def __init__(self):
self.current_vessel = 0
#Creating attack and defend grid.
self.defend_grid = grid.Grid()
self.attack_grid = grid.Grid()
self.root = Tk()
#Labels for the GUI, explain the grid.
Label(text='Attack', font=myfont).grid(row=0, column=0)
Label(text='Defend', font=myfont).grid(row=2, column=0)
#Attack frame and buttons being set to buttons and a 10x10 grid.
self.attack_frame, self.attack_grid_buttons = self.create_grid(
grid.GRID_HEIGHT, grid.GRID_WIDTH, self.attack_button_clicked)
self.attack_frame.grid(row=1, column=0)
#Same as above, except with the defend grid.
self.defend_frame, self.defend_grid_buttons = self.create_grid(
grid.GRID_HEIGHT, grid.GRID_WIDTH, self.defend_button_clicked)
self.defend_frame.grid(row=3, column=0)
#Disables the attack buttons on the attack grid.
self.set_grid('disabled', self.attack_grid_buttons)
#Updates the buttons on the defend and attack grid.
self.update_GUI(self.defend_grid.grid, self.defend_grid_buttons)
self.update_GUI(self.attack_grid.grid, self.attack_grid_buttons)
self.root.mainloop()
def setUp(self):
#linear SAMPLE objects
ct = 4
lim_t = 31
lbpar_t = cd.LbFixParams([ct], [lim_t])
shf_t = 5.0
band_t = 40.0
dt = band_t / ct
diff = [dt]
shf = [shf_t]
band = [band_t]
ptpar = cd.PtFixParams(diff, shf, band)
lb_t = cd.LbGen(lbpar_t)
pt_t = cd.PtGen(ptpar)
dim_lb = (0, 0)
obj_lb = [lb_t]
dims_lb = [dim_lb]
mst_lb = mst.MdimStruct(obj_lb, dims_lb)
dims_pt = [(0, 0)]
obj_pt = [pt_t]
mst_pt = mst.MdimStruct(obj_pt, dims_pt)
genS = gn.Generator(mst_lb, mst_pt)
self.gdS = gd.Grid(genS)
#BOUNDS
diff = [dt]
shf = [shf_t - 5.0]
band = [band_t]
ptpar = cd.PtFixParams(diff, shf, band)
pt_t = cd.PtGen(ptpar)
dims_pt = [(0, 0)]
obj_pt = [pt_t]
mst_pt = mst.MdimStruct(obj_pt, dims_pt)
genB = gn.Generator(mst_lb, mst_pt)
self.gdB = gd.Grid(genB)
return
def buildCoarseGrid(self, type, L, n, X0, hf):
# buildCoarseGrid constructs an indentical coarse grid on each processor
# it then builds a local mesh for each subdomain - including overlap
# Identical Coarse Grid is built on each processor
self.macroGrid = fem.Grid()
self.macroGrid.buildStructuredMesh2D(L, n, X0, 1, 0)
# Type: element or node centric
# Node centric - Overlapping, each domain support of node (assumming linear macro finite element)
allNeighbours, allN2E = self.macroGrid.findNeighbours()
self.NN = allNeighbours[self.rank]
self.N2E = allN2E[self.rank]
self.numNeighbours = len(self.NN)
# Find boundary box for local grid
bottomLeft = self.macroGrid.coords[self.rank][:]
topRight = self.macroGrid.coords[self.rank][:]
l1 = np.zeros(self.numNeighbours + 1)
l1[0] = np.sum(np.abs(self.macroGrid.coords[self.rank][:]))
for i in range(0, self.numNeighbours):
l1[i + 1] = np.sum(np.abs(self.macroGrid.coords[self.NN[i]][:]))
tmp_id_min = np.argmin(l1)
tmp_id_max = np.argmax(l1)
if (tmp_id_min == 0):
bottomLeft = self.macroGrid.coords[self.rank][:]
else:
bottomLeft = self.macroGrid.coords[self.NN[tmp_id_min - 1]][:]
if (tmp_id_max == 0):
topRight = self.macroGrid.coords[self.rank][:]
else:
topRight = self.macroGrid.coords[self.NN[tmp_id_max - 1]][:]
# Build local finite element grid
L_local = topRight - bottomLeft
nf = []
for i in range(0, self.dim):
nf.append(int(np.floor(L_local[i] / hf[i])))
self.fineGrid = fem.Grid()
self.fineGrid.buildStructuredMesh2D(L_local, nf, bottomLeft, 1, 0)
def __init__(self, choice: str = "default"):
if choice == "easy":
self.grid = grid.Grid(8, 11, 10)
elif choice == "medium":
self.grid = grid.Grid(14, 20, 39)
elif choice == "hard":
self.grid = grid.Grid(20, 29, 102)
elif choice == "insane":
self.grid = grid.Grid(26, 38, 194)
else:
self.grid = grid.Grid(14, 20, 39)
def test_single_cell(self):
"""Tests removing each individual cell."""
for i in range(81):
values = list(TEST_VALUES)
p = [None] * 81
p[i] = set([values[i]])
values[i] = None
g = grid.Grid(values)
self.assertEqual(grid.GridStorage(p), solver.possibilities(g))
self.assertEqual(grid.Grid(TEST_VALUES), solver.set_trivial(g))
def problem(self):
""" Show problem on screen """
self._clear_screen()
letters_grid = grid.Grid(self.screen, 200, 5, 350, 50)
letters_grid.draw()
letters_grid.draw_letters(self.letters)
start_grid = grid.Grid(self.screen, 200, 5, 650, 50)
start_grid.draw()
start_grid.draw_letters(self.start_pattern)
pygame.display.flip()
def main(argv) :
if len(argv) < 0 or len(argv) > 4 :
print "Inadequate number of parameters, view README"
return
b = g.get_bot()
sensor_grid = grid.Grid(size, size) # Grid for displaying noisy readings
ml_grid = grid.Grid(size, size) # Grid for displaying most likely pos
accuracy = [] # At each step, how far away is the most likely state
steps = 15 # How many steps the robot should take
print_info = False # Flag for info
print_grid = False # Flag for grid printing
time_per_step = 0.0
if len(argv) >= 1 :
steps = int(argv[0])
if len(argv) >= 2 :
print_info = (int(argv[1]) != 0)
if len(argv) >= 3 :
print_grid = (int(argv[2]) != 0)
if len(argv) >= 4 :
time_per_step = float(argv[3])
count = 0
for i in xrange(steps) :
obs = g.next() # Latest observation
update(obs)
if not obs[0] is "nothing" : # Nothing handling
sensor_grid.set_bot(obs[0][0], obs[0][1])
ml_state = most_likely()
if print_grid : # This prints three parallell grids
ml_grid.set_bot(ml_state.get_x(), ml_state.get_y())
grid_string = g.to_string().split("\n")
sensor_string = sensor_grid.to_string().split("\n")
ml_string = ml_grid.to_string().split("\n")
for j in xrange(len(grid_string)) :
print (grid_string[j] + sensor_string[j]
+ ml_string[j])
bstate = b.to_state()
diff = bstate.diff(ml_state)
accuracy.append(diff)
if print_info :
info = "Actual" + str(bstate.to_string())
info += "\tSensor"
info += str((obs[0], bot.h_to_string(obs[1])))
info += "\tMost likely" + str(ml_state.to_string())
print info + "\n"
print "Off by " + str(diff)
if print_grid :
time.sleep(time_per_step)
s = str(len(accuracy)) + ": "
for x in accuracy :
s += str(abs(x[0]) + abs(x[1])) + " "
print "\nAccuracy as a function of time: "
print s
def test_clear_rows(self):
g = grid.Grid(8, 10)
for i in range(4, 8):
for j in range(10):
g.cells[i][j] = 1
g.cells[5][6] = 0
cleared_lines = g.clear_rows()
self.assertEqual(cleared_lines, 3)
current_grid = grid.Grid(8, 10)
for i in range(10):
current_grid.cells[7][i] = 1
current_grid.cells[7][6] = 0
self.assertEqual(g.cells, current_grid.cells)
def test_row(self):
"""Tests removing a whole row of values.
The column of each should still fix the value."""
values = list(TEST_VALUES)
p = [None] * 81
for i in range(9, 18):
p[i] = set([values[i]])
values[i] = None
g = grid.Grid(values)
self.assertEqual(grid.GridStorage(p), solver.possibilities(g))
self.assertEqual(grid.Grid(TEST_VALUES), solver.set_trivial(g))
def __init__(self, samps = None,
bounds = None,
volume = None,
measure = None,
is_eq = True,
bandlims = None):
#TODO: remove this member
self.is_eq = is_eq
if samps is None:
self.samps = gd.Grid()
self.bounds = gd.Grid()
else:
self.samps = samps
self.bounds = bounds
self.ns = self.samps.get_curr_cards()
if volume is None:
self.volume = vol.Volume()
else:
self.volume = volume
if measure is None:
self.measure = ms.Measure()
else:
self.measure = measure
if bandlims is None:
bounds = deep(self.bounds)
self.bandlims = deep(bounds.get_lims() )
else:
self.bandlims = deep(bandlims)
return
def on_init(self):
pygame.init()
self._display_surf = pygame.display.set_mode((840, 480),
pygame.HWSURFACE)
self._grid = grid.Grid(10, 10)
self._status_window = status_window.StatusWindow()
self._running = True
def __init__(self):
"""Constructor. Class/member variables for playing board and current player's turn."""
self.grid = grid.Grid()
self.player1 = player.ManualPlayer(1)
self.player2 = player.ManualPlayer(2)
self.players = [self.player1, self.player2]
self.pid_turn = 1
def expand_nodes(self, starting_grid, search_method):
"""Take a grid state, add all possible 'next moves' to the frontier"""
node_order = ['up', 'down', 'left', 'right']
if search_method == 'dfs':
node_order = reversed(node_order)
for node in node_order:
# the program is imagining the future!! (maybe change this name...)
imagined_grid = grid.Grid(starting_grid.state)
# pass path history from previous grid to the next grid
# using copy to avoid python's reference bindings
imagined_grid.path_history = copy.copy(starting_grid.path_history)
if imagined_grid.move(node): # returns false if move not possible
imagined_grid.path_history.append(node)
if imagined_grid not in self.frontier and imagined_grid not in self.explored:
if search_method == 'ast':
imagined_grid.score = imagined_grid.manhattan_score(
self.goal_state)
self.ast_frontier.queue.put(
(imagined_grid.score, imagined_grid))
else:
self.frontier.queue.append(imagined_grid)
self.metrics.update_max_fringe()
self.metrics.nodes_expanded += 1
def a_star_search(self):
"""Explore search space using A*-search, using Manhattan Priority
Function as a heuristic."""
self.metrics.start_timer()
initial_grid = grid.Grid(self.initial_state)
initial_grid.score = initial_grid.manhattan_score(self.goal_state)
# TODO: ridiculous parameters
self.ast_frontier.queue.put((initial_grid.score, initial_grid))
# while queue is not empty..
while self.ast_frontier.queue:
# TODO: better name for state. It's a grid. state.state is the state!
lowest_scored = self.ast_frontier.queue.get()
state = lowest_scored[1]
self.metrics.search_depth = len(state.path_history)
self.metrics.update_max_depth()
self.explored.set.add(state)
if self.goal_test(state):
self.metrics.path_to_goal = state.path_history
self.metrics.stop_timer()
self.metrics.measure_ram_useage()
return self.metrics
self.expand_nodes(state, 'ast')
# if we get to here it's gone t**s up
raise ValueError('Shouldn\'t have got to here - gone t**s')
def uninformed_search(self, search_method):
"""Explore search space using either breadth-first or depth-first search"""
self.metrics.start_timer()
initial_grid = grid.Grid(self.initial_state)
self.frontier.queue.append(initial_grid)
# while queue is not empty..
while self.frontier.queue:
# TODO: better name for state. It's a grid. state.state is the state!
if search_method == 'bfs':
state = self.frontier.queue.popleft()
elif search_method == 'dfs':
state = self.frontier.queue.pop()
self.metrics.search_depth = len(state.path_history)
self.metrics.update_max_depth()
self.explored.set.add(state)
if self.goal_test(state):
self.metrics.path_to_goal = state.path_history
self.metrics.stop_timer()
self.metrics.measure_ram_useage()
return self.metrics
self.expand_nodes(state, search_method)
# if we get to here it's gone t**s up
# TODO: this doesn't feel like good design!
raise ValueError('Shouldn\'t have got to here - gone t**s')
def regrid(self, newgrid, newmask=None, interp='nearest'):
'''
Regrids a field to a grid defined in newgrid
interp is interpolation method.
'''
X = newgrid['lon']
Y = newgrid['lat']
newshape = self.time.shape + self.grid['lev'].shape + newgrid[
'lat'].shape
nt, km, jm, im = newshape
x = self.data.reshape(-1, self.grid['lat'].shape[0],
self.grid['lon'].shape[1])
xnew = sp.ma.masked_array(sp.zeros((nt * km, jm, im)))
for ii, xx in enumerate(x):
mm = ~sp.ma.getmaskarray(xx)
xnew[ii] = interpolate.griddata(zip(self.grid['lon'][mm],
self.grid['lat'][mm]),
xx[mm], (X, Y),
method=interp)
xnew = xnew.reshape(newshape)
if newmask is not None:
xnew.mask = newmask
xnew[sp.isnan(xnew)] = sp.ma.masked
self.data = xnew
self.grid = grid.Grid(newgrid['lon'], newgrid['lat'], self.grid['lev'])
def test_square(self):
g = grid.Grid(TEST_VALUES)
self.assertEqual(g.square(0), (1, 2, 3, 4, 5, 6, 7, 8, 9))
self.assertEqual(g.square(1), (4, 5, 6, 7, 8, 9, 1, 2, 3))
self.assertEqual(g.square(3), (2, 3, 4, 5, 6, 7, 8, 9, 1))
self.assertEqual(g.square(8), (9, 1, 2, 3, 4, 5, 6, 7, 8))
self.assertEqual(g.square(7), (6, 7, 8, 9, 1, 2, 3, 4, 5))
def test_init_with_initial_grid(self):
g = grid.Grid(3,
2,
initial_grid=[["RED", "RED"], ["RED", "RED"],
["RED", "RED"]])
self.assertEqual([["RED", "RED"], ["RED", "RED"], ["RED", "RED"]],
g.grid)
def simulate_player(self, possibilities, sunk_ships):
sunk_ships_copy = copy.deepcopy(sunk_ships)
ship_sizes = [5, 4, 3, 3, 2]
random.shuffle(ship_sizes)
temp_grid = grid.Grid()
for size in ship_sizes:
if size in sunk_ships_copy:
possible_locations = [sunk_ships_copy[size]]
sunk_ships_copy.pop(size)
else:
possible_locations = list(possibilities[size])
random.shuffle(possible_locations)
ship_added = False
for location in possible_locations:
square, direction = location
col, row = square
ship_str = ' '.join([str(size), col, row, direction])
new_ship = ship.Ship(ship_str)
if temp_grid.add_ship(new_ship):
ship_added = True
break
if not ship_added: # ran out of locations
return []
if self.shots_hit <= temp_grid.occupied_squares():
return list(temp_grid.occupied_squares())
return []
def setUp(self):
self.tmp_list = files.ListFile('tests/list_tmp.life')
self.tmp_life = files.LifeFile('tests/life_tmp.life')
self.glider = [(1, 1), (1, 2), (1, 3), (2, 1), (3, 2)]
self.game = game.Game(grid.Grid())
for coords in self.glider:
self.game.grid.live(coords)
def get_visits(self):
# Initialize a board for the same state
board = grid.Grid(int(len(self.state)**0.5))
board.set_from_state(self.state)
# Empty list that we are going to fill up
visits = []
# Get all the nodes
b_states = board.get_state(False)
# Match is with legal actions
for b in b_states:
found = False
i = None
for ai in range(len(self.actions)):
# If there is such an ai that
if b == self.actions[ai].action:
found = True
i = ai
# Set to 0 if the action is not legal
if found: visits.append(self.actions[i].visits)
else: visits.append(0)
# Return the complete list
return visits
def __init__(self, pacman_agent):
self.pacman_agent = pacman_agent
self.grid = grid.Grid()
self.pacman = pacman.Pacman(grid.PACMAN_STARTPOS, 0, 0)
self.blinky = ghost.Blinky(grid.GHOST_STARTPOS, 0, 0)
self.inky = ghost.Inky(grid.GHOST_STARTPOS, 0, 0)
self.pinky = ghost.Pinky(grid.GHOST_STARTPOS, 0, 0)
self.clyde = ghost.Clyde(grid.GHOST_STARTPOS, 0, 0)
self.ghosts = pygame.sprite.Group()
self.ghost_house = pygame.sprite.Group()
self.ghosts.add(self.blinky)
self.ghost_house.add(self.inky)
self.ghost_house.add(self.pinky)
self.ghost_house.add(self.clyde)
pygame.time.set_timer(CHANGESTATEEVENT, int(self.ghosts_state * 1000))
pygame.time.set_timer(GHOSTHOUSEEVENT, int(GHOSTHOUSETIME * 1000))
self._font = pygame.font.Font(globals.FONT, globals.FONT_SIZE)
def __init__(self, width, height):
super().__init__(width, height, SCREEN_TITLE)
arcade.set_background_color(arcade.color.WHITE)
self.grid = grid.Grid(width, height, GRID_COLUMNS, GRID_ROWS)
self.player = player.Player(self.grid.column_width,
self.grid.row_height)
self.playerPosition = Position(0, 0)
def __init__(self, img, xml, side, w, h, b, classes):
self.array = numpy.asarray(Image.open(img))
self.array = numpy.asarray([
self.array[:, :, 0] / 255, self.array[:, :, 1] / 255,
self.array[:, :, 2] / 255
],
dtype='float32')
self.side = side
self.w = w
self.h = h
self.b = b
self.classes = classes
self.grid = grid.Grid(self.side, self.w, self.h)
tree = ET.parse(xml)
root = tree.getroot()
for obj in root.iter('object'):
geometry = obj.find('geometry')
box = geometry.find('bndbox')
xmin, ymin = float(box.find('xmin').text), float(
box.find('ymin').text)
xmax, ymax = float(box.find('xmax').text), float(
box.find('ymax').text)
class_num = self.classes.index(obj.find('name').text)
self.grid.insert(box=grid.Box(xmin, ymin, xmax, ymax, class_num))
def test_is_valid(self):
self.assertTrue(grid.Grid(TEST_VALUES).is_valid())
values = list(TEST_VALUES)
values[5] = 9
self.assertFalse(grid.Grid(values).is_valid())
values = list(TEST_VALUES)
values[5] = None
self.assertTrue(grid.Grid(values).is_valid())
self.assertTrue(grid.Grid([None] * len(TEST_VALUES)).is_valid())
values = [None] * len(TEST_VALUES)
values[6] = 3
self.assertTrue(grid.Grid(values).is_valid())
def play(self, M, N, G, board_size):
for anet1 in self.anets:
for anet2 in [a for a in self.anets if a != anet1]:
for i in range(G):
play = grid.Grid(board_size)
# Playing until the board is in terminal state. Anet1 is always the first player
# This ensures that all networks will have a chance to play as first player
while not play.is_terminal()[0]:
# Let the player whose turn it currently is to make the move
an = anet1 if play.get_player() == 1 else anet2
# Get the probability distribution from ANET
pd = an.policy( str(play.get_player()) + play.get_state() )
# Make the best move
play.make_move(play.get_coor( pd.index(h.argmax( pd ))) )
# Save the results of the game
key = "an" + str(self.anets.index(anet1)) + " vs an" + str(self.anets.index(anet2))
# Add the results into the dictionary
if play.is_terminal()[1] == 1:
if key in self.results:
self.results[key][0] += 1
else:
self.results[key] = [1,0]
else:
if key in self.results:
self.results[key][1] += 1
else:
self.results[key] = [0, 1]
def vinterp(self, newgrid, revert_axis=False, newmask=None):
'''
Performs vertical interpolation from self.grid['lev'] to newgrid['lev'].
Levels should be monotonicaly increasing. If levels are decreasing, i.g. pressure levels,
use revert_axis=True.
'''
if revert_axis:
ind = slice(None, None, -1)
else:
ind = slice(None)
lev = self.grid['lev'][ind]
newlev = newgrid['lev'][ind]
nt, km, jm, im = self.dims
kmout = newlev.size
data = self.data[:, ind, :, :]
mm = data.mean()
data[data.mask] = mm
f = interpolate.interp1d(lev, data, axis=1)
self.data = sp.ma.masked_values(f(newlev)[:, ind, :, :], mm)
if newmask is not None:
self.data.mask = newmask
self.grid = grid.Grid(self.grid['lon'], self.grid['lat'],
newgrid['lev'])
def process_text(self, text, lang):
if len(text) == 1:
if self.is_grid_running == True:
if '0' <= text[:1] <= '9':
#send command to grid
self.grid.select_cell(text)
reporter.report_success(text)
return True
else:
self.insert_text(text, lang)
return True
if text == "grid" or text == "griglia":
if self.grid != None and self.is_grid_running == True:
self.grid.stop()
if (self.grid == None):
self.grid = grid.Grid()
self.grid.start()
self.is_grid_running = True
reporter.report_success(text)
return True
# Ignore some token in initial position
if text.startswith('open ') or text.startswith(
'run ') or text.startswith('apri '):
startpos = text.find(" ") + 1
t = text[startpos:]
return self.open_program(t, lang, text.split(" ")[0].strip())
# Keyword in order to go to a web page
elif text.startswith('vai su ') or text.startswith('go to '):
startpos = text.find(" ") + 1
t = text[startpos:]
startpos = t.find(" ") + 1
t = t[startpos:]
self.google_search(t)
return True
# Keyword in order to write with vocal keyboard
elif text.startswith('scrivi ') or text.startswith('write '):
startpos = text.find(" ") + 1
t = text[startpos:]
self.insert_text(t, lang)
return True
else:
status = self.open_program(text, lang, "")
if self.is_grid_running == True and status == True:
self.grid.stop()
if (status == False):
t = text
if text.startswith('chi è ') or text.startswith('cosa è '):
startpos = text.find("è ") + 1
t = text[startpos:]
if text.startswith('who is') or text.startswith('what is'):
startpos = text.find("is ") + 1
t = text[startpos:]
status = self.sparql.run(t, lang[:2])
if (status == True):
reporter.report_success(text)
return status
reporter.report_failure(text)
def staticGridSimulation():
wMap = grid.Grid(tile_num_height, tile_num_width, tile_size)
# Generates random enviroment on the grid
generator = RandomObjects(wMap)
# You can change the number of every type of object you want
generator.create_env(20, 0, 0, 20, 7)
generator.bloatTiles(robot_radius, bloat_factor)
# Starting location
topLeftX = 2.0
topLeftY = 2.0
# Ending Location
botRightX = tile_size * tile_num_width - 2.0
botRightY = tile_size * tile_num_height - 2.0
# Run algorithm to get path
try:
dists, path = search.a_star_search(wMap, (topLeftX, topLeftY),
(botRightX, botRightY),
search.euclidean)
root = Tk()
# start GUI and run animation
simulation = MapPathGUI(root, wMap, path)
simulation.runSimulation(True)
except:
print("C1C0: \"There is no path to the desired location. Beep Boop\"")
if __name__ == "__main__":
staticGridSimulation()
def playgame():
grid = g.Grid()
x = 0
score = 0
while grid.reward(score) == 0:
grid.printGrid()
x, y = raw_input("Enter row, column: ").split()
if x == 'x': break
x = int(x)
y = int(y)
if x < 0 or x >= grid.getNumRows():
print "Invalid row entry"
continue
if y < 0 or y >= grid.getNumCols():
print "Invalid column entry"
continue
round = grid.noisyThrow(x, y)
score += round
print "You got ",round," Current score:", score
if grid.reward(score) == -1:
print "You lose."
return
if grid.reward(score) == 1:
print "You win!"
return