def test_simulator_animate_without_run():
"""Test if animate() method throws an error when called before run()"""
board = sg.Board(size=(10, 10))
board.add(lf.Blinker(length=3), loc=(0, 1))
sim = sg.Simulator(board)
with pytest.raises(ValueError):
sim.animate()
def test_simulator_run():
"""Test if the run() method returns the computed statistics"""
board = sg.Board(size=(10, 10))
board.add(lf.Blinker(length=3), loc=(0, 1))
sim = sg.Simulator(board)
stats = sim.run(sg.rules.conway_classic, iters=10)
assert isinstance(stats, dict)
def test_compute_statistics():
"""Test if compute_statistics() returns a dictionary"""
board = sg.Board(size=(10, 10))
board.add(lf.Blinker(length=3), loc=(0, 1))
sim = sg.Simulator(board)
sim.run(sg.rules.conway_classic, iters=10)
stats = sim.compute_statistics(sim.get_history())
assert isinstance(stats, dict)
def test_simulator_animate():
"""Test if animate() method returns a FuncAnimation"""
board = sg.Board(size=(10, 10))
board.add(lf.Blinker(length=3), loc=(0, 1))
sim = sg.Simulator(board)
sim.run(sg.rules.conway_classic, iters=10)
anim = sim.animate()
assert isinstance(anim, animation.FuncAnimation)
def test_simulator_get_history_shape():
"""Test if get_history() will return the expected shape"""
board = sg.Board(size=(10, 10))
board.add(lf.Blinker(length=3), loc=(0, 1))
sim = sg.Simulator(board)
sim.run(sg.rules.conway_classic, iters=10)
hist = sim.get_history()
assert hist.shape == (10, 10, 10)
def test_simulator_inplace():
"""Test if board state didn't change after a simulation run"""
board = sg.Board(size=(10, 10))
board.add(lf.Glider(), loc=(0, 0))
# Initial board state, must be the same always
init_board = board.state.copy()
# Run simulator
sim = sg.Simulator(board)
sim.run(sg.rules.conway_classic, iters=10)
assert np.array_equal(board.state, init_board)
def test_rule_return_type(rule_name, fn):
board = sg.Board(size=(10, 10))
board.add(lf.Blinker(length=3), loc=(0, 1))
new_state = fn(board.state)
assert isinstance(new_state, (list, np.ndarray))
def test_rule_return_shape(rule_name, fn):
board = sg.Board(size=(10, 10))
board.add(lf.Blinker(length=3), loc=(0, 1))
new_state = fn(board.state)
assert new_state.shape == (10, 10)
import time
import seagull as sg
import cv2
import seagull.lifeforms as lf
import numpy as np
from lib import imageToBinary as i2b
MULTICAST_GROUP = ('239.1.2.3', 8080)
WIDTH = 64
HEIGHT = 32
ITERS = 900
board = sg.Board(size=(HEIGHT, WIDTH))
np.random.seed(42)
noise = np.random.choice([0, 1], size=(HEIGHT, WIDTH))
custom_lf = lf.Custom(noise)
board.add(custom_lf, loc=(0, 0))
sim = sg.Simulator(board)
sock = socket.socket(
socket.AF_INET, # Internet
socket.SOCK_DGRAM) # UDP
ttl = struct.pack('b', 1)
sock.setsockopt(socket.IPPROTO_IP, socket.IP_MULTICAST_TTL, ttl)
sim.run(sg.rules.conway_classic, iters=ITERS)
import seagull as sg from seagull.lifeforms import Pulsar # Initialize board board = sg.Board(size=(19, 60)) # Add three Pulsar lifeforms in various locations board.add(Pulsar(), loc=(1, 1)) board.add(Pulsar(), loc=(1, 22)) board.add(Pulsar(), loc=(1, 42)) # Simulate board sim = sg.Simulator(board) sim.run(sg.rules.conway_classic, iters=1000) # Animate sim.animate()
def make_sprite(
n_sprites: int,
n_iters: int,
repro_rate: int,
stasis_rate: int,
):
"""Main function for creating sprites
Parameters
----------
n_sprites : int
Number of sprites to generate
n_iters : int
Number of iterations to run the simulator
repro_rate : int
Inverse reproduction rate
stasis_rate : int
Stasis rate
"""
logger.info("Initializing board")
board = sg.Board(size=(8, 4))
logger.info("Running simulation")
sprator_list = []
for sprite in range(n_sprites):
noise = np.random.choice([0, 1], size=(8, 4))
custom_lf = lf.Custom(noise)
board.add(custom_lf, loc=(0, 0))
sim = sg.Simulator(board)
sim.run(
custom_rule,
iters=n_iters,
repro_rate=repro_rate,
stasis_rate=stasis_rate,
)
fstate = sim.get_history()[-1]
logger.info(f"Generating sprite/s: {sprite}")
sprator = np.hstack([fstate, np.fliplr(fstate)])
sprator = np.pad(sprator,
mode="constant",
pad_width=1,
constant_values=1)
sprator_with_outline = add_outline(sprator)
sprator_gradient = get_gradient(sprator_with_outline)
sprator_final = combine(sprator_with_outline, sprator_gradient)
sprator_list.append(sprator_final)
# Generate plot based on the grid size
n_grid = int(sqrt(n_sprites))
# Generate random colors as cmap
r = lambda: "#%02X%02X%02X" % (
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255),
)
colors = ["black", "#f2f2f2", r(), r(), r()]
cm.register_cmap(cmap=mpl.colors.LinearSegmentedColormap.from_list(
"custom", colors).reversed())
if n_grid == 1:
fig, axs = plt.subplots(n_grid, n_grid, figsize=(5, 5))
axs = fig.add_axes([0, 0, 1, 1], xticks=[], yticks=[], frameon=False)
axs.imshow(sprator_list[0], cmap="custom_r", interpolation="nearest")
fig.text(0, -0.05, "bit.ly/CellularSprites", ha="left", color="black")
else:
fig, axs = plt.subplots(n_grid, n_grid, figsize=(5, 5))
for ax, sprator in zip(axs.flat, sprator_list):
# TODO: Remove duplicates
# Generate random colors as cmap
r = lambda: "#%02X%02X%02X" % (
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255),
)
colors = ["black", "#f2f2f2", r(), r(), r()]
cm.register_cmap(cmap=mpl.colors.LinearSegmentedColormap.from_list(
"custom", colors).reversed())
ax.imshow(sprator, cmap="custom_r", interpolation="nearest")
ax.set_axis_off()
fig.text(0.125, 0.05, "bit.ly/CellularSprites", ha="left")
return fig
def generate_sprite(
n_iters: int = 1,
extinction: float = 0.125,
survival: float = 0.375,
size: int = 180,
sprite_seed: int = None,
color_seeds: List[int] = None,
):
"""Generate a sprite given various parameters
Parameters
----------
n_iters : int
Number of iterations to run Conway's Game of Life.
extinction : float (0.0 to 1.0)
Controls how many dead cells will stay dead on the next iteration
Default is 0.125 (around 1 cell)
survival: float (0.0 to 1.0)
Controls how many live cells will stay alive on the next iteration.
Default is 0.375 (around 3 cells)
size : int
Size of the generated sprite in pixels. Default is 180 for 180 x 180px.
sprite_seed : int (optional)
Random seed for the Sprite. Default is None
color_seeds : list (optional)
Random seed for the colors. Default is None
Returns
-------
matplotlib.Figure
"""
logger.debug("Initializing board")
board = sg.Board(size=(8, 4))
logger.debug("Seeding the lifeform")
if sprite_seed:
np.random.seed(sprite_seed)
noise = np.random.choice([0, 1], size=(8, 4))
custom_lf = lf.Custom(noise)
board.add(custom_lf, loc=(0, 0))
logger.debug("Running the simulation")
sim = sg.Simulator(board)
sim.run(
_custom_rule,
iters=n_iters,
n_extinct=int(extinction * 8),
n_survive=int(survival * 8),
)
fstate = sim.get_history()[-1]
logger.debug("Adding outline, gradient, and colors")
sprite = np.hstack([fstate, np.fliplr(fstate)])
sprite = np.pad(sprite, mode="constant", pad_width=1, constant_values=1)
sprite_with_outline = _add_outline(sprite)
sprite_gradient = _get_gradient(sprite_with_outline)
sprite_final = _combine(sprite_with_outline, sprite_gradient)
logger.trace("Registering a colormap")
iterator = list(_group(3, color_seeds))[:3] if color_seeds else [None] * 3
random_colors = [_color(seeds) for seeds in iterator]
base_colors = ["black", "#f2f2f2"]
colors = base_colors + random_colors
logger.trace(f"Colors to use: {colors}")
cm.register_cmap(
cmap=mpl.colors.LinearSegmentedColormap.from_list(
"custom", colors
).reversed()
)
logger.debug("Preparing final image")
fig, axs = plt.subplots(1, 1, figsize=(1, 1), dpi=size)
axs = fig.add_axes([0, 0, 1, 1], xticks=[], yticks=[], frameon=False)
axs.imshow(sprite_final, cmap="custom_r", interpolation="nearest")
logger.debug("Successfully generated sprite!")
return fig
