refactor update

Author: paulvangentcom

config.py

@@ -7,17 +7,18 @@
 class config_error(Exception):
  pass
 
-class configuration():
+class Configuration():
  def __init__(self, *args, **kwargs):
  #simulation variables
  self.verbose = False #whether to print infections, recoveries and fatalities to the terminal
  self.simulation_steps = 10000 #total simulation steps performed
  self.tstep = 0 #current simulation timestep
  self.save_data = True #whether to dump data
+ self.save_timesteps = True #dumps population data every time step
 
  #scenario flags
  self.traveling_infects = False
- self.self_isolate = False
+ self.self_isolate = True
  self.lockdown = False
  self.lockdown_percentage = 0.1 #after this proportion is infected, lock-down begins
  self.lockdown_compliance = 0.95 #fraction of the population that will obey the lockdown 

path_planning_demo.py renamed to demo_COVID.py

@@ -45,44 +45,44 @@ def out_of_bounds(population, xbounds, ybounds):
  #determine number of elements that need to be updated
 
  shp = population[:,3][(population[:,1] <= xbounds[:,0]) &
- (population[:,3] < 0)].shape
+  (population[:,3] < 0)].shape
  population[:,3][(population[:,1] <= xbounds[:,0]) &
  (population[:,3] < 0)] = np.clip(np.random.normal(loc = 0.5, 
- scale = 0.5/3,
- size = shp),
- a_min = 0.05, a_max = 1)
+  scale = 0.5/3,
+  size = shp),
+  a_min = 0.05, a_max = 1)
 
  shp = population[:,3][(population[:,1] >= xbounds[:,1]) &
- (population[:,3] > 0)].shape
+  (population[:,3] > 0)].shape
  population[:,3][(population[:,1] >= xbounds[:,1]) &
  (population[:,3] > 0)] = np.clip(-np.random.normal(loc = 0.5, 
- scale = 0.5/3,
- size = shp),
- a_min = -1, a_max = -0.05)
+  scale = 0.5/3,
+  size = shp),
+  a_min = -1, a_max = -0.05)
 
  #update y heading
  shp = population[:,4][(population[:,2] <= ybounds[:,0]) &
- (population[:,4] < 0)].shape
+  (population[:,4] < 0)].shape
  population[:,4][(population[:,2] <= ybounds[:,0]) &
  (population[:,4] < 0)] = np.clip(np.random.normal(loc = 0.5, 
- scale = 0.5/3,
- size = shp),
- a_min = 0.05, a_max = 1)
+  scale = 0.5/3,
+  size = shp),
+  a_min = 0.05, a_max = 1)
 
  shp = population[:,4][(population[:,2] >= ybounds[:,1]) &
- (population[:,4] > 0)].shape
+  (population[:,4] > 0)].shape
  population[:,4][(population[:,2] >= ybounds[:,1]) &
  (population[:,4] > 0)] = np.clip(-np.random.normal(loc = 0.5, 
- scale = 0.5/3,
- size = shp),
- a_min = -1, a_max = -0.05)
+  scale = 0.5/3,
+  size = shp),
+  a_min = -1, a_max = -0.05)
 
  return population
 
 
 def update_randoms(population, pop_size, heading_update_chance=0.02, 
- speed_update_chance=0.02, heading_multiplication=1,
- speed_multiplication=1, speed=0.01):
+  speed_update_chance=0.02, heading_multiplication=1,
+  speed_multiplication=1, speed=0.01):
  '''updates random states such as heading and speed
  
  Function that randomized the headings and speeds for population members
@@ -118,223 +118,25 @@ def update_randoms(population, pop_size, heading_update_chance=0.02,
  update = np.random.random(size=(pop_size,))
  shp = update[update <= heading_update_chance].shape
  population[:,3][update <= heading_update_chance] = np.random.normal(loc = 0, 
- scale = 1/3,
- size = shp) * heading_multiplication
+  scale = 1/3,
+  size = shp) * heading_multiplication
  #y
  update = np.random.random(size=(pop_size,))
  shp = update[update <= heading_update_chance].shape
  population[:,4][update <= heading_update_chance] = np.random.normal(loc = 0, 
- scale = 1/3,
- size = shp) * heading_multiplication
+  scale = 1/3,
+  size = shp) * heading_multiplication
  #randomize speeds
  update = np.random.random(size=(pop_size,))
  shp = update[update <= heading_update_chance].shape
  population[:,5][update <= heading_update_chance] = np.random.normal(loc = speed, 
- scale = speed / 3,
- size = shp) * speed_multiplication
+  scale = speed / 3,
+  size = shp) * speed_multiplication
 
  population[:,5] = np.clip(population[:,5], a_min=0.0001, a_max=0.05)
  return population
 
 
-#########################
-##### PATH PLANNING #####
-#########################
-
-def set_destination(population, destinations):
- '''sets destination of population
-
- Sets the destination of population if destination marker is not 0.
- Updates headings and speeds as well.
-
- Keyword arguments
- -----------------
- population : ndarray
- the array containing all the population information
-
- destinations : ndarray
- the array containing all destinations information
- '''
- 
- #how many destinations are active
- active_dests = np.unique(population[:,11][population[:,11] != 0])
-
- #set destination
- for d in active_dests:
- dest_x = destinations[:,int((d - 1) * 2)]
- dest_y = destinations[:,int(((d - 1) * 2) + 1)]
-
- #compute new headings
- head_x = dest_x - population[:,1]
- head_y = dest_y - population[:,2]
-
- #head_x = head_x / np.sqrt(head_x)
- #head_y = head_y / np.sqrt(head_y)
-
- #reinsert headings into population of those not at destination yet
- population[:,3][(population[:,11] == d) &
- (population[:,12] == 0)] = head_x[(population[:,11] == d) &
- (population[:,12] == 0)]
- population[:,4][(population[:,11] == d) &
- (population[:,12] == 0)] = head_y[(population[:,11] == d) &
- (population[:,12] == 0)]
- #set speed to 0.01
- population[:,5][(population[:,11] == d) &
- (population[:,12] == 0)] = 0.02
-
- return population
-
-
-def check_at_destination(population, destinations, wander_factor=1.5):
- '''check who is at their destination already
-
- Takes subset of population with active destination and
- tests who is at the required coordinates. Updates at destination
- column for people at destination. 
-
- Keyword arguments
- -----------------
- population : ndarray
- the array containing all the population information
-
- destinations : ndarray
- the array containing all destinations information
-
- wander_factor : int or float
- defines how far outside of 'wander range' the destination reached
- is triggered
- '''
-
- #how many destinations are active
- active_dests = np.unique(population[:,11][(population[:,11] != 0)])
-
- #see who is at destination
- for d in active_dests:
- dest_x = destinations[:,int((d - 1) * 2)]
- dest_y = destinations[:,int(((d - 1) * 2) + 1)]
-
- #see who arrived at destination and filter out who already was there
- at_dest = population[(np.abs(population[:,1] - dest_x) < (population[:,13] * wander_factor)) & 
- (np.abs(population[:,2] - dest_y) < (population[:,14] * wander_factor)) &
- (population[:,12] == 0)]
-
- if len(at_dest) > 0:
- #mark those as arrived
- at_dest[:,12] = 1
- #insert random headings and speeds for those at destination
- at_dest = update_randoms(at_dest, len(at_dest), 1, 1)
-
- #at_dest[:,5] = 0.001
-
- #reinsert into population
- population[(np.abs(population[:,1] - dest_x) < (population[:,13] * wander_factor)) & 
- (np.abs(population[:,2] - dest_y) < (population[:,14] * wander_factor)) &
- (population[:,12] == 0)] = at_dest
-
-
- return population
- 
-
-def keep_at_destination(population, destinations, wander_factor=1):
- '''keeps those who have arrived, within wander range
-
- Function that keeps those who have been marked as arrived at their
- destination within their respective wander ranges
-
- Keyword arguments
- -----------------
- population : ndarray
- the array containing all the population information
-
- destinations : ndarray
- the array containing all destinations information
-
- wander_factor : int or float
- defines how far outside of 'wander range' the destination reached
- is triggered
- ''' 
-
- #how many destinations are active
- active_dests = np.unique(population[:,11][(population[:,11] != 0) &
- (population[:,12] == 1)])
-
- for d in active_dests:
- dest_x = destinations[:,int((d - 1) * 2)][(population[:,12] == 1) &
- (population[:,11] == d)]
- dest_y = destinations[:,int(((d - 1) * 2) + 1)][(population[:,12] == 1) &
- (population[:,11] == d)]
-
- #see who is marked as arrived
- arrived = population[(population[:,12] == 1) &
- (population[:,11] == d)]
-
- ids = np.int32(arrived[:,0]) # find unique IDs of arrived persons
- 
- #check if there are those out of bounds
- #replace x oob
- #where x larger than destination + wander, AND heading wrong way, set heading negative
- shp = arrived[:,3][arrived[:,1] > (dest_x + (arrived[:,13] * wander_factor))].shape
-
- arrived[:,3][arrived[:,1] > (dest_x + (arrived[:,13] * wander_factor))] = -np.random.normal(loc = 0.5,
- scale = 0.5 / 3,
- size = shp)
-
-
- #where x smaller than destination - wander, set heading positive
- shp = arrived[:,3][arrived[:,1] < (dest_x - (arrived[:,13] * wander_factor))].shape
- arrived[:,3][arrived[:,1] < (dest_x - (arrived[:,13] * wander_factor))] = np.random.normal(loc = 0.5,
- scale = 0.5 / 3,
- size = shp)
- #where y larger than destination + wander, set heading negative
- shp = arrived[:,4][arrived[:,2] > (dest_y + (arrived[:,14] * wander_factor))].shape
- arrived[:,4][arrived[:,2] > (dest_y + (arrived[:,14] * wander_factor))] = -np.random.normal(loc = 0.5,
- scale = 0.5 / 3,
- size = shp)
- #where y smaller than destination - wander, set heading positive
- shp = arrived[:,4][arrived[:,2] < (dest_y - (arrived[:,14] * wander_factor))].shape
- arrived[:,4][arrived[:,2] < (dest_y - (arrived[:,14] * wander_factor))] = np.random.normal(loc = 0.5,
- scale = 0.5 / 3,
- size = shp)
-
- #slow speed
- arrived[:,5] = np.random.normal(loc = 0.005,
- scale = 0.005 / 3, 
- size = arrived[:,5].shape)
-
- #reinsert into population
- population[(population[:,12] == 1) &
- (population[:,11] == d)] = arrived
- 
- return population
-
-
-def reset_destinations(population, ids=[]):
- '''clears destination markers
-
- Function that clears all active destination markers from the population
-
- Keyword arguments
- -----------------
- population : ndarray
- the array containing all the population information
-
- ids : ndarray or list
- array containing the id's of the population members that need their
- destinations reset
- '''
- 
- 
- if len(ids) == 0:
- #if ids empty, reset everyone
- population[:,11] = 0
- else:
- pass
- #else, reset id's
-
- 
- pass
-
-
 def get_motion_parameters(xmin, ymin, xmax, ymax):
  '''gets destination center and wander ranges
 
@@ -354,4 +156,4 @@ def get_motion_parameters(xmin, ymin, xmax, ymax):
  x_wander = (xmax - xmin) / 2
  y_wander = (ymax - ymin) / 2
 
- return x_center, y_center, x_wander, y_wander
+ return x_center, y_center, x_wander, y_wander