Limited Variable Bathymetry¶

The domain class has two built in methods that give the user a limited ability to alter the bathymetry of the domain. The class polyadcirc.run_framework.random_wall.runSet uses the polyadcirc.pyADCIRC.fort14_management.update() method and polyadcirc.run_framework.domain.domain.add_wall() method to run PADCIRC with variable Manning’s n fields and variable bathymetry.

few_walls¶

This script models a submerged mound by setting the bathymetry to a specified wall_height at nodes contained in a rectangle defined by y_min, y_max, x_min, and x_max. To recude model error the user may desire to refine the mesh in areas where the bathymetry will be altered.

Allow running from the command line using ./few_walls.py:

#! /usr/bin/env/python

Import necessary modules:

import polyadcirc.run_framework.domain as dom
import polyadcirc.run_framework.random_wall as rmw
import numpy as np

Store string references to important directories:

adcirc_dir = '/work/01837/lcgraham/v50_subdomain/work'
grid_dir = adcirc_dir + '/ADCIRC_landuse/Inlet/inputs/poly_walls'
save_dir = adcirc_dir + '/ADCIRC_landuse/Inlet/runs/few_walls'
basis_dir = adcirc_dir
+'/ADCIRC_landuse/Inlet/landuse_basis/gap/beach_walls_2lands'

grid_dir: directory where the fort.15, fort.14, and fort.22 files are stored
save_dir: directory where the RF_directory_*/ are created and job specific data is saved
basis_dir: directory containing the landuse_## folders which each contain a fort.13 file specific to the landuse classification
adcirc_dir: directory containing compiled ADCIRC executables

Set run specific names of script and save_file:

script = "runWALLrun.sh"
save_file = 'py_save_file'

Setting diffrerent script names allows for simulatenous runs of PolyADCIRC with differing grid_dir, save_dir, and basis_dir.

Designate which ADCIRC specific output files to collect data from:

timeseries_files = ["fort.63"]
nontimeseries_files = ["tinun.63", "maxvel.63", "maxele.63", "timemax63"]

The non-timeseries output timemax63 is not an ADCIRC output file. The data from the fort.63 file used to determine time of maximum elevation in a post-processing step within the run_points() method.

Set nprocs to be number of processors per PADCIRC run. Set ppnode to be TpN (tasks per node) or the number of processors per node. On Lonestar, 12 is the number of processors per node. Set NoN to be number of nodes requested by the submission_script.sub. See -pe `` line in submission_script ``<TpN>way<NoN x 12>.:

nprocs = 2
ppnode = 12
NoN = 2
num_of_parallel_runs = (ppnode*NoN)/nprocs # procs_pnode * NoN / nproc

Store directory references and set up random field directories:

main_run = rmn.runSet(grid_dir, save_dir, basis_dir, num_of_parallel_runs,
        base_dir = adcirc_dir, script_name = script)
main_run.initialize_random_field_directories(num_procs = nprocs)

Store fort.14 and fort.15 data in domain:

domain = dom.domain(grid_dir)
domain.update()

Set Manning’s n samples:

lam_domain = np.array([[.07, .15], [.1, .2]])
lam1 = np.linspace(lam_domain[0, 0], lam_domain[0, 1], 20)
lam2 = np.linspace(lam_domain[1, 0], lam_domain[1, 1], 20)
lam4 = 0.012
lam1, lam2, lam4 = np.meshgrid(lam1, lam2, lam4)
lam_samples = np.column_stack((lam1.ravel(), lam2.ravel(), lam4.ravel()))

mann_pts = lam_samples.transpose()

Set wall samples:

num_walls = 6

ymin = np.linspace(1500, -1500, num_walls)
xmin = 1420*np.ones(ymin.shape)
xmax = 1580*np.ones(ymin.shape)
ymax = 1500*np.ones(ymin.shape)
wall_height = -2.5*np.ones(ymax.shape)
# box_limits [xmin, xmax, ymin, ymax, wall_height]
wall_points = np.column_stack((xmin, xmax, ymin, ymax, wall_height))
wall_points = wall_points.transpose()

Tile mann_pts so that the number of columns is mann_pts.shape[1] * num_walls. This samples Manning’s n points on the same regular grid for each point in wall_points. However, this need not be the case. See examples/walls_rand_man.py for an example with varying numbers of random Manning’s n samples per wall sample.

Job Submission Script¶

An example submission script is included in examples/submission_script.sub. You should copy any scripts you wish to modify and run into a separate folder in your $WORK directory. In these examples I am working from $WORK/landuse_bin. You will need to modify the lines that designate the adcirc_dir, grid_dir, save_dir, and basis_dir to match your directory structure.

To run run-lonestar-test you need to modify submission_script.sub so that the line #$ -M youremail@someplace.com has your e-mail. Then you can submit it to the queue using:

$ qsub submission_script.sub

To check on your job you can use the commend:

$ qstat

Currently the output is saved to a python formatted binary file called py_save_file.mat in save_dir.

load_few_walls¶

This script is very similar to examples/load_test.py.

Import necessary modules:

import polyadcirc.run_framework.random_wall as rmw
import polyadcirc.pyADCIRC.plotADCIRC as pa

Set up local directory and file references:

base_dir = '/h1/lgraham/workspace'
grid_dir = base_dir + '/ADCIRC_landuse/Inlet/inputs/poly_walls'
save_dir = base_dir + '/ADCIRC_landuse/Inlet/runs/few_walls'
basis_dir = base_dir + '/ADCIRC_landuse/Inlet/landuse_basis/gap/beach_walls_2lands'

save_file = 'py_save_file.mat'

Load the run set up and data:

main_run, domain, mann_pts, wall_pts, points = rmw.loadmat(save_file, base_dir,
    grid_dir, save_dir, basis_dir)

Now the data is availiable for plotting methods in plotADCIRC.

Determine the total number of samples modeled:

pt_nos = range(points.shape[-1])

Plot the non-timeseries data for all of the samples modeled:

pa.nts_pcolor(main_run.nts_data, domain, points = pt_nos, path = save_dir)