added environmental factors to fireSpread

model.py

@@ -3,6 +3,14 @@
 import util
 
 '''features to add:
+temperature dynamics/dewpoint
+
+stochastic elements
+-wind speed/direciton
+-temperature
+-precipitation
+-humidity
+
 
 '''
 
@@ -47,9 +55,12 @@ def burnout(self):
         for i in range(self.state.shape[0]):
             for j in range(self.state.shape[1]):
                 if self.state[i,j] >= 1:
-                    self.state[i,j] = 0 
+                    self.state[i,j] = 0
                     self.mask[i,j] = 0
 
     def timeStep(self):
         self.fireSpread()
-        self.t += 1
+        self.t += 1
+
+    def visualize(self):
+        return self.state + (1-self.mask)

test.py

@@ -1,7 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import model
-import time
 
 initialState = np.zeros((36,88))
 initialState[18,44] = 0.1
@@ -10,17 +9,22 @@
 tempLow = np.array([60,56,55,59,61,61,57,58,59,64,65,64,62,57])
 precip = np.zeros((14))
 wind = np.array([15,16,12,9,13,17,12,15,10,12,10,9,14,13])
-humid = np.array([49.2,57.1,55.2,41.5,40.0,44.1,45.1,42.5,45.3,38.1,34.3,35.3,50.0,56.9])
+humid = np.array([49.2,57.1,55.2,41.5,40.0,44.1,45.1,42.5,45.3,38.1,34.3,35.3,50.0,56.9])*0.01
 
 testModel = model.firemodel(initialState, tempHigh, tempLow, precip, wind, humid)
 
-plt.figure(1)
-plt.imshow(testModel.state, cmap=plt.get_cmap('inferno'))
+'''plt.figure(1)
+plt.imshow(testModel.visualize(), cmap=plt.get_cmap('inferno'))
 plt.show
 for i in range(7):
     testModel.timeStep()
     #print(testModel.state)
     
     plt.figure(i+2)
-    plt.imshow(testModel.state, cmap=plt.get_cmap('inferno'))
-    plt.show
+    plt.imshow(testModel.visualize(), cmap=plt.get_cmap('inferno'))
+    plt.show'''
+
+for i in range(336):
+    testModel.timeStep()
+
+plt.imshow(testModel.visualize(),cmap=plt.get_cmap('inferno'))

util.py

@@ -1,14 +1,17 @@
 import numpy as np
 from scipy.ndimage import convolve
 
-def findKernel(state,wind,humidity,precip): 
+def findKernel(state,wind,humidity,precip):
     kernel = np.zeros((3,3))
-    kernel[0,0] = wind/15
-    kernel[0,1] = 0.5
-    kernel[1,0] = 0.5
-    kernel[1,2] = 0.5
-    kernel[2,1] = 0.5
-
+    kernel[0,0] = wind/20
+    kernel[0,1] = wind/40
+    kernel[1,0] = wind/40
+    kernel[1,2] = 0.1
+    kernel[2,1] = 0.1
+    kernel[1,1] = 1
+    kernel[2,2] = 0.1
+    kernel *= humidity
+    kernel *= (1-precip)
     return kernel
 
 def maskConvolve(state,kernel,mask):