def run_analysis():
"""Run Climate Linkage Mapper analysis."""
import cc_grass_cwd # Cannot import until configured
cc_copy_inputs() # Clip inputs and create project area raster
# Get zonal statistics for cores and climate
lm_util.gprint("\nCALCULATING ZONAL STATISTICS FROM CLIMATE RASTER")
climate_stats = arcpy.sa.ZonalStatisticsAsTable(cc_env.prj_core_fc,
cc_env.core_fld,
cc_env.prj_climate_rast,
"zstats", "DATA", "ALL")
# Create core pairings table and limit based upon climate threshold
core_pairings = create_pair_tbl(climate_stats)
# Generate link table, calculate CWD and run Linkage Mapper
if int(arcpy.GetCount_management(core_pairings).getOutput(0)) == 0:
lm_util.warn("\nNo core pairs within climate threshold. "
"Program will end")
else:
# Process pairings and generate link table
grass_cores = process_pairings(core_pairings)
if not grass_cores:
lm_util.warn("\nNo core pairs within Euclidean distances. "
"Progam will end")
else:
# Create CWD using Grass
cc_grass_cwd.grass_cwd(grass_cores)
# Run Linkage Mapper
lm_util.gprint("\nRUNNING LINKAGE MAPPER "
"TO CREATE CLIMATE CORRIDORS")
lm_master.lm_master()
def run_analysis():
"""Run Climate Linkage Mapper analysis"""
import cc_grass_cwd # Cannot import until configured
zonal_tbl = "zstats.dbf"
cc_copy_inputs() # Clip inputs and create project area raster
# Get zonal statistics for cores and climate
lm_util.gprint("\nCALCULATING ZONAL STATISTICS FROM CLIMATE RASTER")
climate_stats = arcpy.sa.ZonalStatisticsAsTable(
cc_env.prj_core_fc, cc_env.core_fld, cc_env.prj_climate_rast,
zonal_tbl, "DATA", "ALL")
# Create core pairings table and limit based upon climate threshold
core_pairings = create_pair_tbl(climate_stats)
# Generate link table, calculate CWD and run Linkage Mapper
if int(arcpy.GetCount_management(core_pairings).getOutput(0)) == 0:
arcpy.AddWarning("\nNo core pairs within climate threshold. "
"Program will end")
else:
# Process pairings and generate link table
grass_cores = process_pairings(core_pairings)
if not grass_cores:
arcpy.AddWarning("\nNo core pairs within Euclidean distances. "
"Progam program will end")
else:
# Create CWD using Grass
cc_grass_cwd.grass_cwd(grass_cores)
# Run Linkage Mapper
lm_util.gprint("\nRUNNING LINKAGE MAPPER "
"TO CREATE CLIMATE CORRIDORS")
lm_master.lm_master()
def main():
"""Set path and run model."""
demo_path = (os.path.abspath(
os.path.join(os.path.dirname(os.path.abspath(__file__)), '..')))
sys.path.append(os.path.join(demo_path, '..\\toolbox\\scripts'))
import lm_master
lm_master.lm_master(in_params(demo_path))
def main():
"""Runs Linkage Mapper with coded inputs"""
proj_dir = "V:\\demoProject"
core_fc = "V:\\demoData\\Cores.shp"
core_fl = "core_ID"
resis_rast = "V:\\demoData\\resistances"
# distance_file = "V:\\demoData\\distances_Cores.txt"
arg = (_SCRIPT_NAME, proj_dir, core_fc, core_fl, resis_rast, "true",
"true", "Cost-Weighted & Euclidean", "#", "true", "true", "false",
"4", "Cost-Weighted", "false", "true", "100000", "100000", "100000")
sys.argv = arg
sys.path.append('../toolbox/scripts')
import lm_master
lm_master.lm_master()
def main():
"""Runs Linkage Mapper with coded inputs"""
proj_dir = "V:\\demoProject"
core_fc = "V:\\demoData\\Cores.shp"
core_fl = "core_ID"
resis_rast = "V:\\demoData\\resistances"
# distance_file = "V:\\demoData\\distances_Cores.txt"
arg = (_SCRIPT_NAME, proj_dir, core_fc, core_fl, resis_rast, "true", "true",
"Cost-Weighted & Euclidean", "#",
"true", "true", "false", "4", "Cost-Weighted", "false", "true",
"100000", "100000", "100000")
sys.argv = arg
sys.path.append('../toolbox/scripts')
import lm_master
lm_master.lm_master()
def main(argv=None):
"""Iterate over LM, BM, and restoration tasks."""
if argv is None:
argv = sys.argv # Get parameters from ArcGIS tool dialog
start_time = time.clock()
# USER SETTINGS ######################################################
# Restoration Settings
# ALL input data must be in the same projection
# Set to True to restore highest ROI. Set to False to restore strongest
# barrier
restore_max_roi = argv[1]
# Resistance value of restored habitat. Must be 1 or greater.
restored_resistance_val = argv[2]
# No spaces or special chars in paths or gdb names
restoration_data_gdb = argv[3]
# No spaces in path, avoid using dropbox or network drive
# Project directories will be created in this (iter1, iter2...) as will an
# output geodatabase
output_dir = argv[4]
# Resistance raster. Should be in input GDB
resistance_ras = argv[5]
# Core area feature class. Should be in input GDB 'URWA_HCAs_Doug_Grant'
core_fc = argv[6]
core_fn = argv[7] # Core area field name
radius = argv[8] # Restoration radius in meters
iterations = argv[9] # Number of restorations to perform
# If less than this proportion of ag in circle, don't consider restoring
# circle
min_ag_threshold = argv[10]
# Don't consider barriers below this improvement score (average improvement
# per meter diameter restored)
min_improvement_val = argv[11]
# Average per-m2 parcel cost per pixel. Snapped to resistance raster.
parcel_cost_ras = argv[12]
# Right now this is just a raster with all pixels set to 0.113174
restoration_cost_ras = argv[13]
ag_ras = argv[14] # 1=Ag, 0=Not Ag
# Some restorations benefit multiple corridors.
# 'Maximum' takes the greatest improvement across core area pairs
# 'Sum' adds improvement scores acreoss all pairs.
barrier_combine_method = argv[15]
# Use cwd_thresh = None for no threshold. Use cwd_thresh = X to not
# consider restorations more than X map units away from each core area.
cwd_thresh = argv[16]
# END USER SETTINGS ######################################################
try:
# Setup path and create directories
gprint('Hey! Make sure everything is in the same projection!\n')
gprint('Setting up paths and creating directories')
sys.path.append('..\\toolbox\\scripts')
res_ras = os.path.join(restoration_data_gdb, resistance_ras)
core_fc_path = os.path.join(restoration_data_gdb, core_fc)
# Set up a NEW output gdb (leave previous ones on drive)
i = None
for i in range(1, 200):
output_gdb = 'restorationOutput' + str(i) + '.gdb'
if not arcpy.Exists(os.path.join(output_dir, output_gdb)):
break
gprint('Previous output GDB ' + output_gdb + ' exists. '
'Delete to save disk space.')
arcpy.CreateFileGDB_management(output_dir, output_gdb)
output_gdb = os.path.join(output_dir, output_gdb)
log_file = os.path.join(output_gdb,
'Iterate Barriers' + str(i) + '.py')
# Write a copy of this file to output dir as a record of settings
shutil.copyfile(__file__, log_file)
arcpy.env.cellSize = res_ras
arcpy.env.extent = res_ras
arcpy.env.snapRaster = res_ras
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = output_gdb
arcpy.env.workspace = output_gdb
spatialref = arcpy.Describe(res_ras).spatialReference
mapunits = spatialref.linearUnitName
gprint('Cell size = ' + str(arcpy.env.cellSize) + ' ' + mapunits + 's')
# Calculate fraction of ag within radius of each pixel
gprint('Calculating purchase cost, fraction of ag, etc within radius '
'of each pixel.')
ag_ras = os.path.join(restoration_data_gdb, ag_ras)
in_neighborhood = arcpy.sa.NbrCircle(radius, "MAP")
arcpy.env.extent = ag_ras
out_focal_stats = arcpy.sa.FocalStatistics(ag_ras, in_neighborhood,
"MEAN", "NODATA")
proportion_ag_ras = os.path.join(output_gdb, 'proportionAgRas')
out_focal_stats.save(proportion_ag_ras)
arcpy.env.extent = res_ras
# Calculate purchase cost of circles
parcel_cost_ras = os.path.join(restoration_data_gdb, parcel_cost_ras)
arcpy.env.extent = parcel_cost_ras
out_focal_stats = arcpy.sa.FocalStatistics(parcel_cost_ras,
in_neighborhood, "MEAN",
"DATA")
cost_focal_stats_ras = os.path.join(output_gdb, 'cost_focal_stats_ras')
out_focal_stats.save(cost_focal_stats_ras)
arcpy.env.extent = res_ras
circle_area = float(npy.pi * radius * radius)
outras = arcpy.sa.Raster(cost_focal_stats_ras) * circle_area
purch_cost_ras = os.path.join(output_gdb, 'purchaseCostRaster')
outras.save(purch_cost_ras)
lu.delete_data(cost_focal_stats_ras)
restoration_cost_ras = os.path.join(restoration_data_gdb,
restoration_cost_ras)
outras = (
arcpy.sa.Raster(purch_cost_ras) +
(arcpy.sa.Raster(restoration_cost_ras) * radius * radius * npy.pi))
total_cost_ras = os.path.join(output_gdb, 'totalCostRaster')
outras.save(total_cost_ras)
# Create mask to remove areas without cost data
arcpy.env.extent = total_cost_ras
cost_mask_ras = os.path.join(output_gdb, 'costMaskRaster')
cost_thresh = 0
out_con = arcpy.sa.Con(
(arcpy.sa.Raster(total_cost_ras) > float(cost_thresh)), 1)
out_con.save(cost_mask_ras)
arcpy.env.extent = res_ras
# Create mask to remove areas below ag threshold
out_con = arcpy.sa.Con(
(arcpy.sa.Raster(proportion_ag_ras) > float(min_ag_threshold)), 1)
ag_mask_ras = os.path.join(output_gdb, 'agMaskRaster')
out_con.save(ag_mask_ras)
do_step_1 = 'true'
do_step_2 = 'true'
do_step_5 = 'false'
all_restored_areas_ras = ''
for cur_iter in range(1, iterations + 1):
start_time1 = time.clock()
# Some env settings get changed by linkage mapper and must be
# reset here
arcpy.env.cellSize = res_ras
arcpy.env.extent = res_ras
arcpy.env.snapRaster = res_ras
arcpy.env.scratchWorkspace = output_gdb
arcpy.env.workspace = output_gdb
lu.dashline(1)
gprint('Running iteration number ' + str(cur_iter))
proj_dir = os.path.join(output_dir,
'iter' + str(cur_iter) + 'Proj')
lu.create_dir(output_dir)
lu.delete_dir(proj_dir)
lu.create_dir(proj_dir)
if cur_iter > 1: # Copy previous s2 linktable to new project dir
datapass_dir = os.path.join(proj_dir, 'datapass')
lu.create_dir(datapass_dir)
proj_dir1 = os.path.join(output_dir, 'iter1Proj')
datapass_dir_iter1 = os.path.join(proj_dir1, 'datapass')
s2_link_tbl_iter1 = os.path.join(datapass_dir_iter1,
'linkTable_s2.csv')
s2_link_tbl = os.path.join(datapass_dir, 'linkTable_s2.csv')
shutil.copyfile(s2_link_tbl_iter1, s2_link_tbl)
# Run Linkage Mapper
# Copy distances text file from earlier LM run to the output
# directory- speeds things up!
dist_file = os.path.join(output_dir, core_fc + '_dists.txt')
if not os.path.exists(dist_file):
if cur_iter == 1:
gprint('Will calculate distance file.')
dist_file = '#'
else:
proj_dir1 = os.path.join(output_dir, 'iter1Proj')
dist_file1 = os.path.join(proj_dir1,
core_fc + '_dists.txt')
# Put a copy here for future runs
shutil.copyfile(dist_file1, dist_file)
arcpy.env.scratchWorkspace = output_gdb
arcpy.env.workspace = output_gdb
argv = ('lm_master.py', proj_dir, core_fc_path, core_fn, res_ras,
do_step_1, do_step_2, 'Cost-Weighted & Euclidean',
dist_file, 'true', 'true', 'false', '4', 'Cost-Weighted',
'true', do_step_5, 'true', '200000', '10000', '#', '#',
'#', '#')
gprint('Running ' + str(argv))
lm_master.lm_master(argv)
do_step_1 = 'false' # Can skip for future iterations
do_step_2 = 'false' # Can skip for future iterations
do_step_5 = 'false' # Skipping for future iterations
start_radius = str(radius)
end_radius = str(radius)
radius_step = '0'
save_radius_ras = 'false'
write_pct_ras = 'false'
argv = ('barrier_master.py', proj_dir, res_ras, start_radius,
end_radius, radius_step, barrier_combine_method,
save_radius_ras, write_pct_ras, cwd_thresh)
gprint('Running ' + str(argv))
barrier_master.bar_master(argv)
# Some env settings get changed by linkage mapper and must be
# reset here
arcpy.env.cellSize = res_ras
arcpy.env.extent = res_ras
arcpy.env.snapRaster = res_ras
arcpy.env.scratchWorkspace = output_gdb
arcpy.env.workspace = output_gdb
gprint('Finding restoration circles with max barrier score / ROI')
# Find points with max ROI
prefix = os.path.basename(proj_dir)
if barrier_combine_method == 'Sum':
sum_suffix = 'Sum'
else:
sum_suffix = ''
barrier_fn = (prefix + "_BarrierCenters" + sum_suffix + "_Rad" +
str(radius))
barrier_ras = os.path.join(proj_dir, 'output', 'barriers.gdb',
barrier_fn)
if not arcpy.Exists(barrier_ras):
msg = ('Error: cannot find barrier output: ' + barrier_ras)
lu.raise_error(msg)
if cur_iter > 1:
gprint('Creating mask for previously restored areas')
in_neighborhood = arcpy.sa.NbrCircle(radius, "MAP")
arcpy.env.extent = all_restored_areas_ras
out_focal_stats = arcpy.sa.FocalStatistics(
all_restored_areas_ras, in_neighborhood, "MEAN", "DATA")
all_restored_focal_ras = os.path.join(
output_gdb, 'allRestFocRas_iter' + str(cur_iter))
# Anything > 0 would include a restored area
out_focal_stats.save(all_restored_focal_ras)
arcpy.env.extent = res_ras
rest_mask_ras = os.path.join(
output_gdb, 'restMaskRaster_iter' + str(cur_iter))
minval = 0
out_con = arcpy.sa.Con(
(arcpy.sa.Raster(all_restored_focal_ras) == float(minval)),
1)
out_con.save(rest_mask_ras)
# Candidate areas have not been restored, have cost data, meet
# minimum improvement score criteria, and have enough ag in them
candidate_barrier_ras = os.path.join(
output_gdb, 'candidateBarrierRaster' + '_iter' + str(cur_iter))
if cur_iter > 1:
gprint('Creating candidate restoration raster using barrier '
'results, previous restorations, and selection '
'criteria')
# ROI scores will be in terms of total improvement
# (= score * diameter)
out_calc = (arcpy.sa.Raster(cost_mask_ras) *
arcpy.sa.Raster(ag_mask_ras) *
arcpy.sa.Raster(barrier_ras) *
arcpy.sa.Raster(rest_mask_ras) * (radius * 2))
else:
out_calc = (arcpy.sa.Raster(cost_mask_ras) *
arcpy.sa.Raster(ag_mask_ras) *
arcpy.sa.Raster(barrier_ras) * radius * 2)
min_barrier_score = min_improvement_val * radius * 2
if restored_resistance_val != 1:
out_calc_2 = (out_calc - (2 * radius *
(restored_resistance_val - 1)))
out_con = arcpy.sa.Con(
(out_calc_2 >= float(min_barrier_score)), out_calc_2)
else:
out_con = arcpy.sa.Con((out_calc >= float(min_barrier_score)),
out_calc)
out_con.save(candidate_barrier_ras)
lu.build_stats(candidate_barrier_ras)
purchase_roi_ras = os.path.join(
output_gdb, 'purchaseRoiRaster' + '_iter' + str(cur_iter))
out_calc = (arcpy.sa.Raster(candidate_barrier_ras) /
arcpy.sa.Raster(purch_cost_ras))
out_calc.save(purchase_roi_ras)
lu.build_stats(purchase_roi_ras)
total_roi_ras = os.path.join(
output_gdb, 'purchaseRestRoiRaster' + '_iter' + str(cur_iter))
out_calc = (arcpy.sa.Raster(candidate_barrier_ras) /
arcpy.sa.Raster(total_cost_ras))
out_calc.save(total_roi_ras)
lu.build_stats(total_roi_ras)
max_barrier = float(
arcpy.GetRasterProperties_management(candidate_barrier_ras,
"MAXIMUM").getOutput(0))
gprint('Maximum barrier improvement score: ' + str(max_barrier))
if max_barrier < 0:
arcpy.AddWarning("\nNo barriers found that meet CWD or Ag "
"threshold criteria.")
max_purch_roi = arcpy.GetRasterProperties_management(
purchase_roi_ras, "MAXIMUM")
gprint('Maximum purchase ROI score: ' +
str(max_purch_roi.getOutput(0)))
max_roi = arcpy.GetRasterProperties_management(
total_roi_ras, "MAXIMUM")
gprint('Maximum total ROI score: ' + str(max_roi.getOutput(0)))
if restore_max_roi:
out_point = os.path.join(
output_gdb, 'maxRoiPoint' + '_iter' + str(cur_iter))
gprint('Choosing circle with maximum ROI to restore')
out_con = arcpy.sa.Con(
(arcpy.sa.Raster(total_roi_ras) >= float(
max_roi.getOutput(0))), total_roi_ras)
max_roi_ras = os.path.join(output_gdb, 'max_roi_ras')
out_con.save(max_roi_ras)
# Save max ROI to point
try:
arcpy.RasterToPoint_conversion(max_roi_ras, out_point)
except Exception:
msg = ('Error: it looks like there are no viable '
'restoration candidates.')
lu.raise_error(msg)
else: # Restoring strongest barrier instead
out_point = os.path.join(
output_gdb, 'maxBarrierPoint' + '_iter' + str(cur_iter))
gprint('Choosing circle with maximum BARRIER IMPROVEMENT SCORE'
' to restore')
out_con = arcpy.sa.Con(
(arcpy.sa.Raster(candidate_barrier_ras) >= max_barrier),
candidate_barrier_ras)
max_barrier_ras = os.path.join(output_gdb, 'maxBarrierRaster')
out_con.save(max_barrier_ras)
# Save max barrier to point
try:
arcpy.RasterToPoint_conversion(max_barrier_ras, out_point)
except Exception:
msg = ('Error: it looks like there are no viable '
'restoration candidates.')
lu.raise_error(msg)
gprint('Done evaluating candidate restorations')
result = int(arcpy.GetCount_management(out_point).getOutput(0))
if result > 1:
# Would be better to retain point with max barrier score when
# we have multiple points with same ROI
arcpy.AddWarning('Deleting points with identical '
'ROI/improvement score values')
arcpy.DeleteIdentical_management(out_point, "grid_code", 0.1,
0.1)
arcpy.sa.ExtractMultiValuesToPoints(
out_point,
[[candidate_barrier_ras, "barrierScore"],
[purch_cost_ras, "purchCost"], [total_cost_ras, "totalCost"],
[purchase_roi_ras, "purchaseROI"],
[total_roi_ras, "totalROI"]], "NONE")
arcpy.AddField_management(out_point, "restorationNumber", "SHORT")
arcpy.CalculateField_management(out_point, "restorationNumber",
cur_iter, "PYTHON_9.3")
arcpy.AddField_management(out_point, "radius", "DOUBLE")
arcpy.CalculateField_management(out_point, "radius", radius,
"PYTHON_9.3")
arcpy.AddField_management(out_point, "barrierScore_per_m",
"DOUBLE")
arcpy.CalculateField_management(
out_point, "barrierScore_per_m",
"(float(!barrierScore!) / (!radius! * 2))", "PYTHON_9.3")
gprint('\nCreating restoration circles')
if restore_max_roi:
circle_fc = os.path.join(
output_gdb, 'maxRoiCircle' + '_iter' + str(cur_iter))
else:
circle_fc = os.path.join(
output_gdb, 'maxBarrierCircle' + '_iter' + str(cur_iter))
arcpy.Buffer_analysis(out_point, circle_fc, radius)
gprint('Rasterizing restoration circles')
if restore_max_roi:
circle_ras = os.path.join(
output_gdb, 'maxRoicircle_ras' + '_iter' + str(cur_iter))
else:
circle_ras = os.path.join(
output_gdb,
'maxBarrierCircleRas' + '_iter' + str(cur_iter))
arcpy.FeatureToRaster_conversion(circle_fc, 'totalROI', circle_ras,
arcpy.env.cellSize)
# restore raster
gprint('Digitally restoring resistance raster')
res_ras_restored = os.path.join(
output_gdb, 'resRastRestored' + '_iter' + str(cur_iter))
out_con = arcpy.sa.Con(arcpy.sa.IsNull(circle_ras), res_ras,
restored_resistance_val)
out_con.save(res_ras_restored)
all_restored_areas_ras = os.path.join(
output_gdb, 'allRestoredAreas_iter' + str(cur_iter))
prev_restored_areas_ras = os.path.join(
output_gdb, 'allRestoredAreas_iter' + str(cur_iter - 1))
if cur_iter == 1:
out_con = arcpy.sa.Con(arcpy.sa.IsNull(circle_ras), 0, 1)
else:
# Add this restoration to areas restored
out_con = arcpy.sa.Con(arcpy.sa.IsNull(circle_ras),
prev_restored_areas_ras, 1)
out_con.save(all_restored_areas_ras)
lu.delete_data(circle_ras)
# Use for next iteration resistance raster
res_ras = res_ras_restored
# Add circle into feature class with all circles
if restore_max_roi:
all_circles_fc = os.path.join(output_gdb, "allCirclesMaxROI")
else:
all_circles_fc = os.path.join(output_gdb,
"allCirclesMaxBarriers")
if cur_iter == 1:
arcpy.CopyFeatures_management(circle_fc, all_circles_fc)
else:
arcpy.Append_management(circle_fc, all_circles_fc, "TEST")
gprint('Finished iteration #' + str(cur_iter))
start_time1 = lu.elapsed_time(start_time1)
gprint('\nDone with iterations.')
start_time = lu.elapsed_time(start_time)
gprint('Outputs saved in: ' + output_gdb)
gprint('Back up your project directories if you want to save '
'corridor/barrier results.')
# Return GEOPROCESSING specific errors
except arcpy.ExecuteError:
lu.dashline(1)
gprint('****Iteration script failed. Details follow.****')
lu.exit_with_geoproc_error(_SCRIPT_NAME)
# Return any PYTHON or system specific errors
except Exception:
lu.dashline(1)
gprint('****Iteration script failed. Details follow.****')
lu.exit_with_python_error(_SCRIPT_NAME)
def main():
"""Iterates over LM, BM, and restoration tasks"""
## USER SETTINGS ######################################################
## Restoration Settings
## ALL input data must be in the same projection
start_time = time.clock()
restoreMaxROI = False # Set to True to restore highest ROI
# Set to False to restore strongest barrier
restoredResistanceVal = 1 # Resistance value of restored habitat. Must be 1 or greater.
restorationDataGDB = (
"C:\\barrierClassAnalysis\\RestorationINPUTS_July2013.gdb"
) # No spaces or special chars in paths or gdb names
outputDir = "C:\\barrierClassAnalysis\\output" # No spaces in path, avoid using dropbox or network drive
# Project directories will be created in this (iter1, iter2...)
# as will an output geodatabase
resistanceRaster = "URWA_resis" # Resistance raster. Should be in input GDB
coreFC = "URWA_HCAs_Doug_Grant" # Core area feature class. Should be in input GDB 'URWA_HCAs_Doug_Grant'
coreFN = "HCA_ID" # Core area field name
radius = 450 # restoration radius in meters
iterations = 13 # number of restorations to perform
minAgThreshold = 0.75 # if less than this proportion of ag in circle, don't consider restoring circle
minImprovementVal = (
0
) # Don't consider barriers below this improvement score (average improvement per meter diameter restored)
parcelCostRaster = (
"DougGrantParcelCost_m2_projected_90m"
) # Average per-m2 parcel cost per pixel. Snapped to resistance raster.
restorationCostRaster = "restCostPer_m2" # Right now this is just a raster with all pixels set to 0.113174
agRaster = "ARESmaskp_projected" # 1=Ag, 0 = not Ag
barrierCombineMethod = "Maximum" # Some restorations benefit multiple corridors.
# 'Maximum' takes the greatest improvement across core area pairs
# 'Sum' adds improvement scores acreoss all pairs.
cwdThresh = None # Use cwdThresh = None for no threshold. Use cwdThresh = X to not consider
# restorations more than X map units away from each core area.
## END USER SETTINGS ######################################################
try:
# Setup path and create directories
gprint("Hey! Make sure everything is in the same projection!\n")
gprint("Setting up paths and creating directories")
sys.path.append("..\\toolbox\\scripts")
resRast = os.path.join(restorationDataGDB, resistanceRaster)
coreFCPath = os.path.join(restorationDataGDB, coreFC)
# Set up a NEW output gdb (leave previous ones on drive)
for i in range(1, 200):
outputGDB = "restorationOutput" + str(i) + ".gdb"
if not arcpy.Exists(os.path.join(outputDir, outputGDB)):
break
gprint("Previous output GDB " + outputGDB + " exists. Delete to save disk space.")
arcpy.CreateFileGDB_management(outputDir, outputGDB)
outputGDB = os.path.join(outputDir, outputGDB)
logFile = os.path.join(outputGDB, "Iterate Barriers" + str(i) + ".py")
shutil.copyfile(__file__, logFile) # write a copy of this file to output dir as a record of settings
arcpy.env.cellSize = resRast
arcpy.env.extent = resRast
arcpy.env.snapRaster = resRast
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = outputGDB
arcpy.env.workspace = outputGDB
spatialref = arcpy.Describe(resRast).spatialReference
mapunits = spatialref.linearUnitName
gprint("Cell size = " + str(arcpy.env.cellSize) + " " + mapunits + "s")
# Calculate fraction of ag within radius of each pixel
gprint("Calculating purchase cost, fraction of ag, etc within radius of each pixel.")
agRaster = os.path.join(restorationDataGDB, agRaster)
inNeighborhood = NbrCircle(radius, "MAP")
arcpy.env.extent = agRaster
outFocalStats = arcpy.sa.FocalStatistics(agRaster, inNeighborhood, "MEAN", "NODATA")
proportionAgRaster = os.path.join(outputGDB, "proportionAgRas")
outFocalStats.save(proportionAgRaster)
arcpy.env.extent = resRast
# Calculate purchase cost of circles
parcelCostRaster = os.path.join(restorationDataGDB, parcelCostRaster)
arcpy.env.extent = parcelCostRaster
outFocalStats = arcpy.sa.FocalStatistics(parcelCostRaster, inNeighborhood, "MEAN", "DATA")
costFocalStatsRaster = os.path.join(outputGDB, "costFocalStatsRaster")
outFocalStats.save(costFocalStatsRaster)
arcpy.env.extent = resRast
circleArea = float(npy.pi * radius * radius)
outras = Raster(costFocalStatsRaster) * circleArea
purchCostRaster = os.path.join(outputGDB, "purchaseCostRaster")
outras.save(purchCostRaster)
lu.delete_data(costFocalStatsRaster)
# restCost = npy.pi * radius * radius * restCostPer_m2
restorationCostRaster = os.path.join(restorationDataGDB, restorationCostRaster)
outras = Raster(purchCostRaster) + (Raster(restorationCostRaster) * radius * radius * npy.pi)
totalCostRaster = os.path.join(outputGDB, "totalCostRaster")
outras.save(totalCostRaster)
# lu.build_stats(totalCostRaster)
# Create mask to remove areas without cost data
arcpy.env.extent = totalCostRaster
costMaskRaster = os.path.join(outputGDB, "costMaskRaster")
costThresh = 0
outCon = arcpy.sa.Con((Raster(totalCostRaster) > float(costThresh)), 1)
outCon.save(costMaskRaster)
arcpy.env.extent = resRast
# Create mask to remove areas below ag threshold
outCon = arcpy.sa.Con((Raster(proportionAgRaster) > float(minAgThreshold)), 1)
agMaskRaster = os.path.join(outputGDB, "agMaskRaster")
outCon.save(agMaskRaster)
doStep1 = "true"
doStep2 = "true"
doStep5 = "false"
for iter in range(1, iterations + 1): # xxx
start_time1 = time.clock()
arcpy.env.cellSize = resRast # Some env settings get changed by linkage mapper and must be reset here
arcpy.env.extent = resRast
arcpy.env.snapRaster = resRast
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = outputGDB
arcpy.env.workspace = outputGDB
lu.dashline(1)
gprint("Running iteration number " + str(iter))
projDir = os.path.join(outputDir, "iter" + str(iter) + "Proj")
lu.create_dir(outputDir)
lu.delete_dir(projDir) # xxx
lu.create_dir(projDir)
if iter > 1: # Copy previous s2 linktable to new project directory
datapassDir = os.path.join(projDir, "datapass")
lu.create_dir(datapassDir)
projDir1 = os.path.join(outputDir, "iter1Proj")
datapassDirIter1 = os.path.join(projDir1, "datapass")
s2LinktableIter1 = os.path.join(datapassDirIter1, "linkTable_s2.csv")
s2LinkTable = os.path.join(datapassDir, "linkTable_s2.csv")
shutil.copyfile(s2LinktableIter1, s2LinkTable)
# Run Linkage Mapper
distFile = os.path.join(
outputDir, coreFC + "_dists.txt"
) # Copy distances text file from earlier LM run to the output directory- speeds things up!
if not os.path.exists(distFile):
if iter == 1:
gprint("Will calculate distance file.")
distFile = "#"
else:
projDir1 = os.path.join(outputDir, "iter1Proj")
distFile1 = os.path.join(projDir1, coreFC + "_dists.txt")
shutil.copyfile(distFile1, distFile) # Put a copy here for future runs
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = outputGDB
arcpy.env.workspace = outputGDB
argv = (
"lm_master.py",
projDir,
coreFCPath,
coreFN,
resRast,
doStep1,
doStep2,
"Cost-Weighted & Euclidean",
distFile,
"true",
"true",
"false",
"4",
"Cost-Weighted",
"true",
doStep5,
"10000",
"#",
"#",
)
gprint("Running " + str(argv))
import lm_master # xxx
lm_master.lm_master(argv) # xxx
doStep1 = "false" # Can skip for future iterations
doStep2 = "false" # Can skip for future iterations
doStep5 = "false" # Skipping for future iterations
startRadius = str(radius)
endRadius = str(radius)
radiusStep = "0"
saveRadiusRasters = "false"
writePctRasters = "false"
argv = (
"barrier_master.py",
projDir,
resRast,
startRadius,
endRadius,
radiusStep,
barrierCombineMethod,
saveRadiusRasters,
writePctRasters,
cwdThresh,
)
gprint("Running " + str(argv))
import barrier_master # xxx
barrier_master.bar_master(argv) # xxx
arcpy.env.cellSize = resRast # Some env settings get changed by linkage mapper and must be reset here
arcpy.env.extent = resRast
arcpy.env.snapRaster = resRast
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = outputGDB
arcpy.env.workspace = outputGDB
gprint("Finding restoration circles with max barrier score / ROI")
# Find points with max ROI
PREFIX = os.path.basename(projDir)
if barrierCombineMethod == "Sum":
sumSuffix = "Sum"
else:
sumSuffix = ""
barrierFN = PREFIX + "_BarrierCenters" + sumSuffix + "_Rad" + str(radius)
barrierRaster = os.path.join(projDir, "output", "barriers.gdb", barrierFN)
if not arcpy.Exists(barrierRaster):
msg = "Error: cannot find barrier output: " + barrierRaster
lu.raise_error(msg)
# arcpy.env.cellSize = agMaskRaster
# arcpy.env.extent = agMaskRaster
if iter > 1:
gprint("Creating mask for previously restored areas")
inNeighborhood = NbrCircle(radius, "MAP")
arcpy.env.extent = allRestoredAreasRaster
outFocalStats = arcpy.sa.FocalStatistics(allRestoredAreasRaster, inNeighborhood, "MEAN", "DATA")
allRestoredFocalRaster = os.path.join(outputGDB, "allRestFocRas_iter" + str(iter))
outFocalStats.save(allRestoredFocalRaster) # Anything > 0 would include a restored area and
arcpy.env.extent = resRast
restMaskRaster = os.path.join(outputGDB, "restMaskRaster_iter" + str(iter))
minval = 0
outCon = arcpy.sa.Con((Raster(allRestoredFocalRaster) == float(minval)), 1)
outCon.save(restMaskRaster)
# Candidate areas have not been restored, have cost data, meet
# minimum improvement score criteria, and have enough ag in them
candidateBarrierRaster = os.path.join(outputGDB, "candidateBarrierRaster" + "_iter" + str(iter))
if iter > 1:
gprint(
"Creating candidate restoration raster using barrier results, previous restorations, and selection criteria"
)
outCalc = (
Raster(costMaskRaster)
* Raster(agMaskRaster)
* Raster(barrierRaster)
* Raster(restMaskRaster)
* (radius * 2)
) # ROI scores will be in terms of total improvement (= score * diameter)
else:
outCalc = Raster(costMaskRaster) * Raster(agMaskRaster) * Raster(barrierRaster) * radius * 2
minBarrierScore = minImprovementVal * radius * 2
if restoredResistanceVal != 1:
outCalc2 = outCalc - (2 * radius * (restoredResistanceVal - 1))
outCon = arcpy.sa.Con((outCalc2 >= float(minBarrierScore)), outCalc2)
else:
outCon = arcpy.sa.Con((outCalc >= float(minBarrierScore)), outCalc)
outCon.save(candidateBarrierRaster)
lu.build_stats(candidateBarrierRaster)
purchaseRoiRaster = os.path.join(outputGDB, "purchaseRoiRaster" + "_iter" + str(iter))
outCalc = Raster(candidateBarrierRaster) / Raster(purchCostRaster)
outCalc.save(purchaseRoiRaster)
lu.build_stats(purchaseRoiRaster)
totalRoiRaster = os.path.join(outputGDB, "purchaseRestRoiRaster" + "_iter" + str(iter))
outCalc = Raster(candidateBarrierRaster) / Raster(totalCostRaster)
outCalc.save(totalRoiRaster)
lu.build_stats(totalRoiRaster)
maxBarrier = arcpy.GetRasterProperties_management(candidateBarrierRaster, "MAXIMUM")
gprint("Maximum barrier improvement score: " + str(maxBarrier.getOutput(0)))
if maxBarrier < 0:
arcpy.AddWarning("\nNo barriers found that meet CWD or Ag threshold criteria.")
maxPurchROI = arcpy.GetRasterProperties_management(purchaseRoiRaster, "MAXIMUM")
gprint("Maximum purchase ROI score: " + str(maxPurchROI.getOutput(0)))
maxROI = arcpy.GetRasterProperties_management(totalRoiRaster, "MAXIMUM")
gprint("Maximum total ROI score: " + str(maxROI.getOutput(0)))
if restoreMaxROI:
outPoint = os.path.join(outputGDB, "maxRoiPoint" + "_iter" + str(iter))
gprint("Choosing circle with maximum ROI to restore")
outCon = arcpy.sa.Con((Raster(totalRoiRaster) >= float(maxROI.getOutput(0))), totalRoiRaster)
maxRoiRaster = os.path.join(outputGDB, "maxRoiRaster")
outCon.save(maxRoiRaster)
# Save max ROI to point
try:
arcpy.RasterToPoint_conversion(maxRoiRaster, outPoint)
except:
msg = "Error: it looks like there are no viable restoration candidates."
lu.raise_error(msg)
else: # Restoring strongest barrier instead
outPoint = os.path.join(outputGDB, "maxBarrierPoint" + "_iter" + str(iter))
gprint("Choosing circle with maximum BARRIER IMPROVEMENT SCORE to restore")
outCon = arcpy.sa.Con(
(Raster(candidateBarrierRaster) >= float(maxBarrier.getOutput(0))), candidateBarrierRaster
)
maxBarrierRaster = os.path.join(outputGDB, "maxBarrierRaster")
outCon.save(maxBarrierRaster)
# Save max barrier to point
try:
arcpy.RasterToPoint_conversion(maxBarrierRaster, outPoint)
except:
msg = "Error: it looks like there are no viable restoration candidates."
lu.raise_error(msg)
gprint("Done evaluating candidate restorations")
result = int(arcpy.GetCount_management(outPoint).getOutput(0))
if result > 1:
arcpy.AddWarning(
"Deleting points with identical ROI/improvement score values"
) # Would be better to retain point with max barrier score when we have multiple points with same ROI
arcpy.DeleteIdentical_management(outPoint, "grid_code", 0.1, 0.1)
arcpy.sa.ExtractMultiValuesToPoints(
outPoint,
[
[candidateBarrierRaster, "barrierScore"],
[purchCostRaster, "purchCost"],
[totalCostRaster, "totalCost"],
[purchaseRoiRaster, "purchaseROI"],
[totalRoiRaster, "totalROI"],
],
"NONE",
)
arcpy.AddField_management(outPoint, "restorationNumber", "SHORT")
arcpy.CalculateField_management(outPoint, "restorationNumber", iter)
arcpy.AddField_management(outPoint, "radius", "DOUBLE")
arcpy.CalculateField_management(outPoint, "radius", radius)
arcpy.AddField_management(outPoint, "barrierScore_per_m", "DOUBLE")
arcpy.CalculateField_management(
outPoint, "barrierScore_per_m", "(float(!barrierScore!) / (!radius! * 2))", "PYTHON"
)
gprint("\nCreating restoration circles")
if restoreMaxROI:
circleFC = os.path.join(outputGDB, "maxRoiCircle" + "_iter" + str(iter))
else:
circleFC = os.path.join(outputGDB, "maxBarrierCircle" + "_iter" + str(iter))
arcpy.Buffer_analysis(outPoint, circleFC, radius)
gprint("Rasterizing restoration circles")
if restoreMaxROI:
circleRas = os.path.join(outputGDB, "maxRoiCircleRas" + "_iter" + str(iter))
else:
circleRas = os.path.join(outputGDB, "maxBarrierCircleRas" + "_iter" + str(iter))
arcpy.FeatureToRaster_conversion(circleFC, "totalROI", circleRas, arcpy.env.cellSize)
# restore raster
gprint("Digitally restoring resistance raster")
resRastRestored = os.path.join(outputGDB, "resRastRestored" + "_iter" + str(iter))
outCon = arcpy.sa.Con(IsNull(circleRas), resRast, restoredResistanceVal)
outCon.save(resRastRestored)
allRestoredAreasRaster = os.path.join(outputGDB, "allRestoredAreas_iter" + str(iter))
PrevRestoredAreasRaster = os.path.join(outputGDB, "allRestoredAreas_iter" + str(iter - 1))
if iter == 1:
outCon = arcpy.sa.Con(IsNull(circleRas), 0, 1)
else:
outCon = arcpy.sa.Con(
IsNull(circleRas), PrevRestoredAreasRaster, 1
) # Add this restoration to areas restored
outCon.save(allRestoredAreasRaster)
lu.delete_data(circleRas)
resRast = resRastRestored # Use for next iteration resistance raster
# Add circle into feature class with all circles
if restoreMaxROI:
allCirclesFC = os.path.join(outputGDB, "allCirclesMaxROI")
else:
allCirclesFC = os.path.join(outputGDB, "allCirclesMaxBarriers")
if iter == 1:
arcpy.CopyFeatures_management(circleFC, allCirclesFC)
else:
arcpy.Append_management(circleFC, allCirclesFC, "TEST")
gprint("Finished iteration #" + str(iter))
start_time1 = lu.elapsed_time(start_time1)
gprint("\nDone with iterations.")
start_time = lu.elapsed_time(start_time)
gprint("Outputs saved in: " + outputGDB)
gprint("Back up your project directories if you want to save corridor/barrier results.")
# Return GEOPROCESSING specific errors
except arcpy.ExecuteError:
lu.dashline(1)
gprint("****Iteration script failed. Details follow.****")
lu.exit_with_geoproc_error(_SCRIPT_NAME)
# Return any PYTHON or system specific errors
except:
lu.dashline(1)
gprint("****Iteration script failed. Details follow.****")
lu.exit_with_python_error(_SCRIPT_NAME)
def main():
"""Iterates over LM, BM, and restoration tasks"""
## USER SETTINGS ######################################################
## Restoration Settings
## ALL input data must be in the same projection
start_time = time.clock()
restoreMaxROI = False # Set to True to restore highest ROI
# Set to False to restore strongest barrier
restoredResistanceVal = 1 # Resistance value of restored habitat. Must be 1 or greater.
restorationDataGDB = "C:\\barrierClassAnalysis\\RestorationINPUTS_July2013.gdb" # No spaces or special chars in paths or gdb names
outputDir = "C:\\barrierClassAnalysis\\output" # No spaces in path, avoid using dropbox or network drive
# Project directories will be created in this (iter1, iter2...)
# as will an output geodatabase
resistanceRaster = "URWA_resis"# Resistance raster. Should be in input GDB
coreFC = 'URWA_HCAs_Doug_Grant'# Core area feature class. Should be in input GDB 'URWA_HCAs_Doug_Grant'
coreFN = 'HCA_ID' # Core area field name
radius = 450 # restoration radius in meters
iterations = 13 # number of restorations to perform
minAgThreshold = 0.75 # if less than this proportion of ag in circle, don't consider restoring circle
minImprovementVal = 0 # Don't consider barriers below this improvement score (average improvement per meter diameter restored)
parcelCostRaster = 'DougGrantParcelCost_m2_projected_90m' # Average per-m2 parcel cost per pixel. Snapped to resistance raster.
restorationCostRaster = 'restCostPer_m2' # Right now this is just a raster with all pixels set to 0.113174
agRaster = "ARESmaskp_projected" # 1=Ag, 0 = not Ag
barrierCombineMethod = 'Maximum' # Some restorations benefit multiple corridors.
# 'Maximum' takes the greatest improvement across core area pairs
# 'Sum' adds improvement scores acreoss all pairs.
cwdThresh = None # Use cwdThresh = None for no threshold. Use cwdThresh = X to not consider
# restorations more than X map units away from each core area.
## END USER SETTINGS ######################################################
try:
# Setup path and create directories
gprint('Hey! Make sure everything is in the same projection!\n')
gprint('Setting up paths and creating directories')
sys.path.append('..\\toolbox\\scripts')
resRast = os.path.join(restorationDataGDB, resistanceRaster)
coreFCPath = os.path.join(restorationDataGDB, coreFC)
# Set up a NEW output gdb (leave previous ones on drive)
for i in range (1,200):
outputGDB = 'restorationOutput'+str(i)+'.gdb'
if not arcpy.Exists(os.path.join(outputDir,outputGDB)):
break
gprint('Previous output GDB '+ outputGDB +' exists. Delete to save disk space.')
arcpy.CreateFileGDB_management(outputDir,outputGDB)
outputGDB = os.path.join(outputDir,outputGDB)
logFile = os.path.join(outputGDB,'Iterate Barriers'+str(i)+'.py')
shutil.copyfile(__file__, logFile) #write a copy of this file to output dir as a record of settings
arcpy.env.cellSize = resRast
arcpy.env.extent = resRast
arcpy.env.snapRaster = resRast
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = outputGDB
arcpy.env.workspace = outputGDB
spatialref = arcpy.Describe(resRast).spatialReference
mapunits = spatialref.linearUnitName
gprint('Cell size = ' + str(arcpy.env.cellSize) + ' ' + mapunits +'s')
# Calculate fraction of ag within radius of each pixel
gprint('Calculating purchase cost, fraction of ag, etc within radius of each pixel.')
agRaster = os.path.join(restorationDataGDB, agRaster)
inNeighborhood = NbrCircle(radius, "MAP")
arcpy.env.extent = agRaster
outFocalStats = arcpy.sa.FocalStatistics(agRaster,
inNeighborhood, "MEAN","NODATA")
proportionAgRaster = os.path.join(outputGDB,'proportionAgRas')
outFocalStats.save(proportionAgRaster)
arcpy.env.extent = resRast
# Calculate purchase cost of circles
parcelCostRaster = os.path.join(restorationDataGDB, parcelCostRaster)
arcpy.env.extent = parcelCostRaster
outFocalStats = arcpy.sa.FocalStatistics(parcelCostRaster,inNeighborhood, "MEAN","DATA")
costFocalStatsRaster = os.path.join(outputGDB,'costFocalStatsRaster')
outFocalStats.save(costFocalStatsRaster)
arcpy.env.extent = resRast
circleArea = float(npy.pi * radius * radius)
outras = (Raster(costFocalStatsRaster) * circleArea)
purchCostRaster = os.path.join(outputGDB,'purchaseCostRaster')
outras.save(purchCostRaster)
lu.delete_data(costFocalStatsRaster)
# restCost = npy.pi * radius * radius * restCostPer_m2
restorationCostRaster = os.path.join(restorationDataGDB, restorationCostRaster)
outras = Raster(purchCostRaster) + (Raster(restorationCostRaster) * radius * radius * npy.pi)
totalCostRaster = os.path.join(outputGDB,'totalCostRaster')
outras.save(totalCostRaster)
# lu.build_stats(totalCostRaster)
# Create mask to remove areas without cost data
arcpy.env.extent = totalCostRaster
costMaskRaster = os.path.join(outputGDB,'costMaskRaster')
costThresh = 0
outCon = arcpy.sa.Con((Raster(totalCostRaster) > float(costThresh)), 1)
outCon.save(costMaskRaster)
arcpy.env.extent = resRast
# Create mask to remove areas below ag threshold
outCon = arcpy.sa.Con((Raster(proportionAgRaster) > float(minAgThreshold)), 1)
agMaskRaster = os.path.join(outputGDB, 'agMaskRaster')
outCon.save(agMaskRaster)
doStep1 = 'true'
doStep2 = 'true'
doStep5 = 'false'
for iter in range(1,iterations+1): #xxx
start_time1 = time.clock()
arcpy.env.cellSize = resRast # Some env settings get changed by linkage mapper and must be reset here
arcpy.env.extent = resRast
arcpy.env.snapRaster = resRast
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = outputGDB
arcpy.env.workspace = outputGDB
lu.dashline(1)
gprint('Running iteration number '+str(iter))
projDir = os.path.join(outputDir,'iter' + str(iter)+'Proj')
lu.create_dir(outputDir)
lu.delete_dir(projDir) #xxx
lu.create_dir(projDir)
if iter > 1: # Copy previous s2 linktable to new project directory
datapassDir = os.path.join(projDir,'datapass')
lu.create_dir(datapassDir)
projDir1 = os.path.join(outputDir,'iter1Proj')
datapassDirIter1 = os.path.join(projDir1,'datapass')
s2LinktableIter1 = os.path.join(datapassDirIter1 ,'linkTable_s2.csv')
s2LinkTable = os.path.join(datapassDir ,'linkTable_s2.csv')
shutil.copyfile(s2LinktableIter1, s2LinkTable)
# Run Linkage Mapper
distFile = os.path.join(outputDir, coreFC + '_dists.txt') # Copy distances text file from earlier LM run to the output directory- speeds things up!
if not os.path.exists(distFile):
if iter == 1:
gprint('Will calculate distance file.')
distFile = '#'
else:
projDir1 = os.path.join(outputDir,'iter1Proj')
distFile1 = os.path.join(projDir1, coreFC + '_dists.txt')
shutil.copyfile(distFile1,distFile) # Put a copy here for future runs
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = outputGDB
arcpy.env.workspace = outputGDB
argv = ('lm_master.py', projDir, coreFCPath, coreFN, resRast,
doStep1, doStep2, 'Cost-Weighted & Euclidean', distFile,
'true', 'true', 'false', '4', 'Cost-Weighted', 'true',
doStep5, 'true', '200000', '10000', '#', '#', '#', '#')
gprint('Running ' + str(argv))
cfg.lm_configured = False # Insures lm_master uses current argv
lm_master.lm_master(argv) #xxx
doStep1 = 'false' # Can skip for future iterations
doStep2 = 'false' # Can skip for future iterations
doStep5 = 'false' # Skipping for future iterations
startRadius = str(radius)
endRadius = str(radius)
radiusStep = '0'
saveRadiusRasters= 'false'
writePctRasters = 'false'
argv = ('barrier_master.py', projDir, resRast, startRadius, endRadius, radiusStep, barrierCombineMethod,
saveRadiusRasters, writePctRasters, cwdThresh)
gprint('Running ' + str(argv))
barrier_master.bar_master(argv) #xxx
arcpy.env.cellSize = resRast # Some env settings get changed by linkage mapper and must be reset here
arcpy.env.extent = resRast
arcpy.env.snapRaster = resRast
arcpy.env.overwriteOutput = True
arcpy.env.scratchWorkspace = outputGDB
arcpy.env.workspace = outputGDB
gprint('Finding restoration circles with max barrier score / ROI')
# Find points with max ROI
PREFIX = os.path.basename(projDir)
if barrierCombineMethod == 'Sum':
sumSuffix = 'Sum'
else:
sumSuffix = ''
barrierFN = (PREFIX + "_BarrierCenters" + sumSuffix + "_Rad" + str(radius))
barrierRaster = os.path.join(projDir,'output','barriers.gdb',barrierFN)
if not arcpy.Exists(barrierRaster):
msg = ('Error: cannot find barrier output: '+barrierRaster)
lu.raise_error(msg)
# arcpy.env.cellSize = agMaskRaster
# arcpy.env.extent = agMaskRaster
if iter > 1:
gprint('Creating mask for previously restored areas')
inNeighborhood = NbrCircle(radius, "MAP")
arcpy.env.extent = allRestoredAreasRaster
outFocalStats = arcpy.sa.FocalStatistics(allRestoredAreasRaster,inNeighborhood, "MEAN","DATA")
allRestoredFocalRaster = os.path.join(outputGDB,'allRestFocRas_iter'+str(iter))
outFocalStats.save(allRestoredFocalRaster) # Anything > 0 would include a restored area and
arcpy.env.extent = resRast
restMaskRaster = os.path.join(outputGDB,'restMaskRaster_iter'+str(iter))
minval = 0
outCon = arcpy.sa.Con((Raster(allRestoredFocalRaster) == float(minval)), 1)
outCon.save(restMaskRaster)
# Candidate areas have not been restored, have cost data, meet
# minimum improvement score criteria, and have enough ag in them
candidateBarrierRaster = os.path.join(outputGDB, 'candidateBarrierRaster' + '_iter'+str(iter))
if iter > 1:
gprint('Creating candidate restoration raster using barrier results, previous restorations, and selection criteria')
outCalc = (Raster(costMaskRaster) * Raster(agMaskRaster) * Raster(barrierRaster) * Raster(restMaskRaster) * (radius * 2)) # ROI scores will be in terms of total improvement (= score * diameter)
else:
outCalc = (Raster(costMaskRaster) * Raster(agMaskRaster) * Raster(barrierRaster) * radius * 2)
minBarrierScore = minImprovementVal * radius * 2
if restoredResistanceVal != 1:
outCalc2 = (outCalc - (2 * radius * (restoredResistanceVal - 1)))
outCon = arcpy.sa.Con((outCalc2 >= float(minBarrierScore)), outCalc2)
else:
outCon = arcpy.sa.Con((outCalc >= float(minBarrierScore)), outCalc)
outCon.save(candidateBarrierRaster)
lu.build_stats(candidateBarrierRaster)
purchaseRoiRaster = os.path.join(outputGDB, 'purchaseRoiRaster' + '_iter'+str(iter))
outCalc = Raster(candidateBarrierRaster) / Raster(purchCostRaster)
outCalc.save(purchaseRoiRaster)
lu.build_stats(purchaseRoiRaster)
totalRoiRaster = os.path.join(outputGDB, 'purchaseRestRoiRaster' + '_iter'+str(iter))
outCalc = Raster(candidateBarrierRaster) / Raster(totalCostRaster)
outCalc.save(totalRoiRaster)
lu.build_stats(totalRoiRaster)
maxBarrier = arcpy.GetRasterProperties_management(candidateBarrierRaster,"MAXIMUM")
gprint('Maximum barrier improvement score: '+str(maxBarrier.getOutput(0)))
if maxBarrier < 0:
arcpy.AddWarning("\nNo barriers found that meet CWD or Ag threshold criteria.")
maxPurchROI = arcpy.GetRasterProperties_management(purchaseRoiRaster,"MAXIMUM")
gprint('Maximum purchase ROI score: '+str(maxPurchROI.getOutput(0)))
maxROI = arcpy.GetRasterProperties_management(totalRoiRaster,"MAXIMUM")
gprint('Maximum total ROI score: '+str(maxROI.getOutput(0)))
if restoreMaxROI:
outPoint = os.path.join(outputGDB, 'maxRoiPoint'+'_iter'+str(iter))
gprint('Choosing circle with maximum ROI to restore')
outCon = arcpy.sa.Con((Raster(totalRoiRaster) >= float(maxROI.getOutput(0))), totalRoiRaster)
maxRoiRaster = os.path.join(outputGDB, 'maxRoiRaster')
outCon.save(maxRoiRaster)
# Save max ROI to point
try:
arcpy.RasterToPoint_conversion(maxRoiRaster, outPoint)
except:
msg = ('Error: it looks like there are no viable restoration candidates.')
lu.raise_error(msg)
else: #Restoring strongest barrier instead
outPoint = os.path.join(outputGDB, 'maxBarrierPoint'+'_iter'+str(iter))
gprint('Choosing circle with maximum BARRIER IMPROVEMENT SCORE to restore')
outCon = arcpy.sa.Con((Raster(candidateBarrierRaster) >= float(maxBarrier.getOutput(0))), candidateBarrierRaster)
maxBarrierRaster = os.path.join(outputGDB, 'maxBarrierRaster')
outCon.save(maxBarrierRaster)
# Save max barrier to point
try:
arcpy.RasterToPoint_conversion(maxBarrierRaster, outPoint)
except:
msg = ('Error: it looks like there are no viable restoration candidates.')
lu.raise_error(msg)
gprint('Done evaluating candidate restorations')
result = int(arcpy.GetCount_management(outPoint).getOutput(0))
if result > 1:
arcpy.AddWarning('Deleting points with identical ROI/improvement score values') # Would be better to retain point with max barrier score when we have multiple points with same ROI
arcpy.DeleteIdentical_management(outPoint, "grid_code", 0.1, 0.1)
arcpy.sa.ExtractMultiValuesToPoints(outPoint,
[[candidateBarrierRaster, "barrierScore"],[purchCostRaster, "purchCost"],
[totalCostRaster, "totalCost"],[purchaseRoiRaster, "purchaseROI"],
[totalRoiRaster, "totalROI"]], "NONE")
arcpy.AddField_management(outPoint, "restorationNumber", "SHORT")
arcpy.CalculateField_management(outPoint, "restorationNumber", iter)
arcpy.AddField_management(outPoint, "radius", "DOUBLE")
arcpy.CalculateField_management(outPoint, "radius", radius)
arcpy.AddField_management(outPoint, "barrierScore_per_m", "DOUBLE")
arcpy.CalculateField_management(outPoint, "barrierScore_per_m", "(float(!barrierScore!) / (!radius! * 2))", "PYTHON")
gprint('\nCreating restoration circles')
if restoreMaxROI:
circleFC = os.path.join(outputGDB, 'maxRoiCircle'+'_iter'+str(iter))
else:
circleFC = os.path.join(outputGDB, 'maxBarrierCircle'+'_iter'+str(iter))
arcpy.Buffer_analysis(outPoint, circleFC, radius)
gprint('Rasterizing restoration circles')
if restoreMaxROI:
circleRas = os.path.join(outputGDB, 'maxRoiCircleRas'+'_iter'+str(iter))
else:
circleRas = os.path.join(outputGDB, 'maxBarrierCircleRas'+'_iter'+str(iter))
arcpy.FeatureToRaster_conversion(circleFC, 'totalROI', circleRas, arcpy.env.cellSize)
# restore raster
gprint('Digitally restoring resistance raster')
resRastRestored = os.path.join(outputGDB, 'resRastRestored'+'_iter'+str(iter))
outCon = arcpy.sa.Con(IsNull(circleRas), resRast, restoredResistanceVal)
outCon.save(resRastRestored)
allRestoredAreasRaster = os.path.join(outputGDB, 'allRestoredAreas_iter'+str(iter))
PrevRestoredAreasRaster= os.path.join(outputGDB, 'allRestoredAreas_iter'+str(iter-1))
if iter == 1:
outCon = arcpy.sa.Con(IsNull(circleRas), 0, 1)
else:
outCon = arcpy.sa.Con(IsNull(circleRas), PrevRestoredAreasRaster, 1) # Add this restoration to areas restored
outCon.save(allRestoredAreasRaster)
lu.delete_data(circleRas)
resRast = resRastRestored # Use for next iteration resistance raster
#Add circle into feature class with all circles
if restoreMaxROI:
allCirclesFC = os.path.join(outputGDB,"allCirclesMaxROI")
else:
allCirclesFC = os.path.join(outputGDB,"allCirclesMaxBarriers")
if iter == 1:
arcpy.CopyFeatures_management(circleFC, allCirclesFC)
else:
arcpy.Append_management(circleFC, allCirclesFC, "TEST")
gprint('Finished iteration #'+str(iter))
start_time1 = lu.elapsed_time(start_time1)
gprint('\nDone with iterations.')
start_time = lu.elapsed_time(start_time)
gprint('Outputs saved in: '+outputGDB)
gprint('Back up your project directories if you want to save corridor/barrier results.')
# Return GEOPROCESSING specific errors
except arcpy.ExecuteError:
lu.dashline(1)
gprint('****Iteration script failed. Details follow.****')
lu.exit_with_geoproc_error(_SCRIPT_NAME)
# Return any PYTHON or system specific errors
except:
lu.dashline(1)
gprint('****Iteration script failed. Details follow.****')
lu.exit_with_python_error(_SCRIPT_NAME)