diff --git a/Dockerfile b/Dockerfile index 58216034..eaf7b8b1 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM ghcr.io/ecoextreml/stemmus_scope:1.6.0 +FROM ghcr.io/ecoextreml/stemmus_scope:1.6.1 LABEL maintainer="Bart Schilperoort " LABEL org.opencontainers.image.source = "https://github.com/EcoExtreML/STEMMUS_SCOPE_Processing" diff --git a/PyStemmusScope/bmi/docker_process.py b/PyStemmusScope/bmi/docker_process.py index 7e0cc220..ab65f3e5 100644 --- a/PyStemmusScope/bmi/docker_process.py +++ b/PyStemmusScope/bmi/docker_process.py @@ -83,7 +83,7 @@ class StemmusScopeDocker: """Communicate with a STEMMUS_SCOPE Docker container.""" # Default image, can be overridden with config: - compatible_tags = ("1.6.0",) + compatible_tags = ("1.6.1",) _process_ready_phrase = b"Select BMI mode:" _process_finalized_phrase = b"Finished clean up." diff --git a/PyStemmusScope/bmi/variable_reference.py b/PyStemmusScope/bmi/variable_reference.py index fc7ed14d..2291c115 100644 --- a/PyStemmusScope/bmi/variable_reference.py +++ b/PyStemmusScope/bmi/variable_reference.py @@ -114,6 +114,15 @@ class BmiVariable: grid=0, keys=["gwfluxes", "recharge"], ), + BmiVariable( + name="groundwater_recharge_index", + dtype="int64", + input=False, + output=True, + units="-", + grid=0, + keys=["gwfluxes", "indxRchrg"], + ), # groundwater (coupling) vars BmiVariable( name="groundwater_coupling_enabled", diff --git a/PyStemmusScope/config_io.py b/PyStemmusScope/config_io.py index 33bd7944..6c7bacd1 100644 --- a/PyStemmusScope/config_io.py +++ b/PyStemmusScope/config_io.py @@ -126,6 +126,16 @@ def _copy_data(input_dir: Path, config: dict) -> None: # copy input_data.xlsx shutil.copy(str(config["input_data"]), str(input_dir)) + # copy soil_layers_thickness, this is optional + if "soil_layers_thickness" in config: + if Path(config["soil_layers_thickness"]).is_file(): + shutil.copy(str(config["soil_layers_thickness"]), str(input_dir)) + else: + raise FileNotFoundError( + "The key `soil_layers_thickness` is provided in the config file, " + f"but file {config['soil_layers_thickness']} not found. " + "Remove the key from the config file or provide the file." + ) def _update_config_file( diff --git a/docs/bmi.md b/docs/bmi.md index 13243b17..c47ede7a 100644 --- a/docs/bmi.md +++ b/docs/bmi.md @@ -19,10 +19,10 @@ To use the Docker image, use the `DockerImage` setting in the configuration file ```sh WorkDir=/home/username/tmp/stemmus_scope ... -DockerImage=ghcr.io/ecoextreml/stemmus_scope:1.6.0 +DockerImage=ghcr.io/ecoextreml/stemmus_scope:1.6.1 ``` -It is best to add the version tag here too (`:1.6.0`), this way the BMI will warn you if the version might be incompatible. +It is best to add the version tag here too (`:1.6.1`), this way the BMI will warn you if the version might be incompatible. Note that the `docker` package for python is required here. Install this with `pip install PyStemmusScope[docker]`. Additionally, [Docker](https://docs.docker.com/get-docker/) itself has to be installed. @@ -52,7 +52,7 @@ If you need access to other model variables that are not yet available in the BM A [Docker image is available](https://ghcr.io/ecoextreml/stemmus_scope-grpc4bmi) in which the model as well as the Python BMI have been wrapped in a container. The Docker image is created using the Docker file [here](https://github.com/EcoExtreML/STEMMUS_SCOPE_Processing/blob/main/Dockerfile) and allows communication with a STEMMUS_SCOPE BMI through [grpc4bmi](https://grpc4bmi.readthedocs.io/en/latest/). -Doing so avoids the needs to install PyStemmusScope yourself, only Docker/apptainer and a python environment with grpc4bmi are required. Please note you should not specify `DockerImage` or `ExeFilePath` in the config file if you are using the grpc4bmi interface. +Doing so avoids the needs to install PyStemmusScope yourself, only Docker/apptainer and a python environment with grpc4bmi are required. Please note you should not specify `DockerImage` or `ExeFilePath` in the config file if you are using the grpc4bmi interface. A demonstration is available [here](notebooks/grpc4bmi_demo.ipynb) diff --git a/docs/notebooks/bmi_MODFLOW_coupling.ipynb b/docs/notebooks/bmi_MODFLOW_coupling.ipynb new file mode 100644 index 00000000..17718a88 --- /dev/null +++ b/docs/notebooks/bmi_MODFLOW_coupling.ipynb @@ -0,0 +1,6828 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# STEMMUS_SCOPE & MODFLOW 6 BMI coupling\n", + "\n", + "This Python notebook shows a simple example of BMI coupling between the STEMMUS_SCOPE model and the MODFLOW 6 model. The example is prepared for one of the PLUMBER2 dataset (https://researchdata.edu.au/plumber2-forcing-evaluation-surface-models/1656048): the LooBos site, the Netherlands (NL-Loo). To run this notebook, the following are required:\n", + "- Install the related packages:\n", + " - pyStemmusScope package (https://pystemmusscope.readthedocs.io/en/latest/).\n", + " - modflowapi Python package (https://github.com/MODFLOW-USGS/modflowapi?tab=readme-ov-file#installation).\n", + "- The executable files of both models. \n", + " - STEMMUS_SCOPE executable file can be retrieved from the link (https://github.com/EcoExtreML/STEMMUS_SCOPE/blob/main/run_model_on_snellius/exe/STEMMUS_SCOPE). Note: the STEMMUS_SCOPE executable file is Linux-based only.\n", + " - MODFLOW 6 executable files can be retrieved from the link (https://github.com/MODFLOW-USGS/modflow6/releases). Note: This notebook is based on MODFLOW 6.5.0 (Linux version). \n", + "- The input files of both models are already prepared in advance and are not part of this notebook. The input data files can be accessed via this link (https://zenodo.org/records/13464164). The link also includes: 1) a version of the executable files needed to run the models. Download, unzip and copy the folders to your own working directory (root directory, see below the cell under the header: *1. Set up the root directory*), and 2) a notebook for creating the MODFLOW model files of this example.\n", + " \n", + "In case the user wants to test the notebook on another site, preparing input files for both models is required. Preparing input files for STEMMUS_SCOPE can be done through the PyStemmusScope Python package (https://pystemmusscope.readthedocs.io/en/latest/notebooks/run_model_on_different_infra/). Preparing input files for MODFLOW 6 can be done through the FloPy Python package (https://flopy.readthedocs.io/en/stable/tutorials.html#modflow-6).\n", + "\n", + "\n", + "Notes\n", + "- Units in STEMMUS_SCOPE are centimetres and seconds. The model runs every 30 mins.\n", + "- Units in MODFLOW 6 are meter and day. The model runs every day.\n", + "- STEMMUS_SCOPE runs per time steps.\n", + "- MODFLOW 6 runs per stress periods and time steps. A stress period is the period of time when the model forcing is fixed. Each stress period can be divided into a number of time steps. In each time step, model variables will be updated (but model forcing is still fixed).\n", + "- This example runs for 10 days. STEMMUS_SCOPE has 480 time steps (length of each time step = 30 mins). MODFLOW 6 has 10 stress periods (length of each period = 1 day and each period has 48 time steps), so in total MODFLOW 6 runs 480 times." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import shutil\n", + "import numpy as np\n", + "import xarray as xr\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from PyStemmusScope.config_io import read_config\n", + "from PyStemmusScope.bmi.implementation import StemmusScopeBmi\n", + "from modflowapi import ModflowApi\n", + "from modflowapi.extensions import ApiSimulation\n", + "from cftime import num2pydate\n", + "from rich import print" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "startTime = datetime.now()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Set up the root directory" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "root = os.path.join('.', 'SSM_examples', 'ex1') # the root path where all folders are stored into\n", + "executables_workspace = os.path.join('.', 'SSM_examples', 'executables')\n", + "output_figures_workspace = os.path.join(root, 'output_figures')\n", + "if not os.path.exists(output_figures_workspace):\n", + " os.makedirs(output_figures_workspace)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.1. Set up STEMMUS_SCOPE folder and config file" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "stemmus_scope_workspace = os.path.join(root, 'input', 'NL-Loo_2024-08-12-1341')\n", + "stemmus_scope_config_file = os.path.join(stemmus_scope_workspace, \"NL-Loo_2024-08-12-1341_config.txt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we are using the local executable file we first have to add the Matlab runtime compiler locations to PATH:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
/opt/matlab/MATLAB_Runtime/R2023a/runtime/glnxa64:/opt/matlab/MATLAB_Runtime/R2023a/bin/glnxa64:/opt/matlab/MATLAB_\n",
+       "Runtime/R2023a/sys/os/glnxa64:/opt/matlab/MATLAB_Runtime/R2023a/extern/bin/glnxa64:/opt/matlab/MATLAB_Runtime/R2023\n",
+       "a/sys/opengl/lib/glnxa64\n",
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\n" + ], + "text/plain": [ + "\u001b[35m/opt/matlab/MATLAB_Runtime/R2023a/runtime/\u001b[0m\u001b[95mglnxa64\u001b[0m:\u001b[35m/opt/matlab/MATLAB_Runtime/R2023a/bin/\u001b[0m\u001b[95mglnxa64\u001b[0m:\u001b[35m/opt/matlab/MATLAB_\u001b[0m\n", + "\u001b[35mRuntime/R2023a/sys/os/\u001b[0m\u001b[95mglnxa64\u001b[0m:\u001b[35m/opt/matlab/MATLAB_Runtime/R2023a/extern/bin/\u001b[0m\u001b[95mglnxa64\u001b[0m:\u001b[35m/opt/matlab/MATLAB_Runtime/R2023\u001b[0m\n", + "\u001b[35ma/sys/opengl/lib/\u001b[0m\u001b[95mglnxa64\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Add the matlab runtime compiler locations: \n", + "matlab_version = 'R2023a'\n", + "matlab_path = '/opt/matlab/MATLAB_Runtime/' + matlab_version \n", + " \n", + "os.environ['LD_LIBRARY_PATH'] = ( \n", + " f\"{matlab_path}/runtime/glnxa64:\"\n", + " f\"{matlab_path}/bin/glnxa64:\"\n", + " f\"{matlab_path}/sys/os/glnxa64:\"\n", + " f\"{matlab_path}/extern/bin/glnxa64:\"\n", + " f\"{matlab_path}/sys/opengl/lib/glnxa64\")\n", + "print(os.environ['LD_LIBRARY_PATH'])\n", + "os.environ[\"STEMMUS_SCOPE\"] = read_config(stemmus_scope_config_file)[\"ExeFilePath\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.2. Set up MODFLOW 6 workspace and dll/so file" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "modflow_workspace = os.path.join(root, 'mf6_model')\n", + "modflow_exe_file = os.path.join(executables_workspace, \"libmf6.so\") # for window -> \"libmf6.dll\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Load and initialize models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.1. Initialize the STEMMUS_SCOPE model " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "stemmus_scope = StemmusScopeBmi()\n", + "stemmus_scope.initialize(stemmus_scope_config_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Note: STEMMUS_SCOPE needs to be updated by one timestep before the BMI is accessible" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "stemmus_scope.update()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Define arrays to store the needed STEMMUS_SCOPE variables" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "n_timesteps = int((stemmus_scope.get_end_time() - stemmus_scope.get_current_time())/stemmus_scope.get_time_step())\n", + "n_soil_layers = stemmus_scope.get_grid_size(stemmus_scope.get_var_grid(\"soil_moisture\"))\n", + "\n", + "soil_moisture = np.zeros((n_timesteps, n_soil_layers))\n", + "soil_temperature = np.zeros((n_timesteps, n_soil_layers))\n", + "gw_recharge = np.zeros(1)\n", + "gw_recharge_nstp = np.array([])\n", + "stemmus_scope_time = []\n", + "i = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.2. Initialize the MODFLOW 6 model and get other settings" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "modflow = ModflowApi(modflow_exe_file, working_directory = modflow_workspace)\n", + "modflow_config_file = os.path.join(modflow_workspace, 'mfsim.nam')\n", + "modflow.initialize(modflow_config_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Load the MODFLOW 6 simulation and models" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('gwf', 'gwe')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = ApiSimulation.load(modflow)\n", + "gwf_name = sim.model_names[0] # groundwater flow model \n", + "gwe_name = sim.model_names[1] # groundwater energy (heat) model\n", + "gwf_name, gwe_name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Get number of stress periods (days) and time steps of MODFLOW" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 48)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nper, nstp = sim.nper, sim.nstp\n", + "nper, nstp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Get pointers of MODFLOW API data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "top_tag = modflow.get_var_address(\"TOP\", gwf_name.upper(), \"DIS\") # tag of land surface (top) elevation\n", + "gwhead_tag = modflow.get_var_address(\"X\", gwf_name) # tag of groundwater heads\n", + "gwtemp_tag = modflow.get_var_address(\"X\", gwe_name) # tag of groundwater temperature\n", + "rch_tag = modflow.get_var_address(\"RECHARGE\", gwf_name.upper(), \"RCH_0\") # tag of groundwater recharge" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Get Initial head and temperature of MODFLOW" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2350.0, 10.0, 2600.0)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_elev = modflow.get_value(top_tag)[0] * 100 # multiply by 100 to convert from m to cm\n", + "gwhead_init = modflow.get_value(gwhead_tag)[0] * 100 # multiply by 100 to convert from m to cm\n", + "gwtemp_init = modflow.get_value(gwtemp_tag)[0]\n", + "\n", + "gwhead_init, gwtemp_init, top_elev" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Update MODFLOW 6 by one time step to match with the time step of STEMMUS_SCOPE" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "modflow.update()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Start STEMMUS_SCOPE & MODFLOW coupling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Enable groundwater coupling option in STEMMUS_SCOPE" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "stemmus_scope.set_value(\"groundwater_coupling_enabled\", np.array([True]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Set the MODFLOW 6 initial variables [Land surface elevation, groundwater initial head, and groundwater initial temperature (optional)] in STEMMUS_SCOPE" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "stemmus_scope.set_value(\"groundwater_elevation_top_aquifer\", top_elev)\n", + "stemmus_scope.set_value(\"groundwater_head_bottom_layer\", gwhead_init)\n", + "stemmus_scope.set_value(\"groundwater_temperature\", gwtemp_init) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Iterate through the time steps of both models and exchange variables.\n", + "\n", + "For every time step of STEMMUS_SCOPE:\n", + "- MODFLOW 6 gives groundwater head and groundwater temperature to STEMMUS_SCOPE\n", + "\n", + "For every stress period of MODFLOW 6 (which has 48 time steps of MODFLOW 6):\n", + "- STEMMUS_SCOPE gives groundwater recharge to MODFLOW 6 (recharge_stress_period = sum of 48 recharge_time_step)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Updating MODFLOW Stress period 10, time step 47\n",
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\n" + ], + "text/plain": [ + "Updating MODFLOW Stress period \u001b[1;36m10\u001b[0m, time step \u001b[1;36m47\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "while modflow.get_current_time() < modflow.get_end_time():\n", + " while stemmus_scope.get_current_time() < stemmus_scope.get_end_time(): \n", + " \n", + " # Get STEMMUS_SCOPE variables (soil moisture, soil temperature, groundwater recharge) \n", + " stemmus_scope.get_value(\"soil_moisture\", soil_moisture[i])\n", + " stemmus_scope.get_value(\"soil_temperature\", soil_temperature[i])\n", + " stemmus_scope.get_value(\"groundwater_recharge\", gw_recharge)\n", + " gw_recharge_nstp = np.append(gw_recharge_nstp, gw_recharge)\n", + "\n", + " # Store the current time as a datetime\n", + " stemmus_scope_time.append(num2pydate(stemmus_scope.get_current_time(), stemmus_scope.get_time_units()))\n", + " i+= 1\n", + "\n", + " # Get MODFLOW 6 variables (groundwater head, groundwater temperature)\n", + " gwhead = modflow.get_value(gwhead_tag)[0] * 100\n", + " gwtemp = modflow.get_value(gwtemp_tag)[0]\n", + "\n", + " # Set STEMMUS_SCOPE variables (groundwater head, groundwater temperature)\n", + " stemmus_scope.set_value(\"groundwater_head_bottom_layer\", gwhead)\n", + " stemmus_scope.set_value(\"groundwater_temperature\", gwtemp)\n", + " \n", + " # Set MODFLOW 6 variables (groundwater recharge)\n", + " kstp, kper = sim.kstp, sim.kper \n", + " if kstp == nstp - 1:\n", + " # groundwater recharge per stress period = sum of groundwater recharge per time step \n", + " gw_recharge_nper = np.array([-1 * np.sum(gw_recharge_nstp) / 100]) # multiply by -1 due to different signs of both models\n", + " modflow.set_value(rch_tag, gw_recharge_nper)\n", + " gw_recharge_nstp = np.array([]) \n", + " \n", + " # Track the simulation time of MODFLOW 6 \n", + " print('Updating MODFLOW Stress period ' + str(kper + 1) + ', time step ' + str(kstp + 1))\n", + " # print(kper + 1, kstp + 1, gwhead, gwtemp)\n", + " \n", + " # Update models\n", + " stemmus_scope.update()\n", + " modflow.update()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Finalize models" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "modflow.finalize()\n", + "stemmus_scope.finalize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Export results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Analyze the STEMMUS_SCOPE variables (put the data into xarray DataArray objects)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "gs = stemmus_scope.get_grid_size(1)\n", + "depths = np.ones(gs)\n", + "stemmus_scope.get_grid_z(1, depths)\n", + "\n", + "da_sm = xr.DataArray(\n", + " data=soil_moisture,\n", + " dims=(\"time\", \"depth\"),\n", + " coords={\"time\": np.array(stemmus_scope_time), \"depth\": depths},)\n", + "\n", + "da_t = xr.DataArray(\n", + " data=soil_temperature,\n", + " dims=(\"time\", \"depth\"),\n", + " coords={\"time\": np.array(stemmus_scope_time), \"depth\": depths},)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Show the STEMMUS_SCOPE soil moisture and soil temperature results" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(14,5))\n", + "ax1.set_title(\"Soil Moisture\")\n", + "ax2.set_title(\"Soil Temperature\")\n", + "ax1.set_ylabel(\"depth (m)\")\n", + "ax2.set_ylabel(\"depth (m)\")\n", + "da_sm.plot(y=\"depth\", ax=ax1, cbar_kwargs={'label': \"volumetric moisture content (m3 m-3)\"})\n", + "da_t.plot(y=\"depth\", ax=ax2, cbar_kwargs={'label': \"temperature (deg C)\"})\n", + "fig.savefig(os.path.join(output_figures_workspace, 'soil_variables.png'), dpi = 600, bbox_inches = 'tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
the script took 0:05:45.521026 to run\n",
+       "
\n" + ], + "text/plain": [ + "the script took \u001b[1;92m0:05:45\u001b[0m.\u001b[1;36m521026\u001b[0m to run\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print ('the script took', datetime.now() - startTime, 'to run')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/mkdocs.yml b/mkdocs.yml index b17d44a1..a31e3fb9 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -14,6 +14,7 @@ nav: - "BMI instructions": bmi.md - "BMI demonstration": notebooks/BMI_demo.ipynb - "grpc4bmi demonstration": notebooks/grpc4bmi_demo.ipynb + - "example: coupling modflow and stemmus_scope": notebooks/bmi_MODFLOW_coupling.ipynb - Contributing guide: CONTRIBUTING.md - API reference: reference.md diff --git a/tests/test_config_io.py b/tests/test_config_io.py index c95b0bb5..f45d2e9d 100644 --- a/tests/test_config_io.py +++ b/tests/test_config_io.py @@ -41,3 +41,14 @@ def test_create_io_dir(self): assert Path(input_dir).is_dir() assert Path(output_dir).is_dir() assert Path(config_path).exists() + + def test_without_soil_layers_thickness(self, dummy_config): + dummy_config["soil_layers_thickness"] = "" + with pytest.raises(FileNotFoundError): + config_io.create_io_dir(dummy_config) + + def test_with_soil_layers_thickness(self, dummy_config): + dummy_config["soil_layers_thickness"] = dummy_config["input_data"] + input_dir, _, _ = config_io.create_io_dir(dummy_config) + + assert (Path(input_dir) / "dummy_data.xlsx").exists() diff --git a/tests/test_data/config_file_docker.txt b/tests/test_data/config_file_docker.txt index df00ba25..5a26ed34 100644 --- a/tests/test_data/config_file_docker.txt +++ b/tests/test_data/config_file_docker.txt @@ -13,4 +13,4 @@ StartTime=1996-01-01T00:00 EndTime=1996-01-01T02:00 InputPath= OutputPath= -DockerImage=ghcr.io/ecoextreml/stemmus_scope:1.6.0 \ No newline at end of file +DockerImage=ghcr.io/ecoextreml/stemmus_scope:1.6.1