def __init__(self):
self.console = Console()
self.landscape = Landscape()
self.display = Display(self.landscape, self.console)
self.control = Controller(self.console)
self.cursorX = 2
self.cursorY = 2
def test_is_valid_position():
x = 10
y = 10
nb_patches = 0
landscape = Landscape(x, y, nb_patches)
assert not landscape.is_valid_position(x, y-1)
assert not landscape.is_valid_position(0, -1)
assert landscape.is_valid_position(5, 5)
def test_resources_which_can_be_eaten():
x = 10
y = 10
nb_patches = 0
x_pos = 4
y_pos = 6
resource_dim = 75
landscape = Landscape(x, y, nb_patches)
landscape.land[x_pos, y_pos] = resource_dim
assert landscape.resources_which_can_be_eaten(x_pos, y_pos) == resource_dim
def test_landscape_creation():
x = 10
y = 10
nb_patches = 3
landscape = Landscape(x, y, nb_patches)
assert landscape.land.shape == (x, y)
assert (landscape.land > 0).sum() >= nb_patches
too_many_patch = x * y + 1
with pytest.raises(Exception):
Landscape(x, y, too_many_patch)
def __init__(self, dim_arg=DEFAULT_DIM, move_vec_arg=DEFAULT_MOVE_VECTORS):
self.dim = dim_arg
self.move_vectors = move_vec_arg
self.landscape = Landscape(self.dim)
self.cur_location = ([int(self.dim / 2), int(self.dim / 2)])
self.cur_location = tuple(self.cur_location)
self.path_target = None
self.default_chance = 1 / (self.dim * self.dim)
self.likelihood = np.ones(shape=(self.dim, self.dim))
self.likelihood *= self.default_chance
self.query_counter = np.zeros((self.dim, self.dim))
self.cur_path_target = None
def getLandscape():
# itesm representing end of the world at both edges
image = globals.IMAGESDICT['corner']
negativeRect = pygame.Rect([-150, globals.WINHEIGHT - globals.TILEHEIGHT, globals.TILEWIDTH, globals.TILEHEIGHT])
landscape = Landscape(image, negativeRect)
allLandscapeList.add(landscape)
allSpriteList.add(landscape)
image = globals.IMAGESDICT['corner']
positiveRect = pygame.Rect([globals.LEVEL[0] - globals.TILEWIDTH, globals.WINHEIGHT - globals.TILEHEIGHT, globals.TILEWIDTH, globals.TILEHEIGHT])
landscape = Landscape(image, positiveRect)
allLandscapeList.add(landscape)
allSpriteList.add(landscape)
return
def getInitialObstacles():
""" gets the position of the initial blocks """
# hardcode number of blocks
# will account for movemnet
from random import choice
from globals import TILEWIDTH, TILEHEIGHT, WINHEIGHT, TILEFLOORHEIGHT, LEVEL, HALFWINWIDTH
no_of_blocks = 50
for b in range(no_of_blocks // 2):
# get image
# image = globals.IMAGESDICT['rock']
for y in range(1,5):
image = globals.IMAGESDICT[choice(['ugly tree', 'rock', 'tall tree'])]
# make rect
spaceRect = pygame.Rect((b * TILEWIDTH, y * TILEFLOORHEIGHT, TILEWIDTH, TILEFLOORHEIGHT))
landscape = Landscape(image, spaceRect)
allLandscapeList.add(landscape)
allSpriteList.add(landscape)
image = globals.IMAGESDICT['corner']
negativeRect = pygame.Rect([-150, WINHEIGHT - TILEHEIGHT, TILEWIDTH, TILEHEIGHT])
landscape = Landscape(image, negativeRect)
allLandscapeList.add(landscape)
allSpriteList.add(landscape)
image = globals.IMAGESDICT['corner']
positiveRect = pygame.Rect([LEVEL[0] - TILEWIDTH, WINHEIGHT - TILEHEIGHT, TILEWIDTH, TILEFLOORHEIGHT])
landscape = Landscape(image, positiveRect)
allLandscapeList.add(landscape)
allSpriteList.add(landscape)
bottomRect = pygame.Rect([HALFWINWIDTH, LEVEL[1] - TILEHEIGHT, TILEWIDTH, TILEFLOORHEIGHT])
landscape = Landscape(image, bottomRect)
allLandscapeList.add(landscape)
allSpriteList.add(landscape)
for x in range(0, LEVEL[0], 50):
for y in range(10):
image = globals.IMAGESDICT[choice(['ugly tree', 'rock', 'tall tree'])]
spaceRect = pygame.Rect((x, LEVEL[1] - (y * TILEHEIGHT), TILEWIDTH, TILEFLOORHEIGHT))
landscape = Landscape(image, spaceRect)
if choice([0,1,0]):
allLandscapeList.add(landscape)
allSpriteList.add(landscape)
return
def bayes_update_on_move(self):
# Fetch the observation on boundry crossing from the landscape
clue = self.landscape.get_last_transition()
if clue is None:
return
target_move_vectors = Landscape.get_target_move_vectors()
other_t_ids = np.zeros((self.dim, self.dim)) - 1
num_moves_arr = np.zeros((self.dim, self.dim))
for x in range(self.dim):
for y in range(self.dim):
coords = (x, y)
t_id = self.landscape.t_id_map[coords]
if t_id not in clue:
other_t_id = -1
continue
if t_id == clue[0]:
other_t_id = clue[1]
else:
other_t_id = clue[0]
other_t_ids[coords] = other_t_id
# Calculate the number of moves from (x,y) that match the
# transition description
neighbors = coords + target_move_vectors
for neighbor in neighbors:
neighbor = tuple(neighbor)
if not self.in_bounds(neighbor):
continue
if self.landscape.t_id_map[neighbor] == other_t_id:
num_moves_arr[coords] += 1
new_belief = np.zeros((self.dim, self.dim))
for x in range(self.dim):
for y in range(self.dim):
coords = (x, y)
# Get the t_id of the terrain we're on and the prev terrain
t_id = self.landscape.t_id_map[coords]
other_t_id = other_t_ids[coords]
if other_t_id == -1:
continue
poss_prev_locations = coords - target_move_vectors
for neighbor in poss_prev_locations:
neighbor = tuple(neighbor)
if not self.in_bounds(neighbor):
continue
# neighbor is
if self.landscape.t_id_map[neighbor] != other_t_id:
# Filter out wrong t_id
continue
num_trans = num_moves_arr[neighbor]
new_belief[coords] += ((1 / num_trans) *
self.likelihood[neighbor])
self.likelihood = new_belief
self.likelihood /= np.sum(self.likelihood)
class Simulation: def __init__(self, size=200, r1=3, r2=5, r3=10, r4=10, load_filename=None): self.landscape = Landscape(size, size, self, r1, r2, r3, r4, load_filename) if not load_filename: self.agents = set() for i in range(100): #200 self.add_agent(Agent(self.landscape, self)) def step(self, phase, maNum=10, miNum=400, byNum=2000, brNum=5000, buNum=400, pDecay=0.75, tDecay=0.25, corNum=5): self.landscape.step(phase, maNum, miNum, byNum, brNum, buNum, pDecay, tDecay, corNum) killlist = [] for agent in list(self.agents): agent.step(self.landscape) if agent.water < 0 or agent.resource < 0: killlist.append(agent) for agent in killlist: self.kill(agent) gc.collect() def add_agent(self, agent): self.agents.add(agent) def kill(self, agent): self.agents.discard(agent) self.landscape.remove_agent(agent) def view(self, step): return self.landscape.view(step) def output(self, filedir): self.landscape.output(filedir)
class Main:
def __init__(self):
self.console = Console()
self.landscape = Landscape()
self.display = Display(self.landscape, self.console)
self.control = Controller(self.console)
self.cursorX = 2
self.cursorY = 2
def run(self):
exit = False
while not exit:
self.display.setCursorPosition(self.cursorX, self.cursorY)
self.display.draw()
userinput = self.control.getKey()
if userinput == self.control.QUIT:
exit = True
else:
self.processinput(self.control.buffer)
self.console.gotoLine(self.landscape.height + 3)
self.console.printText(self.landscape.generateCode())
def processinput(self, command):
if command.endswith(self.control.UP) and self.cursorY > 0:
self.cursorY = self.cursorY - 1
if command.endswith(self.control.DOWN
) and self.cursorY < self.landscape.height - 1:
self.cursorY = self.cursorY + 1
if command.endswith(self.control.LEFT) and self.cursorX > 0:
self.cursorX = self.cursorX - 1
if command.endswith(self.control.RIGHT
) and self.cursorX < self.landscape.width - 1:
self.cursorX = self.cursorX + 1
if command.endswith(self.control.SET):
self.landscape.set(self.cursorX, self.cursorY)
if command.endswith(self.control.CLEAR):
self.landscape.clear(self.cursorX, self.cursorY)
if command.endswith(self.control.CLEARLINE):
self.landscape.clearLine(self.cursorY)
if command.endswith(self.control.COPYLINEABOVE) and self.cursorY > 0:
self.landscape.copyLine(self.cursorY - 1, self.cursorY)
def testing(input_a,plan,itern=10):
sum = 0.0
for _ in range(itern):
im_list = input_a
inf = construct_influence_matrix_from_list(im_list)
fit1 = FitnessContributionTable(inf)
land1 = Landscape(fitness_contribution_matrix = fit1)
land1.compute_all_locations_id(plan)
land1.standardize()
sum+= count_local_peak(land1,False)
return sum / float(itern)
def test_add_resources_as_patch():
x = 10
y = 10
nb_patches = 0
x_pos = 4
y_pos = 6
resource_dim = 75
landscape = Landscape(x, y, nb_patches)
landscape.add_resources_as_patch(x_pos, y_pos, resource_dim)
assert landscape.land[x_pos, y_pos] == resource_dim
assert (landscape.land[landscape.land != resource_dim] < resource_dim).all()
assert (landscape.land[landscape.land != resource_dim] > 0).any()
landscape.add_resources_as_patch(x_pos, y_pos, resource_dim)
assert landscape.land[x_pos, y_pos] == Landscape.MAX_VALUE
def data_likelihood(trials,
sbert_model_name,
initial_max_activation=1.0,
initial_decay_rate=0.1,
initial_memory_capacity=5.0,
initial_learning_rate=0.9,
initial_semantic_strength_coeff=1.0):
"""
Generalized cost function for fitting the Landscape model optimized using the numba library.
Output scales inversely with the likelihood that the model and specified parameters would generate the specified
trials. For model fitting, is usually wrapped in another function that fixes and frees parameters for optimization.
**Arguments**:
- trials: int64-array where rows identify a unique trial of responses and columns corresponds to a unique recall
index.
- A configuration for each parameter of model
**Returns** the negative sum of log-likelihoods across specified trials conditional on the specified parameters and
the mechanisms of InstanceCMR.
"""
model = Landscape(sbert_model_name, initial_max_activation,
initial_decay_rate, initial_memory_capacity,
initial_learning_rate, initial_semantic_strength_coeff)
model.experience(np.eye(item_count, item_count + 1, 1))
likelihood = np.ones(np.shape(trials))
for trial_index in range(len(trials)):
trial = trials[trial_index]
for recall_index in range(len(trial)):
recall = trial[recall_index]
likelihood[trial_index, recall_index] = \
model.outcome_probabilities()[recall]
model.force_recall(recall)
if recall == 0:
break
return -np.sum(np.log(likelihood))
def task(self, node, number_of_nodes, pipes):
width = 10 # Landscape width
height = 10 # Landscape height
densitylimit = 50 # Density above which agents move.
number_of_agents = 1000
node_number_of_agents = 0
iterations = 100 # Model iterations before stopping
agents = []
# Setup landcsape.
landscape = Landscape()
landscape.width = width
landscape.height = height
pipe_to_zero = None
# Setup agents
if (node != 0):
node_number_of_agents = int(number_of_agents /
(number_of_nodes - 1))
if (node == (number_of_nodes - 1)):
node_number_of_agents = int(node_number_of_agents +
(number_of_agents %
(number_of_nodes - 1)))
pipe_to_zero = pipes[node - 1]
for i in range(node_number_of_agents):
agents.append(Agent())
agents[i].densitylimit = densitylimit
agents[
i].landscape = landscape # Agents get a reference to the
# landscape to interrogate for densities.
# They could also get a reference to the
# agent list if agents need to talk,
# but here they don't.
# Allocate agents a start location.
x = int(width / 2) # Set to middle for start
y = int(height / 2) # Set to middle for start
agents[i].x = x
agents[i].y = y
# Give the landscape a reference to the agent list so it
# can find out where they all are and calculate densities.
landscape.agents = agents
# Print start map
# Run
for time in range(iterations):
# Send out the local densities to node zero
if (node != 0):
landscape.calc_densities()
densities = landscape.getdensities()
pipe_to_zero.send(densities)
else: # if node is node zero
# Get the local densities in to node zero
densities = []
for x in range(width):
for y in range(height):
densities.append(0)
for i in range(len(pipes)):
# Get the local density from each node i.
local_densities = pipes[i].recv()
# Add node i's density surface to the global surface.
for x in range(width):
for y in range(height):
densities[(y * width) + x] = densities[
(y * width) +
x] + local_densities[(y * width) + x]
# Send out the global densities to the nodes
if (node == 0):
for i in range(len(pipes)):
pipes[i].send(densities)
else:
# Receive the global densities from node zero.
global_densities = pipe_to_zero.recv()
landscape.setdensities(global_densities)
# Move the agents if the density is too high.
for i in range(node_number_of_agents):
agents[i].step()
# Report
if (node == 0):
print("time = ", time, " -------------------")
landscape.setdensities(densities)
for x in range(width):
for y in range(height):
print(landscape.getdensityat(x, y), end=" ")
print("")
class LsFinder:
"""Performs the probabilistic search on a randomly generated landscape
"""
# pylint: disable=too-many-instance-attributes
def __init__(self, dim_arg=DEFAULT_DIM, move_vec_arg=DEFAULT_MOVE_VECTORS):
self.dim = dim_arg
self.move_vectors = move_vec_arg
self.landscape = Landscape(self.dim)
self.cur_location = ([int(self.dim / 2), int(self.dim / 2)])
self.cur_location = tuple(self.cur_location)
self.path_target = None
self.default_chance = 1 / (self.dim * self.dim)
self.likelihood = np.ones(shape=(self.dim, self.dim))
self.likelihood *= self.default_chance
self.query_counter = np.zeros((self.dim, self.dim))
self.cur_path_target = None
def reset_all(self):
"""Resets the finder with a new map and recenters the cur_location
"""
self.landscape = Landscape(self.dim)
self.reset_finder()
def reset_finder(self):
"""Resets the finder with the same map and recenters the cur_location
"""
self.cur_location = ([int(self.dim / 2), int(self.dim / 2)])
self.cur_location = tuple(self.cur_location)
self.likelihood = np.ones(shape=(self.dim, self.dim))
self.likelihood *= self.default_chance
self.query_counter = np.zeros((self.dim, self.dim))
self.cur_path_target = None
def search_cell(self, coords):
debug_print(f"Searching in spot [{coords}]", 8)
self.query_counter[coords] += 1
# searches the cell and moves the target if not found
if self.landscape.query_tile(coords):
# print(f"Target has been found in [{x},{y}]")
return True
self.bayes_update_on_miss(coords)
if self.landscape.is_target_moving:
self.bayes_update_on_move
# print(self.likelihood)
return False
def bayes_update_on_miss(self, miss_coords):
"""Performs the bayesian update on a miss on a certain cell
Parameters
----------
miss_coords : (int, int)
Coordinate tuple of where the miss observation occurred
Returns
-------
float
the new probability that is in the miss_coords spot
"""
false_neg_chance = self.landscape.prob_map[miss_coords]
original_belief = self.likelihood[miss_coords]
new_belief = original_belief * false_neg_chance
self.likelihood[miss_coords] = new_belief
self.likelihood /= np.sum(self.likelihood)
return new_belief
def bayes_update_on_move(self):
# Fetch the observation on boundry crossing from the landscape
clue = self.landscape.get_last_transition()
if clue is None:
return
target_move_vectors = Landscape.get_target_move_vectors()
other_t_ids = np.zeros((self.dim, self.dim)) - 1
num_moves_arr = np.zeros((self.dim, self.dim))
for x in range(self.dim):
for y in range(self.dim):
coords = (x, y)
t_id = self.landscape.t_id_map[coords]
if t_id not in clue:
other_t_id = -1
continue
if t_id == clue[0]:
other_t_id = clue[1]
else:
other_t_id = clue[0]
other_t_ids[coords] = other_t_id
# Calculate the number of moves from (x,y) that match the
# transition description
neighbors = coords + target_move_vectors
for neighbor in neighbors:
neighbor = tuple(neighbor)
if not self.in_bounds(neighbor):
continue
if self.landscape.t_id_map[neighbor] == other_t_id:
num_moves_arr[coords] += 1
new_belief = np.zeros((self.dim, self.dim))
for x in range(self.dim):
for y in range(self.dim):
coords = (x, y)
# Get the t_id of the terrain we're on and the prev terrain
t_id = self.landscape.t_id_map[coords]
other_t_id = other_t_ids[coords]
if other_t_id == -1:
continue
poss_prev_locations = coords - target_move_vectors
for neighbor in poss_prev_locations:
neighbor = tuple(neighbor)
if not self.in_bounds(neighbor):
continue
# neighbor is
if self.landscape.t_id_map[neighbor] != other_t_id:
# Filter out wrong t_id
continue
num_trans = num_moves_arr[neighbor]
new_belief[coords] += ((1 / num_trans) *
self.likelihood[neighbor])
self.likelihood = new_belief
self.likelihood /= np.sum(self.likelihood)
def move_on_path(self):
# TODO implement path walking
pos = self.cur_location
target = self.cur_path_target
if pos == target:
return
elif pos[0] < target[0]:
self.cur_location = (pos[0] + 1, pos[1])
return
elif pos[0] > target[0]:
self.cur_location = (pos[0] - 1, pos[1])
return
elif pos[1] < target[1]:
self.cur_location = (pos[0], pos[1] + 1)
return
elif pos[1] > target[1]:
self.cur_location = (pos[0], pos[1] - 1)
return
def search_target(self,
rule_num,
search_approach,
target_moving_arg=False):
"""Runs a certain algorithm to search for the target on a certain map
"""
search_approach = search_approach.lower()
total_steps = 0
self.landscape.is_target_moving = target_moving_arg
# Rule 1 -> likelihood that the target is in a cell
# Rule 2 -> likelihood that the target is found in a cell
if rule_num == 1:
def score_matrix():
return self.likelihood
elif rule_num == 2:
def score_matrix():
return np.multiply(self.likelihood,
1 - self.landscape.prob_map)
else:
error_print("Invalid rule number", 0)
return -1
# def coords_to_chance(coords):
# coords = tuple(coords)
# if not self.in_bounds(coords):
# return -1
# scores = score_matrix()
# return scores[coords]
def best_global_cell():
index = np.argmax(score_matrix())
index = np.unravel_index(index, (self.dim, self.dim))
return index
def best_close_cell():
'''Smarter algorithm that weighs in distance'''
# TODO write this
scores = np.copy(score_matrix())
dists = np.zeros((self.dim, self.dim))
for x in range(self.dim):
for y in range(self.dim):
coords = (x, y)
dist = cell_dist(coords, self.cur_location)
dists[coords] = dist + 1
# dists -= self.dim
# dists = np.arctan(dists)
# dists *= -(1/pi)
# dists += 1/2
# scores = np.multiply(scores, dists)
scores = np.divide(scores, dists)
index = np.argmax(scores)
index = np.unravel_index(index, (self.dim, self.dim))
return index
# Set the decide_path and agent_search algorithm
if search_approach == 'global':
# No pathing always searches the best global
def decide_path_global():
self.cur_path_target = None
return
def agent_search_global():
coords = best_global_cell()
return self.search_cell(coords)
decide_path = decide_path_global
agent_search = agent_search_global
elif search_approach == 'path_simple':
# Travels to the best cell then searches it
def decide_path_simple():
if self.cur_path_target is None:
self.cur_path_target = best_global_cell()
if self.cur_location == self.cur_path_target:
self.cur_path_target = None
def agent_search_simple():
return self.search_cell(self.cur_location)
decide_path = decide_path_simple
agent_search = agent_search_simple
elif search_approach == 'path_smart':
def decide_path_smart():
if self.cur_path_target is None:
# TODO replace best_global_cell() with scoring func
self.cur_path_target = best_close_cell()
if self.cur_location == self.cur_path_target:
self.cur_path_target = None
def agent_search_smart():
# debug_print(f'Searching: {self.cur_location}', 5)
return self.search_cell(self.cur_location)
decide_path = decide_path_smart
agent_search = agent_search_smart
target_found = False
while not target_found:
total_steps += 1
# Sets the path to none if no movement needed
decide_path()
if self.cur_path_target is None:
target_found = agent_search()
else:
self.move_on_path()
return total_steps
def in_bounds(self, coords):
return self.landscape.in_bounds(coords)
def run_trials(self, num_trials, search_rule):
total_steps = 0
for __ in range(0, num_trials):
self.reset_all()
target_found = False
while not target_found:
target_found = search_rule()
total_steps += 1
return total_steps / num_trials
def p_state(self):
print(self.likelihood)
print('Current Path Target')
print(self.cur_path_target)
print('Current Position')
print(self.cur_location)
def __init__(self, size=200, r1=3, r2=5, r3=10, r4=10, load_filename=None): self.landscape = Landscape(size, size, self, r1, r2, r3, r4, load_filename) if not load_filename: self.agents = set() for i in range(100): #200 self.add_agent(Agent(self.landscape, self))
def parallel_settlements(grid):
# generate a random seed, use it and store it
random_seed = round(np.random.random() * 1000000)
np.random.seed(random_seed)
file_seed = open(directorySettlements + subpath + 'randomSeeds.txt', 'a')
file_seed.write(
str(scenario['interactionW']) + "," + str(grid) + "," +
str(random_seed) + '\n')
file_seed.close()
# read the previously generated landscape object (the grid)
lscape = Landscape()
lscape.pkl_import(path=directoryLandscapes + 'landscape' + str(grid) +
'.pkl')
# start with an empty list of distributions
settlements = []
# loop over multiple distributions
for distribution in range(0, nDistributions):
# print message to keep track of the progress
print("Generating settlement " + str(distribution) + " on grid " +
str(grid))
# sample the random number of individuals
if nIndividualsMin < nIndividualsMax:
n_individuals = np.random.randint(low=nIndividualsMin,
high=nIndividualsMax)
else:
n_individuals = nIndividualsMin
# new settlement object
settlements.append(
Settlement(interaction_w=interactionW, landscape=lscape))
# set the response of the species to the different variables
for index, label in enumerate(varLabel):
settlements[distribution].set_weight(label=label,
weight=varWeight[index])
# place the individuals in the grid using gibbs sampling
settlements[distribution].gibbs(n_individuals=n_individuals,
n_iterations=n_individuals *
nSelections)
# save the distribution
settlements[distribution].export_to_pkl(
path=directorySettlements + subpath + 'settlement' + str(grid) +
'_' + str(distribution) + ".pkl")
from debug_render import QuadtreeRender from engine import Engine from landscape import Landscape from rocket import Rocket engine = Engine() engine.global_scale = 0.1 engine.add_static_object(Landscape()) engine.add_game_object(QuadtreeRender(engine.quadtree)) engine.add_dynamic_object(Rocket()) engine.loop()
def __init__(self, complexity=8, width=1000, height=800, margin=100):
world = Landscape(terrain=[
ground(margin, margin, width - margin, height - margin)
for i in range(complexity)
])
self.board = Simulator(world, width=width, height=height)
print(f"t = {t:4.32}, alive = {len(agentList.agentList)}\n{bMap}")
if PAUSE:
# Print nice statistics and await input
# Empty input == continue to next event
print(nice_statistics(agentList, t))
repeatInput = True
while repeatInput:
uInput = input(f"Input t={t}> ")
repeatInput = interpret(uInput, agentList, calendar, landscape,
calendar.now())
rng = RNG(SEED)
eventList = EventCalendar()
landscape = Landscape(ROWS, COLUMNS, rng=rng)
agentList = AgentList(AGENTS, landscape, eventList, rng=rng)
eventList.set_preevent(preoperation, landscape, eventList)
eventList.set_postevent(postoperation, landscape, eventList, agentList)
## Pre simulation ##
print(f"Seed: {SEED}")
print(nice_statistics(agentList, 0))
init_map = str_map(landscape, showSugar=SHOW_SUGAR)
## Simulation ##
eventList.run(MAX_T)
## Post simulation ##
t = eventList.now()
from landscape import Landscape
from sprite import FrameSprite
from Rain import Rain
from Client import Client
from time import sleep
volcano = FrameSprite("volcano",
shift=(5, 0),
num_frames=3,
update_time=1000,
color_map={
"*": (105, 105, 105),
"@": (255, 0, 0)
})
rain = Rain("rain", zval=100)
background = Landscape()
name = "weather-led-test" if background.test else "weather-led"
client = Client(name, "https://central-socket.herokuapp.com/")
def onWeatherChange(weather):
if (isinstance(weather, list)):
weather = weather[0]
if weather == 'hot':
background.add_sprite(volcano)
elif weather == 'cold':
background.remove_sprite(volcano)
if weather == 'rainy':
background.add_sprite(rain)
import matplotlib.pyplot as plt
import matplotlib.animation as an
from forager import Forager
from landscape import Landscape
from foraging import foraging, display
def update(_):
global l, f
if not f.is_dead:
foraging(l, f)
return display(l, f),
l = Landscape(30, 30, 15)
f = Forager(0, 0)
fig = plt.figure(figsize=(6, 6))
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect=1)
animation = an.FuncAnimation(fig, update, interval=1, blit=True, frames=200)
plt.show()
print('Total move = %d' % f.total_move)
print('Total eaten = %d' % f.total_eaten)
def reset_all(self):
"""Resets the finder with a new map and recenters the cur_location
"""
self.landscape = Landscape(self.dim)
self.reset_finder()
if __name__ == "__main__":
#global printers, landscapes, OSCSERVER_IP
print("waiting 10 secs")
time.sleep(10)
print("MAIN program start\n")
# get my IP address
import os
f = os.popen('ifconfig eth0 | grep inet')
s = f.read()
#print( s )
OSCSERVER_IP = s.split()[1]
landscapes["megalopoli"] = Landscape("megalopoli")
landscapes["montagna"] = Landscape("montagna")
landscapes["isola"] = Landscape("isola")
landscapes["citta"] = Landscape("citta")
landscapes["scogliera"] = Landscape("scogliera")
landscapes["generici"] = Landscape("generici")
print()
# printer stuff
try:
printers.append(Printer("/dev/usb/lp0"))
print("Printer 'lp0' recognized")
except:
print("Unable to create the 'pl0' printer")
try:
from landscape import Landscape
from simulator import Simulator
world = Landscape(
terrain=[[200, 200, 400, 400], [400, 400, 600, 600], [600, 100, 800, 400]])
demo = Simulator(world)
demo.interactive()
from forager import Forager
from landscape import Landscape
from foraging import eat, is_moving, move, foraging
x = 2
y = 2
nb_patches = 0
x_forager = 0
y_forager = 0
landscape = Landscape(x, y, nb_patches)
forager = Forager(x_forager, y_forager)
def test_eat():
forager.stock = Forager.STOCK_MAX - Forager.EAT_MAX_BY_DAY + 2
landscape.land[x_forager, y_forager] = Forager.EAT_MAX_BY_DAY
eat(landscape, forager)
assert landscape.land[x_forager, y_forager] == 2
assert forager.stock == Forager.STOCK_MAX
forager.stock = Forager.STOCK_MAX - Forager.EAT_MAX_BY_DAY - 2
landscape.land[x_forager, y_forager] = Forager.EAT_MAX_BY_DAY
eat(landscape, forager)
assert landscape.land[x_forager, y_forager] == 0
assert forager.stock == Forager.STOCK_MAX - 2
def test_is_moving():
landscape.land[x_forager, y_forager] = 0
assert is_moving(landscape, forager)
landscape.land[x_forager, y_forager] = Forager.EAT_MAX_BY_DAY - 1
forager.stock = 0
from flask import Flask, render_template, request, redirect, session, url_for
import numpy as np
from Treehacks_recommendation import *
from visualisation import *
from landscape import Landscape
from Preprocessing import *
# setup flask
app = Flask(__name__)
app.secret_key = b'_5#y2L"F4Q8z\n\xec]/'
main_input = ""
# set up the model
lsr = Landscape("stsb-distilbert-base")
sentence = None
text_units = None
@app.route('/', methods=["GET", "POST"])
def startpage():
if request.method == 'POST':
session['inputText'] = request.form['inputText']
# print(input_list)
session['outputText'] = textsummary(session['inputText'])
return redirect(url_for('analyze'))
return render_template('interface.html')
@app.route('/text')
def analyze():
import pygame
from settings import Settings
from landscape import Landscape
from dinosaur import Dinosaur
pygame.init()
canvas = pygame.display.set_mode(Settings.screen_size)
landscape = Landscape(canvas)
dinosaur = Dinosaur(canvas, landscape)
clock = pygame.time.Clock()
done = False
while not done:
canvas.fill((0, 0, 0))
landscape.draw()
dinosaur.draw()
pygame.display.flip()
landscape.update()
clock.tick(10)
from landscape import Landscape
from simulator import Simulator
from math import pi, sqrt
world = Landscape(
terrain=[[150, 200, 350, 400], [400, 400, 600, 600], [600, 200, 800, 400]])
demo = Simulator(world)
right = pi / 2
left = -pi / 2
straight = 0
demo.explore(401,
401,
moves=[[right, 5], [left, 100], [right, 100], [right, 250],
[right, 250], [right, 400], [right, 300], [right, 200],
[right, 200], [right, 250], [right, 100], [right, 250],
[straight, 150], [right, 80], [right,
150], [straight, 150],
[straight, 100], [pi / 4, 30], [right, 100], [right, 50],
[right, 250], [right, 200], [right, 300], [right, 200],
[right, 200], [right, 250], [right, 100], [right, 250],
[straight, 150], [right, 80], [right,
150], [straight, 150],
[right, 100], [right, 100], [right, 200], [left, 20],
[right, 20], [left, 20], [right, 20], [left, 20],
[right, 20], [left, 25], [left,
sqrt(200**2 + 200**2)],
[right, 20], [right, 20], [right, 20]])
demo.interactive()