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Veg_FIS.py
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Veg_FIS.py
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# -------------------------------------------------------------------------------
# Name: Veg FIS
# Purpose: Runs the vegetation FIS for the BRAT input table
#
# Author: Jordan Gilbert
#
# Created: 09/2016
# Copyright: (c) Jordan 2016
# Licence: <your licence>
# -------------------------------------------------------------------------------
import arcpy
import skfuzzy as fuzz
from skfuzzy import control as ctrl
import numpy as np
import os
import sys
from SupportingFunctions import make_folder, make_layer, find_available_num_prefix
def main(in_network):
"""
Runs the vegetation FIS for the BRAT input table
:param in_network: The input BRAT network
:return:
"""
scratch = 'in_memory'
# TODO Does this have to be nested? It seems more consistent if it is just defined below.
def veg_cap_fis(model_run):
"""
Vegetation capacity fis function
:param model_run: The model being run, either 'Hpe' or 'ex"
:return:
"""
arcpy.env.overwriteOutput = True
# get list of all fields in the flowline network
fields = [f.name for f in arcpy.ListFields(in_network)]
# set the carrying capacity and vegetation field depending on whether potential or existing run
if model_run == 'Hpe':
out_field = "oVC_Hpe"
riparian_field = "iVeg100Hpe"
streamside_field = "iVeg_30Hpe"
else:
out_field = "oVC_EX"
riparian_field = "iVeg100EX"
streamside_field = "iVeg_30EX"
# check for oVC_* field in the network attribute table and delete if exists
if out_field in fields:
arcpy.DeleteField_management(in_network, out_field)
# get arrays for fields of interest
segid_np = arcpy.da.FeatureClassToNumPyArray(in_network, "ReachID")
riparian_np = arcpy.da.FeatureClassToNumPyArray(in_network, riparian_field)
streamside_np = arcpy.da.FeatureClassToNumPyArray(in_network, streamside_field)
segid_array = np.asarray(segid_np, np.int64)
riparian_array = np.asarray(riparian_np, np.float64)
streamside_array = np.asarray(streamside_np, np.float64)
# check that inputs are within range of fis
# if not, re-assign the value to just within range
riparian_array[riparian_array < 0] = 0
riparian_array[riparian_array > 4] = 4
streamside_array[streamside_array < 0] = 0
streamside_array[streamside_array > 4] = 4
# delete temp arrays
items = [segid_np, riparian_np, streamside_np]
for item in items:
del item
# create antecedent (input) and consequent (output) objects to hold universe variables and membership functions
riparian = ctrl.Antecedent(np.arange(0, 4, 0.01), 'input1')
streamside = ctrl.Antecedent(np.arange(0, 4, 0.01), 'input2')
density = ctrl.Consequent(np.arange(0, 45, 0.01), 'result')
# build membership functions for each antecedent and consequent object
riparian['unsuitable'] = fuzz.trapmf(riparian.universe, [0, 0, 0.1, 1])
riparian['barely'] = fuzz.trimf(riparian.universe, [0.1, 1, 2])
riparian['moderately'] = fuzz.trimf(riparian.universe, [1, 2, 3])
riparian['suitable'] = fuzz.trimf(riparian.universe, [2, 3, 4])
riparian['preferred'] = fuzz.trimf(riparian.universe, [3, 4, 4])
streamside['unsuitable'] = fuzz.trapmf(streamside.universe, [0, 0, 0.1, 1])
streamside['barely'] = fuzz.trimf(streamside.universe, [0.1, 1, 2])
streamside['moderately'] = fuzz.trimf(streamside.universe, [1, 2, 3])
streamside['suitable'] = fuzz.trimf(streamside.universe, [2, 3, 4])
streamside['preferred'] = fuzz.trimf(streamside.universe, [3, 4, 4])
density['none'] = fuzz.trimf(density.universe, [0, 0, 0.1])
density['rare'] = fuzz.trapmf(density.universe, [0, 0.1, 0.5, 1.5])
density['occasional'] = fuzz.trapmf(density.universe, [0.5, 1.5, 4, 8])
density['frequent'] = fuzz.trapmf(density.universe, [4, 8, 12, 25])
density['pervasive'] = fuzz.trapmf(density.universe, [12, 25, 45, 45])
# build fis rule table
rule1 = ctrl.Rule(riparian['unsuitable'] & streamside['unsuitable'], density['none'])
rule2 = ctrl.Rule(riparian['barely'] & streamside['unsuitable'], density['rare'])
rule3 = ctrl.Rule(riparian['moderately'] & streamside['unsuitable'], density['rare'])
rule4 = ctrl.Rule(riparian['suitable'] & streamside['unsuitable'], density['occasional'])
rule5 = ctrl.Rule(riparian['preferred'] & streamside['unsuitable'], density['occasional'])
rule6 = ctrl.Rule(riparian['unsuitable'] & streamside['barely'], density['rare'])
# matBRAT has consequent as 'occasional'
rule7 = ctrl.Rule(riparian['barely'] & streamside['barely'], density['rare'])
rule8 = ctrl.Rule(riparian['moderately'] & streamside['barely'], density['occasional'])
rule9 = ctrl.Rule(riparian['suitable'] & streamside['barely'], density['occasional'])
rule10 = ctrl.Rule(riparian['preferred'] & streamside['barely'], density['occasional'])
rule11 = ctrl.Rule(riparian['unsuitable'] & streamside['moderately'], density['rare'])
rule12 = ctrl.Rule(riparian['barely'] & streamside['moderately'], density['occasional'])
rule13 = ctrl.Rule(riparian['moderately'] & streamside['moderately'], density['occasional'])
rule14 = ctrl.Rule(riparian['suitable'] & streamside['moderately'], density['frequent'])
rule15 = ctrl.Rule(riparian['preferred'] & streamside['moderately'], density['frequent'])
rule16 = ctrl.Rule(riparian['unsuitable'] & streamside['suitable'], density['occasional'])
rule17 = ctrl.Rule(riparian['barely'] & streamside['suitable'], density['occasional'])
rule18 = ctrl.Rule(riparian['moderately'] & streamside['suitable'], density['frequent'])
rule19 = ctrl.Rule(riparian['suitable'] & streamside['suitable'], density['frequent'])
rule20 = ctrl.Rule(riparian['preferred'] & streamside['suitable'], density['pervasive'])
rule21 = ctrl.Rule(riparian['unsuitable'] & streamside['preferred'], density['occasional'])
rule22 = ctrl.Rule(riparian['barely'] & streamside['preferred'], density['frequent'])
rule23 = ctrl.Rule(riparian['moderately'] & streamside['preferred'], density['pervasive'])
rule24 = ctrl.Rule(riparian['suitable'] & streamside['preferred'], density['pervasive'])
rule25 = ctrl.Rule(riparian['preferred'] & streamside['preferred'], density['pervasive'])
# FIS
veg_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5,
rule6, rule7, rule8, rule9, rule10,
rule11, rule12, rule13, rule14, rule15,
rule16, rule17, rule18, rule19, rule20,
rule21, rule22, rule23, rule24, rule25])
veg_fis = ctrl.ControlSystemSimulation(veg_ctrl)
# run fuzzy inference system on inputs and defuzzify output
out = np.zeros(len(riparian_array))
for i in range(len(out)):
veg_fis.input['input1'] = riparian_array[i]
veg_fis.input['input2'] = streamside_array[i]
veg_fis.compute()
out[i] = veg_fis.output['result']
# save fuzzy inference system output as table
columns = np.column_stack((segid_array, out))
# TODO See if possible to skip this step
out_table = os.path.dirname(in_network) + "/" + out_field + "_Table.txt"
np.savetxt(out_table, columns, delimiter=",", header="ReachID, " + out_field, comments="")
ovc_table = scratch + "/" + out_field + "Tbl"
arcpy.CopyRows_management(out_table, ovc_table)
# join the fuzzy inference system output to the flowline network
# create empty dictionary to hold input table field values
tbl_dict = {}
# add values to dictionary
with arcpy.da.SearchCursor(ovc_table, ['ReachID', out_field]) as cursor:
for row in cursor:
tbl_dict[row[0]] = row[1]
# populate flowline network out field
arcpy.AddField_management(in_network, out_field, 'DOUBLE')
with arcpy.da.UpdateCursor(in_network, ['ReachID', out_field]) as cursor:
for row in cursor:
try:
a_key = row[0]
row[1] = tbl_dict[a_key]
cursor.updateRow(row)
# TODO There should be no blank exception statements. What error is this catching?
except:
pass
tbl_dict.clear()
# calculate defuzzified centroid value for density 'none' MF group
# this will be used to re-classify output values that fall in this group
# important: will need to update the array (x) and MF values (mfx) if the
# density 'none' values are changed in the model
x = np.arange(0, 45, 0.01)
mfx_none = fuzz.trimf(x, [0, 0, 0.1])
defuzz_none = round(fuzz.defuzz(x, mfx_none, 'centroid'), 6)
mfx_pervasive = fuzz.trapmf(x, [12, 25, 45, 45])
defuzz_pervasive = round(fuzz.defuzz(x, mfx_pervasive, 'centroid'))
# update vegetation capacity (ovc_*) values in stream network
# set ovc_* to 0 if output falls fully in 'none' category and to 40 if falls fully in 'pervasive' category
with arcpy.da.UpdateCursor(in_network, [out_field]) as cursor:
for row in cursor:
if round(row[0], 6) == defuzz_none:
row[0] = 0.0
if round(row[0]) >= defuzz_pervasive:
row[0] = 40.0
cursor.updateRow(row)
# delete temporary tables and arrays
arcpy.Delete_management(out_table)
arcpy.Delete_management(ovc_table)
items = [columns, out, x, mfx_none, defuzz_none]
for item in items:
del item
# run the combined fis function for both potential and existing
veg_cap_fis('Hpe')
veg_cap_fis('ex')
make_layers(in_network)
def make_layers(input_network):
"""
Makes the layers for the modified output
:param input_network: The path to the network that we'll make a layer from
:return:
"""
arcpy.AddMessage("Making layers...")
intermediates_folder = os.path.dirname(input_network)
veg_folder_name = find_available_num_prefix(intermediates_folder) + "_VegDamCapacity"
veg_folder = make_folder(intermediates_folder, veg_folder_name)
trib_code_folder = os.path.dirname(os.path.abspath(__file__))
symbology_folder = os.path.join(trib_code_folder, 'BRATSymbology')
existing_veg_symbology = os.path.join(symbology_folder, "ExistingVegDamBuildingCapacity.lyr")
historic_veg_symbology = os.path.join(symbology_folder, "HistoricVegDamBuildingCapacity.lyr")
make_layer(veg_folder, input_network, "Existing Veg Dam Building Capacity", existing_veg_symbology, is_raster=False)
make_layer(veg_folder, input_network, "Historic Veg Dam Building Capacity", historic_veg_symbology, is_raster=False)
if __name__ == '__main__':
main(sys.argv[1])