“All” about land use and land classifications¶

Preparing basis vector files from GAP/NLCD data¶

The prep_from_data.py script in examples prepares *.table and *.13 files needed for T:**_manning.table-->fort.13 and creates the n x m matrix of multiplier factors where n = # nodes, and m = # land classification values. It works ok, but could use some polishing.

Import the necessary modules:

import polyadcirc.pyGriddata.table_management as tm
import polyadcirc.pyGriddata.gridObject as go
import polyadcirc.pyGriddata.prep_mesh as prep

Read in the CCAP_Manning.table file in the current working directory.:

table = tm.read_table('CCAP_Manning.table')

To read in multiple *.table files use:

tables = tm.read_tables()

Create necessary gapInfo objects, one for each *.asc file, requires *.asc files in current working directory:

gap15 = tm.gapInfo('15N.asc', table, 2, 15 )
gap16_1 = tm.gapInfo('16N_part1.asc', table, 2, 16 )
gap16_2 = tm.gapInfo('16N_part2.asc', table, 2, 16 )
gap17 = tm.gapInfo('17N.asc', table, 2, 17)

Create a gridInfo object and store references to the gapInfo objects. Currently this assumes that the flagged_fort.14 (or fort.14) file is in the current working directory:

gap_list = [gap15, gap16_1]
grid = go.gridInfo('flagged_fort.14', gap_list)

Explictly set the grid_dir to the current working directory:

grid_dir = '.'
grid = gm.gridInfo(basis_dir, grid_dir, gap_list,
        executable_dir="directory_containing_Griddataexecutable")

Set up files to run Griddata_v1.32.F90 to create the landuse basis folders. Appropriately flag the nodes of the fort.14 file. Finally, run Griddata_v1.32.F90 to map the landuse classification data to the mesh:

prep.prep_all(grid, path = grid_dir)
prep.prep_test(grid, path = grid_dir)

prep.prep_all(grid, path = grid_dir)
prep.prep_test(grid, path = grid_dir)

This code also maps the landuse classifcation data to the mesh for a test case for verifcation purposes. The method compare() generates a set of images in a figs/ folder for visual verification:

prep.compare(basis_dir = grid_dir)

Comparing the Python vs. Griddata_v1.32.F90 Reconstructions¶

To compare the Python vs. the “pure” Griddata_v1.32.F90 constructions, I usually move the test and landuse_*/ folders out of the bin/ directory into another folder. The fort.14 file needs to also be copied to this folder. Within this folder I do the following:

>>> fm.mkdir('figs')
>>> prep.compare(basis_dir = '.')

basis_dir is the folder where the test and landuse_* folders are located in addition to the fort.14 file. A set of plots will be created in figs. The values in the difference.png figure should be very small.

Manufacturing GAP data¶

This section deals mostly with how to use manufacture_gap. The relevant example scripts for this section located in examples/ are

• manu_prep_comp.py, requires rand_Manning.table file
• bands.py, requires rand_Manning.table file

What follows is an explaination of the script bands.py, the script manu_prep_comp.py similar and simpler:

Import the necessary modules:

import polyadcirc.run_framework.domain as dom
import polyadcirc.pyGriddata.manufacture_gap as manu
import polyadcirc.pyGriddata.table_management as tm
import polyadcirc.pyGriddata.gridObject as go
import polyadcirc.pyGriddata.prep_mesh as prep

First determine the limits of the domain you wish to create your mesh for:

grid_dir = '.'

domain = dom.domain(grid_dir)
domain.read_spatial_grid()

x_values = [n.x for n in domain.node.values()]
y_values = [n.y for n in domain.node.values()]
xr = max(x_values)
xl = min(x_values)
yu = max(y_values)
yl = min(y_values)

Divide up the domain into a collection of rectangles determined by x_points and y_points:

x_points = (xl, 750, 1500, xr)
y_points = (yl, -1200, -750, 100, 500, 1150, 1300, yu)

Designate the probablities for each of the four landuse classifications for each rectangle:

p = [[0, 0, 0, 1],
 [0, 0, 0, 1],
 [0, 0, 0, 1],
 [0, 0, 0, 1],
 [0, 0, 0, 1],
 [0, 0, 0, 1],
 [0, 0, 0, 1],
 [0.8, 0.2, 0.0, 0],
 [0.0, 0.2, 0.8, 0],
 [0.8, 0.2, 0, 0],
 [0.2, 0.4, 0.4, 0],
 [0.1, 0.2, 0.7, 0],
 [0.2, 0.4, 0.4, 0],
 [0.7, 0.3, 0, 0],
 [1, 0, 0, 0],
 [0, 0, 1, 0 ],
 [0.9, 0.1, 0, 0 ],
 [0.8, 0.1, 0.1, 0],
 [0.1, 0.2, 0.7, 0],
 [0.2, 0.4, 0.4, 0],
 [0, 0.1, 0.9, 0]]

You can construct data by randomly selecting values from four classifications at a resolution of 30 m and write that out to a file band_sections.asc:

rand_rect = manu.random_patches(x_points, y_points, [1, 2, 3, 4],
    p_sections = p)
manu.write_gapfile(rand_rect, xl, yl, 'band_sections.asc')

For other methods to create random GAP data see polyadcirc.pyGriddata.manufacture_gap.

Finally, create and verify the landuse basis fort.13 files:

table = tm.read_table('rand_Manning.table')
gap_sec = tm.gapInfo('band_sections.asc', table)
gap_list = [gap_sec]
grid = go.gridInfo('flagged_fort.14', gap_list)

prep.prep_all(grid, path = grid_dir)
prep.prep_test(grid, path = grid_dir)

prep.prep_all(grid, path = grid_dir)
prep.prep_test(grid, path = grid_dir)
prep.compare(basis_dir = grid_dir)