1504 lines
143 KiB
Plaintext
1504 lines
143 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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"## Creating a Complex MODFLOW 6 Model with Flopy\n",
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"\n",
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"The purpose of this notebook is to demonstrate the Flopy capabilities for building a more complex MODFLOW 6 model from scratch. This notebook will demonstrate the capabilities by replicating the advgw_tidal model that is distributed with MODFLOW 6."
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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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"### Setup the Notebook Environment"
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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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"3.8.6 | packaged by conda-forge | (default, Oct 7 2020, 18:42:56) \n",
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"[Clang 10.0.1 ]\n",
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"numpy version: 1.18.5\n",
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"matplotlib version: 3.2.2\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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"# For this example, we will set up a model workspace.\n",
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"# Model input files and output files will reside here.\n",
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"model_name = 'advgw_tidal'\n",
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"workspace = os.path.join('data', model_name)\n",
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"if not os.path.exists(workspace):\n",
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" os.makedirs(workspace)"
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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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"source": [
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"data_pth = os.path.join('..', 'data', 'mf6', 'create_tests', \n",
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" 'test005_advgw_tidal')\n",
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"assert os.path.isdir(data_pth)"
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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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"source": [
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"# create simulation\n",
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"sim = flopy.mf6.MFSimulation(sim_name=model_name, version='mf6', exe_name='mf6', \n",
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" sim_ws=workspace)\n",
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"\n",
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"# create tdis package\n",
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"tdis_rc = [(1.0, 1, 1.0), (10.0, 120, 1.0), \n",
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" (10.0, 120, 1.0), (10.0, 120, 1.0)]\n",
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"tdis = flopy.mf6.ModflowTdis(sim, pname='tdis', time_units='DAYS', \n",
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" nper=4, perioddata=tdis_rc)\n",
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"\n",
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"# create gwf model\n",
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"gwf = flopy.mf6.ModflowGwf(sim, modelname=model_name,\n",
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" model_nam_file='{}.nam'.format(model_name))\n",
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"gwf.name_file.save_flows = True\n",
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"\n",
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"# create iterative model solution and register the gwf model with it\n",
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"ims = flopy.mf6.ModflowIms(sim, pname='ims', print_option='SUMMARY', \n",
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" complexity='SIMPLE', outer_hclose=0.0001, \n",
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" outer_maximum=500, under_relaxation='NONE', \n",
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" inner_maximum=100, inner_hclose=0.0001, \n",
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" rcloserecord=0.001, linear_acceleration='CG', \n",
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" scaling_method='NONE', reordering_method='NONE', \n",
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" relaxation_factor=0.97)\n",
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"sim.register_ims_package(ims, [gwf.name])"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# discretization package\n",
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"nlay = 3\n",
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"nrow = 15\n",
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"ncol = 10\n",
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"botlay2 = {'factor':1.0, 'data': [-100 for x in range(150)]}\n",
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"dis = flopy.mf6.ModflowGwfdis(gwf, pname='dis', nlay=nlay, nrow=nrow, ncol=ncol, \n",
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" delr=500.0, delc=500.0, top=50.0, \n",
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" botm=[5.0, -10.0, botlay2], \n",
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" filename='{}.dis'.format(model_name))\n",
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"\n",
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"# initial conditions\n",
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"ic = flopy.mf6.ModflowGwfic(gwf, pname='ic', strt=50.0,\n",
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" filename='{}.ic'.format(model_name))\n",
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"\n",
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"# node property flow\n",
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"npf = flopy.mf6.ModflowGwfnpf(gwf, pname='npf', save_flows=True, \n",
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" icelltype=[1,0,0], \n",
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" k=[5.0, 0.1, 4.0],\n",
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" k33=[0.5, 0.005, 0.1])\n",
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"\n",
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"# output control\n",
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"oc = flopy.mf6.ModflowGwfoc(gwf, pname='oc', budget_filerecord='{}.cbb'.format(model_name),\n",
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" head_filerecord='{}.hds'.format(model_name),\n",
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" headprintrecord=[('COLUMNS', 10, 'WIDTH', 15,\n",
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" 'DIGITS', 6, 'GENERAL')],\n",
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" saverecord=[('HEAD', 'ALL'), ('BUDGET', 'ALL')],\n",
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" printrecord=[('HEAD', 'FIRST'), ('HEAD', 'LAST'), \n",
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" ('BUDGET', 'LAST')])"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# storage package\n",
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"sy = flopy.mf6.ModflowGwfsto.sy.empty(gwf, layered=True)\n",
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"for layer in range(0,3):\n",
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" sy[layer]['data'] = 0.2\n",
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" \n",
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"ss = flopy.mf6.ModflowGwfsto.ss.empty(gwf, layered=True, default_value=0.000001)\n",
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"\n",
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"sto = flopy.mf6.ModflowGwfsto(gwf, pname='sto', save_flows=True, iconvert=1, \n",
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" ss=ss, sy=sy, steady_state={0:True},\n",
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" transient={1:True})"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# well package\n",
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"# test empty with aux vars, bound names, and time series\n",
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"period_two = flopy.mf6.ModflowGwfwel.stress_period_data.empty(gwf, maxbound=3, aux_vars=['var1', 'var2', 'var3'],\n",
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" boundnames=True, timeseries=True)\n",
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"period_two[0][0] = ((0,11,2), -50.0, -1, -2, -3, None)\n",
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"period_two[0][1] = ((2,4,7), 'well_1_rate', 1, 2, 3, 'well_1')\n",
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"period_two[0][2] = ((2,3,2), 'well_2_rate', 4, 5, 6, 'well_2')\n",
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"period_three = flopy.mf6.ModflowGwfwel.stress_period_data.empty(gwf, maxbound=2, aux_vars=['var1', 'var2', 'var3'],\n",
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" boundnames=True, timeseries=True)\n",
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"period_three[0][0] = ((2,3,2), 'well_2_rate', 1, 2, 3, 'well_2')\n",
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"period_three[0][1] = ((2,4,7), 'well_1_rate', 4, 5, 6, 'well_1')\n",
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"period_four = flopy.mf6.ModflowGwfwel.stress_period_data.empty(gwf, maxbound=5, aux_vars=['var1', 'var2', 'var3'],\n",
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" boundnames=True, timeseries=True)\n",
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"period_four[0][0] = ((2,4,7), 'well_1_rate', 1, 2, 3, 'well_1')\n",
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"period_four[0][1] = ((2,3,2), 'well_2_rate', 4, 5, 6, 'well_2')\n",
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"period_four[0][2] = ((0,11,2), -10.0, 7, 8, 9, None)\n",
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"period_four[0][3] = ((0,2,4), -20.0, 17, 18, 19, None)\n",
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"period_four[0][4] = ((0,13,5), -40.0, 27, 28, 29, None)\n",
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"stress_period_data = {}\n",
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"stress_period_data[1] = period_two[0]\n",
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"stress_period_data[2] = period_three[0]\n",
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"stress_period_data[3] = period_four[0]\n",
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"wel = flopy.mf6.ModflowGwfwel(gwf, pname='wel', print_input=True, print_flows=True,\n",
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" auxiliary=[('var1', 'var2', 'var3')], maxbound=5,\n",
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" stress_period_data=stress_period_data, boundnames=True, \n",
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" save_flows=True)\n",
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"\n",
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"# well ts package\n",
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"ts_data =[(0.0, 0.0, 0.0, 0.0),\n",
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" (1.0, -200.0, 0.0, -100.0),\n",
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" (11.0, -1800.0, -500.0, -200.0),\n",
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" (21.0, -200.0, -400.0, -300.0),\n",
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" (31.0, 0.0, -600.0, -400.0)]\n",
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"wel.ts.initialize(filename='well-rates.ts', timeseries=ts_data,\n",
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" time_series_namerecord=[('well_1_rate', 'well_2_rate', 'well_3_rate')],\n",
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" interpolation_methodrecord=[('stepwise', 'stepwise', 'stepwise')])"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Evapotranspiration\n",
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"evt_period = flopy.mf6.ModflowGwfevt.stress_period_data.empty(gwf, 150, nseg=3)\n",
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"for col in range(0, 10):\n",
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" for row in range(0, 15):\n",
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" evt_period[0][col*15+row] = (((0, row, col), 50.0, 0.0004, 10.0, 0.2, 0.5, 0.3, 0.1, None))\n",
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"evt = flopy.mf6.ModflowGwfevt(gwf, pname='evt', print_input=True, print_flows=True, \n",
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" save_flows=True, maxbound=150,\n",
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" nseg=3, stress_period_data=evt_period)"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# General-Head Boundaries\n",
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"ghb_period = {}\n",
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"ghb_period_array = []\n",
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"for layer, cond in zip(range(1, 3), [15.0, 1500.0]):\n",
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" for row in range(0, 15):\n",
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" ghb_period_array.append(((layer, row, 9), 'tides', cond, 'Estuary-L2'))\n",
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"ghb_period[0] = ghb_period_array\n",
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"ghb = flopy.mf6.ModflowGwfghb(gwf, pname='ghb', print_input=True, print_flows=True, \n",
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" save_flows=True, boundnames=True,\n",
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" maxbound=30, stress_period_data=ghb_period)\n",
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"ts_recarray=[]\n",
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"fd = open(os.path.join(data_pth, 'tides.txt'), 'r')\n",
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"for line in fd:\n",
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" line_list = line.strip().split(',')\n",
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" ts_recarray.append((float(line_list[0]), float(line_list[1])))\n",
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"ghb.ts.initialize(filename='tides.ts', timeseries=ts_recarray,\n",
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" time_series_namerecord='tides',\n",
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" interpolation_methodrecord='linear')\n",
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"obs_recarray = {'ghb_obs.csv':[('ghb-2-6-10', 'GHB', (1, 5, 9)), \n",
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" ('ghb-3-6-10', 'GHB', (2, 5, 9))],\n",
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" 'ghb_flows.csv':[('Estuary2', 'GHB', 'Estuary-L2'), \n",
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" ('Estuary3', 'GHB', 'Estuary-L3')]}\n",
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"ghb.obs.initialize(filename='{}.ghb.obs'.format(model_name), digits=10, \n",
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" print_input=True, continuous=obs_recarray)"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"obs_recarray = {'head_obs.csv':[('h1_13_8', 'HEAD', (2, 12, 7))],\n",
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" 'intercell_flow_obs1.csv':[('ICF1_1.0', 'FLOW-JA-FACE', (0, 4, 5), (0, 5, 5))],\n",
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" 'head-hydrographs.csv':[('h3-13-9', 'HEAD', (2, 12, 8)),\n",
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" ('h3-12-8', 'HEAD', (2, 11, 7)),\n",
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" ('h1-4-3', 'HEAD', (0, 3, 2)),\n",
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" ('h1-12-3', 'HEAD', (0, 11, 2)),\n",
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" ('h1-13-9', 'HEAD', (0, 12, 8))]}\n",
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"obs_package = flopy.mf6.ModflowUtlobs(gwf, pname='head_obs', filename='{}.obs'.format(model_name), \n",
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" digits=10, print_input=True,\n",
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" continuous=obs_recarray)"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# River\n",
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"riv_period = {}\n",
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"riv_period_array = [((0,2,0),'river_stage_1',1001.0,35.9,None),\n",
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" ((0,3,1),'river_stage_1',1002.0,35.8,None),\n",
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" ((0,4,2),'river_stage_1',1003.0,35.7,None),\n",
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" ((0,4,3),'river_stage_1',1004.0,35.6,None),\n",
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" ((0,5,4),'river_stage_1',1005.0,35.5,None),\n",
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" ((0,5,5),'river_stage_1',1006.0,35.4,'riv1_c6'),\n",
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" ((0,5,6),'river_stage_1',1007.0,35.3,'riv1_c7'),\n",
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" ((0,4,7),'river_stage_1',1008.0,35.2,None),\n",
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" ((0,4,8),'river_stage_1',1009.0,35.1,None),\n",
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" ((0,4,9),'river_stage_1',1010.0,35.0,None),\n",
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" ((0,9,0),'river_stage_2',1001.0,36.9,'riv2_upper'),\n",
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" ((0,8,1),'river_stage_2',1002.0,36.8,'riv2_upper'),\n",
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" ((0,7,2),'river_stage_2',1003.0,36.7,'riv2_upper'),\n",
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" ((0,6,3),'river_stage_2',1004.0,36.6,None),\n",
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" ((0,6,4),'river_stage_2',1005.0,36.5,None),\n",
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" ((0,5,5),'river_stage_2',1006.0,36.4,'riv2_c6'),\n",
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" ((0,5,6),'river_stage_2',1007.0,36.3,'riv2_c7'),\n",
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" ((0,6,7),'river_stage_2',1008.0,36.2,None),\n",
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" ((0,6,8),'river_stage_2',1009.0,36.1),\n",
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" ((0,6,9),'river_stage_2',1010.0,36.0)]\n",
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"riv_period[0] = riv_period_array\n",
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"riv = flopy.mf6.ModflowGwfriv(gwf, pname='riv', print_input=True, print_flows=True, \n",
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" save_flows='{}.cbc'.format(model_name),\n",
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" boundnames=True, maxbound=20, \n",
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" stress_period_data=riv_period)\n",
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"ts_recarray=[(0.0,40.0,41.0),(1.0,41.0,41.5),\n",
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" (2.0,43.0,42.0),(3.0,45.0,42.8),\n",
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" (4.0,44.0,43.0),(6.0,43.0,43.1),\n",
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" (9.0,42.0,42.4),(11.0,41.0,41.5),\n",
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" (31.0,40.0,41.0)]\n",
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"riv.ts.initialize(filename='river_stages.ts', timeseries=ts_recarray,\n",
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" time_series_namerecord=[('river_stage_1', 'river_stage_2')],\n",
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" interpolation_methodrecord=[('linear', 'stepwise')])\n",
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"obs_recarray = {'riv_obs.csv':[('rv1-3-1', 'RIV', (0,2,0)), ('rv1-4-2', 'RIV', (0,3,1)),\n",
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" ('rv1-5-3', 'RIV', (0,4,2)), ('rv1-5-4', 'RIV', (0,4,3)),\n",
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" ('rv1-6-5', 'RIV', (0,5,4)), ('rv1-c6', 'RIV', 'riv1_c6'),\n",
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" ('rv1-c7', 'RIV', 'riv1_c7'), ('rv2-upper', 'RIV', 'riv2_upper'),\n",
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" ('rv-2-7-4', 'RIV', (0,6,3)), ('rv2-8-5', 'RIV', (0,6,4)),\n",
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" ('rv-2-9-6', 'RIV', (0,5,5,))],\n",
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" 'riv_flowsA.csv':[('riv1-3-1', 'RIV', (0,2,0)), ('riv1-4-2', 'RIV', (0,3,1)),\n",
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" ('riv1-5-3', 'RIV', (0,4,2))],\n",
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" 'riv_flowsB.csv':[('riv2-10-1', 'RIV', (0,9,0)), ('riv-2-9-2', 'RIV', (0,8,1)),\n",
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" ('riv2-8-3', 'RIV', (0,7,2))]}\n",
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"riv.obs.initialize(filename='{}.riv.obs'.format(model_name), digits=10,\n",
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" print_input=True, continuous=obs_recarray)"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"# First recharge package\n",
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"rch1_period = {}\n",
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"rch1_period_array = []\n",
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"col_range = {0:3,1:4,2:5}\n",
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"for row in range(0, 15):\n",
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" if row in col_range:\n",
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" col_max = col_range[row]\n",
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" else:\n",
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" col_max = 6\n",
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" for col in range(0, col_max):\n",
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" if (row == 3 and col == 5) or (row == 2 and col == 4) or (row == 1 and col == 3) or (row == 0 and col == 2):\n",
|
|
" mult = 0.5\n",
|
|
" else:\n",
|
|
" mult = 1.0\n",
|
|
" if row == 0 and col == 0:\n",
|
|
" bnd = 'rch-1-1'\n",
|
|
" elif row == 0 and col == 1:\n",
|
|
" bnd = 'rch-1-2'\n",
|
|
" elif row == 1 and col == 2:\n",
|
|
" bnd = 'rch-2-3'\n",
|
|
" else:\n",
|
|
" bnd = None\n",
|
|
" rch1_period_array.append(((0, row, col), 'rch_1', mult, bnd))\n",
|
|
"rch1_period[0] = rch1_period_array\n",
|
|
"rch1 = flopy.mf6.ModflowGwfrch(gwf, filename='{}_1.rch'.format(model_name), \n",
|
|
" pname='rch_1', fixed_cell=True,\n",
|
|
" auxiliary='MULTIPLIER', auxmultname='MULTIPLIER',\n",
|
|
" print_input=True, print_flows=True, \n",
|
|
" save_flows=True, boundnames=True,\n",
|
|
" maxbound=84, stress_period_data=rch1_period)\n",
|
|
"ts_data =[(0.0, 0.0015), (1.0, 0.0010),\n",
|
|
" (11.0, 0.0015),(21.0, 0.0025),\n",
|
|
" (31.0, 0.0015)]\n",
|
|
"rch1.ts.initialize(filename='recharge_rates_1.ts', timeseries=ts_data,\n",
|
|
" time_series_namerecord='rch_1',\n",
|
|
" interpolation_methodrecord='stepwise')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Second recharge package\n",
|
|
"rch2_period = {}\n",
|
|
"rch2_period_array = [((0,0,2), 'rch_2', 0.5),\n",
|
|
" ((0,0,3), 'rch_2', 1.0),\n",
|
|
" ((0,0,4), 'rch_2', 1.0),\n",
|
|
" ((0,0,5), 'rch_2', 1.0),\n",
|
|
" ((0,0,6), 'rch_2', 1.0),\n",
|
|
" ((0,0,7), 'rch_2', 1.0),\n",
|
|
" ((0,0,8), 'rch_2', 1.0),\n",
|
|
" ((0,0,9), 'rch_2', 0.5),\n",
|
|
" ((0,1,3), 'rch_2', 0.5),\n",
|
|
" ((0,1,4), 'rch_2', 1.0),\n",
|
|
" ((0,1,5), 'rch_2', 1.0),\n",
|
|
" ((0,1,6), 'rch_2', 1.0),\n",
|
|
" ((0,1,7), 'rch_2', 1.0),\n",
|
|
" ((0,1,8), 'rch_2', 0.5),\n",
|
|
" ((0,2,4), 'rch_2', 0.5),\n",
|
|
" ((0,2,5), 'rch_2', 1.0),\n",
|
|
" ((0,2,6), 'rch_2', 1.0),\n",
|
|
" ((0,2,7), 'rch_2', 0.5),\n",
|
|
" ((0,3,5), 'rch_2', 0.5),\n",
|
|
" ((0,3,6), 'rch_2', 0.5)]\n",
|
|
"rch2_period[0] = rch2_period_array\n",
|
|
"rch2 = flopy.mf6.ModflowGwfrch(gwf, filename='{}_2.rch'.format(model_name), \n",
|
|
" pname='rch_2', fixed_cell=True,\n",
|
|
" auxiliary='MULTIPLIER', auxmultname='MULTIPLIER',\n",
|
|
" print_input=True, print_flows=True, save_flows=True,\n",
|
|
" maxbound=20, stress_period_data=rch2_period)\n",
|
|
"ts_data = [(0.0, 0.0016), (1.0, 0.0018),\n",
|
|
" (11.0, 0.0019),(21.0, 0.0016),\n",
|
|
" (31.0, 0.0018)]\n",
|
|
"rch2.ts.initialize(filename='recharge_rates_2.ts', timeseries=ts_data,\n",
|
|
" time_series_namerecord='rch_2',\n",
|
|
" interpolation_methodrecord='linear')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Third recharge package\n",
|
|
"rch3_period = {}\n",
|
|
"rch3_period_array = []\n",
|
|
"col_range = {0:9,1:8,2:7}\n",
|
|
"for row in range(0, 15):\n",
|
|
" if row in col_range:\n",
|
|
" col_min = col_range[row]\n",
|
|
" else:\n",
|
|
" col_min = 6\n",
|
|
" for col in range(col_min, 10):\n",
|
|
" if (row == 0 and col == 9) or (row == 1 and col == 8) or (row == 2 and col == 7) or (row == 3 and col == 6):\n",
|
|
" mult = 0.5\n",
|
|
" else:\n",
|
|
" mult = 1.0\n",
|
|
" rch3_period_array.append(((0, row, col), 'rch_3', mult))\n",
|
|
"rch3_period[0] = rch3_period_array\n",
|
|
"rch3 = flopy.mf6.ModflowGwfrch(gwf, filename='{}_3.rch'.format(model_name), \n",
|
|
" pname='rch_3', fixed_cell=True,\n",
|
|
" auxiliary='MULTIPLIER', auxmultname='MULTIPLIER',\n",
|
|
" print_input=True, print_flows=True, save_flows=True,\n",
|
|
" maxbound=54, stress_period_data=rch3_period)\n",
|
|
"ts_data=[(0.0, 0.0017),(1.0, 0.0020),(11.0, 0.0017),(21.0, 0.0018),(31.0, 0.0020)]\n",
|
|
"rch3.ts.initialize(filename='recharge_rates_3.ts', timeseries=ts_data,\n",
|
|
" time_series_namerecord='rch_3',\n",
|
|
" interpolation_methodrecord='linear')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Create the MODFLOW 6 Input Files and Run the Model\n",
|
|
"\n",
|
|
"Once all the flopy objects are created, it is very easy to create all of the input files and run the model."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# change folder to save simulation\n",
|
|
"#sim.simulation_data.mfpath.set_sim_path(run_folder)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"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 advgw_tidal...\n",
|
|
" writing model name file...\n",
|
|
" writing package dis...\n",
|
|
" writing package ic...\n",
|
|
" writing package npf...\n",
|
|
" writing package oc...\n",
|
|
" writing package sto...\n",
|
|
" writing package wel...\n",
|
|
" writing package ts_0...\n",
|
|
" writing package evt...\n",
|
|
" writing package ghb...\n",
|
|
" writing package ts_1...\n",
|
|
" writing package obs_0...\n",
|
|
" writing package head_obs...\n",
|
|
" writing package riv...\n",
|
|
" writing package ts_2...\n",
|
|
" writing package obs_1...\n",
|
|
" writing package rch_1...\n",
|
|
" writing package ts_3...\n",
|
|
" writing package rch_2...\n",
|
|
" writing package ts_4...\n",
|
|
" writing package rch_3...\n",
|
|
" writing package ts_5...\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# write simulation to new location\n",
|
|
"sim.write_simulation()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['advgw_tidal.nam', 'advgw_tidal.ic', 'recharge_rates_1.ts', 'advgw_tidal.sto', 'tides.ts', 'advgw_tidal.ims', 'well-rates.ts', 'advgw_tidal.ghb', 'advgw_tidal.obs', 'advgw_tidal.riv.obs', 'advgw_tidal.dis', 'advgw_tidal_1.rch', 'advgw_tidal_3.rch', 'advgw_tidal_2.rch', 'advgw_tidal.oc', 'river_stages.ts', 'advgw_tidal.wel', 'advgw_tidal.ghb.obs', 'advgw_tidal.npf', 'advgw_tidal.tdis', 'recharge_rates_2.ts', 'mfsim.nam', 'advgw_tidal.riv', 'recharge_rates_3.ts', 'advgw_tidal.evt']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Print a list of the files that were created\n",
|
|
"# in workspace\n",
|
|
"print(os.listdir(workspace))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Run the Simulation\n",
|
|
"\n",
|
|
"We can also run the simulation from the notebook, but only if the MODFLOW 6 executable is available. The executable can be made available by putting the executable in a folder that is listed in the system path variable. Another option is to just put a copy of the executable in the simulation folder, though this should generally be avoided. A final option is to provide a full path to the executable when the simulation is constructed. This would be done by specifying exe_name with the full path."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"FloPy is using the following executable to run the model: /Users/jdhughes/.local/bin/mf6\n",
|
|
" MODFLOW 6\n",
|
|
" U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
|
|
" VERSION 6.2.0 10/22/2020\n",
|
|
"\n",
|
|
" MODFLOW 6 compiled Oct 29 2020 12:19:52 with IFORT compiler (ver. 19.10.3)\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): 2021/02/18 11:32:47\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": [
|
|
" Solving: Stress period: 2 Time step: 1\n",
|
|
" Solving: Stress period: 2 Time step: 2\n",
|
|
" Solving: Stress period: 2 Time step: 3\n",
|
|
" Solving: Stress period: 2 Time step: 4\n",
|
|
" Solving: Stress period: 2 Time step: 5\n",
|
|
" Solving: Stress period: 2 Time step: 6\n",
|
|
" Solving: Stress period: 2 Time step: 7\n",
|
|
" Solving: Stress period: 2 Time step: 8\n",
|
|
" Solving: Stress period: 2 Time step: 9\n",
|
|
" Solving: Stress period: 2 Time step: 10\n",
|
|
" Solving: Stress period: 2 Time step: 11\n",
|
|
" Solving: Stress period: 2 Time step: 12\n",
|
|
" Solving: Stress period: 2 Time step: 13\n",
|
|
" Solving: Stress period: 2 Time step: 14\n",
|
|
" Solving: Stress period: 2 Time step: 15\n",
|
|
" Solving: Stress period: 2 Time step: 16\n",
|
|
" Solving: Stress period: 2 Time step: 17\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Solving: Stress period: 2 Time step: 18\n",
|
|
" Solving: Stress period: 2 Time step: 19\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Solving: Stress period: 2 Time step: 20\n",
|
|
" Solving: Stress period: 2 Time step: 21\n",
|
|
" Solving: Stress period: 2 Time step: 22\n",
|
|
" Solving: Stress period: 2 Time step: 23\n",
|
|
" Solving: Stress period: 2 Time step: 24\n",
|
|
" Solving: Stress period: 2 Time step: 25\n",
|
|
" Solving: Stress period: 2 Time step: 26\n",
|
|
" Solving: Stress period: 2 Time step: 27\n",
|
|
" Solving: Stress period: 2 Time step: 28\n",
|
|
" Solving: Stress period: 2 Time step: 29\n",
|
|
" Solving: Stress period: 2 Time step: 30\n",
|
|
" Solving: Stress period: 2 Time step: 31\n",
|
|
" Solving: Stress period: 2 Time step: 32\n",
|
|
" Solving: Stress period: 2 Time step: 33\n",
|
|
" Solving: Stress period: 2 Time step: 34\n",
|
|
" Solving: Stress period: 2 Time step: 35\n",
|
|
" Solving: Stress period: 2 Time step: 36\n",
|
|
" Solving: Stress period: 2 Time step: 37\n",
|
|
" Solving: Stress period: 2 Time step: 38\n",
|
|
" Solving: Stress period: 2 Time step: 39\n",
|
|
" Solving: Stress period: 2 Time step: 40\n",
|
|
" Solving: Stress period: 2 Time step: 41\n",
|
|
" Solving: Stress period: 2 Time step: 42\n",
|
|
" Solving: Stress period: 2 Time step: 43\n",
|
|
" Solving: Stress period: 2 Time step: 44\n",
|
|
" Solving: Stress period: 2 Time step: 45\n",
|
|
" Solving: Stress period: 2 Time step: 46\n",
|
|
" Solving: Stress period: 2 Time step: 47\n",
|
|
" Solving: Stress period: 2 Time step: 48\n",
|
|
" Solving: Stress period: 2 Time step: 49\n",
|
|
" Solving: Stress period: 2 Time step: 50\n",
|
|
" Solving: Stress period: 2 Time step: 51\n",
|
|
" Solving: Stress period: 2 Time step: 52\n",
|
|
" Solving: Stress period: 2 Time step: 53\n",
|
|
" Solving: Stress period: 2 Time step: 54\n",
|
|
" Solving: Stress period: 2 Time step: 55\n",
|
|
" Solving: Stress period: 2 Time step: 56\n",
|
|
" Solving: Stress period: 2 Time step: 57\n",
|
|
" Solving: Stress period: 2 Time step: 58\n",
|
|
" Solving: Stress period: 2 Time step: 59\n",
|
|
" Solving: Stress period: 2 Time step: 60\n",
|
|
" Solving: Stress period: 2 Time step: 61\n",
|
|
" Solving: Stress period: 2 Time step: 62\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Solving: Stress period: 2 Time step: 63\n",
|
|
" Solving: Stress period: 2 Time step: 64\n",
|
|
" Solving: Stress period: 2 Time step: 65\n",
|
|
" Solving: Stress period: 2 Time step: 66\n",
|
|
" Solving: Stress period: 2 Time step: 67\n",
|
|
" Solving: Stress period: 2 Time step: 68\n",
|
|
" Solving: Stress period: 2 Time step: 69\n",
|
|
" Solving: Stress period: 2 Time step: 70\n",
|
|
" Solving: Stress period: 2 Time step: 71\n",
|
|
" Solving: Stress period: 2 Time step: 72\n",
|
|
" Solving: Stress period: 2 Time step: 73\n",
|
|
" Solving: Stress period: 2 Time step: 74\n",
|
|
" Solving: Stress period: 2 Time step: 75\n",
|
|
" Solving: Stress period: 2 Time step: 76\n",
|
|
" Solving: Stress period: 2 Time step: 77\n",
|
|
" Solving: Stress period: 2 Time step: 78\n",
|
|
" Solving: Stress period: 2 Time step: 79\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Solving: Stress period: 2 Time step: 80\n",
|
|
" Solving: Stress period: 2 Time step: 81\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Solving: Stress period: 2 Time step: 82\n",
|
|
" Solving: Stress period: 2 Time step: 83\n",
|
|
" Solving: Stress period: 2 Time step: 84\n",
|
|
" Solving: Stress period: 2 Time step: 85\n",
|
|
" Solving: Stress period: 2 Time step: 86\n",
|
|
" Solving: Stress period: 2 Time step: 87\n",
|
|
" Solving: Stress period: 2 Time step: 88\n",
|
|
" Solving: Stress period: 2 Time step: 89\n",
|
|
" Solving: Stress period: 2 Time step: 90\n",
|
|
" Solving: Stress period: 2 Time step: 91\n",
|
|
" Solving: Stress period: 2 Time step: 92\n",
|
|
" Solving: Stress period: 2 Time step: 93\n",
|
|
" Solving: Stress period: 2 Time step: 94\n",
|
|
" Solving: Stress period: 2 Time step: 95\n",
|
|
" Solving: Stress period: 2 Time step: 96\n",
|
|
" Solving: Stress period: 2 Time step: 97\n",
|
|
" Solving: Stress period: 2 Time step: 98\n",
|
|
" Solving: Stress period: 2 Time step: 99\n",
|
|
" Solving: Stress period: 2 Time step: 100\n",
|
|
" Solving: Stress period: 2 Time step: 101\n",
|
|
" Solving: Stress period: 2 Time step: 102\n",
|
|
" Solving: Stress period: 2 Time step: 103\n",
|
|
" Solving: Stress period: 2 Time step: 104\n",
|
|
" Solving: Stress period: 2 Time step: 105\n",
|
|
" Solving: Stress period: 2 Time step: 106\n",
|
|
" Solving: Stress period: 2 Time step: 107\n",
|
|
" Solving: Stress period: 2 Time step: 108\n",
|
|
" Solving: Stress period: 2 Time step: 109\n",
|
|
" Solving: Stress period: 2 Time step: 110\n",
|
|
" Solving: Stress period: 2 Time step: 111\n",
|
|
" Solving: Stress period: 2 Time step: 112\n",
|
|
" Solving: Stress period: 2 Time step: 113\n",
|
|
" Solving: Stress period: 2 Time step: 114\n",
|
|
" Solving: Stress period: 2 Time step: 115\n",
|
|
" Solving: Stress period: 2 Time step: 116\n",
|
|
" Solving: Stress period: 2 Time step: 117\n",
|
|
" Solving: Stress period: 2 Time step: 118\n",
|
|
" Solving: Stress period: 2 Time step: 119\n",
|
|
" Solving: Stress period: 2 Time step: 120\n",
|
|
" Solving: Stress period: 3 Time step: 1\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Solving: Stress period: 3 Time step: 2\n",
|
|
" Solving: Stress period: 3 Time step: 3\n",
|
|
" Solving: Stress period: 3 Time step: 4\n",
|
|
" Solving: Stress period: 3 Time step: 5\n",
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]
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},
|
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{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
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" Solving: Stress period: 4 Time step: 117\n",
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" Solving: Stress period: 4 Time step: 118\n",
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" Solving: Stress period: 4 Time step: 119\n",
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" Solving: Stress period: 4 Time step: 120\n",
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" \n",
|
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" Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:32:49\n",
|
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" Elapsed run time: 1.398 Seconds\n",
|
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" \n",
|
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"\n",
|
|
"WARNING REPORT:\n",
|
|
"\n",
|
|
" 1. NONLINEAR BLOCK VARIABLE 'OUTER_HCLOSE' IN FILE 'advgw_tidal.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 'advgw_tidal.ims' WAS\n",
|
|
" DEPRECATED IN VERSION 6.1.1. SETTING INNER_DVCLOSE TO INNER_HCLOSE VALUE.\n",
|
|
" Normal termination of simulation.\n"
|
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]
|
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},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
|
"\n",
|
|
"Success is: True\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Run the simulation\n",
|
|
"success, buff = sim.run_simulation()\n",
|
|
"print('\\nSuccess is: ', success)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Post-Process Head Results\n",
|
|
"\n",
|
|
"Post-processing MODFLOW 6 results is still a work in progress. There aren't any Flopy plotting functions built in yet, like they are for other MODFLOW versions. So we need to plot the results using general Flopy capabilities. We can also use some of the Flopy ModelMap capabilities for MODFLOW 6, but in order to do so, we need to manually create a SpatialReference object, that is needed for the plotting. Examples of both approaches are shown below.\n",
|
|
"\n",
|
|
"First, a link to the heads file is created with `HeadFile`. The link can then be accessed with the `get_data` function, by specifying, in this case, the step number and period number for which we want to retrieve data. A three-dimensional array is returned of size `nlay, nrow, ncol`. Matplotlib contouring functions are used to make contours of the layers or a cross-section."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Read the binary head file and plot the results\n",
|
|
"# We can use the existing Flopy HeadFile class because\n",
|
|
"# the format of the headfile for MODFLOW 6 is the same\n",
|
|
"# as for previous MODFLOW verions\n",
|
|
"headfile = '{}.hds'.format(model_name)\n",
|
|
"fname = os.path.join(workspace, headfile)\n",
|
|
"hds = flopy.utils.binaryfile.HeadFile(fname)\n",
|
|
"h = hds.get_data()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x720 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# We can also use the Flopy PlotMapView capabilities for MODFLOW 6\n",
|
|
"fig = plt.figure(figsize=(10, 10))\n",
|
|
"ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
|
|
"\n",
|
|
"# Next we create an instance of the ModelMap class\n",
|
|
"modelmap = flopy.plot.PlotMapView(model=gwf, ax=ax)\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",
|
|
"#quadmesh = modelmap.plot_ibound(ibound=ibd)\n",
|
|
"linecollection = modelmap.plot_grid()\n",
|
|
"contours = modelmap.contour_array(h[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Post-Process Flows\n",
|
|
"\n",
|
|
"MODFLOW 6 writes a binary grid file, which contains information about the model grid. MODFLOW 6 also writes a binary budget file, which contains flow information. Both of these files can be read using Flopy capabilities. The MfGrdFile class in Flopy can be used to read the binary grid file. The CellBudgetFile class in Flopy can be used to read the binary budget file written by MODFLOW 6."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"OrderedDict([('NCELLS', 450),\n",
|
|
" ('NLAY', 3),\n",
|
|
" ('NROW', 15),\n",
|
|
" ('NCOL', 10),\n",
|
|
" ('NJA', 2700),\n",
|
|
" ('XORIGIN', 0.0),\n",
|
|
" ('YORIGIN', 0.0),\n",
|
|
" ('ANGROT', 0.0),\n",
|
|
" ('DELR',\n",
|
|
" array([500., 500., 500., 500., 500., 500., 500., 500., 500., 500.])),\n",
|
|
" ('DELC',\n",
|
|
" array([500., 500., 500., 500., 500., 500., 500., 500., 500., 500., 500.,\n",
|
|
" 500., 500., 500., 500.])),\n",
|
|
" ('TOP',\n",
|
|
" array([[50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.],\n",
|
|
" [50., 50., 50., 50., 50., 50., 50., 50., 50., 50.]])),\n",
|
|
" ('BOTM',\n",
|
|
" array([[[ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.],\n",
|
|
" [ 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n",
|
|
" 5.]],\n",
|
|
" \n",
|
|
" [[ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.],\n",
|
|
" [ -10., -10., -10., -10., -10., -10., -10., -10., -10.,\n",
|
|
" -10.]],\n",
|
|
" \n",
|
|
" [[-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.],\n",
|
|
" [-100., -100., -100., -100., -100., -100., -100., -100., -100.,\n",
|
|
" -100.]]])),\n",
|
|
" ('IA',\n",
|
|
" array([ 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 49,\n",
|
|
" 54, 60, 66, 72, 78, 84, 90, 96, 102, 107, 112,\n",
|
|
" 118, 124, 130, 136, 142, 148, 154, 160, 165, 170, 176,\n",
|
|
" 182, 188, 194, 200, 206, 212, 218, 223, 228, 234, 240,\n",
|
|
" 246, 252, 258, 264, 270, 276, 281, 286, 292, 298, 304,\n",
|
|
" 310, 316, 322, 328, 334, 339, 344, 350, 356, 362, 368,\n",
|
|
" 374, 380, 386, 392, 397, 402, 408, 414, 420, 426, 432,\n",
|
|
" 438, 444, 450, 455, 460, 466, 472, 478, 484, 490, 496,\n",
|
|
" 502, 508, 513, 518, 524, 530, 536, 542, 548, 554, 560,\n",
|
|
" 566, 571, 576, 582, 588, 594, 600, 606, 612, 618, 624,\n",
|
|
" 629, 634, 640, 646, 652, 658, 664, 670, 676, 682, 687,\n",
|
|
" 692, 698, 704, 710, 716, 722, 728, 734, 740, 745, 750,\n",
|
|
" 756, 762, 768, 774, 780, 786, 792, 798, 803, 807, 812,\n",
|
|
" 817, 822, 827, 832, 837, 842, 847, 851, 856, 862, 868,\n",
|
|
" 874, 880, 886, 892, 898, 904, 909, 915, 922, 929, 936,\n",
|
|
" 943, 950, 957, 964, 971, 977, 983, 990, 997, 1004, 1011,\n",
|
|
" 1018, 1025, 1032, 1039, 1045, 1051, 1058, 1065, 1072, 1079, 1086,\n",
|
|
" 1093, 1100, 1107, 1113, 1119, 1126, 1133, 1140, 1147, 1154, 1161,\n",
|
|
" 1168, 1175, 1181, 1187, 1194, 1201, 1208, 1215, 1222, 1229, 1236,\n",
|
|
" 1243, 1249, 1255, 1262, 1269, 1276, 1283, 1290, 1297, 1304, 1311,\n",
|
|
" 1317, 1323, 1330, 1337, 1344, 1351, 1358, 1365, 1372, 1379, 1385,\n",
|
|
" 1391, 1398, 1405, 1412, 1419, 1426, 1433, 1440, 1447, 1453, 1459,\n",
|
|
" 1466, 1473, 1480, 1487, 1494, 1501, 1508, 1515, 1521, 1527, 1534,\n",
|
|
" 1541, 1548, 1555, 1562, 1569, 1576, 1583, 1589, 1595, 1602, 1609,\n",
|
|
" 1616, 1623, 1630, 1637, 1644, 1651, 1657, 1663, 1670, 1677, 1684,\n",
|
|
" 1691, 1698, 1705, 1712, 1719, 1725, 1731, 1738, 1745, 1752, 1759,\n",
|
|
" 1766, 1773, 1780, 1787, 1793, 1798, 1804, 1810, 1816, 1822, 1828,\n",
|
|
" 1834, 1840, 1846, 1851, 1855, 1860, 1865, 1870, 1875, 1880, 1885,\n",
|
|
" 1890, 1895, 1899, 1904, 1910, 1916, 1922, 1928, 1934, 1940, 1946,\n",
|
|
" 1952, 1957, 1962, 1968, 1974, 1980, 1986, 1992, 1998, 2004, 2010,\n",
|
|
" 2015, 2020, 2026, 2032, 2038, 2044, 2050, 2056, 2062, 2068, 2073,\n",
|
|
" 2078, 2084, 2090, 2096, 2102, 2108, 2114, 2120, 2126, 2131, 2136,\n",
|
|
" 2142, 2148, 2154, 2160, 2166, 2172, 2178, 2184, 2189, 2194, 2200,\n",
|
|
" 2206, 2212, 2218, 2224, 2230, 2236, 2242, 2247, 2252, 2258, 2264,\n",
|
|
" 2270, 2276, 2282, 2288, 2294, 2300, 2305, 2310, 2316, 2322, 2328,\n",
|
|
" 2334, 2340, 2346, 2352, 2358, 2363, 2368, 2374, 2380, 2386, 2392,\n",
|
|
" 2398, 2404, 2410, 2416, 2421, 2426, 2432, 2438, 2444, 2450, 2456,\n",
|
|
" 2462, 2468, 2474, 2479, 2484, 2490, 2496, 2502, 2508, 2514, 2520,\n",
|
|
" 2526, 2532, 2537, 2542, 2548, 2554, 2560, 2566, 2572, 2578, 2584,\n",
|
|
" 2590, 2595, 2600, 2606, 2612, 2618, 2624, 2630, 2636, 2642, 2648,\n",
|
|
" 2653, 2657, 2662, 2667, 2672, 2677, 2682, 2687, 2692, 2697, 2701],\n",
|
|
" dtype=int32)),\n",
|
|
" ('JA', array([ 1, 2, 11, ..., 300, 440, 449], dtype=int32)),\n",
|
|
" ('IDOMAIN',\n",
|
|
" array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)),\n",
|
|
" ('ICELLTYPE',\n",
|
|
" array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
|
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32))])"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# read the binary grid file\n",
|
|
"fname = os.path.join(workspace, '{}.dis.grb'.format(model_name))\n",
|
|
"bgf = flopy.utils.mfgrdfile.MfGrdFile(fname)\n",
|
|
"\n",
|
|
"# data read from the binary grid file is stored in a dictionary\n",
|
|
"bgf._datadict"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Information from the binary grid file is easily retrieved\n",
|
|
"ia = bgf._datadict['IA'] - 1\n",
|
|
"ja = bgf._datadict['JA'] - 1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# read the cell budget file\n",
|
|
"fname = os.path.join(workspace, '{}.cbb'.format(model_name))\n",
|
|
"cbb = flopy.utils.CellBudgetFile(fname, precision='double')\n",
|
|
"#cbb.list_records()\n",
|
|
"\n",
|
|
"flowja = cbb.get_data(text='FLOW-JA-FACE')[0][0, 0, :]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Printing flows for cell 413\n",
|
|
"Cell 413 flow with cell 263 is 251.46262091207623\n",
|
|
"Cell 413 flow with cell 403 is 0.7176346498656017\n",
|
|
"Cell 413 flow with cell 412 is 439.8629968543804\n",
|
|
"Cell 413 flow with cell 414 is -693.4212447574185\n",
|
|
"Cell 413 flow with cell 423 is 1.3779378787739915\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# By having the ia and ja arrays and the flow-ja-face we can look at\n",
|
|
"# the flows for any cell and process them in the follow manner.\n",
|
|
"k = 2; i = 7; j = 7\n",
|
|
"celln = k * nrow * ncol + i * nrow + j\n",
|
|
"print('Printing flows for cell {}'.format(celln + 1))\n",
|
|
"for ipos in range(ia[celln] + 1, ia[celln + 1]):\n",
|
|
" cellm = ja[ipos] # change from one-based to zero-based\n",
|
|
" print('Cell {} flow with cell {} is {}'.format(celln + 1, cellm + 1, flowja[ipos]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x123fd6970>"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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JCVi+fDnuv/9+jB079pT5pZSYNWsWxo8fj/Hjx0Or1eKZZ545a/bx42QZhjln8ONkT4YfJ8swDPMzgAWcYRjGTWEBZxiGcVP6FHAhxFAhxDYhRJEQokAI8YiS/pwQokYIkaP8TD/75jIMwzB2+nMboQXAY1LKg0IIDYADQohNynvzpJT/d/bMYxiGYXqjTwGXUmoBaJW/DUKIIgDxZ9swhmEY5tSc1hm4EGI4gAkA9ipJDwsh8oQQHwshhvRyzX1CiGwhRHZDQ8OAjGUYhhlszvRxsldffTVCQ0Nx7bXXuqTPnDkTKSkpGDduHO6++26YzWYAwNdff4309HRkZmYiKysLu3btGrDt/RZwIUQQgBUAHpVS6gG8D2AEgExQhN7jE1uklB9JKbOklFmRkZEDNphhGObH4lSPk3388cexZMmSk9JnzpyJ4uJi5Ofno7OzE/PnzwcATJs2Dbm5ucjJycHHH3+Me+65Z8D29UvAhRDeIPH+VEq5EgCklDoppVVKaQPwHwCTBmwNwzDMT4hTPU522rRp0Gg0J6VPnz4dQggIITBp0iRUV1cDoEjf/mjZ9vb2QXnMbJ9n4IJqWQCgSEr5ulN6rHI+DgAzABwesDUMw/xsqXvxRXQXDe7jZH3HpCLmqadOmedMHifbH8xmM5YsWYI333xTTVu1ahWefPJJ1NfXY926dQOuoz93oVwC4LcA8oUQOUraUwBuF0JkApAAKgHcP2BrGIZhfmTsj5O1Y3841UB58MEHMXXqVEyZMkVNmzFjBmbMmIGdO3fi6aefxubNmwdUR3/uQtkFoKdYf/2AamYYhnGir0jZnXj++efR0NCADz/8sMf3p06dioqKCjQ2NiIiIuKM6+FvYjIMwwwi8+fPx8aNG7F06VKXs/Py8nLYHx548OBBmEwmhIeHD6guFnCGYZheONXjZKdMmYJbb70VW7ZsQUJCAjZu3AgAeOCBB6DT6XDRRRchMzNT/R9+VqxYgXHjxiEzMxMPPfQQli1bNuAPMvlxsgzDnDP4cbInw4+TZRiG+RnAAs4wDOOmsIAzDMO4KSzgDMMwbgoLOMMwjJvCAs4wDOOmsIAzDPOzZrAfJztnzhxkZGQgPT0dt9xyC4xGIwCgpaUFM2bMQHp6OiZNmoTDhwf++CgWcIZhmF44k8fJzps3D7m5ucjLy0NiYiLeeecdAMCLL76IzMxM5OXlYfHixXjkkUcGbB8LOMMwTC+cyeNkg4ODAQBSSnR2dqrftiwsLMS0adMAAKmpqaisrIROpxuQff15GiHDMMxZ57svStF43DioZUYMDcKUX48+ZZ6z8TjZ2bNnY/369UhLS8Nrr9H/dZORkYGVK1di8uTJ2LdvH6qqqlBdXY3o6OgzrocjcIZhftbYHydr/7E/u2QgLFy4ELW1tRgzZgyWLVsGAHjiiSfQ0tKCzMxMvP3225gwYQK8vAYWQ3MEzjDMT4K+ImV3w9PTE7fddhteffVVzJ49G8HBwVi4cCEAOl5JSkpCUlLSgOrgCJxhGGaQkFKivLxc/XvNmjVITU0FALS2tsJkMgGgR85OnTpVPS8/UzgCZxiG6YX9+/djxowZaGlpwZo1a/Dss8+ioKAAAD1Otri4GEajEQkJCViwYAGuvPJKzJo1C3q9HlJKZGRk4P333wdATxn83e9+B09PT6SlpWHBggUDto8fJ8swzDmDHyd7Mvw4WYZhmJ8BbnGE0tVuhrnbeq7NGDQG+J9w/MT432nM/9S4uElbbDYJq8V2rs34UfDwEBAegzswbiHge1cfweEdNefaDIZhBpmsmUPQWG0Y8H8t5g6ERAXA1//Uknu6R9puIeCjJ8UgMvHkbzy5JT/eRw5nnR/z8xOm/7jTsNj89bB6diI0ZMj/vIh7eZ/6xFpKiaamJvj5+fW/zL4yCCGGAlgMIAaADcBHUso3hRBhAJYBGA6gEsCvpZQt/a75NIgdEYLYESFno2iGYc4hZnMUqqurUVVzVqTD7fDz80NCQkK/8/cnArcAeExKeVAIoQFwQAixCcBdALZIKV8SQjwB4AkAc8/A5sHD3AV492P1spgAL5/BrdtmA3p4XsJJmDsBT9/+5R1spAQs3eeuj/qDuQs4sAgYdzMQFPnj1y+lexyGn0s7Ld2Al2/f+WxWwMPzlFm8vb17/jKLpRuwWQCfwJPTPbz6LBeWbgDi3PiwlEBXG+Afetar6lNFpJRaKeVB5W8DgCIA8QBuAPCJku0TADeeLSNPMAjI+Qz45kkSQzt73gVeSgTKtzjSmiqAL34HNJQ40uoOAy8PB9b+mUQXINH4+mHg8EpHPqsFWHQt8NFlQH2RIz3/S2DrPwF9rSOt4CvglSRg1zxHWkczsPZPQMkGR1pnK/DuBcAn1ykOprDtRWDHK65735X3A2+ku7anbDPw7d+B5qOOtKM7gXcmAV/OcaR1G4Gd/wfs/cipPWZg6e3AW5mAscGRXvg1sOoB6gM7ez+kvsxb7khrKAFW3gfoCpzSSoF3LwSWzHBcb7UA2R/Tj71/pQR2vQHMG+d6fUsl8M1TgLHeke+r3wPfzAW+nE0CAACmDmDNI0DlLse15i7g46tpfDuaHel5XwCHPiU77OR+Dnx4KY2THWMD2aTNdaR1NAMfTqG6nPnhAyrDma8eBBb8EmipcqRVbKUynf2yeB2wcDqw+21HWreR+ufod440Uzuw4l5gwVW0eKplbjvZN/bPB14f42p7UwWwYS7Q3uRI0+YB75wPbHrGMRamDmDrv8guO5Zu4KuHgHnjgcYyR3rVbmDdX1z9pfRb8o3sj1377cs5QOX3jrT2JrLxs9uAbgOlSQlsfp78y+Z0U8LqP5IP2+eK1QLMvwJ4MR7Y73SvdNUeKnPV/a62b/wbsO8/jjT73H3vAkCvdeq3BTTvnX19xyvAq6Ncr6/NAdY/Dhz7wZHWUAJ8MBl472LH+FgtpDvfPOnq62sfBV5LAWoPOa4/S+dapxUGCiGGA5gAYC+AaCmlFiCRBxA12Ma5YB/wotU0yX94D8imr6WivhjY+BSt2GseoUE1dwJLbiSBWj7b0enf/g0wtwPZC4ASxYl3vw0cWkKiYRfM0m+Ayu9oELa9SGlNFcCKOcDOVx1ppnYS/65WSrM7zOczycm/vNshMFueB9qOA1W7aKIDQMEqYMfLwLZ/AXs/oDRdIZD3OdBaRY5ks5H9qx8mWz+fSQ4hJbW7sQQ4/CVQrdxjv+lpYOs/gA2POxafff8BSjeQWH77N0qrOUD25S6lSQ6QrVteACydwNcP0mspgTWPAnnLSDS72ijvD+8BDUUkXPmK2Od+RgvX2j8BZd9S2vG9wOZnqe0bn6K0zhZaHH94l/LqtcDxfUDBSmDYJdT3pRuV9jxDUfl/b6FFw17PsT00vt8rfdlQAqy8l+z+7v8orb0JWP0HQJtD/WI1U3uWzCCbVt7vmHxrHwXq8qkuu8Ad+IQWlK9+71hAqvYAOZ9Su9b+SWlPK7D0Dipz1QOUJiWw+Tmg6nvqU4POUc/aP5G4dSpHB9+/CeR/ARz/Adj3IaUd2UE+vO1fFLQAtGBs/Btg0AKf30kiYjWT7+79gPLb58qOl4HGUiq7QvHr/f8Bdr4CfH6HQ3ALvgJy/gu0HaNrABLyhddQ/u2Kr3cbgRX3UH3fPAm0Hqf0FXPI/5bdCRjqKO271wCjjuaR3a+L1wG7Xgc2/JVEHKC5e/AT8uGvH6bAKPtjoC4PgKS5ZrVQX67+A9DRRL5mH4sNfwX2vAOs/wstWABdX72P7PtGORTQ5gHr/kzzft1jjjH7/k2gvZ4Co/ZGqmf9X4B9HwGf3upYfLa9SL5RX0C+B9A4bXyK5kGhEhyUbyH/kTbyAymBtmrgrQmuAcgg0W8BF0IEAVgB4FEppf40rrtPCJEthMhuaGjo+4Ke2PEKOZOUwJ73gCHDgWGTaeJaTCQswhO44V0SieK1JIytx4BJ91Gnl35DE/zIdmDas4AmjiZnl57KGX01EBjpiCwOLAQ0scDFf6DyWiopn6cvkPIrcqKOZqBoLWAyUN02CzlI7SHg2G5gwp20kOz7iH7nfQFk3AGMuBw4uJgm2vaXgOhxQOLF5OhSAnvfB7wDgatfBporgKM7SNANWmDsTdSeyl1A7UFyqiueB/yHkH0dzUDOUiD1WsBHQ5E4QCIddx5wwQPA4RUk5HvepS3q+F9TezuaKdI0GYEZHwFWE9lcvkVpz2+Bbj3tQrqN1AcZd5D9P7xPE23XPCAiBQiKdkzcnE+pPb/4G/V/7SGysbOF+r14LTAvDVg+C/ALBX7zGRAQQW1uq6Y+HX8rIK1kp5Rke9x51M6DSyiq2vUG4OVP6fsXkG/kfkbtuOI58ofDK6k/dflA8mW0ABV9TW0vXgdc8HtgSBK1x2Yl30uYRP6y81Vqz553yFcu/iMtXi1VlGbpApJ/QUGGvpYWmMZSYPKfyIbsBUDzEer/oRdQILF/Pi0guUvp2uRf0CJtNQO736J+jB5Huz6blYTc0g1c/RIJbsUWWsS0uUDajSR8R3fQLq14HXDJI2T77repj/a8Cwy9kPrZvlAcWASEJVN78r8kX9/3EeDpA4y5nny1rQYoWQ90twE3vk82HPovUHOQ+iBrDtDZTIGQqYPKzLgdGD6FxsdmpTZEpADxE6kvpCTx9vAG7lxJYvnWebTQjrgcuO1T8vnSbyjYaCoDrnkVCIyidrQ3kR+l3Uj+b1+0DywC4rOAC+4HiteTMO98hfKMu5n8trOF/NJkBG5eQGOX/THNq+r9QPpvyNdzP6e5UrwWuPAhsv37N8nX935I4xg1luaxlORv/mHAr14DGoppF3NwMfVpyNAz079T0C8BF0J4g8T7Uyml/ZxBJ4SIVd6PBVDf07VSyo+klFlSyqzIyDM80wyOp2hn3WMUoVz4IDD5UVrhi1aTyIy4HEi/DQhJpMm79wNylqv+DQTFkOPnfwkIDyDzDmDCTKB8M0V3JiNw6VxKL9lAEVb5FhKsCx+kazY/T84y4U7g8r/TgB/8hAYnNJGEbOSV5Kw7XiXBuupFIGkq1V2ynupJ/zWVq6+mSKqhmER14iwa5PItFBGl3QBMvIuEYtfrJIyxmcCN75GD7PuQHNU7AMiaDZz3O3LWb/9O0fMvniJbi1bTVr0uj/pn4ixaaHa8DBSuBjJn0iJlNZGwHP4SiEkHMm4jZ937PrDnbZo0v3odiB5PO5/85dSerNnAhb+nRWXdn0igLv8bMOle4Mg2mngFXwFp19OE8vInx9/3IQnj7Z8DszdQfxm0ZLd/KDD+FhqLTc/QxJj2DJByDS3WVbuBpnLg/DlUZmcztSdvGfXZL/5GUVXBKuqjoRcCFz8ChI+kiPK712iB+M1S8pcDiyjyt1mAzNup3yq/IwHVV1P/TJhJEbGugHYW42+l4EAImtDZC8m+a1+n6OvgYirXNxiY+jgw+hryyx2v0BnurxeTz2YvBI5up8Ul43Yq06ijxah8M5B1N11vqKX+zF8OJE0hwQyIIKHdv4AWnRkfAH4hJKx5X9DcmXQfcMF9JOobn6Syf/EU+UvRWsfiPPEu6kshgO0vUxnjbgaufIH6JfczErOQodT2Eb+gPLvforG74jkKqnKWkq+b28m3smYrO8m/0GI5+VHg/Hto/Mq+pbkx5jpg5DTgoR+AMdfSOM34kBZ3TRwJ64FFgJcf+eWEmbQ72/Q0YO0GLv0rCW7JBtpV1BcAGb+heWYz0y6oeD29vuQRuibvC7I1fiL52qhfUl9uf4l8/bo3aL7t+w8tSjaL0kcPkI9//RC168IHyf8bS2i8itcDY2cA426hsd/zDgWKo64Ehgw7M/07BX0KuKB7exYAKJJSvu701moAs5S/ZwH4etCts5N+G4lx9gKacBPvIucPjqftm76aJrOHB3DRQ7Rl1ebSwHp60aQs20iTN2kqoImhjvfyo4guYRIQfx5NCkhg4dXkyOf9DgiOowEpWEnOcMkfgeg0Kmfzc3QcMuk+qnvSfYCxjo5mJj9Kk2nCnTTQ6/5Ctg+fTFFj1FgSR00sTZQx15FYf3ozrfzpv6YPGy/+A51zNx8BpvwZ8PZXJt8aEolxN1M9WXNIOHI+pbToscB5vyVh/r08UTcAACAASURBVORammTjbwUiU2hi7J9P7b/gfiA2HYjNALb/mwR3/K3U75c9RYvKke006bx8gAsfoOh17aPUhoTzyVkDI8meiNFA6nXAxNm0W/nP5dSe8+8lO9NvpWizpRK47Anq52EXA/duAe7eSPYAwEUPOxaVsTfSIjlxNm2hF00n28dcRxFefBYtcsIDuPhh8o2wEcCq+0goJt5F45M1h6KrozvJN3wCFGHeTsFBbCYtXhPuBHxDaHzDR5EwZ9xOti79Ddk17hYgdCiJVPYCoKORxj8sGRh1FfVl3jLyXZ9A4KIHKU/uUrpGE0PirK+h45yACFrkRv2S/GTbP0kAzr+H6vcfQue3zRW0Y/LyIV8s30wCPOle8o3MO6nPtr9IvhaSQO338ichjJ9Ivnv+PeTr/72JIuDMmZQ3ZTqJtbmD+igsifp46z8p2j9vFvXlxX8k2wtWAZPuAfyCyS+bK4BvniDhHXYxRceRqVR3YCTtIMfeRDuLz35NR48T76IxD0sGbp4P/P57ICiK5u7EWVTvoSUkwH4hZIPd18feRL6eeQeNy7I7ye/SbgSiUmnuHlpC+bPuJj8fegEdvejySfgB0oP2BprPFz1IfXnB/STMW16gY73I0RRYBUXT7jA2k3YoY2+k+fXpLRQ8TZxFvnXRQ7SYtTfQLuxsIKU85Q+AyTTSyAOQo/xMBxAOYAuAMuV3WF9lTZw4UZ4xbTVS7nhFyqofHGm5y6R8NljKjy6X0majNJtNyq3/knL7y458nW1Szhsv5T9jpawrcKQXfC3lmkelNNQ70r7+A5W5fq4jrfW4lFv+QfXZOfqdlP+IkvKlYVJ2tjrS81dQGeYuem0xSbn4RiqzeL0jX12BlJ/fKWVjuSNt/8eU77+3Smm1UFp3u5QbnpTy0GeONrYck/If0VK+ECFlbY6rTasfkbK12pG2+o9U5tYXnfqjVcrld0tZtsmRdny/lM+FSvn+JVJ2Gx3pW/4h5a43qB329swbT2UWfO3Id2yvlOv/KmXVHkfaxr9RvoW/cqR1t5NNm5+XfXJ4FY2DqYNe22xSfjydytz2b1fbv7hLysI1jrS9H1G+l5OoTrvt2/4t5Yp7pTR3U5q+jsbw2WApyzY7ri/fKuX8K6WsLzm5PcvvdoyFXivl4hlSZi905Kv6gfI9F+q43maTcs97Uq64j3zZbs+7F1HeHa86rm8sl3LRta7+cmAx5ftgqpQWs3K9Wcplv5Vy03NSWq2UZmygfM8GS1m+xXF9zlIp1/3F1d++uIvyrXzAkdbeJOXGv/fcnpeGSdllcKTvX0BjabfHapVywVVSPhviOhbH9km56kEpdUWOtO/fojI/ud7Rlz1hbKRx+FeclC1VTmXulXLl/WSvnfVzqcxdbzrSDDopv5zjqhtHd1G+dyY55q7NRtf98KHDN0ydUr6RTnkLVzuubyynvqw77NQXytxdca8jzWqRcsMTUuYt7719/QRAtuxBU93/YVZtNbRaBoSdOp9eS5FgZMqp85m7aLsaltx33eZO+umrbnMXbR/jJpw6n5R0phiX2fdtUt1GwNO7f7dz6Qqp3X2Vqc2lSNd/yKnztTdStNtXuwH64DcgrO8y+0t7E/Xl8MmnzmezUTQYGNn37VydrXTElDT11Pks3RRRjb6m71sxaw6QD/XVbquZ7nqIm0AR56ko20Tn4cGxp86nzaWIO7KP52vbrLSjCYjo+7ZWQx1FuKGJp87X2UK7q7583WajM/zQYX3fDikl1d2Xr9us9PlK3Hl9t6ehhI6d+rrN0Gajuvtz621TBe1i+jMnT5PeHmbl/gLOMAzzPw4/jZBhGOZ/DBZwhmEYN4UFnGEYxk1hAWcYhnFTWMAZhmHcFBZwhmEYN4UFnGEYxk1hAWcYhnFTWMAZhmHcFBZwhmEYN4UFnGEYxk1hAWcYhnFTWMAZhmHcFBZwhmEYN4UFnGEYxk1hAWcYhnFTWMAZhmHcFBZwhmEYN4UFnGEYxk1hAWcYhnFTWMAZhmHcFBZwhmEYN6VPARdCfCyEqBdCHHZKe04IUSOEyFF+pp9dMxmGYZgT6U8EvgjA1T2kz5NSZio/6wfXLIZhGKYv+hRwKeVOAM0/gi0MwzDMaTCQM/CHhRB5yhHLkN4yCSHuE0JkCyGyGxoaBlAdwzAM48yZCvj7AEYAyASgBfBabxmllB9JKbOklFmRkZFnWB3DMAxzImck4FJKnZTSKqW0AfgPgEmDaxbDMAzTF2ck4EKIWKeXMwAc7i0vwzAMc3bw6iuDEGIpgMsARAghqgE8C+AyIUQmAAmgEsD9Z9FGhmEYpgf6FHAp5e09JC84C7YwDMMwpwF/E5NhGMZNYQFnGIZxU1jAGYZh3BQWcIZhGDeFBZxhGMZNYQFnGIZxU36WAl7d0gGbTfaZr0irR3u3ZVDrlrLvegFA29aJ480dfeYzW21o6zAP1CwXrDbZr7qllMg93trvNvW37h2lDTBbbX3mLdMZ0GHqe3xMFhus/Rjv08HQZUZbZ9/93t5tQU1r56DWDfTPj6SUyK9u61d5XWbrQE06iWNNHf0qt7qlY9B92Gy1wdIPH2puN6GkztBnPinlWemjgeJ2Ai6lxO7yRpcJbrNJvLWlDE+uzDtJcE8Ugn1HmzH55W34Ivu4S5nL9h9DvaFLTdN3mXHNm99h9qL9LtdvKtShvN7okvbUqnw8vjy3T7Fv7TDhqjd24pVvil3Sy3QGVDW1u6Q9+OlBzPp4n8tEPXisBWtya13yvbi+CBkvfIsGQ7ea1mGyoFTn6pQNhm48t7oAb2wudUm32U52zL9/dRhTXtnm0h9tHWYs3lPpsvB9lVODG979HqudbLLaJFYcqHYps73bgjmL9uPTvVU99osz93yyH7M+3ocvD1S7pO8ub4TRqX+b20341Vu78MbmMpd86/K0KDuh7Te++z1u+3CPS1/qu8wntftIgxFPrcrHliKdS7rVJl38SEqJqa9swy/n7XDJV6TV47sy1we2PfJ5Di55aSsMXQ6BajR2Y2epa77DNW24f0k2DlS1oC/e316BC/+9xcXfusxWfF/e6JJvXb4W172zC7srHOkmiw3zvzvicq22rROpT3+DD3dUuFzfZOw+KdDZUqTDvYuzT/LX1g6TS/9q2zox9dVt+Oe6Qpd8X+fUuIyPlBKTX96Gaa+79uXDnx3EY1/kuqQt+v4oHv7sII41nTq4sFhtuPqNnbhncbZLenm98aT+fX5NAW5673t0mhy+UNFgxGd7j7nkW3mwBqlPf4OCWseCKKV0mXcA+crrm0px/5LsfgUhA8XtBHx/ZQvumL8Xv//vQTWtUKvH65tKsXTfcdWJpZT4y/JcjPrbBhw65hi0t7fShN9WUq+mfV/ehLkr8vHMVwVq2p6KJgAk+Hbh6DBZcO/ibFzx+g7VWU0WGz7bewzLD1TjP98dUa//y/JcXPTvLdhU6BCDF9YWolRnxEc7Hfnq2rpw5byduPWDPWrasaYOHDrWiiON7S6LxfNrCvHY8lyXyG95Ngndqxsdi8LbW8vxy3k7sbGgTk37Ivs4Fu2uxBuby9SotbndhCvm7cDkl7ei20IOrO8yY+m+Yy59AAB/XZGLZ74uwPdOYrD3CD1leNl+x2K4pUiHx5bn4vk1jol76FgrthTX42+rDiP3eCsAEpzr39mFm9/fjVolQq3Xd2FbCQnbQaeJ9s3hOtwxfy/e3uIQ640FdTBZbVifr1XHoqXdhD9+fggvrHXU3dphQqFWj+yqFnxz2NEfsxfuR+rT37hEx3//6jA+23vMZRzzq9sw/rmNmPtlnpq272gzWjrM0Om70dphAkD+ds2b3+G3C/bBZHFM3M3KYuC8ID21Mh+/+3if2hcA8NWhGmws0GH2wn2qaB6uacOVr+/AQ58dVNtYpjPg5W+KodN3u1z/8GeHMHP+XhTW6tW0tbla+p2ndfRlQR3+ua7IRaD2HaVx/PeGYuj0tGg3Gbsx8Z+bccO736vtae+2YM4n2dhUqMO6fEeZn+yuROYLm7DiYI2atngPLdYb8h19fry5A48uy8ErG0vUtCONtBA0GrtRpCXbWztMWJunxYqD1S6ByPxdR7E2T4uXvilS0xZ9fxQX/XuLi298sqcKFQ3t2F7SoPp1t8WKK17fgZvf3632b7fFii1F9Wg3WV304P4lB/DUqnzVHgBYr7T38eUOP/jmcB3O/9dmvPatoz2bi3R4a0sZNhbo1Mi+pd2Et7eUnbToDQZuJ+D7jpKobC5yRMLFTlugAsWBK5s61EnzXRmJTnu3Rf0793ibOik+/v4oAFoI7OwqcwiVPSKzOzoA7FTed3awHGVC2WwSa/NqoW3rwpcHHOKWc4zet9ikumV8U3G8ekO3GgU4T45vlQWgtrUTucdbYbLYVGfqMlvVKPI7J3t3K4vYC04i6txH9r9X59TgSEM7Go0mFGkNLtcCUBfDDpMFW4vJwXeUOCLHH47QWOyuaEJzOwnZXqWPVhyoVqPOw05Ri72PirR65FW34UBVC7YrZdrHTuPrhUNO4vSv9YUu1wKOCVXd0qlet6W4HlabxK7yRlWYncvZqUTHHSaLGom9t60cAAmwvZwirUH1jTc2l6LDZMXKQzVqmvOOw77IHTzmqGd/ZbNimyNS/CrHcY29ntc2lZ6Upu+y4JhyfLWpUIeyeiPW5Wmh01Okt6/S4YPZShuONXWoC4U92u40WbG9lMZs4+E69QjJHlB8vv+Y2p5DTrbbF057/+TXtKni5uzr9qMZs9WGF9eToG53EsHVSnubO0zqIvdF9nFICXxX1qD6rXOQ8LVyzXYnH/t8H82fLrMV2rYu1SY7qw7VQNvWha97GBMAOFyjV+p2LKCl9QY1n7HbAiEcc85itam6Yg9kAMdCU6jVq35tD2be3lquBnkFTgtontJH+TVteG1TKWpaBv8oze0EfH9lC4Sgv/NryPFK6vTw8fJAUkSg2oHOWx17pHKkgQZhyqgI1Om7UNnUAbPVpor1seYO9ez3hyNNmDIqAl4eQl1Jnbeodic5rDjTeYmhat1VzR3oMtsUOyit22JFZVM7Jg6jR6fnVJNNedVOk0fZKRyuacPw8ACkxmiQrUxY+yIyJMBbjazzqttgsUmcP3wItG1daOs0w2y1oUSZaDWtnWhRhLWkTo/UGI2LTc4LVo5Sd4VTH+1RBPpgVSvMVprsdiGva6P+uyyFHhFsn9z2PjJZbWo9h2vaEB/qj7BAH3VcnCehXeDt791xQSLK641o6zSj0diN483k+LnVtIBJKZFzvBVXjIkG4BCbTYV1CPbzgpTAJqWPDh1rhYcAxseHqAtXXrVz3WSjTt+Ntk4zRkUFoa3TrC4Azn1kF9byeiMyhoYiyNcLuxU/+NZpt2MX0x+UHcrFI8JRpjPAZqMtt73sfUebYLNJZfFoQ3pCiMv4OPeRvW/KdEYE+HhidHSQKuAHnXaY9kW1qE6PLrMNV4yJQlO7Ccebyde3l9Qj2M8LFQ3tqigdOt6KjIQQCOFY3J0XvgLFDvs8SE8IUfvwSEM7upUIPVfx5Q4TnftflhIJKR39sKlQh9AAb3SZbaqf7D3ajJhgP2QkhKjt2FZSj4ggX6QnhKC4Tq/2udUmMTYuGMebO9HWaYbJYlMDjyMNRjUAqmgwYtLwMABQ50+208K3u5z66NCxVggBXDkmWtUI54V4SxH5emuHCUcbHXPX7uv5NQ7fKFb8pKROj6SIQIQGeKtz2x7Jj4kNxmDjNgIupYTVJnGwqgW3ZQ2Fj5eHul0srjNgVFQQxseHoFBx9MJaPbw8BK7PiEOO8kFbRQOtrDedFw+AOraysR0mqw33TU0GQJPBbLXhaGM7xsWHICkiEKU6um5fZQsmJYUhNUajDuLh2jZofL0wfXwsGgzdqDd0qQN3fUYcqlvI2Y42tsMmgVsmJkAIisYtVhvK6o24fVIiPIQjeq1oMCI5MgijozUoU6KB3Oo2RGp8cVlKlDqR7PX8OmsoAHKsIi1N3NsnUVqhVg+TxYYjDe24PDUKIf7ejj7S6jF5ZASiNL5qdFtRb0RsiB8uTA7H8eZOGLrM6iS6b2oyjjS2o63DjDIlirkhMw4Abe3bOsworjNg5gWJqj0ACdL4+BCMjQtWxSn3eBsignxxYXIYNhfqsLu8EQW1egwLD8DFIyPUMbS39bcXDkOX2Yb8mjY0Gk0wdFlwychwaPy8VFtyjrdi2phoDAnwRokyZnnVrRgdrcGExFCU1lFkbRf8m86LR7FWD4vVprbxpvMSVJtb2k3QtnVhxgTyF3ukWtHQjpToIKTFBqvXFdcZMCY2GBcmh6n57IHF9PGx6DBZUdPaqUbnt08aii6zDcdbOlDd0gl9lwUzJsTD00OgUEu7w7zqNlw9NkbtC7tvjIwKwsRhQ5BzrAVSShTV6eHtKXDLxATsPdoMKSWOKgvxNeNi1bE40tAOQ5cFt0wcqo6Z2WpDUa0eFySHY3h4oDpmB6takJEQglFRQeoiVlxnQICPJ6aPj0VNayeanI49bpmYgOPNnWgwdKuB0nXpcRCChN9kocj2pgkJ8BDkz/Y+GhcfggmJQ5BX3Qqz1YbdFU24ZGQ4UqI16tyz+4F9fIq0epTqDDBZbbg+Iw42CZToDOi2WFHV1I4LksOQGBagLioldQZcnhqFYeEB2Kvs4o80tiNhiD/Gx4eguqUTHSaLetx675Qk1LR2wthtURfSX2clqP1Ai4ce12fEqf5iryc1RoPx8SFqG4u0esSG+GFIoA8GG7cQ8P/bWILpb+3C0cZ2GLotmDhsCFJjNC6dlhKjwdi4YNS2daGlnc49R0YFYVJSGJraTahu6URFgxGeHgKXp0ZDCIpm7BHHNeNi4CFIwI43d8Bik0iOCFRE1ACrTaKkjoRoVLRDwIu1NHHHxjmip7zqNvh6eajiRs5Gjpg5NBTDwgJQqjOgsqkdJosN5w8fgqSIQBRr9bDZJCqb2pEcEYhRUUGqY9kdY2RUELRtXTB0mVHR0I6wQB9V8ErqDGokMfOCYYo9bahoMMJik2ofFdTqYbbaUFpnxNi4YGQODVUjqooGI0ZEBmFUVJDyuh0ldQZVbAGgvMGACmVhuXhEBDS+XiirN6JIEbMr0qIR7OeFkjq6S+RoYzvS4oKRFhdMk85iQ35NK9ITQjA6WoN6QzfumL8Xe482Y1x8CFKiaadQVm9QBcK+SBXX6XFEWYiTFTvLdEa0dpig03cjNUaDUVEa9YOy8nojRkdrkBKjQbsiornHW5EUEYhLRkSg20KLtV0gbsgk0Smo1at135AZB39vT+RWt6Ktg3YFIyKDMCo6CKU6I6SU6viMjtagvJ7SjjTQONp3PmX1BjWwuDGTFoVSnVEVyMyhoRgVFYSCWj3q9F1oNHbjohHhGB4eoOYp0xkxMjIIqTHB0HdZUG/oRkmdASMigzAhMRSGLgtq27pwpNEILw+BaWOilLqN6mJzbUasWvex5g6YrDakRGswOjoIJXXk6/k1bZiQOMRl0S2pM2BUtAbp8eTrhVo9CrW0SN2sCGtedasaKI2ND0bCEH+U1RtwpJF8MGNoCIaFB6JcmVOVTR0YERmI84YNQZfZhnHPbkSDoRsXJodjdLQGjcZutLSbUKwshtemx6rzzC7Ov1GClYLaNlQ2dsAmgZFRFACV6owwWWyoaDAiJUaDtNhglClz8UiDEckRNI4AUFFPfhAT7IfzlQi+TGdQ81+eGo0gX/Jrux9fmRaN8EAfFNbq0WGyoKq5AymKgJfpDOou4WxE34CbCLi/jyeKtHrsUc6cxidQNFeo1aO53YR6A01cZxE9XKNHWlyw2nGlOgMqGoxIDAtAiL83hg4JQGm9ASV1Bnh6CKTFBWNYeCDK6o1qBDEiigb3WHMHSuoM6DLbkBqjQUo0CWt7twVl9UaMjA5CWhzVU1irR351G8bGBSM9IVRNK9cZ4CGAJGVRKNEZ1O1fijLxy+qNqG3rRJfZRuKkOFapzohSnUGZZCQG5fVGitQjAhEX4geN4liFWj1CA7wxNi4YsSF+LlFsakwwxsYFo7jOgGItRS/2PqpsakeX2YqKhnaMiAzEKKWeUp0BJTqDKowAiciRxnYE+XohSuOLkdFBKNUZ1G1kWmwwUmI0KNMZUVFPfTk6Oghj40JgtkrkVbeivN6I8fEhuDw1Sh3n5nYTLk+JQnSwLzR+XlS3sniMiw9GoI+nWjcAdYEtr3csxCkxGoyMDkJZPW2pa1o7MSIySBXRYq0BBbV6jI0Lxth4Zcy01EfRwb6IC/VHckQgCmv1qmiOiw/BaKWNFY00me2LXFunGRUNRtTpu5ASo8GoqCAYuy3Q6btxpLEdyS59aUSRVo8RkUEYq4hgqc6gnrmOitYgTRFM+4JKvh6Cglo6e63Td2FkdJDqByXKWDqPjz3aTgwLQGiAD+JC/FBeb0RJnQFeHgLj4kIwNMyf2qPUPSIqCCkx5AdFWj06TFaMVRZdbVsX3W6nMyA1WoNUZU4Va2lBGu3k/6U6mj9CAMPDAzEqSqPWTX5AQUiZzoialk6YLDYkRwbi4hHhSI4MVI9jLkwOd/J/A4rrDBgdHYToYD9EaXxRUNuG/Oo2hAZ448Ik2okV1urV3RgJeBAqlcXZbJVKHwWpvn5UGZ+RUU4BgxIM2vu3TGdEeYMRIf7eiAjyQUqMRv38BgAyEkKRpmhRmc4IKYHUGOoji02iSKtHRYMRY2I1OBu4hYDbzwY/338cPl4eGBFJ29fWDrP6wUmKIk4A8G1hHRqN3ZgwNBSjFScoUVbSEZGBAEhQypUIPCkiEL5enuRY9UYcsU/SCJooUgJf59In7GNig9UJubuiST03DfH3xtAwfxyuacPh2jakJ4QiUuOLSI0vCmr1KNQakBwZBD9vT6TEaHC0sR0Hqlrg4+mBkVEkBlVN7ShWRN3ZsbYU6dBtsakCASgiqkTLQgiMjQ9GbnUrCmv1SIsNpjRFDIrrDPD2FEiODMTYuBCYLDb1w9XMoaFIidHAJun82thtQXJkEBLDAuDj5aFGGykxGsSH+sPP20Nd5JIjAyGEQEq0BsXK4jEkwBtRGl+MjtaguE6v7lRGRgWp47M8uxo2SeN6WUoUKl6cjkiNr7I7ioIQQo2e8mvakBZH7RkZFYTyemq3r5cH4kP9MTIqCE3tJvUzidSYYIxWhHXv0SZICYyICsSY2GB4CPogs6a1E2PjQjAiMgg+Xh5qH6XEkH1j4+go7tCxVsSH+iMiiNpTUmdUBc9ZmO13eaTEaNQxK9S24VhzB5IjyDdigmkxLa4zIDVWgyBfL8SH+qNIq0eZzoD4UH8E+XphbFwIGgzd2FpUT4FFLInoseYOZFfS9t4eLQPAniNNqNN3ITU2WPWNcp1jfABgpLJjtEfqPl4eqrDaP/NIjgxERkIIbNJxV9HYuBA1KNpZ2oDmdhNSYjQIC/RBdDCJaO7xVqQnhCLE3xuxIX5qoJQwxB9+3p4YFR2EIw3t6s7DvvAdbWxXdwTJkUGICPLF1scuw6oHL8YT16RieHgAUuyLbh0JeEo0jU9aXDAKlUVufHwIPJR+KtTqUaylgGxEZBBSYjSw2CTW5teqvjEymnx9z5EmdJisSI4MwrDwAHh7ChLbeiNSYzQYGhYAXy8PdZEbGUXzbHx8CA7X6HHwWAtCA2jOp8UGo6TOoO5UUmKC1YBh+YHjsNgkxseH9qFyZ4ZbCPj4eEdkPSZGA29PD6QpjrVSuXUpNUaDIYEUbSz5gW5hmpA4BBo/b8SH+uNgVSvKG4wYp5Q1MkqD0noDdpU3YMJQ6txRUfYV24jwQB+EBHgjS/ngYuGuSngIEiJ7GSuUu1xGKhNnbGwINhyuQ4fJqtpsd6zC2jakKZHL6GgNrDaJT/dWITMxlBYPxbHsd1eMjCLHCvDxVO9ZHxMbjKFhAfDz9sAPR5rQaDRhRBRN0vMShyCvug25SvRvr7uiwYjc460YERkEb08P9b0lP1QhPNAHiWEBqhjYb0kcFx+iToKl+46hy2xDxtBQeHgIxyKnRP8AcP7wMLR2mPFVTi3GKIvHuPgQ6Lss2FKsg5eHwLDwQCSFByLAxxPLlPbY+8jTQ+CeyUm484JE9ZxwVFQQ9h1tRnGdQT26GRlFx1klOiOSIgLh4SFUgfki+zhC/L0RHeyrRk92YR0RGYQAHy+kxgTjU+X2ubS4YHh7eiAlWkM7ggajOunsR3GbinTIGk7jnxJD2/kdpQ0I9PHEsPBAVTAX7a6EEMC4uBA1atxSRHfE2EX0/KQwfFtYh5rWTnVXOD6ePgwsUwTCXjcALMs+jtHRGvh5e6p+Y7+ralx8CMKDfBER5IPFuysBABckhWFIoA8ignxQqNXjaFM7kiOpzHFxJDA5x1tVUUyJ0aCiwYjDtW2I0vgi2M8bmco8+O/eKvh4emBUdJBq68pDjnlGv4OxOrcWhm6LOkfsR4t51Q5fHxWlgclqw6pDNUiN1dDiER0Ei02qH4jb/QigOfvApSMghEBMsB8iNb7YVlKPBkO3GsXad5GFWr0a3KXFBaNYa0BudStGRVGgZN+RLPq+EkMCvDEqynE0uEGZZyMiAlU9Wbynio6TYjTw9KAg4nBtm7orJftC0Wm2YsXBaoyPD4EQtHu339Lq5+2BxLAAJEUEwsfTA//94ZgyR3r9f98HhFsIeGiADzyUO08mJdFkTo3RQAhgV3kjQpWoD6CBtH+fwO5so6ODsLlIBymBDMVJJ4+MgJRAl9mmnhOOUbY9Gw5r1S1hVLAfkiICYbLakDk0FH7enogL8UNMsB++Ue48sDuKffIBQMZQh2MVafWobetS37f/NlslLkoOV8ogx1p5qAbJEYGICPKFt6cHfpEaBZ2+G/GhtNJ7egiclzhEnVDJEXSd/RNyKt9ed4gabdgnLu0CPGCTFH0LQeLq4+mBbwrq4O0pneMPLgAAC0hJREFUVPumjo5Ah/LJ/qWj6W6TtNhgZFc2K+2hei4aQW0wWWzIUs4OJyRSP6/Pr0OSMkk8PIQqCDHBfogK9lNtvv/SEXj+hnHqa7uYAHTODgCjooOg09OXYOxnlFnDhyDE3xvati5MG0PRe8bQUHh5CHx5oBpCObay22S/nc4uMGPjgvHDkWaYLDb17N3eLpPFpvarc7SdMTQUnh4CkRpfpMZo0NphRuZQ2nGFK9Gp/TY6e79flByu3plkDxgmJIbiWHMHCmr16vg7n5Xaz5rtvrguX4uIIF/V18fFh6BdGZ/xamAShFWHalxsnzYmGhabRFO7ST2yOn/4EJitEuvytOriER7ki9AAb0gJjI6hBT8s0AexIX7qF4/s7UmNpYADALKG0ViMVs7vjzV34LxEqnuqcidXvaEbV46JcbF1TW4tIoJ8EdbLh3tCCGQkhKq3FabEuI4PlRWqjmen2YrvyhrVAGtEVCA0vl7otthw8cgIeHjQLtTTQ+CL7Gp4eQikK2MxLTVKPb6ZPIr8LWv4EPxwpBmNxm61j+x+KaXjZMA+X3aVN2J0NIm/t7KzBoDwQB+EB/n22MaB4hYCDgDP3zAO905JwmO/TAEABPp6IULplJRoDYRyb+HsS5Lg7SlwxZgoeHlS89KchDVTOZe+ZGS4mjZ5FInTpSmR8PH0QIfJqt6iBlAEBwB/uHwUAHIs+0BGanwRHUx2XDIqAp4eAtNSo5AU4RpROduRHBmk3n5nF7+RUUGIVCbmBckO264ZR04/Y0I8PJRV7BLlQ0vKS5PHPmGC/bxwtXKNc9125/f0EGo7LlVs8Pb0QKoS3VCE7wmA7vwAAC8PgRB/bwDAFWOiVVG/Io36KC7UX61n9sXDATgWNed2A3R7IgDcqNzZ0Rs3T0xQ/x6nXD/Zqd32fvP29MA0RZTsH3QG+nphvDK5zh8eprbHHk3fMjFB7eu0Hvro/KQh6uJ/oTIWzmeY9r4Wgu78ODFt2phoGLot6qLrbK+vl4cahJzntOjaz5VD/L0xcdgQ+Hh5qP0bpaFo226H3df/dMVoAHT8Yff1qcpC62z7hKGhiAjyQYi/t+ob9gUQcPWnqcpceGTaaDXN3oaIIF9ViO6YRHcaRQf7YmgYjX+606Jrb1tUsJ86l64aR79HKMcW7SYrfjk2Wm1PT2QkOMTabsf5w8OQMMQfiWEBamTrLOp2f/H18sScKUkAgInK+Ph6eap+lJ4QgiBf+k/J7EFcYlgAojR+Lv0HOIKIxLAAhAf6QAjgdqUPkiKC1ADTHgQAwF+uoj6cPt75vxAeZKSUP9rPxIkT5WDy91X5ctRT6+X35Q0u6V1mi+wyW9TXTcZuOWzuWpn69w0u+fKOt8qvc2pc0uYs2ieHzV0rjze3q2nVLR1y8e6j0mazqWlvbS6Vw+aulV/sP+ZyvdVqc3mt7zTJ2z/aIy98cbNs7TCp6Z0mi/zmsNalzH+tK5TD5q6Vn++rcmnLaxuLZaOhS007UNUsh81dK298d5dLXdmVTbKt01GHzWaTl726TV7x2nZZ19bpkre13eRi6w8VjXLY3LXyT8sOueRbdbBa5hxrcbE77ekN8orXtrvk21XWIHeU1LukTX1lqxw2d6082mB0Sbec0Ee9UdvaIXOPt7ikDZu7Vg6bu1Y2GbvVtJI6vXxtY7FLe/70+SE5bO5audppfK1Wm9x7pMklX0mdXiY/uU7OWbRfmi1WFxuPNTl8QEop/7SMytxUUKemdZos8uUNRS72bC+pl8PmrpXPry5Q02w2m1y464isamx3udbeHmd/tdlsLn4hpZSLdx+Vw+aulR9sL3dJ33hYK8t0evV1o6FLLdOZtbm1cl1erUuaPV+zk+2tHSYXG6WUcsmeSpn+3Ea5cNcRl3Rjl1nW6x1+abZY1TI7TY72VNQb5Ec7Klza9I81BXLY3LVyV5nr3D2RfUeb5LC5a+Ubm0pPmc9ms8k/Lj0oh81dK4u0bWq6yWKVX+w/5mLPurxaOWzuWvn48hyX6z/YXu7iqy3tpBsjn1rnUpdO3ynbu80uaY99kSOnvbZdfn9Ce3T6TtnRbZEDBUC27EFT3VrALVZbv8VAp++UlY3GPvMV1LSd5Kg90d5tlluLdCdNtIHQabLIj3cdcZnMPWG12uR/dlacJMo9cTr25Ve3ytZ2U5/5vjpUfZJY90Rlo/EkAR4oxVq9ywLXG3VtnfKdrWUuotwbJy66vdFttsoN+f/f3t2FSFXGcRz//tiMwoLethC1V7yLsFi8KULoBeumFyjyqq7sosDuim6yoJAo6S4wkgx6QbBMCEKRorrI3LUtTbPMpFaXddXClgRR/13MszGtMztn19l95hl+H1jOnGfPYZ///mf+c+Y5c54z3PI5d+r0mXjl0z3nvAE0sv3AsRite3OezMlTpys93zd9N3TOQU0jO347Fp/tHq70t6saOXEyvvn1aMvthv78J9Zs2VcpnvqDqVbqC3Uzp9Prp8r/ffPgoUp1Y6Y1K+CKNs4k10pfX1/09/e33tDMzP4jaSAi+ia2FzMGbmZm/+cCbmZWqJYFXNI6SUck7a5ru0LSVkm/pOXMfMnRzMyaqnIE/g6wbELbc8C2iFgEbEvrZmY2i1oW8Ij4Ejg+ofkBYH16vB54sM39MjOzFqY7Bn5NRAwDpOXVzTaUtEJSv6T+0dHRZpuZmdkUzfhJzIhYGxF9EdHX29vbegczM6tkugV8RNI8gLQ80mJ7MzNrswumud9m4HFgdVp+UmWngYGBo5Im3pr8KuBoo+0L1C2xdEsc4Fg6VbfEMltxXNeoseWVmJI+AJZS6+gI8AKwCdgAXAv8DjwSERNPdFYiqb/RFUYl6pZYuiUOcCydqltiyR1HyyPwiFje5Fd3tbkvZmY2Bb4S08ysUJ1QwNfm7kAbdUss3RIHOJZO1S2xZI1jVmcjNDOz9umEI3AzM5sGF3Azs0JlK+CSlknaJ2m/pKInw5J0UNIuSYOSirpjRTfNNtkkllWSDqXcDEq6P2cfq5C0UNLnkvZK+lHSytReXF4miaXEvFwk6VtJ36dYXkzt2fKSZQxcUg/wM3APMATsAJZHxJ5Z70wbSDoI9EVEcRcmSLoTGAPejYibU9urwPGIWJ3eXC+PiGdz9rOKJrGsAsYi4rWcfZuKdHXzvIjYKelSYIDahHFPUFheJonlUcrLi4C5ETEmaQ7wNbASeJhMecl1BL4E2B8RByLiFPAhtRkObZZ102yTTWIpTkQMR8TO9PhvYC8wnwLzMkksxUm3pxxLq3PST5AxL7kK+Hzgj7r1IQpNahLAFkkDklbk7kwbVJ5tshBPS/ohDbF0/LBDPUnXA7cC2yk8LxNigQLzIqlH0iC1+Z+2RkTWvOQq4GrQVvL3GW+PiNuA+4Cn0kd56wxvAjcBi4Fh4PW83alO0iXARuCZiDiRuz/no0EsReYlIs5ExGJgAbBE0s05+5OrgA8BC+vWFwCHM/XlvEXE4bQ8AnxMbYioZF0z22REjKQX3VngLQrJTRpj3Qi8FxEfpeYi89IollLzMi4i/gK+oHa3smx5yVXAdwCLJN0g6ULgMWozHBZH0tx0cgZJc4F7gd2T79XxxmebhCnMNtmJxl9YyUMUkJt0suxtYG9ErKn7VXF5aRZLoXnplXRZenwxcDfwExnzku1KzPS1oTeAHmBdRLycpSPnSdKN1I66oTY52PslxTLTs03OpiaxLKX2MT2Ag8CT4+OVnUrSHcBXwC7gbGp+ntrYcVF5mSSW5ZSXl1uonaTsoXbwuyEiXpJ0JZny4kvpzcwK5SsxzcwK5QJuZlYoF3Azs0K5gJuZFcoF3MysUC7gZmaFcgE3MyvUv2fHSYM6SaEMAAAAAElFTkSuQmCC\n",
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"text/plain": [
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"<Figure size 432x288 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": [
|
|
"fname = 'head-hydrographs.csv'\n",
|
|
"fname = os.path.join(workspace, fname)\n",
|
|
"csv = np.genfromtxt(fname, delimiter=',', dtype=None, names=True)\n",
|
|
"for name in csv.dtype.names[1:]:\n",
|
|
" plt.plot(csv['time'], csv[name], label=name)\n",
|
|
"plt.legend()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"anaconda-cloud": {},
|
|
"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": "3.8.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|