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ijwf_changewind.py
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ijwf_changewind.py
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"""Run basic percolation case with slope and/or wind."""
import os
import numpy as np
import json
from model import wildfire as wf
from model import tensor_utils
from model import gen_conditions
numCases = 1000
casesPerBatch = 100
fieldShape = (110, 110)
neighborhoodSize = 3
nomIgnitionHeat = 3
nomBurnDuration = 3
burnable_p = 0.5
outRoot = r'./out_4'
outDir = outRoot + '_1/'
filePath = lambda run, name, case : outRoot + '_{0}/{1}_{2}.npy'.format(run, name, case)
# Iterate over batches
for initCaseID in range(0, numCases, casesPerBatch):
batchSize = numCases - initCaseID
if batchSize > casesPerBatch:
batchSize = casesPerBatch
# Generate cases and fill batch
batch = wf.FireBatch()
for i in range(batchSize):
loc = gen_conditions.location_rand(((25, 75), (25, 75)))
lit_init = gen_conditions.lit_circle(fieldShape, loc, 5, 'infinite')
lit_source_np = np.expand_dims(lit_init, axis=0)
# Field has some empty spaces dictated by burnable_p. Where there are trees, random density
# density_np_bool = gen_conditions.density_bool(fieldShape, burnable_p)
# density_np_raw = gen_conditions.density(fieldShape, 1, 0.25)
# density_np = density_np_bool * density_np_raw
# density_np = tensor_utils.set_border(density_np, 0, 5)
density_np = gen_conditions.density_patchy(fieldShape, 8, 0.7, 0.6, 1, 0.25)
density_np = tensor_utils.set_border(density_np, 0, 5)
# moisture_np = np.zeros(fieldShape)
moisture_np = gen_conditions.moisture(fieldShape, 0, 0.25)
# terrain_np = np.zeros(fieldShape)
# terrain_np = gen_conditions.terrain_slope(fieldShape,
# np.random.uniform(0, 2*np.pi),
# np.random.uniform(0, np.pi/4))
terrain_np = gen_conditions.terrain_ds(fieldShape, 50, 0.5)
# wind_np = gen_conditions.wind_uniform(fieldShape, (0, 0))
wind_np = gen_conditions.wind_uniform(fieldShape, np.random.uniform(-12, 12, 2))
case = wf.FireCase(fieldShape=fieldShape,
neighborhoodSize=neighborhoodSize,
nomIgnitionHeat=nomIgnitionHeat,
nomBurnDuration=nomBurnDuration,
lit_source_np=lit_source_np,
density_np=density_np,
moisture_np=moisture_np,
terrain_np=terrain_np,
wind_np=wind_np,
boundaryCondition='infinite',
termConditions=['exhausted', 'step'],
termStep=30,
initCaseID=initCaseID,
outDir=outDir,
outInterval=1)
batch.append(case)
# Run model
batch.initialize_tensors()
(buildTime, runTime, fireDuration) = wf.simulate(batch)
batch.export_params(append=True)
wf.export_perfStats(buildTime, runTime, fireDuration, outDir=outDir, append=True)
print("{0} cases completed...".format(initCaseID + batchSize))
print("")
print("Changing wind direction and restarting...")
print("")
with open(outDir + "perfStats.json") as f:
perfStats = json.load(f)
outDir = outRoot + '_2/'
for initCaseID in range(0, numCases, casesPerBatch):
batchSize = numCases - initCaseID
if batchSize > casesPerBatch:
batchSize = casesPerBatch
batch = wf.FireBatch()
for i in range(batchSize):
ID = i + initCaseID
if perfStats['fireDuration'][ID] == 0:
lit_init = np.zeros(fieldShape)
lit_source_np = np.expand_dims(lit_init, axis=0)
fire_np = np.zeros(fieldShape)
density_np = np.load(filePath(1, "density", ID))
else:
# Use final state from previous run as initial condition
lit_fromFile = np.load(filePath(1, "lit", ID))
lit_init = lit_fromFile[-1,:,:]
lit_source_np = np.expand_dims(lit_init, axis=0)
# Rebuild density, removing burnt trees
fire_np = np.load(filePath(1, "fire", ID))
burnDuration_np = np.load(filePath(1, "burnDuration", ID))
density_np = np.load(filePath(1, "density", ID))
density_np = np.where(fire_np[-1] == burnDuration_np, 0, density_np)
terrain_np = np.load(filePath(1, "terrain", ID))
wind_np = gen_conditions.wind_uniform(fieldShape, np.random.uniform(-12, 12, 2))
case = wf.FireCase(fieldShape=fieldShape,
neighborhoodSize=neighborhoodSize,
nomIgnitionHeat=nomIgnitionHeat,
nomBurnDuration=nomBurnDuration,
lit_source_np=lit_source_np,
density_np=density_np,
moisture_np=np.zeros(fieldShape),
terrain_np=terrain_np,
wind_np=wind_np,
boundaryCondition='infinite',
termConditions=['exhausted'],
initCaseID=initCaseID,
outDir=outDir,
outInterval=1)
batch.append(case)
# Run model
batch.initialize_tensors()
(buildTime, runTime, fireDuration) = wf.simulate(batch)
batch.export_params(append=True)
wf.export_perfStats(buildTime, runTime, fireDuration, outDir=outDir, append=True)
print("{0} cases completed...".format(initCaseID + batchSize))
print("")
print("Merging runs...")
print("")
outDir = outRoot + '_3/'
if not os.path.exists(outDir):
os.makedirs(outDir)
for initCaseID in range(0, numCases, casesPerBatch):
batchSize = numCases - initCaseID
if batchSize > casesPerBatch:
batchSize = casesPerBatch
batch = wf.FireBatch()
for i in range(batchSize):
ID = i + initCaseID
for var in ["density", "moisture", "terrain", "ignitionHeat", "burnDuration"]:
shutil.copyfile(filePath(1, var, ID), filePath(3, var, ID))
if perfStats['fireDuration'][ID] < 30:
shutil.copyfile(filePath(1, "lit", ID), filePath(3, "lit", ID))
shutil.copyfile(filePath(1, "fire", ID), filePath(3, "fire", ID))
wind_single = np.load(filePath(1, "wind", ID))
wind = np.repeat(wind_single[np.newaxis, :], perfStats['fireDuration'][ID], axis=0)
np.save(filePath(3, "wind", ID), wind)
else:
lit_1 = np.load(filePath(1, "lit", ID))
lit_2 = np.load(filePath(2, "lit", ID))
lit = np.concatenate((lit_1, lit_2[1:]), axis=0)
np.save(filePath(3, "lit", ID), lit)
fire_1 = np.load(filePath(1, "fire", ID))
fire_2 = np.load(filePath(2, "fire", ID))
fire = np.concatenate((fire_1, fire_2[1:]), axis=0)
np.save(filePath(3, "fire", ID), fire)
shape_1 = lit_1.shape
shape_2 = lit_2.shape
fireDuration_1 = shape_1[0]
fireDuration_2 = shape_2[0]
wind_1_single = np.load(filePath(1, "wind", ID))
wind_2_single = np.load(filePath(2, "wind", ID))
wind_1 = np.repeat(wind_1_single[np.newaxis, :], fireDuration_1, axis=0)
wind_2 = np.repeat(wind_2_single[np.newaxis, :], fireDuration_2, axis=0)
wind = np.concatenate((wind_1, wind_2[1:]), axis=0)
np.save(filePath(3, "wind", ID), wind)