def filter(self, z):
# Prediction
# xp = F * x
self.xp = mm([self.F, self.x])
# Pp = F * P * F' + Q
self.Pp = ma(mm([self.F, self.P, self.F.transpose()]), self.Q)
# Update prediction
# S = H * Pp * H' + R
self.S = ma(mm([self.H, self.Pp, self.H.transpose()]), self.R)
# K = Pp * H' * S^-1
self.K = mm([self.Pp, self.H.transpose(), miv(self.S)])
self.K_list.append(self.K)
# y = z - H * xp
self.y = ms(z, mm([self.H, self.xp]))
# x = xp + K * y
self.x = ma(self.xp, mm([self.K, self.y]))
# P = (I - K * H) * Pp'
I = mid(self.K.shape[0])
self.P = mm([ms(I, mm([self.K, self.H])), self.Pp.transpose()])
self.P_list.append(self.P)
return self.x.tolist()
def step(self):
"""
Advance the Household Agent by one step
Can be residential step or school step
:return:
"""
if self.model.schedule.steps < self.model.residential_steps:
residential_move = True
else:
residential_move = False
if residential_move:
# only step the agents if the number considered is not exhausted
if self.model.total_considered < self.model.residential_moves_per_step:
# move residential
U_res = self.get_res_satisfaction(self.pos)
self.model.res_satisfaction.append(U_res)
# print("U_res",U_res)
if U_res < self.T:
# todo: implement different move schemes, for now only random
# find all empty places
# rank them
# take one with boltzmann probability.
self.evaluate_move(U_res, school=False)
else:
self.model.res_happy += 1
self.model.total_considered += 1
#print("considered",self.model.total_considered)
else:
if self.model.total_considered < self.model.school_moves_per_step:
# school moves
# satisfaction in current school
U = self.get_school_satisfaction(self.school,
self.dist_to_school)
self.model.satisfaction.append(U)
# If unhappy, compared to threshold move:
if U < self.T:
#print('unhappy')
self.evaluate_move(U, school=True)
else:
self.model.happy += 1
if self.model.total_considered > 0:
self.model.percent_happy = np.ma(
self.model.happy / self.model.total_considered)
# label sublot character size and position (e.g. a,b,c,d) lbfs = 20 lbpx = 0.04 lbpy = 0.96 # fig = plt.figure(facecolor='white', figsize = (8.5, 11)) fig = plt.figure(facecolor="white", figsize=(11.0, 8.5)) baroffset = 0.023 # --------- plots plt.subplot(111) ma = Basemap(boundinglat=bounding_lat, lon_0=lon_0, area_thresh=10000.0, projection=projection) xa, ya = ma(*np.meshgrid(lon, lat)) ma.drawcoastlines() ma.drawparallels(np.arange(-90.0, 90.0, 30.0)) ma.drawmeridians(np.arange(-180.0, 180.0, 30.0)) # revhaxby=cmap_map(lambda x: x[::-1], cm.GMT_haxby) # tlvs=np.arange(-20.,22.,1.) # tlvs=(-5000,-4500,-4000,-3500,-3000,-2500,-2000,-1500,-1000,-500,0,500,1000,1500,2000,2500,3000,3500,4000,4500,5000) # tlvs=(0,500,1000,1500,2000,2500,3000,3500,4000,4500,5000) # tlvs=np.arange(0,5000,100) tlvs = np.arange(0, 2000, 100) # cs1 = m1.contourf(x1, y1, obsnaug, lvs, cmap=bcm.GMT_haxby_r) # csa = ma.contourf(xa, ya, etopo, tlvs, cmap=bcm.GMT_no_green) # csa = ma.contourf(xa, ya, etopo, tlvs, cmap=bcm.GMT_globe)
#label sublot character size and position (e.g. a,b,c,d)
lbfs = 20
lbpx = 0.04
lbpy = 0.96
#fig = plt.figure(facecolor='white', figsize = (8.5, 11))
fig = plt.figure(facecolor='white', figsize=(11., 8.5))
baroffset = 0.023
# --------- plots
plt.subplot(111)
ma = Basemap(boundinglat=bounding_lat,lon_0=lon_0,\
area_thresh=10000.,projection=projection)
xa, ya = ma(*np.meshgrid(lon, lat))
ma.drawcoastlines()
ma.drawparallels(np.arange(-90., 90., 30.))
ma.drawmeridians(np.arange(-180., 180., 30.))
#revhaxby=cmap_map(lambda x: x[::-1], cm.GMT_haxby)
#tlvs=np.arange(-20.,22.,1.)
#tlvs=(-5000,-4500,-4000,-3500,-3000,-2500,-2000,-1500,-1000,-500,0,500,1000,1500,2000,2500,3000,3500,4000,4500,5000)
#tlvs=(0,500,1000,1500,2000,2500,3000,3500,4000,4500,5000)
#tlvs=np.arange(0,5000,100)
tlvs = np.arange(0, 2000, 100)
#cs1 = m1.contourf(x1, y1, obsnaug, lvs, cmap=bcm.GMT_haxby_r)
#csa = ma.contourf(xa, ya, etopo, tlvs, cmap=bcm.GMT_no_green)
#csa = ma.contourf(xa, ya, etopo, tlvs, cmap=bcm.GMT_globe)