xarr[var].attrs['units']} + classes_dict['severe_erosion'] = {'min': -5, 'max': -3, 'unit': xarr[var].attrs['units']} + classes_dict['extreme_erosion'] = {'min': -np.inf, 'max': -5, 'unit': xarr[var].attrs['units']} + + prompt = """ + You are a coastal scientist tasked with writing a concise paragraph (maximum 100 words) for a report describing the state of the coast. + Use the dataset below, which contains coastal change rates (positive values indicate accretion, negative values indicate erosion). + Categorize the observed change rates using the coastal erosion classes, also provided below. Ensure the description is clear, professional, + and aligned with the dataset's trends. Begin your paragraph with: "The coast in this area is characterized by...". + + * Dataset: {} + * coastal erosion classes: {} + """.format(str(data_dict), str(classes_dict)) + + + case 'land_sub': + None + + + # case 'slr_RCP26' | 'slr_RCP45' | 'slr_RCP85': + # # Create data dictionary + # data_dict = {} + # data_dict['years'] = {'value': ["2031","2041","2051", "2061", "2071", "2081", "2091", "2101","2111","2121","2131","2141","2151"], 'long_name': 'year'} + # data_dict['high'] = {'value': [slp['value'] for slp in xarr if (slp['msl'] == 'msl_h')], 'long_name': 'Sea level rise projection with high confidence', 'units': 'mm'} + # data_dict['medium'] = {'value': [slp['value'] for slp in xarr if (slp['msl'] == 'msl_m')], 'long_name': 'Sea level rise projection with medium confidence', 'units': 'mm'} + # data_dict['low'] = {'value': [slp['value'] for slp in xarr if (slp['msl'] == 'msl_l')], 'long_name': 'Sea level rise projection with low confidence', 'units': 'mm'} + + # prompt1 = """ + # You are a coastal scientist tasked with writing a concise paragraph (maximum 100 words) for a report describing the state of the coast. + # Use the dataset below, which contains the future sea level rise every ten years from 2031 to 2151. The dataset contains the sea level rise projection with + # high, medium and low confidence at every ten years from 2031. The high confidence and the low confidence projection states the maximum and minimum possible sea level rise, + # while the medium confidence projection states the mean possible sea level rise. + # """ + # match dataset_id: + # case 'slr_RCP26': + # prompt2 = "The dataset is projected based on SSP126 scenario from AR6." + # case 'slr_RCP45': + # prompt2 = "The dataset is projected based on SSP245 scenario from AR6." + # case 'slr_RCP85': + # prompt2 = "The dataset is projected based on SSP585 scenario from AR6." + + # prompt3 = """ + # Please describe the trend of the sea level rise with different confidence of projections and Ensure the description is clear, professional, + # and aligned with the dataset's trends. Begin your paragraph with: "The coast in this area is characterized by...". + + # * Dataset: {} + # """.format(str(data_dict)) + + # prompt = prompt1 + prompt2 + prompt3 + + case 'slr': + # Create data dictionary + data_dict = {} + data_dict['years'] = {'value': ["2031","2041","2051", "2061", "2071", "2081", "2091", "2101","2111","2121","2131","2141","2151"], 'long_name': 'year'} + data_dict['high_end'] = {'value': [slp['value'] for slp in xarr if (slp['ssp'] == 'high_end')], 'long_name': 'Sea level rise projection in high-end scenario', 'units': 'mm'} + data_dict['ssp126'] = {'value': [slp['value'] for slp in xarr if (slp['ssp'] == 'ssp126')], 'long_name': 'Sea level rise projection in SSP126 scenario', 'units': 'mm'} + data_dict['ssp245'] = {'value': [slp['value'] for slp in xarr if (slp['ssp'] == 'ssp245')], 'long_name': 'Sea level rise projection in SSP245 scenario', 'units': 'mm'} + data_dict['ssp585'] = {'value': [slp['value'] for slp in xarr if (slp['ssp'] == 'ssp585')], 'long_name': 'Sea level rise projection in SSP585 scenario', 'units': 'mm'} + + prompt = """ + You are a coastal scientist tasked with writing a concise paragraph (maximum 100 words) for a report describing the state of the coast. + Use the dataset below, which contains the future sea level rise every ten years from 2031 to 2151. + The dataset contains the sea level rise projectionin differente in four different scenarios (i.e. high-end, SSP126, SSP245 and SSP585) every ten years from 2031. + Please describe the trend of the sea level rise in all four scenarios and Ensure the description is clear, professional, + and aligned with the dataset's trends. Begin your paragraph with: "The coast in this area is characterized by.... + + * Dataset: {} + """.format(str(data_dict)) + + + case 'esl_RCP26' | 'esl_RCP45' | 'esl_RCP85': + None + + + case 'future_shoreline_change_2050' | 'future_shoreline_change_2100': + # recalculate average shoreline charnge rate + if dataset_id == 'future_shoreline_change_2050': + rate = xarr.diff('time', 1).sel(time='2050') / (2050 - 2021) + else: + rate = xarr.diff('time', 1).sel(time='2100') / (2100 - 2050) + + rate45 = rate['sp_rcp45_p50'] + rate85 = rate['sp_rcp85_p50'] + + # Create data dictionary + data_dict = {} + data_dict['lon'] = {'value': np.round(xarr['lon'].values, 2), 'long_name': xarr['lon'].attrs['long_name'], 'units': xarr['lon'].attrs['units']} + data_dict['lat'] = {'value': np.round(xarr['lat'].values, 2), 'long_name': xarr['lat'].attrs['long_name'], 'units': xarr['lat'].attrs['units']} + data_dict['RCP45'] = {'value': np.round(rate45.values, 2), 'long_name': 'average shoreline change rate in RCP4.5 scenario', 'units': 'm/yr'} + data_dict['RCP85'] = {'value': np.round(rate85.values, 2), 'long_name': 'average shoreline change rate in RCP8.5 scenario', 'units': 'm/yr'} + + # Create changerate classes dictionary + classes_dict = {} + classes_dict['extreme_accretion'] = {'min': 5, 'max': np.inf, 'unit': 'm/yr'} + classes_dict['severe_accretion'] = {'min': 3, 'max': 5, 'unit': 'm/yr'} + classes_dict['intense_accretion'] = {'min': 1, 'max': 3, 'unit': 'm/yr'} + classes_dict['accretion'] = {'min': 0.5, 'max': 1, 'unit': 'm/yr'} + classes_dict['stable'] = {'min': -0.5, 'max': 0.5, 'unit': 'm/yr'} + classes_dict['erosion'] = {'min': -1, 'max': -0.5, 'unit': 'm/yr'} + classes_dict['intense_erosion'] = {'min': -3, 'max': -1, 'unit': 'm/yr'} + classes_dict['severe_erosion'] = {'min': -5, 'max': -3, 'unit': 'm/yr'} + classes_dict['extreme_erosion'] = {'min': -np.inf, 'max': -5, 'unit': 'm/yr'} + + prompt1 = """ + You are a coastal scientist tasked with writing a concise paragraph (maximum 200 words) for a report describing the state of the coast. + Use the dataset below, which contains the future average coastal change rates (positive values indicate accretion, negative values indicate erosion) + under two scenarios, RCP4.5 (indicated as the 'RCP45' dictionary) and RCP8.5 (indicated as the 'RCP85' dictionary). + """ + + if dataset_id == 'future_shoreline_change_2050': + prompt2 = "The dataset is projected to 2050" + else: + prompt2 = "The dataset is projected to 2100" + + prompt3 = """ + Categorize the observed change rates using the coastal erosion classes, also provided below. Please also describe how different the coastal change rates + is between two periods and describe some key changes on the rates between two periods. Please also be cautious of the change rates with non-realistic values, + which is bigger than 100 m/yr. You should ignore the change rates with this non-realistic values. + Ensure the description is clear, professional, + and aligned with the dataset's trends. Begin your paragraph with: "The coast in this area is characterized by...". + + * Dataset: {} + * coastal erosion classes: {} + """.format(str(data_dict), str(classes_dict)) + + prompt = prompt1 + prompt2 + prompt3 + + return prompt \ No newline at end of file diff --git a/App/functions/report-python-cloud-run/report/utils/stac.py b/App/functions/report-python-cloud-run/report/utils/stac.py index 0e51100b3..30191deeb 100644 --- a/App/functions/report-python-cloud-run/report/utils/stac.py +++ b/App/functions/report-python-cloud-run/report/utils/stac.py @@ -18,7 +18,7 @@ def get_all_zarr_uris(self) -> list[ZarrDataset]: for collection in collections: # we only look at collections that have a child links - if collection.get_item_links(): + #if collection.get_item_links(): ##TODO: to be removed zarr_datasets.append( ZarrDataset( dataset_id=collection.id, diff --git a/App/functions/report-python-cloud-run/requirements.txt b/App/functions/report-python-cloud-run/requirements.txt deleted file mode 100644 index a945e44ad..000000000 --- a/App/functions/report-python-cloud-run/requirements.txt +++ /dev/null @@ -1,13 +0,0 @@ -Flask~=3.0.0 -gunicorn~=21.2.0 -shapely~=2.0.2 -xarray~=2023.12.0 -zarr~=2.16.1 -fsspec~=2023.12.2 -aiohttp~=3.9.1 -pystac~=1.9.0 -pystac-client~=0.7.5 -pymupdf~=1.23.5 -Jinja2~=3.1.0 -matplotlib~=3.8.2 -geopandas~=0.14.1 \ No newline at end of file diff --git a/App/functions/report-python-cloud-run/uv.lock b/App/functions/report-python-cloud-run/uv.lock new file mode 100644 index 000000000..db21cb7be --- /dev/null +++ b/App/functions/report-python-cloud-run/uv.lock @@ -0,0 +1,4143 @@ +version = 1 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version < '3.11' and platform_system == 'Darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.11' and 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