diff --git a/.github/workflows/conda_pip.yml b/.github/workflows/conda_pip.yml index 4c1ff2b2..b8180665 100644 --- a/.github/workflows/conda_pip.yml +++ b/.github/workflows/conda_pip.yml @@ -64,10 +64,12 @@ jobs: SCHISM_ENV: "SCHISM_ENV" DELFT3D_ENV: "DELFT3D_ENV" PYPOSEIDON_ENV: "PYPOSEIDON_ENV" + PYPOS_TEL_ENV: "PYPOS_TEL_ENV" # SCHISM_LOCK: locks/conda-${{ matrix.os }}-schism_${{ matrix.mpi }}.lock DELFT3D_LOCK: locks/conda-${{ matrix.os }}-delft3d_${{ matrix.mpi }}.lock PYPOSEIDON_LOCK: locks/conda-${{ matrix.os }}-binary-p${{ matrix.python }}.lock + PYPOS_TEL_LOCK: locks/conda-${{ matrix.os }}-binary-p${{ matrix.python }}_tel.lock REQUIREMENTS_CI: locks/requirements-ci.txt REQUIREMENTS_VIZ: locks/requirements-viz.txt @@ -96,6 +98,7 @@ jobs: channel_priority: strict channels: - gbrey + - tomsail - conda-forge EOF - run: cat ~/.condarc @@ -128,6 +131,16 @@ jobs: ${{ matrix.os }}-${{ matrix.python }}-${{ env.PYPOSEIDON_ENV }}-${{ hashFiles( env.PYPOSEIDON_LOCK )}} ${{ matrix.os }}-${{ matrix.python }}-${{ env.PYPOSEIDON_ENV }}- + - name: "Cache ${{ env.PYPOS_TEL_ENV }}" + uses: actions/cache@v3 + id: cache-PYPOS_TEL_ENV + with: + path: ~/micromamba/envs/${{ env.PYPOS_TEL_ENV }} + key: ${{ matrix.os }}-${{ matrix.python }}-${{ env.PYPOS_TEL_ENV }}-${{ hashFiles( env.PYPOS_TEL_LOCK )}}-${{ hashFiles( env.REQUIREMENTS_CI )}} + restore-keys: | + ${{ matrix.os }}-${{ matrix.python }}-${{ env.PYPOS_TEL_ENV }}-${{ hashFiles( env.PYPOS_TEL_LOCK )}} + ${{ matrix.os }}-${{ matrix.python }}-${{ env.PYPOS_TEL_ENV }}- + - name: Cache tests/data/ uses: actions/cache@v3 env: @@ -149,6 +162,10 @@ jobs: if: steps.cache-PYPOSEIDON_ENV.outputs.cache-hit != 'true' run: micromamba create --yes --name ${PYPOSEIDON_ENV} -f ${PYPOSEIDON_LOCK} + - name: "Create ${{ env.PYPOS_TEL_ENV }}" + if: steps.cache-PYPOS_TEL_ENV.outputs.cache-hit != 'true' + run: micromamba create --yes --name ${PYPOS_TEL_ENV} -f ${PYPOS_TEL_LOCK} + - name: "Install python dependencies" run: | eval "$(micromamba shell hook)" @@ -157,6 +174,15 @@ jobs: python -mpip install -r "${REQUIREMENTS_CI}" python -mpip install ./ + - name: "Install python dependencies for pyposeidon + telemac" + run: | + eval "$(micromamba shell hook)" + micromamba activate --stack "${PYPOS_TEL_ENV}" + python -mpip install -U pip + python -mpip install -r "${REQUIREMENTS_CI}" + python -mpip install ./ + python -mpip install mpi4py + - name: "Run tests: Basic" run: | eval "$(micromamba shell hook)" @@ -188,3 +214,9 @@ jobs: micromamba activate --stack "${DELFT3D_ENV}" micromamba activate --stack "${PYPOSEIDON_ENV}" make test_delft + + - name: "Run tests: TELEMAC" + run: | + eval "$(micromamba shell hook)" + micromamba activate "${PYPOS_TEL_ENV}" + make test_telemac diff --git a/Makefile b/Makefile index 14b5c797..15148cf6 100644 --- a/Makefile +++ b/Makefile @@ -6,10 +6,10 @@ init: poetry install -E viz --with dev --with docs --with test test: - pytest -vl --durations=10 -m 'not (schism or delft or viz or slow)' -n auto + pytest -vl --durations=10 -m 'not (schism or delft or telemac or viz or slow)' -n auto test_fail: - pytest -vl --lf --runschism --rundelft --runviz + pytest -vl --lf --runschism --rundelft --runtelemac --runviz test_viz: pytest -vl --durations=10 --runviz -m viz @@ -20,8 +20,11 @@ test_schism: test_delft: pytest -vl --durations=10 --rundelft -m delft +test_telemac: + pytest -vl --durations=10 --runtelemac -m telemac + test_full: - pytest -vl --durations=20 --runschism --rundelft --runviz + pytest -vl --durations=20 --runschism --rundelft --runtelemac --runviz conda_lock: ./scripts/generate_envs.sh diff --git a/Tutorial/TELEMAC.ipynb b/Tutorial/TELEMAC.ipynb new file mode 100644 index 00000000..bd8a06b5 --- /dev/null +++ b/Tutorial/TELEMAC.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2e3e73eb", + "metadata": { + "tags": [] + }, + "source": [ + "# The Model module" + ] + }, + { + "cell_type": "markdown", + "id": "b55dcdc7", + "metadata": {}, + "source": [ + "The top module handles the main setup of the instance. \n", + "\n", + "\n", + "- Requires:\n", + "\n", + " - The data folder. See [README](README.md) in this folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7dc5c43", + "metadata": {}, + "outputs": [], + "source": [ + "#to use the full width of the browser window\n", + "from IPython.display import display, HTML\n", + "display(HTML(\"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aca5a378", + "metadata": {}, + "outputs": [], + "source": [ + "# Not neccessarily needed. Just to check version. Must be >= 0.6\n", + "import pyposeidon\n", + "pyposeidon.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cac5ab42", + "metadata": {}, + "outputs": [], + "source": [ + "import pyposeidon.model as pm" + ] + }, + { + "cell_type": "markdown", + "id": "66071b72", + "metadata": {}, + "source": [ + "### Graphics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "356b1f61", + "metadata": {}, + "outputs": [], + "source": [ + "import pyposeidon.utils.pplot # initialize matplolib accessor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc0b09e2", + "metadata": {}, + "outputs": [], + "source": [ + "#optional\n", + "#pyposeidon.utils.pplot.__init__(dark_background=True) # set plt style for pplot graphics below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2817124", + "metadata": {}, + "outputs": [], + "source": [ + "import pyposeidon.utils.hplot # initialize pyviz accessor" + ] + }, + { + "cell_type": "markdown", + "id": "6a6d2620", + "metadata": {}, + "source": [ + "- In order to have within the Notebook the executable binaries installed in the conda env of the Kernel we add the corresponding path to the env PATH. This is not needed when we are running from python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c745b1b", + "metadata": {}, + "outputs": [], + "source": [ + "#Create a folder to save the output\n", + "import os\n", + "if not os.path.exists('test'):\n", + " os.makedirs('test')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fb41bc5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "cpath = numpy.__path__[0].split('/lib/')[0] # get the current kernel path\n", + "\n", + "os.environ['PATH'] += os.pathsep + cpath + '/bin' # add to PATH" + ] + }, + { + "cell_type": "markdown", + "id": "598b91bf", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "id": "93bcdedd", + "metadata": {}, + "source": [ + "The minimum information required is the geometry's extent. In the most simple case that is a lat/lon box that defines the area of interest. Without loss of generality we select below Iceland as a test case. Feel free to modify the coordinates. \n", + "\n", + "In addition, the coastlines need to be provided as internal boundaries. Without loss of generality, natural earth's 'intermediate' resolution (https://www.naturalearthdata.com/downloads/) is used here.\n", + "\n", + "The coastlines can be given as a shapefile or GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15b6d59b", + "metadata": {}, + "outputs": [], + "source": [ + "# use cartopy to get coastlines\n", + "import cartopy.feature as cf\n", + "\n", + "cr='i'\n", + "\n", + "coast = cf.NaturalEarthFeature(\n", + " category='physical',\n", + " name='land',\n", + " scale='{}m'.format({'l':110, 'i':50, 'h':10}[cr]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a2151f9", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gp\n", + "ne_i = gp.GeoDataFrame(geometry = [x for x in coast.geometries()])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae446407", + "metadata": {}, + "outputs": [], + "source": [ + "# observations: get SEASET data\n", + "import pandas as pd\n", + "seaset = pd.read_csv(\"https://raw.githubusercontent.com/tomsail/seaset/main/Notebooks/catalog_full.csv\",\n", + " index_col=0)\n", + "seaset.to_csv(\"data/seaset_full.csv\")\n", + "seaset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0d18560", + "metadata": {}, + "outputs": [], + "source": [ + "#define in a dictionary the properties of the model..\n", + "model={'solver_name':'telemac',\n", + " 'geometry':{'lon_min' : -25,'lon_max' : -12.,'lat_min' : 56.,'lat_max' : 74.},\n", + " 'coastlines':ne_i, # coastlines\n", + "# 'manning':.12, #uniform manning value\n", + "# 'windrot':0.00001, #uniform windrot value\n", + "# 'tag':'schism', # optional tag \n", + " 'mesh_generator' : 'jigsaw', # set mesh generator \n", + " 'start_date':'2017-10-1 0:0:0',\n", + " 'time_frame':'12H',\n", + " 'meteo_source' : ['./data/erai.grib'], #path to meteo files\n", + " 'dem_source' : './data/dem.nc', #path to dem file\n", + "# 'ncores': 4 , # optional number of cores\n", + " 'dt' : 50,\n", + " 'rpath':'./test/telemac/', #location of calc folder\n", + "# 'update':['all'], #set which component should be updated (meteo,dem,model)\n", + " 'monitor':True, # get time series for observation points\n", + " 'obs' : \"data/seaset_full.csv\",\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "dbe6859d", + "metadata": {}, + "source": [ + "## Initialize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5579622", + "metadata": {}, + "outputs": [], + "source": [ + "#initialize a model\n", + "model['mesh_generator'] = 'jigsaw'\n", + "b = pm.set(**model)" + ] + }, + { + "cell_type": "markdown", + "id": "b798571e", + "metadata": {}, + "source": [ + "## Step by Step process" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72e0ce36", + "metadata": {}, + "outputs": [], + "source": [ + "b.create()\n", + "b.mesh.Dataset.pplot.mesh()" + ] + }, + { + "cell_type": "markdown", + "id": "934a4e51", + "metadata": {}, + "source": [ + "TPXO tide have been implemented, but have not been tested in pyposeidon.\n", + "\n", + "We will show here a simple case of surge model, without tide forcing at the boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "287fad5a", + "metadata": {}, + "outputs": [], + "source": [ + "b.mesh.Dataset.type[:] = 'closed' # here for a surge application, we will close all boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acdc35fb", + "metadata": {}, + "outputs": [], + "source": [ + "b.output()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c40436e9", + "metadata": {}, + "outputs": [], + "source": [ + "b.save() # saves the json model file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b088274b", + "metadata": {}, + "outputs": [], + "source": [ + "b.set_obs() # setup station points" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "405739f1", + "metadata": {}, + "outputs": [], + "source": [ + "b.run() # execute" + ] + }, + { + "cell_type": "markdown", + "id": "4bb8672d", + "metadata": {}, + "source": [ + "## ... or simply in one step\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8957dd6", + "metadata": {}, + "outputs": [], + "source": [ + "#initialize a model\n", + "b = pm.set(**model)\n", + "b.execute()" + ] + }, + { + "cell_type": "markdown", + "id": "4fd6fac9", + "metadata": {}, + "source": [ + "The various datasets incorporated in the model can be accessed independently as attributes, namely :" + ] + }, + { + "cell_type": "markdown", + "id": "7c966830", + "metadata": {}, + "source": [ + "- Meteo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3adcd3b0", + "metadata": {}, + "outputs": [], + "source": [ + "#plt.figure() # xarray uses existing ax if any. You might need to create a new figure to do it separately.\n", + "b.meteo.Dataset.msl[2,:,:].plot()" + ] + }, + { + "cell_type": "markdown", + "id": "fda43cb1", + "metadata": {}, + "source": [ + "- Mesh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0cb7609", + "metadata": {}, + "outputs": [], + "source": [ + "b.mesh.Dataset.hplot.mesh() " + ] + }, + { + "cell_type": "markdown", + "id": "4245edff", + "metadata": {}, + "source": [ + "- Dem" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce61dd43", + "metadata": {}, + "outputs": [], + "source": [ + "b.dem.Dataset.elevation.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "6301c07c", + "metadata": {}, + "source": [ + "- Coastlines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7f11425", + "metadata": {}, + "outputs": [], + "source": [ + "b.coastlines.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "a5c6208f", + "metadata": {}, + "source": [ + "- Boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ffdcae7", + "metadata": {}, + "outputs": [], + "source": [ + "b.mesh.boundary.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3e936652", + "metadata": {}, + "source": [ + "## Output" + ] + }, + { + "cell_type": "markdown", + "id": "0642af80", + "metadata": {}, + "source": [ + "The output of the simulations could be in separate files (due to MPI) or specific folders. pyposeidon can incorporate the resulting Datasets with " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "edee32bb", + "metadata": {}, + "outputs": [], + "source": [ + "b.results(remove_zarr=False) # integrate output" + ] + }, + { + "cell_type": "markdown", + "id": "f614eb62", + "metadata": {}, + "source": [ + "using [xarray-selafin](https://github.com/seareport/xarray-selafin/) directly on the selafin files: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35a2c144", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "out = xr.open_dataset(b.rpath + '/results_2D.slf', engine = 'selafin', lazy_loading = False)\n", + "out" + ] + }, + { + "cell_type": "markdown", + "id": "52dfbe98", + "metadata": {}, + "source": [ + "Or using xarray on the generated results: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ed5d149", + "metadata": {}, + "outputs": [], + "source": [ + "! tar -xvf test/telemac/outputs/out_2D.zarr.tar" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10a1de6a", + "metadata": {}, + "outputs": [], + "source": [ + "out = xr.open_dataset('out_2D.zarr')\n", + "out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4256c07", + "metadata": {}, + "outputs": [], + "source": [ + "out.pplot.mesh(x='longitude', y = 'latitude', e='face_nodes')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88bd5215", + "metadata": {}, + "outputs": [], + "source": [ + "it = 2\n", + "u, v = out.u.isel(time = it).values, out.v.isel(time = it).values\n", + "out.pplot.quiver(x='longitude', y = 'latitude', it = 2, u=u, v=v, scale=.5, title='hvel', color='k')" + ] + }, + { + "cell_type": "markdown", + "id": "fc1eeecd", + "metadata": {}, + "source": [ + "### with hvplot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0cf4968", + "metadata": {}, + "outputs": [], + "source": [ + "out.hplot.contourf(var='depth')#,tiles=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3084498c", + "metadata": {}, + "outputs": [], + "source": [ + "out.hplot.mesh(width=800, height=400,tiles=True)" + ] + }, + { + "cell_type": "markdown", + "id": "dab51e7b", + "metadata": {}, + "source": [ + "### Animate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "494c484d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "v = out.pplot.frames(x='longitude', \n", + " y = 'latitude', \n", + " e='face_nodes',\n", + " var='elev',\n", + " title='SSH', \n", + " vmin = -.5, \n", + " vmax =.5) # change to hplot for holoviews\n", + "v" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37ad3906", + "metadata": {}, + "outputs": [], + "source": [ + "v.save('test/elev.mp4')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b06c0fd4", + "metadata": {}, + "outputs": [], + "source": [ + "u, v = out.u.values, out.v.values\n", + "out.pplot.qframes(x='longitude', \n", + " y = 'latitude', \n", + " e='face_nodes',\n", + " u=u,v=v,title='Vel',scale=.5)#, color='w')" + ] + }, + { + "cell_type": "markdown", + "id": "9741c129", + "metadata": {}, + "source": [ + "## Read Model" + ] + }, + { + "cell_type": "markdown", + "id": "dcb38bee", + "metadata": {}, + "source": [ + "If a model is created by `pyposeidon` then there is a `json` file that describes the model. Such a file can be read into `pyposeidon` with " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5c0555f", + "metadata": {}, + "outputs": [], + "source": [ + "a = pyposeidon.model.read('test/telemac/telemac2d_model.json')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80e32da8", + "metadata": {}, + "outputs": [], + "source": [ + "a.execute()" + ] + }, + { + "cell_type": "markdown", + "id": "12ec7eeb", + "metadata": {}, + "source": [ + "However, there might be a model created by other means. `pyposeidon` does its best to incorporate such model with" + ] + }, + { + "cell_type": "markdown", + "id": "aacd277e", + "metadata": {}, + "source": [ + "TELEMAC Works up until here \n", + "\n", + "`read_folder()` class function is yet to be implemented" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39a6005e", + "metadata": {}, + "outputs": [], + "source": [ + "c = pm.set(solver_name='telemac', rfolder = './test/telemac/', load_geo=True, load_meteo=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f5254c4", + "metadata": {}, + "outputs": [], + "source": [ + "c.mesh.Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2b4eaac", + "metadata": {}, + "outputs": [], + "source": [ + "c.meteo.Dataset" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/environments/base-p3.10.yml b/environments/base-p3.10.yml index ec264a47..1dea446d 100644 --- a/environments/base-p3.10.yml +++ b/environments/base-p3.10.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - cartopy - cfgrib @@ -34,3 +35,4 @@ dependencies: - shapely>=2 - tqdm - xarray + - opentelemac diff --git a/environments/base-p3.11.yml b/environments/base-p3.11.yml index 0413cbac..a9df28b4 100644 --- a/environments/base-p3.11.yml +++ b/environments/base-p3.11.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - cartopy - cfgrib @@ -34,3 +35,4 @@ dependencies: - shapely>=2 - tqdm - xarray + - opentelemac diff --git a/environments/base-p3.9.yml b/environments/base-p3.9.yml index e2339bc1..cbdc0fd5 100644 --- a/environments/base-p3.9.yml +++ b/environments/base-p3.9.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - cartopy - cfgrib @@ -34,3 +35,4 @@ dependencies: - shapely>=2 - tqdm - xarray + - opentelemac diff --git a/environments/binary-p3.10.yml b/environments/binary-p3.10.yml index 343e7545..8aff1cfe 100644 --- a/environments/binary-p3.10.yml +++ b/environments/binary-p3.10.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - compilers - eccodes @@ -12,3 +13,4 @@ dependencies: - pip - proj - python=3.10 + - opentelemac diff --git a/environments/binary-p3.11.yml b/environments/binary-p3.11.yml index 93d8e860..b5f1e0c6 100644 --- a/environments/binary-p3.11.yml +++ b/environments/binary-p3.11.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - compilers - eccodes @@ -12,3 +13,4 @@ dependencies: - pip - proj - python=3.11 + - opentelemac diff --git a/environments/binary-p3.9.yml b/environments/binary-p3.9.yml index 3d252b52..5553f61c 100644 --- a/environments/binary-p3.9.yml +++ b/environments/binary-p3.9.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - compilers - eccodes @@ -12,3 +13,4 @@ dependencies: - pip - proj - python=3.9 + - opentelemac diff --git a/environments/full-p3.10.yml b/environments/full-p3.10.yml index 22beb23a..b7747760 100644 --- a/environments/full-p3.10.yml +++ b/environments/full-p3.10.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - black<24.0 - bump2version @@ -52,3 +53,4 @@ dependencies: - spatialpandas - tqdm - xarray + - opentelemac diff --git a/environments/full-p3.11.yml b/environments/full-p3.11.yml index e93f1521..00338981 100644 --- a/environments/full-p3.11.yml +++ b/environments/full-p3.11.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - black<24.0 - bump2version @@ -52,3 +53,4 @@ dependencies: - spatialpandas - tqdm - xarray + - opentelemac diff --git a/environments/full-p3.9.yml b/environments/full-p3.9.yml index d8fd6f77..78b16660 100644 --- a/environments/full-p3.9.yml +++ b/environments/full-p3.9.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - black<24.0 - bump2version @@ -52,3 +53,4 @@ dependencies: - spatialpandas - tqdm - xarray + - opentelemac diff --git a/environments/viz-p3.10.yml b/environments/viz-p3.10.yml index 7cfa8ac6..fe2c1f86 100644 --- a/environments/viz-p3.10.yml +++ b/environments/viz-p3.10.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - cartopy - cfgrib @@ -41,3 +42,4 @@ dependencies: - spatialpandas - tqdm - xarray + - opentelemac diff --git a/environments/viz-p3.11.yml b/environments/viz-p3.11.yml index 9993c5b4..b76f8e5d 100644 --- a/environments/viz-p3.11.yml +++ b/environments/viz-p3.11.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - cartopy - cfgrib @@ -41,3 +42,4 @@ dependencies: - spatialpandas - tqdm - xarray + - opentelemac diff --git a/environments/viz-p3.9.yml b/environments/viz-p3.9.yml index 85cc33e8..71bca02d 100644 --- a/environments/viz-p3.9.yml +++ b/environments/viz-p3.9.yml @@ -1,6 +1,7 @@ channels: - conda-forge - gbrey + - tomsail dependencies: - cartopy - cfgrib @@ -41,3 +42,4 @@ dependencies: - spatialpandas - tqdm - xarray + - opentelemac diff --git a/locks/binary-p3.10_tel.yml b/locks/binary-p3.10_tel.yml new file mode 100644 index 00000000..26b7b738 --- /dev/null +++ b/locks/binary-p3.10_tel.yml @@ -0,0 +1,4579 @@ +# This lock file was generated by conda-lock (https://github.com/conda-incubator/conda-lock). DO NOT EDIT! +# +# A "lock file" contains a concrete list of package versions (with checksums) to be installed. Unlike +# e.g. `conda env create`, the resulting environment will not change as new package versions become +# available, unless you explicitly update the lock file. +# +# Install this environment as "YOURENV" with: +# conda-lock install -n YOURENV --file binary-p3.9.yml +# To update a single package to the latest version compatible with the version constraints in the source: +# conda-lock lock --lockfile binary-p3.9.yml --update PACKAGE +# To re-solve the entire environment, e.g. after changing a version constraint in the source file: +# conda-lock -f ../environments/binary-p3.9.yml -f /home/panos/Prog/poseidon/pyPoseidon/environments/binary-p3.9.yml --lockfile binary-p3.9.yml +metadata: + content_hash: + linux-64: ef3ce57ca78aa6b616a78c3bd7c64b86f25dac5462254100e03fa659dba4138a + channels: + - url: conda-forge + used_env_vars: [] + - url: gbrey + used_env_vars: [] + - url: tomsail + used_env_vars: [] + platforms: + - linux-64 + sources: + - environments/binary-p3.9.yml + - 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DO NOT EDIT! +# +# A "lock file" contains a concrete list of package versions (with checksums) to be installed. Unlike +# e.g. `conda env create`, the resulting environment will not change as new package versions become +# available, unless you explicitly update the lock file. +# +# Install this environment as "YOURENV" with: +# conda-lock install -n YOURENV --file binary-p3.9.yml +# To update a single package to the latest version compatible with the version constraints in the source: +# conda-lock lock --lockfile binary-p3.9.yml --update PACKAGE +# To re-solve the entire environment, e.g. after changing a version constraint in the source file: +# conda-lock -f ../environments/binary-p3.9.yml -f /home/panos/Prog/poseidon/pyPoseidon/environments/binary-p3.9.yml --lockfile binary-p3.9.yml +metadata: + content_hash: + linux-64: 1a2b68f65682f17ad825093f5e64f856327c0bf73065d480e55fa475ccbb6bfc + channels: + - url: conda-forge + used_env_vars: [] + - url: gbrey + used_env_vars: [] + - url: tomsail + used_env_vars: [] + platforms: + - linux-64 + sources: + - environments/binary-p3.9.yml + - 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DO NOT EDIT! +# +# A "lock file" contains a concrete list of package versions (with checksums) to be installed. Unlike +# e.g. `conda env create`, the resulting environment will not change as new package versions become +# available, unless you explicitly update the lock file. +# +# Install this environment as "YOURENV" with: +# conda-lock install -n YOURENV --file binary-p3.9.yml +# To update a single package to the latest version compatible with the version constraints in the source: +# conda-lock lock --lockfile binary-p3.9.yml --update PACKAGE +# To re-solve the entire environment, e.g. after changing a version constraint in the source file: +# conda-lock -f ../environments/binary-p3.9.yml -f /home/panos/Prog/poseidon/pyPoseidon/environments/binary-p3.9.yml --lockfile binary-p3.9.yml +version: 1 +metadata: + content_hash: + linux-64: d6bffb62351d1756ff0e1dc7f0aaa31026596e4a364047cf6d58b2b9b52e3fd8 + channels: + - url: conda-forge + used_env_vars: [] + - url: gbrey + used_env_vars: [] + - url: tomsail + used_env_vars: [] + platforms: + - linux-64 + sources: + - environments/binary-p3.9.yml 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'{{out_1D_file}}' +TIME SERIES COORDINATES FILE 1 = '{{station_coords}}'{% endif %} +/CHECKING THE MESH = YES +/PARALLEL PROCESSORS = {{ ncsize }} +/ +/---------------------------------------------- +/ GENERAL INFORMATIONS - OUTPUTS +/---------------------------------------------- +/ +TITLE = '{{title}}' +VARIABLES FOR GRAPHIC PRINTOUTS = U,V,S / L,H,P +GRAPHIC PRINTOUT PERIOD = {{tstep_graph}} +LISTING PRINTOUT PERIOD = {{tstep_list}} +TIME STEP = {{tstep}} +DURATION = {{duration}} +ORIGINAL DATE OF TIME = {{datestart}} +ORIGINAL HOUR OF TIME = {{timestart}} +MASS-BALANCE = {{ 'YES' if mass_balance else 'NO' }} +/Determines whether a check of the mass-balance over the domain is made or not +/ +/---------------------------------------------- +/ INITIAL CONDITIONS +/---------------------------------------------- +/ +COMPUTATION CONTINUED = {{ 'YES' if computation_continued else 'NO' }} /Determines whether the computation under way is independent result or is following an earlier result +{% if computation_continued %}PREVIOUS COMPUTATION FILE = {{ previous_computation_file }} +PREVIOUS COMPUTATION FILE FORMAT = 'SERAFIND' +{% else %} +INITIAL CONDITIONS = '{{initial_conditions}}' /ZERO ELEVATION, +/ /CONSTANT ELEVATION, +/ /INITIAL ELEVATION, +/ /ZERO DEPTH, +/ /CONSTANT DEPTH (together with INITIAL DEPTH), +/ /SPECIAL, +/ /TPXO SATELLITE ALTIMETRY{% endif %} +/ +/--------------------------------------------------------------------- +/ CREATE RESTART CONDITIONS +/--------------------------------------------------------------------- +{% if hotstart %}RESTART MODE = YES +RESTART FILE = {{ restart_file }} +/RECORD NUMBER IN RESTART FILE = {{ restart_tstep }} +RESTART FILE FORMAT = 'SERAFIND' {% endif %} +/ +/--------------------------------------------------------------------- +/ SPATIAL CONDITIONS - COORDINATE SYSTEM +/--------------------------------------------------------------------- +SPHERICAL COORDINATES = {{ 'YES' if spherical_coords else 'NO' }} +SPATIAL PROJECTION TYPE = {{spatial_proj}} /1=CARTESIAN, NOT GEOREFERENCED + /2=MERCATOR + /3=LATITUDE LONGITUDE +/ 'Option 2 or 3 mandatory for spherical coordinates / Option 3: latitude and longitude in degrees!' +RESULT FILE IN LONGITUDE-LATITUDE = {{ 'YES' if result_ll else 'NO' }} +LATITUDE OF ORIGIN POINT = {{lon_orig}} +LONGITUDE OF ORIGIN POINT = {{lat_orig}} +GEOGRAPHIC SYSTEM = {{geo_sys}} /0="DEFINED BY USER" +/ /1="WGS84 LONGITUDE/LATITUDE IN REAL DEGREES" +/ /2="WGS84 NORTHERN UTM" +/ /3="WGS84 SOUTHERN UTM" +/ /4="LAMBERT" +/ /5="MERCATOR FOR TELEMAC" +ZONE NUMBER IN GEOGRAPHIC SYSTEM = {{zone_no}} +/ +/---------------------------------------------- +/ PHYSICAL PARAMETERS +/---------------------------------------------- +/ +LAW OF BOTTOM FRICTION = {{friction_law}} +FRICTION COEFFICIENT = {{friction_coef}} +DENSITY EFFECTS = {{ 'YES' if density_effects else 'NO' }} +CORIOLIS = {{ 'YES' if coriolis else 'NO' }} +/ALWAYS DEFAULT WHEN TAKING SPHERICAL COORDINATES +WATER DENSITY = {{water_density}} +TURBULENCE MODEL = {{turb_model}} /'1="CONSTANT VISCOSITY"'; +/ /'2="ELDER"'; +/ /'3="K-EPSILON MODEL"'; +/ /'4="SMAGORINSKI"'; +/ /'5="MIXING LENGTH"'; +/ /'6="SPALART-ALLMARAS"' +VELOCITY DIFFUSIVITY = {{velocity_diffusivity}} / Sets the value of the coefficient of global (dynamic+turbulent) viscosity, +/ this value may have a significant effect both on the shapes and sizes of recirculation zones +BOTTOM SMOOTHINGS = {{no_smoothings}} /Number of smoothings on bottom topography. +/ Each smoothing is mass conservative. To be used when interpolation of bathymetry on the mesh gives very rough results +/ +/---------------------------------------------- +/ WIND +/---------------------------------------------- +/ +{% if wind %}WIND = YES /Determines whether the wind effects are to be taken into account or not +AIR PRESSURE = {{ 'YES' if pressure else 'NO' }} +OPTION FOR WIND = {{wind_type}} /'1="constant in time and space"'; +/ /'2="variable in time given by formated file"'; +/ /'3="variable in time and space given by formated file"' +/COEFFICIENT OF WIND INFLUENCE = {{wind_coef}} /Sets the value of the wind driving coefficient +THRESHOLD DEPTH FOR WIND = {{depth_threshold_wind}} {% endif %} +/ +/---------------------------------------------- +/ TSUNAMI +/---------------------------------------------- +{% if tsunami %}OPTION FOR TSUNAMI GENERATION = YES +PHYSICAL CHARACTERISTICS OF THE TSUNAMI = +{{tsunami_fault}} {% endif %} +/ +/--------------------------------------------------------------------- +/ NUMERICAL PARAMETERS +/--------------------------------------------------------------------- +/ +ADVECTION OF U AND V = {{ 'YES' if adv_u_v else 'NO' }} +DIFFUSION OF VELOCITY = {{ 'YES' if diff_velocity else 'NO' }} +PRECONDITIONING = {{preconditioning}} +MATRIX STORAGE = {{matrix_storage}} +/ +INFORMATION ABOUT SOLVER = {{ 'YES' if info_solver else 'NO' }} +SOLVER = {{solver}} +SOLVER OPTION = {{solver_option}} +/ +TYPE OF ADVECTION = {{type_advection}} /Advection schemes for every variable +SUPG OPTION = {{supg_option}} /Upwindinding schemes for every variable +PROPAGATION = {{ 'YES' if propagation else 'NO' }} /Determines whether the propagation step is taken into account +SOLVER ACCURACY = {{solver_accuracy}} /Required accuracy for solving the propagation step +MATRIX-VECTOR PRODUCT = {{matrix_vector_product}} +MASS-LUMPING ON H = {{mass_lumping}} /more stable, artificially smoothed +DISCRETIZATIONS IN SPACE = {{discretizations_space}} /(1)U,V (2)H (3)T (4)K,EPSILON +FREE SURFACE GRADIENT COMPATIBILITY = {{surface_gradient_compatibility}} +INITIAL GUESS FOR U = {{initial_guess_u}} +INITIAL GUESS FOR H = {{initial_guess_h}} +/ +IMPLICITATION FOR DEPTH = {{implicitation_depth}} +IMPLICITATION FOR VELOCITY = {{implicitation_velocity}} +/ +/--------------------------------------------------------------------- +/ TIDES +/--------------------------------------------------------------------- +/ +TIDE GENERATING FORCE = {{ 'YES' if tide_generating_force else 'NO' }} +{% if tide %} +OPTION FOR TIDAL BOUNDARY CONDITIONS = {{ tidal_boundary_conditions|repeat_value(N_bc) }} +OPTION FOR LIQUID BOUNDARIES = {{ liquid_boundaries|repeat_value(N_bc) }} +TIDAL DATA BASE = {{ tidal_database|repeat_value(N_bc) }} +BINARY DATABASE 1 FOR TIDE = '{{db1_tide}}' +BINARY DATABASE 2 FOR TIDE = '{{db2_tide}}' +MINOR CONSTITUENTS INFERENCE = {{ 'YES' if minor_constituents_inference else 'NO' }} +{% endif %} +/ +/--------------------------------------------------------------------- +/ TIDAL FLATS +/--------------------------------------------------------------------- +/ +TIDAL FLATS = {{ 'YES' if tidal_flats else 'NO' }} +OPTION FOR THE TREATMENT OF TIDAL FLATS = {{tidal_flats_option}} +TREATMENT OF NEGATIVE DEPTHS = {{negative_depths_treatment}} +OPTION FOR THE DIFFUSION OF VELOCITIES = {{diffusion_velocities_option}} +CONTINUITY CORRECTION = {{ 'YES' if continuity_correction else 'NO' }} +/ +/---------------------------------------------- +/ TRACERS +/---------------------------------------------- +{% if tracer %} +TYPE OF ADVECTION = 14;5;14 +/ +NUMBER OF TRACERS = {{no_tracers}} +/NAMES OF TRACERS = 'TRACER 1'; 'TRACER 2' +/ +INITIAL VALUES OF TRACERS = 0.; 0. +PRESCRIBED TRACERS VALUES = 0.0;0.0 ; 0.0;0.0 +/ +DIFFUSION OF TRACERS = True +COEFFICIENT FOR DIFFUSION OF TRACERS = 1.;1.{% endif %} \ No newline at end of file diff --git a/pyposeidon/misc/telemac2d.json b/pyposeidon/misc/telemac2d.json new file mode 100644 index 00000000..826e496e --- /dev/null +++ b/pyposeidon/misc/telemac2d.json @@ -0,0 +1,85 @@ +{"params": + { + "geo_file": "geo.slf", + "cli_file": "geo.cli", + "out_2D_file": "results_2D.slf", + "out_1D_file": "results_1D.slf", + "station_coords": "station.in", + "monitor": false, + "inp_wind_file": "input_wind.slf", + "hotstart": true, + "restart_file": "restart_2D.slf", + "restart_tstep": -1, + "title": "TELEMAC 2D Model", + "ncsize": 1, + "tstep_graph": 12, + "tstep_list": 6, + "tstep": 300, + "duration": "None", + "datestart": "None", + "timestart": "None", + "mass_balance": true, + "no_tracers": 1, + "computation_continued": false, + "previous_computation_file": "placeholder.slf", + "initial_conditions": "ZERO ELEVATION", + "spherical_coords": true, + "spatial_proj": 3, + "result_ll": true, + "lon_orig": 0, + "lat_orig": 0, + "geo_sys": 5, + "zone_no": 33, + "friction_law": 2, + "friction_coef": 40, + "density_effects": false, + "coriolis": true, + "water_density": 1025, + "turb_model": 1, + "velocity_diffusivity": 1.0, + "no_smoothings": 0, + "wind": true, + "pressure": true, + "wind_type": 3, + "wind_coef": 3.4E-6, + "depth_threshold_wind": 1, + "tsunami": false, + "tsunami_fault": "100.;210000.;75000.;13.6;13.;36.;13.;0.;0.;3.", + "adv_u_v": true, + "diff_velocity": true, + "preconditioning": 2, + "matrix_storage": 3, + "info_solver": true, + "solver": 7, + "solver_option": 3, + "type_advection": "13;13", + "supg_option": "0;0", + "propagation": true, + "solver_accuracy": "1.E-5", + "matrix_vector_product": 1, + "mass_lumping": 1, + "discretizations_space": "11;11", + "surface_gradient_compatibility": 0.5, + "initial_guess_u": 2, + "initial_guess_h": 2, + "implicitation_depth": 0.55, + "implicitation_velocity": 0.55, + "tide_generating_force": false, + "tidal_boundary_conditions": 1, + "liquid_boundaries": 2, + "tidal_database": 2, + "db1_tide": "TPXO/h_LOCAL", + "db2_tide": "TPXO/uv_LOCAL", + "minor_constituents_inference": true, + "tidal_flats": true, + "tidal_flats_option": 1, + "negative_depths_treatment": 2, + "diffusion_velocities_option": 1, + "continuity_correction": true, + "tracer": false, + "source_x": -266565, + "source_y": 232753, + "source_flow": 1, + "tracer_values": 1000000 + } +} \ No newline at end of file diff --git a/pyposeidon/misc/telemac3d.cas b/pyposeidon/misc/telemac3d.cas new file mode 100755 index 00000000..031c35e9 --- /dev/null +++ b/pyposeidon/misc/telemac3d.cas @@ -0,0 +1,97 @@ +/---------------------------------------------- +/ COMPUTER INFORMATIONS +/---------------------------------------------- +/ +FORTRAN FILE = '{{ fortran_file }}' +GEOMETRY FILE = '{{ geo_file }}' +BOUNDARY CONDITIONS FILE = '{{ cli_file }}' +3D RESULT FILE = '{{ out_3D_file }}' +2D RESULT FILE = '{{ out_2D_file }}' +FORMATTED DATA FILE 1 = '{{ formatted_data_file_1 }}' +DEBUGGER={{ debugger }} +/ +/---------------------------------------------- +/ GENERAL INFORMATIONS - OUTPUTS +/---------------------------------------------- +/ +TITLE = '{{ title }}' +VARIABLES FOR 3D GRAPHIC PRINTOUTS : '{{ variables_3d_graphics }}' +VARIABLES FOR 2D GRAPHIC PRINTOUTS : '{{ variables_2d_graphics }}' +GRAPHIC PRINTOUT PERIOD = {{ graphic_printout_period }} +LISTING PRINTOUT PERIOD = {{ listing_printout_period }} +TIME STEP = {{ time_step }} +NUMBER OF TIME STEPS = {{ number_of_time_steps }} +CHECKING THE MESH = {{ checking_mesh }} +{% if nonlinearities %}NUMBER OF SUB ITERATIONS FOR NON LINEARITIES : {{no_subiter}} {% endif %} +/ +/----------------------------------------------------------------------/ +/ INITIAL CONDITIONS +/----------------------------------------------------------------------/ +ORIGINAL DATE OF TIME = {{ original_date_of_time }} +ORIGINAL HOUR OF TIME = {{ original_hour_of_time }} +/NUMBER OF DROGUES = {{ number_of_drogues }} +/PRINTOUT PERIOD FOR DROGUES = {{ printout_period_drogues }} +/DROGUES FILE = '{{ drogues_file }}' +MASS-BALANCE = {{ mass_balance }} +/ +/---------------------------------------------- +/ PHYSICAL PARAMETERS +/---------------------------------------------- +/ +LAW OF BOTTOM FRICTION = {{ law_of_bottom_friction }} +FRICTION COEFFICIENT FOR THE BOTTOM = {{ friction_coefficient_bottom }} +/TIDE GENERATING FORCE = {{ tide_generating_force }} +/FRICTION COEFFICIENT FOR THE BOTTOM = {{ friction_coefficient_bottom_2 }} +CORIOLIS = {{ coriolis }} +INITIAL CONDITIONS = '{{ initial_conditions }}' +/ +/--------------------------------------------------------------------- +/ SPATIAL CONDITIONS - COORDINATE SYSTEM +/--------------------------------------------------------------------- +SPHERICAL COORDINATES = {{ spherical_coordinates }} +SPATIAL PROJECTION TYPE = {{ spatial_projection_type }} +GEOGRAPHIC SYSTEM = {{ geographic_system }} +RESULT FILE IN LONGITUDE-LATITUDE = {{ result_file_in_longitude_latitude }} +/------------------------------------------------------ +/ NUMERICAL PARAMETERS +/---------------------------------------------- +/ +SCHEME FOR ADVECTION OF DEPTH = {{ scheme_for_advection_of_depth }} +/SCHEME FOR ADVECTION OF VELOCITIES = {{ scheme_for_advection_of_velocities }} +ACCURACY FOR PROPAGATION = {{ accuracy_for_propagation }} +ACCURACY FOR DIFFUSION OF VELOCITIES = {{ accuracy_for_diffusion_of_velocities }} +ACCURACY FOR PPE = {{ accuracy_for_ppe }} +/ +MAXIMUM NUMBER OF ITERATIONS FOR ADVECTION SCHEMES = {{ max_iterations_advection_schemes }} +MAXIMUM NUMBER OF ITERATIONS FOR PROPAGATION = {{ max_iterations_propagation }} +MAXIMUM NUMBER OF ITERATIONS FOR DIFFUSION OF VELOCITIES = {{ max_iterations_diffusion_velocities }} +MAXIMUM NUMBER OF ITERATIONS FOR PPE = {{ max_iterations_ppe }} +MAXIMUM NUMBER OF ITERATIONS FOR DIFFUSION OF K-EPSILON = {{ max_iterations_diffusion_k_epsilon }} +/ +SOLVER FOR PPE = {{ solver_for_ppe }} +SOLVER FOR PROPAGATION = {{ solver_for_propagation }} +OPTION OF SOLVER FOR PROPAGATION = {{ option_solver_for_propagation }} +IMPLICITATION FOR DEPTH = {{ implicitation_for_depth }} +IMPLICITATION FOR VELOCITIES = {{ implicitation_for_velocities }} +COEFFICIENT FOR HORIZONTAL DIFFUSION OF VELOCITIES = {{ coefficient_horizontal_diffusion_velocities }} +COEFFICIENT FOR VERTICAL DIFFUSION OF VELOCITIES = {{ coefficient_vertical_diffusion_velocities }} +FREE SURFACE GRADIENT COMPATIBILITY = {{ free_surface_gradient_compatibility }} +/ +/---------------------------------------------------------------------- +/ VERTICAL DESCRIPTION (LAYERS, MESH, ETC...) +/---------------------------------------------------------------------- +/ +MESH TRANSFORMATION = {{ mesh_transformation }} +NUMBER OF HORIZONTAL LEVELS = {{ number_of_horizontal_levels }} +NUMBER OF BOTTOM SMOOTHINGS = {{ number_of_bottom_smoothings }} +/ +/---------------------------------------------- +/ DRY-WET ELEMENTS +/---------------------------------------------- +/ +/TIDAL FLATS = {{ tidal_flats }} +/MINIMAL VALUE FOR DEPTH = {{ minimal_value_for_depth }} +/TREATMENT OF NEGATIVE DEPTHS = {{ treatment_of_negative_depths }} +/TREATMENT ON TIDAL FLATS FOR VELOCITIES = {{ treatment_on_tidal_flats_for_velocities }} +/OPTION FOR THE TREATMENT OF TIDAL FLATS = {{ option_for_treatment_of_tidal_flats }} +/ \ No newline at end of file diff --git a/pyposeidon/misc/telemac3d.json b/pyposeidon/misc/telemac3d.json new file mode 100644 index 00000000..f24650f9 --- /dev/null +++ b/pyposeidon/misc/telemac3d.json @@ -0,0 +1,62 @@ +{ + "fortran_file": "user_fortran", + "geo_file": "Global_10km_100km.slf", + "cli_file": "Global_10km_100km_t.cli", + "out_3D_file": "res.3d.slf", + "out_2D_file": "res.2d.slf", + "formatted_data_file_1": "tsunamis.txt", + "debugger": 0, + "title": "TELEMAC 3D MODEL", + "variables_3d_graphics": "Z,U,V,W", + "variables_2d_graphics": "U,V,H,S", + "graphic_printout_period": 10, + "listing_printout_period": 10, + "time_step": 60, + "number_of_time_steps": 2000, + "checking_mesh": false, + "nonlinearities": false, + "no_subiter": 20, + "original_date_of_time": "2011;3;11", + "original_hour_of_time": "05;46;0", + "number_of_drogues": 2, + "printout_period_drogues": 60, + "drogues_file": "drogues.dat", + "mass_balance": true, + "law_of_bottom_friction": 5, + "friction_coefficient_bottom": 0.01, + "tide_generating_force": true, + "law_of_bottom_friction_2": 3, + "friction_coefficient_bottom_2": 60.0, + "mesh_transformation": 1, + "coriolis": true, + "initial_conditions": "SPECIAL", + "spherical_coordinates": true, + "spatial_projection_type": 3, + "geographic_system": 1, + "result_file_in_longitude_latitude": true, + "scheme_for_advection_of_depth": 5, + "scheme_for_advection_of_velocities": 14, + "accuracy_for_propagation": 1.0E-3, + "accuracy_for_diffusion_of_velocities": 1.0E-3, + "accuracy_for_ppe": 1.0E-3, + "max_iterations_advection_schemes": 200, + "max_iterations_propagation": 300, + "max_iterations_diffusion_velocities": 200, + "max_iterations_ppe": 200, + "max_iterations_diffusion_k_epsilon": 200, + "solver_for_ppe": 6, + "solver_for_propagation": 7, + "option_solver_for_propagation": 4, + "implicitation_for_depth": 0.6, + "implicitation_for_velocities": 0.6, + "coefficient_horizontal_diffusion_velocities": 1.0, + "coefficient_vertical_diffusion_velocities": 1.0, + "free_surface_gradient_compatibility": 0.5, + "number_of_horizontal_levels": 2, + "number_of_bottom_smoothings": 1, + "tidal_flats": true, + "minimal_value_for_depth": -10.0, + "treatment_of_negative_depths": 2, + "treatment_on_tidal_flats_for_velocities": 0, + "option_for_treatment_of_tidal_flats": 1 +} diff --git a/pyposeidon/misc/tomawac.cas b/pyposeidon/misc/tomawac.cas new file mode 100755 index 00000000..a6ae5d56 --- /dev/null +++ b/pyposeidon/misc/tomawac.cas @@ -0,0 +1,86 @@ +/ TOMAWAC INPUT FILE DEFINITIONS +/******************************************************************** +GEOMETRY FILE : '{{geo_file}}' +BOUNDARY CONDITIONS FILE : '{{cli_file}}' +BINARY WINDS FILE : '{{inp_wind_file}}' +WINDS FILE FORMAT : {{wind_file_format}} +{% if hotstart %} +GLOBAL RESULT FILE = {{ restart_file }} +{% endif %} +/------------------------------------------------------------------/ +2D RESULTS FILE : '{{out_2D_file}}' +2D RESULTS FILE FORMAT : 'SERAFIN' +{% if monitor %}TIME SERIES FILE = '{{out_1D_file}}' +TIME SERIES COORDINATES FILE = '{{station_coords}}'{% endif %} +/------------------------------------------------------------------/ +/ COMPUTATION_ENVIRONMENT +/------------------------------------------------------------------/ +TITLE : '{{title}}' +PERIOD FOR LISTING PRINTOUTS : {{tstep_list}} +PERIOD FOR GRAPHIC PRINTOUTS : {{tstep_graph}} +VARIABLES FOR 2D GRAPHIC PRINTOUTS : HM0;DMOY;TMOY;TPD;SPD;TPR8 + +/------------------------------------------------------------------/ +/ GENERAL_PARAMETERS +/------------------------------------------------------------------/ +NAMES OF VARIABLES : 'PATM UI '; +'TAIR DEGREES '; +'WINDX M/S '; +'WINDY M/S ' +NUMBER OF TIME STEP : {{nb_tsteps}} +TIME STEP : {{tstep}} +DATE OF COMPUTATION BEGINNING : {{datestart}} + +/***** DISCRETISATION ***** +NUMBER OF DIRECTIONS : {{num_directions}} +MINIMAL FREQUENCY : {{min_frequency}} +FREQUENTIAL RATIO : {{freq_ratio}} +NUMBER OF FREQUENCIES : {{num_frequencies}} + +CONSIDERATION OF A STATIONARY CURRENT : {{stationary_current}} +CONSIDERATION OF A WIND : {{consider_wind}} +STATIONARY WIND : {{stationary_wind}} +CONSIDERATION OF TIDE : {{consider_tide}} +MINIMUM WATER DEPTH : {{min_water_depth}} +SPHERICAL COORDINATES : {{spherical_coordinates}} +INFINITE DEPTH : {{infinite_depth}} +/ +/------------------------------------------------------------------/ +/ SOURCES/SINKS CONDITIONS +/------------------------------------------------------------------/ +BAJ MODELING : {{baj_modeling}} +CONSIDERATION OF SOURCE TERMS : {{consider_source_terms}} +WIND GENERATION : {{wind_generation}} +WIND GENERATION COEFFICIENT : {{wind_gen_coef}} +AIR DENSITY : {{air_density}} +WATER DENSITY : {{water_density}} +WIND DRAG COEFFICIENT : {{wind_drag_coef}} +VON KARMAN CONSTANT : {{von_karman_const}} +SHIFT GROWING CURVE DUE TO WIND : {{shift_growing_curve}} +LINEAR WAVE GROWTH : {{linear_wave_growth}} +WHITE CAPPING DISSIPATION : {{whitecap_diss}} +WHITE CAPPING DISSIPATION COEFFICIENT : {{whitecap_diss_coef}} +WHITE CAPPING WEIGHTING COEFFICIENT : {{whitecap_diss_wgt}} +BOTTOM FRICTION DISSIPATION : {{bottom_friction_diss}} +BOTTOM FRICTION COEFFICIENT : {{bottom_friction_coef}} +STANDARD CONFIGURATION PARAMETER : {{standard_config_param}} +NUMBER OF ITERATIONS FOR THE SOURCE TERMS : {{source_terms_iterations}} +DISSIPATION BY STRONG CURRENT : {{dissipation_strong_current}} +DEPTH-INDUCED BREAKING DISSIPATION : {{depth_induced_breaking_diss}} +NUMBER OF BREAKING TIME STEPS : {{breaking_time_steps}} +MAXIMUM VALUE OF THE RATIO HM0 ON D : {{max_ratio_hm0_d}} +WAVE GROWTH LIMITER : {{wave_growth_limiter}} +TRIAD INTERACTIONS : {{triad_interactions}} +NON-LINEAR TRANSFERS BETWEEN FREQUENCIES : {{nonlinear_transfers_freq}} +/VEGETATION TAKEN INTO ACCOUNT : {{vegetation_account}} +/------------------------------------------------------------------/ +/ BOUNDARY_CONDITIONS +/------------------------------------------------------------------/ +/ +/------------------------------------------------------------------/ +/ INITIAL_CONDITIONS +/------------------------------------------------------------------/ +/ +{% if computation_continued %} +PREVIOUS COMPUTATION FILE = {{ previous_computation_file }} +{% endif %} \ No newline at end of file diff --git a/pyposeidon/misc/tomawac.json b/pyposeidon/misc/tomawac.json new file mode 100644 index 00000000..d03f5bc5 --- /dev/null +++ b/pyposeidon/misc/tomawac.json @@ -0,0 +1,57 @@ +{"params": + { + "geo_file": "geo.slf", + "cli_file": "geo.cli", + "inp_wind_file": "input_wind.slf", + "wind_file_format": 3, + "out_2D_file": "results_2D.slf", + "out_1D_file": "results_1D.slf", + "station_coords": "station.in", + "computation_continued": false, + "previous_computation_file": "placeholder.slf", + "hotstart": true, + "restart_file": "restart_2D.slf", + "title": "TOMAWAC Model Simulation", + "tstep_list": 3600, + "tstep_graph": 3600, + "nb_tsteps": 100, + "tstep": 60, + "datestart": "YYYY/MM/DD", + "num_directions": 24, + "min_frequency": 0.03, + "freq_ratio": 1.1, + "num_frequencies": 31, + "stationary_current": "NO", + "consider_wind": "YES", + "stationary_wind": "NO", + "consider_tide": "NO", + "min_water_depth": 0.1, + "spherical_coordinates": "YES", + "infinite_depth": "NO", + "baj_modeling": 0, + "consider_source_terms": "YES", + "wind_generation": 1, + "wind_gen_coef": 1.2, + "air_density": 1.225, + "water_density": 1026.0, + "wind_drag_coef": 0.0012875, + "von_karman_const": 0.41, + "shift_growing_curve": 0.011, + "linear_wave_growth": 1, + "whitecap_diss": 1, + "whitecap_diss_coef": 0.04, + "whitecap_diss_wgt": 1.0, + "bottom_friction_diss": 1, + "bottom_friction_coef": 0.038, + "standard_config_param": 0.25, + "source_terms_iterations": 1, + "dissipation_strong_current": 0, + "depth_induced_breaking_diss": 1, + "breaking_time_steps": 1, + "max_ratio_hm0_d": 1.0, + "wave_growth_limiter": 1, + "triad_interactions": 0, + "nonlinear_transfers_freq": 1, + "vegetation_account": "NO" + } +} diff --git a/pyposeidon/telemac.py b/pyposeidon/telemac.py new file mode 100644 index 00000000..c16c1846 --- /dev/null +++ b/pyposeidon/telemac.py @@ -0,0 +1,1446 @@ +""" +TELEMAC model of pyposeidon. It controls the creation, execution & output of a complete simulation based on SCHISM. + +""" +# Copyright 2018 European Union +# This file is part of pyposeidon. +# Licensed under the EUPL, Version 1.2 or – as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "Licence"). +# Unless required by applicable law or agreed to in writing, software distributed under the Licence is distributed on an "AS IS" basis, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the Licence for the specific language governing permissions and limitations under the Licence. + +import os +import datetime +import numpy as np +import sys +import json +import pandas as pd +import glob +import xarray as xr +import geopandas as gp +import shapely +import jinja2 +from searvey import ioc +import shutil +from scipy.spatial import Delaunay + +# local modules +import pyposeidon +import pyposeidon.mesh as pmesh +import pyposeidon.meteo as pmeteo +import pyposeidon.dem as pdem +from pyposeidon.paths import DATA_PATH +from pyposeidon.utils.get_value import get_value +from pyposeidon.utils.converter import myconverter +from pyposeidon.utils.cpoint import closest_n_points +from pyposeidon.utils import data +from pyposeidon.utils.norm import normalize_column_names +from pyposeidon.utils.norm import normalize_varnames +from pyposeidon.utils.obs import get_obs_data +from pyposeidon.utils.post import export_xarray +from pyposeidon.utils.post import remove + +# telemac (needs telemac stack on conda) +from telapy.api.t2d import Telemac2d +from telapy.api.t3d import Telemac3d +from telapy.api.art import Artemis +from telapy.api.wac import Tomawac +from execution.telemac_cas import TelemacCas +from data_manip.formats.selafin import Selafin +from data_manip.extraction.telemac_file import TelemacFile +from pretel.extract_contour import detecting_boundaries +from pretel.generate_atm import generate_atm +from pretel.extract_contour import sorting_boundaries +from utils.progressbar import ProgressBar +from utils.geometry import get_weights +from utils.geometry import interp +from pretel.manip_telfile import alter +from xarray_selafin.xarray_backend import SelafinAccessor +from mpi4py import MPI + +import numpy as np + +import logging + +from . import tools + + +logger = logging.getLogger(__name__) + + +TELEMAC_NAME = "telemac" + + +def divisors(n): + result = set() + for i in range(1, int(n**0.5) + 1): + if n % i == 0: + result.add(i) + result.add(n // i) + return sorted(result) + + +def calculate_time_step_hourly_multiple(resolution, min_water_depth=0.1, courant_number=50): + """ + Calculate the maximum allowable time step for a shallow water equation model + based on the CFL condition, rounded to the closest divisor of an hour. + + :param resolution: Spatial resolution (Δx) in meters + :param min_water_depth: Minimum water depth (h_min) in meters. Default is 1 meter. + :param courant_number: Courant number (C), typically less than 1 for explicit schemes. Can be higher for implicit schemes. + :return: Maximum allowable time step (Δt) in seconds, rounded to the closest divisor of an hour. + """ + resolution *= 111.389 # degrees to meters conversion factor + raw_time_step = courant_number * resolution / (9.81 * min_water_depth) ** 0.5 + # Get all divisors of an hour (3600 seconds) + hour_divisors = divisors(3600) + # Find the closest divisor of an hour to the raw time step + closest_divisor = min(hour_divisors, key=lambda x: abs(x - raw_time_step)) + return closest_divisor + + +# Helper functions +def df_to_gpd(df, x: str = "longitude", y: str = "latitude"): + gpdf = gp.GeoDataFrame( + df, + geometry=gp.points_from_xy( + df[x], + df[y], + ), + crs="EPSG:4326", + ) + return gpdf + + +def xy_to_ll(x, y): + gpdf = gp.GeoDataFrame(geometry=gp.points_from_xy(x, y), crs="EPSG:3857").to_crs("EPSG:4326") + return gpdf.geometry.x, gpdf.geometry.y + + +def is_ccw(tris, meshx, meshy): + x1, x2, x3 = meshx[tris].T + y1, y2, y3 = meshy[tris].T + return (y3 - y1) * (x2 - x1) > (y2 - y1) * (x3 - x1) + + +def flip(tris): + return np.column_stack((tris[:, 2], tris[:, 1], tris[:, 0])) + + +def is_overlapping(tris, meshx): + PIR = 180 + x1, x2, x3 = meshx[tris].T + return np.logical_or(abs(x2 - x1) > PIR, abs(x3 - x1) > PIR) + + +def get_det_mask(tris, meshx, meshy): + t12 = meshx[tris[:, 1]] - meshx[tris[:, 0]] + t13 = meshx[tris[:, 2]] - meshx[tris[:, 0]] + t22 = meshy[tris[:, 1]] - meshy[tris[:, 0]] + t23 = meshy[tris[:, 2]] - meshy[tris[:, 0]] + return t12 * t23 - t22 * t13 > 0 + + +def contains_pole(x, y): + return np.any(y == 90, axis=0) + + +def fix_glob_connectivity(x, y, ikle2, corrections): + # Assuming slf.meshx, slf.meshy, and slf.ikle2 are NumPy arrays + ikle2 = np.array(ikle2) + # Ensure all triangles are CCW + ccw_mask = is_ccw(ikle2, x, y) + ikle2[~ccw_mask] = flip(ikle2[~ccw_mask]) + + # triangles accross the dateline + m_ = is_overlapping(ikle2, x) + ikle2[m_] = flip(ikle2[m_]) + + # special case : pole triangles + pole_mask = contains_pole(x[ikle2].T, y[ikle2].T) + + # manual additional corrections + if corrections is not None: + for rev in corrections["reverse"]: + ikle2[rev : rev + 1] = flip(ikle2[rev : rev + 1]) + for rem in corrections["remove"]: + pole_mask[rem] = True + + # Check for negative determinant + detmask = ~get_det_mask(ikle2, x, y) + logger.debug(f"reversed {detmask.sum()} triangles") + logger.debug(f"pole triangles: {np.where(pole_mask)[0]}") + logger.debug("[TEMPORARY FIX]: REMOVE THE POLE TRIANGLES") + return ikle2[~pole_mask, :] + + +def write_netcdf(ds, outpath): + fileOut = os.path.splitext(outpath)[0] + ".nc" + ds.to_netcdf(fileOut) + + +def write_meteo(outpath, geo, ds, gtype="grid", ttype="time", input360=False): + lon = ds.longitude.values + if input360: + lon[lon > 180] -= 360 + lat = ds.latitude.values + + if gtype == "grid": + nx1d = len(lon) + ny1d = len(lat) + x = np.tile(lon, ny1d).reshape(ny1d, nx1d).T.ravel() + y = np.tile(lat, nx1d) + else: + x = lon + y = lat + + if ttype == "time": + t0 = pd.Timestamp(ds.time.values[0]) + elif ttype == "step": + t0 = pd.Timestamp(ds.time.values) + seconds = ds.step.values / 1e9 + ds.time = pd.to_datetime(t0 + pd.Timedelta(seconds=seconds)) + + geo = xr.open_dataset(geo, engine="selafin") + + in_xy = np.vstack((x, y)).T + out_xy = np.vstack((geo.x, geo.y)).T + logger.info(f"Geting interp weights from {len(x)} on {len(geo.x)} nodes") + vert, wgts, u_x, g_x = get_weights(in_xy, out_xy) + + data_vars = {} + var_attrs = {} + dtype = np.float64 + dims = ["time", "node"] + shape = (len(ds.time), len(geo.x)) + + coords = { + "x": ("node", geo.x.data), + "y": ("node", geo.y.data), + "time": ds.time, + } + + # Define a mapping from the original variable names to the new ones + var_map = { + "u10": ("WINDX", "WINDX", "M/S"), + "v10": ("WINDY", "WINDY", "M/S"), + "msl": ("PATM", "PATM", "PASCAL"), + "tmp": ("TAIR", "TEMPERATURE", "DEGREES C"), + } + for var in ds.data_vars: + if var in var_map: + # attributes + var_attrs[var_map[var][0]] = (var_map[var][1], var_map[var][2]) + # data + data = np.empty(shape, dtype=dtype) + for it, t_ in enumerate(ds.time): + tmp = np.ravel(np.transpose(ds.isel(time=it)[var].values)) + data[it, :] = interp(tmp, vert, wgts, u_x, g_x) + data_vars[var_map[var][0]] = xr.Variable(dims=dims, data=data) + + atm = xr.Dataset(data_vars=data_vars, coords=coords) + atm.attrs["date_start"] = [t0.year, t0.month, t0.day, t0.hour, t0.minute, t0.second] + atm.attrs["ikle2"] = geo.attrs["ikle2"] + atm.attrs["variables"] = var_attrs + atm.selafin.write(outpath) + + +def get_boundary_settings(boundary_type, glo_node, bnd_node): + settings = { + "lihbor": 5 if boundary_type == "open" else 2, + "liubor": 6 if boundary_type == "open" else 2, + "livbor": 6 if boundary_type == "open" else 2, + "hbor": 0.0, + "ubor": 0.0, + "vbor": 0.0, + "aubor": 0.0, + "litbor": 5 if boundary_type == "open" else 2, + "tbor": 0.0, + "atbor": 0.0, + "btbor": 0.0, + "nbor": glo_node + 1, + "k": bnd_node + 1, + } + return settings + + +def format_value(value, width, precision=3, is_float=False): + if is_float: + return f"{value:{5}.{precision}f}" + else: + return f"{value:{2}}" + + +def detect_boundary_points_optimized(connectivity_table, npoints): + # Building adjacency list from connectivity table + adjacency_list = {i: [] for i in range(npoints)} + for row in connectivity_table: + for i in range(3): + adjacency_list[row[i]].extend(row[np.arange(3) != i]) + + buf = [] + connectivity_bnd_elt = [] + + pbar = ProgressBar(npoints) + for i in range(npoints): + pbar.update(i) + # Directly accessing the connections for each point + connections = adjacency_list[i] + + uniq, count = np.unique(connections, return_counts=True) + temp = uniq[count == 1].tolist() + buf.extend(temp) + + if temp: + if i in temp: + temp.remove(i) + temp.append(i) + connectivity_bnd_elt.append(temp) + + pbar.finish() + bnd_points = np.unique(buf) + + return np.array(connectivity_bnd_elt), bnd_points + + +def write_cli(inTel, ds, outCli=None, tel_module="telemac2d", global_=True): + """ + (This function is a modification of the existing extract_contour() function + in scripts/python3/pretel/extract_contour.py of the TELEMAC scripts) + + Generic function for extraction of contour from a mesh (with our without + boundary file) + + @param inTel (str) Path to the mesh file + @param ds (xr.Dataset) xarray Dataset of the mesh file (used to extract the boundary types) + @param outCli (str) Path to the output contour file + + @returns (list) List of polygons + """ + # TELEMAC script PART + domains = [] + tel = TelemacFile(inTel) + + connectivity_bnd_elt, bnd_points = detect_boundary_points_optimized(tel.ikle2, tel.npoin2) + + boundaries_ordered, bnd_idx_left, bnd_elt_left = sorting_boundaries( + tel, bnd_points, connectivity_bnd_elt, global_=global_ + ) + domains.append(boundaries_ordered) + + i = 1 + while bnd_elt_left.size != 0: + i += 1 + + boundaries_ordered, bnd_idx_left, bnd_elt_left = sorting_boundaries( + tel, bnd_idx_left, bnd_elt_left, global_=global_ + ) + domains.append(boundaries_ordered) + + # custom pyposeidon part + if outCli is None: + outCli = os.path.splitext(inTel)[0] + ".cli" + node_to_type = dict(zip(ds.node.values, ds.type.values)) # mapping from node to type + domains_bnd = [] + lines = [] + bnd_node = 0 + for domain in domains: + poly_bnd = [] + for bnd in domain: + coord_bnd = [] + for i, glo_node in enumerate(bnd[:-1]): # not taking the last node (not repeating) + x, y = tel.meshx[glo_node], tel.meshy[glo_node] + coord_bnd.append((x, y)) + # Determine boundary type for the current node + boundary_type = node_to_type.get(glo_node, "Unknown") + if boundary_type == "open": + # Get previous and next node indices in a circular manner + prev_node = bnd[i - 1] if i > 0 else bnd[-2] # -2 to skip the repeated last node + next_node = bnd[i + 1] if i < len(bnd) - 2 else bnd[0] # Wrapping around to the first node + # Get boundary types for previous and next nodes + prev_boundary_type = node_to_type.get(prev_node, "Unknown") + next_boundary_type = node_to_type.get(next_node, "Unknown") + # If both adjacent nodes are not 'open', then bnd is closed + if prev_boundary_type != "open" and next_boundary_type != "open": + boundary_type = "Unknown" + boundary_settings = get_boundary_settings(boundary_type, glo_node, bnd_node) + + keys_order = [ + "lihbor", + "liubor", + "livbor", + "hbor", + "ubor", + "vbor", + "aubor", + "litbor", + "tbor", + "atbor", + "btbor", + "nbor", + "k", + ] + if tel_module == "telemac2d": + line = " ".join(str(boundary_settings[key]) for key in keys_order) + lines.append(line) + bnd_node += 1 + poly_bnd.append((coord_bnd, bnd)) + domains_bnd.append(poly_bnd) + + # Writing to file + with open(outCli, "w") as f: + for line in lines: + formatted_line = " ".join( + format_value(value, 3, is_float=isinstance(value, float)) for value in line.split() + ) + f.write(f"{formatted_line}\n") + + return domains_bnd + + +def write_cas(outpath, tel_module, params): + # Load the Jinja2 template + env = jinja2.Environment(loader=jinja2.FileSystemLoader(os.path.join(DATA_PATH))) + + # Define the custom filter for tidal bounds + def repeat_value(value, count): + return ";".join([str(value)] * count) + + env.filters["repeat_value"] = repeat_value + template = env.get_template(tel_module + ".cas") + ouput = template.render(params) + with open(os.path.join(outpath, tel_module + ".cas"), "w") as f: + f.write(ouput) + + +class Telemac: + def __init__(self, **kwargs): + """ + Create a TELEMAC model: + by default : TELEMAC2D is used. + + Args: + rfolder str: the TELEMAC module to use. Can be one of the following: telemac3d, telemac2d, artemis, tomawac + + + """ + tel_module = kwargs.get("module", "telemac2d") + if tel_module not in ["telemac3d", "telemac2d", "artemis", "tomawac"]: + raise ValueError("module must be one of the following: telemac3d, telemac2d, artemis, tomawac") + + rfolder = kwargs.get("rfolder", None) + if rfolder: + self.read_folder(**kwargs) + + self.geometry = kwargs.get("geometry", None) + + if self.geometry: + if isinstance(self.geometry, dict): + self.lon_min = self.geometry["lon_min"] + self.lon_max = self.geometry["lon_max"] + self.lat_min = self.geometry["lat_min"] + self.lat_max = self.geometry["lat_max"] + elif self.geometry == "global": + logger.warning("geometry is 'global'") + elif isinstance(self.geometry, str): + try: + geo = gp.GeoDataFrame.from_file(self.geometry) + except: + logger.error("geometry argument not a valid geopandas file") + sys.exit(1) + + ( + self.lon_min, + self.lat_min, + self.lon_max, + self.lat_max, + ) = geo.total_bounds + else: + logger.warning("no geometry given") + + # coastlines + coastlines = kwargs.get("coastlines", None) + + if coastlines is not None: + try: + coast = gp.GeoDataFrame.from_file(coastlines) + except: + coast = gp.GeoDataFrame(coastlines) + + self.coastlines = coast + + if not hasattr(self, "start_date"): + start_date = kwargs.get("start_date", None) + self.start_date = pd.to_datetime(start_date) + + if not hasattr(self, "end_date"): + if "time_frame" in kwargs: + time_frame = kwargs.get("time_frame", None) + self.end_date = self.start_date + pd.to_timedelta(time_frame) + elif "end_date" in kwargs: + end_date = kwargs.get("end_date", None) + self.end_date = pd.to_datetime(end_date) + self.time_frame = self.end_date - self.start_date + + if not hasattr(self, "rdate"): + self.rdate = get_value(self, kwargs, "rdate", self.start_date) + + if not hasattr(self, "end_date"): + # --------------------------------------------------------------------- + logger.warning("model not set properly, No end_date\n") + # --------------------------------------------------------------------- + + self.tstep = kwargs.get("dt", None) + + self.module = tel_module + self.tide = kwargs.get("tide", False) + tpxo_ = kwargs.get("tpxo_source", None) + if tpxo_ is not None: + self.tide = True + + self.atm = kwargs.get("atm", True) + self.monitor = kwargs.get("monitor", False) + + self.solver_name = TELEMAC_NAME + # restart + restart = get_value(self, kwargs, "hotstart", None) + if restart: + self.restart = pd.to_datetime(restart) + else: + self.restart = None + + # specific to meteo grib files + self.gtype = get_value(self, kwargs, "meteo_gtype", "grid") + self.ttype = get_value(self, kwargs, "meteo_ttype", "time") + self.ncsize = get_value(self, kwargs, "ncsize", 1) + # convert -180/180 to 0-360 + self.input360 = get_value(self, kwargs, "meteo_input360", False) + + for attr, value in kwargs.items(): + if not hasattr(self, attr): + setattr(self, attr, value) + + setattr(self, "misc", {}) + + # ============================================================================================ + # CONFIG + # ============================================================================================ + def config(self, config_file=None, output=False, **kwargs): + dic = get_value(self, kwargs, "parameters", None) + # param_file = get_value(self,kwargs,'config_file',None) + + if config_file: + # --------------------------------------------------------------------- + logger.info("reading parameter file {}\n".format(config_file)) + # --------------------------------------------------------------------- + else: + # --------------------------------------------------------------------- + logger.info("using default " + self.module + " json config file ...\n") + # --------------------------------------------------------------------- + + config_file = os.path.join(DATA_PATH, self.module + ".json") + + # Load the parameters from the JSON file + with open(config_file, "r") as json_file: + config = json.load(json_file) + params = config["params"] + + # update key values + if "telemac" in self.module: + params["datestart"] = self.start_date.strftime("%Y;%m;%d") + params["timestart"] = self.start_date.strftime("%H;%M;%S") + elif "tomawac" in self.module: + params["datestart"] = self.start_date.strftime("%Y%m%d%H%M") + + if hasattr(self, "time_frame"): + duration = pd.to_timedelta(self.time_frame).total_seconds() + else: + self.time_frame = self.end_date - self.start_date + duration = self.time_frame.total_seconds() + + params["duration"] = duration + # export grid data every hour + res_min = get_value(self, kwargs, "resolution_min", 0.5) + + if self.tstep: + tstep = self.tstep + else: + tstep = calculate_time_step_hourly_multiple(res_min) + params["tstep"] = tstep + + params["nb_tsteps"] = int(duration / tstep) + params["tstep_graph"] = int(3600 / tstep) + params["tstep_list"] = int(3600 / tstep) + params["ncsize"] = self.ncsize + + # tide + if self.tide: + params["tide"] = True + params["initial_conditions"] = "TPXO SATELLITE ALTIMETRY" + + # hotstart + if self.restart is not None: + hotout = int((self.restart - self.rdate).total_seconds() / (params["tstep"] * params["tstep_graph"])) + params["hotstart"] = True + params["restart_tstep"] = hotout + + if self.monitor: + params["monitor"] = True + + # update + if dic: + for key in dic.keys(): + params[key] = dic[key] + if "params" in kwargs.keys(): + for key in kwargs["params"].keys(): + params[key] = kwargs["params"][key] + + self.params = params + + if output: + # save params + # --------------------------------------------------------------------- + logger.info("output " + self.module + " CAS file ...\n") + # --------------------------------------------------------------------- + path = get_value(self, kwargs, "rpath", "./telemac/") + write_cas(path, self.module, params) + + # ============================================================================================ + # METEO + # ============================================================================================ + def force(self, **kwargs): + meteo_source = get_value(self, kwargs, "meteo_source", None) + + kwargs.update({"meteo_source": meteo_source}) + + flag = get_value(self, kwargs, "update", ["all"]) + + z = {**self.__dict__, **kwargs} # merge self and possible kwargs + + # check if files exist + if flag: + if ("meteo" in flag) | ("all" in flag): + self.meteo = pmeteo.Meteo(**z) + else: + logger.info("skipping meteo ..\n") + else: + self.meteo = pmeteo.Meteo(**z) + + if hasattr(self, "meteDataseto"): + # add 1 hour for Schism issue with end time + ap = self.meteo.Dataset.isel(time=-1) + ap["time"] = ap.time.values + pd.to_timedelta("1H") + + self.meteo.Dataset = xr.concat([self.meteo.Dataset, ap], dim="time") + + def to_force(self, geo, outpath): + # # WRITE METEO FILE + logger.info("saving meteo file.. ") + out_file = os.path.join(outpath, "input_wind.slf") + write_meteo(out_file, geo, self.meteo.Dataset, gtype=self.gtype, ttype=self.ttype, input360=self.input360) + + # ============================================================================================ + # TPXO + # ============================================================================================ + def tpxo(self, tpxo_suffix="tpxo9_atlas_30_v4", **kwargs): + tpxo_source = get_value(self, kwargs, "tpxo_source", None) + path = get_value(self, kwargs, "rpath", "./telemac") + + lat_min = np.round(self.lat_min - 1 if self.lat_min > -89 else self.lat_min, 2) + lat_max = np.round(self.lat_max + 1 if self.lat_max < 89 else self.lat_max, 2) + lon_min = np.round(self.lon_min - 1, 2) + lon_max = np.round(self.lon_max + 1, 2) + + if lon_min < 0 and lon_max < 0: + lon_min += 360 + lon_max += 360 + + if tpxo_source is None: + # --------------------------------------------------------------------- + logger.error("model not set properly: define tpxo_source\n") + raise ValueError("model not set properly: define tpxo_source") + # --------------------------------------------------------------------- + os.makedirs(os.path.join(path, "TPXO"), exist_ok=True) + + logger.info("extracting tides..\n") + # + lines = [] + lines.append(f"{path}/TPXO/input_sources !") + lines.append(f"{path}/TPXO/Model_Local ! Local area Model name") + lines.append(f"{lat_min} {lat_max} ! Lat limits (degrees N)") + lines.append(f"{lon_min} {lon_max} ! Lon limits (degrees E, range -180 +360)") + with open(f"{path}/TPXO/setup.local", "w") as file: + file.write("\n".join(lines)) + + # input sources + lines = [] + lines.append(f"{tpxo_source}/h_*_{tpxo_suffix}") + lines.append(f"{tpxo_source}/u_*_{tpxo_suffix}") + lines.append(f"{tpxo_source}/grid_{tpxo_suffix}") + with open(f"{path}/TPXO/input_sources", "w") as file: + file.write("\n".join(lines)) + + # output files + lines = [] + lines.append(f"{path}/TPXO/h_LOCAL") + lines.append(f"{path}/TPXO/uv_LOCAL") + lines.append(f"{path}/TPXO/grid_LOCAL") + with open(f"{path}/TPXO/Model_Local", "w") as file: + file.write("\n".join(lines)) + + os.system(f"cp {DATA_PATH}/extract_local_model {path}/TPXO/extract_local_model") + os.system(f"{path}/TPXO/extract_local_model<{path}/TPXO/setup.local") + + # ============================================================================================ + # DEM + # ============================================================================================ + def bath(self, **kwargs): + # z = self.__dict__.copy() + + if self.mesh.Dataset is not None: + kwargs["grid_x"] = self.mesh.Dataset.SCHISM_hgrid_node_x.values + kwargs["grid_y"] = self.mesh.Dataset.SCHISM_hgrid_node_y.values + + dpath = get_value(self, kwargs, "dem_source", None) + + kwargs.update({"dem_source": dpath}) + + flag = get_value(self, kwargs, "update", ["all"]) + # check if files exist + if flag: + if ("dem" in flag) | ("all" in flag): + kwargs.update( + { + "lon_min": self.lon_min, + "lat_min": self.lat_min, + "lon_max": self.lon_max, + "lat_max": self.lat_max, + } + ) + self.dem.Dataset = pdem.dem_on_mesh(self.dem.Dataset, **kwargs) + + if kwargs.get("adjust_dem", True): + coastline = kwargs.get("coastlines", None) + if coastline is None: + logger.warning("coastlines not present, aborting adjusting dem\n") + elif "adjusted" in self.dem.Dataset.data_vars.keys(): + logger.info("Dem already adjusted\n") + elif "fval" in self.dem.Dataset.data_vars.keys(): + logger.info("Dem already adjusted\n") + else: + self.dem.adjust(coastline, **kwargs) + try: + try: + bat = -self.dem.Dataset.fval.values.astype(float) # minus for the hydro run + if np.isinf(bat).sum() != 0: + raise Exception("Bathymetry contains Infs") + if np.isnan(bat).sum() != 0: + raise Exception("Bathymetry contains NaNs") + logger.warning("Bathymetric values fval contain NaNs, using ival values ..\n") + + except: + bat = -self.dem.Dataset.ival.values.astype(float) # minus for the hydro run + + self.mesh.Dataset.depth.loc[: bat.size] = bat + + logger.info("updating bathymetry ..\n") + + except AttributeError as e: + logger.info("Keeping bathymetry in hgrid.gr3 due to {}\n".format(e)) + + else: + logger.info("dem from mesh file\n") + + @staticmethod + def mesh_to_slf( + x, y, z, tri, outpath, tag="telemac2d", chezy=None, manning=0.027, friction_type="chezy", **kwargs + ): + # + ds = xr.Dataset( + { + "B": (("time", "node"), z[np.newaxis, :]), + # Add other variables as needed + }, + coords={ + "x": ("node", x), + "y": ("node", y), + "time": [pd.Timestamp.now()], + }, + ) + ds.attrs["ikle2"] = tri + 1 + # bottom friction only in the case of TELEMAC2D + if tag == "telemac2d": + if chezy: + c = np.ones(len(z)) * chezy + else: + if friction_type == "chezy": + c = (abs(z) ** (1 / 6)) / manning + else: + print("only Chezy implemented so far! ") + sys.exit() + ds["W"] = xr.Variable(dims=["node"], data=c) + logger.info("Manning file created..\n") + ds.selafin.write(outpath) + + def to_slf(self, outpath, global_=True, friction_type="chezy", **kwargs): + corrections = get_value(self, kwargs, "mesh_corrections", {"reverse": [], "remove": []}) + + X = self.mesh.Dataset.SCHISM_hgrid_node_x.data + Y = self.mesh.Dataset.SCHISM_hgrid_node_y.data + # depth + Z = self.mesh.Dataset.depth.data + nan_mask = np.isnan(Z) + inf_mask = np.isinf(Z) + if np.any(nan_mask) or np.any(inf_mask): + Z[nan_mask] = 0 + Z[inf_mask] = 0 + logger.info("Z contains . Cleaning needed.") + # connectivity + IKLE2 = self.mesh.Dataset.SCHISM_hgrid_face_nodes.data + remove(outpath) + + if global_: + # adjust triangles orientation on the dateline + # and also suppress the pole triangles (for now) + IKLE2 = fix_glob_connectivity(X, Y, IKLE2, corrections) + + # write mesh + chezy = get_value(self, kwargs, "chezy", None) + manning = get_value(self, kwargs, "manning", 0.027) + self.mesh_to_slf(X, Y, Z, IKLE2, outpath, self.module, chezy, manning, friction_type) + + # ============================================================================================ + # EXECUTION + # ============================================================================================ + def create(self, **kwargs): + if not kwargs: + kwargs = self.__dict__.copy() + + # Set background dem as scale for mesh generation + dpath = get_value(self, kwargs, "dem_source", None) + if dpath: + self.dem = pdem.Dem(**kwargs) + # kwargs.update({"dem_source": self.dem.Dataset}) + else: + logger.info("no dem available\n") + + # Mesh + self.mesh = pmesh.set(type="tri2d", **kwargs) + + # set lat/lon from file + if self.mesh.Dataset is not None: + kwargs.update({"lon_min": self.mesh.Dataset.SCHISM_hgrid_node_x.values.min()}) + kwargs.update({"lon_max": self.mesh.Dataset.SCHISM_hgrid_node_x.values.max()}) + kwargs.update({"lat_min": self.mesh.Dataset.SCHISM_hgrid_node_y.values.min()}) + kwargs.update({"lat_max": self.mesh.Dataset.SCHISM_hgrid_node_y.values.max()}) + + self.lon_min = self.mesh.Dataset.SCHISM_hgrid_node_x.values.min() + self.lon_max = self.mesh.Dataset.SCHISM_hgrid_node_x.values.max() + self.lat_min = self.mesh.Dataset.SCHISM_hgrid_node_y.values.min() + self.lat_max = self.mesh.Dataset.SCHISM_hgrid_node_y.values.max() + + # get bathymetry + if self.mesh.Dataset is not None: + self.bath(**kwargs) + + # get meteo + if self.atm: + self.force(**kwargs) + + # get tide + if self.tide: + self.tpxo(**kwargs) + + self.config(**kwargs) + + def output(self, **kwargs): + path = get_value(self, kwargs, "rpath", "./telemac/") + flag = get_value(self, kwargs, "update", ["all"]) + split_by = get_value(self, kwargs, "meteo_split_by", None) + + if not os.path.exists(path): + os.makedirs(path) + + # Mesh related files + if self.mesh.Dataset is not None: + # WRITE GEO FILE + try: + bat = self.dem.Dataset.fval.values.astype(float) # minus for the hydro run + if np.isnan(bat).sum() != 0: + raise Exception("Bathymetry contains NaNs") + logger.warning("Bathymetric values fval contain NaNs, using ival values ..\n") + + except: + bat = self.dem.Dataset.ival.values.astype(float) # minus for the hydro run + self.mesh.Dataset.depth.loc[: bat.size] = bat + + logger.info("saving geometry file.. ") + geo = os.path.join(path, "geo.slf") + self.to_slf(geo, global_=True) + write_netcdf(self.mesh.Dataset, geo) + + # WRITE METEO FILE + logger.info("saving meteo file.. ") + meteo = os.path.join(path, "input_wind.slf") + if isinstance(self.meteo, pmeteo.Meteo): + pass + elif isinstance(self.meteo, list): + self.meteo = pmeteo.Meteo(self.meteo_source) + + self.atm = write_meteo( + meteo, geo, self.meteo.Dataset, gtype=self.gtype, ttype=self.ttype, input360=self.input360 + ) + + # WRITE BOUNDARY FILE + logger.info("saving boundary file.. ") + domain = write_cli(geo, self.mesh.Dataset) + self.params["N_bc"] = len(domain[0]) + + # WRITE CAS FILE + logger.info("saving CAS file.. ") + write_cas(path, self.module, self.params) + + # --------------------------------------------------------------------- + logger.info("output done\n") + # --------------------------------------------------------------------- + + def run(self, **kwargs): + calc_dir = get_value(self, kwargs, "rpath", "./telemac/") + cwd = os.getcwd() + + # --------------------------------------------------------------------- + logger.info("executing model\n") + # --------------------------------------------------------------------- + comm = MPI.COMM_WORLD + + cas_file = self.module + ".cas" + os.chdir(calc_dir) + if not tools.is_mpirun_installed(): + logger.warning("mpirun is not installed, ending.. \n") + return + + if self.module == "telemac2d": + # Creation of the instance Telemac2d + study = Telemac2d(cas_file, user_fortran=None, comm=comm, stdout=0, recompile=True) + elif self.module == "tomawac": + study = Tomawac(cas_file, user_fortran=None, comm=comm, stdout=0, recompile=True) + else: + raise ValueError("this module", self.module, "is not implemented yet!") + + # Testing construction of variable list + _ = study.variables + + study.set_case() + if self.module == "tomawac": + study.set("MODEL.RESULTFILE", "results_2D.slf") + # Initalization + study.init_state_default() + ntimesteps = study.get("MODEL.NTIMESTEPS") + pbar = ProgressBar(ntimesteps) + for it in range(ntimesteps): + study.run_one_time_step() + pbar.update(it) + + # Ending the run + study.finalize() + pbar.finish() + # + os.chdir(cwd) + + def save(self, **kwargs): + path = get_value(self, kwargs, "rpath", "./telemac/") + + lista = [key for key, value in self.__dict__.items() if key not in ["meteo", "dem", "mesh"]] + dic = {k: self.__dict__.get(k, None) for k in lista} + + mesh = self.__dict__.get("mesh", None) + if isinstance(mesh, str): + dic.update({"mesh": mesh}) + else: + dic.update({"mesh": mesh.__class__.__name__}) + + dem = self.__dict__.get("dem", None) + if isinstance(dem, str): + dic.update({"dem": dem}) + elif isinstance(dem, pdem.Dem): + dic.update({"dem_attrs": dem.Dataset.elevation.attrs}) + + meteo = self.__dict__.get("meteo", None) + if isinstance(meteo, str): + dic.update({"meteo": meteo}) + elif isinstance(meteo, pmeteo.Meteo): + try: + dic.update({"meteo": [meteo.Dataset.attrs]}) + except: + dic.update({"meteo": [x.attrs for x in meteo.Dataset]}) + + coastlines = self.__dict__.get("coastlines", None) + if coastlines is not None: + # Save the path to the serialized coastlines - #130 + coastlines_database = os.path.join(path, "coastlines.json") + coastlines.to_file(coastlines_database) + dic.update({"coastlines": coastlines_database}) + else: + dic.update({"coastlines": None}) + + dic["version"] = pyposeidon.__version__ + + for attr, value in dic.items(): + if isinstance(value, datetime.datetime): + dic[attr] = value.isoformat() + if isinstance(value, pd.Timedelta): + dic[attr] = value.isoformat() + if isinstance(value, pd.DataFrame): + dic[attr] = value.to_dict() + if isinstance(value, gp.GeoDataFrame): + dic[attr] = value.to_json() + + filename = os.path.join(path, f"{self.module}_model.json") + json.dump(dic, open(filename, "w"), indent=4, default=myconverter) + + def execute(self, **kwargs): + self.create(**kwargs) + self.output(**kwargs) + if self.monitor: + self.set_obs() + self.save(**kwargs) + self.run(**kwargs) + + def read_folder(self, rfolder, **kwargs): + self.rpath = rfolder + geo = glob.glob(os.path.join(rfolder, "geo.slf")) + cli = glob.glob(os.path.join(rfolder, "/geo.cli")) + mfiles = glob.glob(os.path.join(rfolder, "/input_wind.slf")) + + mykeys = ["start_year", "start_month", "start_day"] + sdate = [self.params["opt"][x] for x in mykeys] # extract date info from param.nml + sd = "-".join(str(item) for item in sdate) # join in str + sd = pd.to_datetime(sd) # convert to datestamp + + sh = [self.params["opt"][x] for x in ["start_hour", "utc_start"]] # get time attrs + sd = sd + pd.to_timedelta("{}H".format(sh[0] + sh[1])) # complete start_date + + ed = sd + pd.to_timedelta("{}D".format(self.params["core"]["rnday"])) # compute end date based on rnday + + self.start_date = sd # set attrs + self.end_date = ed + + load_mesh = get_value(self, kwargs, "load_mesh", False) + + if load_mesh: + try: + self.mesh = pmesh.set(type="tri2d", mesh_file=hfile) + except: + logger.warning("loading mesh failed") + pass + else: + logger.warning("no mesh loaded") + + load_meteo = get_value(self, kwargs, "load_meteo", False) + + # meteo + if load_meteo is True: + try: + pm = [] + for key, val in mfiles.items(): + msource = xr.open_mfdataset(val) + pm.append(msource) + if len(pm) == 1: + pm = pm[0] + self.meteo = pmeteo.Meteo(meteo_source=pm) + + except: + pm = [] + for key, val in mfiles.items(): + ma = [] + + for ifile in mfiles: + g = xr.open_dataset(ifile) + ts = "-".join(g.time.attrs["base_date"].astype(str)[:3]) + time_r = pd.to_datetime(ts) + try: + times = time_r + pd.to_timedelta(g.time.values, unit="D").round("H") + g = g.assign_coords({"time": times}) + ma.append(g) + except: + logger.warning("loading meteo failed") + break + if ma: + msource = xr.merge(ma) + pm.append(msource) + + if len(pm) == 1: + pm = pm[0] + + self.meteo = pmeteo.Meteo(meteo_source=pm) + + else: + logger.warning("no meteo loaded") + + def hotstart(self, t=None, **kwargs): + filename2d = get_value(self, kwargs, "filename2d", "out_2D.zarr") + ppath = get_value(self, kwargs, "ppath", self.rpath) + out2d = os.path.join(ppath, "outputs", filename2d) + ds = xr.open_dataset(out2d) + t_ = ds.time.values + it = np.where(t_ == t)[0] + xdat = ds.isel(time=it) + hfile = f"hotstart_{t.strftime('%Y%m%d.%H')}.nc" + logger.info("saving hotstart file\n") + xdat.to_netcdf(os.path.join(ppath, f"outputs/{hfile}")) + + ## Any variable + def combine(self, out, g2l, name): + keys = g2l.index.get_level_values(0).unique() + r = [] + for i in range(len(out)): + v = out[i].to_pandas() + v.index += 1 + mask = g2l.loc[keys[i], "local"] + vm = v.loc[mask] + vm.index = g2l.loc[keys[i], "global_n"].values + r.append(vm) + r = pd.concat(r).sort_index() + r.index -= 1 + r.index.name = name + return r + + def combine_(self, var, out, g2l, name): + if len(out[0][var].shape) == 3: + dd = [] + for i in range(out[0][var].shape[2]): + o = [] + for j in range(len(out)): + o.append(out[j][var].loc[{out[j][var].dims[2]: i}]) + r = self.combine(o, g2l, name) + dd.append(xr.DataArray(r.values, dims=out[0][var].dims[:2], name=var)) + + tr = xr.concat(dd, dim=out[0][var].dims[2]) + a, b, c = out[0][var].dims + tr = tr.transpose(a, b, c) + return tr + + elif len(out[0][var].shape) < 3: + o = [] + for j in range(len(out)): + o.append(out[j][var]) + r = self.combine(o, g2l, name) + return xr.DataArray(r.values, dims=list(out[0][var].dims), name=var) + + def xcombine(self, tfs): + # Create dataset + side = [] + node = [] + el = [] + single = [] + + for key in tfs[0].variables: + if "nSCHISM_hgrid_face" in tfs[0][key].dims: + r = self.combine_(key, tfs, self.misc["melems"], "nSCHISM_hgrid_face") + el.append(r) + elif "nSCHISM_hgrid_node" in tfs[0][key].dims: + r = self.combine_(key, tfs, self.misc["mnodes"], "nSCHISM_hgrid_node") + node.append(r) + elif "nSCHISM_hgrid_edge" in tfs[0][key].dims: + r = self.combine_(key, tfs, self.misc["msides"], "nSCHISM_hgrid_edge") + side.append(r) + elif len(tfs[0][key].dims) == 1: + single.append(tfs[0][key]) + + side = xr.merge(side) + el = xr.merge(el) + node = xr.merge(node) + single = xr.merge(single) + + # merge + return xr.merge([side, el, node, single]) + + def tcombine(self, hfiles, sdate, times): + xall = [] + for k in range(times.shape[0]): + tfs = [] + for i in range(len(hfiles)): + tfs.append(xr.open_dataset(hfiles[i]).isel(time=k)) + + xall.append(self.xcombine(tfs)) + + xdat = xr.concat(xall, dim="time") + xdat = xdat.assign_coords(time=times) + + return xdat + + # https://stackoverflow.com/questions/41164630/pythonic-way-of-removing-reversed-duplicates-in-list + @staticmethod + def remove_reversed_duplicates(iterable): + # Create a set for already seen elements + seen = set() + for item in iterable: + # Lists are mutable so we need tuples for the set-operations. + tup = tuple(item) + if tup not in seen: + # If the tuple is not in the set append it in REVERSED order. + seen.add(tup[::-1]) + # If you also want to remove normal duplicates uncomment the next line + # seen.add(tup) + yield item + + def results(self, **kwargs): + lat_coord_standard_name = "latitude" + lon_coord_standard_name = "longitude" + x_units = "degrees_east" + y_units = "degrees_north" + + path = get_value(self, kwargs, "rpath", "./telemac/") + res_min = get_value(self, kwargs, "res_min", 0.5) + filename = get_value(self, kwargs, "filename", "stations.zarr") + filename2d = get_value(self, kwargs, "filename2d", "out_2D.zarr") + remove_zarr = get_value(self, kwargs, "remove_zarr", True) + chunk = get_value(self, kwargs, "chunk", None) + + logger.info("get combined 2D netcdf files \n") + # check for new IO output + res = os.path.join(path, "results_2D.slf") + xc = xr.open_dataset(res, engine="selafin") + dic = {} + varsn = normalize_varnames(xc.variables) + for var, varn in zip(xc.variables, varsn): + dic.update({var: varn}) + xc = xc.rename(dic) + xc["face_nodes"] = xr.Variable(("face", "max_no_vertices"), np.array(xc.attrs["ikle2"]) - 1) + + # set Attrs + xc.longitude.attrs = { + "long_name": "node x-coordinate", + "standard_name": lon_coord_standard_name, + "units": x_units, + "mesh": "TELEMAC_Selafin", + } + + xc.latitude.attrs = { + "long_name": "node y-coordinate", + "standard_name": lat_coord_standard_name, + "units": y_units, + "mesh": "TELEMAC_Selafin", + } + + if "depth" in xc.variables: + xc.depth.attrs = { + "long_name": "Bathymetry", + "units": "meters", + "positive": "down", + "mesh": "TELEMAC_Selafin", + "location": "node", + } + + xc.time.attrs = { + "long_name": "Time", + "base_date": pd.Timestamp(*xc.attrs["date_start"]).strftime("%Y-%m-%d %H:%M:%S"), + "standard_name": "time", + } + + # Dataset Attrs + xc.attrs = { + "Conventions": "CF-1.0, UGRID-1.0", + "title": "TELEMAC Model output", + "source": "TELEMAC model output version vV8P4", + "references": "https://gitlab.pam-retd.fr/otm/telemac-mascaret", + "history": "created by pyposeidon", + "comment": "TELEMAC Model output", + "type": "TELEMAC Model output", + } + os.makedirs(os.path.join(path, "outputs"), exist_ok=True) + out2d = os.path.join(path, "outputs", filename2d) + remove(out2d) + export_xarray(xc, out2d, chunk=chunk, remove_dir=remove_zarr) + + if self.monitor: + logger.info("export observations file\n") + if isinstance(self.stations_mesh_id, dict): + stations = pd.DataFrame(self.stations_mesh_id) + elif isinstance(self.stations_mesh_id, pd.DataFrame): + stations = self.stations_mesh_id + # idx = stations["gindex"] # not need for now + res_1D = Selafin(os.path.join(path, "results_1D.slf")) + t0 = res_1D.datetime + t0pd = pd.Timestamp(t0[0], t0[1], t0[2], t0[3], t0[4], t0[5]) + times = [t0pd + pd.to_timedelta(t_, unit="s") for t_ in res_1D.tags["times"]] + + varsn = normalize_varnames(res_1D.varnames) + data_vars = {} + + # waiting for the xarray Serafin fix + cube = np.zeros((len(times), len(res_1D.varnames), len(res_1D.meshx))) + for it, t_ in enumerate(times): + cube[it, :, :] = res_1D.get_values(it) + + for var, vartel in zip(varsn, res_1D.varnames): + i_v = res_1D.varnames.index(vartel) + data_vars.update({var: (("time", "seaset_id"), cube[:, i_v, :])}) + + mapping = [] + for ii in range(len(res_1D.ikle2.T[0])): + id = res_1D.ikle2.T[0][ii] + mapping.append(int(stations.iloc[id].seaset_id)) + # bug in TELEMAC coord: CONVERT BACK FROM MERCATOR + x2, y2 = xy_to_ll(res_1D.meshx, res_1D.meshy) + # bug #2 res_1D.meshx does not have the same length as stations + # hence why we use only res_1D for the the results + coords = { + "time": times, + "seaset_id": mapping, + "longitude": ("seaset_id", x2), + "latitude": ("seaset_id", y2), + } + ds = xr.Dataset(data_vars=data_vars, coords=coords) + out_obs = os.path.join(path, "outputs", filename) + remove(out_obs) + export_xarray(ds, out_obs, chunk=chunk, remove_dir=remove_zarr) + + logger.info("done with output netCDF files \n") + + def set_obs(self, **kwargs): + path = get_value(self, kwargs, "rpath", "./telemac/") + nspool_sta = get_value(self, kwargs, "nspool_sta", 1) + tg_database = get_value(self, kwargs, "obs", None) + max_dist = get_value(self, kwargs, "max_dist", np.inf) + + if tg_database: + logger.info("get stations from {}\n".format(tg_database)) + tg = pd.read_csv(tg_database, index_col=0).reset_index(drop=True) + else: + logger.info("get stations using searvey\n") + geometry = get_value(self, kwargs, "geometry", None) + if geometry == "global": + tg = ioc.get_ioc_stations() + else: + geo_box = shapely.geometry.box(self.lon_min, self.lat_min, self.lon_max, self.lat_max) + tg = ioc.get_ioc_stations(region=geo_box) + tg = tg.reset_index(drop=True) + ### save in compatible to searvey format + tg["country"] = tg.country.values.astype("str") # fix an issue with searvey see #43 therein + logger.info("save station DataFrame \n") + tg_database = os.path.join(path, "stations.json") + tg.to_file(tg_database) + ##### normalize to be used inside pyposeidon + tgn = normalize_column_names(tg.copy()) + + ##### make sure lat/lon are floats + tgn = tgn.astype({"latitude": float, "longitude": float, "location": str}) + + ## FOR TIDE GAUGE MONITORING + + logger.info("set in-situ measurements locations \n") + + if not self.mesh.Dataset: + logger.warning("no mesh available skipping \n") + return + + gpoints = np.array( + list( + zip( + self.mesh.Dataset.SCHISM_hgrid_node_x.values, + self.mesh.Dataset.SCHISM_hgrid_node_y.values, + ) + ) + ) + + coords = np.array([tgn.longitude.values, tgn.latitude.values]).T + cp, mask = closest_n_points(coords, 1, gpoints, max_dist) + mesh_index = cp + stations = gpoints[cp] + + if len(stations) == 0: + logger.warning("no observations available\n") + + stations = pd.DataFrame(stations, columns=["SCHISM_hgrid_node_x", "SCHISM_hgrid_node_y"]) + stations["z"] = 0 + stations.index += 1 + stations["gindex"] = mesh_index + stations["unique_id"] = stations.index + stations["seaset_id"] = tgn.seaset_id.values[mask] + stations["longitude"] = tgn.longitude.values[mask] + stations["latitude"] = tgn.latitude.values[mask] + # convert to MERCATOR coordinates + # dirty fix (this needs to be fixed in TELEMAC directly) + gdf_mercator = df_to_gpd(stations).to_crs("EPSG:3857") + stations["x"] = gdf_mercator.geometry.x.astype(int) + stations["y"] = gdf_mercator.geometry.y.astype(int) + + self.stations_mesh_id = stations + + logger.info("write out stations.csv file \n") + tgn[mask].to_csv(os.path.join(path, "stations.csv"), index=False) + + logger.info("write out stations.in file \n") + # output to file + with open(os.path.join(path, "station.in"), "w") as f: + f.write(f"1 {stations.shape[0]}\n") # 1st line: number of periods and number of points + f.write( + f"{0} {int(self.params['duration'])} {self.params['tstep']}\n" + ) # 2nd line: period 1: start time, end time and interval (in seconds) + stations.loc[:, ["x", "y", "unique_id", "seaset_id"]].to_csv( + f, header=None, sep=" ", index=False + ) # 3rd-10th line: output points; x coordinate, y coordinate, station number, and station name + + def get_station_sim_data(self, **kwargs): + path = get_value(self, kwargs, "rpath", "./schism/") + + # locate the station files + sfiles = glob.glob(os.path.join(path, "outputs/staout_*")) + sfiles.sort() + + try: + # get the station flags + flags = pd.read_csv(os.path.join(path, "station.in"), header=None, nrows=1, delim_whitespace=True).T + except FileNotFoundError: + logger.error("no station.in file present") + return + + flags.columns = ["flag"] + flags["variable"] = [ + "elev", + "air_pressure", + "windx", + "windy", + "T", + "S", + "u", + "v", + "w", + ] + + vals = flags[flags.values == 1] # get the active ones + + dstamp = kwargs.get("dstamp", self.rdate) + + dfs = [] + for idx in vals.index: + obs = np.loadtxt(sfiles[idx]) + df = pd.DataFrame(obs) + df = df.set_index(0) + df.index.name = "time" + df.columns.name = vals.loc[idx, "variable"] + df.index = pd.to_datetime(dstamp) + pd.to_timedelta(df.index, unit="S") + pindex = pd.MultiIndex.from_product([df.T.columns, df.T.index]) + + r = pd.DataFrame(df.values.flatten(), index=pindex, columns=[vals.loc[idx, "variable"]]) + r.index.names = ["time", "node"] + + r.index = r.index.set_levels(r.index.levels[1] - 1, level=1) + + dfs.append(r.to_xarray()) + + self.station_sim_data = xr.combine_by_coords(dfs) + + def get_output_data(self, **kwargs): + dic = self.__dict__ + + dic.update(kwargs) + + self.data = data.get_output(**dic) + + def get_station_obs_data(self, **kwargs): + self.station_obs_data = get_obs_data(self.obs, self.start_date, self.end_date) + + def open_thalassa(self, **kwargs): + # open a Thalassa instance to visualize the output + return diff --git a/pyposeidon/tools.py b/pyposeidon/tools.py index db02354f..9d6fad3f 100644 --- a/pyposeidon/tools.py +++ b/pyposeidon/tools.py @@ -189,6 +189,10 @@ def get_solver(solver_name: str): from .d3d import d3d solver = d3d + elif solver_name == "telemac": + from .telemac import Telemac + + solver = Telemac else: raise ValueError(f"Unknown solver_name: {solver_name}") return solver diff --git a/pyposeidon/utils/cpoint.py b/pyposeidon/utils/cpoint.py index 2757baaa..cf3cab2c 100644 --- a/pyposeidon/utils/cpoint.py +++ b/pyposeidon/utils/cpoint.py @@ -1,5 +1,38 @@ from scipy.spatial.distance import cdist +from scipy.spatial import cKDTree +import numpy as np def closest_node(node, nodes): return nodes[cdist([node], nodes).argmin()] + + +def closest_n_points(nodes, N, meshXY, dist_max=np.inf): + """ + Find the indices of the N closest points in a set of points. + + Parameters + ---------- + nodes : np.ndarray + The set of points to search. + N : int + The number of points to find. + meshXY : np.ndarray + The grid of points used for the KDTree. + dist_max : float, optional (default np.inf) + the max distance to reach to interpolate from the nearest point. + above that limit, no interpolation is done. the point is removed. + + Returns + ------- + np.ndarray + The indices of the N closest points. + np.boolarray + The mask of the retained stations (with dist_max filter) + """ + + mytree = cKDTree(meshXY) + dist, indice = mytree.query(nodes, range(1, N + 1)) + indice[dist > dist_max] = -1 + mask = indice != -1 + return indice[mask].T, mask.T[0] diff --git a/pyposeidon/utils/norm.py b/pyposeidon/utils/norm.py index 5267ca0d..5f84daab 100644 --- a/pyposeidon/utils/norm.py +++ b/pyposeidon/utils/norm.py @@ -2,8 +2,61 @@ import pandas as pd # to be used for name normalization -LONGITUDE_NAMES = {"longitude", "lon", "x", "Lon", "LONGITUDE", "LON", "X"} -LATITUDE_NAMES = {"latitude", "lat", "y", "Lat", "LATITUDE", "LAT", "Y"} +LONGITUDE_NAMES = {"longitude", "lon", "x", "Lon", "LONGITUDE", "LON", "X", "SCHISM_hgrid_node_x"} +LATITUDE_NAMES = {"latitude", "lat", "y", "Lat", "LATITUDE", "LAT", "Y", "SCHISM_hgrid_node_y"} +LOCATION_NAMES = {"location", "loc", "LOCATION", "Station_Name", "Station", "ioc_code", "provider_id"} + +TEL_MAP_LONG = { + "VELOCITY U ": "u-current-velocity", + "VELOCITY V ": "v-current-velocity", + "FREE SURFACE ": "free-surface", + "WAVE HEIGHT HM0 ": "significant-wave-height", + "MEAN DIRECTION ": "mean-direction", + "MEAN PERIOD TM01": "mean-period-tm01", + "MEAN PERIOD TM02": "mean-period-tm02", + "MEAN PERIOD TMOY": "mean-period-tmoy", + "PEAK PERIOD TPR8": "peak-period-tpr8", + "PEAK PERIOD TPD ": "peak-period-tpd", + "WIND VELOCITY U ": "10m-u-component-of-wind", + "WIND VELOCITY V ": "10m-v-component-of-wind", + "SURFACE PRESSURE": "mean-sea-level-pressure", + "AIR TEMPERATURE ": "air-temperature", +} +TEL_MAP_SHORT = { + "VELOCITY U ": "u", + "VELOCITY V ": "v", + "FREE SURFACE ": "elev", + "WAVE HEIGHT HM0 ": "hm0", + "MEAN DIRECTION ": "dir", + "MEAN PERIOD TM01": "tm01", + "MEAN PERIOD TM02": "tm02", + "MEAN PERIOD TMOY": "tmoy", + "PEAK PERIOD TPR8": "tp", + "PEAK PERIOD TPD ": "tp", + "WIND VELOCITY U ": "u10", + "WIND VELOCITY V ": "v10", + "SURFACE PRESSURE": "msl", + "AIR TEMPERATURE ": "tair", +} + +TEL_MAP_SHORT2 = { + "x": "longitude", + "y": "latitude", + "U": "u", + "V": "v", + "S": "elev", + "HM0": "hm0", + "DIRM": "dir", + "TM01": "tm01", + "TM02": "tm02", + "TMOY": "tmoy", + "TPR8": "tp", + "TPD": "tp", + "WINDX": "u10", + "WINDY": "v10", + "PATM": "msl", + "TAIR": "tair", +} def normalize_coord_names(dataset: xr.Dataset) -> xr.Dataset: @@ -28,6 +81,12 @@ def normalize_coord_names(dataset: xr.Dataset) -> xr.Dataset: def normalize_column_names(dataframe: pd.DataFrame) -> pd.DataFrame: """Return a dataframe with columns containing "longitude" and "latitude" """ cols = dataframe.columns.tolist() + # location + for location in LOCATION_NAMES: + if location in cols: + break + else: + raise ValueError(f"Couldn't normalize location: {cols}") # longitude for lon_name in LONGITUDE_NAMES: if lon_name in cols: @@ -41,8 +100,20 @@ def normalize_column_names(dataframe: pd.DataFrame) -> pd.DataFrame: else: raise ValueError(f"Couldn't normalize latitude: {cols}") - dic = {lon_name: "longitude", lat_name: "latitude"} + dic = {lon_name: "longitude", lat_name: "latitude", location: "location"} new_cols = [dic.get(n, n) for n in cols] dataframe.columns = new_cols return dataframe + + +def normalize_varnames(varnames, mapping=TEL_MAP_SHORT2, mapping1=TEL_MAP_SHORT): + normalized_names = [] + for v in varnames: + if v in mapping: + normalized_names.append(mapping[v]) + elif v in mapping1: + normalized_names.append(mapping1[v]) + else: + normalized_names.append(v.strip().lower()) + return normalized_names diff --git a/pyposeidon/utils/post.py b/pyposeidon/utils/post.py index 8457bd0d..ddb0b526 100644 --- a/pyposeidon/utils/post.py +++ b/pyposeidon/utils/post.py @@ -6,6 +6,9 @@ import numpy as np from tqdm.auto import tqdm import glob +import numcodecs +import tarfile +import shutil import pyposeidon from pyposeidon.utils import data @@ -50,6 +53,88 @@ def get_encoding(ename): } +def remove(path): + try: + if os.path.isfile(path): + os.remove(path) # Remove a file + elif os.path.isdir(path): + if not os.listdir(path): # Check if the directory is empty + os.rmdir(path) + else: + shutil.rmtree(path) + except OSError as e: + print(f"Error: {e.strerror}") + + +def tar_directory(input_directory, output_filename, compress=False): + """ + Tar a Zarr directory. + + Parameters + ---------- + input_directory : str + The path to the Zarr directory to archive. + output_filename : str + The path and filename for the output archive. + compress : bool, optional + Whether to compress the archive with gzip. Default is True. + """ + mode = "w:gz" if compress else "w" + with tarfile.open(output_filename, mode) as tar: + tar.add(input_directory, arcname=os.path.basename(input_directory)) + + +def export_xarray(ds, filename_out, chunk=None, remove_dir=False): + """ + Export an xarray dataset to netcdf or zarr format. + + Parameters + ---------- + ds : xr.Dataset + The xarray dataset to export. + filename_out : str + The path and filename of the output file. + chunk : dict, optional + The chunk size to use when saving the dataset to zarr format, by default None. + example: chunk = {'nodes': 1000, 'time': 100} + + Returns + ------- + None + + Raises + ------ + ValueError + If the filename does not have a .nc or .zarr extension. + """ + if not filename_out.endswith(".nc") and not filename_out.endswith(".zarr"): + raise ValueError("Invalid filename extension. Must be .nc or .zarr") + if filename_out.endswith(".nc"): + encoding = {} + default_encoding = {"zlib": True, "complevel": 1} + for varname in list(ds.variables): + encoding[varname] = default_encoding + if chunk: + chunksizes = dict(ds[varname].sizes) + for key in chunk: + if key in chunksizes: + chunksizes[key] = chunk[key] + encoding[varname]["chunksizes"] = list(chunksizes.values()) + ds.to_netcdf(filename_out, encoding=encoding) + elif filename_out.endswith(".zarr"): + compressor = numcodecs.Blosc(cname="zstd", clevel=1) + if chunk is not None: + ds = ds.chunk(chunk) + encoding = {} + for varname in list(ds.variables): + encoding.update({varname: {"compressor": compressor}}) + ds.to_zarr(filename_out, encoding=encoding, consolidated=True) + tar_directory(filename_out, filename_out + ".tar") + if remove_dir: + remove(filename_out) + logger.info(f"Removed directory {filename_out}") + + def save_leads(stations, st, start_date, dt, leads, rpath="./skill/"): ## save results in lead chunks diff --git a/pyproject.toml b/pyproject.toml index 0055a491..469ab84c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -60,6 +60,7 @@ Shapely = ">=2" #Shapely = { url = "https://github.com/shapely/shapely/archive/refs/tags/1.7.1.zip" } tqdm = "*" xarray = "*" +xarray-selafin = "*" zarr = "*" # viz geoviews = { version = "*", optional = true, extras = ['recommended'] } diff --git a/tests/conftest.py b/tests/conftest.py index 88c08472..d0a3c500 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -30,6 +30,7 @@ def move_tests_to_the_end(items): def pytest_addoption(parser): + parser.addoption("--runtelemac", action="store_true", default=False, help="run telemac tests") parser.addoption("--runschism", action="store_true", default=False, help="run schism tests") parser.addoption("--rundelft", action="store_true", default=False, help="run delft tests") parser.addoption("--runslow", action="store_true", default=False, help="run slow tests") @@ -37,11 +38,13 @@ def pytest_addoption(parser): def pytest_collection_modifyitems(config, items): + should_run_telemac = config.getoption("--runtelemac") should_run_schism = config.getoption("--runschism") should_run_delft = config.getoption("--rundelft") should_run_slow = config.getoption("--runslow") should_run_viz = config.getoption("--runviz") + skip_telemac = pytest.mark.skip(reason="need --runtelemac option to run") skip_schism = pytest.mark.skip(reason="need --runshism option to run") skip_delft = pytest.mark.skip(reason="need --rundelft option to run") skip_slow = pytest.mark.skip(reason="need --runslow option to run") @@ -51,6 +54,8 @@ def pytest_collection_modifyitems(config, items): for item in items: if any(mark in item.keywords for mark in run_last_marks): item.add_marker(RUNLAST_MARK) + if "telemac" in item.keywords and not should_run_telemac: + item.add_marker(skip_telemac) if "schism" in item.keywords and not should_run_schism: item.add_marker(skip_schism) if "delft" in item.keywords and not should_run_delft: diff --git a/tests/test_telemac.py b/tests/test_telemac.py new file mode 100644 index 00000000..67aabf4d --- /dev/null +++ b/tests/test_telemac.py @@ -0,0 +1,102 @@ +import pytest +import pyposeidon +from pyposeidon.utils import data +import os +import multiprocessing + +# from . import DATA_DIR +import pathlib + +# specify package paths +ROOT_DIR = pathlib.Path(__file__).resolve().parent.parent +TEST_DIR = ROOT_DIR / "tests" +DATA_DIR = TEST_DIR / "data" +ROTATED_DIR = DATA_DIR / "rotated" + +DATA_DIR.mkdir(exist_ok=True) +ROTATED_DIR.mkdir(exist_ok=True) + + +MESH_FILE = (DATA_DIR / "hgrid.gr3").as_posix() +DEM_FILE = (DATA_DIR / "dem.nc").as_posix() + +# define in a dictionary the properties of the model.. +case0 = { + "solver_name": "telemac", + "mesh_file": MESH_FILE, + "start_date": "2017-10-1 0:0:0", + "time_frame": "12H", + "meteo_source": [(DATA_DIR / "erai.grib").as_posix()], # meteo file + "dem_source": DEM_FILE, + "update": ["all"], # update only meteo, keep dem + "dt": 50, + "parameters": { + "chezy": 30, + }, +} + +case1 = { + "solver_name": "telemac", + "mesh_file": MESH_FILE, + "start_date": "2017-10-1 0:0:0", + "end_date": "2017-10-2 0:0:0", # optional instead of time_frame + "dem_source": DEM_FILE, + "meteo_source": [(DATA_DIR / "erai.grib").as_posix()], # meteo file + "update": ["all"], # update only meteo, keep dem + "dt": 100, + "parameters": { + "chezy": 100, + }, +} + + +case2 = { + "solver_name": "telemac", + "mesh_file": MESH_FILE, + "start_date": "2011-1-1 0:0:0", + "time_frame": "12H", + "meteo_source": [(DATA_DIR / "era5.grib").as_posix()], # meteo file + "dem_source": DEM_FILE, + "update": ["all"], # update only meteo, keep dem + "dt": 200, +} + + +case3 = { + "solver_name": "telemac", + "mesh_file": MESH_FILE, + "module": "tomawac", + "start_date": "2011-1-1 0:0:0", + "time_frame": "12H", + "meteo_source": [(DATA_DIR / "era5.grib").as_posix()], # meteo file + "dem_source": DEM_FILE, + "update": ["all"], # update only meteo, keep dem + "dt": 1800, +} + + +def telemac(tmpdir, dic): + # initialize a model + rpath = str(tmpdir) + "/" + dic.update({"rpath": rpath}) # use tmpdir for running the model + + b = pyposeidon.model.set(**dic) + + b.execute() + b.results() + zarr_tar = b.rpath + "/outputs/out_2D.zarr.tar" + if os.path.exists(zarr_tar): + a = pyposeidon.model.read(rpath + b.module + "_model.json") # read model + a.execute() + a.results() + zarr_tar = a.rpath + "/outputs/out_2D.zarr.tar" + return os.path.exists(zarr_tar) + else: + return False + + +@pytest.mark.xfail +@pytest.mark.telemac +@pytest.mark.parametrize("case", [case0, case1, case2, case3]) +def test_answer(tmpdir, case): + assert telemac(tmpdir, case) == True