1677 lines
706 KiB
Plaintext
1677 lines
706 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# FloPy\n",
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"\n",
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"## Making Cross Sections of Your Model\n",
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"This notebook demonstrates the cross sectional mapping capabilities of FloPy. It demonstrates these capabilities by loading and running existing models and then showing how the `PlotCrossSection` object and its methods can be used to make nice plots of the model grid, boundary conditions, model results, shape files, etc.\n",
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"\n",
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"### Mapping is demonstrated for MODFLOW-2005 and MODFLOW-6 models in this notebook"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.7.4 (default, Aug 9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]\n",
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"numpy version: 1.18.1\n",
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"matplotlib version: 3.1.2\n",
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"flopy version: 3.3.3\n"
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]
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}
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],
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"source": [
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"import sys\n",
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"import os\n",
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"import platform\n",
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"import numpy as np\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# run installed version of flopy or add local path\n",
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"try:\n",
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" import flopy\n",
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"except:\n",
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" fpth = os.path.abspath(os.path.join('..', '..'))\n",
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" sys.path.append(fpth)\n",
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" import flopy\n",
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"\n",
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"print(sys.version)\n",
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"print('numpy version: {}'.format(np.__version__))\n",
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"print('matplotlib version: {}'.format(mpl.__version__))\n",
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"print('flopy version: {}'.format(flopy.__version__))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set names of the MODFLOW exes\n",
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"# assumes that the executable is in users path statement\n",
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"v2005 = 'mf2005'\n",
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"exe_name_2005 = 'mf2005'\n",
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"vmf6 = 'mf6'\n",
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"exe_name_mf6 = 'mf6'\n",
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"exe_mp = 'mp6'\n",
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"if platform.system() == 'Windows':\n",
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" exe_name_2005 += '.exe'\n",
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" exe_name_mf6 += '.exe'\n",
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" exe_mp += '.exe'\n",
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"\n",
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"#Set the paths\n",
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"loadpth = os.path.join('..', 'data', 'freyberg')\n",
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"modelpth = os.path.join('data')\n",
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"\n",
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"#make sure modelpth directory exists\n",
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"if not os.path.exists(modelpth):\n",
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" os.makedirs(modelpth)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load and Run an Existing MODFLOW-2005 Model\n",
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"A model called the \"Freyberg Model\" is located in the loadpth folder. In the following code block, we load that model, then change into a new workspace (modelpth) where we recreate and run the model. For this to work properly, the MODFLOW-2005 executable (mf2005) must be in the path. We verify that it worked correctly by checking for the presence of freyberg.hds and freyberg.cbc."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"changing model workspace...\n",
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" data\n",
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"FloPy is using the following executable to run the model: .\\mf2005.exe\n",
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"\n",
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" MODFLOW-2005 \n",
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" U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUND-WATER FLOW MODEL\n",
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" Version 1.12.00 2/3/2017 \n",
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"\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Using NAME file: freyberg.nam \n",
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" Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/03/10 17:51:35\n",
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"\n",
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" Solving: Stress period: 1 Time step: 1 Ground-Water Flow Eqn.\n",
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" Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/03/10 17:51:35\n",
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" Elapsed run time: 0.033 Seconds\n",
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"\n",
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" Normal termination of simulation\n",
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"Output file located: freyberg.hds\n",
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"Output file located: freyberg.cbc\n"
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]
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}
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],
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"source": [
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"ml = flopy.modflow.Modflow.load('freyberg.nam', model_ws=loadpth, \n",
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" exe_name=exe_name_2005, version=v2005)\n",
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"ml.change_model_ws(new_pth=modelpth)\n",
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"ml.write_input()\n",
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"success, buff = ml.run_model()\n",
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"if not success:\n",
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" print ('Something bad happened.')\n",
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"files = ['freyberg.hds', 'freyberg.cbc']\n",
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"for f in files:\n",
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" if os.path.isfile(os.path.join(modelpth, f)):\n",
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" msg = 'Output file located: {}'.format(f)\n",
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" print (msg)\n",
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" else:\n",
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" errmsg = 'Error. Output file cannot be found: {}'.format(f)\n",
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" print (errmsg)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Creating a Cross-Section of the Model Grid\n",
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"\n",
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"Now that we have a model, we can use the FloPy plotting utilities to make cross-sections. We'll start by making a Map to show the model grid and basic boundary conditions. Then we'll begin making a cross section using the `PlotCrossSection` class and the `plot_grid()` method of that class."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 576x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# let's take a look at our grid before making a cross section\n",
|
|
"fig = plt.figure(figsize=(8, 8))\n",
|
|
"ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
|
|
"mapview = flopy.plot.PlotMapView(model=ml)\n",
|
|
"ibound = mapview.plot_ibound()\n",
|
|
"wel = mapview.plot_bc(\"WEL\")\n",
|
|
"riv = mapview.plot_bc(\"RIV\")\n",
|
|
"linecollection = mapview.plot_grid()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Next we will make a cross-section of the model grid at column 6."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x360 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# First step is to set up the plot\n",
|
|
"fig = plt.figure(figsize=(15, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"# Next we create an instance of the PlotCrossSection class\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"\n",
|
|
"# Then we can use the plot_grid() method to draw the grid\n",
|
|
"# The return value for this function is a matplotlib LineCollection object,\n",
|
|
"# which could be manipulated (or used) later if necessary.\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"t = ax.set_title('Column 6 Cross-Section - Model Grid')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Ploting Ibound\n",
|
|
"\n",
|
|
"The `plot_ibound()` method can be used to plot the boundary conditions contained in the ibound arrray, which is part of the MODFLOW Basic Package. The `plot_ibound()` method returns a matplotlib PatchCollection object (matplotlib.collections.PatchCollection). If you are familiar with the matplotlib collections, then this may be important to you, but if not, then don't worry about the return objects of these plotting function."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x360 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig = plt.figure(figsize=(15, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"patches = xsect.plot_ibound()\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"t = ax.set_title('Column 6 Cross-Section with IBOUND Boundary Conditions')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x360 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Or we could change the colors!\n",
|
|
"fig = plt.figure(figsize=(15, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"patches = xsect.plot_ibound(color_noflow='red', color_ch='orange')\n",
|
|
"linecollection = xsect.plot_grid(color='green')\n",
|
|
"t = ax.set_title('Column 6 Cross-Section with IBOUND Boundary Conditions')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting Boundary Conditions\n",
|
|
"The `plot_bc()` method can be used to plot boundary conditions on a cross section. It is setup to use the following dictionary to assign colors, however, these colors can be changed in the method call.\n",
|
|
"\n",
|
|
" bc_color_dict = {'default': 'black', 'WEL': 'red', 'DRN': 'yellow',\n",
|
|
" 'RIV': 'green', 'GHB': 'cyan', 'CHD': 'navy'}\n",
|
|
"\n",
|
|
"Just like the `plot_bc()` method for `PlotMapView`, the default boundary condition colors can be changed in the method call.\n",
|
|
"\n",
|
|
"Here, we plot the location of well cells in column 6."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig = plt.figure(figsize=(15, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"patches = xsect.plot_bc('WEL', color='pink')\n",
|
|
"patches = xsect.plot_ibound()\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"t = ax.set_title('Column 6 Cross-Section with Boundary Conditions')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting an Array\n",
|
|
"\n",
|
|
"`PlotCrossSection` has a `plot_array()` method. The `plot_array()` method will only accept 3D arrays for structured grids. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create a random array and plot it\n",
|
|
"a = np.random.random((ml.dis.nlay, ml.dis.nrow, ml.dis.ncol))\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"csa = xsect.plot_array(a)\n",
|
|
"patches = xsect.plot_ibound()\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"t = ax.set_title('Column 6 Cross-Section with Random Data')\n",
|
|
"cb = plt.colorbar(csa, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# plot the horizontal hydraulic conductivities\n",
|
|
"a = ml.lpf.hk.array\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"csa = xsect.plot_array(a)\n",
|
|
"patches = xsect.plot_ibound()\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"t = ax.set_title('Column 6 Cross-Section with Horizontal hydraulic conductivity')\n",
|
|
"cb = plt.colorbar(csa, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Contouring an Array\n",
|
|
"\n",
|
|
"`PlotCrossSection` also has a `contour_array()` method. It also accepts a 3D array for structured grids."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# plot the horizontal hydraulic conductivities\n",
|
|
"a = ml.lpf.hk.array\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"contour_set = xsect.contour_array(a, masked_values=[0], cmap='jet')\n",
|
|
"patches = xsect.plot_ibound()\n",
|
|
"linecollection = xsect.plot_grid(color='grey')\n",
|
|
"t = ax.set_title('Column 6 Cross-Section contour_array() horizontal hydraulic conductivity')\n",
|
|
"cb = plt.colorbar(contour_set, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting Heads\n",
|
|
"\n",
|
|
"We can easily plot results from the simulation by extracting heads using `flopy.utils.HeadFile`. \n",
|
|
"\n",
|
|
"The head can be passed into the `plot_array()` and `contour_array()` using the `head=` keyword argument to fix the top of the colored patch and contour lines at the top of the water table in each cell, respectively. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fname = os.path.join(modelpth, 'freyberg.hds')\n",
|
|
"hdobj = flopy.utils.HeadFile(fname)\n",
|
|
"head = hdobj.get_data()\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"ax.set_title('plot_array() used to plotting Heads')\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"pc = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
|
|
"patches = xsect.plot_ibound(head=head)\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"cb = plt.colorbar(pc, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# contour array on top of heads\n",
|
|
"levels = np.arange(17, 26, 1)\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"ax.set_title('contour_array() and plot_array() of head values')\n",
|
|
"\n",
|
|
"# instantiate the PlotCrossSection object\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"\n",
|
|
"# plot the head array and model grid\n",
|
|
"pc = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
|
|
"patches = xsect.plot_ibound(head=head)\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"\n",
|
|
"# do black contour lines of the head array\n",
|
|
"contour_set = xsect.contour_array(head, masked_values=[999.], head=head, levels=levels, colors='k')\n",
|
|
"plt.clabel(contour_set, fmt='%.1f', colors='k', fontsize=11)\n",
|
|
"\n",
|
|
"cb = plt.colorbar(pc, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting a surface on the cross section\n",
|
|
"\n",
|
|
"The `plot_surface()` method allows the user to plot a surface along the cross section. Here is a short example using head data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"levels = np.arange(10, 30, .5)\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'Column': 5})\n",
|
|
"\n",
|
|
"# contour array and plot ibound\n",
|
|
"ct = xsect.contour_array(head, masked_values=[999.], head=head, levels=levels, linewidths=2.5)\n",
|
|
"pc = xsect.plot_ibound(head=head)\n",
|
|
"\n",
|
|
"#plot the surface and model grid\n",
|
|
"wt = xsect.plot_surface(head, masked_values=[999.], color='blue', lw=2.5)\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"\n",
|
|
"plt.title('contour_array() and plot_surface()')\n",
|
|
"cb = plt.colorbar(ct, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting discharge vectors\n",
|
|
"\n",
|
|
"`PlotCrossSection` has a `plot_vector()` method, which takes `qx`, `qy`, and `qz` vector arrays (ex. specific discharge or flow across a cell faces). The flow array values can be extracted from the cell by cell flow file using the `flopy.utils.CellBudgetFile` object as shown below. Once they are extracted, they either be can be passed to the `plot_vector()` method or they can be post processed into specific discharge using `postprocessing.get_specific_discharge`. Note that `get_specific_discharge()` also takes the head array as an argument. The head array is used by `get_specific_discharge()` to convert the volumetric flow in dimensions of $L^3/T$ to specific discharge in dimensions of $L/T$ and to plot the specific discharge in the center of each saturated cell. For this problem, there is no 'FLOW LOWER FACE' array since the Freyberg Model is a one layer model."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fname = os.path.join(modelpth, 'freyberg.cbc')\n",
|
|
"cbb = flopy.utils.CellBudgetFile(fname)\n",
|
|
"frf = cbb.get_data(text='FLOW RIGHT FACE')[0]\n",
|
|
"fff = cbb.get_data(text='FLOW FRONT FACE')[0]\n",
|
|
"qx, qy, qz = flopy.utils.postprocessing.get_specific_discharge((frf, fff, None), ml, head=head)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"ax.set_title('plot_array() and plot_vector()')\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, ax=ax, line={'Column': 5})\n",
|
|
"csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
|
|
"patches = xsect.plot_ibound(head=head)\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"quiver = xsect.plot_vector(qx, qy, qz, head=head,\n",
|
|
" hstep=2, normalize=True, color='green', \n",
|
|
" scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
|
|
" zorder=10)\n",
|
|
"\n",
|
|
"cb = plt.colorbar(csa, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting a cross section from Shapefile data\n",
|
|
"\n",
|
|
"A shapefile can be used to define the vertices for a instance of the `PlotCrossSection` class. The function `flopy.plot.plotutil.shapefile_get_vertices()` will return a list of vertices for each polyline in a shapefile.\n",
|
|
"\n",
|
|
"Let's plot the shapefiles and the Freyberg model using `PlotMapView` for visualization purposes and then plot the cross-section."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x864 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Setup the figure and PlotMapView. Show a very faint map of ibound and \n",
|
|
"# model grid by specifying a transparency alpha value.\n",
|
|
"\n",
|
|
"# set the modelgrid rotation and offset\n",
|
|
"ml.modelgrid.set_coord_info(xoff=-2419.2189559966773, yoff=297.0427372400354, angrot=-14)\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(12, 12))\n",
|
|
"ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
|
|
"mapview = flopy.plot.PlotMapView(model=ml)\n",
|
|
"\n",
|
|
"# Plot a shapefile of \n",
|
|
"shp = os.path.join(loadpth, 'gis', 'bedrock_outcrop_hole_rotate14')\n",
|
|
"patch_collection = mapview.plot_shapefile(shp, #facecolor='none', \n",
|
|
" edgecolor='green', linewidths=2, alpha=0.5)\n",
|
|
"# Plot a shapefile of a cross-section line\n",
|
|
"shp = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
|
|
"patch_collection = mapview.plot_shapefile(shp, radius=0, lw=3, \n",
|
|
" edgecolor='red', facecolor='None')\n",
|
|
"\n",
|
|
"# Plot a shapefile of well locations\n",
|
|
"shp = os.path.join(loadpth, 'gis', 'wells_locations_rotate14')\n",
|
|
"patch_collection = mapview.plot_shapefile(shp, radius=100, facecolor='red')\n",
|
|
"\n",
|
|
"# Plot the grid and boundary conditions over the top\n",
|
|
"quadmesh = mapview.plot_ibound(alpha = 0.1)\n",
|
|
"linecollection = mapview.plot_grid(alpha=0.1);"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now let's make a cross section based on this arbitrary cross-sectional line. We can load the cross sectional line vertices using `flopy.plot.plotutil.shapefile_get_vertices()`\n",
|
|
"\n",
|
|
"**Note**: in previous examples we passed `line={'column', 5}` to plot a cross section along a column. In this example we pass vertex information into `PlotCrossSection` using `line={'line', line[0]}` where `line[0]` is a list of vertices."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# get the vertices for cross-section lines in a shapefile\n",
|
|
"fpth = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
|
|
"line = flopy.plot.plotutil.shapefile_get_vertices(fpth)\n",
|
|
"\n",
|
|
"# Set up the figure\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"ax.set_title(\"plot_array() along an arbitrary cross-sectional line\")\n",
|
|
"\n",
|
|
"# plot head values along the cross sectional line\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'line': line[0]})\n",
|
|
"csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
|
|
"patches = xsect.plot_ibound(head=head)\n",
|
|
"linecollection = xsect.plot_grid(lw=0.5)\n",
|
|
"cb = fig.colorbar(csa, ax=ax, shrink=0.5)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting geographic coordinates on the x-axis using the `PlotCrossSection` class\n",
|
|
"\n",
|
|
"The default cross section plotting method plots cells with regard to their intersection distance along the cross sectional line defined by the user. While this method is perfectly acceptable and in many cases may be preferred for plotting arbitrary cross sections, a flag has been added to plot based on geographic coordinates.\n",
|
|
"\n",
|
|
"The flag `geographic_coords` defaults to `False` which maintains FloPy's previous method of plotting cross sections. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
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"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# get the vertices for cross-section lines in a shapefile\n",
|
|
"fpth = os.path.join(loadpth, 'gis', 'cross_section_rotate14')\n",
|
|
"line = flopy.plot.plotutil.shapefile_get_vertices(fpth)\n",
|
|
"\n",
|
|
"# Set up the figure\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"ax.set_title(\"plot_array() along an arbitrary cross-sectional line\")\n",
|
|
"\n",
|
|
"# plot head values along the cross sectional line\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml, line={'line': line[0]}, geographic_coords=True)\n",
|
|
"csa = xsect.plot_array(head, masked_values=[999.], head=head, alpha=0.5)\n",
|
|
"patches = xsect.plot_ibound(head=head)\n",
|
|
"linecollection = xsect.plot_grid(lw=0.5)\n",
|
|
"cb = fig.colorbar(csa, ax=ax, shrink=0.5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plotting Cross Sections with MODFLOW-6 models\n",
|
|
"\n",
|
|
"`PlotCrossSection` has support for MODFLOW-6 models and operates in the same fashion for Structured Grids, Vertex Grids, and Unstructured Grids. Here is a short example on how to plot with MODFLOW-6 structured grids using a version of the Freyberg model created for MODFLOW-6|"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"loading simulation...\n",
|
|
" loading simulation name file...\n",
|
|
" loading tdis package...\n",
|
|
" loading model gwf6...\n",
|
|
" loading package dis...\n",
|
|
" loading package ic...\n",
|
|
"WARNING: Block \"options\" is not a valid block name for file type ic.\n",
|
|
" loading package oc...\n",
|
|
" loading package npf...\n",
|
|
" loading package sto...\n",
|
|
" loading package chd...\n",
|
|
" loading package riv...\n",
|
|
" loading package wel...\n",
|
|
" loading package rch...\n",
|
|
" loading ims package gwf_1...\n",
|
|
"writing simulation...\n",
|
|
" writing simulation name file...\n",
|
|
" writing simulation tdis package...\n",
|
|
" writing ims package gwf_1...\n",
|
|
" writing model gwf_1...\n",
|
|
" writing model name file...\n",
|
|
" writing package dis...\n",
|
|
" writing package ic...\n",
|
|
" writing package oc...\n",
|
|
" writing package npf...\n",
|
|
" writing package sto...\n",
|
|
" writing package chd_0...\n",
|
|
" writing package riv_0...\n",
|
|
" writing package wel_0...\n",
|
|
" writing package rch_0...\n",
|
|
"FloPy is using the following executable to run the model: .\\mf6.exe\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" MODFLOW 6\n",
|
|
" U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
|
|
" VERSION 6.2.1 02/18/2021\n",
|
|
"\n",
|
|
" MODFLOW 6 compiled Feb 18 2021 08:24:05 with IFORT compiler (ver. 19.10.2)\n",
|
|
"\n",
|
|
"This software has been approved for release by the U.S. Geological \n",
|
|
"Survey (USGS). Although the software has been subjected to rigorous \n",
|
|
"review, the USGS reserves the right to update the software as needed \n",
|
|
"pursuant to further analysis and review. No warranty, expressed or \n",
|
|
"implied, is made by the USGS or the U.S. Government as to the \n",
|
|
"functionality of the software and related material nor shall the \n",
|
|
"fact of release constitute any such warranty. Furthermore, the \n",
|
|
"software is released on condition that neither the USGS nor the U.S. \n",
|
|
"Government shall be held liable for any damages resulting from its \n",
|
|
"authorized or unauthorized use. Also refer to the USGS Water \n",
|
|
"Resources Software User Rights Notice for complete use, copyright, \n",
|
|
"and distribution information.\n",
|
|
"\n",
|
|
" \n",
|
|
" Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/03/10 17:51:39\n",
|
|
" \n",
|
|
" Writing simulation list file: mfsim.lst\n",
|
|
" Using Simulation name file: mfsim.nam\n",
|
|
" \n",
|
|
" Solving: Stress period: 1 Time step: 1\n",
|
|
" \n",
|
|
" Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/03/10 17:51:39\n",
|
|
" Elapsed run time: 0.140 Seconds\n",
|
|
" \n",
|
|
"\n",
|
|
"WARNING REPORT:\n",
|
|
"\n",
|
|
" 1. NONLINEAR BLOCK VARIABLE 'OUTER_HCLOSE' IN FILE 'freyberg.ims' WAS\n",
|
|
" DEPRECATED IN VERSION 6.1.1. SETTING OUTER_DVCLOSE TO OUTER_HCLOSE VALUE.\n",
|
|
" 2. LINEAR BLOCK VARIABLE 'INNER_HCLOSE' IN FILE 'freyberg.ims' WAS\n",
|
|
" DEPRECATED IN VERSION 6.1.1. SETTING INNER_DVCLOSE TO INNER_HCLOSE VALUE.\n",
|
|
" Normal termination of simulation.\n",
|
|
"Output file located: freyberg.hds\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Output file located: freyberg.cbc\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# load the Freyberg model into mf6-flopy and run the simulation\n",
|
|
"sim_name = 'mfsim.nam'\n",
|
|
"sim_path = os.path.join(\"..\", \"data\", \"mf6-freyberg\")\n",
|
|
"sim = flopy.mf6.MFSimulation.load(sim_name=sim_name, version=vmf6, exe_name=exe_name_mf6, \n",
|
|
" sim_ws=sim_path)\n",
|
|
"\n",
|
|
"newpth = os.path.join('data')\n",
|
|
"sim.set_sim_path(newpth)\n",
|
|
"sim.write_simulation()\n",
|
|
"success, buff = sim.run_simulation()\n",
|
|
"if not success:\n",
|
|
" print ('Something bad happened.')\n",
|
|
"files = ['freyberg.hds', 'freyberg.cbc']\n",
|
|
"for f in files:\n",
|
|
" if os.path.isfile(os.path.join(modelpth, f)):\n",
|
|
" msg = 'Output file located: {}'.format(f)\n",
|
|
" print (msg)\n",
|
|
" else:\n",
|
|
" errmsg = 'Error. Output file cannot be found: {}'.format(f)\n",
|
|
" print (errmsg)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting boundary conditions and arrays\n",
|
|
"\n",
|
|
"This works the same as modflow-2005, however the simulation object can host a number of modflow-6 models so we need to grab a model before attempting to plot with `PlotCrossSection`"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x720 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# get the modflow-6 model we want to plot\n",
|
|
"ml6 = sim.get_model('gwf_1')\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(15, 10))\n",
|
|
"ax = fig.add_subplot(2, 1, 1)\n",
|
|
"\n",
|
|
"# plot boundary conditions\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml6, line={'Column': 5})\n",
|
|
"patches = xsect.plot_bc('WEL_0', color='pink')\n",
|
|
"patches = xsect.plot_ibound()\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"t = ax.set_title('Column 6 Cross-Section with Boundary Conditions')\n",
|
|
"\n",
|
|
"# plot xxxx\n",
|
|
"ax = fig.add_subplot(2, 1, 2)\n",
|
|
"# plot the horizontal hydraulic conductivities\n",
|
|
"a = ml6.npf.k.array\n",
|
|
"\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml6, line={'Column': 5})\n",
|
|
"csa = xsect.plot_array(a)\n",
|
|
"patches = xsect.plot_ibound()\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"t = ax.set_title('Column 6 Cross-Section with Horizontal hydraulic conductivity')\n",
|
|
"cb = plt.colorbar(csa, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting specific discharge with a MODFLOW-6 model\n",
|
|
"\n",
|
|
"MODFLOW-6 includes a the PLOT_SPECIFIC_DISCHARGE flag in the NPF package to calculate and store discharge vectors for easy plotting. The `postprocessing.get_specific_discharge()` method will preprocess the data into vectors and `PlotCrossSection` has the `plot_vector()` method to use this data. The specific discharge array is stored in the cell budget file."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# get the specific discharge from the cell budget file\n",
|
|
"cbc_file = os.path.join(newpth, \"freyberg.cbc\")\n",
|
|
"cbc = flopy.utils.CellBudgetFile(cbc_file, precision='double')\n",
|
|
"spdis = cbc.get_data(text='SPDIS')[-1]\n",
|
|
"qx, qy, qz = flopy.utils.postprocessing.get_specific_discharge(spdis, ml6, head=head)\n",
|
|
"\n",
|
|
"# get the head from the head file\n",
|
|
"head_file = os.path.join(newpth, \"freyberg.hds\")\n",
|
|
"hds = flopy.utils.HeadFile(head_file)\n",
|
|
"head = hds.get_alldata()[0]\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"ax.set_title('plot_array() and plot_vector()')\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=ml6, ax=ax, line={'Column': 5})\n",
|
|
"csa = xsect.plot_array(head, masked_values=[1e30], head=head, alpha=0.5)\n",
|
|
"patches = xsect.plot_ibound(head=head)\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"quiver = xsect.plot_vector(qx, qy, qz, head=head, \n",
|
|
" hstep=2, normalize=True, color='green', \n",
|
|
" scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
|
|
" zorder=10)\n",
|
|
"\n",
|
|
"cb = plt.colorbar(csa, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Vertex cross section plotting with MODFLOW-6 (DISV) \n",
|
|
"\n",
|
|
"FloPy fully supports vertex discretization (DISV) plotting through the `PlotCrossSection` class. The method calls are identical to the ones presented previously for Structured discretization (DIS) and the same matplotlib keyword arguments are supported. Let's run through an example using a vertex model grid."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"writing simulation...\n",
|
|
" writing simulation name file...\n",
|
|
" writing simulation tdis package...\n",
|
|
" writing ims package ims...\n",
|
|
" writing model mp7p2...\n",
|
|
" writing model name file...\n",
|
|
" writing package disv...\n",
|
|
" writing package ic...\n",
|
|
" writing package npf...\n",
|
|
" writing package wel_0...\n",
|
|
"INFORMATION: maxbound in ('gwf6', 'wel', 'dimensions') changed to 1 based on size of stress_period_data\n",
|
|
" writing package rcha...\n",
|
|
" writing package riv_0...\n",
|
|
"INFORMATION: maxbound in ('gwf6', 'riv', 'dimensions') changed to 21 based on size of stress_period_data\n",
|
|
" writing package oc...\n",
|
|
"FloPy is using the following executable to run the model: .\\mf6.EXE\n",
|
|
" MODFLOW 6\n",
|
|
" U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
|
|
" VERSION 6.2.1 02/18/2021\n",
|
|
"\n",
|
|
" MODFLOW 6 compiled Feb 18 2021 08:24:05 with IFORT compiler (ver. 19.10.2)\n",
|
|
"\n",
|
|
"This software has been approved for release by the U.S. Geological \n",
|
|
"Survey (USGS). Although the software has been subjected to rigorous \n",
|
|
"review, the USGS reserves the right to update the software as needed \n",
|
|
"pursuant to further analysis and review. No warranty, expressed or \n",
|
|
"implied, is made by the USGS or the U.S. Government as to the \n",
|
|
"functionality of the software and related material nor shall the \n",
|
|
"fact of release constitute any such warranty. Furthermore, the \n",
|
|
"software is released on condition that neither the USGS nor the U.S. \n",
|
|
"Government shall be held liable for any damages resulting from its \n",
|
|
"authorized or unauthorized use. Also refer to the USGS Water \n",
|
|
"Resources Software User Rights Notice for complete use, copyright, \n",
|
|
"and distribution information.\n",
|
|
"\n",
|
|
" \n",
|
|
" Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/03/10 17:51:41\n",
|
|
" \n",
|
|
" Writing simulation list file: mfsim.lst\n",
|
|
" Using Simulation name file: mfsim.nam\n",
|
|
" \n",
|
|
" Solving: Stress period: 1 Time step: 1\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" \n",
|
|
" Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/03/10 17:51:41\n",
|
|
" Elapsed run time: 0.169 Seconds\n",
|
|
" \n",
|
|
"\n",
|
|
"WARNING REPORT:\n",
|
|
"\n",
|
|
" 1. NONLINEAR BLOCK VARIABLE 'OUTER_HCLOSE' IN FILE 'mp7p2.ims' WAS\n",
|
|
" DEPRECATED IN VERSION 6.1.1. SETTING OUTER_DVCLOSE TO OUTER_HCLOSE VALUE.\n",
|
|
" 2. LINEAR BLOCK VARIABLE 'INNER_HCLOSE' IN FILE 'mp7p2.ims' WAS DEPRECATED\n",
|
|
" IN VERSION 6.1.1. SETTING INNER_DVCLOSE TO INNER_HCLOSE VALUE.\n",
|
|
" Normal termination of simulation.\n",
|
|
"FloPy is using the following executable to run the model: .\\mp7.EXE\n",
|
|
"\n",
|
|
"MODPATH Version 7.2.001 \n",
|
|
"Program compiled Dec 22 2017 11:11:36 with IFORT compiler (ver. 16.0.0) \n",
|
|
" \n",
|
|
" \n",
|
|
"Run particle tracking simulation ...\n",
|
|
"Processing Time Step 1 Period 1. Time = 1.00000E+03 Steady-state flow \n",
|
|
"\n",
|
|
"Particle Summary:\n",
|
|
" 0 particles are pending release.\n",
|
|
" 0 particles remain active.\n",
|
|
" 16 particles terminated at boundary faces.\n",
|
|
" 0 particles terminated at weak sink cells.\n",
|
|
" 0 particles terminated at weak source cells.\n",
|
|
" 0 particles terminated at strong source/sink cells.\n",
|
|
" 0 particles terminated in cells with a specified zone number.\n",
|
|
" 0 particles were stranded in inactive or dry cells.\n",
|
|
" 0 particles were unreleased.\n",
|
|
" 0 particles have an unknown status.\n",
|
|
" \n",
|
|
"Normal termination. \n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"FloPy is using the following executable to run the model: .\\mp7.EXE\n",
|
|
"\n",
|
|
"MODPATH Version 7.2.001 \n",
|
|
"Program compiled Dec 22 2017 11:11:36 with IFORT compiler (ver. 16.0.0) \n",
|
|
" \n",
|
|
" \n",
|
|
"Run particle tracking simulation ...\n",
|
|
"Processing Time Step 1 Period 1. Time = 1.00000E+03 Steady-state flow \n",
|
|
"\n",
|
|
"Particle Summary:\n",
|
|
" 0 particles are pending release.\n",
|
|
" 0 particles remain active.\n",
|
|
" 416 particles terminated at boundary faces.\n",
|
|
" 0 particles terminated at weak sink cells.\n",
|
|
" 0 particles terminated at weak source cells.\n",
|
|
" 0 particles terminated at strong source/sink cells.\n",
|
|
" 0 particles terminated in cells with a specified zone number.\n",
|
|
" 0 particles were stranded in inactive or dry cells.\n",
|
|
" 0 particles were unreleased.\n",
|
|
" 0 particles have an unknown status.\n",
|
|
" \n",
|
|
"Normal termination. \n",
|
|
"Output file located: mp7p2.hds\n",
|
|
"Output file located: mp7p2.cbb\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# build and run vertex model grid demo problem\n",
|
|
"sys.path.append(os.path.join(\"..\", \"common\"))\n",
|
|
"import setup_pmv_demo\n",
|
|
"setup_pmv_demo.run()\n",
|
|
"\n",
|
|
"# check if model ran properly\n",
|
|
"modelpth = os.path.join(\"data\", \"mp7_ex2\", \"mf6\")\n",
|
|
"files = ['mp7p2.hds', 'mp7p2.cbb']\n",
|
|
"for f in files:\n",
|
|
" if os.path.isfile(os.path.join(modelpth, f)):\n",
|
|
" msg = 'Output file located: {}'.format(f)\n",
|
|
" print (msg)\n",
|
|
" else:\n",
|
|
" errmsg = 'Error. Output file cannot be found: {}'.format(f)\n",
|
|
" print (errmsg)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"loading simulation...\n",
|
|
" loading simulation name file...\n",
|
|
" loading tdis package...\n",
|
|
" loading model gwf6...\n",
|
|
" loading package disv...\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" loading package ic...\n",
|
|
" loading package npf...\n",
|
|
" loading package wel...\n",
|
|
" loading package rch...\n",
|
|
" loading package riv...\n",
|
|
" loading package oc...\n",
|
|
" loading ims package mp7p2...\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# load the simulation and get the model\n",
|
|
"vertex_sim_name = \"mfsim.nam\"\n",
|
|
"vertex_sim = flopy.mf6.MFSimulation.load(sim_name=vertex_sim_name, version=vmf6, exe_name=exe_name_mf6, \n",
|
|
" sim_ws=modelpth)\n",
|
|
"vertex_ml6 = vertex_sim.get_model(\"mp7p2\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting a line based cross section through the model grid\n",
|
|
"\n",
|
|
"Because Vertex model grids have no row or column number, cross sections through the model grid must be defined by a set of vertices and passed to the `PlotCrossSection` class using the parameter `line={\"line\": [(), (), ...]`. Below we show an example of setting up a cross sectional line with a MODFLOW-6 vertex grid model.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x864 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"line = np.array([(4700, 0), (4700, 5000), (7250, 10500)])\n",
|
|
"\n",
|
|
"# Let's plot the model grid in map view to look at it\n",
|
|
"fig = plt.figure(figsize=(12, 12))\n",
|
|
"ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
|
|
"ax.set_title(\"Vertex Model Grid (DISV) with cross sectional line\")\n",
|
|
"\n",
|
|
"# use PlotMapView to plot a DISV (vertex) model\n",
|
|
"mapview = flopy.plot.PlotMapView(vertex_ml6, layer=0)\n",
|
|
"linecollection = mapview.plot_grid()\n",
|
|
"\n",
|
|
"# plot the line over the model grid\n",
|
|
"lc = plt.plot(line.T[0], line.T[1], 'r--', lw=2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now we can plot a cross section of the model grid defined by this line"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x360 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig = plt.figure(figsize=(15, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"# Next we create an instance of the PlotCrossSection class\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={'line': line})\n",
|
|
"\n",
|
|
"# Then we can use the plot_grid() method to draw the grid\n",
|
|
"# The return value for this function is a matplotlib LineCollection object,\n",
|
|
"# which could be manipulated (or used) later if necessary.\n",
|
|
"linecollection = xsect.plot_grid()\n",
|
|
"t = ax.set_title('Column 6 Cross-Section - Model Grid')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting Arrays and Contouring with Vertex Model grids\n",
|
|
"\n",
|
|
"`PlotCrossSection` allows the user to plot arrays and contour with DISV based discretization. The `plot_array()` method is called in the same way as using a structured grid. The only difference is that `PlotCrossSection` builds a matplotlib patch collection for Vertex based grids. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# get the head output for stress period 1 from the modflow6 head file\n",
|
|
"head = flopy.utils.HeadFile(os.path.join(modelpth, 'mp7p2.hds'))\n",
|
|
"hdata = head.get_alldata()[0, :, :, :]\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"ax.set_title(\"plot_array()\")\n",
|
|
"\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
|
|
"patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
|
|
"line_collection = xsect.plot_grid()\n",
|
|
"cb = plt.colorbar(patch_collection, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The `contour_array()` method operates in the same way as the sturctured example."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1296x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"levels = np.arange(329, 337, 1)\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"ax.set_title(\"contour_array() with a multi-layer vertex model\")\n",
|
|
"\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
|
|
"patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
|
|
"line_collection = xsect.plot_grid()\n",
|
|
"\n",
|
|
"contour_set = xsect.contour_array(hdata, levels=levels, colors='k')\n",
|
|
"plt.clabel(contour_set, fmt='%.1f', colors='k', fontsize=11)\n",
|
|
"\n",
|
|
"cb = plt.colorbar(patch_collection, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Plotting specific discharge vectors for DISV\n",
|
|
"MODFLOW-6 includes a the PLOT_SPECIFIC_DISCHARGE flag in the NPF package to calculate and store discharge vectors for easy plotting.The `postprocessing.get_specific_discharge()` method will preprocess the data into vectors and `PlotCrossSection` has the `plot_vector()` method to use this data. The specific discharge array is stored in the cell budget file.\n",
|
|
"\n",
|
|
"**Note**: When plotting specific discharge, an arbitrary cross section cannot be used. The cross sectional line must be orthogonal to the model grid"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x864 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# define and plot our orthogonal line\n",
|
|
"line = np.array([(0, 4700), (10000, 4700)])\n",
|
|
"\n",
|
|
"# Let's plot the model grid in map view to look at it\n",
|
|
"fig = plt.figure(figsize=(12, 12))\n",
|
|
"ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
|
|
"ax.set_title(\"Vertex Model Grid (DISV) with cross sectional line\")\n",
|
|
"\n",
|
|
"# use PlotMapView to plot a DISV (vertex) model\n",
|
|
"mapview = flopy.plot.PlotMapView(vertex_ml6, layer=0)\n",
|
|
"linecollection = mapview.plot_grid()\n",
|
|
"\n",
|
|
"# plot the line over the model grid\n",
|
|
"lc = plt.plot(line.T[0], line.T[1], 'r--', lw=2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
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"text/plain": [
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"<Figure size 1296x360 with 2 Axes>"
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]
|
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},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
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"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# plot specific discharge on cross section\n",
|
|
"cbb = flopy.utils.CellBudgetFile(os.path.join(modelpth, 'mp7p2.cbb'))\n",
|
|
"spdis = cbb.get_data(text=\"SPDIS\")[-1]\n",
|
|
"qx, qy, qz = flopy.utils.postprocessing.get_specific_discharge(spdis, vertex_ml6, head=hdata)\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(18, 5))\n",
|
|
"ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
"xsect = flopy.plot.PlotCrossSection(model=vertex_ml6, line={\"line\": line})\n",
|
|
"patch_collection = xsect.plot_array(hdata, head=hdata, alpha=0.5)\n",
|
|
"line_collection = xsect.plot_grid()\n",
|
|
"quiver = xsect.plot_vector(qx, qy, qz, head=hdata, \n",
|
|
" hstep=3, normalize=True, color='green', \n",
|
|
" scale=30, headwidth=3, headlength=3, headaxislength=3,\n",
|
|
" zorder=10)\n",
|
|
"\n",
|
|
"cb = plt.colorbar(patch_collection, shrink=0.75)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plotting using built in styles\n",
|
|
"\n",
|
|
"FloPy's plotting routines can be used with built in styles from the `styles` module. The `styles` module takes advantage of matplotlib's temporary styling routines by reading in pre-built style sheets. Two different types of styles have been built for flopy: `USGSMap()` and `USGSPlot()` styles which can be used to create report quality figures. The styles module also contains a number of methods that can be used for adding axis labels, text, annotations, headings, removing tick lines, and updating the current font.\n",
|
|
"\n",
|
|
"This example will load the Keating groundwater transport model and plot results using `styles`"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Building mf6gwf model...ex-gwt-keating\n",
|
|
"Building mf6gwt model...ex-gwt-keating\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Running mf6gwf model...\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Running mf6gwt model...\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"loading simulation...\n",
|
|
" loading simulation name file...\n",
|
|
" loading tdis package...\n",
|
|
" loading model gwf6...\n",
|
|
" loading package dis...\n",
|
|
" loading package npf...\n",
|
|
" loading package ic...\n",
|
|
" loading package chd...\n",
|
|
" loading package rch...\n",
|
|
" loading package oc...\n",
|
|
" loading ims package flow...\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"setup_pmv_demo.run_keating_model()\n",
|
|
"\n",
|
|
"sim_path = os.path.join('data', 'mf6-gwt-keating', 'mf6gwf')\n",
|
|
"tr_path = os.path.join('data', 'mf6-gwt-keating', 'mf6gwt')\n",
|
|
"sim_name = 'mfsim.nam'\n",
|
|
"sim = flopy.mf6.MFSimulation.load(sim_name=sim_name, version=vmf6, exe_name=exe_name_mf6, \n",
|
|
" sim_ws=sim_path)\n",
|
|
"gwf6 = sim.get_model(\"flow\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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|
|
"text/plain": [
|
|
"<Figure size 3600x2400 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# import styles\n",
|
|
"from flopy.plot import styles\n",
|
|
"\n",
|
|
"# load head file and plot\n",
|
|
"hobj = flopy.utils.HeadFile(os.path.join(sim_path, 'flow.hds'))\n",
|
|
"head = hobj.get_data()\n",
|
|
"\n",
|
|
"with styles.USGSMap():\n",
|
|
" fig, ax = plt.subplots(1, 1, figsize=(12, 8), dpi=300, tight_layout=True)\n",
|
|
" \n",
|
|
" xsect = flopy.plot.PlotCrossSection(model=gwf6, ax=ax, line={'row': 0})\n",
|
|
" pc = xsect.plot_array(head, head=head, cmap=\"jet\")\n",
|
|
" xsect.plot_bc(ftype=\"RCH\", color=\"red\")\n",
|
|
" xsect.plot_bc(ftype=\"CHD\")\n",
|
|
" plt.colorbar(pc, shrink=0.25)\n",
|
|
" \n",
|
|
" # add a rectangle to show the confining layer\n",
|
|
" confining_rect = mpl.patches.Rectangle((3000, 1000), 3000, 100, color=\"gray\", alpha=0.5)\n",
|
|
" ax.add_patch(confining_rect)\n",
|
|
" \n",
|
|
" # set labels using styles\n",
|
|
" styles.xlabel(label=\"x position (m)\")\n",
|
|
" styles.ylabel(label=\"elevation (m)\")\n",
|
|
" styles.heading(letter=\"A.\", heading=\"Simulated hydraulic head\", fontsize=10)\n",
|
|
" ax.set_aspect(1.0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Plotting concentration model results using the `USGSMap()` style"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x648 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# load the transport output file\n",
|
|
"cobj = flopy.utils.HeadFile(os.path.join(tr_path, \"trans.ucn\"), text=\"concentration\")\n",
|
|
"plot_times = [100, 1000, 3000]\n",
|
|
"obs1 = (48, 0, 118) # Layer, row, and column for observation 1\n",
|
|
"obs2 = (76, 0, 358) # Layer, row, and column for observation 2\n",
|
|
"xgrid, _, zgrid = gwf6.modelgrid.xyzcellcenters\n",
|
|
"\n",
|
|
"\n",
|
|
"with styles.USGSPlot():\n",
|
|
" fig, axes = plt.subplots(3, 1, figsize=(15, 9), tight_layout=True)\n",
|
|
" for ix, totim in enumerate(plot_times):\n",
|
|
" heading = \"Time = {}\".format(totim)\n",
|
|
" conc = cobj.get_data(totim=totim)\n",
|
|
" ax = axes[ix]\n",
|
|
" xsect = flopy.plot.PlotCrossSection(model=gwf6, ax=ax, line={\"row\": 0})\n",
|
|
" pc = xsect.plot_array(conc, head=head, cmap=\"jet\", vmin=0, vmax=1)\n",
|
|
" xsect.plot_bc(ftype=\"RCH\", color=\"red\")\n",
|
|
" xsect.plot_bc(ftype=\"CHD\")\n",
|
|
" \n",
|
|
" # plot confining layer\n",
|
|
" confining_rect = mpl.patches.Rectangle((3000, 1000), 3000, 100, color=\"gray\", alpha=0.5)\n",
|
|
" ax.add_patch(confining_rect)\n",
|
|
" \n",
|
|
" # set axis labels and title using styles\n",
|
|
" styles.ylabel(ax=ax, label=\"elevation (m)\", fontsize=10)\n",
|
|
" if ix == 2:\n",
|
|
" styles.xlabel(ax=ax, label=\"x position (m)\", fontsize=10)\n",
|
|
" styles.heading(ax=ax, heading=heading, idx=ix, fontsize=12)\n",
|
|
" \n",
|
|
" ax.set_aspect(1.0)\n",
|
|
" \n",
|
|
" # add observation locations based on grid cell centers\n",
|
|
" for k, i, j in [obs1, obs2]:\n",
|
|
" x = xgrid[i, j]\n",
|
|
" z = zgrid[k, i, j]\n",
|
|
" ax.plot(x, z, mfc=\"yellow\", mec=\"black\", marker=\"o\", ms=\"8\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Summary\n",
|
|
"\n",
|
|
"This notebook demonstrates some of the plotting functionality available with flopy. Although not described here, the plotting functionality tries to be general by passing keyword arguments passed to the `PlotCrossSection` methods down into the `matplotlib.pyplot` routines that do the actual plotting. For those looking to customize these plots, it may be necessary to search for the available keywords by understanding the types of objects that are created by the `PlotCrossSection` methods. The `PlotCrossSection` methods return these matplotlib.collections objects so that they could be fine-tuned later in the script before plotting.\n",
|
|
"\n",
|
|
"Hope this gets you started!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.7.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|