Merge pull request #126 from ArtesiaWater/dev

Release v0.4.0

.github/workflows/ci.yml

@@ -13,16 +13,16 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: [3.9]
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
 

.github/workflows/python-publish.yml

@@ -13,11 +13,11 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
-        python-version: '3.8'
+        python-version: '3.9'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

README.md

@@ -9,17 +9,21 @@
 
 Python package with functions to process, build and visualise MODFLOW models in the Netherlands. 
 
-The functions in nlmod have three main objectives:
-1. Create and adapt the temporal and spatial discretization of a MODFLOW model using an xarray Dataset. These functions are contained in `nlmod.mdims`
+The functions in nlmod have four main objectives:
+1. Create and adapt the temporal and spatial discretization of a MODFLOW model using an xarray Dataset. These functions are contained in `nlmod.dims`
 2. Read data from external sources, project this data on the modelgrid and add this data to an xarray Dataset. These functions are contained in `nlmod.read`
-3. Use data in an xarray Dataset to build modflow packages using flopy.  These functions are contained in `nlmod.mfpackages`
+3. Use data in an xarray Dataset to build modflow packages using flopy.  The functions for modflow 6 packages are in `nlmod.gwf`, for modpath in `nlmod.modpath`.
+4. Visualise modeldata in Python or GIS software. These functions are contained in `nlmod.plot` and `nlmod.gis`.
 
 External data sources that can be read are:
-- REGIS, subsurface model
+- AHN, digital elevation model
+- bgt, surface water level geometries
 - Geotop, subsurface model
-- KNMI, precipitation and evaporation
 - Jarkus, bathymetry
+- KNMI, precipitation and evaporation
+- REGIS, subsurface model
 - Rijkswaterstaat, surface water polygons
+- multiple waterboards, surface water level data
 
 ## Installation
 

docs/examples/00_model_from_scratch.ipynb

@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<IMG SRC=\"https://avatars2.githubusercontent.com/u/31697400?s=400&u=a5a6fc31ec93c07853dd53835936fd90c44f7483&v=4\" WIDTH=125 ALIGN=\"right\">\n",
+    "\n",
+    "\n",
+    "# Building a groundwater model from scratch with `nlmod`\n",
+    "\n",
+    "This notebook shows how to build a basic model from scratch using `nlmod`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import flopy as fp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import nlmod\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nlmod.util.get_color_logger('INFO');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download MODFLOW-binaries\n",
+    "To run MODFLOW, we need to download the MODFLOW-excecutables. We do this with the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not nlmod.util.check_presence_mfbinaries():\n",
+    "    nlmod.util.download_mfbinaries()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extent = [-500, 500, -500, 500]\n",
+    "\n",
+    "top = 0.0\n",
+    "botm = [-10, -15, -30]\n",
+    "\n",
+    "kh = [10, 0.1, 20]\n",
+    "kv = [0.5 * k for k in kh]\n",
+    "\n",
+    "dx = 10.0\n",
+    "dy = 10.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create model dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = nlmod.get_ds(\n",
+    "    extent,\n",
+    "    delr=dx,\n",
+    "    delc=dy,\n",
+    "    layer=len(kh),\n",
+    "    top=top,\n",
+    "    botm=botm,\n",
+    "    kh=kh,\n",
+    "    kv=kv,\n",
+    "    model_ws=\"./scratch_model\",\n",
+    "    model_name=\"from_scratch\",\n",
+    ")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set time discretisation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = nlmod.time.set_ds_time(\n",
+    "    ds, time=[pd.Timestamp.today()], steady_state=True\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start building model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim = nlmod.sim.sim(ds)\n",
+    "tdis = nlmod.sim.tdis(ds, sim)\n",
+    "ims = nlmod.sim.ims(sim, complexity=\"SIMPLE\")\n",
+    "gwf = nlmod.gwf.gwf(ds, sim)\n",
+    "dis = nlmod.gwf.dis(ds, gwf)\n",
+    "npf = nlmod.gwf.npf(ds, gwf)\n",
+    "ic = nlmod.gwf.ic(ds, gwf, starting_head=1.0)\n",
+    "oc = nlmod.gwf.oc(ds, gwf, save_head=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add wells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wells = pd.DataFrame(columns=[\"x\", \"y\", \"top\", \"botm\", \"Q\"], index=range(2))\n",
+    "wells.index.name = \"well no.\"\n",
+    "wells.loc[0] = 100, -50, -5, -10, -100.0\n",
+    "wells.loc[1] = 200, 150, -20, -30, -300.0\n",
+    "wells\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wel = nlmod.gwf.wells.wel_from_df(wells, gwf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add river"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xyriv = [\n",
+    "    (250, -500),\n",
+    "    (300, -300),\n",
+    "    (275, 0),\n",
+    "    (200, 250),\n",
+    "    (175, 500),\n",
+    "]\n",
+    "\n",
+    "riv_layer = 0  # add to first layer\n",
+    "\n",
+    "bed_resistance = 0.1  # days\n",
+    "riv_cond = dx * dy / bed_resistance  # conductance\n",
+    "riv_stage = 1.0  # m NAP\n",
+    "riv_botm = -3.0  # m NAP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "riv_data = nlmod.gwf.surface_water.rivdata_from_xylist( \n",
+    "    gwf, xyriv, riv_layer, riv_stage, riv_cond, riv_botm\n",
+    ")\n",
+    "\n",
+    "riv = fp.mf6.ModflowGwfriv(gwf, stress_period_data={0: riv_data})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write and run Simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nlmod.sim.write_and_run(sim, ds, silent=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load heads"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head = nlmod.util.get_heads_dataarray(ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot heads"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
+    "\n",
+    "pc = nlmod.plot.data_array(\n",
+    "    head.sel(layer=1).isel(time=0),\n",
+    "    ds=ds,\n",
+    "    cmap=\"RdYlBu\",\n",
+    "    ax=ax,\n",
+    "    edgecolor=\"k\",\n",
+    ")\n",
+    "ax.axis(extent)\n",
+    "cbar = ax.figure.colorbar(pc, shrink=1.0)\n",
+    "cbar.set_label(\"head [m NAP]\")\n",
+    "ax.set_title(\"head layer 1\")\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot heads in all layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = nlmod.plot.facet_plot(\n",
+    "    gwf,\n",
+    "    head,\n",
+    "    lbl=\"head [m NAP]\",\n",
+    "    plot_dim=\"layer\",\n",
+    "    period=0,\n",
+    "    cmap=\"RdYlBu\",\n",
+    "    plot_bc={\"WEL\": {}},\n",
+    "    plot_grid=True,\n",
+    ")"
+   ]
+  }
+ ],
+ "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": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "dace5e1b41a98a8e52d2a8eebc3b981caf2c12e7a76736ebfb89a489e3b62e79"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}